Reviewerly/exHarmony huggingface dataset:
Tilt Angle Optimization and Formulation of Annual Adjustment Models for a Solar Power Plant Setup-able Site at Himachal Pradesh, India

Solar tracking devices are quite effective for collecting maximum solar radiations but for vastly spread solar energy collection plant, their usage is suppressed due to large cost involvement. The best alternative to this problem is adjustment of tilt angle at most appropriate position. In this study monthly optimum tilt angle have been identified for a solar power plant setup-able site Kalth (φ 30.85046˚, L 77.06153˚), situated at Himachal Pradesh, India. For diffuse radiation estimation, an isotropic model has been used. By considering the impracticality involved in monthly tilt angle adjustment, various annual adjustment models have been formulated for two, three and four annual adjustments. In order to estimate the increment in solar insolation by adopting these models, Performance Enhancement (PE) have been computed from the conventional method of setting the solar collector tilt equal to latitude angle. The results show that PE is maximum for monthly optimum tilt angles followed by M-4 which is a three annual adjustment model. Based on PE requirement, any of the proposed models can be selected for setting up solar energy collection plant at suggested site.
Applying Case Method in the Training of Future Specialists

Making a brighter presentation and improving assimilation of educational material with the help of the case method allows activating the mental, exploratory and creative abilities of students to optimize the process of assimilation of information. The combination of this method with others allows making future specialists to independently find ways to solve problems. This results in the assimilation of professionally significant special knowledge. The aim of the study was to determine the features of the application of the case method in the educational process in preparing students for future professional activities, identifying sources, structural elements and filling professional cases. The study involved the survey method and further testing to identify indicators of significance at the summative and control stage of the experiment. Methods used: diagnostic programme to study the level of educational activities by Riepkina, Zaika; assessment of the level of control and self-assessment actions; statistical and mathematical interpretation of empirical data with subsequent functional analysis of the research results. The study showed the high efficiency of the case method in the educational environment of higher educational institutions (HEIs). The indicator of students’ self-presentation after the application of the case method has changed significantly. This was reflected in an increased high level to 35.5%, a decreased medium level to 38%, and a slight increase in all levels in the experimental group students. Positive dynamics was revealed on all criteria for diagnosing the effectiveness of the case method in teaching: a decreased percentage of students with negative and relatively low markers and a corresponding increase in the percentage of students with positive markers of knowledge and learning the material. Further research can deal with development and implementation of case systems in view of certain majors, taking into account future professional activities.
Cryptography in Cloud Computing

Cryptography is a key element in establishing trust and enabling services in the digital world. It is represented in a ways that are not accessible to human users. Hence, humans are left out the trust and security in the digital world. Cryptography is necessary in modern communication protocols and to many digital services. A primitive or protocol should be defined to reach the security goal. Beside the introduction part this paper represents the types of cryptography, algorithm of cryptography and techniques of cryptography and the interaction between Government and cryptography.
Does Commonsense help in detecting Sarcasm?

Sarcasm detection is important for several NLP tasks such as sentiment identification in product reviews, user feedback, and online forums.It is a challenging task requiring a deep understanding of language, context, and world knowledge.In this paper, we investigate whether incorporating commonsense knowledge helps in sarcasm detection.For this, we incorporate commonsense knowledge into the prediction process using a graph convolution network with pre-trained language model embeddings as input.Our experiments with three sarcasm detection datasets indicate that the approach does not outperform the baseline model.We perform an exhaustive set of experiments to analyze where commonsense support adds value and where it hurts classification.Our implementation is publicly available at:
GFNet: A Gradient Information Compensation-Based Face Super-Resolution Network

Face super-resolution (FSR) is defined as the generation of high-resolution face images from low-resolution face images. Existing FSR approaches usually improve the performance by combining deep learning with additional tasks such as face parsing and landmark prediction. However, the additional data requires manual labeling, and facial landmark heatmaps and parsing maps cannot represent the intrinsic geometric structure of facial components. In this paper, we introduce a FSR network based on gradient information compensation named GFNet, which consists of feature residual blocks (FRBs) and gradient extraction blocks (GEBs). Specifically, the GEB constructs pixel-level gradient maps directly from the feature maps without requiring data labels and extracts gradient features to compensate for the missing high-frequency components in the face features; the FRB extracts the face features in the network. Furthermore, we introduced a feature fusion mechanism between the GEB and the FRB, which fuses the face features with the gradient features. We evaluate the performance of proposed network on the two public datasets: CelebA-HQ dataset and Helen dataset. Experimental results show that the proposed method is able to reconstruct fine face images, which outperforms the other state-of-the-art methods such as SRResnet, FSRNet, and MSFSR.
Designing Architecture Blockchain of Hyperledger Fabric for Purchasing Strategy

Hyperledger Fabric refers to a blockchain technology regulated by the Linux Foundation, which implements a blockchain network, namely the permission blockchain. The network provides a membership service concept, which means that only certain parties can transact and access data. This research will show the blockchain architecture design of the hyperledger fabric, which is designed for the purchasing strategy of recycled waste products. The design will be used to make purchases for managing distributors in buying recycled waste products from waste banks as raw materials to be processed. The traditional purchasing process using the bank as a third party will take longer and will be more expensive. Therefore, this research aims to improve data security and transparency between the authorities and direct the payment process in an instant and dramatically reduce transaction costs. Besides, this design can also be applied to any e-commerce. The method used is blockchain, which focuses on hyperledger fabric design. The position on the hyperledger fabric is only applied to the transaction processing of the hyperledger fabric. This study's results apply a design to a blockchain system using a hyperledger fabric to overcome several technical problems related to blockchains such as throughput, scalability, and interoperability and contribute to purchasing strategies.
COMBO: State-of-the-Art Morphosyntactic Analysis

We introduce COMBO -a fully neural NLP system for accurate part-of-speech tagging, morphological analysis, lemmatisation, and (enhanced) dependency parsing.It predicts categorical morphosyntactic features whilst also exposes their vector representations, extracted from hidden layers.COMBO is an easy to install Python package with automatically downloadable pre-trained models for over 40 languages.It maintains a balance between efficiency and quality.As it is an end-to-end system and its modules are jointly trained, its training is competitively fast.As its models are optimised for accuracy, they achieve often better prediction quality than SOTA.The COMBO library is available at: https://gitlab.clarin-pl.eu/syntactic-tools/combo.
Comparison of Bubble and Insertion Sort in Rust and Python Language

Sorting is the basic activity in the field of computer science and it is commonly used in searching for information and data. The main goal of sorting is to make reports or records easier to edit, delete and search, etc. It organizes the given data in any sequence. There are many sorting algorithms like insertion sort, bubble sort, radix sort, heap sort, and so forth. Bubble sort and insertion sort are clearly described with algorithms and examples. In this paper, the bubble sort and insertion sort performance analysis is carried out by calculating the time complexity. These algorithm time complexities have been calculated by implementing in the rust and python languages and observed the best case, average case, and worst case. The flowchart shows the complete workflow of this study. The results have been shown graphically and time complexity has been shown in a tabular form. We have compared the efficiency of bubble sort and insertion sort algorithms in the rust and python platforms. The rust language is more efficient than python for both bubble and insertion sort algorithms. However, it is observed insertion sort is more efficient than the bubble sort algorithm.
CRM and customer data: Challenges of conducting business in digital economy

Enterprises are facing difficulties in achieving and maintaining competitive ability amidst globalized markets. In the modern business environment, an effective CRM is an imperative for retaining customers. As CRM systems rely on customer data, it is important to secure data integrity. This paper thoroughly analyses the challenges of enterprises, CRM and customer data. The main goal of this paper is to provide an overview of existing literature and business practice in the domain of CRM. In addition, a model for improvement of CRM is developed. The model is based on the results of conducted review, and as such, it presents an approach towards enhancing CRM systems while taking into consideration the integrity of customer data.
Bateson Information Revisited: A New Paradigm

The goal of this work is to explain a novel information paradigm claiming that all information results from a process, intrinsic to living beings, of self-production; a sensory commensurable, self-referential feedback process immanent to Bateson’s difference that makes a difference. To highlight and illustrate this fundamental process, a simulation based on one-parameter feedback is presented. It simulates a homeorhetic process, innate to organisms, illustrating a self-referenced, autonomous system. The illustrated recursive process is sufficiently generic to be the only basis for information in nature: from the single cell, to multi-cellular organisms, to consideration of all types of natural and non-natural phenomena, including tools and artificial constructions.
Modelling Ceteris Paribus Preferences with Deontic Logic

Abstract We present a formal semantics for deontic logic based on the concept of ceteris paribus preferences. We introduce notions of unconditional obligation and permission as well as conditional obligation and permission that are interpreted relative to this semantics. We show that these notions satisfy some intuitive properties and, at the same time, do not encounter some problems and paradoxes that have been extensively discussed in the deontic logic literature. We prove that the satisfiability problem for our logic is in NP. Finally, we show that the fragment of our logic in which the content of a deontic operator is a literal has an equivalent representation based on conditional preference networks (CP-nets).
A Cross-Modal Image and Text Retrieval Method Based on Efficient Feature Extraction and Interactive Learning CAE

In view of the complexity of the multimodal environment and the existing shallow network structure that cannot achieve high-precision image and text retrieval, a cross-modal image and text retrieval method combining efficient feature extraction and interactive learning convolutional autoencoder (CAE) is proposed. First, the residual network convolution kernel is improved by incorporating two-dimensional principal component analysis (2DPCA) to extract image features and extracting text features through long short-term memory (LSTM) and word vectors to efficiently extract graphic features. Then, based on interactive learning CAE, cross-modal retrieval of images and text is realized. Among them, the image and text features are respectively input to the two input terminals of the dual-modal CAE, and the image-text relationship model is obtained through the interactive learning of the middle layer to realize the image-text retrieval. Finally, based on Flickr30K, MSCOCO, and Pascal VOC 2007 datasets, the proposed method is experimentally demonstrated. The results show that the proposed method can complete accurate image retrieval and text retrieval. Moreover, the mean average precision (MAP) has reached more than 0.3, the area of precision-recall rate (PR) curves are better than other comparison methods, and they are applicable.
Unifying Diagnosis Identification and Prediction Method Embedding the Disease Ontology Structure From Electronic Medical Records

The reasonable classification of a large number of distinct diagnosis codes can clarify patient diagnostic information and help clinicians to improve their ability to assign and target treatment for primary diseases. Our objective is to identify and predict a unifying diagnosis (UD) from electronic medical records (EMRs).We screened 4,418 sepsis patients from a public MIMIC-III database and extracted their diagnostic information for UD identification, their demographic information, laboratory examination information, chief complaint, and history of present illness information for UD prediction. We proposed a data-driven UD identification and prediction method (UDIPM) embedding the disease ontology structure. First, we designed a set similarity measure method embedding the disease ontology structure to generate a patient similarity matrix. Second, we applied affinity propagation clustering to divide patients into different clusters, and extracted a typical diagnosis code co-occurrence pattern from each cluster. Furthermore, we identified a UD by fusing visual analysis and a conditional co-occurrence matrix. Finally, we trained five classifiers in combination with feature fusion and feature selection method to unify the diagnosis prediction.The experimental results on a public electronic medical record dataset showed that the UDIPM could extracted a typical diagnosis code co-occurrence pattern effectively, identified and predicted a UD based on patients' diagnostic and admission information, and outperformed other fusion methods overall.The accurate identification and prediction of the UD from a large number of distinct diagnosis codes and multi-source heterogeneous patient admission information in EMRs can provide a data-driven approach to assist better coding integration of diagnosis.
Multimodal Neural Network Acceleration on a Hybrid CPU-FPGA Architecture: A Case Study

Internet of Things and deep learning (DL) are merging into one domain and enabling outstanding technologies for various classification tasks. Such technologies are based on complex networks that mainly target powerful platforms with rich computing resources, such as servers. Therefore, for resource-constrained embedded systems, new challenges of size, performance (i.e., latency, throughput, and accuracy), and power consumption-aware networks should be addressed, particularly when edge devices handle multimodal data (i.e., different types of real-time sensing data). In this case study, we focus on DeepSense, a time-series multimodal DL framework combining convolutional neural networks (NN) and recurrent NN to process accelerometer and gyroscope data for human activity recognition. We present a field-programmable gate array (FPGA)-based acceleration for DeepSense incorporated into a hardware/software co-design approach to achieve better latency and energy efficiency using the Xilinx Vitis AI framework. The architecture of DeepSense has drawbacks that cannot be easily alleviated by Vitis AI; therefore, we introduced a new methodology of adjusting the framework and its components (i.e., the deep learning processing unit (DPU)) to achieve a custom design suitable for such time-series multimodal NN. We implemented the accelerator on two FPGA boards and performed a quantitative evaluation by varying the DPU parameter settings to support our design approach. We demonstrated the effectiveness of our implementation against the original software implementation on mobile devices by achieving up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.2\times $ </tex-math></inline-formula> improvement in latency and energy consumption, respectively. Through this case study, we provide crucial insights into the FPGA-based accelerator design of multimodal NN and essential aspects to consider for further improvements and adaptation in other application domains.
A Survey of Face Recognition Systems

With the quick grow of multimedia contents, from among this content, face recognition has got a lot of significant, specifically in latest little years. The face as object formed of various recognition characteristics for detect; so, it is still the most challenge research domain for researchers in area of image processing and computer vision. In this survey article, tried to solve the most demanding facial features like illuminations, aging, pose variation, partial occlusion and facial expression. Therefore, it indispensable factors in the system of facial recognition when performed on facial pictures. This paper study the most advanced facial detection techniques too, approaches: Hidden Markov Models, Principal Component Analysis (PCA), Elastic Cluster Plot Matching, Support Vector Machines (SVM), Gabor Waves, Artificial Neural Networks (ANN), Eigen Face, Independent Component Analysis (ICA) and 3D Morphable Model. Additionally to the above works, mentioned various testing facial databases including JAFEE, FEI, Yale, LFW, AT&amp;T(formerly termed as ORL) and AR (Aleix Martinez and Robert Benavente) etc to analyze the results. Even so, the goal of this survey is to present a comprehensive literature review for the face recognition besides its applications after a deepness discussion, some of the experimental results was introduced in the end.
Influence of Critical Thinking Technologies on Improvement of Students Performance During Self-Study

Applicability of the article is preconditioned by the requirements of information-oriented society that dictate modern youth the necessity of work with information ability, to see, to envisage and to correct logic errors. In modern world it is important to conduct reflection of own activity that maintain the necessity of critical thinking development, especially during individual acquisition of knowledge by students of higher educational establishments. Though critical thinking is introduced at school, it should be developed actively also on the next levels of education, including the level of higher education. Aims. To check complex of methods of critical thinking methods directed to the development of intellectual educational skills of students during unsupervised work. Methods: general scientific methods of information analysis were used in the research. Questionnaire method was introduced with the help of methods of the level of competency certification of Yu. Tatur, “critical analysis” of Barret, finding of logical errors of Cheifits. Qualimetric approach of the research of level of competency certification. Semantic differential tear of J. Dease. Statistic analysis was performed with the help of Spearman's rank correlation coefficient. Results. During the research it was revealed that the average rate of rational thinking of all groups of respondents is on the average level and is equal to about 50%. Propensity for sweeping generalizations of students of 1-2 years of study is 10% higher, students of 2-3 years of study is for 10 % more empiric one. Empiric objectness of students of 3-4 years of study is 14,5%, and students of 1-2 years of study is from 11,4% to 12,1%. Conclusions. According to all criteria of diagnostics of efficiency of critical thinking effect on self-guided work if students positive dynamics was revealed. Students of 1-2 years of study showed higher potential for self-guided work than students of 3-4 years of study. Perspectives. Further researches can be organized in direction of development and introduction of critical thinking development for improvement of acquirement of competences with regard of future professional activity.
The Development of Islamic Big Book Learning Media For Early Children's Languages

This article aims to develop Big Bookis learning media (Big Book Islami). The problems that occur are the children's language skills are still not optimal and the learning media used by the teacher is less attractive so that they are less able to improve children's language skills. This article uses a research and development approach. The data collection technique used in this research is a questionnaire and documentation method. Media that have been assessed by material experts and teachers are then given to students of Kindergarten ABA 13 Helvetia Medan as respondents and their language development is assessed by the teacher. The method used to analyze the data is expressed in the distribution of scores on a scale of five against a predetermined rating scale category. The results of Islamic Big Book Learning Media Development are declared suitable for use after being tested by developers, material experts, media experts, and users. Details of the data obtained from the test results include: material expert testing obtained a score of 4 and a presentation value of 80% with a decent category, media expert testing obtained a score of 4, and a presentation value of 80% with a decent category, and the results of children's language development through Islamic Big Book 80 media. .83 with a well-developed category. The results of the study show that Big Bookis media can improve children's language skills in Kindergarten ABA 13 Helvetia Medan.