Reviewerly/exHarmony huggingface dataset:
A State of Art Review on Blockchain Technology

Blockchain could be used for a wide range of supply chain problems, like difficult maintaining records and stock monitoring. Like an option to centralizing servers least subject to corruption and more organized. This is how Blockchain in supply chain business can be helpful. There are also components of their supply chains for big corporations and organizations. As a result, even multinational companies could almost not keep records of any record. The lack of transparency contributes to costs problems and consumer services that dilute brand name at the end of the day. With the aid of sensing devices and RFID tags, recording and provenance monitoring in Blockchain-based supply chains are easier to access because product information might. Blockchain is able to track down a product's background from its roots to where it is now. In particular, this form of reliable origin detection can be used for any aspect of the supply chain to prevent attacks. We develop an Advanced Blockchain Management Framework in which we analyse commodity failure throughout transit and in particular emphasis on the supply chain management for fossil energy.
Evaluating Intertwined Effects of Emoji and Message Framing to Vaccine Communication Strategy

Under the circumstance of continuous variation of COVID-19 virus, verified the temporariness of the vaccines made by various countries. One cannot expect permanent protection by accepting only one dose of vaccine. In order to prepare and respond to the pandemic, many countries are applying different strategies to increase vaccination rates. The WHO appeals to the world to take the vaccine booster shot for community immunity. Relevant authorities then have to provide and spread visual health messages on the booster shot to keep the public informed. This study examine how unofficial organizations can guide and persuade people to adopt relevant health actions more effectively (such as continuous vaccination) by introducing emoji with different emotional valences in different message framing. An online experiment adopted a 2 (emoji: positive versus negative) × 2 (message framing: gain framing versus loss framing) design to investigate the effects of contrary emoji on people's self-efficacy to continuously take the booster shot. In total of 240 university students were recruited to participate in this study. Within two types of message framing, the experiment simulated 4 pieces of health messages on the COVID-19 booster shot released by an unofficial organization, together with emoji of two emotional valences. The results showed that health messages with negative emoji result in stronger self-efficacy to user. Moreover, there is an interaction effect between emoji and message framing on self-efficacy. This study is intended to provide meaningful insights for health communicators, visual designers and health practitioners concerned.
Does ICT Diffusion Drive the Finance-Growth Nexus? Evidence from Sub-Saharan Africa

Technological innovation has become a major channel through which financial development drives economic growth, and the chapter attempted to provide evidence for this, using data for sub-Saharan Africa (SSA). For its theoretical foundation, the study extended the endogenous growth theory by incorporating information and communication technology (ICT) diffusion as a possible catalyst causing financial development to affect economic growth based on the supply-leading and demand-following hypotheses. Following existing literature, the study constructed an ICT diffusion index, using principal component analysis (PCA) and a dataset representing mobile phone subscribers per 100 people, fixed telephone line subscriber per 100 people and broadband Internet subscribers per 100 people. The study used the growth rate of GDP as the dependent variable, while financial development and economic openness were the independent variables. The study adopted the pooled mean group estimator of the autoregressive distributed lag (ARDL) model to analyse the data, which covered the period 2000 to 2020. The results showed that in the long run, while the ICT index drove growth, the interaction of financial development with the ICT index was significantly negative for growth in SSA. Therefore, this result did not support the supply-leading hypothesis. A tenable reason for this finding could be the inability of private credit to drive output expansion and the challenge of financial inclusion in Africa. Therefore, this study led to the recommendation that countries in SSA should channel private credit to value-added activities and increase financial inclusion so that innovation can drive economic growth through financial development.
AI for Patents: A Novel yet Effective and Efficient Framework for Patent Analysis

Patents provide inventors exclusive rights to their inventions by protecting their intellectual property rights. However, analyzing patent documents generally requires knowledge of various fields, considerable human labor, and expertise. Recent studies to alleviate this problem on patent analysis deal only with the analysis of claims and abstract parts, neglecting the descriptions that contain essential technical cores. Moreover, few studies use a deep learning approach to handle the entire patent analysis process, including preprocessing, summarization, and key-phrase generation. Therefore, we propose a novel multi-stage framework that can aid in analyzing patent documents by using the description part of the patent rather than abstracts or claims with deep learning. The framework comprises two stages: key-sentence extraction and key-phrase generation tasks. These stages are based on the T5 model structure, transformer-based architecture that uses a text-to-text approach. To further improve the framework&#x2019;s performance, we employed two key factors: i) post-training the model with a patent-related raw corpus for encouraging the model&#x2019;s comprehension of the patent domain, and ii) utilizing a text rank algorithm for efficient training based on the priority score of each sentence. We verified that our key-phrase generation method of the framework shows higher performance in both superficial and semantic evaluation than other extraction methods. In addition, we provided the validity and effectiveness of our methods through quantitative and qualitative analysis, demonstrating the practical functionality of our methods. We also provided a practical contribution to the patent analysis by releasing the framework as a demo system.
Comparative Study of Time Optimization Algorithms for Traveling Salesman Problem

Brute-force-based solution using backtracking method of traveling salesman problem (TSP) algorithm which is an NP-hard problem is not improved in terms of space and time complexity. The algorithm is muddled to apply in graphs that contain a larger number of well-connected nodes. To handle this huge time complexity, approximation/divide and conquer methods, heuristic methods, and nature-inspired methods are applied. The use of a keen and numerical procedure speeds up the enhanced TSP by multiple times. In this paper, two approaches using the divide and conquer technique and nature-inspired algorithms like recursion with bitmasking and genetic algorithm, respectively, are applied to minimize the running time of the TSP algorithm, and their performance is also analyzed and compared on the different number of nodes.
Augmented Reality Supported Real-Time Data Processing Using Internet of Things Sensor Technology

Internet of things (IoT) devices increasingly permeate everyday life and provide vital and convenient information. Augmented reality (AR) enables the embedding of this information in the environment using visualizations that can contextualize data for various applications such as Smart Home. Current applications providing a visual representation of the information are often limited to graphs or bar charts, neglecting the variety of possible coherence between the subject and the visualization. We present a setup for real-time AR-based visualizations of data collected by IoT devices. Three distinct battery-powered IoT microcontroller systems were designed and programmed. Each is outfitted with numerous sensors, i.e. for humidity or temperature, to interact with the developed AR application through a network connection. The AR application was developed using Unity3D and the Vuforia AR SDK for Android-based mobile devices with the goal of providing processed and visualized information that is comprehensible for the respective context. Inspired by weather applications for mobile devices, the visualization contains animated dioramas, with changing attributes based on the input data from the IoT microcontroller. This work contains the configuration of the IoT microcontroller hardware, the network interface used, the development process of the AR application, and its usage, complemented by possible future extensions described in an outlook.
LFC-SSD: Multiscale Aircraft Detection Based on Local Feature Correlation

Interpreting airborne remote sensing images plays an important role in aviation control and battlefield situational awareness. However, highly dynamic aircraft detection remains challenging, owing to variable object sizes, flexible attitudes, and motion blur. This study develops a multi-scale airborne aircraft dataset benchmark to overcome aircraft detection challenges, such as high intraclass variance, multiple scales and angles, motion blur, and partial occlusion. We also propose a trained-from-scratch aircraft detector, the local feature correlation single shot multibox detector (LFC-SSD), to detect multi-scale aircraft. The LFC-SSD comprises a local correlation feature extraction module, called &#x201C;Right-Residual,&#x201D; and a feature fusion module using a reverse feature pyramid network (R-FPN). Right-Residual extends the global receptive field by aggregating contextual information while learning non-adjacent region features efficiently. R-FPN utilizes multi-path information transfer horizontally with recursive integration to enhance the robust representation of the location information of multi-scale object features. In addition, a specific default boxes method is designed for remote-sensing images of aircraft. Extensive experimental results confirm the significant improvement of the proposed method over several existing state-of-the-art methods.
Shrinkage Linear with Quadratic Gaussian Discriminant Analysis for Big Data Classification

Generation of massive data is increasing in big data industries due to the evolution of modern technologies. The big data industries include data source from sensors, Internet of Things, digital and social media. In particular, these big data systems consist of data extraction, preprocessing, integration, analysis, and visualization mechanism. The data encountered from the sources are redundant, incomplete and conflict. Moreover, in real time applications, it is a tedious process for the interpretation of all the data from different sources. In this paper, the gathered data are preprocessed to handle the issues such as redundant, incomplete and conflict. For that, it is proposed to have a generalized dimensionality reduction technique called Shrinkage Linear Discriminate Analysis (SLDA). As a result, the Shrinkage Linear Discriminate Analysis (LDA) will improve the performance of the classifier with generalization. Even though, dimensionality reduction systems improve the performance of the classifier, the irrelevant features get degraded by the performance of the system further. Hence, the relevant and the most important features are selected using Pearson correlation-based feature selection technique which selects the subset of correlated features for improving the performance of the classification system. The selected features are classified using the proposed Quadratic-Gaussian Discriminant Analysis (QGDA) classifier. The proposed evolution techniques are tested with the localization and the cover data sets from machine learning University of California Irvine (UCI) repository. In addition to that, the proposed techniques on datasets are evaluated with the evaluation metrics and compared to the other similar methods which prove the efficiency of the proposed classification system. It has achieved better performance. The acquired accuracy is over 91% for all the experiment on these datasets. Based on the results evaluated in terms of training percentage and mapper, it is meaningful to conclude that the proposed method could be used for big data classification.
Compare and Evaluate One Hour Ahead Solar Power Forecasting with ANN Tuned Using Genetic Algorithm and Hybrid Genetics-Based Particle Swarm Optimization

Climate change is one of the most pressing issues in recent times. The main reason behind this is the emission of greenhouse gases. Burning of fossil fuels in thermal power plants for electricity generation contributes to this majorly. This can be reduced by adopting renewable energy resources like wind, solar etc. Solar PV power generation systems are being increasingly adopted due to technological advancements and cost reduction. Solar power generation is not reliable due to variability in the irradiance and temperature values throughout the day. If this variability can be forecasted this will help in proper load scheduling and make the system more reliable. The major focus of the study is to reduce the time for forecasting and to minimize the error for short-time duration i.e., One Hour ahead forecasting. In this paper solar PV power forecasting is done using two methods Artificial Neural Network (ANN) tuned using Genetic algorithm (GA), Artificial Neural Network tuned using Hybrid Genetics-based Particle Swarm Optimization (PSO) respectively and the results are compared. The ANN is simulated and the tuning of ANN as well as verification of results is done using MATLAB.
An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database.However, there is no standard guideline for data transformation from relational database to NoSQL database.A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processing time when compared to the relational database query processing time.However, these approaches produced redundant tables in the resulted schema that in turn consume large unnecessary storage size and produce high query processing time due to the generated schema with redundant column families in the transformed column-oriented database.In this paper, an efficient data transformation technique from relational database to column-oriented database is proposed.The proposed schema transformation technique is based on the combination of denormalization approach, data access pattern and multiple-nested schema.In order to validate the proposed work, the proposed technique is implemented by transforming data from MySQL database to MongoDB database.A benchmark transformation technique is also performed in which the query processing time and the storage size are compared.Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storage space usage due to the reduced number of column families in the column-oriented database.
Malignant Mesothelioma Subtyping of Tissue Images via Sampling Driven Multiple Instance Prediction

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epitheliod, Sarcomatoid, and Biphasic. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variablity. In this work, we propose the first end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an instance-based sampling scheme for training deep convolutional neural networks on this task that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterization of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 243 tissue micro-array cores with an AUROC of $$0.87\,\pm \,0.04$$ for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS.
Review of Word Sense Disambiguation and It’S Approaches

In present era, Natural Language Processing (NLP) is critical for improving human-machine communication. It is a broad interest to process textual data and gathers valuable and exact information from these texts. NLP compiles the text and sends the data to a computer for further processing. The current state of NLP's mathematical model for proper understanding of word meaning is unclear, and the meaning of words in context is unclear, evoking multiple senses. The spread and improvement of Natural Language Processing applications are being hampered by ambiguity in interpreting the precise meaning of texts such as machine translation (MT), Human-Machine interfaces, and so on. The approach of discovering the correct interpretation of ambiguous word in a given sentence is accepted as Word Sense Disambiguation (WSD).WSD is recognized as being one of natural language processing's more challenging and unsolved problems. Many ambiguities in natural languages are apparent, and researchers are offering to solve the problem in a variety of languages to achieve good disambiguation. These ambiguities must be solved in order to make sense including its texts and advance NLP processing and applications. WSD has a number of NLP applications for which it could be a problem, such as Machine Translation (MT), Information Retrieval (IR), Dialogues, Speech Synthesis (SS), and Question Answering (QA). The effectiveness of many strategies directly applied to WSD, such as Dictionary and Knowledge-based, Supervised, Semi-Supervised and Unsupervised approach, is compared in this study.
Research and Implementation of Student Management System Based on Java EE Technology

With the development of information technology, information technology is gradually extended to the campus management, administrative office, the existing student management system can not meet the actual needs because of its adaptability. With the increase of college enrollment, the complexity of student management system is getting higher and higher. It is urgent to use computer to realize student information management and statistics, and provide better services for teachers and students. Based on the Java EE platform, this paper develops a student management system with certain characteristics, which has good reliability, portability and security, and has important reference significance for promoting the development of student management and the realization of informatization in Colleges and universities.
Policy Gradient for Arabic to English Neural Machine Translation

In this work, we present a strategy of using the policy gradient training method from deep reinforcement learning to train a Seq2Seq model. This strategy is based on the combination of the classical cross-entropy and the policy gradient in the training phase. To evaluate the effectiveness of this strategy, we compare two Seq2Seq models trained with two different training methods to translate from Arabic to English. The first method is the cross-entropy, and the second one is a combination of cross-entropy and policy gradient with different amounts. Experimental results show that the second training method leads to improve the performance of the sequence-to-sequence model by 0.71 BLEU points.
Cooperative Suppression of Negative Effects Associated With Multicollinearity and Abnormal Data for Aeromagnetic Compensation

Compensating the magnetic interference related to the airplane maneuvers is essential to high-precision aeromagnetic measurement. In estimating the compensation coefficients, aeromagnetic compensation based on the Tolles-Lawson model needs to address the reduction in accuracy and robustness arising from several negative effects. Because existing methods only suppress single effects independently, the scope of their application is very limited. To address this problem, a cooperative suppression method of the negative effects associated with multicollinearity and abnormal data is proposed in this study. For this method, abnormal data produced by the dead-zone effect of a classical optically-pumped magnetic sensor are assigned lower weights, thereby reducing their negative influence in the estimation of the compensation coefficient. Moreover, a ridge parameter is added to further reduce the multicollinearity of the compensation coefficient. Theoretical evaluation shows that, in the presence of 20% abnormal data and 0.5 correlation, this method reduces the mean square error for the coefficient to 0.76 compared with 4.39 for the least-squares method, 3.43 for the ridge method, 2.13 for the robust method. For practicality verification, we built an experimental platform and mounted it on an aeromagnetic survey airplane, and then performed a compensation flight test. The results demonstrate an improved ratio for our method of 12.79, which is significantly higher than 0.56 for the least-squares method, 2.73 for the ridge method, 4.29 for the robust method, and 7.95 for the state-of-the-art commercial compensator.
Analysis of Load Balancing Detection Methods Using Hidden Markov Model for Secured Cloud Computing Environment

Cloud computing is rapidly growing nowadays and stands a creative computing paradigm which provides Internet facilities to meet users' storage needs. Various interconnected technologies exist in Cloud. Each one has some weaknesses which raise many concerns about privacy and security. One among Cloud computing's main security issues is to safeguard against network encroachments affecting the security, availability, and legitimacy of Cloud services and resources provided. Hidden Markov model is a state-based transition model by which the transition of states is been used to estimate using the distributions of probabilities from another state to previous and next another state. This model can also be used here on this paper for both the identification and prevention of intrusion. Except in the cloud setting where security issues and confidentiality are main concerns, identification and prevention of attack is done. Compared to some other current intrusion detection methodology the recommended methodology applied here is successful. This model also increases the real positive rate of intrusion detection systems and reduces the false positive rate.
A Method for Improving Accuracy of DeepLabv3+ Semantic Segmentation Model Based on Wavelet Transform

Deep Learning algorithms based on convolutional neural network (CNN) have achieved huge success in semantic segmentation. However, in these networks, sub-sampling will cause a large loss of image detailed information. In this work, we design a novel method for recovering some of the lost pixels. We use two-dimensional discrete Wavelet Transform (DWT) to extract image boundary detailed information and combine the segmentation result of the convolutional network to recover some of the lost details. We analyze the influence of the algorithm parameters and wavelet function on the final prediction. In our experiments, our algorithm has accuracy improvement compared to the deep network on the PASCAL VOC 2012 dataset.