OpenRaiser/Intern-Atlas huggingface dataset:
Probing the Natural Language Inference Task with Automated Reasoning Tools
Venue: arXiv cs.AI 2020
Year: 2020

The Natural Language Inference (NLI) task is an important task in modern NLP, as it asks a broad question to which many other tasks may be reducible: Given a pair of sentences, does the first entail the second? Although the state-of-the-art on current benchmark datasets for NLI are deep learning-based, it is worthwhile to use other techniques to examine the logical structure of the NLI task. We do so by testing how well a machine-oriented controlled natural language (Attempto Controlled English) can be used to parse NLI sentences, and how well automated theorem provers can reason over the resulting formulae. To improve performance, we develop a set of syntactic and semantic transformation rules. We report their performance, and discuss implications for NLI and logic-based NLP.
DeepClaw: A Robotic Hardware Benchmarking Platform for Learning Object Manipulation
Venue: arXiv cs.RO 2020
Year: 2020

We present DeepClaw as a reconfigurable benchmark of robotic hardware and task hierarchy for robot learning. The DeepClaw benchmark aims at a mechatronics perspective of the robot learning problem, which features a minimum design of robot cell that can be easily reconfigured to host robot hardware from various vendors, including manipulators, grippers, cameras, desks, and objects, aiming at a streamlined collection of physical manipulation data and evaluation of the learned skills for hardware benchmarking. We provide a detailed design of the robot cell with readily available parts to build the experiment environment that can host a wide range of robotic hardware commonly adopted for robot learning. We also propose a hierarchical pipeline of software integration, including localization, recognition, grasp planning, and motion planning, to streamline learning-based robot control, data collection, and experiment validation towards shareability and reproducibility. We present benchmarking results of the DeepClaw system for a baseline Tic-Tac-Toe task, a bin-clearing task, and a jigsaw puzzle task using three sets of standard robotic hardware. Our results show that tasks defined in DeepClaw can be easily reproduced on three robot cells. Under the same task setup, the differences in robotic hardware used will present a non-negligible impact on the performance metrics of robot learning. All design layouts and codes are hosted on Github for open access.
Gradual Relation Network: Decoding Intuitive Upper Extremity Movement Imaginations Based on Few-Shot EEG Learning
Venue: arXiv cs.NE 2020
Year: 2020

Brain-computer interface (BCI) is a communication tool that connects users and external devices. In a real-time BCI environment, a calibration procedure is particularly necessary for each user and each session. This procedure consumes a significant amount of time that hinders the application of a BCI system in a real-world scenario. To avoid this problem, we adopt the metric based few-shot learning approach for decoding intuitive upper-extremity movement imagination (MI) using a gradual relation network (GRN) that can gradually consider the combination of temporal and spectral groups. We acquired the MI data of the upper-arm, forearm, and hand associated with intuitive upper-extremity movement from 25 subjects. The grand average multiclass classification results under offline analysis were 42.57%, 55.60%, and 80.85% in 1-, 5-, and 25-shot settings, respectively. In addition, we could demonstrate the feasibility of intuitive MI decoding using the few-shot approach in real-time robotic arm control scenarios. Five participants could achieve a success rate of 78% in the drinking task. Hence, we demonstrated the feasibility of the online robotic arm control with shortened calibration time by focusing on human body parts but also the accommodation of various untrained intuitive MI decoding based on the proposed GRN.
Joint Optimal Software Caching, Computation Offloading and Communications Resource Allocation for Mobile Edge Computing
Venue: arXiv cs.IT 2020
Year: 2020

As software may be used by multiple users, caching popular software at the wireless edge has been considered to save computation and communications resources for mobile edge computing (MEC). However, fetching uncached software from the core network and multicasting popular software to users have so far been ignored. Thus, existing design is incomplete and less practical. In this paper, we propose a joint caching, computation and communications mechanism which involves software fetching, caching and multicasting, as well as task input data uploading, task executing (with non-negligible time duration) and computation result downloading, and mathematically characterize it. Then, we optimize the joint caching, offloading and time allocation policy to minimize the weighted sum energy consumption subject to the caching and deadline constraints. The problem is a challenging two-timescale mixed integer nonlinear programming (MINLP) problem, and is NP-hard in general. We convert it into an equivalent convex MINLP problem by using some appropriate transformations and propose two low-complexity algorithms to obtain suboptimal solutions of the original non-convex MINLP problem. Specifically, the first suboptimal solution is obtained by solving a relaxed convex problem using the consensus alternating direction method of multipliers (ADMM), and then rounding its optimal solution properly. The second suboptimal solution is proposed by obtaining a stationary point of an equivalent difference of convex (DC) problem using the penalty convex-concave procedure (Penalty-CCP) and ADMM. Finally, by numerical results, we show that the proposed solutions outperform existing schemes and reveal their advantages in efficiently utilizing storage, computation and communications resources.
Comparing quaternary and binary multipliers
Venue: arXiv cs.AR 2020
Year: 2020

We compare the implementation of a 8x8 bit multiplier with two different implementations of a 4x4 quaternary digit multiplier. Interfacing this binary multiplier with quaternary to binary decoders and binary to quaternary encoders leads to a 4x4 multiplier that outperforms the best direct implementation of a 4x4 quaternary multiplier. The far greater complexity of the 1-digit multipliers and 1-digit adders used in this direct implementation compared to the binary 1-bit multipliers and full adders cannot be compensated by the reduced count of quaternary operators. As the best quaternary multiplier includes the corresponding binary one, it means that there is no opportunity to get less interconnects, less chip area, less power dissipation with the quaternary multiplier.
Incremental Multiple Longest Common Sub-Sequences
Venue: arXiv cs.DS 2020
Year: 2020

We consider the problem of updating the information about multiple longest common sub-sequences. This kind of sub-sequences is used to highlight information that is shared across several information sequences, therefore it is extensively used namely in bioinformatics and computational genomics. In this paper we propose a way to maintain this information when the underlying sequences are subject to modifications, namely when letters are added and removed from the extremes of the sequence. Experimentally our data structure obtains significant improvements over the state of the art.
Topological Influence and Locality in Swap Schelling Games
Venue: arXiv cs.GT 2020
Year: 2020

Residential segregation is a wide-spread phenomenon that can be observed in almost every major city. In these urban areas residents with different racial or socioeconomic background tend to form homogeneous clusters. Schelling's famous agent-based model for residential segregation explains how such clusters can form even if all agents are tolerant, i.e., if they agree to live in mixed neighborhoods. For segregation to occur, all it needs is a slight bias towards agents preferring similar neighbors. Very recently, Schelling's model has been investigated from a game-theoretic point of view with selfish agents that strategically select their residential location. In these games, agents can improve on their current location by performing a location swap with another agent who is willing to swap. We significantly deepen these investigations by studying the influence of the underlying topology modeling the residential area on the existence of equilibria, the Price of Anarchy and on the dynamic properties of the resulting strategic multi-agent system. Moreover, as a new conceptual contribution, we also consider the influence of locality, i.e., if the location swaps are restricted to swaps of neighboring agents. We give improved almost tight bounds on the Price of Anarchy for arbitrary underlying graphs and we present (almost) tight bounds for regular graphs, paths and cycles. Moreover, we give almost tight bounds for grids, which are commonly used in empirical studies. For grids we also show that locality has a severe impact on the game dynamics.
AI in society and culture: decision making and values
Venue: arXiv cs.OH 2020
Year: 2020

With the increased expectation of artificial intelligence, academic research face complex questions of human-centred, responsible and trustworthy technology embedded into society and culture. Several academic debates, social consultations and impact studies are available to reveal the key aspects of the changing human-machine ecosystem. To contribute to these studies, hundreds of related academic sources are summarized below regarding AI-driven decisions and valuable AI. In details, sociocultural filters, taxonomy of human-machine decisions and perspectives of value-based AI are in the focus of this literature review. For better understanding, it is proposed to invite stakeholders in the prepared large-scale survey about the next generation AI that investigates issues that go beyond the technology.
A Large-scale Industrial and Professional Occupation Dataset
Venue: arXiv cs.SI 2020
Year: 2020

There has been growing interest in utilizing occupational data mining and analysis. In today's job market, occupational data mining and analysis is growing in importance as it enables companies to predict employee turnover, model career trajectories, screen through resumes and perform other human resource tasks. A key requirement to facilitate these tasks is the need for an occupation-related dataset. However, most research use proprietary datasets or do not make their dataset publicly available, thus impeding development in this area. To solve this issue, we present the Industrial and Professional Occupation Dataset (IPOD), which comprises 192k job titles belonging to 56k LinkedIn users. In addition to making IPOD publicly available, we also: (i) manually annotate each job title with its associated level of seniority, domain of work and location; and (ii) provide embedding for job titles and discuss various use cases. This dataset is publicly available at https://github.com/junhua/ipod.
Digraphie des langues ouest africaines : Latin2Ajami : un algorithme de translitteration automatique
Venue: arXiv cs.CL 2020
Year: 2020

The national languages of Senegal, like those of West Africa country in general, are written with two alphabets : the Latin alphabet that draws its strength from official decreesm and the completed Arabic script (Ajami), widespread and well integrated, that has little institutional support. This digraph created two worlds ignoring each other. Indeed, Ajami writing is generally used daily by populations from Koranic schools, while writing with the Latin alphabet is used by people from the public school. To solve this problem, it is useful to establish transliteration tools between these two scriptures. Preliminary work (Nguer, Bao-Diop, Fall, khoule, 2015) was performed to locate the problems, challenges and prospects. This present work, making it subsequently fell into this. Its objective is the study and creation of a transliteration algorithm from latin towards Ajami.
Role of Apache Software Foundation in Big Data Projects
Venue: arXiv cs.SE 2020
Year: 2020

With the increase in amount of Big Data being generated each year, tools and technologies developed and used for the purpose of storing, processing and analyzing Big Data has also improved. Open-Source software has been an important factor in the success and innovation in the field of Big Data while Apache Software Foundation (ASF) has played a crucial role in this success and innovation by providing a number of state-of-the-art projects, free and open to the public. ASF has classified its project in different categories. In this report, projects listed under Big Data category are deeply analyzed and discussed with reference to one-of-the seven sub-categories defined. Our investigation has shown that many of the Apache Big Data projects are autonomous but some are built based on other Apache projects and some work in conjunction with other projects to improve and ease development in Big Data space.
An Overview of Self-Similar Traffic: Its Implications in the Network Design
Venue: arXiv cs.NI 2020
Year: 2020

The knowledge about the true nature of the traffic in computer networking is a key requirement in the design of such networks. The phenomenon of self-similarity is a characteristic of the traffic of current client/server packet networks in LAN/WAN environments dominated by network technologies such as Ethernet and the TCP/IP protocol stack. The development of networks traffic simulators, which take into account this attribute, is necessary for a more realistic description the traffic on these networks and their use in the design of resources (contention elements) and protocols of flow control and network congestion. In this scenario it is recommended do not adopt standard traffic models of the Poisson type.
Interface-resolved simulation of the evaporation and combustion of a fuel droplet suspended in normal gravity
Venue: arXiv cs.CE 2020
Year: 2020

An interface-resolved simulation of the combustion of a fuel droplet suspended in normal gravity is presented in this work, followed by an extensive analysis on the physical aspects involved. The modeling is based on DropletSMOKE++, a multiphase solver developed for the modeling of droplet vaporization and combustion in convective conditions. A wide range of phenomena can be described by the model, including the interface advection, the phase-change, the combustion chemistry, non-ideal thermodynamics and multicomponent mixtures. To our knowledge, this is the most detailed simulation performed on this configuration, providing a useful theoretical and numerical support for the experimental activity on this field. A recent experimental work is used as a reference, in which a methanol droplet is suspended on a quartz fiber and ignited at different oxygen concentrations. The numerical analysis offers a detailed insight into the physics of the problem and a satisfactory agreement with the experiments in terms of diameter decay, radial temperature profiles and sensitivity to the oxygen concentration. The vaporization rate is affected by the thermal conduction from the fiber, due to the high temperatures involved. Moreover, the fiber perturbs the flame itself, providing quenching at its surface. The combustion physics is compared to the one predicted at zero-gravity, evidencing a lower standoff-ratio, a higher flame temperature and an intense internal circulation. The distribution of the species around the droplet shows (i) a local accumulation of intermediate oxidation products at the fiber surface and (ii) water absorption in the liquid phase, affecting the vaporization rate.
Harvesting and Refining Question-Answer Pairs for Unsupervised QA
Venue: arXiv cs.CL 2020
Year: 2020

Question Answering (QA) has shown great success thanks to the availability of large-scale datasets and the effectiveness of neural models. Recent research works have attempted to extend these successes to the settings with few or no labeled data available. In this work, we introduce two approaches to improve unsupervised QA. First, we harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a corpus of question-answer pairs (named as RefQA). Second, we take advantage of the QA model to extract more appropriate answers, which iteratively refines data over RefQA. We conduct experiments on SQuAD 1.1, and NewsQA by fine-tuning BERT without access to manually annotated data. Our approach outperforms previous unsupervised approaches by a large margin and is competitive with early supervised models. We also show the effectiveness of our approach in the few-shot learning setting.
Training robust neural networks using Lipschitz bounds
Venue: arXiv cs.LG 2020
Year: 2020

Due to their susceptibility to adversarial perturbations, neural networks (NNs) are hardly used in safety-critical applications. One measure of robustness to such perturbations in the input is the Lipschitz constant of the input-output map defined by an NN. In this work, we propose a framework to train multi-layer NNs while at the same time encouraging robustness by keeping their Lipschitz constant small, thus addressing the robustness issue. More specifically, we design an optimization scheme based on the Alternating Direction Method of Multipliers that minimizes not only the training loss of an NN but also its Lipschitz constant resulting in a semidefinite programming based training procedure that promotes robustness. We design two versions of this training procedure. The first one includes a regularizer that penalizes an accurate upper bound on the Lipschitz constant. The second one allows to enforce a desired Lipschitz bound on the NN at all times during training. Finally, we provide two examples to show that the proposed framework successfully increases the robustness of NNs.
Seeing the Forest and the Trees: Detection and Cross-Document Coreference Resolution of Militarized Interstate Disputes
Venue: arXiv cs.CL 2020
Year: 2020

Previous efforts to automate the detection of social and political events in text have primarily focused on identifying events described within single sentences or documents. Within a corpus of documents, these automated systems are unable to link event references -- recognize singular events across multiple sentences or documents. A separate literature in computational linguistics on event coreference resolution attempts to link known events to one another within (and across) documents. I provide a data set for evaluating methods to identify certain political events in text and to link related texts to one another based on shared events. The data set, Headlines of War, is built on the Militarized Interstate Disputes data set and offers headlines classified by dispute status and headline pairs labeled with coreference indicators. Additionally, I introduce a model capable of accomplishing both tasks. The multi-task convolutional neural network is shown to be capable of recognizing events and event coreferences given the headlines' texts and publication dates.
Mathematical foundations of stable RKHSs
Venue: arXiv cs.LG 2020
Year: 2020

Reproducing kernel Hilbert spaces (RKHSs) are key spaces for machine learning that are becoming popular also for linear system identification. In particular, the so-called stable RKHSs can be used to model absolutely summable impulse responses. In combination e.g. with regularized least squares they can then be used to reconstruct dynamic systems from input-output data. In this paper we provide new structural properties of stable RKHSs. The relation between stable kernels and other fundamental classes, like those containing absolutely summable or finite-trace kernels, is elucidated. These insights are then brought into the feature space context. First, it is proved that any stable kernel admits feature maps induced by a basis of orthogonal eigenvectors in l2. The exact connection with classical system identification approaches that exploit such kind of functions to model impulse responses is also provided. Then, the necessary and sufficient stability condition for RKHSs designed by formulating kernel eigenvectors and eigenvalues is obtained. Overall, our new results provide novel mathematical foundations of stable RKHSs with impact on stability tests, impulse responses modeling and computational efficiency of regularized schemes for linear system identification.
Regularized Estimation of Information via High Dimensional Canonical Correlation Analysis
Venue: arXiv cs.IT 2020
Year: 2020

In recent years, there has been an upswing of interest in estimating information from data emerging in a lot of areas beyond communications. This paper aims at estimating the information between two random phenomena by using consolidated second-order statistics tools. The squared-loss mutual information is chosen for that purpose as a natural surrogate of Shannon mutual information. The rationale for doing so is developed for i.i.d. discrete sources -mapping data onto the simplex space-, and for analog sources -mapping data onto the characteristic space-, highlighting the links with other well-known related concepts in the literature based on local approximations of information-theoretic measures. The proposed approach gains in interpretability and scalability for its use on large datasets, providing physical interpretation to the free regularization parameters. Moreover, the structure of the proposed mapping allows resorting to Szegö's theorem to reduce the complexity for high dimensional mappings, exhibiting strong dualities with spectral analysis. The performance of the proposed estimators is analyzed using Gaussian mixtures.
Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches
Venue: arXiv cs.CL 2020
Year: 2020

An interesting and frequent type of multi-word expression (MWE) is the headless MWE, for which there are no true internal syntactic dominance relations; examples include many named entities ("Wells Fargo") and dates ("July 5, 2020") as well as certain productive constructions ("blow for blow", "day after day"). Despite their special status and prevalence, current dependency-annotation schemes require treating such flat structures as if they had internal syntactic heads, and most current parsers handle them in the same fashion as headed constructions. Meanwhile, outside the context of parsing, taggers are typically used for identifying MWEs, but taggers might benefit from structural information. We empirically compare these two common strategies--parsing and tagging--for predicting flat MWEs. Additionally, we propose an efficient joint decoding algorithm that combines scores from both strategies. Experimental results on the MWE-Aware English Dependency Corpus and on six non-English dependency treebanks with frequent flat structures show that: (1) tagging is more accurate than parsing for identifying flat-structure MWEs, (2) our joint decoder reconciles the two different views and, for non-BERT features, leads to higher accuracies, and (3) most of the gains result from feature sharing between the parsers and taggers.
Experiences from Exporting Major Proof Assistant Libraries
Venue: arXiv cs.LO 2020
Year: 2020

The interoperability of proof assistants and the integration of their libraries is a highly valued but elusive goal in the field of theorem proving. As a preparatory step, in previous work, we translated the libraries of multiple proof assistants, specifically the ones of Coq, HOL Light, IMPS, Isabelle, Mizar, and PVS into a universal format: OMDoc/MMT. Each translation presented tremendous theoretical, technical, and social challenges, some universal and some system-specific, some solvable and some still open. In this paper, we survey these challenges and compare and evaluate the solutions we chose. We believe similar library translations will be an essential part of any future system interoperability solution and our experiences will prove valuable to others undertaking such efforts.