Paper Group ANR 256
From First-Order Logic to Assertional Logic. Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection. Linear Additive Markov Processes. Neural Network Multitask Learning for Traffic Flow Forecasting. Forecasting using incomplete models. Production Ready Chatbots: Generate if not Retrieve. Counterexample Guided Indu …
From First-Order Logic to Assertional Logic
| Title | From First-Order Logic to Assertional Logic |
| Authors | Yi Zhou |
| Abstract | First-Order Logic (FOL) is widely regarded as one of the most important foundations for knowledge representation. Nevertheless, in this paper, we argue that FOL has several critical issues for this purpose. Instead, we propose an alternative called assertional logic, in which all syntactic objects are categorized as set theoretic constructs including individuals, concepts and operators, and all kinds of knowledge are formalized by equality assertions. We first present a primitive form of assertional logic that uses minimal assumed knowledge and constructs. Then, we show how to extend it by definitions, which are special kinds of knowledge, i.e., assertions. We argue that assertional logic, although simpler, is more expressive and extensible than FOL. As a case study, we show how assertional logic can be used to unify logic and probability, and more building blocks in AI. |
| Tasks | |
| Published | 2017-01-12 |
| URL | http://arxiv.org/abs/1701.03322v2 |
| http://arxiv.org/pdf/1701.03322v2.pdf | |
| PWC | https://paperswithcode.com/paper/from-first-order-logic-to-assertional-logic |
| Repo | |
| Framework | |
Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection
| Title | Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection |
| Authors | Devu Manikantan Shilay, Kin Gwn Lorey, Tianshu Weiz, Teems Lovetty, Yu Cheng |
| Abstract | A significant challenge in energy system cyber security is the current inability to detect cyber-physical attacks targeting and originating from distributed grid-edge devices such as photovoltaics (PV) panels, smart flexible loads, and electric vehicles. We address this concern by designing and developing a distributed, multi-modal anomaly detection approach that can sense the health of the device and the electric power grid from the edge. This is realized by exploiting unsupervised machine learning algorithms on multiple sources of time-series data, fusing these multiple local observations and flagging anomalies when a deviation from the normal behavior is observed. We particularly focus on the cyber-physical threats to the distributed PVs that has the potential to cause local disturbances or grid instabilities by creating supply-demand mismatch, reverse power flow conditions etc. We use an open source power system simulation tool called GridLAB-D, loaded with real smart home and solar datasets to simulate the smart grid scenarios and to illustrate the impact of PV attacks on the power system. Various attacks targeting PV panels that create voltage fluctuations, reverse power flow etc were designed and performed. We observe that while individual unsupervised learning algorithms such as OCSVMs, Corrupt RF and PCA surpasses in identifying particular attack type, PCA with Convex Hull outperforms all algorithms in identifying all designed attacks with a true positive rate of 83.64% and an accuracy of 95.78%. Our key insight is that due to the heterogeneous nature of the distribution grid and the uncertainty in the type of the attack being launched, relying on single mode of information for defense can lead to increased false alarms and missed detection rates as one can design attacks to hide within those uncertainties and remain stealthy. |
| Tasks | Anomaly Detection, Time Series |
| Published | 2017-09-26 |
| URL | http://arxiv.org/abs/1709.08830v1 |
| http://arxiv.org/pdf/1709.08830v1.pdf | |
| PWC | https://paperswithcode.com/paper/catching-anomalous-distributed-photovoltaics |
| Repo | |
| Framework | |
Linear Additive Markov Processes
| Title | Linear Additive Markov Processes |
| Authors | Ravi Kumar, Maithra Raghu, Tamas Sarlos, Andrew Tomkins |
| Abstract | We introduce LAMP: the Linear Additive Markov Process. Transitions in LAMP may be influenced by states visited in the distant history of the process, but unlike higher-order Markov processes, LAMP retains an efficient parametrization. LAMP also allows the specific dependence on history to be learned efficiently from data. We characterize some theoretical properties of LAMP, including its steady-state and mixing time. We then give an algorithm based on alternating minimization to learn LAMP models from data. Finally, we perform a series of real-world experiments to show that LAMP is more powerful than first-order Markov processes, and even holds its own against deep sequential models (LSTMs) with a negligible increase in parameter complexity. |
| Tasks | |
| Published | 2017-04-05 |
| URL | http://arxiv.org/abs/1704.01255v1 |
| http://arxiv.org/pdf/1704.01255v1.pdf | |
| PWC | https://paperswithcode.com/paper/linear-additive-markov-processes |
| Repo | |
| Framework | |
Neural Network Multitask Learning for Traffic Flow Forecasting
| Title | Neural Network Multitask Learning for Traffic Flow Forecasting |
| Authors | Feng Jin, Shiliang Sun |
| Abstract | Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks. In contrast to STL, multitask learning (MTL) has the potential to improve generalization by transferring information in training signals of extra tasks. In this paper, MTL based neural networks are used for traffic flow forecasting. For neural network MTL, a backpropagation (BP) network is constructed by incorporating traffic flows at several contiguous time instants into an output layer. Nodes in the output layer can be seen as outputs of different but closely related STL tasks. Comprehensive experiments on urban vehicular traffic flow data and comparisons with STL show that MTL in BP neural networks is a promising and effective approach for traffic flow forecasting. |
| Tasks | |
| Published | 2017-12-24 |
| URL | http://arxiv.org/abs/1712.08862v1 |
| http://arxiv.org/pdf/1712.08862v1.pdf | |
| PWC | https://paperswithcode.com/paper/neural-network-multitask-learning-for-traffic |
| Repo | |
| Framework | |
Forecasting using incomplete models
| Title | Forecasting using incomplete models |
| Authors | Vanessa Kosoy |
| Abstract | We consider the task of forecasting an infinite sequence of future observations based on some number of past observations, where the probability measure generating the observations is “suspected” to satisfy one or more of a set of incomplete models, i.e. convex sets in the space of probability measures. This setting is in some sense intermediate between the realizable setting where the probability measure comes from some known set of probability measures (which can be addressed using e.g. Bayesian inference) and the unrealizable setting where the probability measure is completely arbitrary. We demonstrate a method of forecasting which guarantees that, whenever the true probability measure satisfies an incomplete model in a given countable set, the forecast converges to the same incomplete model in the (appropriately normalized) Kantorovich-Rubinstein metric. This is analogous to merging of opinions for Bayesian inference, except that convergence in the Kantorovich-Rubinstein metric is weaker than convergence in total variation. |
| Tasks | Bayesian Inference |
| Published | 2017-05-12 |
| URL | https://arxiv.org/abs/1705.04630v6 |
| https://arxiv.org/pdf/1705.04630v6.pdf | |
| PWC | https://paperswithcode.com/paper/forecasting-using-incomplete-models |
| Repo | |
| Framework | |
Production Ready Chatbots: Generate if not Retrieve
| Title | Production Ready Chatbots: Generate if not Retrieve |
| Authors | Aniruddha Tammewar, Monik Pamecha, Chirag Jain, Apurva Nagvenkar, Krupal Modi |
| Abstract | In this paper, we present a hybrid model that combines a neural conversational model and a rule-based graph dialogue system that assists users in scheduling reminders through a chat conversation. The graph based system has high precision and provides a grammatically accurate response but has a low recall. The neural conversation model can cater to a variety of requests, as it generates the responses word by word as opposed to using canned responses. The hybrid system shows significant improvements over the existing baseline system of rule based approach and caters to complex queries with a domain-restricted neural model. Restricting the conversation topic and combination of graph based retrieval system with a neural generative model makes the final system robust enough for a real world application. |
| Tasks | |
| Published | 2017-11-27 |
| URL | http://arxiv.org/abs/1711.09684v1 |
| http://arxiv.org/pdf/1711.09684v1.pdf | |
| PWC | https://paperswithcode.com/paper/production-ready-chatbots-generate-if-not |
| Repo | |
| Framework | |
Counterexample Guided Inductive Optimization Applied to Mobile Robots Path Planning (Extended Version)
| Title | Counterexample Guided Inductive Optimization Applied to Mobile Robots Path Planning (Extended Version) |
| Authors | Rodrigo F. Araújo, Alexandre Ribeiro, Iury V. Bessa, Lucas C. Cordeiro, João E. C. Filho |
| Abstract | We describe and evaluate a novel optimization-based off-line path planning algorithm for mobile robots based on the Counterexample-Guided Inductive Optimization (CEGIO) technique. CEGIO iteratively employs counterexamples generated from Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) solvers, in order to guide the optimization process and to ensure global optimization. This paper marks the first application of CEGIO for planning mobile robot path. In particular, CEGIO has been successfully applied to obtain optimal two-dimensional paths for autonomous mobile robots using off-the-shelf SAT and SMT solvers. |
| Tasks | |
| Published | 2017-08-14 |
| URL | http://arxiv.org/abs/1708.04028v1 |
| http://arxiv.org/pdf/1708.04028v1.pdf | |
| PWC | https://paperswithcode.com/paper/counterexample-guided-inductive-optimization |
| Repo | |
| Framework | |
Black-box Testing of First-Order Logic Ontologies Using WordNet
| Title | Black-box Testing of First-Order Logic Ontologies Using WordNet |
| Authors | Javier Álvez, Paqui Lucio, German Rigau |
| Abstract | Artificial Intelligence aims to provide computer programs with commonsense knowledge to reason about our world. This paper offers a new practical approach towards automated commonsense reasoning with first-order logic (FOL) ontologies. We propose a new black-box testing methodology of FOL SUMO-based ontologies by exploiting WordNet and its mapping into SUMO. Our proposal includes a method for the (semi-)automatic creation of a very large benchmark of competency questions and a procedure for its automated evaluation by using automated theorem provers (ATPs). Applying different quality criteria, our testing proposal enables a successful evaluation of a) the competency of several translations of SUMO into FOL and b) the performance of various automated ATPs. Finally, we also provide a fine-grained and complete analysis of the commonsense reasoning competency of current FOL SUMO-based ontologies. |
| Tasks | |
| Published | 2017-05-29 |
| URL | http://arxiv.org/abs/1705.10217v3 |
| http://arxiv.org/pdf/1705.10217v3.pdf | |
| PWC | https://paperswithcode.com/paper/black-box-testing-of-first-order-logic |
| Repo | |
| Framework | |
Reliability and Sharpness in Border Crossing Traffic Interval Prediction
| Title | Reliability and Sharpness in Border Crossing Traffic Interval Prediction |
| Authors | Lei Lin, John Handley, Adel Sadek |
| Abstract | Short-term traffic volume prediction models have been extensively studied in the past few decades. However, most of the previous studies only focus on single-value prediction. Considering the uncertain and chaotic nature of the transportation system, an accurate and reliable prediction interval with upper and lower bounds may be better than a single point value for transportation management. In this paper, we introduce a neural network model called Extreme Learning Machine (ELM) for interval prediction of short-term traffic volume and improve it with the heuristic particle swarm optimization algorithm (PSO). The hybrid PSO-ELM model can generate the prediction intervals under different confidence levels and guarantee the quality by minimizing a multi-objective function which considers two criteria reliability and interval sharpness. The PSO-ELM models are built based on an hourly traffic dataset and compared with ARMA and Kalman Filter models. The results show that ARMA models are the worst for all confidence levels, and the PSO-ELM models are comparable with Kalman Filter from the aspects of reliability and narrowness of the intervals, although the parameters of PSO-ELM are fixed once the training is done while Kalman Filter is updated in an online approach. Additionally, only the PSO-ELMs are able to produce intervals with coverage probabilities higher than or equal to the confidence levels. For the points outside of the prediction levels given by PSO-ELMs, they lie very close to the bounds. |
| Tasks | |
| Published | 2017-11-13 |
| URL | http://arxiv.org/abs/1711.04848v1 |
| http://arxiv.org/pdf/1711.04848v1.pdf | |
| PWC | https://paperswithcode.com/paper/reliability-and-sharpness-in-border-crossing |
| Repo | |
| Framework | |
The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study
| Title | The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study |
| Authors | Myoung Hoon Ha, Byung-Ro Moon |
| Abstract | A neural network-based chart pattern represents adaptive parametric features, including non-linear transformations, and a template that can be applied in the feature space. The search of neural network-based chart patterns has been unexplored despite its potential expressiveness. In this paper, we formulate a general chart pattern search problem to enable cross-representational quantitative comparison of various search schemes. We suggest a HyperNEAT framework applying state-of-the-art deep neural network techniques to find attractive neural network-based chart patterns; These techniques enable a fast evaluation and search of robust patterns, as well as bringing a performance gain. The proposed framework successfully found attractive patterns on the Korean stock market. We compared newly found patterns with those found by different search schemes, showing the proposed approach has potential. |
| Tasks | |
| Published | 2017-04-06 |
| URL | http://arxiv.org/abs/1706.05283v1 |
| http://arxiv.org/pdf/1706.05283v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-evolution-of-neural-network-based-chart |
| Repo | |
| Framework | |
Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning
| Title | Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning |
| Authors | Nicolò Cesa-Bianchi, Pierre Gaillard, Claudio Gentile, Sébastien Gerchinovitz |
| Abstract | We investigate contextual online learning with nonparametric (Lipschitz) comparison classes under different assumptions on losses and feedback information. For full information feedback and Lipschitz losses, we design the first explicit algorithm achieving the minimax regret rate (up to log factors). In a partial feedback model motivated by second-price auctions, we obtain algorithms for Lipschitz and semi-Lipschitz losses with regret bounds improving on the known bounds for standard bandit feedback. Our analysis combines novel results for contextual second-price auctions with a novel algorithmic approach based on chaining. When the context space is Euclidean, our chaining approach is efficient and delivers an even better regret bound. |
| Tasks | |
| Published | 2017-02-27 |
| URL | http://arxiv.org/abs/1702.08211v2 |
| http://arxiv.org/pdf/1702.08211v2.pdf | |
| PWC | https://paperswithcode.com/paper/algorithmic-chaining-and-the-role-of-partial |
| Repo | |
| Framework | |
Nonbacktracking Bounds on the Influence in Independent Cascade Models
| Title | Nonbacktracking Bounds on the Influence in Independent Cascade Models |
| Authors | Emmanuel Abbe, Sanjeev Kulkarni, Eun Jee Lee |
| Abstract | This paper develops upper and lower bounds on the influence measure in a network, more precisely, the expected number of nodes that a seed set can influence in the independent cascade model. In particular, our bounds exploit nonbacktracking walks, Fortuin-Kasteleyn-Ginibre (FKG) type inequalities, and are computed by message passing implementation. Nonbacktracking walks have recently allowed for headways in community detection, and this paper shows that their use can also impact the influence computation. Further, we provide a knob to control the trade-off between the efficiency and the accuracy of the bounds. Finally, the tightness of the bounds is illustrated with simulations on various network models. |
| Tasks | Community Detection |
| Published | 2017-05-24 |
| URL | http://arxiv.org/abs/1706.05295v2 |
| http://arxiv.org/pdf/1706.05295v2.pdf | |
| PWC | https://paperswithcode.com/paper/nonbacktracking-bounds-on-the-influence-in |
| Repo | |
| Framework | |
Integrating Three Mechanisms of Visual Attention for Active Visual Search
| Title | Integrating Three Mechanisms of Visual Attention for Active Visual Search |
| Authors | Amir Rasouli, John K. Tsotsos |
| Abstract | Algorithms for robotic visual search can benefit from the use of visual attention methods in order to reduce computational costs. Here, we describe how three distinct mechanisms of visual attention can be integrated and productively used to improve search performance. The first is viewpoint selection as has been proposed earlier using a greedy search over a probabilistic occupancy grid representation. The second is top-down object-based attention using a histogram backprojection method, also previously described. The third is visual saliency. This is novel in the sense that it is not used as a region-of-interest method for the current image but rather as a noncombinatorial form of look-ahead in search for future viewpoint selection. Additionally, the integration of these three attentional schemes within a single framework is unique and not previously studied. We examine our proposed method in scenarios where little or no information regarding the environment is available. Through extensive experiments on a mobile robot, we show that our method improves visual search performance by reducing the time and number of actions required. |
| Tasks | |
| Published | 2017-02-14 |
| URL | http://arxiv.org/abs/1702.04292v1 |
| http://arxiv.org/pdf/1702.04292v1.pdf | |
| PWC | https://paperswithcode.com/paper/integrating-three-mechanisms-of-visual |
| Repo | |
| Framework | |
A novel graph structure for salient object detection based on divergence background and compact foreground
| Title | A novel graph structure for salient object detection based on divergence background and compact foreground |
| Authors | Chenxing Xia, Hanling Zhang, Keqin Li |
| Abstract | In this paper, we propose an efficient and discriminative model for salient object detection. Our method is carried out in a stepwise mechanism based on both divergence background and compact foreground cues. In order to effectively enhance the distinction between nodes along object boundaries and the similarity among object regions, a graph is constructed by introducing the concept of virtual node. To remove incorrect outputs, a scheme for selecting background seeds and a method for generating compactness foreground regions are introduced, respectively. Different from prior methods, we calculate the saliency value of each node based on the relationship between the corresponding node and the virtual node. In order to achieve significant performance improvement consistently, we propose an Extended Manifold Ranking (EMR) algorithm, which subtly combines suppressed / active nodes and mid-level information. Extensive experimental results demonstrate that the proposed algorithm performs favorably against the state-of-art saliency detection methods in terms of different evaluation metrics on several benchmark datasets. |
| Tasks | Object Detection, Saliency Detection, Salient Object Detection |
| Published | 2017-11-30 |
| URL | http://arxiv.org/abs/1711.11266v1 |
| http://arxiv.org/pdf/1711.11266v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-novel-graph-structure-for-salient-object |
| Repo | |
| Framework | |
CNN training with graph-based sample preselection: application to handwritten character recognition
| Title | CNN training with graph-based sample preselection: application to handwritten character recognition |
| Authors | Frédéric Rayar, Masanori Goto, Seiichi Uchida |
| Abstract | In this paper, we present a study on sample preselection in large training data set for CNN-based classification. To do so, we structure the input data set in a network representation, namely the Relative Neighbourhood Graph, and then extract some vectors of interest. The proposed preselection method is evaluated in the context of handwritten character recognition, by using two data sets, up to several hundred thousands of images. It is shown that the graph-based preselection can reduce the training data set without degrading the recognition accuracy of a non pretrained CNN shallow model. |
| Tasks | |
| Published | 2017-12-06 |
| URL | http://arxiv.org/abs/1712.02122v3 |
| http://arxiv.org/pdf/1712.02122v3.pdf | |
| PWC | https://paperswithcode.com/paper/cnn-training-with-graph-based-sample |
| Repo | |
| Framework | |
