niryuu/sni-each-converted huggingface dataset:
An efficient, and intuitive algorithm is presented for the identification of speakers from a long dataset (like YouTube long discussion, Cocktail party recorded audio or video).The goal of automatic speaker identification is to identify the number of different speakers and prepare a model for that speaker by extraction, characterization and speaker-specific information contained in the speech signal. It has many diverse application specially in the field of Surveillance , Immigrations at Airport , cyber security , transcription in multi-source of similar sound source, where it is difficult to assign transcription arbitrary. The most commonly speech parameterization used in speaker verification, K-mean, cepstral analysis, is detailed. Gaussian mixture modeling, which is the speaker modeling technique is then explained. Gaussian mixture models (GMM), perhaps the most robust machine learning algorithm has been introduced to examine and judge carefully speaker identification in text independent. The application or employment of Gaussian mixture models for monitoring & Analysing speaker identity is encouraged by the familiarity, awareness, or understanding gained through experience that Gaussian spectrum depict the characteristics of speaker's spectral conformational pattern and remarkable ability of GMM to construct capricious densities after that we illustrate 'Expectation maximization' an iterative algorithm which takes some arbitrary value in initial estimation and carry on the iterative process until the convergence of value is observed We have tried to obtained 85 ~ 95% of accuracy using speaker modeling of vector quantization and Gaussian Mixture model ,so by doing various number of experiments we are able to obtain 79 ~ 82% of identification rate using Vector quantization and 85 ~ 92.6% of identification rate using GMM modeling by Expectation maximization parameter estimation depending on variation of parameter.
Output: SPEAKER IDENTIFICATION FROM YOUTUBE OBTAINED DATA
This paper presents a simple end-to-end model for speech recognition, combining a convolutional network based acoustic model and a graph decoding. It is trained to output letters, with transcribed speech, without the need for force alignment of phonemes. We introduce an automatic segmentation criterion for training from sequence annotation without alignment that is on par with CTC [6] while being simpler. We show competitive results in word error rate on the Librispeech corpus [18] with MFCC features, and promising results from raw waveform.
Output: Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
The success of semi-supervised learning crucially relies on the scalability to a huge amount of unlabelled data that are needed to capture the underlying manifold structure for better classification. Since computing the pairwise similarity between the training data is prohibitively expensive in most kinds of input data, currently, there is no general readyto-use semi-supervised learning method/tool available for learning with tens of millions or more data points. In this paper, we adopted the idea of two low-rank label propagation algorithms, GLNP (Global Linear Neighborhood Propagation) and Kernel Nyström Approximation, and implemented the parallelized version of the two algorithms accelerated with Nesterov’s accelerated projected gradient descent for Big-data Label Propagation (BigLP). The parallel algorithms are tested on five real datasets ranging from 7000 to 10,000,000 in size and a simulation dataset of 100,000,000 samples. In the experiments, the implementation can scale up to datasets with 100,000,000 samples and hundreds of features and the algorithms also significantly improved the prediction accuracy when only a very small percentage of the data is labeled. The results demonstrate that the BigLP implementation is highly scalable to big data and effective in utilizing the unlabeled data for semi-supervised learning.
Output: Low-rank Label Propagation for Semi-supervised Learning with 100 Millions Samples
In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve the problem of kernel function selection and kernel parameter tuning, we proposed to construct an optimal kernel by weighted linear combination of some candidate kernels. The learning of the classifier parameter and the kernel weight are unified in a single objective function considering to minimize the upper boundary of the given multivariate performance measure. The objective function is optimized with regard to classifier parameter and kernel weight alternately in an iterative algorithm by using cutting plane algorithm. The developed algorithm is evaluated on two different pattern classification methods with regard to various multivariate performance measure optimization problems. The experiment results show the proposed algorithm outperforms the competing methods.
Output: Multiple kernel multivariate performance learning using cutting plane algorithm
Several authors have recently developed risk-sensitive policy gradient methods<lb>that augment the standard expected cost minimization problem with a measure of<lb>variability in cost. These studies have focused on specific risk-measures, such as<lb>the variance or conditional value at risk (CVaR). In this work, we extend the pol-<lb>icy gradient method to the whole class of coherent risk measures, which is widely<lb>accepted in finance and operations research, among other fields. We consider<lb>both static and time-consistent dynamic risk measures. For static risk measures,<lb>our approach is in the spirit of policy gradient algorithms and combines a standard<lb>sampling approach with convex programming. For dynamic risk measures, our ap-<lb>proach is actor-critic style and involves explicit approximation of value function.<lb>Most importantly, our contribution presents a unified approach to risk-sensitive<lb>reinforcement learning that generalizes and extends previous results.
Output: Policy Gradient for Coherent Risk Measures
Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or domain models in hand. Previous approaches either discover plans by maximally “matching” observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing domain models to best explain the observed actions, assuming complete domain models are available. In real world applications, however, target plans are often not from plan libraries and complete domain models are often not available, since building complete sets of plans and complete domain models are often difficult or expensive. In this paper we view plan libraries as corpora and learn vector representations of actions using the corpora; we then discover target plans based on the vector representations. Our approach is capable of discovering underlying plans that are not from plan libraries, without requiring domain models provided. We empirically demonstrate the effectiveness of our approach by comparing its performance to traditional plan recognition approaches in three planning domains.
Output: Discovering Underlying Plans Based on Distributed Representations of Actions
We study expressive power of shallow and deep neural networks with piece-wise linear activation functions. We establish new rigorous upper and lower bounds for the network complexity in the setting of approximations in Sobolev spaces. In particular, we prove that deep ReLU networks more efficiently approximate smooth functions than shallow networks. In the case of approximations of 1D Lipschitz functions we describe adaptive depth-6 network architectures more efficient than the standard shallow architecture.
Output: Error bounds for approximations with deep ReLU networks
In probabilistic logic entailments, even moderate size problems can yield linear constraint systems with so many variables that exact methods are impractical. This difficulty can be remedied in many cases of interest by introducing a three­ valued logic (true, false, and "don't care"). The three-valued approach allows the construction of "compressed" constraint systems which have the same solution sets as their two-valued counterparts, but which may involve dramatically fewer variables. Techniques to calculate point estimates for the posterior probabilities of entailed sentences are discussed. 1. PROLIFERATION OF WORLDS An entailment problem in Nilsson's (1986) probabilistic logic derives an estimate for the prior probability of one sentence (hereafter, the "target") from the priors for a set of other ("source") sentences. The prior beliefs about the source sentences establish constraints of the form P=VW L wi = l Wi <': 0 sum over all "worlds"
Output: Compressed Constraints in Probabilistic Logic and Their Revision
Concept maps can be used to concisely represent important information and bring structure into large document collections. Therefore, we study a variant of multidocument summarization that produces summaries in the form of concept maps. However, suitable evaluation datasets for this task are currently missing. To close this gap, we present a newly created corpus of concept maps that summarize heterogeneous collections of web documents on educational topics. It was created using a novel crowdsourcing approach that allows us to efficiently determine important elements in large document collections. We release the corpus along with a baseline system and proposed evaluation protocol to enable further research on this variant of summarization.
Output: Connecting the dots: Summarizing and Structuring Large Document Collections Using Concept Maps
We extend the model of Multi-armed Bandit with unit switching cost to incorporate a metric between the actions. We consider the case where the metric over the actions can be modeled by a complete binary tree, and the distance between two leaves is the size of the subtree of their least common ancestor, which abstracts the case that the actions are points on the continuous interval r0, 1s and the switching cost is their distance. In this setting, we give a new algorithm that establishes a regret of r Op ? kT ` T {kq, where k is the number of actions and T is the time horizon. When the set of actions corresponds to whole r0, 1s interval we can exploit our method for the task of bandit learning with Lipschitz loss functions, where our algorithm achieves an optimal regret rate of r ΘpT 2{3q, which is the same rate one obtains when there is no penalty for movements. As our main application, we use our new algorithm to solve an adaptive pricing problem. Specifically, we consider the case of a single seller faced with a stream of patient buyers. Each buyer has a private value and a window of time in which they are interested in buying, and they buy at the lowest price in the window, if it is below their value. We show that with an appropriate discretization of the prices, the seller can achieve a regret of r OpT 2{3q compared to the best fixed price in hindsight, which outperform the previous regret bound of r OpT 3{4q for the problem.
Output: Bandits with Movement Costs and Adaptive Pricing
In this paper, we provide two axiomatizations of algebraic expected utility, which is a particular generalized expected utility, in a von NeumannMorgenstern setting, i.e. uncertainty representation is supposed to be given and here to be described by a plausibility measure valued on a semiring, which could be partially ordered. We show that axioms identical to those for expected utility entail that preferences are represented by an algebraic expected utility. This algebraic approach allows many previous propositions (expected utility, binary possibilistic utility,...) to be unified in a same general framework and proves that the obtained utility enjoys the same nice features as expected utility: linearity, dynamic consistency, autoduality of the underlying uncertainty representation, autoduality of the decision criterion and possibility of modeling decision maker’s attitude toward uncertainty.
Output: Axiomatic Foundations for a Class of Generalized Expected Utility: Algebraic Expected Utility
s prominently include information that is relevant to the population group of interest, and intervention, comparison and disease of interest. The vector space model further measures the similarity between the query and citation based on concepts (as opposed to just terms or words themselves).
Output: A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical Knowledge Summarization: Combining Concept Extraction, Vector Similarity and Query Expansion for High Precision
We propose a label propagation approach to geolocation prediction based on Modified Adsorption, with two enhancements: (1) the removal of “celebrity” nodes to increase location homophily and boost tractability; and (2) the incorporation of text-based geolocation priors for test users. Experiments over three Twitter benchmark datasets achieve state-of-the-art results, and demonstrate the effectiveness of the enhancements.
Output: Twitter User Geolocation Using a Unified Text and Network Prediction Model
In this paper the behavior of various be­ lief network learning algorithms is stud­ ied. Selecting belief networks with cer­ tain minimallity properties turns out to be NP-hard, which justifies the use of search heuristics. Search heuristics based on the Bayesian measure of Cooper and Her­ skovits and a minimum description length (MDL) measure are compared with re­ spect to their properties for both limit­ ing and finite database sizes. It is shown that the MDL measure has more desir­ able properties than the Bayesian mea­ sure. Experimental results suggest that for learning probabilities of belief net­ works smoothing is helpful.
Output: Properties of Bayesian Belief Network Learning Algorithms
Gaussian Process bandit optimization has emerged as a powerful tool for optimizing noisy black box functions. One example in machine learning is hyper-parameter optimization where each evaluation of the target function requires training a model which may involve days or even weeks of computation. Most methods for this so-called “Bayesian optimization” only allow sequential exploration of the parameter space. However, it is often desirable to propose batches or sets of parameter values to explore simultaneously, especially when there are large parallel processing facilities at our disposal. Batch methods require modeling the interaction between the different evaluations in the batch, which can be expensive in complex scenarios. In this paper, we propose a new approach for parallelizing Bayesian optimization by modeling the diversity of a batch via Determinantal point processes (DPPs) whose kernels are learned automatically. This allows us to generalize a previous result as well as prove better regret bounds based on DPP sampling. Our experiments on a variety of synthetic and real-world robotics and hyper-parameter optimization tasks indicate that our DPP-based methods, especially those based on DPP sampling, outperform state-of-the-art methods.
Output: Batched Gaussian Process Bandit Optimization via Determinantal Point Processes
Training Recurrent Neural Networks is more troublesome than feedforward ones because of the vanishing and exploding gradient problems detailed in Bengio et al. (1994). In this paper we attempt to understand the fundamental issues underlying the exploding gradient problem by exploring it from an analytical, a geometric and a dynamical system perspective. Our analysis is used to justify the simple yet effective solution of norm clipping the exploded gradient. In the experimental section, the comparison between this heuristic solution and standard SGD provides empirical evidence towards our hypothesis as well as it shows that such a heuristic is required to reach state of the art results on a character prediction task and a polyphonic music prediction one.
Output: Understanding the exploding gradient problem
Graph partitioning, a well studied problem of parallel computing has many applications in diversified fields such as distributed computing, social network analysis, data mining and many other domains. In this paper, we introduce FGPGA, an efficient genetic approach for producing feasible graph partitions. Our method takes into account the heterogeneity and capacity constraints of the partitions to ensure balanced partitioning. Such approach has various applications in mobile cloud computing that include feasible deployment of software applications on the more resourceful infrastructure in the cloud instead of mobile hand set. Our proposed approach is light weight and hence suitable for use in cloud architecture. We ensure feasibility of the partitions generated by not allowing over-sized partitions to be generated during the initialization and search. Our proposed method tested on standard benchmark datasets significantly outperforms the state-of-the-art methods in terms of quality of partitions and feasibility of the solutions.
Output: FGPGA: An Efficient Genetic Approach for Producing Feasible Graph Partitions
The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose modeling of a real-world MTD web application as a repeated Bayesian game. We then formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To incorporate this model into a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.
Output: Moving Target Defense for Web Applications using Bayesian Stackelberg Games
As the complexity of deep neural networks (DNNs) trend to grow to absorb the increasing sizes of data, memory and energy consumption has been receiving more and more attentions for industrial applications, especially on mobile devices. This paper presents a novel structure based on functional hashing to compress DNNs, namely FunHashNN. For each entry in a deep net, FunHashNN uses multiple low-cost hash functions to fetch values in the compression space, and then employs a small reconstruction network to recover that entry. The reconstruction network is plugged into the whole network and trained jointly. FunHashNN includes the recently proposed HashedNets [7] as a degenerated case, and benefits from larger value capacity and less reconstruction loss. We further discuss extensions with dual space hashing and multi-hops. On several benchmark datasets, FunHashNN demonstrates high compression ratios with little loss on prediction accuracy.
Output: Functional Hashing for Compressing Neural Networks
It has been proved that large-scale realistic Knowledge Based Machine Translation (KBMT) applications require acquisition of huge knowledge about language and about the world. This knowledge is encoded in computational grammars, lexicons and domain models. Another approach – which avoids the need for collecting and analyzing massive knowledgeis the Example Based approach, which is the topic of this paper. We show through the paper that using Example Based in its native form is not suitable for translating into Arabic. Therefore a modification to the basic approach is presented to improve the accuracy of the translation process. The basic idea of the new approach is to improve the technique by which template-based approaches select the appropriate templates. It relies on extracting, from a parallel Bilingual Corpus, all possible templates that could match parts of the source sentence. These templates are selected as suitable candidate chunks for the source sentence. The corresponding Arabic templates are also extracted and represented by a diredted graph. Each branch represents one possible string of templates candidate to represent the target sentence. The shortest continuous path or the most probable tree branch is selected to represent the target sentence. Finally the Arabic translation of the selected tree branch is generated.
Output: The Best Templates Match Technique For Example Based Machine Translation
We present a new decision rule, maximin safety, that seeks to maintain a large margin from the worst outcome, in much the same way minimax regret seeks to minimize distance from the best. We argue that maximin safety is valuable both descriptively and normatively. Descriptively, maximin safety explains the well-known decoy effect, in which the introduction of a dominated option changes preferences among the other options. Normatively, we provide an axiomatization that characterizes preferences induced by maximin safety, and show that maximin safety shares much of the same behavioral basis with minimax regret.
Output: Maximin Safety: When Failing to Lose is Preferable to Trying to Win
In recent years we have seen rapid and significant progress in automatic image description but what are the open problems in this area? Most work has been evaluated using text-based similarity metrics, which only indicate that there have been improvements, without explaining what has improved. In this paper, we present a detailed error analysis of the descriptions generated by a state-of-the-art attentionbased model. Our analysis operates on two levels: first we check the descriptions for accuracy, and then we categorize the types of errors we observe in the inaccurate descriptions. We find only 20% of the descriptions are free from errors, and surprisingly that 26% are unrelated to the image. Finally, we manually correct the most frequently occurring error types (e.g. gender identification) to estimate the performance reward for addressing these errors, observing gains of 0.2–1 BLEU point per type.
Output: Room for improvement in automatic image description: an error analysis
Retrieving spoken content with spoken queries, or query-byexample spoken term detection (STD), is attractive because it makes possible the matching of signals directly on the acoustic level without transcribing them into text. Here, we propose an end-to-end query-by-example STD model based on an attention-based multi-hop network, whose input is a spoken query and an audio segment containing several utterances; the output states whether the audio segment includes the query. The model can be trained in either a supervised scenario using labeled data, or in an unsupervised fashion. In the supervised scenario, we find that the attention mechanism and multiple hops improve performance, and that the attention weights indicate the time span of the detected terms. In the unsupervised setting, the model mimics the behavior of the existing query-by-example STD system, yielding performance comparable to the existing system but with a lower search time complexity.
Output: QUERY-BY-EXAMPLE SPOKEN TERM DETECTION USING ATTENTION-BASED MULTI-HOP NETWORKS
For many low-resource or endangered languages, spoken language resources are more likely to be annotated with translations than with transcriptions. Recent work exploits such annotations to produce speech-to-translation alignments, without access to any text transcriptions. We investigate whether providing such information can aid in producing better (mismatched) crowdsourced transcriptions, which in turn could be valuable for training speech recognition systems, and show that they can indeed be beneficial through a smallscale case study as a proof-of-concept. We also present a simple phonetically aware string averaging technique that produces transcriptions of higher quality.
Output: A case study on using speech-to-translation alignments for language documentation
Ensembling is a well-known technique in neural machine translation (NMT) to improve system performance. Instead of a single neural net, multiple neural nets with the same topology are trained separately, and the decoder generates predictions by averaging over the individual models. Ensembling often improves the quality of the generated translations drastically. However, it is not suitable for production systems because it is cumbersome and slow. This work aims to reduce the runtime to be on par with a single system without compromising the translation quality. First, we show that the ensemble can be unfolded into a single large neural network which imitates the output of the ensemble system. We show that unfolding can already improve the runtime in practice since more work can be done on the GPU. We proceed by describing a set of techniques to shrink the unfolded network by reducing the dimensionality of layers. On JapaneseEnglish we report that the resulting network has the size and decoding speed of a single NMT network but performs on the level of a 3-ensemble system.
Output: Unfolding and Shrinking Neural Machine Translation Ensembles
Recent research shows that most Brazilian students have serious problems regarding their reading skills. The full development of this skill is key for the academic and professional future of every citizen. Tools for classifying the complexity of reading materials for children aim to improve the quality of the model of teaching reading and text comprehension. For English, Feng’s work [11] is considered the state-of-art in grade level prediction and achieved 74% of accuracy in automatically classifying 4 levels of textual complexity for close school grades. There are no classifiers for nonfiction texts for close grades in Portuguese. In this article, we propose a scheme for manual annotation of texts in 5 grade levels, which will be used for customized reading to avoid the lack of interest by students who are more advanced in reading and the blocking of those that still need to make further progress. We obtained 52% of accuracy in classifying texts into 5 levels and 74% in 3 levels. The results prove to be promising when compared to the state-of-art work.
Output: Automatic Classification of the Complexity of Nonfiction Texts in Portuguese for Early School Years
It is hypothesized that creativity arises from the self-mending capacity of an internal model of the world, or worldview. The uniquely honed worldview of a creative individual results in a distinctive style that is recognizable within and across domains. It is further hypothesized that creativity is domaingeneral in the sense that there exist multiple avenues by which the distinctiveness of one’s worldview can be expressed. These hypotheses were tested using art students and creative writing students. Art students guessed significantly above chance both which painting was done by which of five famous artists, and which artwork was done by which of their peers. Similarly, creative writing students guessed significantly above chance both which passage was written by which of five famous writers, and which passage was written by which of their peers. These findings support the hypothesis that creative style is recognizable. Moreover, creative writing students guessed significantly above chance which of their peers produced particular works of art, supporting the hypothesis that creative style is recognizable not just within but across domains.
Output: Recognizability of Individual Creative Style Within and Across Domains: Preliminary Studies
The GLEU metric was proposed for evaluating grammatical error corrections using n-gram overlap with a set of reference sentences, as opposed to precision/recall of specific annotated errors (Napoles et al., 2015). This paper describes improvements made to the GLEU metric that address problems that arise when using an increasing number of reference sets. Unlike the originally presented metric, the modified metric does not require tuning. We recommend that this version be used instead of the original version.1
Output: GLEU Without Tuning
We present a first step towards a framework for defining and manipulating normative documents or contracts described as ContractOriented (C-O) Diagrams. These diagrams provide a visual representation for such texts, giving the possibility to express a signatory’s obligations, permissions and prohibitions, with or without timing constraints, as well as the penalties resulting from the non-fulfilment of a contract. This work presents a CNL for verbalising C-O Diagrams, a web-based tool allowing editing in this CNL, and another for visualising and manipulating the diagrams interactively. We then show how these proof-ofconcept tools can be used by applying them to a small example.
Output: A CNL for Contract-Oriented Diagrams
The speed of convergence of the Expectation Maximization (EM) algorithm for Gaussian mixture model fitting is known to be dependent on the amount of overlap among the mixture components. In this paper, we study the impact of mixing coefficients on the convergence of EM. We show that when the mixture components exhibit some overlap, the convergence of EM becomes slower as the dynamic range among the mixing coefficients increases. We propose a deterministic anti-annealing algorithm, that significantly improves the speed of convergence of EM for such mixtures with unbalanced mixing coefficients. The proposed algorithm is compared against other standard optimization techniques like BFGS, Conjugate Gradient, and the traditional EM algorithm. Finally, we propose a similar deterministic antiannealing based algorithm for the Dirichlet process mixture model and demonstrate its advantages over the conventional variational Bayesian approach.
Output: Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients
We introduce the “exponential linear unit” (ELU) which speeds up learning in<lb>deep neural networks and leads to higher classification accuracies. Like rectified<lb>linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PRe-<lb>LUs), ELUs also avoid a vanishing gradient via the identity for positive values.<lb>However ELUs have improved learning characteristics compared to the units with<lb>other activation functions. In contrast to ReLUs, ELUs have negative values which<lb>allows them to push mean unit activations closer to zero. Zero means speed up<lb>learning because they bring the gradient closer to the unit natural gradient. We<lb>show that the unit natural gradient differs from the normal gradient by a bias shift<lb>term, which is proportional to the mean activation of incoming units. Like batch<lb>normalization, ELUs push the mean towards zero, but with a significantly smaller<lb>computational footprint. While other activation functions like LReLUs and PRe-<lb>LUs also have negative values, they do not ensure a noise-robust deactivation state.<lb>ELUs saturate to a negative value with smaller inputs and thereby decrease the<lb>propagated variation and information. Therefore ELUs code the degree of pres-<lb>ence of particular phenomena in the input, while they do not quantitatively model<lb>the degree of their absence. Consequently, dependencies between ELUs are much<lb>easier to model and distinct concepts are less likely to interfere.<lb>We found that ELUs lead not only to faster learning, but also to better general-<lb>ization performance once networks have many layers (≥ 5). ELU networks were<lb>among top 10 reported CIFAR-10 results and yielded the best published result on<lb>CIFAR-100, without resorting to multi-view evaluation or model averaging. On<lb>ImageNet, ELU networks considerably speed up learning compared to a ReLU<lb>network with the same architecture, obtaining less than 10% classification error<lb>for a single crop, single model network.
Output: EXPONENTIAL LINEAR UNITS (ELUS)
A method is presented for the rhythmic pars­<lb>ing problem: Given a sequence of observed<lb>musical note onset times, we simultaneously<lb>estimate the corresponding notated rhythm<lb>and tempo process. A graphical model is<lb>developed that<lb>represents the evolution of<lb>tempo and rhythm and relates these hid­<lb>den quantities to an observable performance.<lb>The rhythm variables are discrete and the<lb>tempo and observation variables are contin­<lb>uous. We show how to compute the<lb>glob­<lb>ally most likely configuration of the tempo<lb>and rhythm variables given an observation of<lb>note onset times. Preliminary experiments<lb>are presented on a small data set. A gen­<lb>eralization to computing MAP estimates for<lb>arbitrary conditional Gaussian distributions<lb>is outlined.
Output: A Mixed Graphical Model for Rhythmic Parsing
The properties of local optimal solutions in multi-objective combinatorial optimization problems are crucial for the effectiveness of local search algorithms, particularly when these algorithms are based on Pareto dominance. Such local search algorithms typically return a set of mutually nondominated Pareto local optimal (PLO) solutions, that is, a PLO-set. This paper investigates two aspects of PLO-sets by means of experiments with Pareto local search (PLS). First, we examine the impact of several problem characteristics on the properties of PLO-sets for multi-objective NK-landscapes with correlated objectives. In particular, we report that either increasing the number of objectives or decreasing the correlation between objectives leads to an exponential increment on the size of PLO-sets, whereas the variable correlation has only a minor effect. Second, we study the running time and the quality reached when using bounding archiving methods to limit the size of the archive handled by PLS, and thus, the maximum size of the PLO-set found. We argue that there is a clear relationship between the running time of PLS and the difficulty of a problem instance.
Output: Local Optimal Sets and Bounded Archiving on Multi-objective NK-Landscapes with Correlated Objectives
Topic models provide a useful method for dimensionality reduction and exploratory data<lb>analysis in large text corpora. Most approaches to topic model inference have been based on<lb>a maximum likelihood objective. Efficient algorithms exist that approximate this objective,<lb>but they have no provable guarantees. Recently, algorithms have been introduced that provide<lb>provable bounds, but these algorithms are not practical because they are inefficient and not ro-<lb>bust to violations of model assumptions. In this paper we present an algorithm for topic model<lb>inference that is both provable and practical. The algorithm produces results comparable to the<lb>best MCMC implementations while running orders of magnitude faster.
Output: A Practical Algorithm for Topic Modeling with Provable Guarantees
Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e. pieces of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how natural language processing and data mining techniques may be used to find ways of determining their veracity. In this survey we introduce and discuss two types of rumours that circulate on social media; long-standing rumours that circulate for long periods of time, and newly-emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far towards the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for detection and resolution of rumours.
Output: Detection and Resolution of Rumours in Social Media: A Survey
Today data mining techniques are exploited in medical science for diagnosing, overcoming and treating diseases. Neural network is one of the techniques which are widely used for diagnosis in medical field. In this article efficiency of nine algorithms, which are basis of neural network learning in diagnosing cardiovascular diseases, will be assessed. Algorithms are assessed in terms of accuracy, sensitivity, transparency, AROC and convergence rate by means of 10 fold cross validation. The results suggest that in training phase, Lonberg-M algorithm has the best efficiency in terms of all metrics, algorithm OSS has maximum accuracy in testing phase, algorithm SCG has the maximum transparency and algorithm CGB has the maximum sensitivity. Keywords— cardiovascular disease; neural network; learning algorithms.
Output: Comparing learning algorithms in neural network for diagnosing cardiovascular disease
Multimedia reasoning, which is suitable for, among others, multimedia content analysis and high-level video scene interpretation, relies on the formal and comprehensive conceptualization of the represented knowledge domain. However, most multimedia ontologies are not exhaustive in terms of role definitions, and do not incorporate complex role inclusions and role interdependencies. In fact, most multimedia ontologies do not have a role box at all, and implement only a basic subset of the available logical constructors. Consequently, their application in multimedia reasoning is limited. To address the above issues, VidOnt, the very first multimedia ontology with SROIQ(D) expressivity and a DL-safe ruleset has been introduced for next-generation multimedia reasoning. In contrast to the common practice, the formal grounding has been set in one of the most expressive description logics, and the ontology validated with industry-leading reasoners, namely HermiT and FaCT++. This paper also presents best practices for developing multimedia ontologies, based on my ontology engineering approach.
Output: A Novel Approach to Multimedia Ontology Engineering for Automated Reasoning over Audiovisual LOD Datasets
We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources, in a distributed setting. In contrast, the proposed method approximates the solution as a fixed-size vector (of larger dimension than the input space) using Random Fourier Features. This paves the way to use standard linear combine-then-adapt techniques. To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented. Conditions for asymptotic convergence and boundness of the networkwise regret are also provided. The simulated tests illustrate the performance of the proposed scheme.
Output: Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features
We propose a new active learning (AL) method for text classification based on convolutional neural networks (CNNs). In AL, one selects the instances to be manually labeled with the aim of maximizing model performance with minimal effort. Neural models capitalize on word embeddings as features, tuning these to the task at hand. We argue that AL strategies for neural text classification should focus on selecting instances that most affect the embedding space (i.e., induce discriminative word representations). This is in contrast to traditional AL approaches (e.g., uncertainty sampling), which specify higher level objectives. We propose a simple approach that selects instances containing words whose embeddings are likely to be updated with the greatest magnitude, thereby rapidly learning discriminative, task-specific embeddings. Empirical results show that our method outperforms baseline AL approaches.
Output: Active Discriminative Word Embedding Learning
We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model that generates a sequence ŷ = {y0 . . . yT }, by maximizing p(y|x) = ∏ t p(yt|x; {y0 . . . yt−1}). Lexical constraints take the form of phrases or words that must be present in the output sequence. This is a very general way to incorporate additional knowledge into a model’s output without requiring any modification of the model parameters or training data. We demonstrate the feasibility and flexibility of Lexically Constrained Decoding by conducting experiments on Neural Interactive-Predictive Translation, as well as Domain Adaptation for Neural Machine Translation. Experiments show that GBS can provide large improvements in translation quality in interactive scenarios, and that, even without any user input, GBS can be used to achieve significant gains in performance in domain adaptation scenarios.
Output: Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search
Feature selection plays an important role in the data mining process. It is needed to deal with the excessive number of features, which can become a computational burden on the learning algorithms. It is also necessary, even when computational resources are not scarce, since it improves the accuracy of the machine learning tasks, as we will see in the upcoming sections. In this review, we discuss the different feature selection approaches, and the relation between them and the various machine learning algorithms.
Output: Survey on Feature Selection
Robust belief revision methods are crucial in streaming data situations for updating existing knowledge (or beliefs) with new incoming evidence. Bayes conditioning is the primary mechanism in use for belief revision in data fusion systems that use probabilistic inference. However, traditional conditioning methods face several challenges due to inherent data/source imperfections in big-data environments that harness soft (i.e., human or human-based) sources in addition to hard (i.e., physicsbased) sensors. The objective of this paper is to investigate the most natural extension of Bayes conditioning that is suitable for evidence updating in the presence of such uncertainties. By viewing the evidence updating process as a thought experiment, an elegant strategy is derived for robust evidence updating in the presence of extreme uncertainties that are characteristic of big-data environments. In particular, utilizing the Fagin-Halpern conditional notions, a natural extension to Bayes conditioning is derived for evidence that takes the form of a general belief function. The presented work differs fundamentally from the Conditional Update Equation (CUE) and authors own extensions of it. An overview of this development is provided via illustrative examples. Furthermore, insights into parameter selection under various fusion contexts are also provided.
Output: Evidence Updating for Stream-Processing in Big-Data: Robust Conditioning in Soft and Hard Data Fusion Environments
Image Registration implies mapping images having varying orientation, multi-modal or multi-temporal images to map to one coordinate system. Digital Elevation models (DEM) are images having terrain information embedded into them. DEM-to-DEM registration incorporate registration of DEMs having different orientation, may have been mapped at different times, or may have been processed using different resolutions. Though very important only a handful of methods for DEM registration exist, most of which are for DEM-to-topographical map or DEMto-Remote Sensed Image registration. Using cognitive mapping concepts for DEM registration, has evolved from this basic idea of using the mapping between the space to objects and defining their relationships to form the basic landmarks that need to be marked, stored and manipulated in and about the environment or other candidate environments, namely, in our case, the DEMs. The progressive two-level encapsulation of methods of geo-spatial cognition includes landmark knowledge and layout knowledge and can be useful for DEM registration. Space-based approach, that emphasizes on explicit extent of the environment under consideration, and object-based approach, that emphasizes on the relationships between objects in the local environment being the two paradigms of cognitive mapping can be methodically integrated in this three-architecture for DEM registration. Initially, P-model based segmentation is performed followed by landmark formation for contextual mapping that uses contextual pyramid formation. Apart from landmarks being used for registration key-point finding, Euclidean distance based deformation calculation has been used for transformation and change detection. Initially, P-model based segmentation is performed followed by landmark formation for contextual mapping that uses contextual pyramid formation. Landmarks have been categorized to belong to either being flat-plain areas without much variation in the land heights; peaks that can be found when there is gradual increase in height as compared to the flat areas; valleys, marked with gradual decrease in the height seen in DEM; and finally, ripple areas with very shallow crests and nadirs. For the final storage of co-registered DEMs, fractal based compression has been found to give good results in terms of space and computation requirements. In this paper, an attempt has been made to implement DEM-DEM registration based on human spatial cognition method of recollection. This method may further be extended for DEM-totopographic map and DEM-to-remote sensed image registration. Experimental results further cement the fact that DEM registration may be effectively done using the proposed method.
Output: Cognitive-mapping and contextual pyramid based Digital Elevation Model Registration and its effective storage using fractal based compression
This work uses the L-system to construct a tree structure for the text sequence and derives its complexity [1]. It serves as a measure of structural complexity of the text. It is applied to anomaly detection in data transmission. Keyword: text complexity, anomaly detection, structural complexity, rewriting rule, context-free grammar, L-system
Output: Syntactic sensitive complexity for symbol-free sequence
Low dimensional representations of words allow<lb>accurate NLP models to be trained on limited<lb>annotated data. While most representations ig-<lb>nore words’ local context, a natural way to in-<lb>duce context-dependent representations is to per-<lb>form inference in a probabilistic latent-variable<lb>sequence model. Given the recent success of<lb>continuous vector space word representations,<lb>we provide such an inference procedure for con-<lb>tinuous states, where words’ representations are<lb>given by the posterior mean of a linear dynam-<lb>ical system. Here, efficient inference can be<lb>performed using Kalman filtering. Our learn-<lb>ing algorithm is extremely scalable, operating<lb>on simple cooccurrence counts for both param-<lb>eter initialization using the method of moments<lb>and subsequent iterations of EM. In our exper-<lb>iments, we employ our inferred word embed-<lb>dings as features in standard tagging tasks, ob-<lb>taining significant accuracy improvements. Fi-<lb>nally, the Kalman filter updates can be seen as a<lb>linear recurrent neural network. We demonstrate<lb>that using the parameters of our model to ini-<lb>tialize a non-linear recurrent neural network lan-<lb>guage model reduces its training time by a day<lb>and yields lower perplexity.
Output: A Linear Dynamical System Model for Text