Paper Group ANR 1281
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models. Effects of data ambiguity and cognitive biases on the interpretability of machine learning models in humanitarian decision making. The Runtime of the Compact Genetic Algorithm on Jump Functions. Learning Variable Ordering Heuristics for Solving Constraint Satisfaction P …
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models
| Title | Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models |
| Authors | Soham Deshmukh, Rahul Rade, Dr. Faruk Kazi |
| Abstract | Cyber threat intelligence is one of the emerging areas of focus in information security. Much of the recent work has focused on rule-based methods and detection of network attacks using Intrusion Detection algorithms. In this paper we propose a framework for inspecting and modelling the behavioural aspect of an attacker to obtain better insight predictive power on his future actions. For modelling we propose a novel semi-supervised algorithm called Fusion Hidden Markov Model (FHMM) which is more robust to noise, requires comparatively less training time, and utilizes the benefits of ensemble learning to better model temporal relationships in data. This paper evaluates the performances of FHMM and compares it with both traditional algorithms like Markov Chain, Hidden Markov Model (HMM) and recently developed Deep Recurrent Neural Network (Deep RNN) architectures. We conduct the experiments on dataset consisting of real data attacks on a Cowrie honeypot system. FHMM provides accuracy comparable to deep RNN architectures at significant lower training time. Given these experimental results, we recommend using FHMM for modelling discrete temporal data for significantly faster training and better performance than existing methods. |
| Tasks | Intrusion Detection |
| Published | 2019-05-28 |
| URL | https://arxiv.org/abs/1905.11824v1 |
| https://arxiv.org/pdf/1905.11824v1.pdf | |
| PWC | https://paperswithcode.com/paper/attacker-behaviour-profiling-using-stochastic |
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Effects of data ambiguity and cognitive biases on the interpretability of machine learning models in humanitarian decision making
| Title | Effects of data ambiguity and cognitive biases on the interpretability of machine learning models in humanitarian decision making |
| Authors | David Paulus, Gerdien de Vries, Bartel Van de Walle |
| Abstract | The effectiveness of machine learning algorithms depends on the quality and amount of data and the operationalization and interpretation by the human analyst. In humanitarian response, data is often lacking or overburdening, thus ambiguous, and the time-scarce, volatile, insecure environments of humanitarian activities are likely to inflict cognitive biases. This paper proposes to research the effects of data ambiguity and cognitive biases on the interpretability of machine learning algorithms in humanitarian decision making. |
| Tasks | Decision Making |
| Published | 2019-11-12 |
| URL | https://arxiv.org/abs/1911.04787v1 |
| https://arxiv.org/pdf/1911.04787v1.pdf | |
| PWC | https://paperswithcode.com/paper/effects-of-data-ambiguity-and-cognitive |
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The Runtime of the Compact Genetic Algorithm on Jump Functions
| Title | The Runtime of the Compact Genetic Algorithm on Jump Functions |
| Authors | Benjamin Doerr |
| Abstract | In the first and so far only mathematical runtime analysis of an estimation-of-distribution algorithm (EDA) on a multimodal problem, Hasen"ohrl and Sutton (GECCO 2018) showed for any $k = o(n)$ that the compact genetic algorithm (cGA) with any hypothetical population size $\mu = \Omega(ne^{4k} + n^{3.5+\varepsilon})$ with high probability finds the optimum of the $n$-dimensional jump function with jump size $k$ in time $O(\mu n^{1.5} \log n)$. We significantly improve this result for small jump sizes $k \le \frac 1 {20} \ln n -1$. In this case, already for $\mu = \Omega(\sqrt n \log n) \cap \text{poly}(n)$ the runtime of the cGA with high probability is only $O(\mu \sqrt n)$. For the smallest admissible values of $\mu$, our result gives a runtime of $O(n \log n)$, whereas the previous one only shows $O(n^{5+\varepsilon})$. Since it is known that the cGA with high probability needs at least $\Omega(\mu \sqrt n)$ iterations to optimize the unimodal OneMx function, our result shows that the cGA in contrast to most classic evolutionary algorithms here is able to cross moderate-sized valleys of low fitness at no extra cost. For large $k$, we show that the exponential (in $k$) runtime guarantee of Hasen"ohrl and Sutton is tight and cannot be improved, also not by using a smaller hypothetical population size. We prove that any choice of the hypothetical population size leads to a runtime that, with high probability, is at least exponential in the jump size $k$. This result might be the first non-trivial exponential lower bound for EDAs that holds for arbitrary parameter settings. |
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| Published | 2019-08-18 |
| URL | https://arxiv.org/abs/1908.06527v1 |
| https://arxiv.org/pdf/1908.06527v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-runtime-of-the-compact-genetic-algorithm |
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Learning Variable Ordering Heuristics for Solving Constraint Satisfaction Problems
| Title | Learning Variable Ordering Heuristics for Solving Constraint Satisfaction Problems |
| Authors | Wen Song, Zhiguang Cao, Jie Zhang, Andrew Lim |
| Abstract | Backtracking search algorithms are often used to solve the Constraint Satisfaction Problem (CSP). The efficiency of backtracking search depends greatly on the variable ordering heuristics. Currently, the most commonly used heuristics are hand-crafted based on expert knowledge. In this paper, we propose a deep reinforcement learning based approach to automatically discover new variable ordering heuristics that are better adapted for a given class of CSP instances. We show that directly optimizing the search cost is hard for bootstrapping, and propose to optimize the expected cost of reaching a leaf node in the search tree. To capture the complex relations among the variables and constraints, we design a representation scheme based on Graph Neural Network that can process CSP instances with different sizes and constraint arities. Experimental results on random CSP instances show that the learned policies outperform classical hand-crafted heuristics in terms of minimizing the search tree size, and can effectively generalize to instances that are larger than those used in training. |
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| Published | 2019-12-23 |
| URL | https://arxiv.org/abs/1912.10762v1 |
| https://arxiv.org/pdf/1912.10762v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-variable-ordering-heuristics-for |
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Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings
| Title | Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings |
| Authors | Christine Bauer, Eva Zangerle |
| Abstract | In this paper, we focus on recommendation settings with multiple stakeholders with possibly varying goals and interests, and argue that a single evaluation method or measure is not able to evaluate all relevant aspects in such a complex setting. We reason that employing a multi-method evaluation, where multiple evaluation methods or measures are combined and integrated, allows for getting a richer picture and prevents blind spots in the evaluation outcome. |
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| Published | 2019-12-14 |
| URL | https://arxiv.org/abs/2001.04348v1 |
| https://arxiv.org/pdf/2001.04348v1.pdf | |
| PWC | https://paperswithcode.com/paper/leveraging-multi-method-evaluation-for-multi |
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Capsule Networks Need an Improved Routing Algorithm
| Title | Capsule Networks Need an Improved Routing Algorithm |
| Authors | Inyoung Paik, Taeyeong Kwak, Injung Kim |
| Abstract | In capsule networks, the routing algorithm connects capsules in consecutive layers, enabling the upper-level capsules to learn higher-level concepts by combining the concepts of the lower-level capsules. Capsule networks are known to have a few advantages over conventional neural networks, including robustness to 3D viewpoint changes and generalization capability. However, some studies have reported negative experimental results. Nevertheless, the reason for this phenomenon has not been analyzed yet. We empirically analyzed the effect of five different routing algorithms. The experimental results show that the routing algorithms do not behave as expected and often produce results that are worse than simple baseline algorithms that assign the connection strengths uniformly or randomly. We also show that, in most cases, the routing algorithms do not change the classification result but polarize the link strengths, and the polarization can be extreme when they continue to repeat without stopping. In order to realize the true potential of the capsule network, it is essential to develop an improved routing algorithm. |
| Tasks | |
| Published | 2019-07-31 |
| URL | https://arxiv.org/abs/1907.13327v1 |
| https://arxiv.org/pdf/1907.13327v1.pdf | |
| PWC | https://paperswithcode.com/paper/capsule-networks-need-an-improved-routing |
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Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition
| Title | Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition |
| Authors | Suraj Tripathi, Abhiram Ramesh, Abhay Kumar, Chirag Singh, Promod Yenigalla |
| Abstract | This paper proposes a Convolutional Neural Network (CNN) inspired by Multitask Learning (MTL) and based on speech features trained under the joint supervision of softmax loss and center loss, a powerful metric learning strategy, for the recognition of emotion in speech. Speech features such as Spectrograms and Mel-frequency Cepstral Coefficient s (MFCCs) help retain emotion-related low-level characteristics in speech. We experimented with several Deep Neural Network (DNN) architectures that take in speech features as input and trained them under both softmax and center loss, which resulted in highly discriminative features ideal for Speech Emotion Recognition (SER). Our networks also employ a regularizing effect by simultaneously performing the auxiliary task of reconstructing the input speech features. This sharing of representations among related tasks enables our network to better generalize the original task of SER. Some of our proposed networks contain far fewer parameters when compared to state-of-the-art architectures. |
| Tasks | Emotion Recognition, Metric Learning, Speech Emotion Recognition |
| Published | 2019-06-19 |
| URL | https://arxiv.org/abs/1906.08873v2 |
| https://arxiv.org/pdf/1906.08873v2.pdf | |
| PWC | https://paperswithcode.com/paper/learning-discriminative-features-using-center |
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Discriminative Sleep Patterns of Alzheimer’s Disease via Tensor Factorization
| Title | Discriminative Sleep Patterns of Alzheimer’s Disease via Tensor Factorization |
| Authors | Yejin Kim, Xiaoqian Jiang, Luyao Chen, Xiaojin Li, Licong Cui |
| Abstract | Sleep change is commonly reported in Alzheimer’s disease (AD) patients and their brain wave studies show decrease in dreaming and non-dreaming stages. Although sleep disturbance is generally considered as a consequence of AD, it might also be a risk factor of AD as new biological evidence shows. Leveraging National Sleep Research Resource (NSRR), we built a unique cohort of 83 cases and 331 controls with clinical variables and EEG signals. Supervised tensor factorization method was applied for this temporal dataset to extract discriminative sleep patterns. Among the 30 patterns extracted, we identified 5 significant patterns (4 patterns for AD likely and 1 pattern for normal ones) and their visual patterns provide interesting linkage to sleep with repeated wakefulness, insomnia, epileptic seizure, and etc. This study is preliminary but findings are interesting, which is a first step to provide quantifiable evidences to measure sleep as a risk factor of AD. |
| Tasks | EEG |
| Published | 2019-05-14 |
| URL | https://arxiv.org/abs/1905.05827v1 |
| https://arxiv.org/pdf/1905.05827v1.pdf | |
| PWC | https://paperswithcode.com/paper/discriminative-sleep-patterns-of-alzheimers |
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BowNet: Dilated Convolution Neural Network for Ultrasound Tongue Contour Extraction
| Title | BowNet: Dilated Convolution Neural Network for Ultrasound Tongue Contour Extraction |
| Authors | M. Hamed Mozaffari, Won-Sook Lee |
| Abstract | Ultrasound imaging is safe, relatively affordable, and capable of real-time performance. One application of this technology is to visualize and to characterize human tongue shape and motion during a real-time speech to study healthy or impaired speech production. Due to the noisy nature of ultrasound images with low-contrast characteristic, it might require expertise for non-expert users to recognize organ shape such as tongue surface (dorsum). To alleviate this difficulty for quantitative analysis of tongue shape and motion, tongue surface can be extracted, tracked, and visualized instead of the whole tongue region. Delineating the tongue surface from each frame is a cumbersome, subjective, and error-prone task. Furthermore, the rapidity and complexity of tongue gestures have made it a challenging task, and manual segmentation is not a feasible solution for real-time applications. Employing the power of state-of-the-art deep neural network models and training techniques, it is feasible to implement new fully-automatic, accurate, and robust segmentation methods with the capability of real-time performance, applicable for tracking of the tongue contours during the speech. This paper presents two novel deep neural network models named BowNet and wBowNet benefits from the ability of global prediction of decoding-encoding models, with integrated multi-scale contextual information, and capability of full-resolution (local) extraction of dilated convolutions. Experimental results using several ultrasound tongue image datasets revealed that the combination of both localization and globalization searching could improve prediction result significantly. Assessment of BowNet models using both qualitatively and quantitatively studies showed them outstanding achievements in terms of accuracy and robustness in comparison with similar techniques. |
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| Published | 2019-06-10 |
| URL | https://arxiv.org/abs/1906.04232v1 |
| https://arxiv.org/pdf/1906.04232v1.pdf | |
| PWC | https://paperswithcode.com/paper/bownet-dilated-convolution-neural-network-for |
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News Cover Assessment via Multi-task Learning
| Title | News Cover Assessment via Multi-task Learning |
| Authors | Zixun Sun, Shuang Zhao, Chengwei Zhu, Xiao Chen |
| Abstract | Online personalized news product needs a suitable cover for the article. The news cover demands to be with high image quality, and draw readers’ attention at same time, which is extraordinary challenging due to the subjectivity of the task. In this paper, we assess the news cover from image clarity and object salience perspective. We propose an end-to-end multi-task learning network for image clarity assessment and semantic segmentation simultaneously, the results of which can be guided for news cover assessment. The proposed network is based on a modified DeepLabv3+ model. The network backbone is used for multiple scale spatial features exaction, followed by two branches for image clarity assessment and semantic segmentation, respectively. The experiment results show that the proposed model is able to capture important content in images and performs better than single-task learning baselines on our proposed game content based CIA dataset. |
| Tasks | Multi-Task Learning, Semantic Segmentation |
| Published | 2019-07-17 |
| URL | https://arxiv.org/abs/1907.07581v2 |
| https://arxiv.org/pdf/1907.07581v2.pdf | |
| PWC | https://paperswithcode.com/paper/news-cover-assessment-via-multi-task-learning |
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An External Knowledge Enhanced Multi-label Charge Prediction Approach with Label Number Learning
| Title | An External Knowledge Enhanced Multi-label Charge Prediction Approach with Label Number Learning |
| Authors | Duan Wei, Li Lin |
| Abstract | Multi-label charge prediction is a task to predict the corresponding accusations for legal cases, and recently becomes a hot topic. However, current studies use rough methods to deal with the label number. These methods manually set parameters to select label numbers, which has an effect in final prediction quality. We propose an external knowledge enhanced multi-label charge prediction approach that has two phases. One is charge label prediction phase with external knowledge from law provisions, the other one is number learning phase with a number learning network (NLN) designed. Our approach enhanced by external knowledge can automatically adjust the threshold to get label number of law cases. It combines the output probabilities of samples and their corresponding label numbers to get final prediction results. In experiments, our approach is connected to some state of-the art deep learning models. By testing on the biggest published Chinese law dataset, we find that our approach has improvements on these models. We future conduct experiments on multi-label samples from the dataset. In items of macro-F1, the improvement of baselines with our approach is 3%-5%; In items of micro-F1, the significant improvement of our approach is 5%-15%. The experiment results show the effectiveness our approach for multi-label charge prediction. |
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| Published | 2019-07-04 |
| URL | https://arxiv.org/abs/1907.02205v1 |
| https://arxiv.org/pdf/1907.02205v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-external-knowledge-enhanced-multi-label |
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Efficient Capon-Based Approach Exploiting Temporal Windowing For Electric Network Frequency Estimation
| Title | Efficient Capon-Based Approach Exploiting Temporal Windowing For Electric Network Frequency Estimation |
| Authors | Georgios Karantaidis, Constantine Kotropoulos |
| Abstract | Electric Network Frequency (ENF) fluctuations constitute a powerful tool in multimedia forensics. An efficient approach for ENF estimation is introduced with temporal windowing based on the filter-bank Capon spectral estimator. A type of Gohberg-Semencul factorization of the model covariance matrix is used due to the Toeplitz structure of the covariance matrix. Moreover, this approach uses, for the first time in the field of ENF, a temporal window, not necessarily the rectangular one, at the stage preceding spectral estimation. Krylov matrices are employed for fast implementation of matrix inversions. The proposed approach outperforms the state-of-the-art methods in ENF estimation, when a short time window of $1$ second is employed in power recordings. In speech recordings, the proposed approach yields highly accurate results with respect to both time complexity and accuracy. Moreover, the impact of different temporal windows is studied. The results show that even the most trivial methods for ENF estimation, such as the Short-Time Fourier Transform, can provide better results than the most recent state-of-the-art methods, when a temporal window is employed. The correlation coefficient is used to measure the ENF estimation accuracy. |
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| Published | 2019-08-22 |
| URL | https://arxiv.org/abs/1908.08813v2 |
| https://arxiv.org/pdf/1908.08813v2.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-capon-based-approach-exploiting |
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Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks
| Title | Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks |
| Authors | Philipp Kratzer, Marc Toussaint, Jim Mainprice |
| Abstract | Human movement prediction is difficult as humans naturally exhibit complex behaviors that can change drastically from one environment to the next. In order to alleviate this issue, we propose a prediction framework that decouples short-term prediction, linked to internal body dynamics, and long-term prediction, linked to the environment and task constraints. In this work we investigate encoding short-term dynamics in a recurrent neural network, while we account for environmental constraints, such as obstacle avoidance, using gradient-based trajectory optimization. Experiments on real motion data demonstrate that our framework improves the prediction with respect to state-of-the-art motion prediction methods, as it accounts to beforehand unseen environmental structures. Moreover we demonstrate on an example, how this framework can be used to plan robot trajectories that are optimized to coordinate with a human partner. |
| Tasks | motion prediction |
| Published | 2019-10-04 |
| URL | https://arxiv.org/abs/1910.01843v2 |
| https://arxiv.org/pdf/1910.01843v2.pdf | |
| PWC | https://paperswithcode.com/paper/prediction-of-human-full-body-movements-with |
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State Alignment-based Imitation Learning
| Title | State Alignment-based Imitation Learning |
| Authors | Fangchen Liu, Zhan Ling, Tongzhou Mu, Hao Su |
| Abstract | Consider an imitation learning problem that the imitator and the expert have different dynamics models. Most of the current imitation learning methods fail because they focus on imitating actions. We propose a novel state alignment-based imitation learning method to train the imitator to follow the state sequences in expert demonstrations as much as possible. The state alignment comes from both local and global perspectives and we combine them into a reinforcement learning framework by a regularized policy update objective. We show the superiority of our method on standard imitation learning settings and imitation learning settings where the expert and imitator have different dynamics models. |
| Tasks | Imitation Learning |
| Published | 2019-11-21 |
| URL | https://arxiv.org/abs/1911.10947v1 |
| https://arxiv.org/pdf/1911.10947v1.pdf | |
| PWC | https://paperswithcode.com/paper/state-alignment-based-imitation-learning-1 |
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MuMMER: Socially Intelligent Human-Robot Interaction in Public Spaces
| Title | MuMMER: Socially Intelligent Human-Robot Interaction in Public Spaces |
| Authors | Mary Ellen Foster, Bart Craenen, Amol Deshmukh, Oliver Lemon, Emanuele Bastianelli, Christian Dondrup, Ioannis Papaioannou, Andrea Vanzo, Jean-Marc Odobez, Olivier Canévet, Yuanzhouhan Cao, Weipeng He, Angel Martínez-González, Petr Motlicek, Rémy Siegfried, Rachid Alami, Kathleen Belhassein, Guilhem Buisan, Aurélie Clodic, Amandine Mayima, Yoan Sallami, Guillaume Sarthou, Phani-Teja Singamaneni, Jules Waldhart, Alexandre Mazel, Maxime Caniot, Marketta Niemelä, Päivi Heikkilä, Hanna Lammi, Antti Tammela |
| Abstract | In the EU-funded MuMMER project, we have developed a social robot designed to interact naturally and flexibly with users in public spaces such as a shopping mall. We present the latest version of the robot system developed during the project. This system encompasses audio-visual sensing, social signal processing, conversational interaction, perspective taking, geometric reasoning, and motion planning. It successfully combines all these components in an overarching framework using the Robot Operating System (ROS) and has been deployed to a shopping mall in Finland interacting with customers. In this paper, we describe the system components, their interplay, and the resulting robot behaviours and scenarios provided at the shopping mall. |
| Tasks | Motion Planning |
| Published | 2019-09-15 |
| URL | https://arxiv.org/abs/1909.06749v1 |
| https://arxiv.org/pdf/1909.06749v1.pdf | |
| PWC | https://paperswithcode.com/paper/mummer-socially-intelligent-human-robot |
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