Reviewerly/exHarmony huggingface dataset:
Mobile application to digitize handwritten patient records in peruvian public hospitals

This research has shown that public hospitals need a digitization model that allows the availability of medical records for patient medical consultation within a repository that unifies their medical information. This article presents MEDREC APP, a mobile application aimed at public hospitals of the Ministry of Health (MINSA), in order to expedite the immediate obtaining of the medical history in digital format, and to visualize their medical care. The main function of this solution is based on Optical Character Recognition (OCR) as a text extraction process by capturing images of handwritten medical history formats and registering them within the mobile application in digital format. As part of the validation process, indicators were defined, surveys and interviews were conducted with our users of the application: the medical staff. The results obtained show that the average time to obtain a medical history is between 30 and 15 minutes. By including the mobile solution to be presented, the time was reduced to 6 minutes, which means a reduction of 9 minutes, equivalent to approximately 70%.
Flexible Tensor Learning for Multi-View Clustering With Markov Chain

Multi-view clustering has gained great progress recently, which employs the representations from different views for improving the final performance. In this paper, we focus on the problem of multi-view clustering based on the Markov chain by considering low-rank constraints. Since most existing methods fail to simultaneously characterize the relations among different entries in a tensor from the global perspective and describe local structures of similarity matrices of a tensor, we propose a novel Flexible Tensor Learning for Multi-view Clustering with the Markov chain (FTLMCM) to solve this problem. We also construct transition probability matrices based on the Markov chain to fully utilize the connection between the Markov chain and spectral clustering. Specifically, the low-rank constraints of the tensor, the frontal slices and the lateral slices of the tensor are imposed on the objective function of the proposed method to achieve these goals. Besides, these three constraints can be optimized jointly to achieve mutual refinement. FTLMCM also uses the tensor rotation to better explore the relationships among different views. We formulate FTLMCM as a problem of low-rank tensor recovery and solve it with the augmented Lagrangian multiplier. Experiments on six different benchmark data sets under six metrics demonstrate that the proposed method is able to achieve better clustering performance.
Heavy Metal Pollution Assessment in surface and groundwater in the Ponce Enríquez mining area, Ecuador.

Mining activities have been carried out in the Ponce Enriquez area (Ecuador) since more than 40 years. However, illegal and anti-technical mining practices have caused pollution and environmental degradation of the water resources. This study aims to assess the pollution degree of surface water and groundwater in the Tenguel and Siete watersheds. Concentrations of Pb, Fe, Zn, Mn, Cr, Cu, As, and Cd in 18 samples from rivers and wells were assessed. The level of water contamination was evaluated by the Metal Index (MI). As a result, an enrichment in the concentration of Cu, Pb and As was evidenced, which exceeded the maximum permissible limit of the Ecuadorian regulations, up to 128, 12 and 10 times, respectively. Regarding the MI, 60% of the surface water samples were seriously affected, with the Siete river being the most polluted among the water courses in the zone. The overview of the pollution degree in Ponce Enriquez water resources can serve as an input for decision makers on water protection issues.
VISION ALGORITHM DEVELOPMENT ARTIFICIAL TO DETECT THE DROWSINESS IN DRIVERS HEAVY MACHINERY MINERS

The main objective of this research is to develop an artificial vision algorithm that detects the drowsiness of heavy machinery mining drivers, using an artificial vision architecture with Python software, importing face detection libraries such as shape_predictor_68_face_landmarks with which it will be detected and They will identify each important point. In the input stage, the face will be detected with a camera placed in a strategic point of the vehicle and/or machine. Then the software detects and measures the required points. A threshold <0.22 was used at a time of 60 fps (1sec) to determine if the individual is blinking continuously or due to fatigue, if this is the case an alert message will be sent. In the tests carried out we obtained average positive results of 96.48%.
Forecasting irrigation scheduling based on deep learning models using IoT

Potatoes are one of the staple foods in Boyacá and play an important role in family nutrition and food security in Colombia. Therefore, timely and accurate information on the irrigation of this crop is relevant in agricultural decision-making, the sustainable development of its production and the reduction of unnecessary water consumption. This study estimated the irrigation prescription of a potato crop from crop meteorological information with the support of IoT technologies to solve the problem of inefficient water dosing in the crop. Two deep learning models were developed, a One-Dimensional Convolutional Neural Network (1D-CNN) and a short-term long-term memory neural network (LSTM). Training data was collected daily from a potato crop from 2018 to 2020 using two weather stations located in the UsoChicamocha irrigation district. To predict irrigation prescriptions, deep learning architectures were trained using Python® by selecting input climatic variables measured with a subsystem of sensors installed in the crop and an actuation subsystem with control of Latch-type solenoid valves, both remotely controlled wirelessly. The algorithms were validated by calculating precision metrics such as MSE and coefficient of determination. The results showed that the LSTM model surpassed the 1D-CNN model, obtaining training and validation errors less than 0.096 and presenting greater precision in the estimation of crop irrigation, giving a coefficient of determination R 2 between 0.881 and 0.919. Irrigation prediction algorithms using deep learning techniques achieved promising results and serve as a decision support tool for farmers to automatically decide when and how much water to irrigate.
The Students’ Perception on Using Spotify Towards Their English Pronunciation

Abstract: The purpose of this study is to investigate students' perceptions of the use of Spotify applications in improving their pronunciation ability. The participants of this study were 10 sixth-semester English education students at Palangka Raya University. The research design used in this study was qualitative description. Data were collected through a questionnaire and interview that assessed the effectiveness of using the Spotify application for students to improve their pronunciation ability. For the interview, there were 5 items and 10 questionnaire items. The results of this study revealed that using the Spotify application helped students improve their pronunciation, intonation, and vocabulary.
Pharmaceutical Drug Traceability by Blockchain and IoT in Enterprise Systems

Pharmaceutical drug traceability is a regulatory compliance adopted by most nations in the world. A comprehensive analysis was carried out to explain the benefits of adopting enterprise system for pharmaceutical drug traceability. Counterfeit drugs are medicines that are fake and have been produced using incorrect potency, or incorrect ingredients used to manufacture these drugs. Solving the drug counterfeiting problems by identifying the most effective and innovative technologies for protecting people's health is of essence these days for the world. Drug serialization is essential concept for drug traceability in the pharmaceutical supply chain. Blockchain is the latest stringent technology that makes drug distribution more secure in the supply chain. The blockchain-based drug traceability is a distributed shared data platform that shares information that is irreversible, reliable, responsible, and transparent in the PSC. Blockchain uses two powerful module, Hyperledger Fabric and Besu to satisfy important criteria for medication traceability, such as privacy, trust, transparency, security, authorization and authentication, and scalability. Researchers in Health informatics can use blockchain designs as a useful road map to develop and implement end-to-end pharmaceutical drug traceability in the supply chain to prevent drug counterfeiting. Industrial IoT is also a key component for the pharmaceutical industry. IoT systems in pharmaceutical drug traceability can be beneficial as they are based on automation and computational methodologies.
Hybrid DAER Based Cross-Modal Retrieval Exploiting Deep Representation Learning

Information retrieval across multiple modes has attracted much attention from academics and practitioners. One key challenge of cross-modal retrieval is to eliminate the heterogeneous gap between different patterns. Most of the existing methods tend to jointly construct a common subspace. However, very little attention has been given to the study of the importance of different fine-grained regions of various modalities. This lack of consideration significantly influences the utilization of the extracted information of multiple modalities. Therefore, this study proposes a novel text-image cross-modal retrieval approach that constructs a dual attention network and an enhanced relation network (DAER). More specifically, the dual attention network tends to precisely extract fine-grained weight information from text and images, while the enhanced relation network is used to expand the differences between different categories of data in order to improve the computational accuracy of similarity. The comprehensive experimental results on three widely-used major datasets (i.e., Wikipedia, Pascal Sentence, and XMediaNet) show that our proposed approach is effective and superior to existing cross-modal retrieval methods.
Teaching Strategies and the Reading Comprehension Competence of Grade 7 Students

Reading comprehension plays a pivotal role in understanding the material at hand. Without this skill, readers would encounter difficulties comprehending what they read, ultimately impeding their academic performance. The study investigated the effectiveness of teaching strategies, including Vocabulary Knowledge, Context Clues, Identifying the Main Idea and Key Details, and Sequencing a Passage into Ordinal Series, to improve the reading comprehension competence of Grade 7 students. The initial scores on the pretest for all measures were relatively low, ranging from 1.98 to 2.66. However, the post test scores showed a significant increase, ranging from 3.47 to 4.58. The t-values for all measures were highly significant (p &lt; .001), indicating that the improvements observed between the pretest and post test scores were not due to chance. The study included a sample size of 64 respondents, with 63 degrees of freedom for each measure. In summary, the results of the study indicated that the implementation of various teaching strategies resulted in significant improvements in all aspects of reading comprehension. The negative t-values demonstrate statistically significant enhancements in scores from the pretest to the post test. Moreover, the p-values (all less than .001) indicate that these improvements were highly unlikely to have occurred by chance alone. Therefore, it can be concluded that strategies such as reading aloud in the classroom, using the QAR strategy (Question-Answer Relationship), and incorporating contextual clues were effective in enhancing the students' reading comprehension competence.
Secure Data Computation Using Deep Learning and Homomorphic Encryption: A Survey

Deep learning and its variant techniques have surpassed classical machine algorithms due to their high performance gaining remarkable results and are used in a broad range of applications. However, adopting deep learning models over the cloud introduces privacy and security issues for data owners and model owners, including computational inefficiency, expansion in ciphertext, error accumulation, security and usability trade-offs, and deep learning model attacks. With homomorphic encryption, computations on encrypted data can be performed without disclosing its content. This research examines the basic concepts of homomorphic encryption limitations, benefits, weaknesses, possible applications, and development tools concentrating on neural networks. Additionally, we looked at systems that integrate neural networks with homomorphic encryption in order to maintain privacy. Furthermore, we classify modifications made on neural network models and architectures that make them computable via homomorphic encryption and the effect of these changes on performance. This paper introduces a thorough review focusing on the privacy of homomorphic cryptosystems targeting neural network models and identifies existing solutions, analyzes potential weaknesses, and makes recommendations for further research.
REDUCE – A Python Module for Reducing Inconsistency in Pairwise Comparison Matrices

This paper introduces REDUCE, a Python module designed to minimize inconsistency in multiplicative pairwise comparisons (PC), a fundamental technique in Multi-Criteria Decision Making (MCDM).Pairwise comparisons are extensively used in various fields, including engineering science and numerical simulation methods, to compare different options based on a set of criteria.However, human errors in perception and judgment often lead to inconsistencies in pairwise comparison matrices (PCM).REDUCE addresses this issue by implementing several algorithms that identify and correct inaccurate data in PCMs, thereby reducing the inconsistency ratio.These algorithms do not require expert intervention, making REDUCE a valuable tool for both scientific research and small to medium enterprises that may not have access to costly commercial software or dedicated decision-making experts.The main functionality of the module is incorporating iterative algorithms for inconsistency reduction.The REDUCE library, written in Python and utilizing auxiliary libraries such as NumPy, SciPy, and SymPy, offers 21 functions categorized into data input helpers, consistency ratio (CR) reduction algorithms, PCM indexes, and support functions.Performance testing indicates that the library can efficiently handle matrices of varying sizes, particularly those ranging from 3x3 to 10x10, and its use significantly accelerates the process compared to spreadsheets, especially when dealing with large quantities of matrices.The library has already been used in several research papers and application tools, and its availability as a free resource opens up opportunities for small and medium-sized enterprises to leverage multi-criteria decision-making methods.Currently, there are no publicly available libraries for this solution.The authors believe that the proposed module may contribute to do better decision-making process in pairwise comparisons, not only for the circle of scientists but also for small and medium enterprises that usually cannot afford expensive commercial software and do not employ full-time experts in decision-making as they rely on the experience of their employees and free online resources.It should also contributes to the transition to Industry 4.0 and advances research in fields such as fuzzy logic, preference programming, and constructive consistent approximations.
Towards a Novel Identity Check Using Latest W3C standards &amp; Hybrid Blockchain for Paperless Verification

With the advent of W3C standards such as DID, VCs, and DPKI beyond 2020, the industry has reached a new level where a technological infrastructure overhaul is possible.By employing blockchain and other Decentralized Ledger Technologies, it is believed that we can eliminate the requirement for paper-based verification.Researchers are aware of the technological components we possess at present and are trying to bring forth their sets of POCs.Additionally, governments ranging from developing to developed countries are taking industrial initiatives that incorporate these technologies.This research also evaluates the latest events and cases to find the need for paperless verification.Previous development conducted in the domains of Information Systems and Public forensics has presented us with various issues at both infrastructural and user levels.It also introduced us to the presence of lots of gaps present that can be improved with a more improvised form of decentralized paperless solution.Researchers have pointed out that the modern day identity check and forensic solutions will face difficulties with blockchain compatibility, since most of those previous components will require built-in integration with a decentralized environment.As the latest researches suggest the key to this integration is now possible with the proper application of the W3C standards.In this paper, we propose an architecture that interlinks the latest decentralized W3C standards with a permissioned blockchain for implementing paperless verification and identity check.
The Use of Integral Citation in English Students’ Final Project Reports

This paper is concerned with integral citation employed by English students of Politeknik Negeri Bandung in their Final Project Reports (FPR). Thus, the aims of this study were to investigate the most integral citation type used by the students in their FPR. In conducting the study, 18 FPR focusing on analysis produced in 2022 were collected as data source. Applying qualitative method and text analysis, the results reveal several things. First, the students favor integral citation than non-integral citation. Second, verb controlling appears more than naming. In verb controlling citation, lexical verb state is commonly used compared with other lexical verbs. Whereas in naming, the phrase according to + the cited authors is the mostly used. this study has implication in improving the students’ academic skills, especially in doing citation.
Analisis Daya Tahan Keamanan Data Pada Steganografi Berbasis Teks Dalam Struktur Kalimat Bahasa Indonesia

Data security in text-based steganography needs to be analyzed in depth to ensure the sustainability and security of the method. The purpose of this research is to analyze the durability of data security in text-based steganography. The analysis approach consists of steps to identify and evaluate the vulnerability of text steganography methods with Indonesian sentence patterns. The first step is to review previous relevant work in this field to understand the vulnerabilities that have been identified previously. The text insertion model carried out is dictionary-based as many as 1,929 words which are divided into seven-word classes that correspond to sentence patterns in Indonesian, namely adj (adjective), adv (adverb), nom (noun), num (numeral), par (particle), pro (pronominal) and ver (verb). Each word class is organized into sentence patterns and each has the same bit length of eight bits. The results of the durability analysis show that input data with a length of one word still can be hacked if the message insertion process with a choice of short sentence patterns. Whereas with a choice of sentence patterns that are more than two words it will start to be difficult to hack and even cannot be hacked because the number of possibilities is very large and even infinite or infinity.
Deep learning with ExtendeD Exponential Linear Unit (DELU)

Activation functions are crucial parts of artificial neural networks. From the first perceptron created artificially up to today, many functions are proposed. Some of them are currently in common use, such as Rectified Linear Unit (ReLU) and Exponential Linear Unit (ELU) and other ReLU variants. In this article we propose a novel activation function, called ExtendeD Exponential Linear Unit (DELU). After its introduction and presenting its basic properties, by making various simulations with different datasets and architectures, we show that it may perform better than other activation functions in certain cases. While also inheriting most of the good properties of ReLU and ELU, DELU offers an increase of success in comparison with them by slowing the alignment of neurons in early stages of training process. In experiments, DELU performed better than other activation functions in general, for Fashion MNIST, CIFAR-10 and CIFAR-100 classification tasks with different sized Residual Neural Networks (ResNet). Specifically, DELU managed to reduce the error rate by sufficiently high confidence levels in CIFAR datasets in comparison with ReLU and ELU networks. In addition, DELU is compared in an image segmentation example as well. Also, compatibility of DELU is tested with different initializations, and statistical methods are employed to verify these success rates by using Z-score analysis, which may be considered as a different view of success assessment in neural networks.
Estimating Social Background Profiling of Indian Speakers by Acoustic Speech Features

Social background profiling of speakers refers to estimating the geographical origin of speakers by their speech features. Methods for accent profiling that use linguistic features, require phoneme alignment and transcription of the speech samples. This paper proposes a purely acoustic accent profiling model, composed of multiple convolutional networks with global average-pooling layers, to classify the temporal sequence of acoustic features. The bottleneck representations of the convolutional networks, trained with the original signals and their low-pass filtered copies, are fed to a Support Vector Machine classifier for final prediction. The model has been analysed for a speech dataset of Indian speakers from social backgrounds spread across India. It has been shown that up to 85% accuracy is achievable for classifying the geographic origin of speakers corresponding to regional Indian languages; 17% higher than the benchmark deep learning model using the same features. Results have also indicated that classification of accents is easier using the second language of the speakers, as compared to their native language.
Short-term PV Power Prediction Based on SAGA-FCM and XGBoost

Abstract The stable and economic operation of the power grid cannot be achieved without accurate prediction of PV plant power. To address the problem of unstable clustering quality of the fuzzy c-mean clustering algorithm, this paper proposes to combine the genetic algorithm and simulated annealing algorithm to optimize the FCM clustering algorithm, and then combine it with XGBoost to construct a short-term PV power generation prediction model. Firstly, the robustness of the clustering algorithm is improved. Secondly, the differences in power generation characteristics under different weather types are considered, and the prediction models under three typical weather types are separately trained. Finally, to verify the improvement of the model on prediction accuracy, different clustering methods are used to compare the prediction results, and the results show that the algorithm proposed in this paper has higher accuracy compared with the other three methods.