Reviewerly/exHarmony huggingface dataset:
Secure Data Sharing with Interplanetary File System for Pharmaceutical Data

Growth of Internet Technology signs Internet of Things as fourth Industrial Revolution. It enables communication between devices without the support of human. Embedded devices with Smart Sensor extract data from external environment and perform actuation according to the application. In spite of obtaining time driven data, this technology has downfalls due to various security issues as it relays on wireless technology for device communication. Hence it demands security solution to address this problem. This paper investigates the security flaws encountered in pharmaceutical supply chain. This study analyzes the problem with existing system in counterfeiting data tampering and user privacy for secure exchange of user data. Data security is improved with Block Chain adoption where Interplanetary File System (IPFS) is used here to enable tamper proof data storage and data retrieval. Cryptographic Smart Contracts for Device Authentication and User Access control scheme are written in Solidity Language and executed on Remix IDE. Transaction and Execution cost are evaluated for Advanced Encryption Standard (AES) algorithm and Rivest-Shamir-Adelman (RSA) algorithm in terms of ether as gas consumption unit. Artificial Intelligence (AI) is used here to forecast demand production with accurate supply chain information from Block chain without fraudulent activity. Regression analysis is performed and evaluated with metrics as mean square error (MSE), root mean square error (RMSE) and R-squared value on three regression models such as Long-Short Term Memory (LSTM), Ordinary Least Square (OLS) and Decision Tree. From the study it is found that LSTM outperforms other models with MSE value of 0.375 whereas OLS and Decision Tree outputs with 1.375 and 2.58 MSE respectively. Integration of AI with Block Chain provides business solution against data tampering in supply chain process.
Socio-cognitive Evolution Strategies

Socio-cognitive computing is a paradigm developed for the last several years, it consists in introducing into metaheuristics mechanisms inspired by inter-individual learning and cognition. It was successfully applied in hybridizing ACO and PSO metaheuristics. In this paper we have followed our previous experiences in order to hybridize the acclaimed evolution strategies. The newly constructed hybrids were applied to popular benchmarks and compared with their referential versions.
Research on the Innovation Ways of Computer-assisted Teaching and Student Management Talent Training Model

Abstract In the 21st century, human society will enter the information age. What will educational science in the information age look like? We cannot give a definite conclusion because there are too many uncertain factors. However, one thing is certain. That is, the laws of education in the information age can only be discovered in education and teaching activities that use computers and networks as technological platforms. Mastering educational information technology is the basic condition for exploring the laws of education and teaching in the information society. It can be seen that high schools must carry out computer teaching and improve the quality of computer teaching to the greatest extent. The author of this article has a humble opinion on computer-assisted school education and teaching and student management personnel training.
Face Detection System for Health Care Units Using Raspberry PI

Computer vision has one of its primary applications as facial detection and recognition system, which can perform two essential functions of identifying and verifying an individual. An image capturing system with embedded computing as one of its significant parts extracts image information and requires no external processing system. To deliver results to other devices for the corresponding inputs, we use an interface device. The designed system is fast and efficient. It coordinates efficiently with the image recognition unit and the recognition algorithm. The system ensures a smooth flow of data stream between the two devices, namely, the Raspberry PI board and the camera. It also handles the data stream flow to occur smoothly between the camera and the Raspberry PI board. During this pandemic era, the application has a wide range of applicability for healthcare systems, helping in a contactless authentication of patients.
Paper Recommendation Based on Author-paper Interest and Graph Structure

The recommendation system can recommend information to users efficaciously, which helps many users to obtain information in different fields. The paper recommendation is a research topic to provide authors with personalized papers of interest. However, most existing approaches equally treat title and abstract as the input to learn the representation of a paper, ignoring the author's interest and structure information of the academic network. In the paper recommendation system, authors and papers and the interaction of their information have a crucial impact on the efficiency and accuracy of the recommendations. However, most recommendation systems are usually designed based only on users. Therefore, we propose a method based on the author's periodic interest and academic graph network structure to obtain as much effective information as possible to recommend papers. Extensive offline experiments on large-scale real data show that our method outperforms the representative baselines.
Implementing Blockchain in the Airline Sector

Blockchain is a distributed database with specific properties and elements in the idea of Bitcoin, which is a combination of peer to peer networks, software, and protocols. Currently, many companies start to adopt blockchain in their systems to enhance the rate of security and data integrity. However, blockchain has proved its ability in different life fields, such as education, healthcare, government, and industry. However, as a result of the increasing rate of airline transportation issues, there was a high demand to activate blockchain in aviation to prove robustness solutions. However, airline transportation had been evolving by decades, and to preserve these rates of evolution, it needs to integrate new technologies in the airline sector. The main features of the blockchain that make it useful in improving air transportation routes are its ability to store any information such as events, object transactions, and electronic transactions. Moreover, the blockchain can provide legal support for flights and managing passenger information and airline flights to solve issues related to transparency, and control. In this paper we give the first steps to a scientific comprehensive literature review that present a detailed of the current investigations and studies in introducing blockchain in the airline sector. Besides, the paper introduce the main challenges, assumptions, advantages, and future work in integrating blockchain in airline sector as a way for enhancing public transportation and human life.
Animation Storyboard Script Description for Computer Scene State Transition Network

With the development of science and technology, especially the development and application of information technology, computer-aided animation production has gradually become a research hotspot because of its extensive practical needs. Based on the idea of graph structure, this paper proposes a dynamic web crawler model based on state transition, which uses the state transition process to simulate dynamic events to trigger changes to the web page structure. Through the analysis and modeling of the traditional animation storyboard script, the basic elements of the animation storyboard script are extracted. Combined with XML technology to design a standardized animation storyboard script description method, the key technology of the method is given. Schema files are used to set the structure and constraint conditions of animation storyboards, and DOM technology is used to realize the analysis of standardized animation storyboards. Compared with the traditional animation storyboard script edited in Microsoft Word, WPS and other formats, the standardized animation storyboard script adds relevant animation storyboard structure information. This structural information will make the various processing of the animation storyboard script using the computer system more convenient and efficient. Using this feature, it supports the annotation of animation materials and efficient browsing and retrieval.
Principled Decision-Making Workflow with Hierarchical Bayesian Models of High-Throughput Dose-Response Measurements

We present a case study applying hierarchical Bayesian estimation on high-throughput protein melting-point data measured across the tree of life. We show that the model is able to impute reasonable melting temperatures even in the face of unreasonably noisy data. Additionally, we demonstrate how to use the variance in melting-temperature posterior-distribution estimates to enable principled decision-making in common high-throughput measurement tasks, and contrast the decision-making workflow against simple maximum-likelihood curve-fitting. We conclude with a discussion of the relative merits of each workflow.
Dual-Regularization Complementary Learning for Image Classification

Deep supervised learning has achieved great success in image classification. However, most existing methods require high-quality labeled images, which are not easy to obtain. In this paper, we focus on learning with the complementary label specifying classes an image does not belong to, which is easier to obtain. Previous methods produce much lower performance than learning with true labels due to the inherent ambiguity of complementary labels. To deal with the ambiguity of complementary labels, we propose a new complementary learning method called Dual-regularization Complementary Learning (DRCL). Specially, we train two deep neural networks simultaneously and enforce them to regularize the outputs of each other. In this way, the two networks can learn from each other and tend to generate consistent outputs even when supervised by the ambiguous complementary labels. Experiments on four datasets demonstrate the superiority of our approach over state-of-the-art methods.
Intelligent Auxiliary Teaching of Educational Psychology from the Perspective of Multimedia

The intelligent auxiliary teaching supported by computer multimedia is a mode of the intelligent auxiliary teaching of educational psychology, and it is the product of the combination of artificial intelligence technology and teaching technology. The intelligent auxiliary teaching of educational psychology uses advanced technical means such as computer networks, multimedia, and artificial intelligence to construct a network-based intelligent teaching environment. Based on this, we analyzed an intelligent auxiliary teaching application system supported by computer multimedia for the intelligent auxiliary teaching of modern educational psychology, which is an intelligent network multimedia teaching support environment. This article analyzes the problems in the use of multimedia in educational psychology classrooms, and puts forward reasonable suggestions for the problems. This article proposes improved methods to improve multimedia teaching and optimize the structure of classroom teaching. This can improve teaching efficiency, and can truly give play to the advantages of multimedia teaching.
Pmae: Pseudo Multi-Label Attention Ensemble

Multi-label classification has been recognized to be challenging since the complex correlations among labels and the combinatorial nature of the output label space. Motivated by the label dimension reduction and ensemble learning, in this paper, we propose a novel multi-label classification algorithm based on ensemble learning, termed PMAE: Pseudo Multi-Label Attention Ensemble. Different from existing landmark-based methods which reduce the predicted label space by selecting a few real labels as landmarks in a "hard" manner, the proposed PMAE develops the encoder-decoder mechanism to effectively utilize the low-dimensional pseudo-label representation space in a "soft" manner, which can capture the underlying correlations among labels and lead to more robust performance in complex scenarios. Moreover, we conduct the feature embedding based on multiple attention networks integrated, then, exponent and hinge diversity regularization are introduced to ensure the diversity and complementarity of multiple learners. Experimental results on multiple bench-mark datasets show the superiority of our proposed method over other state-of-the-arts.
Research on Parallelization of Frequent Itemsets Mining Algorithm

FP-growth is a depth-first mining algorithm based on recursion and pattern growth. However, recursive mode can easily bring huge cost of time and space. Therefore, this paper proposes an improved non-recursive serial algorithm NRFP-growth and a parallel algorithm GPFP-growth. The NRFP-growth algorithm introduces the data structure of FP-array to store data sets, and uses the structure of ItemPoss-map to mine frequent itemsets. The GPFP-growth algorithm is based on NRFP-growth, and uses GPU to accelerate the process of mining frequent itemsets. In order to test the performance of the improved algorithm, this paper selects four data sets with different characteristics, and takes the classical serial algorithm as the benchmark to test the time and space performance of the serial improved algorithm, as well as the speedup ratio performance and scalability of the parallel algorithm.
Implementation of a Control System in a Dual Axis Cylindrical-Parabolic Solar Tracking System

When considering effects such as population growth, global warming, reduction of oil reservoirs, and the proposal of policies committed with zero carbon, it is indicated that the worldwide renewable and non-renewable energy supplies should be prepared to fulfill the upcoming energy demand. Thus one of the renewable energy sources that exhibit more technological growth is the photovoltaic solar energy. However, other alternatives such as solar tracking plants (STP) consist of various stages, one of which is the solar tracking system that is constituted by electronic control systems to guarantee its correct operation and stability to the solar trajectory. Thus, this study develops two control systems to verify features of oscillation and settling time when there are uncertain environmental conditions such as clear and cloudy sky global horizontal irradiation (GHI). Those control systems were implemented in a prototype developed in the laboratory, which meets the following features: solar trajectory tracking in an elevation and azimuth angle, cylindrical-parabolic solar capturing by means of reflective material and reflected rays receiver tube where the internal and external temperature data are measured and collected through a SD memory. Results show that the PI control system exhibits steady-state stability at 24 [ms] with an overshoot of 1,17%, which is an excellent condition in the trajectory with GHI (clear sky), and besides, it shows no oscillations in the presence of disturbances.
<scp>NIC</scp>: An innovative supervised machine learning computational framework having network‐based interactional connections

Abstract Models based on computational intelligence have many applications in various problems. Such systems are generally set up and designed based on a collection of data and information. In some real problems, the implementation of experimental studies or doing tests are costly and time‐consuming. Therefore, a model which requires fewer data than the existing soft computing methods can be useful and applicable. In this article, a network‐based interactional connection system is proposed as a new supervised machine learning computational framework for problems with small data. This model, which is inspired by the connections between neurons of the brain, utilizes the series and parallel structures with interactional connections to determine the best estimation. The proposed approach uses less unknown parameters than existing models and gives a suitable response in a few steps. The learning process starts with one generation and continues until an acceptable prediction is found, and the coefficients are determined. However, it may have more generations in a sequential format for better prediction and providing more accurate answers. To evaluate the performance of the proposed computational system, three engineering problems were investigated as a numerical study. The results also compared with the predicted values of four well‐known techniques.
Analysis of image watermarking with a discrete wavelet transform for digital data security

Abstract One of the ways to secure digital data is image watermarking. The method for image watermarking is used with discrete wavelet transform (DWT). The problem in this article is how the performance of DWT for image watermarking. The stages of the research method are: (1) Collecting the dataset that will be used as the trial host (2) Processing data with watermarking techniques, (3) Inserting data using DWT (4) checking the quality of each method, (6) analyzing the results each method. This research has revealed the stages of data security on image watermarking with DWT and the results show that the images provided with watermarking are well secured.
Unsupervised Method for 3D Brain Magnetic Resonance Image Segmentation

Research at Digital Imaging and Communication in Medicine (DICOM) is very useful research in the field of health. In brain images, the problem encountered is when you want to divide or segment each part of the brain. In previous studies, some of research are still segmenting from 2-dimensional images, where the results will be different for each image slice. Therefore, in this research, we conducted the Magnetic Resonance Image (MRI) segmentation of the brain from the 3-dimensional plane to prevent the information contained in the images from being lost. In the early stages, MRI images will be converted to NifTi format to obtain 3-dimensional volume. The pre-processing is added as a modification from previous research, such as, convert image to grayscale, bias field correction, and skull stripping method to remove the skull (non-brain tissue) so that only brain tissue remains from the human brain. The segmentation process is done using multi-otsu thresholding. The experimental result shows that our method has successfully got three different brain tissue named white matter (WM), gray matter (GM), cerebrospinal fluid (CSF).
Nuances of Data Pre-Processing and its Impact on Business

Sentiment analysis (SA), is an open-ended field of research about computational remedy of classifying the human being's opinion about something. The data pre-processing plays a decisive position in SA which is obtained by accelerating the effectively classified instances through the selection of suitable preprocessing methods. When using both the Rule-based and Decision Tree algorithms separately, the missing of data happens that can cause the incorrect classifications of critiques and make the SA procedure not that effective. On the component of the above, this paper explains approximately the importance of pre- processing by introducing a novel approach of combining both the Rule-based and Decision Tree algorithms. The RBDT is a proposed approach which handles the lacking of information values effectively by making sure no word is missed during the classification process. The Experimental results classified the proposed technique to be more effective in achieving accuracy of 90% and time complexity of 0.079 seconds in predicting all the words in the dataset rather than using both the algorithms individually.
Linguistic Summaries Using Interval-Valued Fuzzy Representation of Imprecise Information - An Innovative Tool for Detecting Outliers

The practice of textual and numerical information processing often involves the need to analyze and test a database for the presence of items that differ substantially from other records. Such items, referred to as outliers, can be successfully detected using linguistic summaries. In this paper, we extend this approach by the use of non-monotonic quantifiers and interval-valued fuzzy sets. The results obtained by this innovative method confirm its usefulness for outlier detection, which is of significant practical relevance for database analysis applications.
Budget‐aware scheduling algorithms for scientific workflows with stochastic task weights on infrastructure as a service Cloud platforms

Summary This paper introduces several budget‐aware algorithms to deploy scientific workflows on Infrastructure as a Service Cloud platforms, where users can request Virtual Machines (VMs) of different types, each with specific cost and computing resources. We use a realistic application/platform model with stochastic task weights, and VMs communicating through a Cloud storage. We extend two well‐known algorithms, MinMin and HEFT , and make scheduling decisions based upon machine availability and remaining budget. During the mapping process, the budget‐aware algorithms make conservative assumptions to avoid exceeding the initial budget; we further improve the results with refined versions that aim at rescheduling some tasks onto faster VMs, thereby spending any budget fraction leftover by the first allocation. These refined variants are much more time‐consuming than the former algorithms, so there is a trade‐off to find in terms of scalability. We report an extensive set of simulations with workflows from the Pegasus benchmark suite. Most of the time, our budget‐aware algorithms succeed in achieving efficient makespans while enforcing the given budget, and this despite the uncertainty in task weights.
Image-Text Matching: Methods and Challenges

Image-text matching has gained increasing popularity, as it bridges the heterogeneous image-text gap and plays an essential role in understanding image and language. In the recent few years, there have been extensive studies of matching visual content and textual data with deep architectures. This article reports an overview of image-text matching, especially the most relevant and recent image-text methods and presents a taxonomy of these methods based on alignment level as well as describes the main approaches. Further, we identify the benefits and limitations of each method in this review and provide readers with prominent challenges that can be helpful for researchers in the cross-modal community.
Residual Attention Block Search for Lightweight Image Super-Resolution

Recently, lightweight neural networks with different manual designs have presented a promising performance in single image super-resolution (SR). However, these designs rely on too much expert experience. To address this issue, we focus on searching a lightweight block for efficient and accurate image SR. Due to the frequent use of various residual blocks and attention mechanisms in SR methods, we propose the residual attention search block (RASB) which combines an operation search block (OSB) with an attention search block (ASB). The former is used to explore the suitable operation at the proper position, and the latter is applied to discover the optimal connection of various attention mechanisms. Moreover, we build the modified residual attention network (MRAN) with stacked found blocks and a refinement module. Extensive experiments demonstrate that our MRAN achieves a better trade-off against the state-of-the-art methods in terms of accuracy and model complexity.