OpenRaiser/Intern-Atlas huggingface dataset:
Workspace monitoring and planning for safe mobile manipulation
Venue: arXiv cs.RO 2020
Year: 2020

In order to enable physical human-robot interaction where humans and (mobile) manipulators share their workspace and work together, robots have to be equipped with important capabilities to guarantee human safety. The robots have to recognize possible collisions with the human co-worker and react anticipatorily by adapting their motion to avert dangerous situations while they are executing their task. Therefore, methods have been developed that allow to monitor the workspace of mobile manipulators using multiple depth sensors to gather information about the robot environment. This encompasses both 3D information about obstacles in the close robot surroundings and the prediction of obstacle motions in the entire monitored space. Based on this information, a collision-free robot motion is planned and during the execution the robot continuously reacts to unforeseen dangerous situations by adapting its planned motion, slowing down or stopping. For the demonstration of a manufacturing scenario, the developed methods have been implemented on a prototypical mobile manipulator. The algorithms handle both robot platform and manipulator in a uniform manner so that an overall optimization of the path and of the collision avoidance behavior is possible. By integrating the monitoring, planning, and interaction control components, the task of grasping, placing and delivering objects to humans in a shared workspace is demonstrated.
Training Multilingual Machine Translation by Alternately Freezing Language-Specific Encoders-Decoders
Venue: arXiv cs.CL 2020
Year: 2020

We propose a modular architecture of language-specific encoder-decoders that constitutes a multilingual machine translation system that can be incrementally extended to new languages without the need for retraining the existing system when adding new languages. Differently from previous works, we simultaneously train $N$ languages in all translation directions by alternately freezing encoder or decoder modules, which indirectly forces the system to train in a common intermediate representation for all languages. Experimental results from multilingual machine translation show that we can successfully train this modular architecture improving on the initial languages while falling slightly behind when adding new languages or doing zero-shot translation. Additional comparison of the quality of sentence representation in the task of natural language inference shows that the alternately freezing training is also beneficial in this direction.
DAPES: Named Data for Off-the-Grid File Sharing with Peer-to-Peer Interactions
Venue: arXiv cs.NI 2020
Year: 2020

This paper introduces DAta-centric Peer-to-peer filE Sharing (DAPES), a data sharing protocol for scenarios with intermittent connectivity and user mobility. DAPES provides a set of semantically meaningful hierarchical naming abstractions that facilitate the exchange of file collections via local connectivity. This enables peers to "make the most" out of the limited connection time with other peers by maximizing the utility of individual transmissions to provide data missing by most connected peers. DAPES runs on top of Named-Data Networking (NDN) and extends NDN's data-centric network layer abstractions to achieve communication over multiple wireless hops through an adaptive hop-by-hop forwarding/suppression mechanism. We have evaluated DAPES through real-world experiments in an outdoor campus setting and extensive simulations. Our results demonstrate that DAPES achieves 50-71% lower overheads and 15-33% lower file sharing delays compared to file sharing solutions that rely on IP-based mobile ad-hoc routing.
Variational Inference and Learning of Piecewise-linear Dynamical Systems
Venue: arXiv cs.LG 2020
Year: 2020

Modeling the temporal behavior of data is of primordial importance in many scientific and engineering fields. Baseline methods assume that both the dynamic and observation equations follow linear-Gaussian models. However, there are many real-world processes that cannot be characterized by a single linear behavior. Alternatively, it is possible to consider a piecewise-linear model which, combined with a switching mechanism, is well suited when several modes of behavior are needed. Nevertheless, switching dynamical systems are intractable because of their computational complexity increases exponentially with time. In this paper, we propose a variational approximation of piecewise linear dynamical systems. We provide full details of the derivation of two variational expectation-maximization algorithms, a filter and a smoother. We show that the model parameters can be split into two sets, static and dynamic parameters, and that the former parameters can be estimated off-line together with the number of linear modes, or the number of states of the switching variable. We apply the proposed method to a visual tracking problem, namely head-pose tracking, and we thoroughly compare our algorithm with several state of the art trackers.
Threat Detection and Investigation with System-level Provenance Graphs: A Survey
Venue: arXiv cs.CR 2020
Year: 2020

With the development of information technology, the border of the cyberspace gets much broader, exposing more and more vulnerabilities to attackers. Traditional mitigation-based defence strategies are challenging to cope with the current complicated situation. Security practitioners urgently need better tools to describe and modelling attacks for defence. The provenance graph seems like an ideal method for threat modelling with powerful semantic expression ability and attacks historic correlation ability. In this paper, we firstly introduce the basic concepts about system-level provenance graph and proposed typical system architecture for provenance graph-based threat detection and investigation. A comprehensive provenance graph-based threat detection system can be divided into three modules, namely, "data collection module", "data management module", and "threat detection modules". Each module contains several components and involves many research problem. We systematically analyzed the algorithms and design details involved. By comparison, we give the strategy of technology selection. Moreover, we pointed out the shortcomings of the existing work for future improvement.
Fast and automated biomarker detection in breath samples with machine learning
Venue: arXiv cs.CV 2020
Year: 2020

Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, subjective and may introduce errors. We propose a system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. The new proposed approach showed to outperform the expert-led analysis by detecting a significantly higher number of VOCs in just a fraction of time while maintaining high specificity. These results suggest that the proposed method can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency.
Depth-Optimized Delay-Aware Tree (DO-DAT) for Virtual Network Function Placement
Venue: arXiv cs.NI 2020
Year: 2020

With the constant increase in demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while ensuring continual improvements to network performance. Although Network Function Virtualization (NFV) has been identified as a solution, several challenges must be addressed to ensure its feasibility. In this paper, we present a machine learning-based solution to the Virtual Network Function (VNF) placement problem. This paper proposes the Depth-Optimized Delay-Aware Tree (DO-DAT) model by using the particle swarm optimization technique to optimize decision tree hyper-parameters. Using the Evolved Packet Core (EPC) as a use case, we evaluate the performance of the model and compare it to a previously proposed model and a heuristic placement strategy.
Efficient tree-structured categorical retrieval
Venue: arXiv cs.DS 2020
Year: 2020

We study a document retrieval problem in the new framework where $D$ text documents are organized in a {\em category tree} with a pre-defined number $h$ of categories. This situation occurs e.g. with taxomonic trees in biology or subject classification systems for scientific literature. Given a string pattern $p$ and a category (level in the category tree), we wish to efficiently retrieve the $t$ \emph{categorical units} containing this pattern and belonging to the category. We propose several efficient solutions for this problem. One of them uses $n(\logσ(1+o(1))+\log D+O(h)) + O(Δ)$ bits of space and $O(|p|+t)$ query time, where $n$ is the total length of the documents, $σ$ the size of the alphabet used in the documents and $Δ$ is the total number of nodes in the category tree. Another solution uses $n(\logσ(1+o(1))+O(\log D))+O(Δ)+O(D\log n)$ bits of space and $O(|p|+t\log D)$ query time. We finally propose other solutions which are more space-efficient at the expense of a slight increase in query time.
On the Significance of Consecutive Ballots in Paxos
Venue: arXiv cs.DC 2020
Year: 2020

In this paper we examine the Paxos protocol and demonstrate how the discrete numbering of ballots can be leveraged to weaken the conditions for learning. Specifically, we define the notion of consecutive ballots and use this to define Consecutive Quorums. Consecutive Quorums weakens the learning criterion such that a learner does not need matching $accept$ messages sent in the $same \; ballot$ from a majority of acceptors to learn a value. We prove that this modification preserves the original safety and liveness guarantees of Paxos. We define $Consecutive \; Paxos$ which encapsulates the properties of discrete consecutive ballots. To establish the correctness of these results, we, in addition to a paper proof, formally verify the correctness of a State Machine Replication Library built on top of an optimized version of Multi-Paxos modified to reflect $Consecutive \; Paxos$.
Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings
Venue: arXiv cs.CL 2020
Year: 2020

Word embeddings are the standard model for semantic and syntactic representations of words. Unfortunately, these models have been shown to exhibit undesirable word associations resulting from gender, racial, and religious biases. Existing post-processing methods for debiasing word embeddings are unable to mitigate gender bias hidden in the spatial arrangement of word vectors. In this paper, we propose RAN-Debias, a novel gender debiasing methodology which not only eliminates the bias present in a word vector but also alters the spatial distribution of its neighbouring vectors, achieving a bias-free setting while maintaining minimal semantic offset. We also propose a new bias evaluation metric - Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections. Experiments based on a suite of evaluation metrics show that RAN-Debias significantly outperforms the state-of-the-art in reducing proximity bias (GIPE) by at least 42.02%. It also reduces direct bias, adding minimal semantic disturbance, and achieves the best performance in a downstream application task (coreference resolution).
Graph2Speak: Improving Speaker Identification using Network Knowledge in Criminal Conversational Data
Venue: arXiv cs.SI 2020
Year: 2020

Criminal investigations mostly rely on the collection of speech conversational data in order to identify speakers and build or enrich an existing criminal network. Social network analysis tools are then applied to identify the most central characters and the different communities within the network. We introduce two candidate datasets for criminal conversational data, Crime Scene Investigation (CSI), a television show, and the ROXANNE simulated data. We also introduce the metric of conversation accuracy in the context of criminal investigations. By re-ranking candidate speakers based on the frequency of previous interactions, we improve the speaker identification baseline by 1.2% absolute (1.3% relative), and the conversation accuracy by 2.6% absolute (3.4% relative) on CSI data, and by 1.1% absolute (1.2% relative), and 2% absolute (2.5% relative) respectively on the ROXANNE simulated data.
GFPNet: A Deep Network for Learning Shape Completion in Generic Fitted Primitives
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose an object reconstruction apparatus that uses the so-called Generic Primitives (GP) to complete shapes. A GP is a 3D point cloud depicting a generalized shape of a class of objects. To reconstruct the objects in a scene we first fit a GP onto each occluded object to obtain an initial raw structure. Secondly, we use a model-based deformation technique to fold the surface of the GP over the occluded object. The deformation model is encoded within the layers of a Deep Neural Network (DNN), coined GFPNet. The objective of the network is to transfer the particularities of the object from the scene to the raw volume represented by the GP. We show that GFPNet competes with state of the art shape completion methods by providing performance results on the ModelNet and KITTI benchmarking datasets.
Reconfigurable Intelligent Surface Empowered Device-to-Device Communication Underlaying Cellular Networks
Venue: arXiv cs.IT 2020
Year: 2020

Reconfigurable intelligent surface (RIS) is a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the overall network's spectrum efficiency (SE) (i.e., sum rate of D2D users and cellular users) and energy efficiency (EE), respectively, by jointly optimizing the resource reuse indicators, the transmit power and the RIS's passive beamforming, under the signal-to-interference-plus-noise ratio constraints and other practical constraints. To solve the non-convex problems, we first propose an efficient user-pairing scheme based on relative channel strength to determine the resource reuse indicators. Then, the transmit power and the RIS's passive beamforming are jointly optimized to maximize the SE by a proposed iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. Moreover, the EE-maximization problem is solved by an alternating algorithm integrated with Dinkelbach's method. Also, the convergence and complexity of both algorithms are analyzed. Numerical results show that the proposed design achieves significant SE and EE enhancements compared to traditional underlaying D2D network without RIS and other benchmarks.
Transfer Learning for British Sign Language Modelling
Venue: arXiv cs.CL 2020
Year: 2020

Automatic speech recognition and spoken dialogue systems have made great advances through the use of deep machine learning methods. This is partly due to greater computing power but also through the large amount of data available in common languages, such as English. Conversely, research in minority languages, including sign languages, is hampered by the severe lack of data. This has led to work on transfer learning methods, whereby a model developed for one language is reused as the starting point for a model on a second language, which is less resourced. In this paper, we examine two transfer learning techniques of fine-tuning and layer substitution for language modelling of British Sign Language. Our results show improvement in perplexity when using transfer learning with standard stacked LSTM models, trained initially using a large corpus for standard English from the Penn Treebank corpus
Does the First Mover Advantage Exist on GitHub?
Venue: arXiv cs.SI 2020
Year: 2020

Collaborative consensus-finding is an integral element of many Web services and greatly determines the quality of information, content, and products that are available through the Web. That also means that the dynamics of democratic consensus-finding strengthen collective resilience against potential threats that attempt to degrade information, content, and products and affect Web data, users, behaviors, and even beyond as well as offline life. Even on Web platforms that are open to all, the influence of some first mover authors may shape future discussion and collaboration, which is comparable to academic citation networks for instance. In a social coding network such as GitHub, activities of a set of users can have influence on other users who can get interested in further actions, possibly contributing to a new project together with influential users. In this paper, we analyze the effect of contribution activities on gaining influence in this and comparable networks that provide users the functionality and aims for reaching collaborative goals on the Web.For this purpose, we present an empirical approach to identify the top influential users by using network features and contribution characteristics, which we find in existing and newly collected data set. We find that early adopter dynamics exist in the GitHub community, where early adopters have more followers in the end as expected. However, we also see counterexamples that arise due to the social networking behavior of late adopters, and due to the aging effect of older repositories and users. We publicly share the source code and the data sets for reproducing our paper.
Controlling the Size of Supercompiled Programs using Multi-result Supercompilation
Venue: arXiv cs.PL 2020
Year: 2020

Supercompilation is a powerful program transformation technique with numerous interesting applications. Existing methods of supercompilation, however, are often very unpredictable with respect to the size of the resulting programs. We consider an approach for controlling result size, based on a combination of multi-result supercompilation and a specific generalization strategy, which avoids code duplication. The current early experiments with this method show promising results -- we can keep the size of the result small, while still performing powerful optimizations.
Joint optimization of TWT mechanism and scheduling for IEEE 802.11ax
Venue: arXiv cs.NI 2020
Year: 2020

IEEE 802.11ax as the newest Wireless Local Area Networks (WLANS) standard brings enormous improvements in network throughput, coverage and energy efficiency in densely populated areas. Unlike previous IEEE 802.11 standards where power saving mechanisms have a limited capability and flexibility, 802.11ax comes with a different mechanism called Target Wake Time (TWT) where stations (STAs) wake up only after each TWT interval and different STAs can wake up at different time instance depending on their application requirements. As an example, for a periodic data arrival occurring in IoT applications, STA can wake up by following the data period and go to sleep mode for a much longer time, and STAs with high traffic volume can have shorter TWT interval to wake up more frequency. Moreover, as multi-user transmission capability is added to 802.11ax, multiple STAs can have the same TWT interval and wake up at the same time, and hence there is a great opportunity to have collision-free transmission by scheduling multiple STAs on appreciate TWT intervals to reduce energy consumption and also increase network throughput. In this paper, we investigate the problem of STAs scheduling and TWT interval assignment together to reduce overall energy consumption of the network. We propose an algorithm that dynamically selects STAs to be served and assigns them the most suitable TWT interval given their traffic and channel conditions. We analyze our algorithm through Lyapunov optimization framework and show that our algorithm is arbitrarily close to the optimal performance at the price of increased queue sizes. Simulation results show that our algorithm consumes less power and support higher traffic compared to a benchmark algorithm that operates randomly for TWT assignment.
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
Venue: arXiv cs.CV 2020
Year: 2020

Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and gathers the results using switch functions. Combining them results in DetectoRS, which significantly improves the performances of object detection. On COCO test-dev, DetectoRS achieves state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation. The code is made publicly available.
Exploring Context-Aware Conversational Agents in Software Development
Venue: arXiv cs.SE 2020
Year: 2020

Software development is a complex endeavor that depends on a wide variety of contextual factors involving a large amount of distributed information. This knowledge could include: technology-related tasks, software operating environments and stakeholder requirements. A major roadblock to using this knowledge in software development is that most of this information is implicit and captured in the developers' minds (tacit) or spread through volumes of documentation. Developers, as they work often have to maintain mental models of these tasks as they produce the software. As a result, context can be easily lost or forgotten and developers often use trial-and-error approaches while finishing the project. This study aims at analyzing whether supporting software developers with a chatbot during task execution can improve the overall development experience. The chatbot can assist the developers in executing different tasks based on implicit contextual information. We propose an implementation to explore the viability of using textual chatbots to assist developers automatically and proactively with software development project activities that recur.
On tensor rank and commuting matrices
Venue: arXiv cs.CC 2020
Year: 2020

Obtaining superlinear lower bounds on tensor rank is a major open problem in complexity theory. In this paper we propose a generalization of the approach used by Strassen in the proof of his 3n/2 border rank lower bound. Our approach revolves around a problem on commuting matrices: Given matrices Z_1,...,Z_p of size n and an integer r>n, are there commuting matrices Z'_1,...,Z'_p of size r such that every Z_k is embedded as a submatrix in the top-left corner of Z'_k? As one of our main results, we show that this question always has a positive answer for r larger than rank(T)+n, where T denotes the tensor with slices Z_1,..,Z_p. Taking the contrapositive, if one can show for some specific matrices Z_1,...,Z_p and a specific integer r that this question has a negative answer, this yields the lower bound rank(T) > r-n. There is a little bit of slack in the above rank(T)+n bound, but we also provide a number of exact characterizations of tensor rank and symmetric rank, for ordinary and symmetric tensors, over the fields of real and complex numbers. Each of these characterizations points to a corresponding variation on the above approach. In order to explain how Strassen's theorem fits within this framework we also provide a self-contained proof of his lower bound.