Reviewerly/exHarmony huggingface dataset:
Opinion Mining

The machine learning is the emerging research domain, from which number of emerging trends are available, among them opinion mining is the one technology attraction through which the we could get analysis of the interested domain or we can say about the review from the customer towards any product or we can say any upcoming trending information. These two are the emerging words and we can say it's the buzz word in the information technology. As you will see that its widely use by the corporate sector to uplift the business next level. Before two decade you will not read any words e.g., Opinion mining or Sentiment analysis, but in the last two decade these words have given a new life to information technology domain as well as to the business. The important question which runs in the mind is why use sentiment analysis or opinion mining. The information technology has given number of new programming languages, new innovation and within that the data mining has given this trends to the users. The chapter is covering the three major concept's which comes under the machine learning e.g., Decision tree, Bayesian network and Support vector machine. The chapter is describing the basic inputs, and how it helps in supporting stakeholders by adopting these technologies.
Effective vector representation for the Korean named-entity recognition

Named-entity recognition, part of information extraction, is the task of finding the position of a proper names in a sentence and assigning it to the correct category. Existing studies have access to Korean named-entity recognition by a morphological-level method that performs named-entity recognition processes by using the results of morphological analysis as input. While this method has the advantage of using various linguistic clues, it suffers from the error propagation problem of morphological analysis. In this paper, we propose an effective method for Korean syllable-level named-entity recognition to solve the above problem. Firstly, we suggest an approach to use the syllable bi-gram vector representation for Korean syllable-level named-entity recognition. Secondly, influenced by the linguistic characteristics of Korean, we suggest a novel way to make the joint vector representation of syllable bi-gram and Korean eojeol’s positional information. In the experiment, we have evaluated our methods on the two Korean named-entity recognition corpora using Bi-directional LSTM-CRFs as a sequence labeler. Experimental results verify that our methods significantly improve the performance of syllable-level named-entity recognition and have similar performance to existing morphological-level named-entity recognition. Besides, additional experiments have shown that our syllable-level named-entity recognition is not only more robust but also faster than traditional morphological-level named-entity recognition by eliminating the morphological analysis process.
HaloDPC: An Improved Recognition Method on Halo Node for Density Peak Clustering Algorithm

The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or form low-density clusters. Therefore, halo is adopted to improve the performance of DPC in processing low-density nodes. This paper explores the potential reasons for adopting halos instead of low-density nodes, and proposes an improved recognition method on Halo node for Density Peak Clustering algorithm (HaloDPC). The proposed HaloDPC has improved the ability to deal with varying densities, irregular shapes, the number of clusters, outlier and hub node detection. This paper presents the advantages of the HaloDPC algorithm on several test cases.
The generic construction of continuous leakage-resilient identity-based cryptosystems

In the leakage setting, the adversary is allowed to access the internal secret states of a cryptosystem, such as the private key of user and the master secret key. In the continuous leakage model, there is no predetermined bound on the leakage of internal secret states; therefore, it is more challenging. In this paper, we present a new primitive called updatable identity-based hash proof system (U-IB-HPS), which can serve as a fundamental tool for constructing continuous leakage-resilient identity-based cryptosystems. With the U-IB-HPS, we obtain the generic constructions of identity-based cryptosystem with continuous leakage-resilience, such as the continuous leakage-resilient identity-based encryption with leakage amplification, the continuous leakage-resilient identity-based hybrid encryption with leakage amplification, etc. As a feature of our generic constructions, we allow the leakage of multiple keys, i.e., leakage of the master secret key and the private key of user. In addition, we present a generic framework of one-round identity-based authenticated key exchange (AKE) protocol with continuous leakage resilience, which ensures the session key security even if the adversary performs continuous leakage attacks during the execution of a session. We also present instantiations of U-IB-HPS from bilinear groups and composite order bilinear groups, respectively. The security of our instantiations is based on the standard static assumptions, such as the decisional bilinear Diffie–Hellman assumption, the corresponding complexity assumptions over composite order bilinear groups, etc. Our instantiations can be easily extended to construct the updatable hierarchical identity-based hash proof system.
Large-scale parallel similarity search with Product Quantization for online multimedia services

The similarity search in high-dimensional spaces is a core operation found in several online multimedia retrieval applications. With the popularity of these applications, they are required to handle very large and increasing datasets, while keeping the response time low. This problem is worsened in the context of online applications, mostly due to the fact that load on these systems vary during the execution according to the users demands. Those variations require the application to adapt during the execution in order to minimize the response times. In this paper, we address these challenges with an efficient parallelization of the Product Quantization Approximate Nearest Neighbor Search (PQANNS) indexing. This method is capable of answering queries with a reduced memory demand and, coupled with a distributed memory parallelization proposed here, can efficiently handle very large datasets. We have also proposed mechanisms to minimize the query response times in online scenarios in which the query rates vary at run-time. For this sake, our strategies tune the parallelism configurations and task granularity during the execution. The parallelism and granularity tuning approaches (ADAPT and ADAPT+G) have shown, for instance, to reduce the query response times by a factor of 6.4× in comparison with the best static configuration of parallelism and task granularity. Further, the distributed memory execution using 128 nodes/3584 CPU cores has attained a parallel efficiency of 0.97 with a dataset of 256 billion SIFT vectors.
Detection and Tracking of Multipath Targets in Over-the-Horizon Radar

In skywave over-the-horizon radar, the ionosphere often supports multiple propagation paths, and a single target can appear as a family of returns. This paper presents two new tracking approaches for this environment: The first uses measurement fusion, and the second uses group-target track-before-detect algorithm. In contrast to previous studies, we find that the biggest benefit is not improved detection sensitivity but rather associating the whole pattern of multipath returns significantly improves the ability to follow maneuvers.
Optimal deployment of components of cloud-hosted application for guaranteeing multitenancy isolation

One of the challenges of deploying multitenant cloud-hosted services that are designed to use (or be integrated with) several components is how to implement the required degree of isolation between the components when there is a change in the workload. Achieving the highest degree of isolation implies deploying a component exclusively for one tenant; which leads to high resource consumption and running cost per component. A low degree of isolation allows sharing of resources which could possibly reduce cost, but with known limitations of performance and security interference. This paper presents a model-based algorithm together with four variants of a metaheuristic that can be used with it, to provide near-optimal solutions for deploying components of a cloud-hosted application in a way that guarantees multitenancy isolation. When the workload changes, the model-based algorithm solves an open multiclass QN model to determine the average number of requests that can access the components and then uses a metaheuristic to provide near-optimal solutions for deploying the components. Performance evaluation showed that the obtained solutions had low variability and percent deviation when compared to the reference/optimal solution. We also provide recommendations and best practice guidelines for deploying components in a way that guarantees the required degree of isolation.
Autonomous trenching with hierarchically organized primitives

Abstract In this article, a solution for autonomous trenching is described. It uses a software architecture built for autonomous excavation tasks which is organized hierarchically. Spatially limited motions are solved with basic primitives defined by the presented architecture. Their execution is controlled by activation which is done as required. Each of the primitives implements a very general control law allowing easy re-use. Bigger tasks can be solved by running multiple in parallel. Additionally, the architecture describes phases in which an excavation task can be split. Each of these phases uses a set of primitives to solve an individual part of the digging process. To implement the system, a behavior-based approach is used providing the ability of adaption according to the changing environment and unexpected events. The performance of the system is shown by running it on a real excavator that uses the system to dig a trench autonomously.
Peer recommendation in dynamic attributed graphs

In this paper, we propose a novel model to recommend possible research peers to a user efficiently. We model all the authors along with their attributes as an attributed graph and then perform community search on this attributed graph to find the most appropriate peer for a user. We propose algorithms to recommend a dynamic attributed graph efficiently and create a real-time community search model by deploying an incremental training algorithm. We also propose dynamic weighted attributes for each node (peer). Given a node and set of attributes (query), the proposed model is capable of self-expansion of the attribute set leading to a more significant match between two nodes. Our experimental results show that our proposed model achieved substantial performance gains over the existing models. In a nutshell, we present an intelligent system that incorporates the changes going on in the research world and suggest up-to-date recommendations. The proposed model is also robust enough to ensure that the recommendations do not suffer due to poor queries.
Bayesian updating rules and AGM belief revision

We interpret the problem of updating beliefs as a choice problem (selecting a posterior from a set of admissible posteriors) with a reference point (prior). We use AGM belief revision to define the support of admissible posteriors after arrival of information, which applies also to zero probability events. We study two classes of updating rules for probabilities: 1) “lexicographic” updating rules where posteriors are given by a lexicographic probability system 2) “minimum distance” updating rules which select the posterior closest to the prior by some metric. We show that an updating rule is lexicographic if and only if it is Bayesian, AGM-consistent and satisfies a weak form of path independence. While not all lexicographic updating rules have a minimum distance representation, we study a sub-class of lexicographic rules, which we call “support-dependent” rules, which admit a minimum distance representation. Finally, we apply our approach to the problem of updating preferences.
Abnormal region detection in cervical smear images based on fully convolutional network

Automation-assisted cervical screening via liquid-based cytology has achieved great success using segmentation and classification methods. This work tries to do abnormal region detection on field of view cervical cell images based on deep learning, which is a novel way to solve cervical cytological screening problem. Since some abnormal nuclei gather in groups, the proposed method chooses abnormal regions instead of abnormal nuclei as the detection targets in order to locate the abnormal regions for the further diagnosis of the pathologists. In this study, a novel abnormal region detection approach for cervical screening is proposed based on a size-sensitive fully convolutional network (R-FCN). Due to the regular feature distribution, a fewer-layer convolutional neural backbone network is designed for more efficient feature extraction and less running time. In addition, a new measure named hit degree is defined to describe the degree how closely each detected region and the corresponding ground truth matches up. Experimental results show that an average precision of 93.2% is achieved for abnormal region detection in cervical smear images. The proposed method is promising for the development of computer-aided systems in clinical cervical cytological screening.
Exploring Deep Recurrent Convolution Neural Networks for Subjectivity Classification

Subjectivity classification aims to discriminate whether the given text presents opinions or states facts, which is an important foundation and component of sentiment analysis task.The characteristics of microblog (e.g., short text, full noise, casual expression, and incomplete grammatical structure) bring new challenges to microblog text subjectivity classification task.The existing subjectivity classification approaches of microblog text mainly depend on the techniques of artificially constructing emotional features that are commonly based on the bag-of-words model, which will lead to the curse of dimensionality and data sparsity problem.Furthermore, microblog text usually contains the left-handed compliment or implicit representation.It is difficult to find and describe this implicit semantic representation by artificially constructing emotional features, which seriously affects the subjectivity classification accuracy.To address these problems, we introduce a deep recurrent convolution neural network model for subjectivity classification of microblog text with the multi-features combination.In our model, we apply the convolution neural networks to automatically learn classification features and use the recurrent neural networks to abstract and memorize the sequence semantic relations of microblog text.We also employ pre-trained word embedding to reduce the representation dimension of the text, which avoids dimension disasters and data sparsity issues.We perform experiments on six commonly used Twitter datasets.The experimental results demonstrate that our proposed model outperforms the state-of-the-art approaches on several datasets.Compared with the baseline methods, the accuracy and the average F1-measure of our proposed model for subjectivity classification are improved by a minimum of 1.21% and 2.82%, respectively.
Multi-phase image segmentation with the adaptive deformable mesh

This paper proposes a method for image segmentation using a deformable triangle mesh in the image domain. We define a piecewise constant function by labeling the mesh triangles with different phases, each representing a segment of an image. Our method finds the optimal mesh configuration and triangle labeling that minimize the piecewise constant Mumford-Shah functional. Contributions of this paper include a force model that moves mesh vertices towards the solution, and an adaptivity model that further adapts the mesh by introducing or removing vertices. The results demonstrate the advantages of our method over traditional methods like snakes and level set. Our approach supports multi-phase segmentation incurring no particular overhead. Furthermore, the use of an adaptive mesh facilitates accurate segmentation with a very compact representation. The biggest challenge of deformable meshes, changes to the topology of the segments, is handled by employing Deformable Simplical Complex (DSC), a method for explicit interface tracking.
Recurrent Poisson Process Unit for Speech Recognition

Over the past few years, there has been a resurgence of interest in using recurrent neural network-hidden Markov model (RNN-HMM) for automatic speech recognition (ASR). Some modern recurrent network models, such as long shortterm memory (LSTM) and simple recurrent unit (SRU), have demonstrated promising results on this task. Recently, several scientific perspectives in the fields of neuroethology and speech production suggest that human speech signals may be represented in discrete point patterns involving acoustic events in the speech signal. Based on this hypothesis, it may pose some challenges for RNN-HMM acoustic modeling: firstly, it arbitrarily discretizes the continuous input into the interval features at a fixed frame rate, which may introduce discretization errors; secondly, the occurrences of such acoustic events are unknown. Furthermore, the training targets of RNN-HMM are obtained from other (inferior) models, giving rise to misalignments. In this paper, we propose a recurrent Poisson process (RPP) which can be seen as a collection of Poisson processes at a series of time intervals in which the intensity evolves according to the RNN hidden states that encode the history of the acoustic signal. It aims at allocating the latent acoustic events in continuous time. Such events are efficiently drawn from the RPP using a sampling-free solution in an analytic form. The speech signal containing latent acoustic events is reconstructed/sampled dynamically from the discretized acoustic features using linear interpolation, in which the weight parameters are estimated from the onset of these events. The above processes are further integrated into an SRU, forming our final model, called recurrent Poisson process unit (RPPU). Experimental evaluations on ASR tasks including ChiME-2, WSJ0 and WSJ0&amp;1 demonstrate the effectiveness and benefits of the RPPU. For example, it achieves a relative WER reduction of 10.7% over state-of-the-art models on WSJ0.
Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs

Counting the number of true instances of a clause is arguably a major bottleneck in relational probabilistic inference and learning. We approximate counts in two steps: (1) transform the fully grounded relational model to a large hypergraph, and partially-instantiated clauses to hypergraph motifs; (2) since the expected counts of the motifs are provably the clause counts, approximate them using summary statistics (in/outdegrees, edge counts, etc). Our experimental results demonstrate the efficiency of these approximations, which can be applied to many complex statistical relational models, and can be significantly faster than state-of-the-art, both for inference and learning, without sacrificing effectiveness.
Nonclassicality of superposition of photon-added two-mode coherent states

We investigate nonclassical properties of the quantum state generated by adding any number of photons to the two-mode coherent states. This novel quantum state is also a superposition of photon-added two-mode CSs (PATMCS). It is shown that the normalization factor of the PATMCS is related to the Laguerre polynomials. The quantum statistical properties, squeezing effects, entanglement and nonlocality are also discussed analytically. Our results show that the PATMCS is a new nonclassical, squeezing and entangled state with negative Wigner function, negative quadrature squeezing and violation of the Bell-CHSH inequality.
Cost-Sensitive Label Propagation for Semi-Supervised Face Recognition

In real-world applications, different kinds of learning and prediction errors are likely to incur different costs for the same system. Moreover, in practice, the cost label information is often available only for a few training samples. In a semi-supervised setting, label propagation is critical to infer the cost information for unlabeled training data. The existing methods typically conduct label propagation independently ahead of supervised cost-sensitive learning. The precomputed label information is kept fixed, which may become suboptimal in the subsequent learning process and hence degrade the overall system performance. In this paper, we develop a unified cost-sensitive framework for semi-supervised face recognition that can jointly optimize the inferred label information and the classifier in an iterative manner. Our experiments on face benchmark datasets demonstrate that in comparison with the state-of-the-art methods for label propagation and cost-sensitive learning, the proposed approach can significantly improve the overall system performance, especially in terms of classification errors associated with high costs.
Furnace thermal efficiency modeling using an improved convolution neural network based on parameter-adaptive mnemonic enhancement optimization

Thermal efficiency is a very important factor in furnaces. Accurate thermal efficiency modeling plays a key role in optimizing and improving thermal efficiency. Industrial process data own complex characteristics of high nonlinearity and time series. Convolutional neural network (CNN) has been widely used to build models due to the strong ability in processing high-nonlinear and time-series data. However, in the traditional CNN method, the repeatability of calculations is not considered when updating weights; only the latest updated weights are reserved. As a result, some important computation experience is abandoned, which weakens the performance of CNN. In order to solve this problem, an improved CNN method integrating with parameter-adaptive mnemonic enhancement optimization (AMEO-CNN) is proposed in this paper. In the proposed AMEO-CNN, previous computational experiences are adaptively reserved to minimize output errors using optimized weights. The continuity of the algorithm and the anisotropy of the initial values are proved through theoretical analyses. In order to validate the performance of the proposed AMEO-CNN model, thermal efficiency modeling simulations of an industrial heating furnace are carried out. Compared with other three models, simulation results show that the proposed AMEO-CNN model can achieve higher modeling accuracy, which verifies the effectiveness of the proposed AMEO-CNN method.