Reviewerly/exHarmony huggingface dataset:
Semi-controlled Learning in Information Processing Problems

The article substantiates the need for further research of known methods and the development of new methods of machine learning – semi-supervized learning. It is shown that knowledge of the probability distribution density of the initial data obtained using unlabeled data should carry information useful for deriving the conditional probability distribution density of labels and input data. If this is not the case, semi-supervised learning will not provide any improvement over supervised learning. It may even happen that the use of unlabeled data reduces the accuracy of the prediction. For semi-supervised learning to work, certain assumptions must hold, namely: the semi-supervised smoothness assumption, the clustering assumption (low-density partitioning), and the manifold assumption. A new hybrid semi-supervised learning algorithm using the label propagation method has been developed. An example of using the proposed algorithm is given.
Bibliometric Analysis of Climate Change Publications in Tanzania from 1964 to 2021

Climate change has affected lives of all human beings around the globe making it a growing area of research concern among scientists worldwide. Researchers have used bibliometrics to analyse research productivity and the impact of climate change research output. This study aimed at analysing the trends of research output on climate change in Tanzania from 1964 to 2021. Specifically, the study investigates the growth of publications and citation impact on climate change in Tanzania during the period under study. Data were searched, retrieved and analysed from Dimensions.ai. The results show that the trend of publications on climate change in Tanzania is growing with peak production in 2020 and 2021. Researchers in developed nations like USA, UK, and Germany have significantly contributed to the production of publications on the subject and so are their citations. Renowned public universities and research institutions in Tanzania have contributed more publications on the subject. Research outputs published in Open Access are increasingly contributing to climate change scholarly research output. Researchers from developed countries are highly cited compared to local researchers. Comparing the productivity of research institutions in Tanzania, the University of Dar es Salaam, Sokoine University of Agriculture, and the Nelson Mandela Institute of Science and Technology have a large share of publications and citations on climate change. Publications from high Impact Factor open-access journals are highly cited. This study recommends further studies on climate change using bibliometrics including assessing the impact of climate change publications on policy influence in the country.
Emotional Intelligence Attention Unsupervised Learning Using Lexicon Analysis for Irony Based Advertising

Social media platforms have made increasing use of irony in recent years. Users can express their ironic thoughts with audio, video, and images attached to text content. When you use irony, you are making fun of a situation or trying to make a point. It can also express frustration or highlight the absurdity of a situation. The use of irony in social media is likely to continue to increase, no matter the reason. By using syntactic information in conjunction with semantic exploration, we show that attention networks can be enhanced. Using learned embedding, unsupervised learning encodes word order into a joint space. By evaluating the entropy of an example class and adding instances, the active learning method uses the shared representation as a query to retrieve semantically similar sentences from a knowledge base. In this way, the algorithm can identify the instance with the maximum uncertainty and extract the most informative example from the training set. An ironic network trained for each labelled record is used to train a classifier (model). The partial training model and the original labelled data generate pseudo-labels for the unlabeled data. To correctly predict the label of a dataset, a classifier (attention network) updates the pseudo-labels for the remaining datasets. After the experimental evaluation of the 1,021 annotated texts, the proposed model performed better than the baseline models, achieving an F1 score of 0.63 on ironic tasks and 0.59 on non-ironic tasks. We also found that the proposed model generalized well to new instances of datasets.
Understanding phishing awareness among students in tertiary institutions and setting-up defensive mechanisms against the attackers

The average loss by companies to phishing in 2021 is $14.8 million, more than triple what it was in 2015. That translates to hundreds of billions of dollars in total losses from phishing attacks on global businesses, and the vulnerability of these attacks is every day increasing, particularly among the younger generation less than 40 years of age. This paper begins with a background exposition on phishing trends and highlights previous findings concerning users' susceptibility to phishing attacks. It however explores the term Phishing itself, its kinds, types and some basic measures necessary for defense against phishing activities. The research was employed with a major focus on the email aspect of phishing. Alongside the website aspect of phishing, the certificate of a website was also considered. The purpose of this study was to identify the level of student awareness related to specific phishing tactics. Findings revealed that while students are unlikely to provide personal information in response to an email/SMS request, they can be easily tricked by numerous other tactics. This paper reports the findings of the study in addition to listing suggested points to employ for creating phishing awareness.
Image Processing from Unmanned Aerial Vehicle Using Modified YOLO Detector

Identifying objects from drone images is a state-of-the-art task for artificial neural networks. Since drones are always moving at different altitudes, the scale of the object varies greatly, making it difficult to optimize the networks. Moreover, flying at high speeds and low altitudes leads to blurred images of densely populated objects during movement, which is a problem when solving the problem of recognizing and classifying small sized objects. This paper addresses the above problem solutions and solves them by applying an additional prediction model to identify objects of different scales. We also modify the loss function to penalize larger objects more and vice versa to encourage recognition of smaller objects. To achieve improvements, we use advanced techniques such as multiscale testing, image blurring, object rotation, and data distortion. Experiments with a large data set show that our model has good performance in drone images. Compared to the baseline model (YOLOv5), our model shows significant improvements in object recognition and classification.
A Comparison of Different Loss Computations in Siamese Networks for Authorship Verification

In this study, we consider the author verification problem as a binary classification problem where the aim is to identify whether the two inputs belong to the same author or not. For this task, we have used several Siamese convolutional neural networks-based models. The first model employs a Siamese network which is trained using binary cross-entropy loss after the absolute distance computation. In addition to this baseline model, we have implemented another model which employs a concatenation operation. Moreover, two contrastive loss-based models have also been implemented for the same task. Two publicly available benchmark datasets, IAM and CVL, have been used in the study. Training, validation and test datasets that have been used to train and test each of those models have been generated from those datasets. Experimental results show that the performances of the two cross-entropy loss-based models are similar and much better than that of the contrastive loss-based models.
Calibrating a Three-Viewpoints Thermal Camera with Few Correspondences

This paper proposes the approach to calibration of a thermal camera that has three viewpoints. This work estimates the intrinsic parameters of the camera by establishing geometric relationship between real-world scene points and image points. This work estimates the extrinsic parameters, i.e., rotation and translation of the camera using vanishing points. The present work is different from the existed work on the calibration from three perspectives. The first one is that this work calibrates thermal camera with three viewpoints whose relative rotation is greater than 180-degree. The second one is that the viewpoints shares few overlapped areas due to the structure of the camera, so the proposed work is more challenging and difficult than the previous work on the calibration. The third one is that projected image of a scene requires quality enhancement for the calibration. Since few overlapped images are given a priori, feature matching cannot be used for the calibration. Instead of using correspondence matching, vanishing points are exploited to estimate relative rotations of cameras. The intrinsic parameters are estimated by capturing thermal-checkerboard followed by image processing. Experimental results show the accuracy and stability of the proposed approach for estimation of the intrinsic and the extrinsic parameters.
Eyes on the Road

To what extent can artificial intelligence do away with the need for humans to enforce the rules of the road? James Allen reports on the new technologies that are changing the way local authorities are keeping their roads safe and prosecuting offenders
A Novel Approach to Secure Files Using Color Code Authentication

In this modern era, where technologies are being evolved day by day and data is shared on various communication platforms in a huge volume and variety. There is a need of implementing security and privacy in every technology. The files that are being shared need to be protected with strong encryption. So that the confidential data will remain, secure and protected from unauthorized access. Data is a raw form of information that can be stored or transferred digitally over the network. Cybersecurity is applied to protect data from unauthorized access in order to maintain its integrity and availability. Information security refers to the use of defensive computerized protection measures to prevent unauthorized access to computers, databases, and websites. It protects customers by making the encrypting of communications and the validation of distinct clients helpful. This allows us to communicate critical data in a surprising manner. Nevertheless, there are several approaches available; the current state of the data security framework includes categorization, trustworthiness, and righteousness while accessing or modifying confidential internal records. This paper proposes an approach for securing the data from unauthorized access using color-coding authentication. It secures confidential data from being mishandled and information from being passed into the wrong hands by encrypting it and securing it using a process of automated passwords. Security is about the assurance of benefits. The validation model depends on some shading mixes. Thus, this paper aims for making sure about classified documents for conveying data utilizing encryption, unscrambling, and shading code validation.
Token Bases Valid and Secure Payment System Using SHA-256

Every connected device must be safe, with no easily compromised card, account, or personal information secured to it, to address the growing security concerns for retailers, consumers, and financial institutions. As a result of that tokenization will be critical in the future of linked devices. Implementing the EMV mechanisms approach, which describes the adoption of payment tokens as a replacement for PANs and how security can be improved by limiting their use to a specific environment, is one way to handle this challenge. By using the approach to hide sensitive data and restrict access to it, this study influences the use of Network tokenization as an alternate strategy for guaranteeing data security in modern cloud computing systems The research also looked at Host Card Emulation (HCE) and Near Field Communication (NFC), which are all utilized in the transaction process to encrypt data from both sides Java Spring Boot and Angular JS were used for both the backend and frontend of the web services, and hash and salting algorithms were employed to ascertain the token value from the cardholder. Tokenization systems are easy to set up and may be used in a variety of ways in IT systems to maintain data security.
Supplemental Material: Crustal conductivity footprint of the orogenic gold district in the Red Lake greenstone belt, western Superior craton, Canada

&lt;p&gt;Details of MT imaging and resolution tests and supplemental figures. &lt;/p&gt;
The Impact of the Pandemic on Higher Education: New Challenges and Perspectives

The aim of this article is to analyze the experience of the transition to distance learning during the Covid-19 pandemic in domestic universities. The article considers some of the features of distance learning, as well as the advantages and disadvantages that have become obvious during this experience. The authors pay attention to the actions of public authorities in response to new circumstances in the period of pandemic. The proponents note that universities have passed major efficiency tests of their work and have successfully sorted out the problem of providing high quality education outside the walls of the universities. As a result of this complicated experience, new soft-skills have been formed for both teachers and students; moreover, the concept of an innovator teacher has been expanded. The article addresses the issues of teaching foreign languages in higher education in the context of distance learning while maintaining the high level inherent in classes in a university classroom. The authors draw attention to the psychological system of distance learning, talk about the importance of the emotional ties and face to face communication during the educational process and emphasize that the development of a digital learning environment will expand students’ access to highquality curricula, in foreign languages in particular, regardless of the form of study. In conclusion, the authors believe that it is too early to draw final conclusion about the results of distance learning, but they consider it will become an integral part of the entire educational process in the nearest future. This educational process will be a continuation of that close inter-university cooperation aimed at interaction with the aim of sharing experiences and expanding the prospects for the use of hybrid forms of education in the future.
Science and Theology: The Prospects for Fruitful Mutually Beneficial Cooperation

The main scientific problems of our time - the hard problem of consciousness, the problem of completing the second quantum revolution and the problem of the ‘great silence’ of the Universe - can be solved by turning to theological tradition. The fact is that modern physics builds a model of the world through the introduction of numerical values. These values allow us to compare the elements of the real physical world with mathematical objects - numbers. This mapping exists in the process of realising procedures of measurement, which is a study of the relation of one physical element to another. Thus objective science describes the world not as it is ‘in itself’, but only via the projection of different elements of the world on devices of measurement. The physical theories that result from this practise are thus theories of relations. By virtue of the ‘relativity’ of mathematical (structural) theories, the physical theories are opened up to substantive interpretations. The uniqueness of the current situation is that today we seem to have reached the limits of structural knowledge. ‘Deeper’ structures of the Universe that could be accounted for in the formal language of mathematics are not there. Thus it follows that the structures of the Universe that we can observe now are fundamental, ontological. But does this achievement mean that we have reached the limits of knowledge? Not at all! Further movement is possible in the direction of filling the mathematical syntactic structure with semantic existential content. As science originated as the study of the text of the Book of the World, complementary to that of the Bible, then when one searches for the semantic interpretation of the mathematical structures revealed by science it is reasonable to turn to the Bible as a source of meanings for filling formal mathematical structures of the physical world. The problem of interpreting (mathematical) texts is a hermeneutic problem and, therefore, traditionally theological. It is logical to presume that the correspondence between the internal mathematical (‘psychical’) model and the external physical world is not limited to only the structural similarity but can be extended to the ontological sphere. This will make it possible to create a new conceptual ‘two-dimensional’ language. Such a language would allow us to describe both the objectivity of the ‘external’ physical world - quantum world and the world of the Universe as a whole - and the subjectivity of the ‘inner’ psychical world.
ANALYSIS OF THE STUDENTS’ PROBLEMS IN LEARNING VOCABULARY AT SMP SATAP PALEA

The main problem of the research was ‘what are the problems faced by the students in learning vocabulary at the SMP Satap Palea?’ The research was case study. The subjects of the research were students and an English teacher in SMP Satap Palea at grade seven. The data of research were collected through test and interview. The data were analyzed through three steps adopted from Miles and Huberman; data reduction, data display and conclusion drawing. The result of this research showed that the result of test of the students of SMP Satap Palea was low. The problem faced by the students were lack or low motivation in learning English vocabulary, the students had low interest in learning English and lack of strategy used in learning vocabulary. It was indicated by the indifferent attitude or their reluctance to memorize English vocabulary. This reluctance impacted their mastery of English vocabulary. The students of SMP Satap Pelae rarely repeated their English lesson at home. The result of interview also indicated that student’s environment did not show positive impact for the students to learn, including the students peer.
USAGE OF INFORMATION TECHNOLOGY IN THE INDUSTRIAL REVOLUTION 4.0 ERA TO DEVELOP A MODERN DEFENSE STRATEGY

The defense strategy has both the opportunity and challenge of the Industrial Revolution 4.0, which is beginning to change the way of life, the way of working, and the way of relating to one another. Industrial Revolution 4.0 is a revolutionary change based on information technology marked by the emergence of digital technology, the Internet of Things, artificial intelligence, Big Data, and unmanned systems as well. This study examines the influence of the presence of the Industrial Revolution 4.0 on the national defense perspective to find new technologies and implement human resource development strategies using the method of review of literature in descriptive. Information technology has a role to completely replace various activities and the quality of human action. Advances in information technology development have become increasingly competitive and have consequences for applications in formulating defense strategies. This study examines academic perspectives using qualitative methods through the analysis of literature studies from various sources such as books, journals, and others, to develop modern defense strategies using information technology of the Industrial Revolution 4.0. The conclusion of the research shows that the use of information technology in the Industrial Revolution 4.0 facilitates the process of making decisions and policies more quickly, precisely, and accurately in developing a modern national defense strategy.
Supplemental Material: Genesis and source affinities of heterogeneous ultramafic rocks in the North Kongling Complex, Yangtze craton: Architecture of a Paleoproterozoic accretionary orogen

&lt;p&gt;Table S1: Mineral major elements of harzburgites and pyroxenites from the North Kongling Complex. Table S2: Mineral trace elements of harzburgites from the North Kongling Complex. Table S3: LA-ICP-MS zircon U-Pb data of harzburgites and pyroxenites from the North Kongling Complex. Table S4: LA-ICP-MS zircon rare earth element values (ppm) of harzburgites and pyroxenites from the North Kongling Complex. Table S5: Whole-rock major and trace elements of harzburgites and pyroxenites from the North Kongling Complex. Table S6: Whole-rock Sm-Nd isotopic compositions of harzburgites and pyroxenites from the North Kongling Complex. Table S7: Whole-rock Re-Os isotopic compositions of harzburgites and pyroxenites from the North Kongling Complex.&lt;/p&gt;
Identifying encephalopathy in patients admitted to an intensive care unit: Going beyond structured information using natural language processing

Background Encephalopathy is a severe co-morbid condition in critically ill patients that includes different clinical constellation of neurological symptoms. However, even for the most recognised form, delirium, this medical condition is rarely recorded in structured fields of electronic health records precluding large and unbiased retrospective studies. We aimed to identify patients with encephalopathy using a machine learning-based approach over clinical notes in electronic health records. Methods We used a list of ICD-9 codes and clinical concepts related to encephalopathy to define a cohort of patients from the MIMIC-III dataset. Clinical notes were annotated with MedCAT and vectorized with a bag-of-word approach or word embedding using clinical concepts normalised to standard nomenclatures as features. Machine learning algorithms (support vector machines and random forest) trained with clinical notes from patients who had a diagnosis of encephalopathy (defined by ICD-9 codes) were used to classify patients with clinical concepts related to encephalopathy in their clinical notes but without any ICD-9 relevant code. A random selection of 50 patients were reviewed by a clinical expert for model validation. Results Among 46,520 different patients, 7.5% had encephalopathy related ICD-9 codes in all their admissions (group 1, definite encephalopathy), 45% clinical concepts related to encephalopathy only in their clinical notes (group 2, possible encephalopathy) and 38% did not have encephalopathy related concepts neither in structured nor in clinical notes (group 3, non-encephalopathy). Length of stay, mortality rate or number of co-morbid conditions were higher in groups 1 and 2 compared to group 3. The best model to classify patients from group 2 as patients with encephalopathy (SVM using embeddings) had F1 of 85% and predicted 31% patients from group 2 as having encephalopathy with a probability &amp;gt;90%. Validation on new cases found a precision ranging from 92% to 98% depending on the criteria considered. Conclusions Natural language processing techniques can leverage relevant clinical information that might help to identify patients with under-recognised clinical disorders such as encephalopathy. In the MIMIC dataset, this approach identifies with high probability thousands of patients that did not have a formal diagnosis in the structured information of the EHR.