Reviewerly/exHarmony huggingface dataset:
Workflow Management Through Database Migration in Cloud Computing Environment

Cloud computing (CC) is a new concept compared with conventional methods to information technology, in which companies and enterprises implement open cloud systems on a pay-per-use basis to minimize costs. Virtualization is a pervasively implemented strategy in modern data centers (DCs) to optimize the utilization of energy, minimize greenhouse gas emissions, and decrease operating costs. The migration of virtual machines (VM) is commonly used within and through DCs to address a range of virtualized cloud system needs. For example, server consolidation needs power management via VM migration. Load balancing, fault tolerance, network repair, and reducing the violation rate of service-level agreement (SLA) often include live VM migration. The VM migration process is very resource-intensive and involves smart solutions to prevent saturation of the network bandwidth which reduces server downtime.
Spontaneous Temporal Grouping Neural Network for Long-Term Memory Modeling

The capacity of long-term memory is an important issue in sequence learning, but it remains challenging for the problems of vanishing gradients or out-of-order dependencies. Inspired by human memory, in which long-term memory is broken into fragments and then can be recalled at appropriate times, we propose a neural network via spontaneous temporal grouping in this article. In the architecture, the segmented layer is used for spontaneous sequence segmentation under guidance of the reset gates which are driven to be sparse in the training process; the cascading layer is used to collect information from the temporal groups, where a filtered long short-term memory with chrono-initialization is proposed to alleviate the gradient vanishing phenomenon, and random skip connections are adopted to capture complex dependencies among the groups. Furthermore, the advantage of our neural architecture in long-term memory is demonstrated via a new measurement method. In experiments, we compare the performance with multiple models on several algorithmic or classification tasks, and both of the sequences with fixed lengths like the MNISTs and with varying lengths like the speech utterances are adopted. The results in different criteria have demonstrated the superiority of our proposed neural network.
Design of Restricted Coulomb Energy Neural Network Processor for Multi-modal Sensor Fusion

This paper proposes a restricted coulomb energy neural network (RCE-NN) with an improved learning algorithm and presents the hardware architecture design and FPGA implementation results. The proposed learning algorithm divides each neuron region in the learning process and measures the reliability with different factors for each region. In addition, it applies a process of gradual radius reduction by a pre-defined reduction rate. In performance evaluations using two datasets, RCE-NN with the proposed learning algorithm showed high recognition accuracy with fewer neurons compared to existing RCE-NNs. The proposed RCE-NN processor was implemented in an Intel-Altera Cyclone IV FPGA with 26,702 logic elements, 13,096 registers and 131,072bits memory and operated at the clock frequency of 150 MHz.
Caption Generation from Road Images for Traffic Scene Construction

In this paper, an image captioning network is proposed for traffic scene modeling, which incorporates element attention into the encoder-decoder mechanism to generate more reasonable scene captions. Firstly, the traffic scene elements are detected and segmented according to their clustered locations. Then, the image captioning network is applied to generate the corresponding caption of each subregion. The static and dynamic traffic elements are appropriately organized to construct a 3D corridor scene model. The semantic relationships between the traffic elements are specified according to the captions. The constructed 3D scene model can be utilized for the offline test of unmanned vehicles. The evaluations and comparisons based on the TSD-max and COCO datasets prove the effectiveness of the proposed framework.
Application of Machine Learning Algorithms to Determine the Authorship of Text Fragments

The article describes the main approaches to the automated determination of authorship of texts based on the analysis of copyright styles. The architectures of a convolutional neural network, a multilayer perceptron, and LSTM neural network were proposed to solve this problem. Also, experiments were conducted in which the effectiveness of each of the proposed approaches was evaluated using the example of the task of determining the authorship of English-language poems.
Two-stage Generative Adversarial Recovery Network for MR Brain Images Containing Tumors

Brain image registration (BIR) plays an important role in neuroscience. However, for the registration of brain image containing tumors, the existence of tumor could cause great influence to BIR. One possible solution for getting rid of such influence is to recover the tumor brain image to “normal” appearance brain image (no tumor). Most of existing methods for tumor brain image recovery are based on low-rank, which is time consuming and low recovery quality. In this paper, we propose a novel deep-learning based method for tumor brain image recovery. Specifically, a two-stage generative adversarial network comprising a region recovery stage and an image recovery stage is presented. For the input tumor brain image, the region recovery stage first generates a recovered brain region image containing three different regions (i.e., the gray matter, the white matter and the cerebrospinal fluid). The recovered brain region image is used in the image recovery stage as priori information to get the final “normal” appearance brain image. Both stages are trained under the generative adversarial framework. The experimental results demonstrate that the registration accuracy of tumor brain images can be significantly enhanced by our network as compared to the state-of-the-art image recovery methods.
Survey for Dependent on Neuro Fuzzy Algorithm for Framework ID

Abstract This article focuses on hybrid neuro-fuzzy techniques, its advantages and disadvantages. The techniques of fuzzy logic and neural networks are commonly used for artificial intelligence. At the point when they are utilized in a joined manner, they are called Neuro-Fuzzy Systems. In the Neuro-Fuzzy Systems fuzzy sets and fuzzy rules are adjusted using input output patterns. The Neuro-fuzzy systems are implemented by different authors in different models. The Neuro-Fuzzy frameworks have the accompanying attributes: quick learning, versatility on-line, to modify itself with the point of getting the little worldwide blunder conceivable and little computational multifaceted nature. The obtaining of information, the pre-handling of information and preparing information are additionally significant for the achievement of the use of the neuro-fuzzy models
Multi-rate Sampling Wavelet Lifting Scheme Based Video Compression with Enhanced Adaptive Rood Search Integral Projections

This paper presents the research on video compression for videos by developing a multi rate wavelet lifting scheme method which works better for both colour and grayscale videos along with Enhanced Adaptive Rood Search with Integral Projections for motion Estimation. In wavelet lifting scheme sampling is performed at different rates at the upper and lower branches. It is a powerful alternative to traditional convolution involving forward and inverse filter banks with the total amount of arithmetic computations required is substantially lesser. The ratio used is 3:2 for the upper branch and 3:1 for lower branch of lifting scheme, more low frequency coefficients are preserved as compared to high frequency coefficients to have a better picture quality with a small compromise in compression ratio. The Listless speck has been used as the Encoder and an Enhanced Adaptive Rood Search technique has been developed for motion Estimation as it improved over the problem with Adaptive Rood Search which does not consider the diagonal direction. The proposed method has produced better compression results with quality and reduced latency than the existing ones as validated in the experimentation.
Automatic Segmentation of Liver from Abdominal Computed Tomography Images using Energy Feature

Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography (CT) images.The segmentation of hepatic organ is more intricate task, owing to the fact that it possesses a sizeable quantum of vascularization.This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans.The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not.This involves segmentation of the region of interest (ROI) from the segmented liver, extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted features.In this work, the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering (FCM) which is one of the algorithms to segment the images.The dataset used in this method has been collected from various repositories and scan centers.The outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and consistency.It gives better results when compared with other existing algorithms.Fast execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.
The Controllability of Quantum Correlation Under Geometry and Entropy Discords

Quantum correlation plays a critical role in the maintenance of quantum information processing and nanometer device design.In the past two decades, several quantitative methods had been proposed to study the quantum correlation of certain open quantum systems, including the geometry and entropy style discord methods.However, there are differences among these quantification methods, which promote a deep understanding of the quantum correlation.In this paper, a novel time-dependent three environmental open system model is established to study the quantum correlation.This system model interacts with two independent spin-environments (two spin-environments are connected to the other spin-environment) respectively.We have calculated and compared the changing properties of the quantum correlation under three kinds of geometry and two entropy discords, especially for the freezing phenomenon.At the same time, some original and novel changing behaviors of the quantum correlation under different timedependent parameters are studied, which is helpful to achieve the optimal revival of the quantum discord and the similar serrated form of the freezing phenomenon.Finally, it shows the controllability of the freezing correlation and the robustness of these methods by adjusting time-dependent parameters.This work provides a new way to control the quantum correlation and design nanospintronic devices.
Key success factors for Autonomous Agile Software Teams at the small-scale

Purpose – This thesis is to identifying key success factors for autonomous agile teams at the small-scale. Furthermore the purpose leads to an improvement of their working process.Method – This stu ...
Discrete Data Clustering

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An Optimized Algorithm for Speeding up Universal Adversarial Perturbation Generation

Universal adversarial perturbation, which is a security vulnerability in convolutional neural networks (CNNs), can fool convolutional neural network models on a set of images by a single perturbation vector. One recent algorithm, named UAP, generates universal adversarial perturbation iteratively by aggregating the smallest adversarial perturbations with respect to each image, but it ignored the orientations of perturbation vectors; consequently, the magnitude of the universal adversarial perturbation cannot efficiently increase at each iteration, thereby resulting in slow universal adversarial perturbation generation. Hence, to expedite the generation of universal adversarial perturbation, we propose an optimized algorithm to generate universal adversarial perturbation based on the orientations of perturbation vectors and aggregate adversarial perturbations with similar orientations. The proposed algorithm is compared with the original algorithm on ImageNet dataset, experimental results show that our proposed algorithm is more efficient and can reduce the number of training images compared with the UAP with nearly the same fooling rate.
Written in Code: Exploring the Negative Effects of Acronyms in News Headlines

Through two experiments (N1 = 131, N2 = 520), this study looks at whether the negative effects of acronyms and abbreviations in headlines are based on their presence or their difficulty. In all, it finds support for a difficulty effect; people had lower content and source perceptions when they were shown a headline with unfamiliar acronym(s) compared with ones they knew, and they were more frustrated with those articles. These differences were moderated by need for cognition. In terms of a presence effect, people did perceive some differences in articles with acronyms in their headlines compared to those without, particularly if they were paying close attention, but those differences were much less pronounced. In other words, readers don’t seem to be inherently bothered by the presence of acronyms in headlines; they seem to be bothered by the ones they don’t understand. These findings suggest that journalists should strive to explain acronyms and abbreviations in headlines, rather than worry about avoiding them altogether. Implications for journalistic practice, reader engagement, and dual-processing theories of persuasion are discussed.
Nonnegative Feature Learning by Regularized Nonnegative Matrix Factorization

This paper investigated a feature learning problem, where localized and part-based representations are embedded in dimension-reduction progress. We propose a subspace learning model based on nonnegative matrix factorization (NMF) framework. The proposed NMF model incorporates wisely the spatial location information constraint to the base matrix, the manifold and max-margin penalty to the projective feature matrix for enhancing the capability of exploiting much information of the original data. Experiments on facial expression recognition scenery reveal powerful data representation ability of the proposed NMF method that enables dictionary learning and feature learning jointly.
Wrist-worn Physical Activity Recognition: A Fusion Learning Approach

With the advantages of technology in sensors on wrist-worn devices, many researchers have investigated and utilized the signals to recognize the physical activity in a living context. However, these studies are still faced with challenging problems, especially improving the recognition rate. Without using the recorded sensor data of target subjects, we aim to create a cross-subject activity recognition framework composed of three important models by using the fusion approach of machine learning. Performance was tested on four public datasets with different window sizes. Our experimental results demonstrate that our proposed framework achieves the highest recognition rate when compared with state-of-the-art models. In some datasets, our framework achieves more than 80% success in the recognition rate. In the other datasets, our proposed model can achieve up to 90%. In summary, our proposed framework is capable of being an activity recognition tool for classifying and identifying the physical activities in real world scenarios.
Clustering Analysis of Usability in Web Sites of Higher Technological Institutes of Ecuador

Techniques for evaluating usability continue to be innovated. This document shares the application of a heuristic-based framework for measuring web usability - SIRIUS, complemented by two machine learning techniques for clustering: a) Hierarchical, with the Ward.2 method and Euclidean; and b) K-means clustering. For data processing, CRISP-DM has been proposed as a general method. Since our objective is to evaluate the usability characteristics of the websites of the Technical and Technological Institutes of Ecuador, data has been obtained from the web portals of 83 Institutes (34 public and 49 private). As a result, three clusters have been obtained, which encompass the 10 aspects of the framework, and which allow us to identify the levels of usability of technological institutes. As a result, 18 institutes have been categorized into the group of websites with above-average usability (cluster1), 32 institutes with below-average usability (cluster2), and the remainder with an acceptable degree of usability. The method used and proposed has made it possible to have a general usability map of the web portals of the technical and technological institutes of a country, as input for decision-making.
Integration of Pipeline SCADA and Pipeline WebGIS Based on OPC

Based on the analysis and comparison of the main methods of current pipeline SCADA and GIS integration, an OPC-based pipeline SCADA and GIS integration method is proposed. A custom data access component is developed with OPC interfaces to implement the real-time data accessing to the SCADA system and the real-time data transfer to the Pipeline Spatial Data Model (PSDM).
Gibbs Base Random Partitions

As a typical family of random partitions on $$\mathcal {P}_{n,k}$$ , the set of partitions of n into k parts, the conditional distribution of Pitman’s random partition, termed as the Gibbs base random partition, GBRP $$(\alpha )$$ , is investigated. The set $$\mathcal {P}_{n,k}$$ is a lattice with respect to majorization partial order with unique minimum and maximum, and GBRP $$(\alpha )$$ has TP2 with respect to this order. The main purpose of this paper is to study such a family of random partitions and the inference on its parameter.
Python Based Face Recognition for Person Identification Using PCA and 2DPCA Techniques

In this paper we proposed a face recognition techniques based on Principal component analysis and two Dimension Principal Component Analysis using python. Many researcher's use Matlab software but we use Python because it's open source and easily available. Python provides many good libraries like Open CV, PIL, and Sklearn to solve the face recognition problem. The size of python is very less as compared to matlab so we can avoid the computational burden and it takes less time to execute the program that's why we get fast and better results on large database. We have used EIF-I and Face 94 database and we applied PCA and 2DPCA on both the database but we got better accuracy using 2DPCA with Face 94 database.
중국인 학습자의 한국어 자기발화수정 연구 - 수정 특징 및 그 원인 분석을 중심으로 -

This study aims to analyze the characteristics and its causes of self-repair in Chinese learners’ Korean speech. For this purpose, 15 Chinese advanced learners and 15 Korean native speakers were took as the research subjects. Through the comparative analysis of their conversation materials, the characteristics of self-repair in learners’ speech were summarized into five points as follows. First, by tape, it mainly focused on the covert repair. Second, by strategy, long pauses and meaningless editing terms frequently appeared. Third, by structure, most repair includes the editing phase. Fourth, by location, the proportion of repairs occurring within word segments is significant. Fifth, by result, although native speakers can detect and repair most of the targets to be repaired successfully, learners can only repair a limited number of targets and often fail. These characteristics may have a negative impact on the fluency of speech, causing the speech to sound intermittent or long and cumbersome. Next, based on the results of interview with Chinese learners, the causes of learners’ self-repair characteristics were classified into learners’ insufficient language ability, translational speaking habits, a lack of awareness of self-repair and psychological factors such as negative attitude toward repair and excessive tension when using a foreign language.(Seoul National University)
Towards a Blockchain and Smart Contracts-Based Privacy Framework for Decentralized Data Processing

Analysing and processing data are not exempt of privacy policies, particularly when talking about personal data. This problem becomes more complex and cumbersome when sharing the data between parties, entities or even across borders. Different policies, implications and interests may apply, and the risks of losing control and privacy protection arise. Yet, new forms of analysing, protecting and sharing data should be considered in order to safeguard the privacy. This paper introduces a general framework for privacy assurance in data processing applications. This framework is based on blockchain and smart contract technology that have received increased attention recently. Our solution reduces the need for a credible centralized authority and maintains the privacy of data and information for data processing applications.
Long-Term Prediction of Physical Interactions: A Challenge for Deep Generative Models

Anticipating salient events by actively predicting the sensory stream is a core skill of intelligent agents. In recent years, a variety of unsupervised deep learning approaches have been proposed for modeling our ability to predict the physical dynamics of visual scenes: In this paper we conduct a systematic evaluation of state-of-the-art models, considered capable of learning the spatio-temporal structure of synthetic videos of bouncing objects. We show that, though most of the models obtain high accuracy on the standard benchmark of predicting the next frame of a given sequence, they all fall short when probed with the generation of multiple future frames. Our simulations thus show that the ability to perform one-step-ahead prediction does not imply that the model has captured the underlying dynamics of the environment, suggesting that the gap between deep generative models and human observers has yet to be filled.
Cross-lingual Emotion Intensity Prediction

Emotion intensity prediction determines the degree or intensity of an emotion that the author expresses in a text, extending previous categorical approaches to emotion detection. While most previous work on this topic has concentrated on English texts, other languages would also benefit from fine-grained emotion classification, preferably without having to recreate the amount of annotated data available in English in each new language. Consequently, we explore cross-lingual transfer approaches for fine-grained emotion detection in Spanish and Catalan tweets. To this end we annotate a test set of Spanish and Catalan tweets using Best-Worst scaling. We compare six cross-lingual approaches, e.g., machine translation and cross-lingual embeddings, which have varying requirements for parallel data – from millions of parallel sentences to completely unsupervised. The results show that on this data, methods with low parallel-data requirements perform surprisingly better than methods that use more parallel data, which we explain through an in-depth error analysis. We make the dataset and the code available at https://github.com/jerbarnes/fine-grained_cross-lingual_emotion.