Paper Group ANR 1012
Towards a Question Answering System over the Semantic Web. A Pipeline for Creative Visual Storytelling. 2-gram-based Phonetic Feature Generation for Convolutional Neural Network in Assessment of Trademark Similarity. Nearly Optimal Dynamic $k$-Means Clustering for High-Dimensional Data. New Hybrid Neuro-Evolutionary Algorithms for Renewable Energy …
Towards a Question Answering System over the Semantic Web
| Title | Towards a Question Answering System over the Semantic Web |
| Authors | Dennis Diefenbach, Andreas Both, Kamal Singh, Pierre Maret |
| Abstract | Thanks to the development of the Semantic Web, a lot of new structured data has become available on the Web in the form of knowledge bases (KBs). Making this valuable data accessible and usable for end-users is one of the main goals of Question Answering (QA) over KBs. Most current QA systems query one KB, in one language (namely English). The existing approaches are not designed to be easily adaptable to new KBs and languages. We first introduce a new approach for translating natural language questions to SPARQL queries. It is able to query several KBs simultaneously, in different languages, and can easily be ported to other KBs and languages. In our evaluation, the impact of our approach is proven using 5 different well-known and large KBs: Wikidata, DBpedia, MusicBrainz, DBLP and Freebase as well as 5 different languages namely English, German, French, Italian and Spanish. Second, we show how we integrated our approach, to make it easily accessible by the research community and by end-users. To summarize, we provided a conceptional solution for multilingual, KB-agnostic Question Answering over the Semantic Web. The provided first approximation validates this concept. |
| Tasks | Question Answering |
| Published | 2018-03-02 |
| URL | http://arxiv.org/abs/1803.00832v1 |
| http://arxiv.org/pdf/1803.00832v1.pdf | |
| PWC | https://paperswithcode.com/paper/towards-a-question-answering-system-over-the |
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A Pipeline for Creative Visual Storytelling
| Title | A Pipeline for Creative Visual Storytelling |
| Authors | Stephanie M. Lukin, Reginald Hobbs, Clare R. Voss |
| Abstract | Computational visual storytelling produces a textual description of events and interpretations depicted in a sequence of images. These texts are made possible by advances and cross-disciplinary approaches in natural language processing, generation, and computer vision. We define a computational creative visual storytelling as one with the ability to alter the telling of a story along three aspects: to speak about different environments, to produce variations based on narrative goals, and to adapt the narrative to the audience. These aspects of creative storytelling and their effect on the narrative have yet to be explored in visual storytelling. This paper presents a pipeline of task-modules, Object Identification, Single-Image Inferencing, and Multi-Image Narration, that serve as a preliminary design for building a creative visual storyteller. We have piloted this design for a sequence of images in an annotation task. We present and analyze the collected corpus and describe plans towards automation. |
| Tasks | Visual Storytelling |
| Published | 2018-07-21 |
| URL | http://arxiv.org/abs/1807.08077v1 |
| http://arxiv.org/pdf/1807.08077v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-pipeline-for-creative-visual-storytelling |
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2-gram-based Phonetic Feature Generation for Convolutional Neural Network in Assessment of Trademark Similarity
| Title | 2-gram-based Phonetic Feature Generation for Convolutional Neural Network in Assessment of Trademark Similarity |
| Authors | Kyung Pyo Ko, Kwang Hee Lee, Mi So Jang, Gun Hong Park |
| Abstract | A trademark is a mark used to identify various commodities. If same or similar trademark is registered for the same or similar commodity, the purchaser of the goods may be confused. Therefore, in the process of trademark registration examination, the examiner judges whether the trademark is the same or similar to the other applied or registered trademarks. The confusion in trademarks is based on the visual, phonetic or conceptual similarity of the marks. In this paper, we focus specifically on the phonetic similarity between trademarks. We propose a method to generate 2D phonetic feature for convolutional neural network in assessment of trademark similarity. This proposed algorithm is tested with 12,553 trademark phonetic similar pairs and 34,020 trademark phonetic non-similar pairs from 2010 to 2016. As a result, we have obtained approximately 92% judgment accuracy. |
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| Published | 2018-02-10 |
| URL | http://arxiv.org/abs/1802.03581v1 |
| http://arxiv.org/pdf/1802.03581v1.pdf | |
| PWC | https://paperswithcode.com/paper/2-gram-based-phonetic-feature-generation-for |
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Nearly Optimal Dynamic $k$-Means Clustering for High-Dimensional Data
| Title | Nearly Optimal Dynamic $k$-Means Clustering for High-Dimensional Data |
| Authors | Wei Hu, Zhao Song, Lin F. Yang, Peilin Zhong |
| Abstract | We consider the $k$-means clustering problem in the dynamic streaming setting, where points from a discrete Euclidean space ${1, 2, \ldots, \Delta}^d$ can be dynamically inserted to or deleted from the dataset. For this problem, we provide a one-pass coreset construction algorithm using space $\tilde{O}(k\cdot \mathrm{poly}(d, \log\Delta))$, where $k$ is the target number of centers. To our knowledge, this is the first dynamic geometric data stream algorithm for $k$-means using space polynomial in dimension and nearly optimal (linear) in $k$. |
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| Published | 2018-02-01 |
| URL | http://arxiv.org/abs/1802.00459v2 |
| http://arxiv.org/pdf/1802.00459v2.pdf | |
| PWC | https://paperswithcode.com/paper/nearly-optimal-dynamic-k-means-clustering-for |
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New Hybrid Neuro-Evolutionary Algorithms for Renewable Energy and Facilities Management Problems
| Title | New Hybrid Neuro-Evolutionary Algorithms for Renewable Energy and Facilities Management Problems |
| Authors | L. Cornejo-Bueno |
| Abstract | This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of life. This means that predictions over the characteristics on which renewable energies depend are necessary, in order to know the amount of energy that will be obtained at any time. The second topic tackled in this thesis is related to the basic parameters that influence in different marine activities and airports, whose knowledge is necessary to develop a proper facilities management in these environments. Within this work, a study of the state-of-the-art Machine Learning have been performed to solve the problems associated with the topics above-mentioned, and several contributions have been proposed: One of the pillars of this work is focused on the estimation of the most important parameters in the exploitation of renewable resources. The second contribution of this thesis is related to feature selection problems. The proposed methodologies are applied to multiple problems: the prediction of $H_s$, relevant for marine energy applications and marine activities, the estimation of WPREs, undesirable variations in the electric power produced by a wind farm, the prediction of global solar radiation in areas from Spain and Australia, really important in terms of solar energy, and the prediction of low-visibility events at airports. All of these practical issues are developed with the consequent previous data analysis, normally, in terms of meteorological variables. |
| Tasks | Feature Selection |
| Published | 2018-06-05 |
| URL | http://arxiv.org/abs/1806.02654v1 |
| http://arxiv.org/pdf/1806.02654v1.pdf | |
| PWC | https://paperswithcode.com/paper/new-hybrid-neuro-evolutionary-algorithms-for |
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Mammographic Image Enhancement using Digital Image Processing Technique
| Title | Mammographic Image Enhancement using Digital Image Processing Technique |
| Authors | Ardymulya Iswardani, Wahyu Hidayat |
| Abstract | Abstract PURPOSES this study aims to perform microcalsification detection by performing image enhancement in mammography image by using transformation of negative image and histogram equalization. image mammography with .pgm format changed to. jpg format then processed into negative image result then processed again using histogram equalization. the results of the image enhancement process using negative image techniques and equalization histograms are compared and validated with MSE and PSNR on each mammographic image. CONCLUSION: Image enhancement process on mammography image can be done, however there are only some image that have improved quality, this affected by threshold usage, which have important role to get better visualization on mammographic image. Keywords-component; Image enhancement, image negative, histogram equalization, mammographic, breast cancer |
| Tasks | Image Enhancement |
| Published | 2018-06-29 |
| URL | http://arxiv.org/abs/1806.11496v1 |
| http://arxiv.org/pdf/1806.11496v1.pdf | |
| PWC | https://paperswithcode.com/paper/mammographic-image-enhancement-using-digital |
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Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
| Title | Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge |
| Authors | Takayuki Nishio, Ryo Yonetani |
| Abstract | We envision a mobile edge computing (MEC) framework for machine learning (ML) technologies, which leverages distributed client data and computation resources for training high-performance ML models while preserving client privacy. Toward this future goal, this work aims to extend Federated Learning (FL), a decentralized learning framework that enables privacy-preserving training of models, to work with heterogeneous clients in a practical cellular network. The FL protocol iteratively asks random clients to download a trainable model from a server, update it with own data, and upload the updated model to the server, while asking the server to aggregate multiple client updates to further improve the model. While clients in this protocol are free from disclosing own private data, the overall training process can become inefficient when some clients are with limited computational resources (i.e. requiring longer update time) or under poor wireless channel conditions (longer upload time). Our new FL protocol, which we refer to as FedCS, mitigates this problem and performs FL efficiently while actively managing clients based on their resource conditions. Specifically, FedCS solves a client selection problem with resource constraints, which allows the server to aggregate as many client updates as possible and to accelerate performance improvement in ML models. We conducted an experimental evaluation using publicly-available large-scale image datasets to train deep neural networks on MEC environment simulations. The experimental results show that FedCS is able to complete its training process in a significantly shorter time compared to the original FL protocol. |
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| Published | 2018-04-23 |
| URL | http://arxiv.org/abs/1804.08333v2 |
| http://arxiv.org/pdf/1804.08333v2.pdf | |
| PWC | https://paperswithcode.com/paper/client-selection-for-federated-learning-with |
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Personalized Classifier for Food Image Recognition
| Title | Personalized Classifier for Food Image Recognition |
| Authors | Shota Horiguchi, Sosuke Amano, Makoto Ogawa, Kiyoharu Aizawa |
| Abstract | Currently, food image recognition tasks are evaluated against fixed datasets. However, in real-world conditions, there are cases in which the number of samples in each class continues to increase and samples from novel classes appear. In particular, dynamic datasets in which each individual user creates samples and continues the updating process often have content that varies considerably between different users, and the number of samples per person is very limited. A single classifier common to all users cannot handle such dynamic data. Bridging the gap between the laboratory environment and the real world has not yet been accomplished on a large scale. Personalizing a classifier incrementally for each user is a promising way to do this. In this paper, we address the personalization problem, which involves adapting to the user’s domain incrementally using a very limited number of samples. We propose a simple yet effective personalization framework which is a combination of the nearest class mean classifier and the 1-nearest neighbor classifier based on deep features. To conduct realistic experiments, we made use of a new dataset of daily food images collected by a food-logging application. Experimental results show that our proposed method significantly outperforms existing methods. |
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| Published | 2018-04-08 |
| URL | http://arxiv.org/abs/1804.04600v1 |
| http://arxiv.org/pdf/1804.04600v1.pdf | |
| PWC | https://paperswithcode.com/paper/personalized-classifier-for-food-image |
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Winter Road Surface Condition Recognition Using A Pretrained Deep Convolutional Network
| Title | Winter Road Surface Condition Recognition Using A Pretrained Deep Convolutional Network |
| Authors | Guangyuan Pan, Liping Fu, Ruifan Yu, Matthew Muresan |
| Abstract | This paper investigates the application of the latest machine learning technique deep neural networks for classifying road surface conditions (RSC) based on images from smartphones. Traditional machine learning techniques such as support vector machine (SVM) and random forests (RF) have been attempted in literature; however, their classification performance has been less than desirable due to challenges associated with image noises caused by sunlight glare and residual salts. A deep learning model based on convolutional neural network (CNN) is proposed and evaluated for its potential to address these challenges for improved classification accuracy. In the proposed approach we introduce the idea of applying an existing CNN model that has been pre-trained using millions of images with proven high recognition accuracy. The model is extended with two additional fully-connected layers of neurons for learning the specific features of the RSC images. The whole model is then trained with a low learning rate for fine-tuning by using a small set of RSC images. Results show that the proposed model has the highest classification performance in comparison to the traditional machine learning techniques. The testing accuracy with different training dataset sizes is also analyzed, showing the potential of achieving much higher accuracy with a larger training dataset. |
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| Published | 2018-12-17 |
| URL | http://arxiv.org/abs/1812.06858v1 |
| http://arxiv.org/pdf/1812.06858v1.pdf | |
| PWC | https://paperswithcode.com/paper/winter-road-surface-condition-recognition |
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Corpus Conversion Service: A machine learning platform to ingest documents at scale [Poster abstract]
| Title | Corpus Conversion Service: A machine learning platform to ingest documents at scale [Poster abstract] |
| Authors | Peter W J Staar, Michele Dolfi, Christoph Auer, Costas Bekas |
| Abstract | Over the past few decades, the amount of scientific articles and technical literature has increased exponentially in size. Consequently, there is a great need for systems that can ingest these documents at scale and make their content discoverable. Unfortunately, both the format of these documents (e.g. the PDF format or bitmap images) as well as the presentation of the data (e.g. complex tables) make the extraction of qualitative and quantitive data extremely challenging. We present a platform to ingest documents at scale which is powered by Machine Learning techniques and allows the user to train custom models on document collections. We show precision/recall results greater than 97% with regard to conversion to structured formats, as well as scaling evidence for each of the microservices constituting the platform. |
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| Published | 2018-05-15 |
| URL | http://arxiv.org/abs/1805.09687v1 |
| http://arxiv.org/pdf/1805.09687v1.pdf | |
| PWC | https://paperswithcode.com/paper/corpus-conversion-service-a-machine-learning |
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Triangular Architecture for Rare Language Translation
| Title | Triangular Architecture for Rare Language Translation |
| Authors | Shuo Ren, Wenhu Chen, Shujie Liu, Mu Li, Ming Zhou, Shuai Ma |
| Abstract | Neural Machine Translation (NMT) performs poor on the low-resource language pair $(X,Z)$, especially when $Z$ is a rare language. By introducing another rich language $Y$, we propose a novel triangular training architecture (TA-NMT) to leverage bilingual data $(Y,Z)$ (may be small) and $(X,Y)$ (can be rich) to improve the translation performance of low-resource pairs. In this triangular architecture, $Z$ is taken as the intermediate latent variable, and translation models of $Z$ are jointly optimized with a unified bidirectional EM algorithm under the goal of maximizing the translation likelihood of $(X,Y)$. Empirical results demonstrate that our method significantly improves the translation quality of rare languages on MultiUN and IWSLT2012 datasets, and achieves even better performance combining back-translation methods. |
| Tasks | Machine Translation |
| Published | 2018-05-13 |
| URL | http://arxiv.org/abs/1805.04813v2 |
| http://arxiv.org/pdf/1805.04813v2.pdf | |
| PWC | https://paperswithcode.com/paper/triangular-architecture-for-rare-language |
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Neuron inspired data encoding memristive multi-level memory cell
| Title | Neuron inspired data encoding memristive multi-level memory cell |
| Authors | Aidana Irmanova, Alex Pappachen James |
| Abstract | Mapping neuro-inspired algorithms to sensor backplanes of on-chip hardware require shifting the signal processing from digital to the analog domain, demanding memory technologies beyond conventional CMOS binary storage units. Using memristors for building analog data storage is one of the promising approaches amongst emerging non-volatile memory technologies. Recently, a memristive multi-level memory (MLM) cell for storing discrete analog values has been developed in which memory system is implemented combining memristors in voltage divider configuration. In given example, the memory cell of 3 sub-cells with a memristor in each was programmed to store ternary bits which overall achieved 10 and 27 discrete voltage levels. However, for further use of proposed memory cell in analog signal processing circuits data encoder is required to generate control voltages for programming memristors to store discrete analog values. In this paper, we present the design and performance analysis of data encoder that generates write pattern signals for 10 level memristive memory. |
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| Published | 2018-03-14 |
| URL | http://arxiv.org/abs/1803.05132v1 |
| http://arxiv.org/pdf/1803.05132v1.pdf | |
| PWC | https://paperswithcode.com/paper/neuron-inspired-data-encoding-memristive |
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Physical Signal Classification Via Deep Neural Networks
| Title | Physical Signal Classification Via Deep Neural Networks |
| Authors | Benjamin Epstein, Roy H. Olsson III |
| Abstract | A Deep Neural Network is applied to classify physical signatures obtained from physical sensor measurements of running gasoline and diesel-powered vehicles and other devices. The classification provides information on the target identities as to vehicle type and even vehicle model. The physical measurements include acoustic, acceleration (vibration), geophonic, and magnetic. |
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| Published | 2018-11-15 |
| URL | http://arxiv.org/abs/1811.06349v1 |
| http://arxiv.org/pdf/1811.06349v1.pdf | |
| PWC | https://paperswithcode.com/paper/physical-signal-classification-via-deep |
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Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash
| Title | Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash |
| Authors | Puneet Kohli, Anjali Chadha |
| Abstract | Human lives are important. The decision to allow self-driving vehicles operate on our roads carries great weight. This has been a hot topic of debate between policy-makers, technologists and public safety institutions. The recent Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has strengthened the argument that autonomous vehicle technology is still not ready for deployment on public roads. In this work, we analyze the Uber car crash and shed light on the question, “Could the Uber Car Crash have been avoided?". We apply state-of-the-art Computer Vision models to this highly practical scenario. More generally, our experimental results are an evaluation of various image enhancement and object recognition techniques for enabling pedestrian safety in low-lighting conditions using the Uber crash as a case study. |
| Tasks | Image Enhancement, Object Recognition |
| Published | 2018-05-30 |
| URL | http://arxiv.org/abs/1805.11815v1 |
| http://arxiv.org/pdf/1805.11815v1.pdf | |
| PWC | https://paperswithcode.com/paper/enabling-pedestrian-safety-using-computer |
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A robust hierarchical nominal classification method based on similarity and dissimilarity using loss function and an improved version of the deck of cards method
| Title | A robust hierarchical nominal classification method based on similarity and dissimilarity using loss function and an improved version of the deck of cards method |
| Authors | Ana Sara Costa, Salvatore Corrente, Salvatore Greco, José Rui Figueira, José Borbinha |
| Abstract | Cat-SD is a multiple criteria decision aiding method for dealing with nominal classification problems. Actions are assessed according to multiple criteria and assigned to one or more categories. A set of reference actions is used for defining each category. The assignment of an action to a given category depends on the comparison of the action to each reference set according to likeness thresholds. Distinct sets of criteria weights, interaction coefficients, and likeness thresholds can be defined per category. We propose to apply Multiple Criteria Hierarchy Process (MCHP) to Cat-SD. An adapted MCHP is proposed to take into account possible interaction effects between criteria structured in a hierarchical way. On the basis of the known deck of cards method, we also consider an imprecise elicitation of parameters permitting to take into account interactions and antagonistic effects between criteria. The elicitation procedure we are proposing can be applied to any Electre method. With the purpose of exploring the assignments obtained by Cat-SD considering possible sets of parameters, we propose to apply the Stochastic Multicriteria Acceptability Analysis (SMAA). The SMAA methodology allows to draw statistical conclusions on the classification of the actions. The proposed method, SMAA-hCat-SD, helps the decision maker to check the effects of the variation of parameters on the classification at different levels of the hierarchy. We propose also a procedure, based on the concept of loss function, to obtain a final classification fulfilling some requirements given by the decision maker and taking into account the hierarchy of criteria and the probabilistic assignments obtained applying SMAA. Also this procedure can be applied to any classification Electre method. |
| Tasks | |
| Published | 2018-12-12 |
| URL | https://arxiv.org/abs/1812.08596v2 |
| https://arxiv.org/pdf/1812.08596v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-robust-hierarchical-nominal-classification |
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