Reviewerly/exHarmony huggingface dataset:
Monitoring And Controlling Of IoT Based Solar Wind Hybrid System

As the demand for non-conventional recourses is increasing every day. It is necessary to increase the power production and installation of non-conventional power plants. It is not economical. It explains a combination of solar and wind systems called a solar wind hybrid system, power monitoring and controlling. Present Windmills and solar plants have several obstacles. Many wind farms are far from power using regions and the solar plant requires occupies more space; tracking these farms are difficult to manage and maintain. Wind farms could be built deepinside of the sea, possibly between mountains or in the forests. When small defects arise, employees are needed to travel to certain places to clear the fault. It necessitates a large amount of manpower, time, and is fraught with financial difficulties. Similar techniques are used to take readings of a specific plant.The IOT concept can be used to resolve these limitations. For data logging, most current products use a memory card or a computer. This knowledge is most easily accessible on personal computer. IoT may also help with this problem. A low-cost monitoring and control system is proposed in this paper for wind turbines and solar plants, as well as a statistic logging feature. Any of the parameters of windmill plants, such aswith these proposed devices, power generation, magnitude, voltage and currents, humidity and temperature can all be regulated and used anywhere in the world.
An Efficient Recommender System Based on Collaborative Filtering Recommendation and Cluster Ensemble

In the last few years, cluster ensembles have emerged as powerful techniques that integrate multiple clustering methods into recommender systems. Such integration leads to improving the performance, quality and the accuracy of the generated recommendations. This paper proposes a novel recommender system based on a cluster ensemble technique for big data. The proposed system incorporates the collaborative filtering recommendation technique and the cluster ensemble to improve the system performance. Besides, it integrates the Expectation-Maximization method and the HyperGraph Partitioning Algorithm to generate new recommendations and enhance the overall accuracy. We use two real-world datasets to evaluate our system: TED Talks and MovieLens. The experimental results show that the proposed system outperforms the traditional methods that utilize single clustering techniques in terms of recommendation quality and predictive accuracy. Most importantly, the results indicate that the proposed system provides the highest precision, recall, accuracy, F1, and the lowest Root Mean Square Error regardless of the used similarity strategy.
Survey of UAV Path Planning Based on Swarm Intelligence Optimization

Nowadays, the unmanned aerial vehicle (UAV) is widely used in various military and civil scenarios due to its advantages of flexibility, low cost and expansibility. As a critical technology to improve the degree of UAV autonomy, path planning has become a hot issue in recent years. UAV path planning is an optimization problem with a series of constraints essentially, whose optimal or sub-optimal solution can be obtained by swarm intelligence (SI) optimization with the superiorities of expansibility, parallel processing and compatibility for the UAV group. Therefore, this paper summarizes UAV path planning approaches based on SI optimization in recent years, and analyzes application dimension, ability of avoiding local optimum, expansibility for UAV group and real-time performance of different approaches. Finally, we suggest the applicable scenarios of various algorithms.
An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing

The classification of a respondent's opinions online into positive and negative classes using a minimal number of questions is gradually changing and helps turn techniques into practices. A survey incorporating convolutional neural networks (CNNs) into web-based computerized adaptive testing (CAT) was used to collect perceptions on My Health Bank (MHB) from users in Taiwan. This study designed an online module to accurately and efficiently turn a respondent's perceptions into positive and negative classes using CNNs and web-based CAT.In all, 640 patients, family members, and caregivers with ages ranging from 20 to 70 years who were registered MHB users were invited to complete a 3-domain, 26-item, 5-category questionnaire asking about their perceptions on MHB (PMHB26) in 2019. The CNN algorithm and k-means clustering were used for dividing respondents into 2 classes of unsatisfied and satisfied classes and building a PMHB26 predictive model to estimate parameters. Exploratory factor analysis, the Rasch model, and descriptive statistics were used to examine the demographic characteristics and PMHB26 factors that were suitable for use in CNNs and Rasch multidimensional CAT (MCAT). An application was then designed to classify MHB perceptions.We found that 3 construct factors were extracted from PMHB26. The reliability of PMHB26 for each subscale beyond 0.94 was evident based on internal consistency and stability in the data. We further found the following: the accuracy of PMHB26 with CNN yields a higher accuracy rate (0.98) with an area under the curve of 0.98 (95% confidence interval, 0.97-0.99) based on the 391 returned questionnaires; and for the efficiency, approximately one-third of the items were not necessary to answer in reducing the respondents' burdens using Rasch MCAT.The PMHB26 CNN model, combined with the Rasch online MCAT, is recommended for improving the accuracy and efficiency of classifying patients' perceptions of MHB utility. An application developed for helping respondents self-assess the MHB cocreation of value can be applied to other surveys in the future.
DECA: DoD Enterprise Cloud Architecture Concept for Cloud-Based Cross Domain Solutions

The Department of Defense (DoD) battlefield exists both in cyber and the physical world. Information sharing is a top priority for the DoD in support of our warfighters and allies. To maintain military technological advantage and superiority, access to information and the capacity to process it are critical components for empowering the warfighter for mission success. The volume of information shared has increased exponentially, necessitating the development of a DoD enterprise cloud capable of sustaining, and supporting strategic worldwide DoD missions through effective information sharing. However, the existing U.S. Government cloud design does not support enterprise use, and legacy software and hardware applications such as a Cross Domain Solution (CDS) will need to be re-architected, certified, accredited, and authorized for future enterprise cloud use. A CDS is a requirement for information sharing in both unclassified and classified systems and information transmission from one system to another, but there must also be a DoD enterprise cloud structure to leverage the CDS in the U.S. Government cloud. The purpose of this research is to explore the future possibilities of using an enterprise cloud CDS and to present a conceptual design for a DoD enterprise cloud architecture that will save the DoD time and money in the certification process while also allowing efficient information sharing across multiple DoD Command and Control (C2) systems. To have this architecture design approved and accredited by the DoD for future use, we adhere to the Federal Risk and Authorization Management Program (FedRAMP), a process required for federal agency cloud deployments and the National Institute of Standards and Technology (NIST) standards. We use existing systems from across the DoD and allies found in the open literature as a baseline.
Real-Time Face Mask Detection Using CNN in Covid-19 Aspect

The COVID-19 pandemic has reshaped everyone’s life as we know it. Many measures and proposals have been suggested to prevent the virus’s rapid spread since scientists detected it. In the midst of many of these, one major piece of advice was to “wear a mask!”. The mask or face coverings can protect the wearer from contracting the virus or spreading it to others. Hence, to ensure that the public is not wearing a mask, one of the applications we have developed is face mask detection, which uses biometrics to map the features of a human face, analyse the data, and determine whether the detected face is a positive image or a non-face or negative image by placing a box around it. The main ideology of studying and researching face mask detection is to use computer technology to directly mimic the action and manner of human facial features and develop a system that reduces the amount of effort required to identify whether or not the person is wearing a mask and notifying them with the assistance of the proposed model.
Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data

In many real-world machine learning applications, samples belong to a set of domains e.g., for product reviews each review belongs to a product category.In this paper, we study multi-domain imbalanced learning (MIL), the scenario that there is imbalance not only in classes but also in domains.In the MIL setting, different domains exhibit different patterns and there is a varying degree of similarity and divergence among domains posing opportunities and challenges for transfer learning especially when faced with limited or insufficient training data.We propose a novel domain-aware contrastive knowledge transfer method called DCMI to (1) identify the shared domain knowledge to encourage positive transfer among similar domains (in particular from head domains to tail domains); (2) isolate the domain-specific knowledge to minimize the negative transfer from dissimilar domains.We evaluated the performance of DCMI on three different datasets showing significant improvements in different MIL scenarios.
Understanding Domain Knowledge in Initialization Method for K-Mean Clustering Algorithm in Medical Images

This work serves as a preliminary study to investigate and identify the applicability of domain knowledge as an initialization method for K-Mean (KM), typically in medical images. For this purpose, 20 breast histopathology images were used as data set and the evaluations are focused on the clustering of the hyperchromatic nucleus. The iteration numbers and clustering results (i.e., accuracy, over-segmentation, and under-segmentation) are benchmarked with KM++ and the conventional random initialization method. The domain knowledge initialization method is found promising by achieving lower iteration numbers (<9), higher percentage in accuracy (85.5% (±2.27)), and lower percentages in over-segmentation (8.25% (±2.23)), and under-segmentation (7.00% (±2.14)). From this study, we hypothesize that the domain knowledge initialization method has the potential to be implemented as an initialization method and is posited to overperform some established initialization methods, typically for clustering tasks in medical images.
Web Recommendation by Exploiting User Profile and Collaborative Filtering

Collaborative filtering is a methodology which thinks about the profile of the active client as well as the neighborhood of the active client with comparable inclinations, while suggesting the things. Collaborative filtering implies that individuals work together to help each other in the documents they access, by utilizing their responses. In the recent period, the recommender framework has been utilized by huge number of specialists to perk up the web search. Content based methodology is an additional method used in recommender frameworks. To make better recommendations, in this work, we aim to focus around client preferences as opposed to item/product data and exploit collaborative filtering. We build and update the user profiles for representing users' information. We propose a recommendation system based on preferences of similar users. We also derive the concealed feature that would be the main source of user preference. We apply rigorous IR benchmarking processes to evaluate the efficiency and robustness of the proposed approaches.
Predicting Invasive Ductal Carcinoma Based on Convolutional Neural Network

Since the last century, the diagnosis of cancer has been a difficult problem among the world. Currently, the diagnosis of cancer is mainly made by medical experts that they need to speed a lot of time to observe pathological patches. However, this method requires tedious work and is insufficient for diagnosing all patients in low- and middle-income countries that lack medical resources. Which will result the delays in the diagnosis of the patient's condition and decreases the survival rate of patients. Therefore, I created a Convolutional Neural Network (CNN) model to automatically diagnose Invasive Ductal Carcinoma (IDC) in order to help medical experts reduce tedious work. I created a CNN-based model with nine 2D-convolutional layers and three max-pooling layers. The data passing through these layers will eventually output a dichotomous result, i.e., IDC or non-IDC. Then, I chose a dataset of 162 patients from Kaggle to test the performance of the model. The result is satisfactory, the model has 88% accuracy and 95% Area Under Curve (AUC) score in the automatic diagnosis of IDC. Based on this promising result, the model performance well to solve the problem that automatically diagnose IDC for the purpose of assisting medical expert.
Managing, storing, and sharing long-form recordings and their annotations

The technique of long-form recordings via wearables is gaining momentum in different fields of research, notably linguistics and neurology. This technique, however, poses several technical challenges, some of which are amplified by the peculiarities of the data, including their sensitivity and their volume. In this paper, we begin by outlining key problems related to the management, storage, and sharing of the corpora that emerge when using this technique. We continue by proposing a multi-component solution to these problems, specifically in the case of daylong recordings of children. As part of this solution, we release ChildProject, a Python package for performing the operations typically required by such datasets and for evaluating the reliability of annotations using a number of measures commonly used in speech processing and linguistics. This package builds upon an annotation management system, which allows the importation of annotations from a wide range of existing formats, as well as upon data validation procedures, which assert the conformity of the data, or, alternatively, produce detailed and explicit error reports. Our proposal could be generalized to populations other than children and beyond linguistics.
Bi-Objective Search Method for Bayesian Network Structure Learning

Bayesian network (BN) is a probability graph model, which makes uncertain reasoning logically clearer and more understandable. Structure learning is the first step to learn a BN model. And the score + search methods are a kind of the effective methods to learn the structure. This paper proposes a Bi-Objective Search (BOS) method for Bayesian network structure learning, which considers two objectives, i.e., the log-likelihood score and network complexity. To avoid the illegal structures, BOS samples edges and generates permutations to add directions to the edges for the initial population. To improve the diversity, BOS designs the genetic operators to generate new solutions. The new approach is applied to a set of discrete Bayesian networks, and the experimental results show that the algorithm is superior to the existing algorithms in BN structure learning.
Micro Activities Recognition and Macro Worksteps Classification for Industrial IoT Processes

Automated understanding of worksteps in industrial assembly work is important for IoT-based assistive guidance technologies in employee-machine collaboration and for industrial IoT (IIoT) environments. Our aim is to identify macro worksteps using depth images and micro activities of employees during the assembly of ATM machines for auxiliary purposes in their daily complex tasks using hand-operated tools. Due to the advance of inertial measurement unit (IMU) technologies and pattern recognition systems [11], IMU based sensing together with machine learning have gained momentum on workstep recognition and were selected for this study in combination with a depth camera sensor which is mounted on a ceiling with a top-down angle. In this work the focus is at a seamless embedding of non-impeding body-worn IMUs or their integration into smart tools and IoT devices, and the depth sensor ensuring the privacy of the operators[5][7][8], allowing for unobstructed monitoring of tools’ usage pattern and thus assembly workstep recognition. The results for this study are evidenced with empirical observations of assembly workstep executions by (i) hand screwing, (ii) screw driver screwing, (iii) machine screwing, (iv) wrench screwing, with the null class being disproportionally dominant in the data set. Deep Learning models including LSTM, Temporal Convolutional Networks (TCN) and CNN architectures are proposed for the detection of micro activities and macro worksteps and the identification of the current workstep which will be beneficial also for the recognition of the transition between each two consecutive macro worksteps. A sophisticated counting mechanism of the classified activities is recognized as the next research challenge, with the extraction of features from each IMU sensor and the temporal information from the depth sensor, integrated in an IoT system.
Blockchain Based Model for Consent Management and Data Transparency Assurance

Data privacy issues and regulations are on the rise. Privacy and transparency concerns are quickly defining a new era in data management and service operation across applications, platforms and industries. With a continuous flow of revisited privacy laws worldwide, adopted in different regulations such as the General Data Protection Regulation (GDPR), the urge to enhance transparency, and provide efficient and dynamic control over individual consent to use personal data, has peaked. Importantly, data-centered service providers must implement tools and integrate frameworks that are accessible, with reliable updates and viability assurances across the entire information life cycle. Data transparency and consent management platforms become then an essential tool that allows service providers to communicate their privacy practices and build up users' trust, while giving consumers enhanced control over their privacy preferences. In this context, we present a consent management system based on multiple blockchains. We assure a scalable user-friendly solution using a web-based blockchain model, performing a decentralized solution and a separation between the private data and consent data for more security and privacy assurance.
Slicing-Based Enhanced Method for Privacy-Preserving in Publishing Big Data

Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan, conduct, and assess scientific research.However, publishing and processing big data poses a privacy concern related to protecting individuals' sensitive information while maintaining the usability of the published data.Several anonymization methods, such as slicing and merging, have been designed as solutions to the privacy concerns for publishing big data.However, the major drawback of merging and slicing is the random permutation procedure, which does not always guarantee complete protection against attribute or membership disclosure.Moreover, merging procedures may generate many fake tuples, leading to a loss of data utility and subsequent erroneous knowledge extraction.This study therefore proposes a slicingbased enhanced method for privacy-preserving big data publishing while maintaining the data utility.In particular, the proposed method distributes the data into horizontal and vertical partitions.The lower and upper protection levels are then used to identify the unique and identical attributes' values.The unique and identical attributes are swapped to ensure the published big data is protected from disclosure risks.The outcome of the experiments demonstrates that the proposed method could maintain data utility and provide stronger privacy preservation.
Stylometric Study of the Fiction Using Sketch Engine

The paper deals with a stylometric study of I. Asimov’s idiostyle considering a corpus-based approach. For the analysis of stylometric features the I. Asimov “Foundation” cycle text corpus was created. The quantitative and statistical processing of the text corpus is done via Sketch Engine tool that enables comparison of phrases and words in the following variants: lemma, token, subcorpus. The last parameter is important for distinguishing individual authorial features, comparing their combinability and identifying the dynamics of idiostyle. The following stylometric features of a text corpus by I. Asimov are described: quantitative morphological and lexical characteristics of the vocabulary, quantitative characteristics of occasionalisms’ word formation and statistical estimation of occasionalisms’ collocations. It is sated that the frequency of occasionalisms in the cycle of novels undergoes chronological change, as well as their combinability. In this paper, a method of occasionalisms’ automated extraction due to keyness score was proposed, however, it requires the subsequent manual verification.
Routing Single Photons from a Trapped Ion Using a Photonic Integrated Circuit

Trapped ions are promising candidates for nodes of a scalable quantum network due to their long-lived qubit coherence times and high-fidelity single- and two-qubit gates. Future quantum networks based on trapped ions will require a scalable way to route photons between different nodes. Photonic integrated circuits from fabrication foundries provide a compact solution to this problem. However, these circuits typically operate at telecommunication wavelengths that are incompatible with the strong dipole emissions of trapped ions. In this work, we demonstrate the routing of single photons from a trapped ion using a photonic integrated circuit. We employ quantum frequency conversion to match the emission of the ion to the operating wavelength of a foundry-fabricated silicon nitride photonic integrated circuit, achieving a total transmission of 31.0% \ifmmode\pm\else\textpm\fi{} 0.9% through the device. Using programmable phase shifters, we switch the single photons between the output channels of the circuit and demonstrate a 50:50 beam splitting condition. These results constitute an important step towards programmable routing and entanglement distribution in large-scale quantum networks and distributed quantum computers.
GameOfThronesQA: Answer-Aware Question-Answer Pairs for TV Series

In this paper, we offer a corpus of question answer pairs related to the TV series generated from paragraph contexts. The data set called GameofThronesQA V1.0 contains 5237 unique question answer pairs from the Game Of Thrones TV series across the eight seasons. In particular, we provide a pipeline approach for answer aware question generation, where the answers are extracted based on the named entities from the TV series. This is different to the traditional methods which generate questions first and find the relevant answers later. Furthermore, we provide a comparative analysis of the generated corpus with the benchmark datasets such as SQuAD, TriviaQA, WikiQA and TweetQA. The snapshot of the dataset is provided as an appendix for review purpose and will be released to public later.