Reviewerly/exHarmony huggingface dataset:
FACIAL ATTENDANCE SYSTEM USING MTCNN AND FEATURE MAPPING

Artificial intelligence and Computer Vision are some of the fields which have shown rapid growth in recent years.These technologies have greatly reduced the effort we used to put in our day to day work and facial detection and Recognition are such revolutionary technologies.While many approaches are possible, on base, this technology works by recognizing the features of an individual face and matches it with the data which is stored in the database.Face is the representation of one's identity.Hence, we have proposed a full-fledged automated attendance system based on facial detection and recognition.Smart Attendance using Real-Time Face Recognition is a real-world solution which comes with day to day activities capable of handling everyday student / employee attendance systems.Using feature mapping, this system is capable of detecting multiple faces, saving the facial features using feature mapping and at last recognition based on the database.The Computer vision will be able to find and recognize human faces fast and precisely in images or videos that are being captured through a surveillance camera or webcam.Numerous algorithms and techniques have been developed for improving the performance of face recognition but to get the best results we have created a compounded deep learning model.Directly streaming the live video, faces will be detected, mapped and recognized and automatically saved into the database with the date and time of entry.Later the authorities can easily check the database for the complete information.Improving upon the existing systems and creating a more robust model for better and more efficient processes.
Sequential Learning of Cryptocurrency Volatility Dynamics: Evidence Based on a Stochastic Volatility Model with Jumps in Returns and Volatility

This paper studies the dynamics of cryptocurrency volatility using a stochastic volatility model with simultaneous and correlated jumps in returns and volatility. We estimate the model using an efficient sequential learning algorithm that allows for learning about multiple unknown model parameters simultaneously, with daily data on four popular cryptocurrencies. We find that these cryptocurrencies have quite different volatility dynamics. In particular, they exhibit different return-volatility relationships: While Ethereum and Litecoin show a negative relationship, Chainlink displays a positive one and interestingly, Bitcoin’s one changes from negative to positive in June 2016. We also provide evidence that the sequential learning algorithm helps better detect large jumps in the cryptocurrency market in real time. Overall, incorporating volatility jumps helps better capture the dynamic behavior of highly volatile cryptocurrencies.
Editorial Computational Pathology

Until recent years, histopathologists have analyzed tissue sections and diagnosed diseases including cancer primarily by using a microscope. The introduction of high resolution and high throughput digital scanners has enabled digitizing entire glass slides to generate high-resolution whole-slide images (WSI), de facto giving rise to the field of Digital Pathology. Since then, an increasing number of pathology laboratories have transitioned to a digital pipeline, which offers advantages such as remote diagnosis (e.g., for a second opinion), reduction of some routine work in the lab and partly reduction of physical storage. However, perhaps the most revolutionizing aspect of digital pathology is that it enables image analysis in pathology using machine learning. This field has come to be known as Computational Pathology.
The Effect of Information Communication Technology (ICT) Infrastructure availability on FDI inflow in D8 Countries

This This paper analyzes the effect of ICT infrastructure availability on FDI inflow in D8countries (Bangladesh, Indonesia, Iran, Egypt, Nigeria, Malaysia, Pakistan and Turkey). Panel data for the period 1997-2018 has been used and the analysis has been done using the fix effect model suggested by Hausman specification test. The result shows positive and significant effect between ICT infrastructure and FDI inflows, along with other controlling variables like market size, trade openness, in case of macroeconomic variable that is exchange rate it has negative but significant effect on FDI inflows.
A Markov Chain Monte Carlo Algorithm for Spatial Segmentation

Spatial data are very often heterogeneous, which indicates that there may not be a unique simple statistical model describing the data. To overcome this issue, the data can be segmented into a number of homogeneous regions (or domains). Identifying these domains is one of the important problems in spatial data analysis. Spatial segmentation is used in many different fields including epidemiology, criminology, ecology, and economics. To solve this clustering problem, we propose to use the change-point methodology. In this paper, we develop a new spatial segmentation algorithm within the framework of the generalized Gibbs sampler. We estimate the average surface profile of binary spatial data observed over a two-dimensional regular lattice. We illustrate the performance of the proposed algorithm with examples using artificially generated and real data sets.
Proses pemanfaatan modul bonggol jagung berbentuk balok menjadi material utama desain lampu

The result of the exploration of corncob is a module in the form of a block at this stage of the corncob research with this form will be used as the main component of the product, this research was conducted with a material exploration approach, to explore the potential material of corncob which is expected to be a product that has novelty value. so an experiment is needed on corncobs. The experiments carried out were (1) the development of the physical treatment of the previous square module which had been explored by the industry to find the potential contained in the corncob. (2) Experiment with the corncob square module by using assembly methods and techniques in order to obtain new shapes and potentials. The research was conducted to obtain a product design with an emphasis on material aspects in order to obtain the characteristics of the corncob material. This research provides new opportunities and hopes for the community to have new creations in utilizing the corncob module.
Indoor Scene Recognition using ResNet-18

Deep learning allows computational models consisting of multiple neural layers of processing to learn data representation at multiple abstraction levels.Such approaches have greatly strengthened state-of-the-art speech recognition, visual object recognition, scene recognition, NLPs, object detection, and many other fields such as drug discovery, distant surgeries and genomics.Scene Recognition is an area of visual recognition where we design and automate our system to recognize and identify the scene of the image.Automatic Scene Recognition or Scene Analysis is one of the hot topics in Deep Learning.If we look at the contributions of Deep Learning in this decade, we had come to know that Scene Recognition has been an obvious concern for scientists as it has great significance in security and surveillance too.Object recognition and recognition of indoor scenes plays a significant role in the cognition of service robots in the field.The development of deep learning has made fine-tuning of CNN (Convolutional Neural Network) on target datasets a common way of solving classification problems.Nonetheless, this approach cannot achieve adequate results easily for indoor scene classification due to over fitting when the datasets for scene preparation are inadequate.In order to compare techniques that participate to better accuracies, we have applied two different techniques to achieve our results i.e.Fine Tuning and concept of Freezing Layers.Within this project, a system of classification of the indoor scene is proposed to solve this issue.We are using ResNet-18 which contains 18 deep layers for classification.Furthermore, we are using transfer learning and performing classification on scenes based images.
An IOT Based Private Blockchain Framework for Attendance Management Using QR Code

Blockchain and Internet of Things are considered as most disruptive technologies of the decade.Internet of Things has established its existence in several areas including manufacturing, smart home system to IT enabled Services on the other several use cases are available for blockchain mentioning its successful application in finances to supply change management, electronic health care record etc. Researchers are also trying to integrate blockchain and Internet of Things.This paper introduces the primary work carried to integrate blockchain and internet of things.To integrate blockchain and internet of things it is essential that all the participating devices work in an environment that allows them to communicate and initiate transactions thereby allowing the successful creation of block and blockchain.The major contribution of this paper includes development of a private blockchain that allows various users of system to perform their activities as per the rules or smart contracts defined while they are the part of blockchain.We have developed a private blockchain framework that utilizes a novel method to create the blocks and blockchain using SHA-256 algorithm, QR Codes and stores the information in blockchain at a particular timeframe.The proposed private blockchain framework is explained in terms of use case taken for marking attendance of students using mobile phones and teacher's laptop which participate in the blockchain creation.The rest of the paper is organized in five sections.Initially a short introduction of the proposed system is given then in second section related work is presented.Third section describes the proposed system architecture, implementation details are highlighted then in last section conclusion and directions to future work are given.
How to Mitigate Security-Related Stress: The Role of Psychological Capital

In an organizational context, individuals are prone to feel stressed by overwhelming and complicated security requirements, which can result in noncompliance with security policies and guidelines.While previous research has mainly focused on identifying distinct dimensions of securityrelated stress (SRS) and their behavioral impact, this paper is the first to examine factors for mitigating SRS.A study with 150 participants reveals that psychological capital (PsyCap) -here comprising of domain-specific self-efficacy and resilience -may work as such a means as it significantly reduces perceived SRS.However, the positive effect of PsyCap diminishes when becoming a victim of cybercriminals.Said differently: victims displaying high or low PsyCap tend to feel more stress compared to non-victims.Our findings imply that organizations should invest in measures that help their employees to develop positive mental capabilities before experiencing an information security incident.
AUTOMATIC HINDI TEXT SUMMARIZATION USING SELECTION AND ELIMINATION APPROACH

In recent years, the shoot up of web information has required to make intensive research in the field of automatic text summarization which is the part of the Natural Language Processing (NLP).This paper concentrates on different algorithms for text summarization of Hindi documents and to check which methods among them, are given the best summary.This paper has used selection and elimination based approach in order to make a summarizer for Hindi text documents.To make final summary several features are taken into account, including sentence resemblance to the first 100 and last 100 words and find sentence frequency on the basis of the word frequency, and sentence similarity by using Dice Coefficient and cluster the summary obtained by both methods and the Jaccard's Coefficient of dissimilarity is used to remove the sentences which are highly dissimilar in the summary.The results of the proposed algorithm are compared over different algorithms used for text summarization using Hindi corpus posted by IIT Bombay to identify which approach contains the most important points of the original text.
Digital Educational Resources in Sciences for Visual Impaired Students in Times of COVID - 19

Pandemics have forced humanity to face great challenges over time, education is part of this challenge, moving around the world from face-to-face to virtual and teleworking spaces that involve teaching-learning methodologies transformation, not only for teachers, but for students and their families. This situation has increased social differentiation in Colombia and vulnerability is even greater for the population made up of students with visual disabilities in secondary education, since a large part of the resources that can be accessed from the web do not have the basic parameters of accessibility proposed by the Web Accessibility initiative, those are in languages different than Spanish, or it are not open access or do not comply in a relevant way with basic standards of competencies in Natural Sciences proposed by Ministerio de Educacion de Colombia (Organism that established Education Politics in Colombia).
Indoor Navigation using Augmented Reality

This paper introduces the idea of Indoor Navigation using Augmented Reality.With the increase in complex building structures, people of different age may find it difficult to navigate within such structures.This paper provides a solution for this trouble using available and accessible resources.Using this application, a shop within a shopping mall, a specific room in a hotel etc.,can be easily located and the user is provided with reasonably accurate visual assistance through their smartphone to reach his preferred location.The proposed system is inspired by augmented reality and 2-D Visual markers are the fundamental elements used in this system.Multiple visual markers are placed within the complex building structure and on scanning these visual markers using the application, users are led to their destination using arrows displayed on the screen.The application allows the user to choose their preferred destination and also permits them to change their destination in between.
Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes

Malaria is a disease caused by the Plasmodium parasite that transmitted by female Anopheles mosquitoes. Malaria can become a dangerous disease if late have the medical treatment. The late medical treatment happened because of misdiagnosis and lack of medical staff, especially in the countryside. This problem can cause severe malaria that has complications. This study creates a system prediction to classify the severe malaria disease using Classification and Regression Tree (CART) method and the probability of malaria complication using Naïve Bayes method. The first step of this study is classifying the patients that have symptom are infected severe malaria or not based on the model that has been built. The next step, if the patient classified severe malaria then the data predicted if there any probability of complication by the malaria. There are 8 possibilities of complication malaria which are convulsion, hypoglycemia, hyperpyrexia, and the combinations of these four. The first step will evaluate by using F-score, precision and recall while the second step will evaluate by using accuracy. The highest result F-score, precision and recall are 0.551, 0.471 and 0.717. The highest accuracy 81.2% which predicted the complication is Hypoglycemia.
Handling the imbalanced data with missing value elimination SMOTE in the classification of the relevance education background with graduates employment

&lt;span id="docs-internal-guid-cd6caed5-7fff-5f99-0341-cb32fa5ad787"&gt;&lt;span&gt;The imbalanced data affect the accuracy of models, especially for precision and sensitivity, it makes difficult to find information on minority class. The problem is identified in the tracer study dataset Universitas Sriwijaya that has 2934 data. The label attribute is divided into several label classes, namely not tight, somewhat-tight, tight, very tight, and tightest. The number of the tightest and very tight is 27% and 38.6% of the number majority classes. In the study, the SMOTE is combined with eliminating the missing value of data to handle the imbalanced data. The method was evaluated by the classification methods KNN, ANN, and C4.5. The results of these methods show a significant increase in accuracy as a whole and a significant increase in the precision and sensitivity of minority classes. The precision and sensitivity of both the majority and minority are not too different, although the number of the minority is very less compared to the majority class. the information on minority classes can be obtained with quite high precision and sensitivity. As a conclusion, the proposed method is passably to improve accuracy and greatly affects the increase in sensitivity and precision.&lt;/span&gt;&lt;/span&gt;
Teachers’ assessments of the changes in higher education in Russia: Transformation or stagnation?

The article presents the assessments by Russian university teachers of the changes that have resulted from the reform of higher education. The assessment of the result of higher education reform was built through the optics of the micro-level studying and the bottom-top strategy for summarizing the results. The authors used a list of questions about teachers’ assessment of the conditions for academic mobility, the success of the transition to a two-level system, the perception of competition in the academic market, the possibility of scientific work and academic mobility, and also the degree and type of participation in the development of master’s degree programs. The results were based on data obtained in the framework of the project “The Russian Masters’ early growth”, a recipient of the Vladimir Potanin Foundation scholarship program. The study showed that university teachers perceived the reform of higher education rather negatively, they noted an increase in useless paperwork, a low level of academic mobility, lack of academic freedom, and difficulty in conducting scientific research. Competition between teachers was assessed as low, and respondents also marked that the academic labor market in Russia is not well-established. The authors concluded that the tasks of higher education reform have not been solved, and on the part of teachers there is an institutional distrust of the ongoing reforms.
New temperature dependent models for estimating global solar radiation across the midland climatic zone of Nigeria

Authentic information of the availability of global solar radiation is significant to agro/hydro meteorologists, atmospheric Physicists and solar energy engineers for the purpose of local and international marketing, designs and manufacturing of solar equipment. In this study, five new proposed temperature dependent models were evaluated using measured monthly average daily global solar radiation, maximum and minimum temperature meteorological data during the period of thirty one years (1980-2010). The new models were compared with three existing temperature dependent models (Chen et al., Hargreaves and Samani and Garcia) using seven different statistical validation indicators of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t â€“ test, Nash â€“ Sutcliffe Equation (NSE) and Index of Agreement (IA) to ascertain the suitability of global solar radiation estimation in five different locations (Zaria, Bauchi, Jos, Minna and Yola) situated in the Midland climatic zone of Nigeria. In each location, the result shows that a new empirical regression model was found more accurate when compared to the existing models and are therefore recommended for estimating global solar radiation in the location and regions with similar climatic information where only temperature data are available. The evaluated existing Hargreaves and Samani and Garcia temperature based models for Jos were compared to those available in literature and was found more suitable for estimating global solar radiation for the location. The comparison between the measured and estimated temperature dependent models depicts slight overestimation and underestimation in some months with good fitting in the studied locations. However, the recommended models give the best fitting.Â Â Â
Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport

Users voluntarily generate large amounts of textual content by expressing their opinions, in social media and specialized portals, on every possible issue, including transport and sustainability. In this work we have leveraged such User Generated Content to obtain a high accuracy sentiment analysis model which automatically analyses the negative and positive opinions expressed in the transport domain. In order to develop such model, we have semiautomatically generated an annotated corpus of opinions about transport, which has then been used to fine-tune a large pretrained language model based on recent deep learning techniques. Our empirical results demonstrate the robustness of our approach, which can be applied to automatically process massive amounts of opinions about transport. We believe that our method can help to complement data from official statistics and traditional surveys about transport sustainability. Finally, apart from the model and annotated dataset, we also provide a transport classification score with respect to the sustainability of the transport types found in the use case dataset.
Machine Translation Utilizing the Frequent-Item Set Concept

In this paper, we introduce new concepts in the machine translation paradigm. We treat the corpus as a database of frequent word sets. A translation request triggers association rules joining phrases present in the source language, and phrases present in the target language. It has to be noted that a sequential scan of the corpus for such phrases will increase the response time in an unexpected manner. We introduce the pre-processing of the bilingual corpus through proposing a data structure called Corpus-Trie (CT) that renders a bilingual parallel corpus in a compact data structure representing frequent data items sets. We also present algorithms which utilize the CT to respond to translation requests and explore novel techniques in exhaustive experiments. Experiments were performed on specific language pairs, although the proposed method is not restricted to any specific language. Moreover, the proposed Corpus-Trie can be extended from bilingual corpora to accommodate multi-language corpora. Experiments indicated that the response time of a translation request is logarithmic to the count of unrepeated phrases in the original bilingual corpus (and thus, the Corpus-Trie size). In practical situations, 5–20% of the log of the number of the nodes have to be visited. The experimental results indicate that the BLEU score for the proposed CT system increases with the size of the number of phrases in the CT, for both English-Arabic and English-French translations. The proposed CT system was demonstrated to be better than both Omega-T and Apertium in quality of translation from a corpus size exceeding 1,600,000 phrases for English-Arabic translation, and 300,000 phrases for English-French translation.