Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Multi-Node Hadoop Cluster Based on Large Data Sets

The purpose of this paper is to assess the performance of a Hadoop cluster in MapReduce and Spark using two different clusters, one with 5 slave nodes, and another with 9 slave nodes. For the experiment, the HiBenchmark workloads WordCount and TeraSort are used with varied data scale from 50GB to 600GB. We have chosen a few different parameters and replaced their default values with the tuned values, allowing us to analyze the effects of such changes in each job's runtime. The results show that for both WordCount and Terasort workloads, depending on the tuned parameters, MapReduce and Spark achieved 64% and around 60% performance improvement at each data point. Besides, we also got slightly interesting results of speed-up progress by 1% using extra slave nodes. These results show that cluster performance can be improved by changing default values of a few parameters and adding additional slave nodes.
Effect of Training Achievement on DiSC Behavior Types of Flight Instructors and Student Pilots

This study wanted to confirm whether the DiSC behavior types of student pilots and instructors in the training course of Helicopter pilots affect the flight training achievement. The results of the study are as follows. First, in the distribution of DiSC behavior types of student pilots and instructors, student pilots had the largest number of Dominance with 40.2 percent, while Instructors had the highest number of conscientiousness with 46.5 percent. Second, it was analyzed that the flight training achievement was influenced by the behavior type of student pilots. In basic flight and aircraft type conversion flight, Steadiness had the highest achievement in flight training, and in tactical flight, conscientiousness was the highest. Third, the behavior patterns of student pilots and instructors have been confirmed to have interaction effects in tactical flight.
Cooperative Anomaly Detection Model and Real-Time Update Strategy for Industrial Stream Data

With the development of industrial Internet technology, more and more devices are brought into the industrial big data platform. To improve the efficiency of device maintenance, the industrial big data platform needs to monitor the abnormal data of the device. However, most of the current anomaly detection algorithms are offline and they can’t be updated in real-time. To solve this problem, this paper proposes an anomaly detection model for the industrial stream. The model realizes anomaly detection by cooperatively calling 3 σ and DBSCAN algorithm. The model has the advantages of low cost, fast speed, and easy to use. On this basis, this paper presents a real-time update strategy for this model, which further improves the accuracy of the model. The experimental results of water pump equipment monitoring data show the effectiveness of this method.
Applying dimension reduction in modern data science and quantitative analysis

Researchers in applied settings are often confronted with high dimensional data spaces. While more data is generally considered a good thing, researchers may also be interested in learning the structure that characterizes the data space. In this sense, the goal of the researcher is to make the complex data space simpler, and thus reduce and project it onto a lower dimensional subspace. This paper highlights the impact of the code from my manuscript forthcoming with Cambridge University Press, to bring researchers into the applied, modern landscape of dimension reduction for solving real problems using open-source code.
DenseRecognition of Spoken Languages

In the present study, we have considered a large number (27) of Indian languages for recognition from their speech signals of different sources. A dense convolutional network architecture (DenseNet) has been used for this classification task. Dynamic elimination of low energy frames from the input speech signal has been considered as a preprocessing operation. Mel-spectrogram of pre-processed speech signal is fed as input to the DenseNet architecture. Language recognition performance of this architecture has been compared with that of several state-of-the-art deep architectures which include a convolutional neural network (CNN), ResNet, CNN-BLSTM and DenseNet-BLSTM hybrid architectures. Additionally, we obtained recognition performances of a stacked BLSTM architecture fed with different sets of handcrafted features for comparison purpose. Simulations for both speaker independent and speaker dependent scenarios have been performed on two different standard datasets which include (i) IITKGP-MLILSC dataset of news clips in 27 different Indian languages and (ii) Linguistic Data Consortium (LDC) dataset of telephonic conversations in 5 different Indian languages. In each case, recognition performance of the DenseNet architecture along with Mel-spectrogram features has been found to be significantly better than all other frameworks implemented in this study.
Processing Requirements for the Lifecycle of 8K Video Content in IoT Applications

Abstract New display resolutions such as 8K bring new demands on supporting processor technology. The author analyzes, from a compute viewpoint, the typical lifecycle of video in consumer entertainment alongside an equivalent IoT market lifecycle for large‐format displays.
Optimization of Edge Extraction Algorithm for Objects in Complex Background

Abstract In the field of artificial intelligence, machine vision is expected because of its low cost and easy popularization. Feature extraction is one of the core steps of machine vision. When we use Canny algorithm, which extracts edges by gray level change, we cannot directly separate the object from the background. The edge information of the object will also be lost when the Gaussian filter smoothes the image. The research proposes an improved edge extraction algorithm based on Canny algorithm. We use the image difference method and object envelopment method to separate the object from the background, so the data to be processed is greatly reduced and the speed and accuracy of the algorithm are improved. Then we use bilateral filtering instead of Gaussian filtering, which increases the weight of gray difference on the basis of Gaussian filtering, so we can to retain more edge information. Finally, we improve the gradient operator, by determining the gradient operator coefficient according to the inverse ratio of the Euclidean distance between the pixel and the center pixel. The average performance of the new gradient operator in the center of the picture is better than that of Sobel operator. Experimental simulation shows that the algorithm has good detection accuracy. Compared with traditional algorithms, the improved algorithm is not affected by complex backgrounds, and reduce the influence of light. The improved algorithm has clear edges in the center area of the image.
Extracting of Near-bit Attitude Signals Using DWT

Steerable drilling is the key technology in oil/gas drilling development. However, the near-bit measurements are usually contaminated by noises resulting from drilling tool vibration and high-speed rotary. This study proposes a new extraction scheme, which can remove the vibration and rotary noises powerfully and extract the useful gravity measurements based on the discrete wavelet transform. Illustration examples are provided to verify the effectiveness and usefulness of our proposed measures.
Fine Grained Security in Cloud with Cryptographic Access Control

In recent years cloud services has been increasingly on demand for IT consumers due to its need for less investment towards their infrastructure. Though the architecture of cloud is robust and flexible towards new data sets, but it lags in handling the issues related to security. Especially when applied in applications related to social networks, Financial transactions. The data storage in virtual machine may rise to problems such as authentication, collusion resilience and service hijacking in cloud environment. To overcome such issues, we tried to tailor the existing KP-ABE by introducing sessions and named it as KPAC to fulfill the requirement which can be adaptable to cloud environment with concerning to the needs of security. Further, we tried to prove our approach is resilient to the attacks mentioned.
Design and implementation of the data prediction model based on PSO-ELM

Extreme Learning Machine (ELM) is currently a very popular type of machine learning algorithm, which is used to train a single layer feedforward neural network (SLFN). Aiming at the drawback that the ELM method cannot independently optimize the optimal network, this study uses the particle swarm algorithm to select the optimal hidden layer deviation and input weight matrix. The proposed PSO-ELM method is compared with the traditional ELM method in simulation. The accuracy of the prediction results on different gasoline octane content data has been improved. Finally, the conclusion and some potential research are summarized.
Quantum computing: A measurement and analysis review

Summary Computing methodologies with rapid proliferation and need for greater concurrency have evolved over the years from parallel and distributed computing to latest technologies like cloud computing. A paradigm shift in computing methodologies has been witnessed in the form of exponential speed up, optimization of problem solutions, and solvability of few classically unsolvable problems with emergence of quantum computing. The study in this article presents observations on executing basic quantum operations that play a vital role in quantum computing. The results and analysis demonstrate how parameters of time, deviation, and shots have a significant effect on outcomes of quantum circuits executed which can be a reference point for the community in general engaged in designing of quantum circuits, protocols, algorithms, and quantum hardware to leverage quantum characteristics and properties. The study by means of experimental use cases provides a rigorous review of qubit behavior, standard, and entangled qubit measurement with practical results over a simulator and real quantum hardware for variable qubit configurations using a selected platform of IBM Q Experience. With the paradigm shift from classical to quantum computing, this article provides basic but very vital observations which can be brought to use by anyone intending to build quantum applications.
Learning non-rigid surface reconstruction from spatia-temporal image patches

We present a method to reconstruct a dense spatiotemporal depth map of a non-rigidly deformable object directly from a video sequence. The estimation of depth is performed locally on spatio-temporal patches of the video, and then the full depth video of the entire shape is recovered by combining them together. Since the geometric complexity of a local spatiotemporal patch of a deforming non-rigid object is often simple enough to be faithfully represented with a parametric model, we artificially generate a database of small deforming rectangular meshes rendered with different material properties and light conditions, along with their corresponding depth videos, and use such data to train a convolutional neural network. We tested our method on both synthetic and Kinect data and experimentally observed that the reconstruction error is significantly lower than the one obtained using conventional non-rigid structure from motion approaches.
Research Findings in the Domain of Security Assurance in DevOps

This chapter of the book provides an overview of the approach and outcomes of the research project. It consists of the following sections: Research approach and deliverables provides an overview of the rationale behind the research methods selected for the project and the outcomes of the literature review. A detailed overview of the security activities is identified during the research project. This chapter contains an in-depth analysis performed for each of the security activities and design factors identified during the previous research steps.
Second compression for pixelated images under edge-based compression algorithms: JPEG-LS as an example

This paper details the examination of a particular case of data compression, where the compression algorithm removes the redundancy from data, which occurs when edge-based compression algorithms compress (previously compressed) pixelated images. The newly created redundancy can be removed using another round of compression. This work utilized the JPEG-LS as an example of an edge-based compression algorithm for compressing pixelated images. The output of this process was subjected to another round of compression using a more robust but slower compressor (PAQ8f). The compression ratio of the second compression was, on average, 18%, which is high for random data. The results of the second compression were superior to the lossy JPEG. Under the used data set, lossy JPEG needs to sacrifice 10% on average to realize nearly total lossless compression ratios of the two-successive compressions. To generalize the results, fast general-purpose compression algorithms (7z, bz2, and Gzip) were used too.
Lifelong Classification in Open World With Limited Storage Requirements

Abstract This letter focuses on the problem of lifelong classification in the open world, the goal of which is to achieve an endless process of learning. However, incremental data sets (like the streaming data) in the open world, where the new classes may be emerging, are unsuited for classical classification methods. For addressing this problem, existing methods usually retrain the whole observed data sets with the complex computation and the expensive storage cost. This letter attempts to improve the performance of classification in the open world and decomposes the problem into three subproblems: (1) to reject unknown instances, (2) to classify accepted instances, and (3) to cut the cost of learning. Rejecting unknown instances refers to recognize those instances whose classes are unknown according to the learner, which could reduce the computation of the retraining process and eliminate the storage of historical data sets. We employ outlier detection for rejecting instances and a variant artificial neural network for classifying with fewer weights. Results on several experiments show that the work is effective. Source code can be found at https://github.com/wangbi1988/Lifelong-learning-in-Open-World-Classification.
Learning embeddings for cross-time geographic areas represented as graphs

Geographic entities from the vertical aerial images can be viewed as discrete objects and represented as nodes in a graph, linked to each other by edges capturing their spatial relationships. Over time, the natural and man made landscape may evolve and thus also their graph representations.
Energy and SLA-driven MapReduce Job Scheduling Framework for Cloud-based Cyber-Physical Systems

Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.
Analysis and fast feature selection technique for real-time face detection materials using modified region optimized convolutional neural network

For face-related applications like face recognition and face verification, face detection is vital. Law enforcement agencies extensively use face recognition applications for the detection of known offenders. Security cameras are increasingly deployed everywhere, and these cameras’ captured data can be used for tagging offenders. This is done by offering the face detection system with input images, which are then processed. Later on, attempts will be done to tag the security cameras’ captured data with this processed input data. Effective utilization of deep learning techniques in order to efficiently carry out this task. Computer vision’s various tasks have been taken over by deep Convolutional Neural Networks (CNNs). Of late, the key paradigms for face detection are the region-based CNN (R-CNN) detection models due to their quicker processing speed and progressively better performance. Nevertheless, the huge feature set causes these models’ to be inefficient for real-time face detection. A Grammatical Evolution (GE) Region-Based Fully Conventional Neural Network (R-FCN) for face detection has been proposed in this work in which the GE heuristics are utilized for tuning the hyper-parameters. The WIDER face dataset and Face Detection Dataset and Benchmark (FDDB) dataset were employed for conducting the assessments. In comparison to other advanced paradigms, the proposed technique’s superiority is evident from the experiments conducted.
Unsupervised Domain Adaptation for Object Detection in Cultural Sites

The ability to detect objects in cultural sites from the egocentric point of view of the user can enable interesting applications for both the visitors and the manager of the site. Unfortunately, current object detection algorithms have to be trained on large amounts of labeled data, the collection of which is costly and time-consuming. While synthetic data generated from the 3D model of the cultural site can be used to train object detection algorithms, a significant drop in performance is generally observed when such algorithms are deployed to work with real images. In this paper, we consider the problem of unsupervised domain adaptation for object detection in cultural sites. Specifically, we assume the availability of synthetic labeled images and real unlabeled images for training. To study the problem, we propose a dataset containing 75244 synthetic and 2190 real images with annotations for 16 different artworks. We hence investigate different domain adaptation techniques based on image-to-image translation and feature alignment. Our analysis points out that such techniques can be useful to address the domain adaptation issue, while there is still plenty of space for improvement on the proposed dataset. We release the dataset at our web page to encourage research on this challenging topic: https://iplab.dmi.unict.it/EGO-CH-OBJ-ADAPT/.
The generative adversarial network improved by channel relationship learning mechanisms

Although recent deep generative models are able to generate high-resolution, diverse natural samples from complex datasets, the generated samples still exist some problems in terms of images structure and detailed texture. In this paper, we propose a novel network architecture–SEDA-GAN that can learn the potential relationship in the dimension of the channel to enhance the generation performance of GAN. The proposed architecture applies Squeeze-and-Excitation(SE) block for feature recalibration to model channel-interdependencies within the GAN feature, and it also incorporates a dual-attention(DA) model with a channel attention mechanism in the GAN framework that can obtain global dependencies between channels. After conducting some comparative experiments on CIFAR and ImageNet datasets by using model BIGGAN as a baseline, our model performance has a certain improvement when evaluating on Fr e ́ chet Inception Distance(FID) and Inception Score(IS) respectively.
An Algorithm Controlling Response Time of Multi-Service Application Based On Bayesian Optimization

In the research field of cloud auto-scaling, controlling application-level response time through resource provision to meet the predefined threshold is vital. But there still exists many challenges in the multi-service application scenario. This paper first points out that, in a multi-service application, increasing or decreasing the instance number of one service may affect other services' response time. This phenomena brings more complexity in designing an auto-scaling strategy as the application-level response time is constructed and affected by the time of every service in it. To hide those affections among internal services, the change in application-level response time under various services' instance number configurations is modelled as a black-box optimization problem. Then, an improved Bayesian Optimization approach is proposed to resolve it. As the improved part, a heuristic method will dynamically and gradually expand the search space according to each service's response time. So a group of services can be scaled simultaneously and more than one instance can be added into a service at one scaling action. The experiments results conducted on the popular kubernetes platform indicate that our method is faster than other traditional ones in controlling the application-level response time down to the predefined threshold value.
Intelligent Data Processing of Marine Target Tracking Process Based on Fuzzy clustering

Aiming at the inaccurate tracking and the difficulty of the track initialization during multi-target tracking in marine rescue scenes, this article proposes an intelligent processing method for marine tracking data based on fuzzy clustering. The measurement information received by the sensor of the measuring device is subjected to the fuzzy clustering process in a period of time, then the entropy from the subjection matrix is used to judge the change in the number of targets. In this way, the number of the target quantity can be determined and the target can be tracked more accurate. This article also analyzes and improves the method through simulation experiments to solve to problems such as inaccurate clustering in real situations. And the method can meet the needs of tracking velocity and tracking accuracy.
Reducing Wrong Labels using Conflict Score in Distant Supervision for Relation Extraction in Bangla Language

The research area of information extraction (IE) aims to extract structured information such as types of entities and relations between them, from unstructured textual data like newswires, blogs, governmental documents etc. Relation extraction (RE) deals with the automatic detection of relationships between concepts mentioned in free texts. Knowledge-based distant supervision (DS) uses structured data to heuristically label a training corpus. However, this heuristic can generate some noisy labeled data. In this paper, we propose a method using conflict score in DS to reduce the number of wrong labels for Bangla sentences.