OpenRaiser/Intern-Atlas huggingface dataset:
Derived metrics for the game of Go -- intrinsic network strength assessment and cheat-detection
Venue: arXiv cs.AI 2020
Year: 2020

The widespread availability of superhuman AI engines is changing how we play the ancient game of Go. The open-source software packages developed after the AlphaGo series shifted focus from producing strong playing entities to providing tools for analyzing games. Here we describe two ways of how the innovations of the second generation engines (e.g.~score estimates, variable komi) can be used for defining new metrics that help deepen our understanding of the game. First, we study how much information the search component contributes in addition to the raw neural network policy output. This gives an intrinsic strength measurement for the neural network. Second, we define the effect of a move by the difference in score estimates. This gives a fine-grained, move-by-move performance evaluation of a player. We use this in combating the new challenge of detecting online cheating.
Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Network
Venue: arXiv cs.HC 2020
Year: 2020

Chess game position analysis is important in improving ones game. It requires entry of moves into a chess engine which is, cumbersome and error prone. We present ARChessAnalyzer, a complete pipeline from live image capture of a physical chess game, to board and piece recognition, to move analysis and finally to Augmented Reality (AR) overlay of the chess diagram position and move on the physical board. ARChessAnalyzer is like a scene analyzer - it uses an ensemble of traditional image and vision techniques to segment the scene (ie the chess game) and uses Convolution Neural Networks (CNNs) to predict the segmented pieces and combine it together to analyze the game. This paper advances the state of the art in the first of its kind end to end integration of robust detection and segmentation of the board, chess piece detection using the fine-tuned AlexNet CNN and chess engine analyzer in a handheld device app. The accuracy of the entire chess position prediction pipeline is 93.45\% and takes 3-4.5sec from live capture to AR overlay. We also validated our hypothesis that ARChessAnalyzer, is faster at analysis than manual entry for all board positions for valid outcomes. Our hope is that the instantaneous feedback this app provides will help chess learners worldwide at all levels improve their game.
Indoor Localization Techniques Within a Home Monitoring Platform
Venue: arXiv cs.CY 2020
Year: 2020

This paper details a number of indoor localization techniques developed for real-time monitoring of older adults. These were developed within the framework of the i-Light research project that was funded by the European Union. The project targeted the development and initial evaluation of a configurable and cost-effective cyber-physical system for monitoring the safety of older adults who are living in their own homes. Localization hardware consists of a number of custom-developed devices that replace existing luminaires. In addition to lighting capabilities, they measure the strength of a Bluetooth Low Energy signal emitted by a wearable device on the user. Readings are recorded in real time and sent to a software server for analysis. We present a comparative evaluation of the accuracy achieved by several server-side algorithms, including Kalman filtering, a look-back heuristic as well as a neural network-based approach. It is known that approaches based on measuring signal strength are sensitive to the placement of walls, construction materials used, the presence of doors as well as existing furniture. As such, we evaluate the proposed approaches in two separate locations having distinct building characteristics. We show that the proposed techniques improve the accuracy of localization. As the final step, we evaluate our results against comparable existing approaches.
Analysis of an M/G/1 system for the optimization of the RTG performances in the delivery of containers in Abidjan Terminal
Venue: arXiv cs.PF 2020
Year: 2020

In front of the major challenges to increase its productivity while satisfying its customer, it is today important to establish in advance the operational performances of the RTG Abidjan Terminal. In this article, by using an M/G/1 retrial queue system, we obtained the average number of parked delivery trucks and as well as their waiting time. Finally, we used Matlab to represent them graphically then analyze the RTG performances according to the traffic rate.
Multidisciplinary Design Optimization of Reusable Launch Vehicles for Different Propellants and Objectives
Venue: arXiv cs.NE 2020
Year: 2020

Identifying the optimal design of a new launch vehicle is most important since design decisions made in the early development phase limit the vehicles' later performance and determines the associated costs. Reusing the first stage via retro-propulsive landing increases the complexity even more. Therefore, we develop an optimization framework for partially reusable launch vehicles, which enables multidisciplinary design studies. The framework contains suitable mass estimates of all essential subsystems and a routine to calculate the needed propellant for the ascent and landing maneuvers. For design optimization, the framework can be coupled with a genetic algorithm. The overall goal is to reveal the implications of different propellant combinations and objective functions on the launcher's optimal design for various mission scenarios. The results show that the optimization objective influences the most suitable propellant choice and the overall launcher design, concerning staging, weight, size, and rocket engine parameters. In terms of gross lift-off weight, liquid hydrogen seems to be favorable. When optimizing for a minimum structural mass or an expandable structural mass, hydrocarbon-based solutions show better results. Finally, launch vehicles using a hydrocarbon fuel in the first stage and liquid hydrogen in the upper stage are an appealing alternative, combining both fuels' benefits.
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily
Venue: arXiv cs.LG 2020
Year: 2020

Meta learning algorithms have been widely applied in many tasks for efficient learning, such as few-shot image classification and fast reinforcement learning. During meta training, the meta learner develops a common learning strategy, or experience, from a variety of learning tasks. Therefore, during meta test, the meta learner can use the learned strategy to quickly adapt to new tasks even with a few training samples. However, there is still a dark side about meta learning in terms of reliability and robustness. In particular, is meta learning vulnerable to adversarial attacks? In other words, would a well-trained meta learner utilize its learned experience to build wrong or likely useless knowledge, if an adversary unnoticeably manipulates the given training set? Without the understanding of this problem, it is extremely risky to apply meta learning in safety-critical applications. Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem. In particular, we formally define key elements of adversarial attacks unique to meta learning and propose the first attacking algorithm against meta learning under various settings. We evaluate the effectiveness of the proposed attacking strategy as well as the robustness of several representative meta learning algorithms. Experimental results demonstrate that the proposed attacking strategy can easily break the meta learner and meta learning is vulnerable to adversarial attacks. The implementation of the proposed framework will be released upon the acceptance of this paper.
PRAGMA: Interactively Constructing Functional Brain Parcellations
Venue: arXiv cs.HC 2020
Year: 2020

A prominent goal of neuroimaging studies is mapping the human brain, in order to identify and delineate functionally-meaningful regions and elucidate their roles in cognitive behaviors. These brain regions are typically represented by atlases that capture general trends over large populations. Despite being indispensable to neuroimaging experts, population-level atlases do not capture individual differences in functional organization. In this work, we present an interactive visualization method, PRAGMA, that allows domain experts to derive scan-specific parcellations from established atlases. PRAGMA features a user-driven, hierarchical clustering scheme for defining temporally correlated parcels in varying granularity. The visualization design supports the user in making decisions on how to perform clustering, namely when to expand, collapse, or merge parcels. This is accomplished through a set of linked and coordinated views for understanding the user's current hierarchy, assessing intra-cluster variation, and relating parcellations to an established atlas. We assess the effectiveness of PRAGMA through a user study with four neuroimaging domain experts, where our results show that PRAGMA shows the potential to enable exploration of individualized and state-specific brain parcellations and to offer interesting insights into functional brain networks.
Optimal Load Balanced Demand Distribution under Overload Penalties
Venue: arXiv cs.DS 2020
Year: 2020

Input to the Load Balanced Demand Distribution (LBDD) consists of the following: (a) a set of service centers; (b) a set of demand nodes and; (c) a cost matrix containing the cost of assignment for each (demand node, service center) pair. In addition, each service center is also associated with a notion of capacity and a penalty which is incurred if it gets overloaded. Given the input, the LBDD problem determines a mapping from the set of n demand vertices to the set of k service centers, n being much larger than k. The objective is to determine a mapping that minimizes the sum of the following two terms: (i) the total cost between demand units and their allotted service centers and, (ii) total penalties incurred. The problem of LBDD has a variety of applications. An instance of the LBDD problem can be reduced to an instance of the min-cost bi-partite matching problem. The best known algorithm for min-cost matching in an unbalanced bipartite graph yields a complexity of O($n^3k$). This paper proposes novel allotment subspace re-adjustment based approach which allows us to characterize the optimality of the mapping without invoking matching or mincost flow. This approach yields an optimal solution with time complexity $O(nk^3 +nk^2 log n)$, and also allows us to efficiently maintain an optimal allotment under insertions and deletions.
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling
Venue: arXiv cs.NE 2020
Year: 2020

Training a deep neural network requires a large amount of single-task data and involves a long time-consuming optimization phase. This is not scalable to complex, realistic environments with new unexpected changes. Humans can perform fast incremental learning on the fly and memory systems in the brain play a critical role. We introduce Sparse Meta Networks -- a meta-learning approach to learn online sequential adaptation algorithms for deep neural networks, by using deep neural networks. We augment a deep neural network with a layer-specific fast-weight memory. The fast-weights are generated sparsely at each time step and accumulated incrementally through time providing a useful inductive bias for online continual adaptation. We demonstrate strong performance on a variety of sequential adaptation scenarios, from a simple online reinforcement learning to a large scale adaptive language modelling.
Enclave-Aware Compartmentalization and Secure Sharing with Sirius
Venue: arXiv cs.CR 2020
Year: 2020

Hardware-assisted trusted execution environments (TEEs) are critical building blocks of many modern applications. However, they have a one-way isolation model that introduces a semantic gap between a TEE and its outside world. This lack of information causes an ever-increasing set of attacks on TEE-enabled applications that exploit various insecure interactions with the host OSs, applications, or other enclaves. We introduce Sirius, the first compartmentalization framework that achieves strong isolation and secure sharing in TEE-assisted applications by controlling the dataflows within primary kernel objects (e.g. threads, processes, address spaces, files, sockets, pipes) in both the secure and normal worlds. Sirius replaces ad-hoc interactions in current TEE systems with a principled approach that adds strong inter- and intra-address space isolation and effectively eliminates a wide range of attacks. We evaluate Sirius on ARM platforms and find that it is lightweight ($\approx 15K$ LoC) and only adds $\approx 10.8\%$ overhead to enable TEE support on applications such as httpd, and improves the performance of existing TEE-enabled applications such as the Darknet ML framework and ARM's LibDDSSec by $0.05\%-5.6\%$.
Accurate TLS Fingerprinting using Destination Context and Knowledge Bases
Venue: arXiv cs.CR 2020
Year: 2020

Network fingerprinting is used to identify applications, provide insight into network traffic, and detect malicious activity. With the broad adoption of TLS, traditional fingerprinting techniques that rely on clear-text data are no longer viable. TLS-specific techniques have been introduced that create a fingerprint string from carefully selected data features in the client_hello to facilitate process identification before data is exchanged. Unfortunately, this approach fails in practice because hundreds of processes can map to the same fingerprint string. We solve this problem by presenting a TLS fingerprinting system that makes use of the destination address, port, and server name in addition to a carefully constructed fingerprint string. The destination context is used to disambiguate the set of processes that match a fingerprint string by applying a weighted naive Bayes classifier, resulting in far greater performance.
Leveraging Clickstream Trajectories to Reveal Low-Quality Workers in Crowdsourced Forecasting Platforms
Venue: arXiv cs.HC 2020
Year: 2020

Crowdwork often entails tackling cognitively-demanding and time-consuming tasks. Crowdsourcing can be used for complex annotation tasks, from medical imaging to geospatial data, and such data powers sensitive applications, such as health diagnostics or autonomous driving. However, the existence and prevalence of underperforming crowdworkers is well-recognized, and can pose a threat to the validity of crowdsourcing. In this study, we propose the use of a computational framework to identify clusters of underperforming workers using clickstream trajectories. We focus on crowdsourced geopolitical forecasting. The framework can reveal different types of underperformers, such as workers with forecasts whose accuracy is far from the consensus of the crowd, those who provide low-quality explanations for their forecasts, and those who simply copy-paste their forecasts from other users. Our study suggests that clickstream clustering and analysis are fundamental tools to diagnose the performance of crowdworkers in platforms leveraging the wisdom of crowds.
Dynamic Context-guided Capsule Network for Multimodal Machine Translation
Venue: arXiv cs.CL 2020
Year: 2020

Multimodal machine translation (MMT), which mainly focuses on enhancing text-only translation with visual features, has attracted considerable attention from both computer vision and natural language processing communities. Most current MMT models resort to attention mechanism, global context modeling or multimodal joint representation learning to utilize visual features. However, the attention mechanism lacks sufficient semantic interactions between modalities while the other two provide fixed visual context, which is unsuitable for modeling the observed variability when generating translation. To address the above issues, in this paper, we propose a novel Dynamic Context-guided Capsule Network (DCCN) for MMT. Specifically, at each timestep of decoding, we first employ the conventional source-target attention to produce a timestep-specific source-side context vector. Next, DCCN takes this vector as input and uses it to guide the iterative extraction of related visual features via a context-guided dynamic routing mechanism. Particularly, we represent the input image with global and regional visual features, we introduce two parallel DCCNs to model multimodal context vectors with visual features at different granularities. Finally, we obtain two multimodal context vectors, which are fused and incorporated into the decoder for the prediction of the target word. Experimental results on the Multi30K dataset of English-to-German and English-to-French translation demonstrate the superiority of DCCN. Our code is available on https://github.com/DeepLearnXMU/MM-DCCN.
Multivariate Time-series Anomaly Detection via Graph Attention Network
Venue: arXiv cs.LG 2020
Year: 2020

Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications. Recent approaches have achieved significant progress in this topic, but there is remaining limitations. One major limitation is that they do not capture the relationships between different time-series explicitly, resulting in inevitable false alarms. In this paper, we propose a novel self-supervised framework for multivariate time-series anomaly detection to address this issue. Our framework considers each univariate time-series as an individual feature and includes two graph attention layers in parallel to learn the complex dependencies of multivariate time-series in both temporal and feature dimensions. In addition, our approach jointly optimizes a forecasting-based model and are construction-based model, obtaining better time-series representations through a combination of single-timestamp prediction and reconstruction of the entire time-series. We demonstrate the efficacy of our model through extensive experiments. The proposed method outperforms other state-of-the-art models on three real-world datasets. Further analysis shows that our method has good interpretability and is useful for anomaly diagnosis.
On FGLM Algorithms with Tropical Gröbner bases
Venue: arXiv cs.SC 2020
Year: 2020

Let K be a field equipped with a valuation. Tropical varieties over K can be defined with a theory of Gr{ö}bner bases taking into account the valuation of K. Because of the use of the valuation, the theory of tropical Gr{ö}bner bases has proved to provide settings for computations over polynomial rings over a p-adic field that are more stable than that of classical Gr{ö}bner bases. In this article, we investigate how the FGLM change of ordering algorithm can be adapted to the tropical setting. As the valuations of the polynomial coefficients are taken into account, the classical FGLM algorithm's incremental way, monomo-mial by monomial, to compute the multiplication matrices and the change of basis matrix can not be transposed at all to the tropical setting. We mitigate this issue by developing new linear algebra algorithms and apply them to our new tropical FGLM algorithms. Motivations are twofold. Firstly, to compute tropical varieties, one usually goes through the computation of many tropical Gr{ö}bner bases defined for varying weights (and then varying term orders). For an ideal of dimension 0, the tropical FGLM algorithm provides an efficient way to go from a tropical Gr{ö}bner basis from one weight to one for another weight. Secondly, the FGLM strategy can be applied to go from a tropical Gr{ö}bner basis to a classical Gr{ö}bner basis. We provide tools to chain the stable computation of a tropical Gr{ö}bner basis (for weight [0,. .. , 0]) with the p-adic stabilized variants of FGLM of [RV16] to compute a lexicographical or shape position basis. All our algorithms have been implemented into SageMath. We provide numerical examples to illustrate time-complexity. We then illustrate the superiority of our strategy regarding to the stability of p-adic numerical computations.
Policy Gradient Reinforcement Learning for Policy Represented by Fuzzy Rules: Application to Simulations of Speed Control of an Automobile
Venue: arXiv cs.AI 2020
Year: 2020

A method of a fusion of fuzzy inference and policy gradient reinforcement learning has been proposed that directly learns, as maximizes the expected value of the reward per episode, parameters in a policy function represented by fuzzy rules with weights. A study has applied this method to a task of speed control of an automobile and has obtained correct policies, some of which control speed of the automobile appropriately but many others generate inappropriate vibration of speed. In general, the policy is not desirable that causes sudden time change or vibration in the output value, and there would be many cases where the policy giving smooth time change in the output value is desirable. In this paper, we propose a fusion method using the objective function, that introduces defuzzification with the center of gravity model weighted stochastically and a constraint term for smoothness of time change, as an improvement measure in order to suppress sudden change of the output value of the fuzzy controller. Then we show the learning rule in the fusion, and also consider the effect by reward functions on the fluctuation of the output value. As experimental results of an application of our method on speed control of an automobile, it was confirmed that the proposed method has the effect of suppressing the undesirable fluctuation in time-series of the output value. Moreover, it was also showed that the difference between reward functions might adversely affect the results of learning.
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
Venue: arXiv cs.LG 2020
Year: 2020

The contemporary process-aware information systems possess the capabilities to record the activities generated during the process execution. To leverage these process specific fine-granular data, process mining has recently emerged as a promising research discipline. As an important branch of process mining, predictive business process management, pursues the objective to generate forward-looking, predictive insights to shape business processes. In this study, we propose a conceptual framework sought to establish and promote understanding of decision-making environment, underlying business processes and nature of the user characteristics for developing explainable business process prediction solutions. Consequently, with regard to the theoretical and practical implications of the framework, this study proposes a novel local post-hoc explanation approach for a deep learning classifier that is expected to facilitate the domain experts in justifying the model decisions. In contrary to alternative popular perturbation-based local explanation approaches, this study defines the local regions from the validation dataset by using the intermediate latent space representations learned by the deep neural networks. To validate the applicability of the proposed explanation method, the real-life process log data delivered by the Volvo IT Belgium's incident management system are used.The adopted deep learning classifier achieves a good performance with the Area Under the ROC Curve of 0.94. The generated local explanations are also visualized and presented with relevant evaluation measures that are expected to increase the users' trust in the black-box-model.