Paper Group ANR 128
A new cut-based genetic algorithm for graph partitioning applied to cell formation. JU_KS_Group@FIRE 2016: Consumer Health Information Search. Joint Hand Detection and Rotation Estimation by Using CNN. Reconstructing undirected graphs from eigenspaces. Multimodal Affect Recognition using Kinect. Recursive Decomposition for Nonconvex Optimization. H …
A new cut-based genetic algorithm for graph partitioning applied to cell formation
| Title | A new cut-based genetic algorithm for graph partitioning applied to cell formation |
| Authors | Boulif Menouar |
| Abstract | Cell formation is a critical step in the design of cellular manufacturing systems. Recently, it was tackled using a cut-based-graph-partitioning model. This model meets real-life production systems requirements as it uses the actual amount of product flows, it looks for the suitable number of cells, and it takes into account the natural constraints such as operation sequences, maximum cell size, cohabitation and non-cohabitation constraints. Based on this model, we propose an original encoding representation to solve the problem by using a genetic algorithm. We discuss the performance of this new GA in comparison to some approaches taken from the literature on a set of medium sized instances. Given the results we obtained, it is reasonable to assume that the new GA will provide similar results for large real-life problems. Keywords: Group Technology, Manufacturing Cell Formation, Graph Partitioning, Graph Cuts, Genetic Algorithm, Encoding representation. |
| Tasks | graph partitioning |
| Published | 2016-12-16 |
| URL | http://arxiv.org/abs/1612.05536v1 |
| http://arxiv.org/pdf/1612.05536v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-new-cut-based-genetic-algorithm-for-graph |
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JU_KS_Group@FIRE 2016: Consumer Health Information Search
| Title | JU_KS_Group@FIRE 2016: Consumer Health Information Search |
| Authors | Kamal Sarkar, Debanjan Das, Indra Banerjee, Mamta Kumari, Prasenjit Biswas |
| Abstract | In this paper, we describe the methodology used and the results obtained by us for completing the tasks given under the shared task on Consumer Health Information Search (CHIS) collocated with the Forum for Information Retrieval Evaluation (FIRE) 2016, ISI Kolkata. The shared task consists of two sub-tasks - (1) task1: given a query and a document/set of documents associated with that query, the task is to classify the sentences in the document as relevant to the query or not and (2) task 2: the relevant sentences need to be further classified as supporting the claim made in the query, or opposing the claim made in the query. We have participated in both the sub-tasks. The percentage accuracy obtained by our developed system for task1 was 73.39 which is third highest among the 9 teams participated in the shared task. |
| Tasks | Information Retrieval |
| Published | 2016-12-24 |
| URL | http://arxiv.org/abs/1612.08178v1 |
| http://arxiv.org/pdf/1612.08178v1.pdf | |
| PWC | https://paperswithcode.com/paper/ju_ks_groupfire-2016-consumer-health |
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Joint Hand Detection and Rotation Estimation by Using CNN
| Title | Joint Hand Detection and Rotation Estimation by Using CNN |
| Authors | Xiaoming Deng, Ye Yuan, Yinda Zhang, Ping Tan, Liang Chang, Shuo Yang, Hongan Wang |
| Abstract | Hand detection is essential for many hand related tasks, e.g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction. However, hand detection in uncontrolled environments is challenging due to the flexibility of wrist joint and cluttered background. We propose a deep learning based approach which detects hands and calibrates in-plane rotation under supervision at the same time. To guarantee the recall, we propose a context aware proposal generation algorithm which significantly outperforms the selective search. We then design a convolutional neural network(CNN) which handles object rotation explicitly to jointly solve the object detection and rotation estimation tasks. Experiments show that our method achieves better results than state-of-the-art detection models on widely-used benchmarks such as Oxford and Egohands database. We further show that rotation estimation and classification can mutually benefit each other. |
| Tasks | Object Detection |
| Published | 2016-12-08 |
| URL | http://arxiv.org/abs/1612.02742v1 |
| http://arxiv.org/pdf/1612.02742v1.pdf | |
| PWC | https://paperswithcode.com/paper/joint-hand-detection-and-rotation-estimation |
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Reconstructing undirected graphs from eigenspaces
| Title | Reconstructing undirected graphs from eigenspaces |
| Authors | Yohann De Castro, Thibault Espinasse, Paul Rochet |
| Abstract | In this paper, we aim at recovering an undirected weighted graph of $N$ vertices from the knowledge of a perturbed version of the eigenspaces of its adjacency matrix $W$. For instance, this situation arises for stationary signals on graphs or for Markov chains observed at random times. Our approach is based on minimizing a cost function given by the Frobenius norm of the commutator $\mathsf{A} \mathsf{B}-\mathsf{B} \mathsf{A}$ between symmetric matrices $\mathsf{A}$ and $\mathsf{B}$. In the Erd\H{o}s-R'enyi model with no self-loops, we show that identifiability (i.e., the ability to reconstruct $W$ from the knowledge of its eigenspaces) follows a sharp phase transition on the expected number of edges with threshold function $N\log N/2$. Given an estimation of the eigenspaces based on a $n$-sample, we provide support selection procedures from theoretical and practical point of views. In particular, when deleting an edge from the active support, our study unveils that our test statistic is the order of $\mathcal O(1/n)$ when we overestimate the true support and lower bounded by a positive constant when the estimated support is smaller than the true support. This feature leads to a powerful practical support estimation procedure. Simulated and real life numerical experiments assert our new methodology. |
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| Published | 2016-03-26 |
| URL | http://arxiv.org/abs/1603.08113v3 |
| http://arxiv.org/pdf/1603.08113v3.pdf | |
| PWC | https://paperswithcode.com/paper/reconstructing-undirected-graphs-from |
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Multimodal Affect Recognition using Kinect
| Title | Multimodal Affect Recognition using Kinect |
| Authors | Amol Patwardhan, Gerald Knapp |
| Abstract | Affect (emotion) recognition has gained significant attention from researchers in the past decade. Emotion-aware computer systems and devices have many applications ranging from interactive robots, intelligent online tutor to emotion based navigation assistant. In this research data from multiple modalities such as face, head, hand, body and speech was utilized for affect recognition. The research used color and depth sensing device such as Kinect for facial feature extraction and tracking human body joints. Temporal features across multiple frames were used for affect recognition. Event driven decision level fusion was used to combine the results from each individual modality using majority voting to recognize the emotions. The study also implemented affect recognition by matching the features to the rule based emotion templates per modality. Experiments showed that multimodal affect recognition rates using combination of emotion templates and supervised learning were better compared to recognition rates based on supervised learning alone. Recognition rates obtained using temporal feature were higher compared to recognition rates obtained using position based features only. |
| Tasks | Emotion Recognition |
| Published | 2016-07-09 |
| URL | http://arxiv.org/abs/1607.02652v1 |
| http://arxiv.org/pdf/1607.02652v1.pdf | |
| PWC | https://paperswithcode.com/paper/multimodal-affect-recognition-using-kinect |
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Recursive Decomposition for Nonconvex Optimization
| Title | Recursive Decomposition for Nonconvex Optimization |
| Authors | Abram L. Friesen, Pedro Domingos |
| Abstract | Continuous optimization is an important problem in many areas of AI, including vision, robotics, probabilistic inference, and machine learning. Unfortunately, most real-world optimization problems are nonconvex, causing standard convex techniques to find only local optima, even with extensions like random restarts and simulated annealing. We observe that, in many cases, the local modes of the objective function have combinatorial structure, and thus ideas from combinatorial optimization can be brought to bear. Based on this, we propose a problem-decomposition approach to nonconvex optimization. Similarly to DPLL-style SAT solvers and recursive conditioning in probabilistic inference, our algorithm, RDIS, recursively sets variables so as to simplify and decompose the objective function into approximately independent sub-functions, until the remaining functions are simple enough to be optimized by standard techniques like gradient descent. The variables to set are chosen by graph partitioning, ensuring decomposition whenever possible. We show analytically that RDIS can solve a broad class of nonconvex optimization problems exponentially faster than gradient descent with random restarts. Experimentally, RDIS outperforms standard techniques on problems like structure from motion and protein folding. |
| Tasks | Combinatorial Optimization, graph partitioning, Problem Decomposition |
| Published | 2016-11-08 |
| URL | http://arxiv.org/abs/1611.02755v1 |
| http://arxiv.org/pdf/1611.02755v1.pdf | |
| PWC | https://paperswithcode.com/paper/recursive-decomposition-for-nonconvex |
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Hierarchical Clustering via Spreading Metrics
| Title | Hierarchical Clustering via Spreading Metrics |
| Authors | Aurko Roy, Sebastian Pokutta |
| Abstract | We study the cost function for hierarchical clusterings introduced by [arXiv:1510.05043] where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters. It was also shown in [arXiv:1510.05043] that a top-down algorithm returns a hierarchical clustering of cost at most $O\left(\alpha_n \log n\right)$ times the cost of the optimal hierarchical clustering, where $\alpha_n$ is the approximation ratio of the Sparsest Cut subroutine used. Thus using the best known approximation algorithm for Sparsest Cut due to Arora-Rao-Vazirani, the top down algorithm returns a hierarchical clustering of cost at most $O\left(\log^{3/2} n\right)$ times the cost of the optimal solution. We improve this by giving an $O(\log{n})$-approximation algorithm for this problem. Our main technical ingredients are a combinatorial characterization of ultrametrics induced by this cost function, deriving an Integer Linear Programming (ILP) formulation for this family of ultrametrics, and showing how to iteratively round an LP relaxation of this formulation by using the idea of \emph{sphere growing} which has been extensively used in the context of graph partitioning. We also prove that our algorithm returns an $O(\log{n})$-approximate hierarchical clustering for a generalization of this cost function also studied in [arXiv:1510.05043]. Experiments show that the hierarchies found by using the ILP formulation as well as our rounding algorithm often have better projections into flat clusters than the standard linkage based algorithms. We also give constant factor inapproximability results for this problem. |
| Tasks | graph partitioning |
| Published | 2016-10-28 |
| URL | http://arxiv.org/abs/1610.09269v1 |
| http://arxiv.org/pdf/1610.09269v1.pdf | |
| PWC | https://paperswithcode.com/paper/hierarchical-clustering-via-spreading-metrics |
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Learning Compact Structural Representations for Audio Events Using Regressor Banks
| Title | Learning Compact Structural Representations for Audio Events Using Regressor Banks |
| Authors | Huy Phan, Marco Maass, Lars Hertel, Radoslaw Mazur, Ian McLoughlin, Alfred Mertins |
| Abstract | We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on the input signal. The regressors are class-specific and trained using the random regression forests framework. Given an input signal, each regressor estimates the onset and offset positions of the target event. The estimation confidence scores output by a regressor are then used to quantify how the target event aligns with the temporal structure of the corresponding category. Our proposed descriptor has two advantages. First, it is compact, i.e. the dimensionality of the descriptor is equal to the number of event classes. Second, we show that even simple linear classification models, trained on our descriptor, yield better accuracies on audio event classification task than not only the nonlinear baselines but also the state-of-the-art results. |
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| Published | 2016-04-29 |
| URL | http://arxiv.org/abs/1604.08716v1 |
| http://arxiv.org/pdf/1604.08716v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-compact-structural-representations |
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Applying Ontological Modeling on Quranic Nature Domain
| Title | Applying Ontological Modeling on Quranic Nature Domain |
| Authors | A. B. M. Shamsuzzaman Sadi, Towfique Anam, Mohamed Abdirazak, Abdillahi Hasan Adnan, Sazid Zaman Khan, Mohamed Mahmudur Rahman, Ghassan Samara |
| Abstract | The holy Quran is the holy book of the Muslims. It contains information about many domains. Often people search for particular concepts of holy Quran based on the relations among concepts. An ontological modeling of holy Quran can be useful in such a scenario. In this paper, we have modeled nature related concepts of holy Quran using OWL (Web Ontology Language) / RDF (Resource Description Framework). Our methodology involves identifying nature related concepts mentioned in holy Quran and identifying relations among those concepts. These concepts and relations are represented as classes/instances and properties of an OWL ontology. Later, in the result section it is shown that, using the Ontological model, SPARQL queries can retrieve verses and concepts of interest. Thus, this modeling helps semantic search and query on the holy Quran. In this work, we have used English translation of the holy Quran by Sahih International, Protege OWL Editor and for querying we have used SPARQL. |
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| Published | 2016-04-12 |
| URL | http://arxiv.org/abs/1604.03318v1 |
| http://arxiv.org/pdf/1604.03318v1.pdf | |
| PWC | https://paperswithcode.com/paper/applying-ontological-modeling-on-quranic |
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On the Parametric Study of Lubricating Oil Production using an Artificial Neural Network (ANN) Approach
| Title | On the Parametric Study of Lubricating Oil Production using an Artificial Neural Network (ANN) Approach |
| Authors | Masood Tehrani, Mary Ahmadi |
| Abstract | In this study, an Artificial Neural Network (ANN) approach is utilized to perform a parametric study on the process of extraction of lubricants from heavy petroleum cuts. To train the model, we used field data collected from an industrial plant. Operational conditions of feed and solvent flow rate, Temperature of streams and mixing rate were considered as the input to the model, whereas the flow rate of the main product was considered as the output of the ANN model. A feed-forward Multi-Layer Perceptron Neural Network was successfully applied to capture the relationship between inputs and output parameters. |
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| Published | 2016-11-21 |
| URL | http://arxiv.org/abs/1701.06551v1 |
| http://arxiv.org/pdf/1701.06551v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-parametric-study-of-lubricating-oil |
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Neural Discourse Relation Recognition with Semantic Memory
| Title | Neural Discourse Relation Recognition with Semantic Memory |
| Authors | Biao Zhang, Deyi Xiong, Jinsong Su |
| Abstract | Humans comprehend the meanings and relations of discourses heavily relying on their semantic memory that encodes general knowledge about concepts and facts. Inspired by this, we propose a neural recognizer for implicit discourse relation analysis, which builds upon a semantic memory that stores knowledge in a distributed fashion. We refer to this recognizer as SeMDER. Starting from word embeddings of discourse arguments, SeMDER employs a shallow encoder to generate a distributed surface representation for a discourse. A semantic encoder with attention to the semantic memory matrix is further established over surface representations. It is able to retrieve a deep semantic meaning representation for the discourse from the memory. Using the surface and semantic representations as input, SeMDER finally predicts implicit discourse relations via a neural recognizer. Experiments on the benchmark data set show that SeMDER benefits from the semantic memory and achieves substantial improvements of 2.56% on average over current state-of-the-art baselines in terms of F1-score. |
| Tasks | Word Embeddings |
| Published | 2016-03-12 |
| URL | http://arxiv.org/abs/1603.03873v1 |
| http://arxiv.org/pdf/1603.03873v1.pdf | |
| PWC | https://paperswithcode.com/paper/neural-discourse-relation-recognition-with |
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Using Visual Analytics to Interpret Predictive Machine Learning Models
| Title | Using Visual Analytics to Interpret Predictive Machine Learning Models |
| Authors | Josua Krause, Adam Perer, Enrico Bertini |
| Abstract | It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power. However, inspecting input-output relationships of those models using visual analytics, while treating them as black-box, can help to understand the reasoning behind outcomes without sacrificing predictive quality. We identify a space of possible solutions and provide two examples of where such techniques have been successfully used in practice. |
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| Published | 2016-06-17 |
| URL | http://arxiv.org/abs/1606.05685v2 |
| http://arxiv.org/pdf/1606.05685v2.pdf | |
| PWC | https://paperswithcode.com/paper/using-visual-analytics-to-interpret |
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A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids
| Title | A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids |
| Authors | Enrico De Santis, Antonello Rizzi, Alireza Sadeghian |
| Abstract | Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in commercial and industrial environment. In this paper we present an interesting application of the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model equipped by renewable sources and an energy storage system, taking into account the economical profit in energy trading with the main-grid. In particular, this study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover a minimal fuzzy rules set in a Fuzzy Logic Controller (FLC) adopted to perform decision making in the microgrid. The HGA rationale focuses on a particular encoding scheme, based on control genes and parametric genes applied to the optimization of the FIS parameters, allowing to perform a reduction in the structural complexity of the RB. This approach will be referred in the following as fuzzy-HGA. Results are compared with a simpler approach based on a classic fuzzy-GA scheme, where both FIS parameters and rule weights are tuned, while the number of fuzzy rules is fixed in advance. Experiments shows how the fuzzy-HGA approach adopted for the synthesis of the proposed controller outperforms the classic fuzzy-GA scheme, increasing the accounting profit by 67% in the considered energy trading problem yielding at the same time a simpler RB. |
| Tasks | Decision Making |
| Published | 2016-04-16 |
| URL | http://arxiv.org/abs/1604.04789v3 |
| http://arxiv.org/pdf/1604.04789v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-hierarchical-genetic-optimization-of-a |
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Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression
| Title | Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression |
| Authors | Julien Gout, Markus Quade, Kamran Shafi, Robert K. Niven, Markus Abel |
| Abstract | Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far reaching applications in many domains, including engineering and medicine. In this paper, we formulate the synchronization control in dynamical systems as an optimization problem and present a multi-objective genetic programming-based approach to infer optimal control functions that drive the system from a synchronized to a non-synchronized state and vice-versa. The genetic programming-based controller allows learning optimal control functions in an interpretable symbolic form. The effectiveness of the proposed approach is demonstrated in controlling synchronization in coupled oscillator systems linked in networks of increasing order complexity, ranging from a simple coupled oscillator system to a hierarchical network of coupled oscillators. The results show that the proposed method can learn highly-effective and interpretable control functions for such systems. |
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| Published | 2016-12-15 |
| URL | http://arxiv.org/abs/1612.05276v2 |
| http://arxiv.org/pdf/1612.05276v2.pdf | |
| PWC | https://paperswithcode.com/paper/learning-optimal-control-of-synchronization |
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An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems
| Title | An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems |
| Authors | Eneko Osaba, Xin-She Yang, Fernando Diaz, Pedro Lopez-Garcia, Roberto Carballedo |
| Abstract | Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric traveling salesman problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student’s $t$-test, the Holm’s test, and the Friedman test. We have also compared the convergence behaviour shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases. |
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| Published | 2016-04-14 |
| URL | http://arxiv.org/abs/1604.04138v1 |
| http://arxiv.org/pdf/1604.04138v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-improved-discrete-bat-algorithm-for |
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