Reviewerly/exHarmony huggingface dataset:
Managing Supply Chain Digitalization With Blockchain Technology

Digitization of manufacturing supply chain operations is essential in changing management practices and enhancing business transparency-related aspects. The digitalization of supply chain business processes often exploits emerging technologies such as radio frequency identification (RFID), the internet of things (IoT), data analytics, cloud computing, and blockchain technology to reshape supply chain management. The IoT technology integrates various smart objects (or things) to form a network, share data among the connected objects, store data, and process data to support business applications. However, some of the IoT infrastructural components are a shortage of computational processing power and local data storing ability, and these components are very vulnerable to the privacy and security of collected data. This chapter presents an information system architecture consisting of IoT and blockchain technology to maintain data security and transparency and how this helps improve business operations.
Blockchain-Based Secure Transactions

Traditional transactions have several issues, like physical cash, for instance, boarding fake bank notes and also building stock to use money significantly less complex. In this digital marketing world, hackers are using many ways and techniques to scam money. The three most common online transaction frauds in India are scam using QR codes, UPI frauds, remote access/screen sharing frauds. Protection and privacy are the main anticipated features in the field of online transactions, which can be fulfilled by blockchain technology. This proposal is crucial as it is the first attempt to apply blockchain technology to payment services. In the proposed system, due to high level of fraud, blockchain technology is used. Hackers attack one block and change their address, and hence, transaction is blocked and amount will also be refunded using blockchain approach hashing algorithms. The main steps of blockchain used in the proposed system are transaction data, changing blocks with a hash, and creating signature (hash) using cryptographic hash function.
Deployment of Blockchain Technologies in Africa

The problem of this chapter is to understand how technological developments, in particular blockchain applications, affect the performance of the supply chains of companies based in Cameroon. This work will allow us to analyze the conditions for the success of the deployment of the supply chain in Cameroon in a dynamic of improving its performance. The empirical research carried out consisted in carrying out 18 semi-structured interviews lasting an average of 60 minutes with actors in the supply chain sector in Cameroon. The researchers adopted a qualitative type methodology based on a thematic content analysis. The results obtained are of two kinds: on the one hand, the results related to the challenges and conditions of success of the blockchain; and on the other hand, the results related to the contribution of the blockchain to the performance of the supply chains in Cameroon.
Blockchain Arbitration

Blockchain technology offers substantial benefits to supply chains (SCs). Along with transparency and traceability and other undeniable advantages, blockchain technology enables smart contract integration. Blockchain and smart contracts are, however, not flawless and can create a new category of disputes. The recognition of these disputes by traditional legal institutions is questionable. This chapter presents the concept of blockchain arbitration that allows for smart contract and blockchain dispute settlement via a decentralized resolution platform. The chapter reveals that despite existing challenges, blockchain arbitration can enhance the resolution process in terms of flexibility, cost, and time reduction, increase trust among SC stakeholders, and boost overall SC performance.
Analyzing and Improving the Scalability of In-Memory Indices for Managed Search Engines

Managed search engines, such as Apache Solr and Elastic- search, host huge inverted indices in main memory to offer fast response times. This practice faces two challenges. First, limited DRAM capacity necessitates search engines aggres- sively compress indices to reduce their storage footprint. Unfortunately, our analysis with a popular search library shows that compression slows down queries (on average) by up to 1.7× due to high decompression latency. Despite their performance advantage, uncompressed indices require 10× more memory capacity, making them impractical. Second, indices today reside off-heap, encouraging unsafe memory accesses and risking eviction from the page cache.
Prototyping in smart product design: Investigating prototyping tools to support communication in the early stage smart product development

Smart product design (SPD) is more complex than traditional product design (TPD), requiring intense interdisciplinary collaboration. It is well understood that prototypes are an effective means to facilitate communicating design concepts. However, there is limited research concerning prototyping preferences of design disciplines involved in SPD. The research explores the use of prototyping in smart product development. Specifically, it aims to understand the preferences and dimensions of fidelity of different prototyping tools that can support communication in the SPD process for various stakeholders. This paper presents two surveys. The first investigates the use of prototyping tools by different disciplines involved in SPD versus TPD. The second compares elements of key prototyping tools used across disciplines within SPD. Results show that the interactivity and environment dimensions of fidelity of prototyping tools motivate differences in preference between SPD and TPD. Moreover, user depiction and contextual information are key elements of prototyping that support designers in communicating interactive and environmental qualities of SPD concepts.
The life and activities of Choi Byeong-jun, an anti-Japanese independence activist

From 1908 to 1919, Choi Byeong-jun devoted himself to the anti-Japanese armed struggle in the northern Gando in Manchuria and Primorsky Krai in Russia for more than 10 years before and after the Japanese invasion of the Korean Empire and its forcible annexation. He was a very noteworthy figure in the early history of the anti-Japanese independence movement in that he acted as a comrade of Lee Beom-yoon, Choi Jae-hyeong, Ahn Jung-geun, and Hong Beom-do, whom we are familiar with, and that it allows us to know the flow of the anti-Japanese independence movement at the time just by looking at his life. Above all, Choi Byung-jun was a true patriot and comrade whom Hong Beom-do trusted the most. In the early days of his activities, he fought passionately for the Righteous Army, and in the middle and latter half of his career, he faithfully fulfilled his role as a contact officer. In addition, he raised his two sons and Hong Beom-do's son as excellent patriotic youths, which served as a foundation for them to play a major role in the anti-Japanese armed struggle in the 1920s. Besides that, however, there are three major historical meanings that we can learn from Choi Byung-jun's life. First, he did not cling to ideologies s and beliefs that were only instrumental in achieving the great goal of independence, and showed a flexible and humble attitude that allowed him to humble himself at any time if he was considered a better person than himself. Secondly, Choi Byeong-jun, together with Hong Beom-do, was the person who established the basic framework of the Korean anti-Japanese armed struggle, that is, the war of independence. Lastly, Choi Byung-jun gave up his life to save his own son and Hong Beom-do's son. This cannot be explained only in the dimension of paternal love. He did not sacrifice himself as a mere biological father, but sacrificed himself to save the next generation of the independence movement.
An Optimal Approach for PV Power Optimization Based on Neural Network

The primary energy sources in the world are fossil fuels like coal and oil. Overuse will result in issues like the greenhouse impact and energy depletion. Finding high-efficiency, clean, and renewable energy is a must in order to address the conflict between the ongoing rise in energy consumption and the deterioration of the natural environment. In order to optimize the Photovoltaic (PV) power in distribution system, this paper propose an efficient algorithm based on neural network. The main idea is to consider the main factors for evaluating the system performance through principle factor analysis technique and deploys them as input for the prediction. Then, the spatial search algorithm is used to select the optimal weight threshold of the neural network. Simulation results show that the proposed algorithm has better performance as compared with existing methods.
Study on Ultra-short Term Distributed Photovoltaic Power Forecasting in the Haze Day Based on All-sky Images Combined with Support Vector Regression

Due to the influence of many local random mutation factors, the ultra-short-term prediction of distributed photovoltaic power is faced with great challenges. This study is a modeling study for accurate prediction of photovoltaic power in hazy days based on all-sky image observation data. Firstly, based on the all-sky images, digital image processing technology is used to extract radiation-related image features of haze days, including image brightness, power spectrum, smoothness and mean of singular value. Secondly, the support vector regression model is established by combining image features and radiation attenuation coefficient. Finally, via determining the suitable forecasting time horizon, the surface irradiance can be forecasted based on the support vector regression model, and the photovoltaic power ultra-short-term prediction is realized using the irradiance-power convert model. The experimental results show that the ultra-short-term prediction method of photovoltaic power based on the whole sky image features and support vector regression has a good prediction effect when the optimal prediction time is determined, which provides an important practical basis for the accurate prediction of ultrashort-term power of distributed photovoltaic power stations.
Learning Enabled Data Center Operation Scheme for Participating in Power Market

As the key industrial infrastructure in the digitalera, the energy consumption of data center is increasing rapidly. which leads to a significant increase in the energy cost for data centers. Meanwhile, the nature of job scheduling can provide time-flexible loads for data centers. Therefore, Data centers can participate in the power market bidding as large energy customers and sense the market electricity price signal for job scheduling. This can not only improve the operating profit of data centers but also further coordinate with the power grid to maximize social welfare. In this paper, we establish a decision model for data centers that perceives market prices. Then, we provide the joint optimal strategy of job scheduling and bidding for the data center via a deep reinforcement learning approach. Finally, we validate our method through numerical experiments. The proposed strategy allows for greater flexibility and higher profits for data centers compared to other strategies.
Data Mining Application for the Generation of User Profiles in Serious Games Aimed at Attention and Memory Training

Serious games designed for cognitive stimulation have proven to be effective for training skills such as attention, memory, and reaction time. As with any other highly interactive system, serious games generate large amounts of data, which directly reflect the player’s decisions. Therefore, it is possible to evaluate users’ performance and customize training according to their progress to provide a cognitive challenge appropriate to their abilities. One of the possible approaches to enabling personalized training and increasing user engagement is the identification of player profiles. This study aims to reveal the influence of player characteristics (demographic, socioeconomic, and behavioral) on performance and identify the most significant attributes to generate profiles. For this purpose, a data mining model based on the CRISP-DM methodology was developed. The results showed that the variables age, daily physical activity time, and computer use time directly influence the cognitive performance of the game users’. Finally, four-player profiles were discovered: 1) High Cognitive Performance, 2) Average Cognitive Performance, 3) Low Cognitive Performance I, and 4) Low Cognitive Performance II.
Virtual Learning Environment for Children with Down Syndrome Through Leap Motion and Artificial Intelligence

The implementation of virtual and immersive environments has increased significantly in recent years, due to their ease of remote interaction, feedback provided, and learning acquired autonomously. This article is focused on the educational area, mainly math, for children with Down Syndrome and proposes the development of two applications as immersive environments full of visual stimuli and kinesthetic interaction, for learning and reinforcement of the basic topics required for preschool levels. The design and implementation were conducted using virtual reality (VR) tools in conjunction with the Leap Motion Controller (LMC) sensor Software Development Kit (SDK), in addition to having a supervised learning of artificial intelligence. The data for training the neural network, was obtained by creating and connecting a bidirectional local server. The neural network, was programmed using the Matlab artificial neural network toolbox, considering the local server to obtain data and feedback. The results are presented the same ones that are interesting.
An Infrared Detection Scheme for Distribution Network Power Equipment Based on Deep Convolutional Auto-Encoder for Small Negative Samples

Infrared detection is frequently used to detect abnormal heating of power equipment in the distribution network. In this paper, the infrared picture of the equipment is reconstructed using the DCAE structure, and the abnormal heating area is discovered using the pixel residual between the reconstructed image and the original image, in order to keep the power grid safe and stable. The experimental results reveal that the DCAE architecture employed in this study has a recognition accuracy of 94.36%, which has greater advantages than the existing standard detection approaches. At the same time, because it does not require the massive data set required for traditional training, it is more suited to the field of infrared detection, where the number of abnormal fever samples is modest.
Financial Credit Risk Measurement Using a Binary Classification Model

Currently, financial institutions have several problems in the analysis of information to grant a credit or a loan, causing losses that involve collection expenses, notifications, legal processes, among others. Thanks to the digital transformation and technological progress, Artificial Intelligence and especially Machine Learning can be used to analyze customer data and predict non-compliance with the payment of their obligations to institutions. The objective of this work is to apply CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and the Random Forest (XGBoost), Logistic Regression and Neural Networks of supervised learning models, to implement a predictive model that allows evaluating credit risk. With the result of the application of the predictive model, it is concluded that the use of Machine Learning tools helps to optimize the evaluation of credit risk in financial entities. Once CRISP-DM methodology was used for the analysis, development, and evaluation of the models, it was concluded that the most efficient model is the Random Forest. Based on this experience, future work could implement this type of model in other areas such as: fraud detection, customer segmentation or a recommendation engine that can suggest financial products and services based on customer needs and behaviors.
Infrared Image Detection of Transmission Line Insulators Utilizing a CNN Combination Implemented With Small Sample Data Augmentation and Transfer Learning

The abnormal heat identification of transmission line insulators has always been a necessary link in power inspection, and the improvement of its intelligence is an inevitable development trend. Infrared image recognition of insulators often requires a large number of infrared image data, which is one of the biggest resistance obstacles at present. In this paper, we use the small sample set for data augmentation and introduce transfer learning at the same time. First of all, the original set of 150 small samples is expanded to nearly 2000, and then the image recognition is carried out through the pre-trained VGG16 network, and the final recognition accuracy can reach 92.65%, which has certain advantages compared with the current infrared recognition methods of various small samples.
Facial Recognition System with Liveness Detection Integrated into Odoo for Attendance Registration in a Software Laboratory

Facial recognition is currently adaptable to any context where the identification of individuals is required and has succeeded in replacing traditional form-filling authentication methods. Moreover, due to the current Covid-19 health crisis, face recognition turns out to be one of the main technologies useful for authentication without requiring the manipulation of community instruments. This paper presents the development of a facial recognition system with liveness detection integrated into Odoo ERP for the registration of attendance to a software laboratory, providing an alternative attendance registration that does not require the manipulation of community use instruments, shortening registration times and facilitating the creation of reports of laboratory use. The Design Thinking framework and its five phases, empathy, definition, ideation, prototyping, and testing, were used for the development of the project. The final face recognition system with liveness detection was developed using face-recognition and three-dimensional neural networks (3D-CNN). The system averages 95% prediction accuracy, requires an average of 9.2 s per person for registration and facilitates timely reporting of lab usage.
Emotion Evaluation from Young Adults to Old Seniors using Films Emotionally Labelled

Objectives: Investigation of facial expressions related to emotional reactions from four age ranges, including both non pathological women and men, when viewing successive film clips emotionally-labeled as neutral, happy, surprised, angry, scared, disgusted, and sad reactions.Method: Emotions were evaluated by having different age groups view a video composed of a succession of short, emotionally-labeled film clips to elicit neutral, happy, surprised, angry, scared, disgusted, and sad reactions.The groups consisted of young adults (YA) ranging from 20 to 39 years old, older adults (OA) ranging from 40 to 59 years old, young seniors (YS) ranging from 60 to 79 years old and older seniors (OS) ranging from 80 to 91 years old.Results: Viewing emotions by age group revealed that the first emotion declining significantly with aging is happiness.A high level of happiness seems to be a signature of younger adults.From age sixty to eighty, the amount of emotion increased significantly, specifically anger.No significant difference was seen in arousal between the four groups.A highly significant difference was observed in the valence between the adults and the seniors.Conclusion: This study allowed us to discriminate by each group's respective amounts of emotion and the normal evolution of emotions at different ages.This testing method, which is easy to apply, could allow us to distinguish normal processes occurring during aging from pathological processes occurring in neurological diseases.
Colombian Sign Language Classification Based on Hands Pose and Machine Learning Techniques

New technologies can improve the inclusion of deaf (and hearing loss) people in different scenarios. In the present work, a classification of the Colombian sign language alphabet was implemented. For this, the employment of the media-pipe hands pose tool was used to feature extraction process. Then, three machine learning models: support vector classifiers, artificial neural networks and random forest, were trained to determine the best proposal. Results show how a neural network with one hidden layer obtained the best performance with 99.41%. The support vector classifier reached an accuracy of 99.12%, and the worse result was achieved by the random forest model with 96.67% in the classification. The proposal can contribute with advances in the sign language recognition in the Colombian context, which has been worked in different approaches with more complex models to do similar classifications.
The Study on NLP-based Semantic Analysis Technology to Improve the Accuracy of English Translation

In this paper, the semantic analysis technology based on NLP(natural language processing) is proposed to optimize the English translation model in order to improve the efficiency of the error correction and accuracy of English machine translation by building the NLP corpus, constructing semantic feature analysis model of English translation text error correction. A grammar error detection model is used as a discriminator, in addition, N-gram information and NLP corpus information is added into the corpus model parameters in English machine translation: first, use the sequence label error detection model to mark the characters with problems in sentences; Second, use the end-to-end model to correct only the wrong characters to reduce the reasoning time; then, the semantic features are added to Transformer's attention calculation through NLP's semantic analysis method to improve the error correction efficiency of the model. According to the sentence error category, the pre-training sentence pairs of the specified error category are generated, and the masked characters in sentences of different languages are predicted through auto-regressive decoding; Besides, the large-scale sentences are corrected into corresponding correct sentences in the case of unknown languages, thereafter reaching the aim of realizing semantic analysis technology based on NLP to improve the accuracy of English translation. The test results show that this method has good error correction ability by reducing the error rate, thus improves the accuracy of English machine translation.
Research on the Application of Deep Reinforcement Learning Algorithm for Sensor Networks in Intelligent Environment Control

Real-time search of objects, data, and services has grown to be a crucial and practical problem in the IoT world in recent years due to the fast deployment of numerous Internet of Things (IoT) sensors. In addition to offering solutions for real-time sensor data collecting in the IoT, IoT data prediction models also provide more useful applications than conventional IoT event detection models. Using complicated time series created by several types of sensors, we have developed an enhanced neural network model in this research for multi-dimensional feature selection and outlier identification. Comparing our approach to conventional data prediction methods, we can see an improvement in the stability and accuracy of long-term forecasts for IoT sensor data. Finally, we compare the training capabilities of traditional neural network models with random forest models to assess the efficacy of our prediction model using sensor data from the Intel Berkeley Research Laboratory. Our model achieves an accuracy rate that is respectively more than 12% and 68% higher than the two comparison models, while the corresponding loss values are less than 0.325 and 0.595, respectively.