Reviewerly/exHarmony huggingface dataset:
Research on Reliability Optimization Design of Mechanical Structure Based on Computational Intelligence

In order to extend the actual service life of mechanical equipment components and realize the reasonable protection of applied structural components, the reliability optimization design of mechanical structure based on computational intelligence is studied. With the help of wavelet neural network, the established reliability and reliability index are obtained, and then the basic theory of reliability and computational intelligence technology are studied through the reliability optimization of mechanical zero structure. On this basis, the application concepts of reliability design method are defined, and the final calculation result of reliability optimization value is obtained by combining fuzzy set and level cut set, so as to realize the reliability optimization design of mechanical structure based on computational intelligence. The experimental results show that, compared with the traditional design method, the optimization design method supported by computational intelligence technology can better improve the disadvantages of mechanical structure object, and has strong practical application value in extending the reliability application time.
PAxSPL: A Generic Framework to Support the Planning of SPL Reengineering

Extractive Software Product Line (SPL) is a well-known approach that organizations can use to transform their legacy applications into an SPL. In this sense, the SPL reengineering process emerges as a possible strategy for applying the extractive SPL. Available artifacts used to perform the SPL reengineering may change, requiring software engineers to adapt their approaches as a means to satisfying the companies&amp;apos; scenarios. However, there is a lack of an approach supporting this adaptation considering different scenarios. To address these limitations we propose the Prepare, Assemble, and Execute Framework SPL reengineering (PAxSPL). PAxSPL is composed of three different aspects: a process, guidelines, and a supporting tool. For evaluating PAxSPL, we extracted eight different scenarios from the literature. Results evidenced that PAxSPL is customizable to a variety of scenarios with different reengineering artifacts, techniques, and activities.
Package AdvEMDpy: Algorithmic Variations of Empirical Mode Decomposition in Python

This paper introduces a new Empirical Mode Decomposition (EMD) Python package called AdvEMDpy that is demonstrably more flexible and which generalises in numerous important ways the existing EMD packages available in Python, R, and MATLAB. The extensions introduced by this AdvEMDpy package both significantly improve the options available to the modeller when applying variations of the EMD methodology as well as also improving the statistical robustness and efficiency of methods otherwise available in existing packages. Unlike many of the functions available for the EMD procedure in existing packages, this EMD package, AdvEMDpy, was developed for customisation by the user, to ensure that a broader class of linear, non-linear, and non-stationary time series analysis could be performed. The intrinsic mode functions (IMFs) extracted from various time series contain multi-frequency implicit structures which warrant further study and as such maximum customisability and intricate knowledge of the algorithm and algorithmic variations is required for users to exploit the full power of the EMD approach to time series decomposition. The edge effect is the most familiar and troublesome problem present in EMD. Various techniques, of varying intricacy from numerous works, have been developed, refined, and offered in AdvEMDpy. In addition to edge effects, numerous preprocessing, post-processing, detrended fluctuation analysis techniques, stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, and algorithmic variations have been incorporated and exposed to the user of AdvEMDpy for fine-tuning of applications of EMD.
Rancang Bangun Website Sekolah Untuk Peningkatan Kapasitas Sekolah Dasar Negeri Menjadi Sekolah Adiwiyata, Terakreditasi dan Role Model Di Balikpapan Tengah

The world of education, from elementary school to university levels, is required to be able to adapt to the progress of science and technology that is growing so rapidly, especially in the field of information. And the current state of the Covid-19 pandemic requires every institution to help reduce the number of its spread. One of the lines of society affected by COVID-19 is schools, especially elementary schools that do not yet have school website facilities as a medium of information to the public. In this community service activity, the implementing team designed the website for the SDN 020 Central Balikpapan school as a partner. The problems faced by partners are the location of schools that are not strategically located in the Central Balikpapan area, not having informative media for various aspects of interest, the existence of an online PPDB policy from the Education Office to suppress the spread of Covid19, To overcome these problems, SDN 020 Central Balikpapan must have websites. The benefits of online PPDB activities can be carried out so that parents do not need to go to school during registration, can meet the requirements as an Adiwiyata school, and can facilitate the achievement of school accreditation. The method of implementing community service activities by the team consists of building communication with partners, designing school websites, providing training on the use of websites in the form of filling out content and managing databases, implementing websites in schools, socializing school websites to the community, maintaining and repairing websites after implementation. This series of community service activities is planned for a maximum of 8 months. The service activity took place successfully and could help school operators in data processing and selecting prospective students easily and quickly
Envelope Imbalance Learning Algorithm based on Multilayer Fuzzy C-means Clustering and Minimum Interlayer discrepancy

Imbalanced learning is important and challenging since the problem of the classification of imbalanced datasets is prevalent in machine learning and data mining fields. Sampling approaches are proposed to address this issue, and cluster-based oversampling methods have shown great potential as they aim to simultaneously tackle between-class and within-class imbalance issues. However, all existing clustering methods are based on a one-time approach. Due to the lack of a priori knowledge, improper setting of the number of clusters often exists, which leads to poor clustering performance. Besides, the existing methods are likely to generate noisy instances. To solve these problems, this paper proposes a deep instance envelope network-based imbalanced learning algorithm with the multilayer fuzzy c-means (MlFCM) and a minimum interlayer discrepancy mechanism based on the maximum mean discrepancy (MIDMD). This algorithm can guarantee high quality balanced instances using a deep instance envelope network in the absence of prior knowledge. In the experimental section, thirty-three popular public datasets are used for verification, and over ten representative algorithms are used for comparison. The experimental results show that the proposed approach significantly outperforms other popular methods.
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining

Although the state-of-the-art traditional representation learning (TRL) models show competitive performance on knowledge graph completion, there is no parameter sharing between the embeddings of entities, and the connections between entities are weak. Therefore, neighbor aggregation-based representation learning (NARL) models are proposed, which encode the information in the neighbors of an entity into its embeddings. However, existing NARL models either only utilize one-hop neighbors, ignoring the information in multi-hop neighbors, or utilize multi-hop neighbors by hierarchical neighbor aggregation, destroying the completeness of multi-hop neighbors. In this paper, we propose a NARL model named RMNA, which obtains and filters horn rules through a rule mining algorithm, and uses selected horn rules to transform valuable multi-hop neighbors into one-hop neighbors, therefore, the information in valuable multi-hop neighbors can be completely utilized by aggregating these one-hop neighbors. In experiments, we compare RMNA with the state-of-the-art TRL models and NARL models. The results show that RMNA has a competitive performance.
Convolutional Deep Exponential Families

We describe convolutional deep exponential families (CDEFs) in this paper. CDEFs are built based on deep exponential families, deep probabilistic models that capture the hierarchical dependence between latent variables. CDEFs greatly reduce the number of free parameters by tying the weights of DEFs. Our experiments show that CDEFs are able to uncover time correlations with a small amount of data.
Controllable and Interpretable Singing Voice Decomposition via Assem-VC

We propose a singing decomposition system that encodes time-aligned linguistic content, pitch, and source speaker identity via Assem-VC. With decomposed speaker-independent information and the target speaker's embedding, we could synthesize the singing voice of the target speaker. In conclusion, we made a perfectly synced duet with the user's singing voice and the target singer's converted singing voice.
Obstacle Avoidance Using CTC and Virtual Structure for Leader-Follower Formation on Mobile Robot

This research examined the development of the combination of virtual structure and leader-follower as an obstacle avoidance method in the formation control of a mobile robot. The formation of the robots are designed with the Separation Bearing Control (SBC) approach between the leader robot (RL) and the virtual robot (RV). The virtual robot is used as a virtual follower and a reference trajectory for the follower robot (RF). When the follower robot detects an obstacle, the follower robot trajectory is adjusted using a trajectory planner for obstacle avoidance. After passing the obstacle, the follower robot will track its position back in formation using virtual robot position and heading as reference. Leader robot and follower are perturbed by disturbances. In order to ensure the achievement of small error tracking, a controller is designed using the integration of kinematic and dynamics controllers with disturbance observer. The kinematic and dynamics controllers are designed using input-output linearisation (IOL) method and computed torque control (CTC). The effectiveness of the proposed method is verified by the simulation result.Keywords: CTC, leader follower, obstacle avoidance, SBC, virtual structure.
Volcanogenic Deposits of Non-ferrous Metals in the Lesser Caucasus and Eastern Pontides

The paper presents brief characteristics of geological environments of ore deposit occurrences in Turkey, Georgia and Armenia. They can be attributed to Kuroko-type deposits, being distinguished by the character of ore accumulation. To the west, in Turkey, there are epigenetic and hydrothermal-sedimentary copper-zinc deposits that were formed in deep restricted basinal settings. An example of the latter is the Chayeli deposit.To the east, in the Caucasus, we have predominantly only epigenetic deposits. Besides, in the Bolnisi mining district (Georgia) there is the Madneuli deposit which represents an example of polyformational deposit. Here, within the restricted territory, have been concentrated:barite, barite-polymetallic, gold-bearing secondary quartzite, large-scale stockworks of copper ores. Judged by the 87Sr/86Sr ratios, some volcanites which are spatially associated with ores, might have been products of the “differentiation” of undepleted mantle, or other magmas that were generated in the lower part of the earth crust.In the Alaverdi ore district in Armenia, there are Jurassic volcanodepressions that host copper, copper-zinc and barite-sulfide ores. All the deposits of the Alaverdi district, porphyry copper including, contain economic reserves of ores.On the basis of available literature material and our own data, there has been created a mental-logical geological-genetic model of volcanogenic deposits.
Data Capsule: A Self-Contained Data Model as an Access Policy Enforcement Strategy

In this paper, we introduce a data capsule model, a self-contained and self-enforcing data container based on emerging self-sovereign identity standards, blockchain, and attribute-based encryption. A data capsule allows for a transparent, privacy-respecting, and secure exchange of personal data, enabling a progressive trust scheme in a semi-trusted environment. Each data capsule is bundled with its own access policy structure and verifiable data, drastically reducing the number of interactions needed among the user, the service providers, and data custodians. Moreover, by relying on the decentralized nature of blockchain and attribute-based encryption our proposed model ensures the access policies published by service providers are public, transparent, and strictly followed.
Detecting the Immensity of Expression in WhatsApp Users Using ‘R’ Language

In the present scenario the different social media apps/sites have empoweredevery person to effortlessly express & share their ideas and emotional state with the society or the publicin and around the world. People, these days are comfortable in sending text for communication, which can be accomplished through different messaging platforms of social media available today such as WhatsApp,Twitter, Insta, Snapchat, Facebook , etc. so that instead of speaking their feelings out they find it very easy and convenient to express them. Sharing of ideas are not only done through texting but it can also take a form of calling.This process can be established among more than two people. The transmission of media is established worldwide in a group or  between two individuals. WhatsApp is a platform where audio and vedio calls could be made and it is also used extensively for exchanging messages and media files.The aim of this piece of research is to recommend an effective method that can distinguish the level of positivity in WhatsApp users. The major goal is to set down a sense of comprehending theopinion levels in different users. The open-source data science technologies like Anaconda and R language have been used in this paper to understand the sentiment level of the communication in WhatsApp.
The articel of journal Pengaruh model pembelajaran RCCDE terhadap hasil belajar siswa kelas VII MTs Miftahul Ulum Sumber Baru: Journal

Research on student learning outcomes of class VII MTs. Miftahul Ulum Karang Bayat Sumber Baru for the 2019/2020 school year with 42 respondents from 125 students who were taken by stratified random sampling. The method used in this study is a test method with a Quasi Experimental Design.Students who are used as subjects in the research sample, have the same ability, because both classes get the same science material. The experimental class uses the Reading Concept Map Connection Discussion Evaluation (RCCDE) learning model and the control class uses the conventional method.It is known from the results of data analysis calculations that the use of the Reading Concept Map Connection Discussion Evaluation (RCCDE) learning model can affect learning outcomes, this is evidenced by the results of the ANOVA test scores which obtain Sig 0.000 < 0.05 (0.050). From the analysis of the data above, it is continued with the paired samples test. the results of the calculation of this analysis obtained a value of Sig (2-tailed) of 0.000 <0.05, it can be concluded that there is a difference in student learning outcomes between the pre-test of the experimental class and the post-test of the experimental class.Keywords : RCCDE Model, Learning Outcomes, Air Pollution
Development Of Personal Safety Box Using QR Code And Image Processing

The aim of this project is for people who need a place to keep their privacy or to preserve an important thing like important documents, jewelry, money, and other things.Therefore, developing a personal security box using the Quick Response code (QR code) may help to ensure that owner item remain safe and secure. The Quick Response (QR) code is an encrypted two-dimensional bar-code that can store encoded information, and the human eye cannot interpret the 2D bar-code pattern. The QR code is used as a key to unlock the security box in this project when the box's owner scans the QR code image that is created bythe QR code generator android app. Furthermore, the project used Raspberry Pi to process the QR code detection input and send the output to the servo motor to open the safety box lock.
Security Validation Model in Cloud Computing Environment

Private, Public cloud or a unified cloud system, client’s absence of a successful secure computable assessment techniques for handling the security circumstance of its own data foundation overall. This paper gives a quantifiable security assessment framework for various mists that can be gotten to by reliable API. The assessment framework incorporates security checking motor, security recuperation motor, secure computable assessment system, graphical presentation segment &amp; so on. Secure assessment system makes out of many assessment components comparing various fields, for example, figuring, stockpiling, organize, support, application security and so forth. Every component is doled out 3 tuples on the liabilities, score &amp; fix strategy. Framework receives "1 vote" system for a field to check its point &amp; includes synopsis as overall score, &amp; to make high security. We implement the computable assessment for various cloud environment clients dependent on the G Cloud phase. It displays active security examining for one or different clouds with pictorial diagrams &amp; clients to adjust arrangement, expand activity &amp; fix liabilities, in order to increase secureness of cloud assets.
Edge detection algorithm of low SNR image based on mixed filtering

Aiming at the available edge detection algorithms can not effectively extract the edge information for low SNR image, a new edge detection algorithm for low SNR image based on mixed filter is proposed in this paper. Which using a mixed filter to achieve the effects of edge protection and denoising, and then acquires the image edge by high effective differential operators. Experimental results show that under the interference of different concentrations of salt-and-pepper noise, the proposed algorithm can not only filter out noise better, but also retain more edge information and improve edge connectivity than the available edge detection algorithms.
Integration of Machine Learning with Neutron Scattering: Hamiltonian Tuning in Spin Ice with Pressure

Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy$_2$Ti$_2$O$_7$, using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system.
1910년대 만주ㆍ연해주지역 의병세력과 독립운동

Since 1904, Kitamajima(北間島) and Primorsky Krai(沿海州) have emerged as independent movement bases. Anti-Japanese armed groups including prisoners gathered in Primorsky Krai to form Primorsky Krai troops. In the process, a list of people who share the same aspirations with the righteous army was created, and that is the “uiwonan(義員案)”. Looking at the Primorsky Krai troops through the “uiwonan(義員案)” first, Koreans who lived in the Tumen River basin and emigrated to Kitamajima(北間島) area participated. Secondly, the mountain gunner righteous army unit and Gyeongseong righteous army(鏡城義兵) who fought the righteous army war in the Hamgyeong-do(함경도) province participated. Third, people who live in the fierce battlefield of the righteous army war can be seen as a righteous army force. Fourth, in the 1910s, Primorsky Krai's troops(연해주 의병세력) were those who settled in Primorsky Krai (沿海州), Kitamajima(北間島). Fifth, the conversion of the righteous army to the independent army is to inherit the purpose and spirit of the righteous army war, which is to protect the national rights and the national spirit.
Application of neural network algorithm for schizophrenia diagnosis

Schizophrenia is a prolonged mental condition that affects functional impairment in work, interpersonal relationships, and self-care. This research was aimed at developing a neural network model to diagnose schizophrenia using text data acquired from patients’ records. The model was developed from datasets obtained from Neuropsychiatric Hospital in Yaba and the Lagos University Teaching Hospital, both in Lagos, Nigeria, using Python programming language and is provided with significant features from data sets to learn patterns within the training data and perform classification on the test data. The results show that the model produced a test accuracy of 85%, specificity of 95% and a precision of 93%. These results indicate that the model can be used for effective computer-aided diagnosis of schizophrenia.
A Plug-and-Play Universal Photonic Processor for Quantum Information Processing

Large-scale and programmable quantum photonic processors are needed to control photonic quantum computations that lead to a quantum advantage. Here, we present a universal 12-mode quantum photonic processor which is the largest of its kind to date. The processor is remotely controllable and seamlessly integrated with a dedicated control software, making the device fully plug-and-play.
3D Inversion of Airborne Magnetic Data and Validation of it by Using Sourk Mine Drilling Data

The airborne magnetic method is one of the most significantly applied approaches in mineral exploration. This magnetic method has been widely used in recent years due to its extensive coverage, lower cost than other methods and coverage of impassable areas and forests. For many years in Iran, the magnetic method has been utilized by authorities to survey ore deposits and to prepare structural and geological maps. This paper presents part of the Dehshir area exploration data of airborne surveys by helicopter with flight line spacing of 250 meters and flight height of 40 meters conducted by Geological Survey and Mineral Exploration of Iran. Furthermore, among the potential areas in the Dehshir area is the studied hereby area of Block C2 of Sourk mine. This region in Yazd province with a 4.1 km2 area is located at 95 km northwest of Yazd city. The purpose of this paper is to present a three-dimensional model of magnetic data and its validation using drilling data. The three-dimensional modeling of magnetic data has been processed by Inversion method. Next, the locations of boreholes were proposed for drilling. Then, the boreholes data is used to evaluate the geophysical method model by comparing the three-dimensional modeling of magnetic data and three-dimensional modeling of the proposed drilled boreholes data.