Reviewerly/exHarmony huggingface dataset:
A combination of algorithm agglomerative hierarchical cluster (AHC) and K-means for clustering tourism in Madura-Indonesia

The development approach through the tourism sector is one of the programs launched by the government since 2016. However, the development approach is not carried out in all areas because the number of accommodation and public facilities is minimal and uneven, one of which is in Madura. With so many tourist objects in Madura, it is necessary to distribute the development of public facilities and analyze tourism that has a non-strategic distance to public facilities to help increase tourist visits. This study builds a system for clustering tourist attractions in each district in Madura based on the distance to public facilities which include hotels, gas stations, restaurants, and mosques which are important criteria and considerations for tourists in visiting a tourist location. The method used in this research is a combination of the AHC method with K-Means. The test results of the AHC, K-Means method, and the combination of AHC and K-Means methods using the Silhouette Coefficient method indicate that the AHC and K-Means combination method is the best method with a Silhouette Coefficient value of 0.8055 for k=2 and is classified as a strong structure, for the K method. -Means produces the highest Silhouette Coefficient value of 0.638. While the AHC method produces the highest Silhouette Coefficient value of 0.707.
State Based IoT Security for Tamper Prevention

There is a global exponential increase in the number of connected devices, which leads to an increase in the vulnerabilities of IoT devices. Most IoT devices receive their data from an external environment. It can be any sensor data, but sometimes this data is sent into the cloud without any encryption due to the limited space in IoT devices. This paper aims to tackle this issue by using stateful inspection of the data and determining its authenticity with the help of the previous values received by the gateway and removing devices which do not satisfy the stateful inspection.
Deploying Transactional Smart Contracts using Multisignature Boolean Formulas

Smart contracts have been established as the standard mechanism for online crypto-currency transactions in blockchains such as Bitcoin and Ethereum. The application scope of smart contracts is predicted to expand beyond crypto-currency in upcoming years, and this emerging technology is likely to play a significant role in other major domains. Smart contracts are usually created using a special purpose programming language. In this paper, we explore an alternative mechanism based on propositional calculus, using multisignature and lock-based boolean variables. Higher order boolean quantifiers have been avoided, and from a security standpoint, the design is deliberately kept Turing-incomplete to guarantee bounded runtime and well-defined termination paths for any valid smart contract (for homogeneous locks, our validation checks run in linear time for best case and quadratic time for worst case). Keeping in mind a widening scope for smart contracts, the applicability of our design is not confined to blockchain transactions, but can be easily integrated into other application domains.
GBMPSO: Hybrid Gradient Boosting Machines with Particle Swarm Optimization in Cell Segmentation Data

This paper describes a hybrid computing model, named GBMPSO, that enhances the performance of gradient boosting machines (GBMs) whose hyperparameters are optimized by particle swarm optimization algorithm. GBMs are tree-based ensemble algorithms that are used for various machine learning tasks. Particle swarm optimization is a stochastic, metaheuristic swarm-based optimization algorithm. The performance of GBMPSO is tested on a cell segmentation dataset. Our experiment shows the outperformance of GBMPSO over a random search-based GBM and Xgboost, highlighting the potential superiority of GBMPSO over other models and its application to classification problems of other problem domains.
Creating the Entities Use Case

While working with data, you began with data retrieval. You used remote HTTP services to make GET calls and show the data on the screen in an Angular application. You created the ability to cache resources including data calls. As you progress, applications need to create, update, and delete data. In an application that supports offline functionality, the create, update, and delete actions are complex.
Research on the Application and Analysis of Mechanical Equipment Intelligence Based on Knowledge Graph

Abstract With the rapid evolution of manufacturing technology, mechanical equipment products are being replaced more frequently, and competition in the industry is becoming increasingly fierce. The collection and analysis of mechanical equipment technical intelligence is a key need for many enterprises. Using artificial intelligence technology to build an intelligence analysis system for all types of data is the inevitable trend of intelligence analysis. This paper proposes an intelligent processing and analysis framework of mechanical equipment intelligence. Based on the construction of knowledge graph, the knowledge embedding model is used to complete knowledge reasoning, predict development trends for mechanical equipment, and allow for more intelligent decision-making. A case study involving 3D printing shows the functions of the intelligence process and analysis framework.
Additive Noise Model Structure Learning Based on Spatial Coordinates

Abstract A new algorithm named SCB (Spatial Coordinates Based) algorithm is presented for structure learning of additive noise model, which can effectively deal with nonlinear arbitrarily distributed data. This paper makes three specific contributions. Firstly, SC (Spatial Coordinates) coefficient is proposed to use as a standard of independence test and CSC (Conditional Spatial Coordinates) coefficient as a standard of conditional independence test. Secondly, it is proved that the CSC coefficient conforms to the standard normal distribution and the HSIC independence test can be regarded as a special case of the SC coefficient. Finally, based on the SC coefficient, the SCB algorithm is proposed, and the experimental comparison with some existing algorithms on seven classical networks shows that the SCB algorithm has better performance. In particular, SCB algorithm can deal with large sample, high dimensional nonlinear data, and maintain good accuracy and time performance.
Fine-Graine Visual Classification with Aggregated Object Localization and Salient Feature Suppression

Abstract Fine-grained visual classification (FGVC) is desired to classify sub-classes of objects in the same super-class. For the FGVC tasks, it is necessary to find subtle yet discriminative information from local areas. However, traditional FGVC approaches tended to extract strong discriminative features, and overlook some subtle yet useful features. Besides, current methods ignore the influence of background noises on feature extraction. Therefore, aggregated object localization combined with salient feature suppression are proposed, which is a stacked network. First, the feature maps extracted by the coarse network are fed into aggregated object localization to obtain complete foreground object in an image. Secondly, the refined features obtained through zooming in complete foreground object are fed into fine network. Finally, through finer network processing, the feature maps are fed into salient feature suppression module to find more valuable region discriminative features for classification. Experiment results on two datasets show that our proposed method can get superior result compared with state-of-the-art methods.
Distributed fitness landscape analysis for cooperative search with domain decomposition

Fitness landscape analysis is often employed to quantify the properties of optimization problems and hence gain a better understanding of these problems and the behavior of the algorithms applied to them. The calculation of various landscape features requires complete knowledge of the boundaries and constraints of the entire search space. Many real-world applications of distributed optimization exhibit an inherent domain decomposition, i.e., the decision variables for a cooperative search are in the hands of multiple actors. Thus, knowledge about the overall search space - likewise distributed - is not available at a central location. In this paper, we propose an approach for distributed computation and subsequent composition of fitness landscape features. We evaluate the approach with a set of well-known continuous benchmark functions and examine the features for correlation with algorithm performance and their suitability for feature-based algorithm parameterization. The results show that the distributedly computed features provide useful insights into the nature of the problems and that especially the heterogeneity of the sub-search spaces is a relevant factor in the optimized design of the exchange mechanisms of distributed heuristics.
SAT vs. Substitution Boxes of DES like Ciphers

One of the applied methods of symmetric ciphers investigations is cryptanalysis that uses the SAT problem. In this case, cipher testing starts with encoding the cipher's algorithm into a propositional Boolean formula. Then chosen randomly plaintext and cryptographic key are also encoded as formulas. Specialized to solve the SAT problem programs called SAT solvers can count the values of encrypted text for such input data. The final cipher analysis consists of providing the SAT solver with encoding formulas: plaintext and ciphertext. With such data, the SAT solver calculates the key value, i.e. performs cryptanalysis of the cipher with plaintext and ciphertext. In this work, we examine how SAT techniques behave for certain limited versions of the DES cipher and various types of S-boxes. To get the best result, we compared many, selected SAT solvers.
A Light Model for Super-Resolution of Remote Sensing Images

Abstract Remote sensing image super-resolution is an important research topic, which helps to improve the quality of remote sensing images. However, because remote sensing images are usually quite large while the satellite equipment often have low computing capacity and small memory, it is appealing to develop lightweight and fast models to perform the super-resolution task for the remote sensing images. In this paper, we empirically study the effectiveness of conventional super-resolution approaches for remote sensing images, and propose an effective way to reduce the model parameters and computational cost. Specifically, motivated by Res2Net, we design a new multi-scale hierarchy residual block to replace the ResBlock in EDSR to provide a more diverse receptive field for each residual block. The proposed modified method has fewer parameters and faster speed compared with the original EDSR. Moreover, we also build two benchmark super-resolution datasets (i.e., DOTA-SR and LEVIR-SR) from DOTA and LEVIR-CD, respectively, for experimental evaluation. We perform experiments on the two datasets, and results show that our method is light and have comparable performance over the existing super-resolution baselines.
LWCNet: Lightweight Complex Neural Network for Real-Time Channel Estimation

Convolutional neural networks (CNNs) have been widely adopted in various fields and the wireless communication domain is no exception; researchers leveraged CNNs for communication tasks such as channel estimation. They showed the potential of CNNs for these applications but suggested CNNs were insufficient to be considered for real world implementation due to the limited applicability and high execution latency. In this paper, we propose a Lightweight Complex Neural Network (LWCNet) composed of Complex Number Adapter (CNA), re-parameterizable multi-path operations, and progressive learning to cover various resource block configurations. LWCNet outperforms not only conventional linear channel estimation methods as least square (LS) estimator and linear minimum mean square error (LMMSE) estimator but also deep learning based estimators. Moreover, LWCNet estimates channels with various numbers of subcarriers within 0.5 ms using a modern graphics processing unit.
A Meta-heuristic-based Scheduling of Transactions for Medical Blockchain Systems

When medical information and documents are exchanged outside of the secure medical facilities networks, patient' privacy is at risk. This can compromise biomedical studies, patients privacy, and all other medical stakeholders' data. Therefore, blockchain is more and more adopted as infrastructure for Electronic Medical Records Management Systems (EMRMS) to address secure data sharing, access management and critical data handling issues. In this paper, we consider the problem of reducing transactions scheduling delays in blockchains as a main issue impacting the performances of EMRMS. We suggest a novel approach that aims to design a structured and weighted queue of transactions request that can promote differentiated level of service for requested transactions. A logical scheduling strategy based on a hybridization of the Genetic Algorithm and the Variable Neigh-borhood Search (VNS) algorithm for scheduling transactions is also proposed.
Employing the Social Group Optimization Algorithm to Find the Best Hyper parameters of Support Vector Machine

Support Vector Machine (SVM) is one of the most widely used algorithms for solving classification and regression problems. SVM parameters such as the kernel and the penalty (C) parameters greatly affect the classification accuracy. For the purpose of improving classification accuracy within a record implementation time, a Social Group Optimization (SGO) algorithm was proposed to find the best combination of SVM parameters through which to improve the performance and to obtain the highest classification accuracy and speed of execution. Five different types of datasets (Iris, Wine, Glass, Stat log and Car) were used that all of them were taken from the (UCI) repository. Moreover, a medical dataset, which is taken from (Dread, Alia, 2019), was used to verify the proposed algorithm. The results of the proposed algorithm were compared with the Grid Search algorithm (GS). The comparison results showed a preference for the (SGO) algorithm compared to the (GS) algorithm in terms of classification accuracy and speed of implementation for all the used datasets in this work.
Real Time Big Data Sentiment Analysis and Classification of Facebook

Many peoples use Facebook to connect and share their views on various issues, with the majority of user-generated content consisting of textual information. Since there is so much actual data from people who are posting messages on their situation in real time thoughts on a range of subjects in everyday life, the collection and analysis of these data, which may well be helpful for political decision or public opinion monitoring, is a worthwhile research project. Therefore, in this paper doing to analyze for public text post on Facebook stream in real time through environment Hadoop ecosystem by using apache spark with NLTK python. The post or feeds are gathered form the Facebook API in real time the data stored database used Apache spark to quick query processing the text partitions in each data nodes (machine). Also used Amazon cloud based Hadoop cluster ecosystem into processing of huge data and eliminate on-site hardware, IT support, and other operational difficulties and installation configuration Hadoop such as Hadoop distribution file system and Apache spark. By using the principle of decision dictionary, emotion analysis is used as positive, negative, or neutral and execution two algorithms in machine learning (naive bias &amp; support vector machine) to build model predict the outcome demonstrates a high level of precision in sentiment analysis.
Design of Double-Masking Current-Mode CMOS Circuit Against Power Analysis Attacks

In order to enhance the ability of current-mode CMOS circuits to resist power analysis attacks, we introduce dual-masking logic to form dual-masking current-mode CMOS logic (DM-CML). There are 4 possibilities for the mask value, which is random and unpredictable. When the attacker cannot obtain the accurate mask value, the actual calculation result of the circuit cannot be accurately obtained by attackers. The less correlation between data and power consumption enhances circuit ability to resist power analysis attacks. Through the simulation experiment of Hspice, the results show that the DM-CML has the correct logic function and constant power consumption, which enhances its ability to resist power attacks.
Efficient Multilingual Deep Learning Model for Keyword Categorization

Keywords categorization is an essential tool for SEO (Search Engine Optimization), digital marketers, and online advertising. Keywords represent one of the most valuable pieces of information to infer the users' intents and interests. An effective keyword categorization method allows understanding what types of content are in the greatest demand and can help improve future content strategies or marketing/ad campaigns. In this paper, we present a novel deep learning model for multilingual keyword categorization. The model relies on fastText multilingual word embeddings, and its architecture is inspired by the DeepSets model. To make use of (training) words not included in the pre-trained fastText embeddings, we initialize them as the average embedding overall of the co-occurrent words. Then, we fine-tune these representations by allowing the network to back-propagate the error to the input. We assess the quality of our proposal on a real-world dataset provided by a Spanish company where keywords are categorized upon the Google Product Taxonomy (GPT). Empirical results show that our model can achieve high accuracy scores while being extremely efficient.
Machine Learning approach for resolving anaphors in the Punjabi Language

The paper presents an attempt to resolve anaphors in the Punjabi language using Machine Learning strategy. The Punjabi language has limited resources and no corpus is available which can be used for the task, so we prepared our own. Punjabi Shallow Parser has been used as a tool to provide information from the text such as part of speech (POS) tags, number, person, gender of NPs and chunking information. Animacy, NER, Pronoun Type features and linkages of anaphors to associated entities are manually annotated on the data. The features retrieved from the annotated corpus were used to train different classifiers, which were then evaluated using Precision, Recall, and F-Score. Analysis of the results shows that ensemble classifiers have performed better than other classifiers. We also tested the contribution of different features in resolving anaphors by training classifiers with different subsets of features. The accuracy of the pre-processing tool(Punjabi Shallow Parser), and the quality of the annotation task completed are also the factors that affect the system's performance. Because this is the first attempt to resolve anaphora in Punjabi, the results could be improved by using a larger corpus and adopting a rule-based strategy in conjunction with a machine learning approach, which is the future scope of the current work.
A Multi-task Text Classification Model Based on Label Embedding Learning

Abstract Different text classification tasks have specific task features and the performance of text classification algorithm is highly affected by these task-specific features. It is crucial for text classification algorithms to extract task-specific features and thus improve the performance of text classification in different text classification tasks. The existing text classification algorithms use the attention-based neural network models to capture contextualized semantic features while ignores the task-specific features. In this paper, a text classification algorithm based on label-improved attention mechanism is proposed by integrating both contextualized semantic and task-specific features. Through label embedding to learn both word vector and modified-TF-IDF matrix, the task-specific features can be extracted and then attention weights are assigned to different words according to the extracted features, so as to improve the effectiveness of the attention-based neural network models on text classification. Experiments are carried on three text classification task data sets to verify the performance of the proposed method, including a six-category question classification data set, a two-category user comment data set, and a five-category sentiment data set. Results show that the proposed method has an average increase of 3.02% and 5.85% in F1 value compared with the existing LSTMAtt and SelfAtt models.
Visual Analysis of Citespace Based Wearable Sports Equipment Research Progress

Through the theoretical framework and practice of the research on wearable sports equipment, the evolution of domestic wearable sports equipment in the past nine years and the forefront of hot spots are summarized. In this paper, CNKI was used as the data source, literature research method, scientific measurement method, and knowledge graph software Citespace and SATI were used to draw knowledge graph of authors, institutions, journals and keywords, and to make statistics of frequency, frequency and intermediary centrality. The research literature on wearable sports equipment was divided into three stages in time, and the authors, institutions and journal publications were analyzed. The cluster map was used to reveal the research hotspots, and finally the conclusions were drawn.
The Systematic Model for the Promotion of Library and Information Resources and Services

Based on the study of used by library the marketing technologies and marketing policy, linking such technologies into system, the author has built the systematic model for promotion of library and information resources and services. The model reflects the sequence of actions taken within the subsystems of the library’s marketing policy, focusing on the need to monitor both the use of resources and services and changes in the state of the target audience for a qualitative and quantitative assessment of the implemented promotion strategy. The model also takes into account the need for different promotion strategies, depending both on the lifecycle of the resource or service and its uniqueness, as well as on the attitude of users toward the library as a whole. In addition, the model can be adapted to different types of libraries by expanding or reducing certain sequences within a particular marketing policy subsystem.
A discharge detection method in high-voltage direct current system using wavelet transform with adaptive wavelet level decision

This paper proposes a method that uses adaptive decision-making of wavelet level for detecting high-voltage direct current (HVDC) discharge in wavelet transform. Identification and detection of HVDC discharge are essential study subjects for pipeline safety and optimal operation of electrical power systems. This method overcomes the disadvantage that wavelet packet transform needs to determine the level in advance. The decomposition level of wavelet packet transform is controlled by calculating relatively wavelet energy change to decide its wavelet level. Our proposal extracts richer features of HVDC discharge by comparing other feature extraction algorithms. The second primary discovery is that a wavelet-based application framework is designed to detect the HVDC discharge and further protect the energy pipeline. These discoveries have application value in the protection of power systems and provide opportunities and brighter perspectives along with valuable studies in the detection and classification of time-series data.