Reviewerly/exHarmony huggingface dataset:
Fake Product Identification using Block-Chain

Counterfeit products are a major problem that affects businesses and consumers worldwide.They pose a threat to public safety, harm legitimate manufacturers, and result in significant economic losses.Authentifi is a decentralised platform that proposes a solution to this challenge by utilising blockchain technology to create a secure and transparent system for verifying product authenticity.The Authentifi platform enables manufacturers, retailers, and consumers to verify the authenticity of products, thereby reducing the prevalence of counterfeit goods.This research paper discusses the technical aspects of the Authentifi platform, including design and implementation.The paper evaluates the feasibility, efficiency, and security of the platform through a series of experiments and simulations.The platform's use of blockchain technology ensures that the information stored on the system is immutable, tamper-proof, and transparent, providing a high level of security.Authentifi's decentralisation also ensures that the system is resistant to single points of failure and malicious attacks.The experiments and simulations conducted demonstrate that Authentifi is an effective solution for counterfeit product identification.The platform's performance is compared with traditional methods of product identification, such as barcodes and QR codes.The simulations assess the platform's resilience to various attacks, including data breaches and denial of service attacks.Authentifi's benefits for manufacturers, retailers, and consumers include an increase in consumer confidence in the products they purchase, protection of legitimate manufacturers from losses due to counterfeiting, and improved public safety.The research suggests that Authentifi has the potential to significantly reduce the negative impact of counterfeit products on the global economy.In conclusion, Authentifi is a promising solution for counterfeit product identification that provides benefits for all stakeholders.The platform's use of blockchain technology ensures a secure and transparent system that can reduce the prevalence of counterfeit goods.The research suggests that Authentifi has the potential to significantly reduce the negative impact of counterfeit products on the global economy.
The Effectiveness of Contextualized Digital Game-Based Learning Resource in Improving Kindergarten Pupil’s Alphabet Knowledge Level

The achievement of pupils in their future studies depends on their reading skills. One of the strongest foundations in reading success includes alphabet knowledge. Nowadays, the utilization of information communication technology (ICT) influences young children’s learning and emergent literacy skills. Hence, this study explored the effectiveness of digital game-based learning resource in improving pupils’ alphabet knowledge specifically on vowels. An alphabet knowledge checklist on vowels was used to collect the data. One key finding from the study is that most of them have fair alphabet knowledge of vowels with an overall pre-test mean score of 17.91. Findings revealed that interactive learning resource was effective as reflected by a substantial increase of 8.95 points. The t-test for paired samples results in a t-value of -10.11 and a p-value of 0.000. Thus, the difference between the posttest and pretest is significant. Hence, digital game-based learning resource is an effective non-print instructional material in kindergarten for the improvement of alphabet knowledge on vowels. The study recommends the creation of contextualized digital game-based learning resource using consonants to further validate the result. A similar resource may also be suggested to other grade levels to confirm its effectiveness in improving alphabet knowledge.
Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.
Prediction for Solar Energy Different Climatic Conditions to Harvest Maximum Energy

Sustainable development and renewable energy are gaining popularity worldwide as climate change has a growing effect on governments and municipalities. In this regard, a gadget that predicts sustainable energy providers’ power production over a year for a particular place might help make sustainable energy investments considerably more efficient. This study is based on secondary sources of data. These reviews aid in selecting the most appropriate forecasting strategy for a given set of demands or criteria. In terms of application, finding the optimality of precise predictions regarding solar energy and key planning issues is a new phenomenon that is the focus of this study. Using solar energy predictions improves the accuracy of energy production forecasts. It not only does better than any prediction (based on the average of previous research), but it also gets the most energy out of it.
Quality evaluation method of raw seismic data based on wavelet consistency

Summary With the continuous development of seismic exploration technology, the quality evaluation of acquired seismic data is particularly important. It is an important basis for evaluating the quality of acquired data, formulating processing workflow and parameter selection, and an important means for analyzing whether the acquired data meet the exploration needs. Based on the improvement of the raw seismic data quality evaluation method, this paper proposes a seismic data evaluation method based on wavelet consistency. This method uses the correlation principle to extract zero phase wavelet. through the extraction of the raw seismic data common shot gather or common receiver gather of the time of the first zero-cross position, quantitative, systematic analysis, so as to effectively achieve the raw seismic data consistency quality evaluation.
English Language Learning with Flashcard Media and Educational Posters to Improve Language Education

Supporting learning media is an important role in increasing the level of English proficiency students, the teacher is responsible for implementing learning media in teaching process. One of them, in using flashcard and posters. This research is sociolinguistic research that focuses on efforts to improve education through flashcards and educational posters at SDN Haurseah 1 (one). The purpose of this study was to describe the effectiveness of language learning using flashcard media and educational posters and to find out the importance of learning English in an effort to improve language education and improvement in English vocabulary by using interactive and educative learning media. The research method used in this research is classroom action research. The results showed that the media flashcards and educational posters used as media for learning English at SDN Haurseah 1 were effective and had a significant effect on improving language education. So, it can be said that the development of learning media is an effort to improve the quality of education.
BTDA: Two-factor dynamic identity authentication scheme for data trading based on alliance chain

With the increase in the market share of data trading, the risks such as identity authentication and authority management are increasingly intensified. Aiming at the problems of centralization of identity authentication, dynamic changes of identities, and ambiguity of trading authority in data trading, a two-factor dynamic identity authentication scheme for data trading based on alliance chain (BTDA) is proposed. Firstly, the use of identity certificates is simplified to solve the problems of large calculation and difficult storage. Secondly, a two-factor dynamic authentication strategy is designed, which uses distributed ledger to achieve dynamic identity authentication throughout the data trading. Finally, a simulation experiment is carried out on the proposed scheme. The theoretical comparison and analysis with similar schemes show that the proposed scheme has lower cost, higher authentication efficiency and security, easier authority management, and can be widely used in various fields of data trading scenarios.
COMPARISON OF TWO MATHEMATICAL MODELS FOR FISHEYE CAMERAS APPLIED IN THE ORB-SLAM

Abstract. Fisheye lens cameras are becoming increasingly popular for vSLAM applications due to their wide field of view (FoV), providing more features to be tracked in a single image shot. However, the complex lens geometry involved in the image formation process still limits their full potential, especially when points in the hyperhemispherical field are unmodeled. In this paper, we compare two adaptations of ORB-SLAM for fisheye lens cameras, considering the use of the rigorous projection model (equisolid-angle) versus the use of the generic projection model (EUCM). The ORB-SLAM versions were adapted for real-time processing on the Nvidia Jetson TX2 board. The experiment was conducted using hyperhemispherical images obtained with a Ricoh Theta S camera. Our results showed that the trajectory estimated with the equisolid-angle ORB-SLAM had smaller discrepancies, compared to the reference trajectory, than the EUCM ORB-SLAM. This suggests that a rigorous photogrammetric model with a suitable treatment of hyperhemispherical points is beneficial for trajectory estimation.
CONCEPTUALIZATION OF A SATELLITE, UAS AND UGV DOWNSCALING APPROACH FOR ABANDONED WASTE DETECTION AND WASTE TO ENERGY PROSPECTS

Abstract. The aim of this research is to develop a multiparametric downscaling analysis for the detection of abandoned waste in the environment. This methodology, using a multi-technological approach, involves the adoption VHR satellite images, Unmanned Aircraft System (UAS) and Unmanned Ground Vehicles (UGV). The identified Warning Areas (WA) will be investigated through an in-situ analysis with air quality measurement devices based on advanced sensors mounted on drones. The creation of a Cadastre Accumulation of Abandoned Materials (CAMA) and the related APP will allow the administrations to monitor the phenomenon. Finally, the waste product analysis, retrieved by means of UAS dataset computation, allows to retrieve some interesting prospects regarding Waste to Energy framework. Here, preliminary results obtained by the on-going INTESA Project are presented.
Leakage-Resilient Anonymous Multi-Receiver Certificate-Based Key Encapsulation Scheme

Key encapsulation schemes in public key system (PKS) can be used to protect sensitive or private data. Unlike traditional PKS and identity-based PKS, certificate-based PKS (CB-PKS) not only avoids the establishment of complex public key infrastructures, but also does not encounter the key escrow problem. Anonymous multi-receiver certificate-based encryption (AMR-CBE) or anonymous multi-receiver certificate-based key encapsulation (AMR-CB-KE) scheme enables a sender to use multiple receivers’ public keys to perform one-time encryption process for a message and send the encrypted ciphertext to these receivers, while these receivers do not know the other receiver’s identity. However, the existing AMR-CBE and AMR-CB-KE schemes cannot resist side-channel attacks. Attackers with the ability of such attacks can continuously obtain part (several bits) of the secret keys and then calculate the complete secret keys. In such a case, such attacks make a cryptographic scheme (including AMR-CBE and AMR-CB-KE schemes) insecure. Leakage-resilient cryptography is an important research topic to resist side-channel attacks. In this paper, we propose the first leakage-resilient anonymous multi-receiver certificate-based key encapsulation (LR-AMR-CB-KE) scheme. Based on the discrete logarithm and hash function assumptions, we demonstrate the scheme has the indistinguishability of two ciphertexts against chosen ciphertext attacks (IND-CCA) and the anonymous indistinguishability of two identities against chosen ciphertext attacks (ANON-IND-CCA) for two types of attackers in CB-PKS settings.
High dimensional time series anomaly detection based on generated adversarial network and spatial correlation

The production data collected by a large number of sensors in actual industrial production have multiple sources, high collection frequency and multiple dimensions, and the existing cleaning methods are difficult to accurately model the changeable time series. Therefore, a high dimensional time series cleaning method based on generative adversarial network and spatial correlation is proposed. Firstly, the time series with multiple dimensions are piecewise aggregated data processing. Secondly, the correlation analysis is carried out by mathematical statistical method, and the weighted repair is made after the abnormal value is found in the sequence. Finally, the long and short term memory cycling neural network training data set was used to complete the data cleaning, and the accuracy rate, recall rate and F1 score were calculated to evaluate the abnormal detection performance. The experimental results show that the cleaning efficiency and accuracy of this method are significantly improved.
Network forum based on blockchain structure: a new anonymous community conception

The solution to solve the problem of network communication security that has attracted increasing attention in the world. A type of network forum is proposed, which is based on blockchain structure and has good anonymity and decentralization characteristics. In the above case, the user's speech will not be subject to any supervision or make the user to suffer adverse consequences. At present, various cryptocurrency systems based on blockchain structure have been running stably for a long time, which provides a relatively clear technical outline and high-security guarantee for network forums based on a similar structure. However, the network forum described in this paper does not involve financial transactions, but the main content is the user's speech. To satisfy the functionality, the block addition rule based on Karma value and the central computing power allocator concept is proposed.
Fertilizer sales forecasting model based on transformer-BiGRU

Forecasting company's future fertilizer sales based on fertilizer sales data are important for reducing production costs for fertilizer companies. Fertilizer sales data have the feature of contextual time-series correlation, and the information of time-series correlation is not considered in the time series (fertilizer price, sales volume, etc.) prediction, which leads to low prediction accuracy. To address the above problems, a Transformer-BiGRU model is pro-posed in this paper. Transformer consists of a multi-headed self-attention, which is capable of mining the multi-feature dependencies between data, and is therefore chosen for multi-feature extraction of fertilizer sales data. However, parameters of transformer are passed in one direction, which cannot extract the temporal information well. Therefore, Bi-directional Gated Recurrent Unit (BiGRU) is introduced to extract the temporal correlation of fertilizer sales data by using its own forward and backward propagation to solve the former problem of poor ex-traction of contextual temporal information. The experimental results show that the Transformer-BiGRU model has the advantage of extracting the temporal correlation of multi-feature data and improves the accuracy of fertilizer sales prediction.
Research on image classification mechanism based on self-supervised learning

Image classification is an essential method to dispose the practical issues including the medical image classification, detection of objectives and process downstream tasks. However, current researchers has utilized the neural network or machine learning model to classify the images based on the image characteristic, which is extremely relies the obtained image data and the training model is a black box. Inspired by the supervised learning algorithm, we proposed a novel self-supervised structure to classify the image data-set. The model structure is consisted of three primary operations including three layers of random transformation, a main neural network layer and prediction layer. In this paper, we specifically demonstrate each components and test our model on a written number data-set. From our extensive experimental results, our proposed mechanism can identify the correct labels in image data-set with acceptable accuracy and reasonable computation cost.
Collaborative variational factorisation machine for recommender system

At present, the recommendation system is confronting the huge challenge of data sparsity and high complexity of algorithm. Like the traditional collaborative filtering recommendation methods, they are difficult to adapt to the data sparse environment, resulting in low prediction accuracy. To address the issues, this paper presents a novel factorisation machine based on Collaborative filtering framework called collaborative variational factorisation machine (CVFM) that considers the user-user relation with the interaction data for Recommender systems. First, the user-item explicit ratings are used to build the user-user relationship by the similarity calculation. Next, we develop a variational factorisation machine to exploit the inherent relationship of latent variables from interaction information. The experimental results on three different sparsity datasets show that the presented CVFM is superior to other popular method in prediction accuracy, at the same time, maintain the stability of our algorithm with dealing with sparse data.
Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot Strips

Breast cancer has become a global health problem, ranking first in incidences and fifth in mortality in women around the world. In Mexico, the first cause of death in women is breast cancer. This work uses deep learning techniques to discriminate between healthy and breast cancer patients, based on the banding patterns obtained from the Western Blot strip images of the autoantibody response to antigens of the T47D tumor line. The reaction of antibodies to tumor antigens occurs early in the process of tumorigenesis, years before clinical symptoms. One of the main challenges in deep learning is the design of the architecture of the convolutional neural network. Neuroevolution has been used to support this and has produced highly competitive results. It is proposed that neuroevolve convolutional neural networks (CNN) find an optimal architecture to achieve competitive ranking, taking Western Blot images as input. The CNN obtained reached 90.67% accuracy, 90.71% recall, 95.34% specificity, and 90.69% precision in classifying three different classes (healthy, benign breast pathology, and breast cancer).
Special Issue on Data Analysis and Artificial Intelligence for IoT

The Internet of Things (IoT) has become an increasingly popular technology in recent years, enabling interconnectivity and communication between devices and systems [...]
SWOT Analysis of Innovative and Creative Human Resource Management Strategies During the COVID-19 Pandemic

The challenges of the COVID-19 has extraordinary and uncertain circumstances that are changing the economic landscape, requiring deep thinking and flexibility to manage human resources well. Companies with the right human resource management strategy during the covid19 Pandemic will have negative factors. Therefore, what is the human resource management strategy at Prima Indonesia University during the Pandemic. The purpose of this study is to identify strategic human resource management during a pandemic. This study uses research from the review results in the form of qualitative and quantitative by using the AHP method and analyzed by the SWOT matrix, namely EFAS and IFAS. The results of the study concluded that the management strategy of human resources amid a pandemic was in Quadran I. The aggressive strategy found at Prima Indonesia University as the most reliable strength, namely the availability of Information Technology assets. It focused on companies using aggressive strategy to develop to maintain the top position to cover the shortfall. The Information Technology found at the prime Indonesian universities has high quality with an IFAS value of 4.39.
Evaluating monitoring array performance using upper limit magnitude prediction

Summary We use the unique dataset of the Preston New Road induced seismicity monitoring to evaluate the predictive power of the upper limit magnitude methodology. The upper limit magnitude methodology is based on extreme value theory and provides an upper limit on next record-breaking magnitudes based on observed seismicity. We show that this methodology provides reliable upper bound for the magnitudes during the hydraulic fracturing and we additionally evaluate its performance assuming different values of magnitude completeness corresponding to different induced seismicity monitoring networks ranging from downhole to surface monitoring arrays. We show that a higher magnitude of completeness does not results in reduced performance of the upper limit magnitude methodology. Finally, we show that it is more important to consider prior seismicity than drilling a deep dedicated monitoring borehole for induced seismicity monitoring.