OpenRaiser/Intern-Atlas huggingface dataset:
Strongly Chordal Graph Generation using Intersection Graph Characterisation
Venue: arXiv cs.DS 2019
Year: 2019

Strongly chordal graphs are a subclass of chordal graphs. Farber also established a number of different characterisations for this class of graphs. These include an intersection graph characterisation that is analogous to a similar characterisation for chordal graphs. Seker et al. exploited this characterisation of chordal graphs to obtain an algorithm for generating them. In this paper, we propose an algorithm to show that strongly chordal graphs can also be generated using their intersection graph characterisation.
Collecting MIB Data from Network Managed by SNMP using Multi Mobile Agents
Venue: arXiv cs.NI 2019
Year: 2019

Network anomalies are destructive to networks. Intrusion detection systems monitor network component behavior to detect unusual activity (i.e., possible threats). Application-layer Simple Network Management Protocol (SNMP) has been used for decades via TCP/IP protocol to manage network devices. Raw data security evaluation in intrusion detection incurs latency in detection. Management Information Base (MIB) combined with SNMP is a solution for this, the traditional approach of SNMP is centralized. Thus, rendering it unreliable and non-adaptive to network changes when it comes to distributed network. In distributed network, using single or multiple light Mobile Agents are an optimal solution for data gathering as they can move from one source node to another, executing naturally at each. This helps complete tasks without increasing the network overheads, and contributes to decreasing latency
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
Venue: arXiv cs.LG 2019
Year: 2019

Robustness of Deep Reinforcement Learning (DRL) algorithms towards adversarial attacks in real world applications such as those deployed in cyber-physical systems (CPS) are of increasing concern. Numerous studies have investigated the mechanisms of attacks on the RL agent's state space. Nonetheless, attacks on the RL agent's action space (AS) (corresponding to actuators in engineering systems) are equally perverse; such attacks are relatively less studied in the ML literature. In this work, we first frame the problem as an optimization problem of minimizing the cumulative reward of an RL agent with decoupled constraints as the budget of attack. We propose a white-box Myopic Action Space (MAS) attack algorithm that distributes the attacks across the action space dimensions. Next, we reformulate the optimization problem above with the same objective function, but with a temporally coupled constraint on the attack budget to take into account the approximated dynamics of the agent. This leads to the white-box Look-ahead Action Space (LAS) attack algorithm that distributes the attacks across the action and temporal dimensions. Our results shows that using the same amount of resources, the LAS attack deteriorates the agent's performance significantly more than the MAS attack. This reveals the possibility that with limited resource, an adversary can utilize the agent's dynamics to malevolently craft attacks that causes the agent to fail. Additionally, we leverage these attack strategies as a possible tool to gain insights on the potential vulnerabilities of DRL agents.
Common Library 1.0: A Corpus of Victorian Novels Reflecting the Population in Terms of Publication Year and Author Gender
Venue: arXiv cs.DL 2019
Year: 2019

Research in 19th-century book history, sociology of literature, and quantitative literary history is blocked by the absence of a collection of novels which captures the diversity of literary production. We introduce a corpus of 75 Victorian novels sampled from a 15,322-record bibliography of novels published between 1837 and 1901 in the British Isles. This corpus, the Common Library, is distinctive in the following way: the shares of novels in the corpus associated with sociologically important subgroups match the shares in the broader population. For example, the proportion of novels written by women in 1880s in the corpus is approximately the same as in the population. Although we do not, in this particular paper, claim that the corpus is a representative sample in the familiar sense--a sample is representative if "characteristics of interest in the population can be estimated from the sample with a known degree of accuracy" (Lohr 2010, p. 3)--we are confident that the corpus will be useful to researchers. This is because existing corpora--frequently convenience samples--are conspicuously misaligned with the population of published novels. They tend to over-represent novels published in specific periods and novels by men. The Common Library may be used alongside or in place of these non-representative convenience corpora.
Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering
Venue: arXiv cs.CL 2019
Year: 2019

Many algorithms for Knowledge-Based Question Answering (KBQA) depend on semantic parsing, which translates a question to its logical form. When only weak supervision is provided, it is usually necessary to search valid logical forms for model training. However, a complex question typically involves a huge search space, which creates two main problems: 1) the solutions limited by computation time and memory usually reduce the success rate of the search, and 2) spurious logical forms in the search results degrade the quality of training data. These two problems lead to a poorly-trained semantic parsing model. In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions. With search space constrained by predicted operators, sufficient search paths can be explored, more valid logical forms can be derived, and operators possibly causing spurious logical forms can be avoided. As a result, a larger proportion of questions in a weakly supervised training set are equipped with logical forms, and fewer spurious logical forms are generated. Such high-quality training data directly contributes to a better semantic parsing model. Experimental results on one of the largest KBQA datasets (i.e., CSQA) verify the effectiveness of our approach: improving the precision from 67% to 72% and the recall from 67% to 72% in terms of the overall score.
Understanding the Impact of Text Highlighting in Crowdsourcing Tasks
Venue: arXiv cs.HC 2019
Year: 2019

Text classification is one of the most common goals of machine learning (ML) projects, and also one of the most frequent human intelligence tasks in crowdsourcing platforms. ML has mixed success in such tasks depending on the nature of the problem, while crowd-based classification has proven to be surprisingly effective, but can be expensive. Recently, hybrid text classification algorithms, combining human computation and machine learning, have been proposed to improve accuracy and reduce costs. One way to do so is to have ML highlight or emphasize portions of text that it believes to be more relevant to the decision. Humans can then rely only on this text or read the entire text if the highlighted information is insufficient. In this paper, we investigate if and under what conditions highlighting selected parts of the text can (or cannot) improve classification cost and/or accuracy, and in general how it affects the process and outcome of the human intelligence tasks. We study this through a series of crowdsourcing experiments running over different datasets and with task designs imposing different cognitive demands. Our findings suggest that highlighting is effective in reducing classification effort but does not improve accuracy - and in fact, low-quality highlighting can decrease it.
From Few to More: Large-scale Dynamic Multiagent Curriculum Learning
Venue: arXiv cs.AI 2019
Year: 2019

A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale multiagent settings due to the complex dynamics between the environment and agents and the explosion of state-action space. In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents. We propose three transfer mechanisms across curricula to accelerate the learning process. Moreover, due to the fact that the state dimension varies across curricula,, and existing network structures cannot be applied in such a transfer setting since their network input sizes are fixed. Therefore, we design a novel network structure called Dynamic Agent-number Network (DyAN) to handle the dynamic size of the network input. Experimental results show that DyMA-CL using DyAN greatly improves the performance of large-scale multiagent learning compared with state-of-the-art deep reinforcement learning approaches. We also investigate the influence of three transfer mechanisms across curricula through extensive simulations.
#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment
Venue: arXiv cs.CL 2019
Year: 2019

Inspired by the recent social movement of #MeToo, we are building a chatbot to assist survivors of sexual harassment cases (designed for the city of Maastricht but can easily be extended). The motivation behind this work is twofold: properly assist survivors of such events by directing them to appropriate institutions that can offer them help and increase the incident documentation so as to gather more data about harassment cases which are currently under reported. We break down the problem into three data science/machine learning components: harassment type identification (treated as a classification problem), spatio-temporal information extraction (treated as Named Entity Recognition problem) and dialogue with the users (treated as a slot-filling based chatbot). We are able to achieve a success rate of more than 98% for the identification of a harassment-or-not case and around 80% for the specific type harassment identification. Locations and dates are identified with more than 90% accuracy and time occurrences prove more challenging with almost 80%. Finally, initial validation of the chatbot shows great potential for the further development and deployment of such a beneficial for the whole society tool.
IoT Forensic -- A digital investigation framework for IoT systems
Venue: arXiv cs.NI 2019
Year: 2019

Security issues, threats, and attacks in relation with the IoT have been identified as promising and challenging area of research. Eventually, the need for a forensics methodology for investigating IoT-related crime is therefore essential. However, the IoT poses many challenges for forensics investigators. These include the wide range and variety of information, the unclear lines of differentiation between networks, for example private networks increasingly fading into public networks. Further, integration of a large number of objects in IoT forensic interest, along with the relevance of identified and collected devices makes forensic of IoT devices more complicated. The scope of this paper is to present a framework for IoT forensic. We aimed at the study and development of the link to support digital investigations of IoT devices and tackle emerging challenges in digital forensics. We emphasize on various steps for digital forensic with respect to IoT devices.
Computing Derivatives for PETSc Adjoint Solvers using Algorithmic Differentiation
Venue: arXiv cs.MS 2019
Year: 2019

Most nonlinear partial differential equation (PDE) solvers require the Jacobian matrix associated to the differential operator. In PETSc, this is typically achieved by either an analytic derivation or numerical approximation method such as finite differences. For complex applications, hand-coding the Jacobian can be time-consuming and error-prone, yet computationally efficient. Whilst finite difference approximations are straight-forward to implement, they have high arithmetic complexity and low accuracy. Alternatively, one may compute Jacobians using algorithmic differentiation (AD), yielding the same derivatives as an analytic derivation, with the added benefit that the implementation is problem independent. In this work, the operator overloading AD tool ADOL-C is applied to generate Jacobians for time-dependent, nonlinear PDEs and their adjoints. Various strategies are considered, including compressed and matrix-free approaches. In numerical experiments with a 2D diffusion-reaction model, the performance of these strategies has been studied and compared to the hand-derived version.
Discriminative Video Representation Learning Using Support Vector Classifiers
Venue: arXiv cs.CV 2019
Year: 2019

Most popular deep models for action recognition in videos generate independent predictions for short clips, which are then pooled heuristically to assign an action label to the full video segment. As not all frames may characterize the underlying action---many are common across multiple actions---pooling schemes that impose equal importance on all frames might be unfavorable. In an attempt to tackle this problem, we propose discriminative pooling, based on the notion that among the deep features generated on all short clips, there is at least one that characterizes the action. To identify these useful features, we resort to a negative bag consisting of features that are known to be irrelevant, for example, they are sampled either from datasets that are unrelated to our actions of interest or are CNN features produced via random noise as input. With the features from the video as a positive bag and the irrelevant features as the negative bag, we cast an objective to learn a (nonlinear) hyperplane that separates the unknown useful features from the rest in a multiple instance learning formulation within a support vector machine setup. We use the parameters of this separating hyperplane as a descriptor for the full video segment. Since these parameters are directly related to the support vectors in a max-margin framework, they can be treated as a weighted average pooling of the features from the bags, with zero weights given to non-support vectors. Our pooling scheme is end-to-end trainable within a deep learning framework. We report results from experiments on eight computer vision benchmark datasets spanning a variety of video-related tasks and demonstrate state-of-the-art performance across these tasks.
Optimizing Generalized Rate Metrics through Game Equilibrium
Venue: arXiv cs.LG 2019
Year: 2019

We present a general framework for solving a large class of learning problems with non-linear functions of classification rates. This includes problems where one wishes to optimize a non-decomposable performance metric such as the F-measure or G-mean, and constrained training problems where the classifier needs to satisfy non-linear rate constraints such as predictive parity fairness, distribution divergences or churn ratios. We extend previous two-player game approaches for constrained optimization to a game between three players to decouple the classifier rates from the non-linear objective, and seek to find an equilibrium of the game. Our approach generalizes many existing algorithms, and makes possible new algorithms with more flexibility and tighter handling of non-linear rate constraints. We provide convergence guarantees for convex functions of rates, and show how our methodology can be extended to handle sums of ratios of rates. Experiments on different fairness tasks confirm the efficacy of our approach.
User Evaluation of a Multi-dimensional Statistical Dialogue System
Venue: arXiv cs.CL 2019
Year: 2019

We present the first complete spoken dialogue system driven by a multi-dimensional statistical dialogue manager. This framework has been shown to substantially reduce data needs by leveraging domain-independent dimensions, such as social obligations or feedback, which (as we show) can be transferred between domains. In this paper, we conduct a user study and show that the performance of a multi-dimensional system, which can be adapted from a source domain, is equivalent to that of a one-dimensional baseline, which can only be trained from scratch.
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Venue: arXiv cs.AI 2019
Year: 2019

As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, present different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360 (http://aix360.mybluemix.net/), an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics. Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability. For data scientists and other users of the toolkit, we have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. We also discuss enhancements to bring research innovations closer to consumers of explanations, ranging from simplified, more accessible versions of algorithms, to tutorials and an interactive web demo to introduce AI explainability to different audiences and application domains. Together, our toolkit and taxonomy can help identify gaps where more explainability methods are needed and provide a platform to incorporate them as they are developed.
Chunked Lectures: A new model for conducting online lectures within Information Technology higher education
Venue: arXiv cs.CY 2019
Year: 2019

The primary aim of this study in this paper aside from presenting student preference survey data and analytics data with relation to learning videos, is to address the attention span issues with our cohort of Information Technology students specifically and propose a new model being chunked lectures for delivering online lectures within the Information Technology discipline which may manage attention span more effectively and thus may also increase comprehension and retention of knowledge.
Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records
Venue: arXiv cs.CL 2019
Year: 2019

Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general domain, both datasets and models are limited in biomedical and clinical domains. The BioCreative/OHNLP organizers have made the first attempt to annotate 1,068 sentence pairs from clinical notes and have called for a community effort to tackle the Semantic Textual Similarity (BioCreative/OHNLP STS) challenge. We developed models using traditional machine learning and deep learning approaches. For the post challenge, we focus on two models: the Random Forest and the Encoder Network. We applied sentence embeddings pre-trained on PubMed abstracts and MIMIC-III clinical notes and updated the Random Forest and the Encoder Network accordingly. The official results demonstrated our best submission was the ensemble of eight models. It achieved a Person correlation coefficient of 0.8328, the highest performance among 13 submissions from 4 teams. For the post challenge, the performance of both Random Forest and the Encoder Network was improved; in particular, the correlation of the Encoder Network was improved by ~13%. During the challenge task, no end-to-end deep learning models had better performance than machine learning models that take manually-crafted features. In contrast, with the sentence embeddings pre-trained on biomedical corpora, the Encoder Network now achieves a correlation of ~0.84, which is higher than the original best model. The ensembled model taking the improved versions of the Random Forest and Encoder Network as inputs further increased performance to 0.8528. Deep learning models with sentence embeddings pre-trained on biomedical corpora achieve the highest performance on the test set.
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video
Venue: arXiv cs.CV 2019
Year: 2019

We present a self-supervised learning-based pipeline for dense 3D reconstruction from full-length monocular endoscopic videos without a priori modeling of anatomy or shading. Our method only relies on unlabeled monocular endoscopic videos and conventional multi-view stereo algorithms, and requires neither manual interaction nor patient CT in both training and application phases. In a cross-patient study using CT scans as groundtruth, we show that our method is able to produce photo-realistic dense 3D reconstructions with submillimeter mean residual errors from endoscopic videos from unseen patients and scopes.
Lightweight Record-and-Replay for Intermittent Tests Failures
Venue: arXiv cs.SE 2019
Year: 2019

In this paper we present lightweight record-and-replay (RR). In contrast to traditional "fully deterministic" RR solutions, lightweight RR focuses on handling nondeterminism arising from thread communication for programs with concurrent, message-passing architectures. By decreasing nondeterminism in programs, lightweight RR decreases the number of intermittent failures in program's test suites. We evaluated the effectiveness of lightweight RR on Servo, a highly concurrent web browser. Our evaluation shows lightweight RR is effective at greatly reducing intermittent failures for some tests, but not others. Lightweight RR performance overhead remains a work in progress, but log sizes are quite small. We believe with further work lightweight RR could prove useful for lowering nondeterminism in programs at a negligible performance overhead.
Trading-Off Static and Dynamic Regret in Online Least-Squares and Beyond
Venue: arXiv cs.LG 2019
Year: 2019

Recursive least-squares algorithms often use forgetting factors as a heuristic to adapt to non-stationary data streams. The first contribution of this paper rigorously characterizes the effect of forgetting factors for a class of online Newton algorithms. For exp-concave and strongly convex objectives, the algorithms achieve the dynamic regret of $\max\{O(\log T),O(\sqrt{TV})\}$, where $V$ is a bound on the path length of the comparison sequence. In particular, we show how classic recursive least-squares with a forgetting factor achieves this dynamic regret bound. By varying $V$, we obtain a trade-off between static and dynamic regret. In order to obtain more computationally efficient algorithms, our second contribution is a novel gradient descent step size rule for strongly convex functions. Our gradient descent rule recovers the order optimal dynamic regret bounds described above. For smooth problems, we can also obtain static regret of $O(T^{1-β})$ and dynamic regret of $O(T^βV^*)$, where $β\in (0,1)$ and $V^*$ is the path length of the sequence of minimizers. By varying $β$, we obtain a trade-off between static and dynamic regret.