Reviewerly/exHarmony huggingface dataset:
Accurate single image super‐resolution using cascading dense connections

In recent years, deep learning methods, especially deep convolutional neural network, have been successfully applied to the single-image super-resolution (SISR) task. In this Letter, the authors propose an accurate SISR method by introducing cascading dense connections in a very deep network. In detail, they construct the cascading dense network (CDN) to fully make use of the hierarchical features from all the convolutional layers, which implements a cascading mechanism upon the dense connected convolutional layers. The cascading dense connection in the CDN enables short and long paths to be built directly from the output to each layer, alleviating the vanishing-gradient problem of very deep networks. Extensive experiments show that CDN achieves state-of-the-art performance on traditional metrics (PSNR and SSIM). Also, they introduce object recognition as the additional evaluation metric for SISR, which further demonstrates the effectiveness of the authors’ method.
Mapping Theoretical and Methodological Perspectives for Understanding Speech Interface Interactions

The use of speech as an interaction modality has grown considerably through the integration of Intelligent Personal Assistants (IPAs- e.g. Siri, Google Assistant) into smartphones and voice based devices (e.g. Amazon Echo). However, there remain significant gaps in using theoretical frameworks to understand user behaviours and choices and how they may applied to specific speech interface interactions. This part-day multidisciplinary workshop aims to critically map out and evaluate theoretical frameworks and methodological approaches across a number of disciplines and establish directions for new paradigms in understanding speech interface user behaviour. In doing so, we will bring together participants from HCI and other speech related domains to establish a cohesive, diverse and collaborative community of researchers from academia and industry with interest in exploring theoretical and methodological issues in the field.
A novel selective clustering framework for appropriate labeling of the clusters based on K-means algorithm

Clustering is one of the main methods of data mining. K-means algorithm is one of the most common clustering algorithms due to its efficiency and ease of use. One of the challenges of clustering is to identify the appropriate label for each cluster. The selection of a label is done in such a way as to provide a proper description of the cluster records. In some cases, choosing the appropriate label is not easy due to the results and structure of each cluster. The aim of this study is to present an algorithm based on the K-means clustering in order to facilitate the allocation of labels to each cluster. Moreover, in many data mining issues, the data set contains a large number of fields and therefore, the identification of the fields and the extraction of subsets from the required fields is an important issue. With the help of the proposed algorithm, the important and influential variables of the data set would be identified and the subset of the required fields would be selected.
Building relatedness explanations from knowledge graphs

Knowledge graphs (KGs) are a key ingredient to complement search results, discover entities and their relations and support several knowledge discovery tasks. We face the problem of building relatedness explanations, that is, graphs that can explain how a pair of entities is related in a KG. Explan ations can be used in a variety of tasks; from exploratory search to query answering. We formalize the notion of explanation and present two algorithms. The first, E4D (Explanations from Data), assembles explanations starting from all paths interlinking the source and target entity in the data. The second algorithm E4S (Explanations from Schema) builds explanations focused on a specific relatedness perspective expressed by providing a predicate. E4S first generates candidate explanation patterns at the level of schema; then, it assembles explanations by proceeding to their verification in the data. Given a set of paths, found by E4D or E4S, we describe different criteria to build explanations based on information-theory, diversity, and their combination. As a concrete use-case of relatedness explanations, we introduce relatedness-based KG querying, which revisits the query-by-example paradigm from the perspective of relatedness explanations. We implemented all the machinery in the RECAP tool, which is based on RDF and SPARQL. We discuss an evaluation of the explanation building algorithms and a comparison of RECAP with related systems on real-world data.
Classifying Heterogeneous Sequential Data by Cyclic Domain Adaptation: An Application in Land Cover Detection

Recent advances in processing remote sensing data have provided unprecedented potential for monitoring land covers. However, it is extremely challenging to deploy an automated monitoring system for different regions and across different years given the involved data heterogeneity over space and over time. The heterogeneity exists on two aspects. First, for many land covers, the distinguishing temporal patterns are only visible in certain discriminative period. Due to the change of weather conditions, the discriminative period can shift across space and time, which causes heterogeneity to the sequential data. Second, the collected remote sensing data are affected by acquisition devices and natural variables, e.g., precipitation and sunlight. In this paper, we introduce a novel framework to effectively detect land covers using the sequential remote sensing data. At the same time, we propose new learning strategies based on attention networks and domain adaptation to addresses the aforementioned challenges. The evaluation on two real-world applications - cropland mapping and burned area detection, demonstrate that the proposed method can effectively detect land covers under different weather conditions.
CHURN PREDICTION IN TELECOM SECTOR USING MACHINE LEARNING

As everyday life appears to be increasingly dependent on the use of telecom products and services, the global market for telecommunication is slated to grow at a phenomenal rate over the coming years.The global telecommunications market is continually transforming on account of the ongoing innovations and developments taking place consistently and at a fast pace.So for the growth of telecommunication market the loyalty of the customer is a key factor.In a highly competitive market, telecom operators can retain their values by building an affinity with subscribers, along with a pre-established expectation of trust driving loyalty.Keeping existing customers and acquiring new customers is a powerful weapon in today's market.But nowadays there are a lot of churn customers in the telecommunication industries.This leads to a need of a domain expert for predicting the churn customers.We implement churn prediction using various supervised algorithm for predicting churn.
Methodology for fine-grained monitoring of the quality perceived by users on 360VR contents

To properly evaluate the performance of 360VR-specific encoding and transmission schemes, and particularly of the solutions based on viewport adaptation, it is necessary to consider not only the bandwidth saved, but also the quality of the portion of the scene actually seen by users over time. With this motivation, we propose a robust, yet flexible methodology for accurately assessing the quality within the viewport along the visualization session. This procedure is based on a complete analysis of the geometric relations involved. Moreover, the designed methodology allows for both offline and online usage thanks to the use of different approximations. In this way, our methodology can be used regardless of the approach to properly evaluate the implemented strategy, obtaining a fairer comparison between them.
Convolutional Neural Network based for Automatic Text Summarization

In recent times, the apps for the processing of a natural language has been formed and generated through the use of intelligent and soft computing methods that allow computer systems to practically mimic practices related to the process of human texts like the detection of plagiarism, determination of the pattern as well as machine translation, Thereafter, Text summarization serves as the procedure of abridging writing within consolidated structures.'Automatic text summarization' or the ATS is when a computer system is used to create a text summarization.In this study, the researchers have introduced a novel ATS system, i.e., CNN-ATS, which is a convolutional neural network that enables to Automatic text summarization using a text matrix representation.CNN-ATS is a deep learning system that was used to evaluate the improvements resulting from the increase in the depth to determine the better CNN configurations, assess the sentences, and determine the most informative one.Sentences deemed important are extracted for document summarization.The researchers have investigated this novel convolutional network depth for determining its accuracy during the informative sentences selection for each input text document.The experiment findings of the proposed method are based on the Convolutional Neural Network that uses 26 different configurations.It demonstrates that the resulting summaries have the potential to be better compared to other summaries.DUC 2002 served as the data warehouse.Some of the news articles were used as input in this experiment.Through this method, a new matrix representation was utilized for every sentence.The system summaries were examined by using the ROUGE tool kit at 95% confidence intervals, in which results were extracted by employing average recall, F-measure and precision from ROUGE-1, 2, and L.
A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking

In this paper, we propose a novel fuzzy expectation maximization (FEM) based Takagi-Sugeno (T-S) fuzzy particle filtering (FEMTS-PF) algorithm for a passive sensor system. In order to incorporate target spatial-temporal information into particle filtering, we introduce a T-S fuzzy modeling algorithm, in which an improved FEM approach is proposed to adaptively identify the premise parameters, and the model probability is adjusted by the premise membership functions. In the proposed FEM, the fuzzy parameter is derived by the fuzzy C-regressive model clustering method based on entropy and spatial-temporal characteristics, which can avoid the subjective influence caused by the artificial setting of the initial value when compared to the traditional FEM. Furthermore, using the proposed T-S fuzzy model, the algorithm samples particles, which can effectively reduce the particle degradation phenomenon and the parallel filtering, can realize the real-time performance of the algorithm. Finally, the results of the proposed algorithm are evaluated and compared to several existing filtering algorithms through a series of Monte Carlo simulations. The simulation results demonstrate that the proposed algorithm is more precise, robust and that it even has a faster convergence rate than the interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF).
THE STUDY OF ACTIVATION FUNCTIONS IN DEEP LEARNING FOR PEDESTRIAN DETECTION AND TRACKING

Abstract. Pedestrian detection and tracking remains a highlight research topic due to its paramount importance in the fields of video surveillance, human-machine interaction, and tracking analysis. At present time, pedestrian detection is still an open problem because of many challenges of image representation in the outdoor and indoor scenes. In recent years, deep learning, in particular Convolutional Neural Networks (CNNs) became the state-of-the-art in terms of accuracy in many computer vision tasks. The unsupervised learning of CNNs is still an open issue. In this paper, we study a matter of feature extraction using a special activation function. Most of CNNs share the same architecture, when each convolutional layer is followed by a nonlinear activation layer. The activation function Rectified Linear Unit (ReLU) is the most widely used as a fast alternative to sigmoid function. We propose a bounded randomized leaky ReLU working in such manner that the angle of linear part with the highest input values is tuned during learning stage, and this linear part can be directed not only upward but also downward using a variable bias for its starting point. The bounded randomized leaky ReLU was tested on Caltech Pedestrian Dataset with promising results.
An Attribute Generalization Mix-Zone Without Privacy Leakage

In the road network, mix-zone is usually considered as an efficient application that provides a balance between privacy protection and service quality. At the same time, the mix-zone can also resist the attack of tracking without any affection in the quality of feedback result of navigation. However, just as the old adage goes, nobody is perfect, the mix-zone also has two non-negligible deficiencies. The first one is that the mix-zone is difficult to cloak or generalize all types of the attributes emitted by the user. The other one is that the mix-zone is difficult to resist the attack initials by the disguiser who participates in the process of constructing the anonymous group. In order to cope with above-mentioned two problems, this paper provides an uncorrelated mix-zone based on the conception of attribute generalization and homomorphic encryption. In this algorithm, the process of attribute generalization is executed by an agency who is the winner in the private bidding, and the value of generalized attributes value is calculated by the secure multi-party computation under the principle of homomorphic encryption. Accordingly, the private information of the user in the mix-zone is preserved and any entities, no matter the agency or other participants, are prevented from any information about the user as there is no information been exposed. Furthermore, as the attribute of each user is generalized, for the mix-zone leavers, their attributes emitted are similar to each other; it will be difficult for the adversary to correlate any user with the attribute grasped before the user enter the mix-zone. Therefore, the potential hazard of adversary tracking the user with attribute comparison is solved. At last, in order to demonstrate the performance of our proposed algorithm in theoretical and practical fields, several analyses of security and simulation experiments are proposed, and the verification of security as well as the results of simulations and comparisons can further demonstrate the priority.
Compositional Coding for Collaborative Filtering

Efficiency is crucial to the online recommender systems, especially for the ones which needs to deal with tens of millions of users and items. Because representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computation in the Hamming space, in recent years, we have witnessed an emerging research effort in exploiting binary hashing techniques for CF methods. However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data.
A None-Sparse Inference Accelerator that Distills and Reuses the Computation Redundancy in CNNs

Prior research on energy-efficient Convolutional Neural Network (CNN) inference accelerators mostly focus on exploiting the model sparsity, i.e., zero patterns in weight and activations, to reduce the on-chip storage and computation overhead. In this work, we found in addition to zero patterns, a larger group of repetitive patterns and values exists in the working-set of CNN inference task, which is defined as computation redundancy and induces unnecessary performance and storage overhead in CNN accelerators. Based on this observation, we proposed a redundancy-free architecture that detects and eliminates the repetitive computation and storage patterns in CNN for more efficient network inference. The architecture consists of two parts: the off-line parameter analyzer that extracts the repetitive patterns in the 3D tensor of parameters, and the dataflow accelerator. The proposed accelerator at first preprocesses the weight patterns and the dynamically generated activations, and then cache these intermediate results in special P2-cache banks for further usage in convolution or full-connection stage. It is evaluated in experiments that the proposed Cavoluche architecture removes up to 89% of the repetitive operations from the layer inference process and reduce 77% of on-chip storage space to store both redundancy-free weight and activations. It is seen in experiments that the implementation of Cavoluche outperforms the state-of-the-art mobile GPGPU in both performance and energy-efficiency. When compared to the latest sparsity base accelerators, Cavoluche also achieves better operation elimination effects.
An efficient retrieval algorithm of encrypted speech based on inverse fast Fouriertransform and measurement matrix

In this paper, we present an efficient retrieval algorithm for encrypted speech based on an inverse fast Fourier transform and measurement matrix. Our approach improves query performance, as well as retrieval efficiency and accuracy, compared to existing content-based encrypted speech retrieval methods. Our proposed algorithm constructs a perceptual hash scheme using perceptual hash sequences from original speech files. By classifying the sequences and applying run-length compression, we decrease the cloud storage required for the hash index. We secure the speech database by encrypting it with Henon chaos scrambling, which offers excellent resistance to attacks. Experimental results show that the robustness, discrimination, and feature extraction efficiency of our proposed method are better than the existing alternatives, with good recall and precision ratios and with high retrieval efficiency and accuracy.
IMPLEMENTING BI-TEMPORAL PROPERTIES INTO VARIOUS NOSQL DATABASE CATEGORIES

NoSQL database systems have emerged and developed at an accelerating rate in the last years. Attractive properties such as scalability and performance, which are needed by many applications today, contributed to their increasing popularity. Time is very important aspect in many applications. Many NoSQL database systems do not offer built in management for temporal properties. In this paper, we discuss how we can embed temporal properties in NoSQL databases. We review and differentiate between the most popular NoSQL stores. Moreover, we propose various solutions to modify data models for embedding bitemporal properties in two of the most popular categories of NoSQL databases (Key-value stores and Column stores). In addition, we give examples of how to represent bitemporal properties using Redis Key-value store and Cassandra column oriented store. This work can be used as basis for designing and implementing temporal operators and temporal data management in NoSQL databases.
White Blood Cell Detection and Counting based on Genetic Algorithm

This paper proposes an approach for detecting and counting white blood cells (WBCs) by taking the power of Genetic Algorithm (GA) and Otsu thresholding method. This technique is based on image binarization in HSV color space using Otsu thresholding method and applying GA for output improvement. In fact, Otsu uses HSV values in order to detect WBCs which causes false detection because of HSV values' similarity. There are some intensity values in RGB color space which corresponding HSV values are similar. This similarity makes mistake in WBC detection. Thus, GA removes false areas based on within-class variance reduction as a fitness function. This combination causes output improvement in order to maintain correct areas as WBC. Experimental results show the superiority of this approach over the comparing methods.
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision

Yanlin Feng, Xiaojun Wan. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Pricing Personal Data Based on Information Entropy

Personal data is increasingly being traded online, for which data marketplace services have emerged to facilitate. Personal data is increasingly valuable to individuals and institutions. Be aware of the growing value of personal data, compensated use of personal data has become an inevitable trend. Therefore, how to reasonably price personal data becomes an urgent problem to be solved. We propose a novel personal data pricing method, which is based on information entropy and compute the price as the entropy of each data tuple. Our method can reasonably price personal data according to the value of the information entropy. This study shows that our model is arbitrage-freedom and can be adjusted dynamically.
Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium

Machine learning (ML) allows the exploration and progressive improvement of very complex high-dimensional data patterns that can be utilised to optimise specific classification and prediction tasks, outperforming traditional statistical approaches. An enormous acceleration of ready-to-use tools and artificial intelligence (AI) applications, shaped by the emergence, refinement, and application of powerful ML algorithms in several areas of knowledge, is ongoing. Although such progress has begun to permeate the medical sciences and clinical medicine, implementation in cardiovascular medicine and research is still in its infancy.To lay out the theoretical framework, purpose, and structure of a novel AI consortium.We have established a new Dutch research consortium, the CVON-AI, supported by the Netherlands Heart Foundation, to catalyse and facilitate the development and utilisation of AI solutions for existing and emerging cardiovascular research initiatives and to raise AI awareness in the cardiovascular research community. CVON-AI will connect to previously established CVON consortia and apply a cloud-based AI platform to supplement their planned traditional data-analysis approach.A pilot experiment on the CVON-AI cloud was conducted using cardiac magnetic resonance data. It demonstrated the feasibility of the platform and documented excellent correlation between AI-generated ventricular function estimates as compared to expert manual annotations. The resulting AI solution was then integrated in a web application.CVON-AI is a new consortium meant to facilitate the implementation and raise awareness of AI in cardiovascular research in the Netherlands. CVON-AI will create an accessible cloud-based platform for cardiovascular researchers, demonstrate the clinical applicability of AI, optimise the analytical methodology of other ongoing CVON consortia, and promote AI awareness through education and training.