Reviewerly/exHarmony huggingface dataset:
Automatic Text Simplification Using LSTM Encoder Decoder Model

The text simplification is a process of simplifying the natural language in such a way that it became easier to understand and to read the language. Text simplification is a part of the natural language processing which is a vast field of research. Here we have proposed several models of machine learning to achieve our goal. The text simplification process can be of any type, it may involve syntax simplification or can involve semantic simplification. In our paper we have taken a historical dataset named Declaration of Independence. The data set is containing 15,000 words to train and test our models to achieve best result. The process of text simplification is similar to the process of text summarization but not same. In the process of text summarization we mainly focus on summarizing the text which may involve minimizing the length of the word. But in text simplification process we focus on how to simplify the text to make it easier to read and understand the language. Sometimes the results of text simplification can be longer while reducing the explanation for the difficult text. Here in this paper we have used several methods of machine learning like Naive Bayes Classifier, LSTM Network and LSTM Encoding Decoding to develop our model and also to measure the performance of these methods. We observed that LSTM Encoder Decoder Network out break the others by achieving highest accuracy with 87%.
Virtual-SRE For Monitoring Large Scale Time-series Data

Monitoring online time-series data and providing real-time alerts are crucial for a variety of industry applications. But doing it at a large scale is a challenging task. In this paper, we introduce Virtual-SRE, an anomaly detection framework which utilizes human knowledge and is suitable for large-scale applications. The proposed approach starts without any human labeling. Instead, human knowledge is obtained through feedbacks. A joint data and model optimization process is proposed to ensure that accurate models can be built. This makes it easily applicable to large-scale and heterogeneous tasks while enjoying the benefits of supervised learning. The proposed framework has been deployed to monitor thousands of time-series data streams and obtained favorable results.
Gradient, Texture Driven Based Dynamic-Histogram Equalization For Medical Image Enhancement

In many clinical diagnostic measurements, medical images play some significant role but often suffer from various types of noise and low-luminance, which causes some notable changes in overall system accuracy with misdiagnosis rate. To improve the visual appearance of object regions in medical images, image enhancement techniques are used as potential pre-processing techniques. Due to its simplicity and easiness of implementation, histogram equalization is widely preferred in many applications. But due to its mapping function based image transformation during enhancement process affect the biomedical patterns which are essential for diagnosis. To mitigate these issues in medical images, a new method based on gradient computations and Texture Driven based Dynamic histogram equalization (GTDDHE) is accomplished to increase the visual perception. The spatial texture pattern is also included to ensure the texture retention and associated control over its variations during histogram modifications. Experimental results on MRI, CT images, eyes images from medical image datasets and quantitative analysis by PSNR, structural similarity index measurement (SSIM), information entropy (IE) and validated that the proposed method offers improved quality with maximum retention of biomedical patterns across all types of medical images.
2D IMAGE OBJECT DETECTION AIDED BY GENERATIVE ADVERSARIAL NETWORKS A LITERATURE REVIEW

Object Detection (OD) is one of the most important tasks in 2D image processing.Multiple math models have been proposed and frameworks based in Deep Convolutional Networks such as R-CNN, SSD and YOLO are most common.Generative Adversarial Nets (GAN's) represent a prominent field of study in machine learning and it has been applied to many tasks with exciting results.The objective of this work is to assess the potential of GAN's applied to OD tasks and the proposed frameworks as field of study.The methodology used was a systemically review of 14 papers.The conclusion shows that even though OD and GAN's are popular themes, there are not many developments done in the intersection of both subjects.Therefore, OD with GAN applied tasks are an excellent field to explore in future works.
Utilizing Blockchain Technology in the Supply Chain Management in New Zealand Manufacturing: The Theoretical Perspective

Blockchain technologies have been established and developed for a range of applications. Despite advancements in the development of systems, the Blockchain still re-quires additional development from a New Zealand perspective. Additionally, the Blockchain is the only known cryptographic Block. It can significantly enhance man-factures' supply chain management, the Internet of Things, and industrial solutions. The author completed many New Zealand efforts involving entrepreneurial networks and supply chains. The current increase in interest in Blockchain technology and its ability to track all sorts of transactions more openly and securely motivates us to in-vestige the supply chain potential that Blockchain provides. This study determines whether Blockchain is a suitable fit for usage in the supply chain of a New Zealand business. To address this, the research employs a paradigm from the literature called the Technology Acceptance Model. The Blockchain enables us to create permissioned or nonpermissioned distributed ledgers in which stakeholders may interact. It explains how Blockchain works and the hash algorithm technique that enables information to be more secure. Additionally, it focuses on supply chain management and the com-laxities of a manufacturer's supply chain. We summarize the procedures followed by a manufacturer and the issues encountered along the supply chain. A methodology is presented for resolving these concerns using public and private blockchains. The suggested approach can increase openness and validity across the supply chain and im-prove communication between the various stakeholders. Additionally, we highlight several concerns that should be addressed before using Blockchain
Data Centers

The requirements on modern data centers grow continuously. An integration of wireless links at THz! frequencies promise a high data rate and new flexibility. In this book chapter, we summarize the benefits and challenges of THz! communication in a data center and present channel measurements for the ToR! and IR! use case. It is shown that multipath propagation in combination with high side lobes from beam-steerable antennas might cause ISI! even in directional point-to-point communication.
Deep plug-and-play and deep unfolding methods for image restoration

Model-based methods and learning-based methods have been the two dominant strategies for solving various image restoration problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based methods are flexible for handling different image restoration problems but are usually time-consuming with sophisticated priors for the purpose of good performance; meanwhile, learning-based methods are continuously showing superior effectiveness and efficiency over traditional model-based methods, largely due to the end-to-end training, but generally lack the flexibility to handle different image restoration tasks. In this chapter, we attempt to provide a gentle introduction to deep plug-and-play methods and deep unfolding methods, which have shown great promise by leveraging both learning-based methods and model-based methods. The main idea of deep plug-and-play methods is that, with the aid of variable splitting techniques, a learning-based denoiser can implicitly serve as the image prior for model-based image restoration methods, while the main idea of deep unfolding methods is that, by unfolding the model-based methods via variable splitting algorithms, an end-to-end trainable, iterative network can be obtained by replacing the corresponding subproblems with neural modules. As a result, the deep plug-and-play methods and deep unfolding methods can inherit the flexibility of model-based methods, while maintaining the advantages of learning-based methods. Experimental results on two representative image restoration tasks, including deblurring and superresolution, demonstrate the flexibility and effectiveness of deep plug-and-play methods and deep unfolding methods.
A Multi-Reservoir Echo State Network with Multiple-Size Input Time Slices for Nonlinear Time-Series Prediction

A novel multi-reservoir echo state network incorporating the scheme of extracting features from multiple-size input time slices is proposed in this paper. The proposed model, Multi-size Input Time Slices Echo State Network (MITSESN), uses multiple reservoirs, each of which extracts features from each of the multiple input time slices of different sizes. We compare the prediction performances of MITSESN with those of the standard echo state network and the grouped echo state network on three benchmark nonlinear time-series datasets to show the effectiveness of our proposed model. Moreover, we analyze the richness of reservoir dynamics of all the tested models and find that our proposed model can generate temporal features with less linear redundancies under the same parameter settings, which provides an explanation about why our proposed model can outperform the other models to be compared on the nonlinear time-series prediction tasks.
Facial Age Progression using Conditional Generative Adversarial Network with Heritable Visual Features

Age progression of face images has been an important tool to search for missing children. Many studies on age progression were recently conducted by conditional Generative Adversarial Networks (cGAN) based methods. However, these methods cannot estimate facial aging from a child’s face in the early childhood stage, which exhibits drastic facial shape changes over time. Thus, the problem of age progression from young children remains challenging in the field. In this study, we propose a cGAN based two-stage age progression model considering heritable facial features from parents and child to generate candidates for age-progressed face images from a young child’s face image.
Research on Person Re-identification Algorithm based on Attention and Pose Estimation

Pedestrian re-identification (re-ID) refers to the technology of re identifying the currently photographed pedestrian in other visual fields in the non-overlapping monitoring field. However, in the re-ID dataset, the pose of pedestrians in different images varies greatly. Therefore, this paper designs a re-ID algorithm based on attention and pose estimation. The attention discrimination module in the algorithm can learn the weight importance of features in channel and spatial dimension, and apply it to the global feature vector to obtain the global feature vector with good robustness and strong discrimination. Another pose enhancement module can enhance the foreground features of the image based on the position of key points, and divide the local features of pedestrian components. The two network modules start from extracting global features and local features of image respectively, and jointly optimize to form a multi granularity end-to-end network model. In addition, our algorithm uses the pretrained ResNet50 model as backbone to learn deep features of images, and uses the Adam optimization algorithm to minimize the loss function. Experimental results on Market-1501 dataset and DukeMTMC-reID dataset show that our algorithm can achieve better retrieval performance.
Mining On-shelf High-utility Quantitative Itemsets

A recently emerged branch of utility-based research, called high-utility quantitative itemset mining (HUQIM), has been widely applied in real-life, and it considers not only the utility factor but also the quantity with ranges of itemsets. However, most existing utility-mining algorithms assume that pat-terns always appear regardless of the period. For instance, some products may sell well at certain times of the year. Considering the rich information in the database, such as quantity and time, we propose an effective and efficient approach for discovering on-shelf high-utility quantitative itemsets (OHUQIs). To avoid scanning the database multiple times, we adopt a data structure to maintain some necessary information, and thus, OHUQI only accesses the database twice. Several pruning strategies are also designed to prune a large number of unpromising itemsets in advance to shrink the search space. Finally, the subsequent experimental results show that OHUQI performs well on several real-world datasets.
Study on the Impact of Gamification on the Continuous Use of Online Education Users: A Stimulus-Organism-Response (S-O-R) Perspective

近几年在线教育快速发展, 特别是随着COVID-19的爆发, 越来越多的⼈开始通过在线教育平台进行学习。本研究从在线教育平台中的游戏元素切入, 分析影响⽤户持续使用的因素。基于刺激机体反应框架(S-O-R Framework), 在先行研究的基础上, 提出假设, 建⽴研究模型。对在线教育⽤户进⾏问卷调查后, 使⽤PLS-SEM以及SmartPls3.0进行假设验证。研究结果表明游戏化元素对在线教育用户的享受和持续使用有显著的积极影响, 同时发现用户的适应性显著的正向调节了游戏化元素和用户享受之间的关系。研究指出, 为了更好地激发用户兴趣, 在线教育企业应结合课程内容和用户特点, 精心设计游戏化体验。
Optimized method of subset generation for ensemble classification

Multiclass learning problems involve training classification models to accurately classify data instances into class labels. The number of class labels that are incorporated in a classification model directly affects its training time and accuracy. It has been noted that the existing models, when used on larger datasets having an extensive number of classes, often fail in achieving an accuracy usable for functional, real-world scenarios and tend to overfit on training data. Present research attempts to optimize the performance of classifiers by creating custom classification strategies, and neural network architectures for specific classification tasks and datasets. The efficiency of one such optimization of classifiers, classifier ensembles can further be improved by altering methods of subset generation from the dataset and correspondingly, class prediction. In this regard, we propose a method of constructing ensembles which aims to increase the accuracy of any given classifier while reducing training time by using a two step approach- one step for group formation as a part of constructing ensembles, and another for relative probability calculations which combines the result of ensembles. The proposed method is implemented and experiments are done on the Fashion MNIST (10 class labels), NIH Chest X-Ray + COVID-19 (16 class labels), Kuzushiji-49 (49 class labels), and CIFAR-100 (100 class labels) datasets. Experimental results show the efficacy of the proposed method which has achieved an increase of 6.33%, 9.32%, 8.59%, and 12.27% in accuracies of respective datasets and at the end, results are debated.
Scalable Outlier Detection Using Distance Projections

Data exploration is a critical component in data science. However, this has become increasingly more difficult for a large, high-dimensional dataset that contains dirty records. In this paper, we propose a solution for detecting outliers in large high-dimensional datasets using distance-based projections. Our proposed solution first maps the high-dimensional data to multiple one dimension values using their distances to a random set of reference points. For each projected value of a data point, we compute a local outlier factor score and then obtain a combined score that can be used to detect outliers. Our solution is computationally much cheaper than traditional methods. We demonstrate the effectiveness of our solutions using both simulation and real data studies.
Privacy Preserving Mining Sequential Pattern based on Random Data Perturbation

Privacy preserving data mining is a hot research direction of data mining in the big data environment. If Data mining has been used properly, it will endanger the privacy and the information. Inspired by that, we researched a method via random data perturbation method to keep privacy preserving in sequential patterns mining. Its strategy is as follows: First, noisy events are added into each event sequence of the original database. Then, an algorithm PP-Span (Privacy-Preserving Sequential pattern mining) is proposed to reconstruct the frequent sequences from these noise-added data sequences. Our results showed that it can outperform a lot of traditional methods.
DRP-DBAS: Dynamic Resource Provisioning for Deadline and Budget Aware Workflow Scheduling in IaaS Clouds

The cloud being the latest technology, provides immense opportunities to solve large-scale scientific problems. This computing paradigm’s success enables the IaaS provider to offer infinite resources based on the pay-per-use model. Although the workflow scheduling problem has been widely investigated, however, most of them are concerned with a single QoS constraint and ignores the consideration of multiple QoS constrained problems. Therefore, this paper proposes a novel strategy of dynamic resource provisioning and scheduling approach for deadline-budget constrained workflows (DRP-DBAS). The algorithm intends to minimize makespan while subject to deadline and budget constraints for the hourly-based cost model of the IaaS cloud. For resource provisioning, the number of instances leased is based on the budget available. For the scheduling, the HEFT algorithm has been extended with the deadline and budget constraint. Further, the new scheduling approach incorporates the clustering mechanism to cluster the pipelined task, which reduces the overall execution time and enhances the algorithm’s performance. The DRP-DBAS is compared against the existing BDSD, BDAS, and GRP-HEFT and the obtained result proves the efficacy of the DRP-DBAS algorithm. DRP-DBAS outperforms the other algorithm by achieving a PSR of 56.11%, followed by GRP-HEFT with PSR 44.61%, BDAS with PSR 39.13%, and BDSD with PSR 26.13%.
Development of Online Supervision System for Industrial Education Pre-Teachers

In case of coronavirus disease starting in 2019 situations, it is a novel syndrome virus which caused due to the outbreak of highly dangerous. This virus is easily spreadable to human by human. The one of measures for prevention for the human in coronavirus disease situations that the human has been working from home and decreasing face-to-face contacted. This paper presents the development of online supervision system that instead of traditional supervision or onsite face-to-face supervision for industrial education pre-teacher students. It has been designed on web application to convenient for supervisors, mentors and pre-teacher students. They will send about report, assignment and teaching video on the system. Supervisors and mentors can comment, check the report or assignment and approve pre-teacher video teaching on the web daily or weekly. In addition, the proposed system can be decreased the expenses for official travel and a lot of papers when compare with traditional supervision or onsite face-to-face supervision.
Answering Fuzzy Queries over Fuzzy DL-Lite Ontologies

Abstract A prominent problem in knowledge representation is how to answer queries taking into account also the implicit consequences of an ontology representing domain knowledge. While this problem has been widely studied within the realm of description logic ontologies, it has been surprisingly neglected within the context of vague or imprecise knowledge, particularly from the point of view of mathematical fuzzy logic. In this paper, we study the problem of answering conjunctive queries and threshold queries w.r.t. ontologies in fuzzy DL-Lite. Specifically, we show through a rewriting approach that threshold query answering w.r.t. consistent ontologies remains in ${AC}^{0}$ in data complexity, but that conjunctive query answering is highly dependent on the selected triangular norm, which has an impact on the underlying semantics. For the idempotent Gödel t-norm, we provide an effective method based on a reduction to the classical case.
Range-Measurement Sensor to Improve the Authentication Workflow for Users of a Hospital Information System: A Proof-of-Concept Study

In this study, we developed an authentication rangefinder (AR) system for hospital information system (HIS) terminals to support the user authentication workflows. The logoff process of the AR system is triggered if no object is placed at least 90 cm in front of the HIS terminal laptop for ≥5 s. We conducted an anonymous survey of medical staff who used the AR system. 33/42(78%) respondents acknowledged an improvement in the logoff process. This study indicates that the AR system improves the user authentication workflow.
Machine Learning Classification of Malicious Resident Space Objects

In the increasingly contested space domain, the threat of intentional and unintentional space access degradation and collisions becomes more likely and more difficult to anticipate with current space situational awareness tools. In this paper, an initial space mission classification hierarchy is proposed alongside a framework for identifying, classifying, and contextualizing potentially unobserved threats to vital space assets. This framework incorporates several levels of applied machine learning to interpret and add context to patterns in satellite data, including the use of Random Forests for time series analysis. By walking through the application of the framework to several threat scenario case studies, the preliminary usefulness of a threat identification tool to the space operations community is proven out.
The Development Process of a Quick SMS Auto Responder App for Car Drivers or Busy People

The aim of this work is to redefine SMS messaging. Sometimes a person cannot respond to an SMS, since has no possibility to do this, because he is driving a car, or he is doing something and has no free hands, for example, when driving a car. To respond to an SMS can be done with an app which has predefined messages and this way the sender can get a quick response, but the receiver can continue his previous task, like driving a car or doing other things. When receiving an SMS message, the receiver can reply to it just by tapping on a predefined message.