niryuu/sni-each-converted huggingface dataset:
We study the computational complexity of candidate control in elections with few voters (that is, we take the number of voters as a parameter). We consider both the standard scenario of adding and deleting candidates, where one asks if a given candidate can become a winner (or, in the destructive case, can be precluded from winning) by adding/deleting some candidates, and a combinatorial scenario where adding/deleting a candidate automatically means adding/deleting a whole group of candidates. Our results show that the parameterized complexity of candidate control (with the number of voters as the parameter) is much more varied than in the setting with many voters.
Output: Elections with Few Voters: Candidate Control Can Be Easy
Machine learning has been used to detect new malware in recent years, while malware authors have strong motivation to attack such algorithms.Malware authors usually have no access to the detailed structures and parameters of the machine learning models used by malware detection systems, and therefore they can only perform black-box attacks. This paper proposes a generative adversarial network (GAN) based algorithm named MalGAN to generate adversarial malware examples, which are able to bypass black-box machine learning based detection models. MalGAN uses a substitute detector to fit the black-box malware detection system. A generative network is trained to minimize the generated adversarial examples’ malicious probabilities predicted by the substitute detector. The superiority of MalGAN over traditional gradient based adversarial example generation algorithms is that MalGAN is able to decrease the detection rate to nearly zero and make the retraining based defensive method against adversarial examples hard to work.
Output: Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
We investigate the use of pivot languages for phrase-based statistical machine translation (PB-SMT) between related languages with limited parallel corpora. We show that subword-level pivot translation via a related pivot language is: (i) highly competitive with the best direct translation model and (ii) better than a pivot model which uses an unrelated pivot language, but has at its disposal large parallel corpora to build the source-pivot (S-P) and pivot-target (P-T) translation models. In contrast, pivot models trained at word and morpheme level are far inferior to their direct counterparts. We also show that using multiple related pivot languages can outperform a direct translation model. Thus, the use of subwords as translation units coupled with the use of multiple related pivot languages can compensate for the lack of a direct parallel corpus. Subword units make pivot models competitive by (i) utilizing lexical similarity to improve the underlying S-P and P-T translation models, and (ii) reducing loss of translation candidates during pivoting.
Output: Utilizing Lexical Similarity for pivot translation involving resource-poor, related languages
Learning. Abstract: Document categorization is a technique where the category of a document is determined. In this paper three well-known supervised learning techniques which are Support Vector Machine(SVM), Naïve Bayes(NB) and Stochastic Gradient Descent(SGD) compared for Bengali document categorization. Besides classifier, classification also depends on how feature is selected from dataset. For analyzing those classifier performances on predicting a document against twelve categories several feature selection techniques are also applied in this article namely Chi square distribution, normalized TFIDF (term frequency-inverse document frequency) with word analyzer. So, we attempt to explore the efficiency of those three-classification algorithms by using two different feature selection techniques in this article.
Output: A Comparative Study on Different Types of Approaches to Bengali document Categorization
The increasing diversity of languages used on the web introduces a new level of complexity to Information Retrieval (IR) systems. We can no longer assume that textual content is written in one language or even the same language family. In this paper, we demonstrate how to build massive multilingual annotators with minimal human expertise and intervention. We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase. Our approach does not require NER human annotated datasets or language specific resources like treebanks, parallel corpora, and orthographic rules. The novelty of approach lies therein using only language agnostic techniques, while achieving competitive performance. Our method learns distributed word representations (word embeddings) which encode semantic and syntactic features of words in each language. Then, we automatically generate datasets from Wikipedia link structure and Freebase attributes. Finally, we apply two preprocessing stages (oversampling and exact surface form matching) which do not require any linguistic expertise. Our evaluation is two fold: First, we demonstrate the system performance on human annotated datasets. Second, for languages where no gold-standard benchmarks are available, we propose a new method, distant evaluation, based on statistical machine translation.
Output: POLYGLOT-NER: Massive Multilingual Named Entity Recognition
The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks. This paper re-evaluates state-of-the-art architectures in light of the new Kinetics Human Action Video dataset. Kinetics has two orders of magnitude more data, with 400 human action classes and over 400 clips per class, and is collected from realistic, challenging YouTube videos. We provide an analysis on how current architectures fare on the task of action classification on this dataset and how much performance improves on the smaller benchmark datasets after pre-training on Kinetics. We also introduce a new Two-Stream Inflated 3D ConvNet (I3D) that is based on 2D ConvNet inflation: filters and pooling kernels of very deep image classification ConvNets are expanded into 3D, making it possible to learn seamless spatio-temporal feature extractors from video while leveraging successful ImageNet architecture designs and even their parameters. We show that, after pre-training on Kinetics, I3D models considerably improve upon the state-of-the-art in action classification, reaching 80.7% on HMDB-51 and 98.0% on UCF-101.
Output: Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Case-based reasoning (CBR) based on description logics (DLs) has gained a lot of attention lately. Adaptation is a basic task in CBR that can be modeled as a knowledge base revision problem which has been solved in propositional logic. However, in DLs, adaptation is still a challenge problem since existing revision operators only work well for DLs of the DL-Lite family. It is difficult to design revision algorithms that are syntax-independent and fine-grained. In this paper, we present a new method for adaptation based on the tractable DL EL⊥. Following the idea of adaptation as revision, we firstly extend the logical basis for describing cases from propositional logic to the DL EL⊥, and then present a formalism for adaptation based on EL⊥. With this formalism, we show that existing revision operators and algorithms in DLs do not work for it, and then present our adaptation algorithm. Our algorithm is syntax-independent and fine-grained, and satisfies the requirements on revision operators.
Output: Algorithm for Adapting Cases Represented in a Tractable Description Logic
Recent works have highlighted scale invariance or symmetry present in the weight space of a typical deep network and the adverse effect it has on the Euclidean gradient based stochastic gradient descent optimization. In this work, we show that a commonly used deep network, which uses convolution, batch normalization, reLU, max-pooling, and sub-sampling pipeline, possess more complex forms of symmetry arising from scaling-based reparameterization of the network weights. We propose to tackle the issue of the weight space symmetry by constraining the filters to lie on the unit-norm manifold. Consequently, training the network boils down to using stochastic gradient descent updates on the unit-norm manifold. Our empirical evidence based on the MNIST dataset shows that the proposed updates improve the test performance beyond what is achieved with batch normalization and without sacrificing the computational efficiency of the weight updates.
Output: Understanding symmetries in deep networks
Advances in sensing technologies and the growth of the internet have resulted in an explosion in the size of modern datasets, while storage and processing power continue to lag behind. This motivates the need for algorithms that are efficient, both in terms of the number of measurements needed and running time. To combat the challenges associated with large datasets, we propose a general framework for active hierarchical clustering that repeatedly runs an off-the-shelf clustering algorithm on small subsets of the data and comes with guarantees on performance, measurement complexity and runtime complexity. We instantiate this framework with a simple spectral clustering algorithm and provide concrete results on its performance, showing that, under some assumptions, this algorithm recovers all clusters of size Ω(log n) using O(n log n) similarities and runs in O(n log n) time for a dataset of n objects. Through extensive experimentation we also demonstrate that this framework is practically alluring.
Output: Efficient Active Algorithms for Hierarchical Clustering
We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016. The attention-based encoder-decoder models can be used for the generation of corrections, in addition to error identification, which is of interest for certain end-user applications. We show that a character-based encoder-decoder model is particularly effective, outperforming other results on the AESW Shared Task on its own, and showing gains over a word-based counterpart. Our final model—a combination of three character-based encoder-decoder models, one word-based encoder-decoder model, and a sentence-level CNN—is the highest performing system on the AESW 2016 binary prediction Shared Task.
Output: Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction
An emerging way to deal with highdimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate the notion of joint harmonic analysis to a full-fledged framework denoted as Time-Vertex Signal Processing, that links together the time-domain signal processing techniques with the new tools of graph signal processing. This entails three main contributions: (a) We provide a formal motivation for harmonic time-vertex analysis as an analysis tool for the state evolution of simple Partial Differential Equations on graphs. (b) We improve the accuracy of joint filtering operators by up-to two orders of magnitude. (c) Using our joint filters, we construct time-vertex dictionaries analyzing the different scales and the local time-frequency content of a signal. The utility of our tools is illustrated in numerous applications and datasets, such as dynamic mesh denoising and classification, still-video inpainting, and source localization in seismic events. Our results suggest that joint analysis of time-vertex signals can bring benefits to regression and learning.
Output: A Time-Vertex Signal Processing Framework Scalable processing and meaningful representations for time-series on graphs
Compounding is a highly productive word-formation process in some languages that is often problematic for natural language processing applications. In this paper, we investigate whether distributional semantics in the form of word embeddings can enable a deeper, i.e., more knowledge-rich, processing of compounds than the standard string-based methods. We present an unsupervised approach that exploits regularities in the semantic vector space (based on analogies such as “bookshop is to shop as bookshelf is to shelf”) to produce compound analyses of high quality. A subsequent compound splitting algorithm based on these analyses is highly effective, particularly for ambiguous compounds. German to English machine translation experiments show that this semantic analogy-based compound splitter leads to better translations than a commonly used frequency-based method.
Output: Splitting Compounds by Semantic Analogy
The EM-algorithm is a general procedure to get maximum likelihood estimates if part of the observations on the variables of a network are missing. In this paper a stochastic version of the algorithm is adapted to probabilistic neural networks describing the associative dependency of variables. These networks have a proba­ bility distribution, which is a special case of the distribution generated by proba­ bilistic inference networks. Hence both types of networks can be combined al­ lowing to integrate probabilistic rules as well as unspecified associations in a sound way. The resulting network may have a number of interesting features including cycles of probabilistic rules, hidden 'un­ observable' variables, and uncertain and contradictory evidence.
Output: Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm
<lb>In multi-agent domains, an agent’s action may not just change the world and the agent’s knowledge and beliefs<lb>about the world, but also may change other agents’ knowledge and beliefs about the world and their knowledge and<lb>beliefs about other agents’ knowledge and beliefs about the world. Similarly, the goals of an agent in a multi-agent<lb>world may involve manipulating the knowledge and beliefs of other agents’ and again, not just their knowledge<lb>about the world, but also their knowledge about other agents’ knowledge about the world. The goal of this paper is to<lb>present an action language, called mA+, that has the necessary features to address the above aspects in representing<lb>and reasoning about actions and change in multi-agent domains.<lb>This action language can be viewed as a generalization of the single-agent action languages extensively studied<lb>in the literature, to the case of multi-agent domains. The language allows the representation of and reasoning about<lb>different types of actions that an agent can perform in a domain where many other agents might be present—such as<lb>world-altering actions, sensing actions, and announcement/communication actions. The action language also allows<lb>the specification of agents’ dynamic awareness of action occurrences which has future implications on what agents’<lb>know about the world and other agents’ knowledge about the world. The language mA+ considers three different<lb>types of awareness: full awareness, partial awareness, and complete oblivion of an action occurrence and its effects.<lb>This keeps the language simple, yet powerful enough to address a large variety of knowledge manipulation scenarios<lb>in multi-agent domains.<lb>The semantics of the language relies on the notion of state, which is described by a pointed Kripke model and<lb>is used to encode the agent’s knowledge and the real state of the world. The semantics is defined by a transition<lb>function that maps pairs of actions and states into sets of states. The paper illustrates properties of the action theories,<lb>including properties that guarantee finiteness of the set of initial states and their practical implementability. Finally,<lb>the paper relates mA+ to other related formalisms that contribute to reasoning about actions in multi-agent domains.
Output: An Action Language for Multi-Agent Domains: Foundations
Retention of residual skills for persons who partially lose their cognitive or physical ability is of utmost importance. Research is focused on developing systems that provide need-based assistance for retention of such residual skills. This paper describes a novel cognitive collaborative control architecture CA, designed to address the challenges of developing needbased assistance for wheelchair navigation. Organization of CA is detailed and results from simulation of the proposed architecture is presented. For simulation of our proposed architecture, we have used ROS (Robot Operating System) as a control framework and a 3D robotic simulator called USARSim (Unified System for Automation and Robot Simulation).
Output: C3A: A Cognitive Collaborative Control Architecture For an Intelligent Wheelchair
In recent years, Deep Neural Networks (DNN) based methods have achieved remarkable performance in a wide range of tasks and have been among the most powerful and widely used techniques in computer vision, speech recognition and Natural Language Processing. However, DNN-based methods are both computational-intensive and resource-consuming, which hinders the application of these methods on embedded systems like smart phones. To alleviate this problem, we introduce a novel Fixed-point Factorized Networks (FFN) on pre-trained models to reduce the computational complexity as well as the storage requirement of networks. Extensive experiments on large-scale ImageNet classification task show the effectiveness of our proposed method.
Output: Fixed-point Factorized Networks
Language students are most engaged while reading texts at an appropriate difficulty level. However, existing methods of evaluating text difficulty focus mainly on vocabulary and do not prioritize grammatical features, hence they do not work well for language learners with limited knowledge of grammar. In this paper, we introduce grammatical templates, the expert-identified units of grammar that students learn from class, as an important feature of text difficulty evaluation. Experimental classification results show that grammatical template features significantly improve text difficulty prediction accuracy over baseline readability features by 7.4%. Moreover, we build a simple and human-understandable text difficulty evaluation approach with 87.7% accuracy, using only 5 grammatical template features.
Output: Grammatical Templates: Improving Text Difficulty Evaluation for Language Learners
Music emotion recognition (MER) is usually regarded as a multi-label tagging task, and each segment of music can inspire specific emotion tags. Most researchers extract acoustic features from music and explore the relations between these features and their corresponding emotion tags. Considering the inconsistency of emotions inspired by the same music segment for human beings, seeking for the key acoustic features that really affect on emotions is really a challenging task. In this paper, we propose a novel MER method by using deep convolutional neural network (CNN) on the music spectrograms that contains both the original time and frequency domain information. By the proposed method, no additional effort on extracting specific features required, which is left to the training procedure of the CNN model. Experiments are conducted on the standard CAL500 and CAL500exp dataset. Results show that, for both datasets, the proposed method outperforms state-of-the-art methods.
Output: CNN BASED MUSIC EMOTION CLASSIFICATION
Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical. We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs.
Output: Learning the Semantics of Structured Data Sources
Understanding the ways in which participants in public discussions frame their arguments is important in understanding how public opinion is formed. In this paper, we adopt the position that it is time for more computationallyoriented research on problems involving framing. In the interests of furthering that goal, we propose the following specific, interesting and, we believe, relatively accessible question: In the controversy regarding the use of genetically-modified organisms (GMOs) in agriculture, do proand anti-GMO articles differ in whether they choose to adopt a more “scientific” tone? Prior work on the rhetoric and sociology of science suggests that hedging may distinguish popular-science text from text written by professional scientists for their colleagues. We propose a detailed approach to studying whether hedge detection can be used to understanding scientific framing in the GMO debates, and provide corpora to facilitate this study. Some of our preliminary analyses suggest that hedges occur less frequently in scientific discourse than in popular text, a finding that contradicts prior assertions in the literature. We hope that our initial work and data will encourage others to pursue this promising line of inquiry. Publication venue: ACL Workshop on ExtraPropositional Aspects of Meaning in Computational Linguistics, 2012
Output: Hedge detection as a lens on framing in the GMO debates: A position paper
Pattern-based methods of IS-A relation extraction rely heavily on so called Hearst patterns. These are ways of expressing instance enumerations of a class in natural language. While these lexico-syntactic patterns prove quite useful, they may not capture all taxonomical relations expressed in text. Therefore in this paper we describe a novel method of IS-A relation extraction from patterns, which uses morpho-syntactical annotations along with grammatical case of noun phrases that constitute entities participating in IS-A relation. We also describe a method for increasing the number of extracted relations that we call pseudo-subclass boosting which has potential application in any pattern-based relation extraction method. Experiments were conducted on a corpus of about 0.5 billion web documents in Polish language.
Output: Grammatical Case Based IS-A Relation Extraction with Boosting for Polish
We present an unsupervised explainable word embedding technique, called EVE, which is built upon the structure of Wikipedia. The proposed model defines the dimensions of a semantic vector representing a word using humanreadable labels, thereby it readily interpretable. Specifically, each vector is constructed using the Wikipedia category graph structure together with the Wikipedia article link structure. To test the effectiveness of the proposed word embedding model, we consider its usefulness in three fundamental tasks: 1) intruder detection — to evaluate its ability to identify a non-coherent vector from a list of coherent vectors, 2) ability to cluster — to evaluate its tendency to group related vectors together while keeping unrelated vectors in separate clusters, and 3) sorting relevant items first — to evaluate its ability to rank vectors (items) relevant to the query in the top order of the result. For each task, we also propose a strategy to generate a task-specific human-interpretable explanation from the model. These demonstrate the overall effectiveness of the explainable embeddings generated by EVE. Finally, we compare EVE with the Word2Vec, FastText, and GloVe embedding techniques across the three tasks, and report improvements over the state-of-the-art.
Output: EVE: Explainable Vector Based Embedding Technique Using Wikipedia
In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).
Output: Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting
Many ubiquitous computing projects have addressed health and wellness behaviors such as healthy eating. Healthy meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people’s food preferences and making healthy recommendations that appeal to their palate is challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period. We propose Yum-me, a personalized healthy-meal recommender system designed to meet individuals’ health goals, dietary restrictions, and finegrained food preferences. Marrying ideas from user preference learning and healthy eating promotion, Yum-me enables a simple and accurate food preference profiling procedure via an image-based online learning framework, and projects the learned profile into the domain of healthy food options to find ones that will appeal to the user. We present the design and implementation of Yum-me, and further discuss the most critical component of it: FoodDist, a state-of-the-art food image analysis model. We demonstrate FoodDist’s superior performance through careful benchmarking, and discuss its applicability across a wide array of dietary applications. We validate the feasibility and effectiveness of Yum-me through a 60-person user study, in which Yum-me improves the recommendation acceptance rate by 42.63% over the traditional food preference survey. ACM Classification
Output: Yum-me: Personalized Healthy Meal Recommender System
-This study proposes a framework of Uncertainty-based Group Decision Support System (UGDSS). It provides a platform for multiple criteria decision analysis in six aspects including (1) decision environment, (2) decision problem, (3) decision group, (4) decision conflict, (5) decision schemes and (6) group negotiation. Based on multiple artificial intelligent technologies, this framework provides reliable support for the comprehensive manipulation of applications and advanced decision approaches through the design of an integrated multi-agents architecture.
Output: Towards a Reliable Framework of Uncertainty-based Group Decision Support System
The detection and identification of extreme weather events in large scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme weather events when large amounts of labeled data are available. However, there are many different types of spatially localized climate patterns of interest (including hurricanes, extra-tropical cyclones, weather fronts, blocking events, etc.) found in simulation data for which labeled data is not available at large scale for all simulations of interest. We present a multichannel spatiotemporal encoder-decoder CNN architecture for semi-supervised bounding box prediction and exploratory data analysis. This architecture is designed to fully model multichannel simulation data, temporal dynamics and unlabelled data within a reconstruction and prediction framework so as to improve the detection of a wide range of extreme weather events. Our architecture can be viewed as a 3D convolutional autoencoder with an additional modified one-pass bounding box regression loss. We demonstrate that our approach is able to leverage temporal information and unlabelled data to improve localization of extreme weather events. Further, we explore the representations learned by our model in order to better understand this important data, and facilitate further work in understanding and mitigating the effects of climate change.
Output: SEMI-SUPERVISED DETECTION OF EXTREME WEATHER EVENTS IN LARGE CLIMATE DATASETS
Backward simulation is an approximate inference technique for Bayesian belief networks. It differs from existing simulation methods in that it starts simulation from the known evidence and works backward (i.e., contrary to the direction of the arcs). The technique's focus on the evidence leads to improved convergence in situations where the posterior beliefs are dominated by the evidence rather than by the prior probabilities. Since this class of situations is large, the technique may make practical the application of approximate inference in Bayesian belief networks to many real�world problems.
Output: Backward Simulation in Bayesian Networks
Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making problems in such environments. In recent years, attempts were made to apply methods from reinforcement learning to construct decision support systems for action selection in Markovian environments. Although conventional methods in reinforcement learning have proved to be useful in problems concerning sequential decision-making, they cannot be applied in their current form to decision support systems, such as those in medical domains, as they suggest policies that are often highly prescriptive and leave little room for the user’s input. Without the ability to provide flexible guidelines, it is unlikely that these methods can gain ground with users of such systems. This paper introduces the new concept of non-deterministic policies to allow more flexibility in the user’s decision-making process, while constraining decisions to remain near optimal solutions. We provide two algorithms to compute non-deterministic policies in discrete domains. We study the output and running time of these method on a set of synthetic and real-world problems. In an experiment with human subjects, we show that humans assisted by hints based on non-deterministic policies outperform both human-only and computer-only agents in a web navigation task.
Output: Non-Deterministic Policies in Markovian Decision Processes
Despite being so vital to success of Support Vector Machines, the principle of separating margin maximisation is not used in deep learning. We show that minimisation of margin variance and not maximisation of the margin is more suitable for improving generalisation in deep architectures. We propose the Halfway loss function that minimises the Normalised Margin Variance (NMV) at the output of a deep learning models and evaluate its performance against the Softmax Cross-Entropy loss on the MNIST, smallNORB and CIFAR-10 datasets.
Output: Effects of the optimisation of the margin distribution on generalisation in deep architectures
Literature on Constraint Satisfaction exhibits the definition of several “structural” properties that can be possessed by CSPs, like (in)consistency, substitutability or interchangeability. Current tools for constraint solving typically detect such properties efficiently by means of incomplete yet effective algorithms, and use them to reduce the search space and boost search. In this paper, we provide a unifying framework encompassing most of the properties known so far, both in CSP and other fields’ literature, and shed light on the semantical relationships among them. This gives a unified and comprehensive view of the topic, allows new, unknown, properties to emerge, and clarifies the computational complexity of the various detection problems. In particular, among the others, two new concepts, fixability and removability emerge, that come out to be the ideal characterisations of values that may be safely assigned or removed from a variable’s domain, while preserving problem satisfiability. These two notions subsume a large number of known properties, including inconsistency, substitutability and others. Because of the computational intractability of all the property-detection problems, by following the CSP approach we then determine a number of relaxations which provide sufficient conditions for their tractability. In particular, we exploit forms of language restrictions and local reasoning.
Output: A Unifying Framework for Structural Properties of CSPs: Definitions, Complexity, Tractability
In this work we extend to the interval-valued setting the notion of an overlap functions and we discuss a method which makes use of interval-valued overlap functions for constructing OWA operators with interval-valued weights. . Some properties of intervalvalued overlap functions and the derived interval-valued OWA operators are analysed. We specially focus on the homogeneity and migrativity properties.
Output: Generalized Interval-valued OWA Operators with Interval Weights Derived from Interval-valued Overlap Functions
We can program a Real-Time (RT) music improvisation system in C++ without a formal semantic or we can model it with process calculi such as the Non-deterministic Timed Concurrent Constraint (ntcc) calculus. “A Concurrent Constraints Factor Oracle (FO) model for Music Improvisation” (Ccfomi) is an improvisation model specified on ntcc. Since Ccfomi improvises non-deterministically, there is no control on choices and therefore little control over the sequence variation during the improvisation. To avoid this, we extended Ccfomi using the Probabilistic Non-deterministic Timed Concurrent Constraint calculus. Our extension to Ccfomi does not change the time and space complexity of building the FO, thus making our extension compatible with RT. However, there was not a ntcc interpreter capable of RT to execute Ccfomi. We developed Ntccrt –a RT capable interpreter for ntcc– and we executed Ccfomi on Ntccrt. In the future, we plan to extend Ntccrt to execute our extension to Ccfomi.
Output: Probabilistic Extension to the Concurrent Constraint Factor Oracle Model for Music Improvisation
Device-free (DF) localization is an emerging technology that allows the detection and tracking of entities that do not carry any devices not participate actively in the localization process. Typically, DF systems require a large number of transmitters and receivers to achieve acceptable accuracy, which is not available in many scenarios such as homes and small businesses. In this paper, we introduce MonoStream as an accurate single-stream DF localization system that leverages the rich Channel State Information (CSI) as well as MIMO information from the physical layer to provide accurate DF localization with only one stream. To boost its accuracy and attain low computational requirements, MonoStream models the DF localization problem as an object recognition problem and uses a novel set of CSI-context features and techniques with proven accuracy and efficiency. Experimental evaluation in two typical testbeds, with a side-by-side comparison with the state-of-the-art, shows that MonoStream can achieve an accuracy of 0.95m with at least 26% enhancement in median distance error using a single stream only. This enhancement in accuracy comes with an efficient execution of less than 23ms per location update on a typical laptop. This highlights the potential of MonoStream usage for real-time DF tracking applications.
Output: MonoStream: A Minimal-Hardware High Accuracy Device-free WLAN Localization System
Determinantal Point Processes (Dpps) are elegant probabilistic models of repulsion and diversity over discrete sets of items. But their applicability to large sets is hindered by expensive cubic-complexity matrix operations for basic tasks such as sampling. In light of this, we propose a new method for approximate sampling from discrete k-Dpps. Our method takes advantage of the diversity property of subsets sampled from a Dpp, and proceeds in two stages: first it constructs coresets for the ground set of items; thereafter, it efficiently samples subsets based on the constructed coresets. As opposed to previous approaches, our algorithm aims to minimize the total variation distance to the original distribution. Experiments on both synthetic and real datasets indicate that our sampling algorithm works efficiently on large data sets, and yields more accurate samples than previous approaches.
Output: Efficient Sampling for k-Determinantal Point Processes
A BN20 network is a two level belief net in which parent interactions are modeled using the noisy-or interaction model. In this paper we discuss application of the SPI local expression language [1] to effi­ cient inference in large BN20 networks. In particular, we show that there is sig­ nificant structure which can be exploited to improve over the Quickscore result. We further describe how symbolic tech­ niques can provide information which can significantly reduce the computation required for computing all cause poste­ rior marginals. Finally, we present a novel approximation technique with pre­ liminary experimental results.
Output: Symbolic Probabilistic Inference in large BN20 networks
Generic text embeddings are successfully used in a variety of tasks. However, they are often learnt by capturing the co-occurrence structure from pure text corpora, resulting in limitations of their ability to generalize. In this paper, we explore models that incorporate visual information into the text representation. Based on comprehensive ablation studies, we propose a conceptually simple, yet well performing architecture. It outperforms previous multimodal approaches on a set of well established benchmarks. We also improve the state-of-the-art results for image-related text datasets, using orders of magnitude less data.
Output: Better Text Understanding Through Image-To-Text Transfer
Traditional neural networks assume vectorial inputs as the network is arranged as layers of single line of computing units called neurons. This special structure requires the non-vectorial inputs such as matrices to be converted into vectors. This process can be problematic. Firstly, the spatial information among elements of the data may be lost during vectorisation. Secondly, the solution space becomes very large which demands very special treatments to the network parameters and high computational cost. To address these issues, we propose matrix neural networks (MatNet), which takes matrices directly as inputs. Each neuron senses summarised information through bilinear mapping from lower layer units in exactly the same way as the classic feed forward neural networks. Under this structure, back prorogation and gradient descent combination can be utilised to obtain network parameters efficiently. Furthermore, it can be conveniently extended for multimodal inputs. We apply MatNet to MNIST handwritten digits classification and image super resolution tasks to show its effectiveness. Without too much tweaking MatNet achieves comparable performance as the state-of-the-art methods in both tasks with considerably reduced complexity.
Output: Matrix Neural Networks
Extracting time expressions from free text is a fundamental task for many applications. We analyze the time expressions from four datasets and observe that only a small group of words are used to express time information, and the words in time expressions demonstrate similar syntactic behaviour. Based on the observations, we propose a type-based approach, named SynTime, to recognize time expressions. Specifically, we define three main syntactic types, namely time token, modifier, and numeral, to group time-related regular expressions over tokens. On the types we design simple heuristic rules to recognize time expressions. In recognition, SynTime first identifies the time tokens from raw text, then searches their surroundings for modifiers and numerals to form time segments, and finally merges the time segments to time expressions. As a light-weight rule-based tagger, SynTime runs in real time, and can be easily expanded by simply adding keywords for the text of different types and of different domains. Experiment results show that SynTime outperforms state-of-the-art methods on benchmark datasets and tweets data.
Output: Time Expression Analysis and Recognition Using Syntactic Types and Simple Heuristic Rules
Various tasks in decision making and decision support systems require selecting a preferred subset of a given set of items. Here we focus on problems where the individual items are described using a set of characterizing attributes, and a generic preference specification is required, that is, a specification that can work with an arbitrary set of items. For example, preferences over the content of an online newspaper should have this form: At each viewing, the newspaper contains a subset of the set of articles currently available. Our preference specification over this subset should be provided offline, but we should be able to use it to select a subset of any currently available set of articles, e.g., based on their tags. We present a general approach for lifting formalisms for specifying preferences over objects with multiple attributes into ones that specify preferences over subsets of such objects. We also show how we can compute an optimal subset given such a specification in a relatively efficient manner. We provide an empirical evaluation of the approach as well as some worst-case complexity results.
Output: Generic Preferences over Subsets of Structured Objects
Sampling from hierarchical Bayesian models is often difficult for MCMC methods, because of the strong correlations between the model parameters and the hyperparameters. Recent Riemannian manifold Hamiltonian Monte Carlo (RMHMC) methods have significant potential advantages in this setting, but are computationally expensive. We introduce a new RMHMC method, which we call semi-separable Hamiltonian Monte Carlo, which uses a specially designed mass matrix that allows the joint Hamiltonian over model parameters and hyperparameters to decompose into two simpler Hamiltonians. This structure is exploited by a new integrator which we call the alternating blockwise leapfrog algorithm. The resulting method can mix faster than simpler Gibbs sampling while being simpler and more efficient than previous instances of RMHMC.
Output: Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models
Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.
Output: A Mixture Model for Learning Multi-Sense Word Embeddings
Outlier detection in a large-scale database is a significant and complex issue in knowledge discovering field. As the data distributions are obscure and uncertain in high dimensional space, most existing solutions try to solve the issue taking into account the two intuitive points: first, outliers are extremely far away from other points in high dimensional space; second, outliers are detected obviously different in projected-dimensional subspaces. However, for a complicated case that outliers are hidden inside the normal points in all dimensions, existing detection methods fail to find such inner outliers. In this paper, we propose a method with twice dimension-projections, which integrates primary subspace outlier detection and secondary point-projection between subspaces, and sums up the multiple weight values for each point. The points are computed with local density ratio separately in twice-projected dimensions. After the process, outliers are those points scoring the largest values of weight. The proposed method succeeds to find all inner outliers on the synthetic test datasets with the dimension varying from 100 to 10000. The experimental results also show that the proposed algorithm can work in low dimensional space and can achieve perfect performance in high dimensional space. As for this reason, our proposed approach has considerable potential to apply it in multimedia applications helping to process images or video with large-scale attributes.
Output: Finding Inner Outliers in High Dimensional Space
In this article, how word embeddings can be used as features in Chinese sentiment classification is presented. Firstly, a Chinese opinion corpus is built with a million comments from hotel review websites. Then the word embeddings which represent each comment are used as input in different machine learning methods for sentiment classification, including SVM, Logistic Regression, Convolutional Neural Network (CNN) and ensemble methods. These methods get better performance compared with N-gram models using Naive Bayes (NB) and Maximum Entropy (ME). Finally, a combination of machine learning methods is proposed which presents an outstanding performance in precision, recall and F1 score. After selecting the most useful methods to construct the combinational model and testing over the corpus, the final F1 score is 0.920.
Output: An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding
The way experts manage uncertainty usually changes depending on the task they are performing. This fact has lead us to consider the problem of communicating modules (task implementations) in a large and structured knowledge based system when modules have different uncertainty calculi. In this paper, the analysis of the communication problem is made assuming that (i) each uncertainty calculus is an inference mechanism defining an entailment relation, and therefore the communication is considered to be inference-preserving, and (ii) we restrict ourselves to the case which the different uncertainty calculi are given by a class of truth­ functional Multiple-valued Logics.
Output: Combining Multiple-valued Logics in Modular Expert Systems
We present an OWL 2 ontology representing the Saint Gall plan, one of the most ancient documents arrived intact to us, that describes the ideal model of a Benedictine monastic complex, and that inspired the design of many European
Output: The Shape of a Benedictine Monastery: The SaintGall Ontology