Reviewerly/exHarmony huggingface dataset:
Pemanfaatan Learning Management System (LMS) sebagai Pendukung Pembelajaran Online di SMK Muhammadiyah 1 Lamongan

SMK Muhammadiyah 1 Lamongan is one of the vocational institutions that provide education using the national curriculum. During a covid19 pandemic, SMK Muhammadiyah 1 Lamongan has a role to apply the learning as online. However, some teachers have difficulty to implement online learning. According to the vice-headmaster curriculum stated that some teachers still has difficulty using LMS application to support online learning. Based on the problem above, the society service aims to assist the teacher to use the LMS application that is Edmodo. The main target is the teachers can know the use of LMS Edmodo. Keywords: e-learning, learning management system, SMK Muhammadiyah 1 Lamongan
АТАКА СОЦИАЛЬНОЙ ИНЖЕНЕРИИ В АСПЕКТЕ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ

The article examines the concept of social engineering as one of the most dangerous threats to information security of industrial systems and various other industries. The most common methods of social engineering are presented and advice is given on organizing counteraction to it. Even the most advanced protection system can sometimes be useless. The proposed work provides a wealth of information on information security and social engineering.
Remove the Labels

In virtually every workplace, some qualities are used to describe employees: Raghu is a hard worker, Mohan is lazy, Anil is very intelligent, and Rita talks a lot. These descriptions often become labels that at best help to generally convey a person’s qualities, but at worst put them into a box that does not really fit, leading to a host of problems. Every individual possesses a number of qualities, including many that are thought of as opposites. Many times, we are hardworking, lethargic, brilliant and chatty. In fact, most of these qualities or descriptors can be used to characterize virtually anyone, and they make sense only partly. Yet, we choose to label our colleagues at work and define them to be so and so. We feel it makes it easier for us to understand them and thus make working with them easier. People like to label things and other people. It helps them organize and simplify their worlds. But having a label at work can be damaging. In reality, however, labels are a lot more dangerous than they are useful! This paper talks about the dangers of labeling people at the workplace and how it can become detrimental to employee engagement and organizational productivity.
PREDIKSI TINGKAT KESUKSESAN PROMOSI BANK DENGAN ALGORITMA DNN

Telemarketing is one effective way for promoting products. However, it is often difficult to measure the success of telemarketing. Therefore, a way to predict the success rate of telemarketing, and hence strategies could be planned to increase the success rate. In this study, we evaluate several implementations of machine learning for prediction the success of telemarketing. The evaluated methods are Deep Neural Network (DNN), Random Forest, and K-nearest neighbor (K-NN). We validate our experiments using 10-fold cross validation and our experiments show that DNN with 3 hidden layers outperforms other methods. Accuracy of 90% is achieved with the DNN. It is better than Random Forest and KNN that achieve accuracies of algorithm and 88% and 89%.Keywords— Bank Marketing, DNN, KNN, Random Forest.
Compact Spiral Asymmetric encryption Distributed Ledger –Secured and authentication Mobile Payment System in Higher Institutions

Looking at the higher learning institutions, there is no question that the current methods for paying student fees are inefficient, inconvenient, and wasteful of time. In addition, the rise in the number of students studying in higher learning institutions has led to long frustrating queues and overcrowding in most financial institutions during payment of student fees. This paper sought to design and implement a secure block chain-based payment system for higher learning institutions in developing countries. Students are to use the proposed payment system to pay tuition fees and other student fees to their respective higher educational institution. In addition, students are to use the proposed payment to pay for goods and services provided by the institution and other merchants in the institution’s premises. In this study, object oriented software development methodology was used to implement the proposed payment system. The proposed system consists of a mobile e-wallet, RESTful API, and blockchain as the core component of the API.
ПРОФЕССИОНАЛЬНОЕ РАЗВИТИЕ МЕНЕДЖЕРА ОБРАЗОВАНИЯ: СОВРЕМЕННЫЕ ВЫЗОВЫ

The article reveals the problems of professional development of an education manager facing the education system. The components of the professional competence of an education manager have been determined. The stages of development of a draft professional standard Head of an educational organization (management in the field of education) are highlighted and disclosed. The new contexts of the development of the system of additional professional education in the Russian Federation have been updated.
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation

To improve the resilience of distributed training to worst-case, or Byzantine node failures, several recent methods have replaced gradient averaging with robust aggregation methods. Such techniques can have high computational costs, often quadratic in the number of compute nodes, and only have limited robustness guarantees. Other methods have instead used redundancy to guarantee robustness, but can only tolerate limited numbers of Byzantine failures. In this work, we present DETOX, a Byzantine-resilient distributed training framework that combines algorithmic redundancy with robust aggregation. DETOX operates in two steps, a filtering step that uses limited redundancy to significantly reduce the effect of Byzantine nodes, and a hierarchical aggregation step that can be used in tandem with any state-of-the-art robust aggregation method. We show theoretically that this leads to a substantial increase in robustness, and has a per iteration runtime that can be nearly linear in the number of compute nodes. We provide extensive experiments over real distributed setups across a variety of large-scale machine learning tasks, showing that DETOX leads to orders of magnitude accuracy and speedup improvements over many state-of-the-art Byzantine-resilient approaches.
СОВРЕМЕННЫЕ СРЕДСТВА ПРОВЕРКИ И ОЦЕНИВАНИЯ СФОРМИРОВАННЫХ У КУРСАНТОВ ПРОФЕССИОНАЛЬНЫХ УМЕНИЙ В ДИАГНОСТИКО-СТИМУЛИРУЮЩЕЙ СРЕДЕ ВОЕННОГО ВУЗА

The formation of professional skills is one of the urgent problems in training specialists at a military university, especially in the created diagnostic and stimulating educational environment. The authors of the article turn to the analysis of a complex of pedagogical conditions that reflect the phased development of actions, the sequence of using the point-rating system when teaching cadets. The purpose of the article is to consider the technology (forms, techniques, means) for checking the pedagogical conditions for the implementation of the model of the formation of professional skills (cognitive, practical, generalized) in cadets in the process of the actions they have mastered (search, reproductive, productive), based on the developed point-rating system, a didactic bank of control and measuring materials for assessing the educational achievements of cadets at a low, sufficient, increased level in the diagnostic and stimulating environment of a military university. To achieve this goal, the authors use the methods of theoretical and problem analysis, systematize the data obtained in previous studies on this issue. The result of the research carried out in the article is the characteristic of the complex of pedagogical conditions for organizing the training of cadets in the diagnostic and stimulating environment of a military university. The results obtained in the article can be used in the development of teaching materials for cadets of various specialties, as well as for undergraduate and graduate students.
An Efficient Named Entity Recognition Model In Medical Health Records Using Water Wave Optimization With Kernel Extreme Learning Machine

The current research proposes an effective framework for identifying named entities within medical health records.The objectives of this study are achieved through the utilisation of water wave optimisation in combination with kernel extreme learning machine (KELM).The aim of the proposed model is to accurately identify and classify medical terminologies, diagnoses, and treatments mentioned in the textual data of medical records.The implementation of the water wave optimisation algorithm has been utilised to optimise the hyperparameters of the KELM algorithm.The selection of hyperparameters, such as the kernel function and regularisation parameter, is considered crucial for achieving optimal performance.The research evaluates the effectiveness of the suggested framework on a publicly accessible medical dataset.The results suggest that the proposed model outperforms existing state-of-the-art models in terms of accuracy, precision, recall, and F1-score.The proposed model exhibits the potential to improve the accuracy and efficiency of named entity recognition in medical records, thereby leading to better patient outcomes and improved healthcare delivery.
Correction to: Blockchain-assisted secured data management framework for health information analysis based on Internet of Medical Things

A Correction to this paper has been published: https://doi.org/10.1007/s00779-021-01626-0
Portuguese Sentiment Analysis Applied to a Reality Show using Twitter and NLP in real time

The motivation for this study was to measure the impact that Twitter publications have on voting and the choosing of winners.To this end, an experimental study was carried out based on a set of data collected from tweets (published on Twitter) related to the reality show "Big Brother -A Revolução", broadcast on a television station in Portugal, TVI.The procedure adopted for conducting the experiment consisted of creating a completely self-contained service, built from scratch for this project, and the correspondent implementation, in order to allow the collection, storage, cleaning, pre-processing and analysis of as many tweets as possible, as long as they are associated with the program.A tool to analyze the polarity (positive, negative or neutral) of the sentiment was implemented and applied to the phrase (or phrases) contained in the tweet and stored in a database.Then, running in the database, the tweets were divided according to how they referred to one or more competitors.Throughout the time that the reality show existed, the results of this experiment were public presented in daily/weekly summaries and posted on Twitter through a "Twitter bot".
Introducing RDA: A Guide to the Basics after 3R

The goal of Introducing RDA is more ambitious than its title suggests. This second edition, which like the toolkit it describes has been completely revised and reorganized, sets out to do more than...
FUTURE TEACHER TRAINING FOR THE USE OF AUGMENTED REALITY IN THE EDUCATIONAL PROCESS OF A PRESCHOOL EDUCATION INSTITUTION

The article substantiates the importance of future teacher training for the use of augmented reality (AR) in the educational process of a preschool education institution (PEI). Scientific sources on the problem of AR application in the field of education are analysed. The aspects of the research of the problem of AR application are defined in the field of education done by modern foreign and national scientists, in particular, the use of AR applications in education; introduction of 3D technologies, virtual reality (VR) and augmented reality in the educational process of preschool and primary school; application of 3d technology, virtual and augmented reality in a higher education institution; increasing the efficiency of training and motivation of students on the basis of using AR applications in smartphones; the formation of reading culture by means of augmented reality technology; prospects for the use of augmented reality within the linguistic and literary field of preschool and primary education. The specifics of publications of fiction works with AR applications are analysed that are appropriate to use in work with preschool children; the possibilities of books for preschool children created with the help of augmented reality technology are demonstrated. The possibilities of using AR in work with preschoolers are considered. The urgency of the use of AR for the effective education and development of preschoolers is determined. The problems of application of AR in the educational process of modern national PEI are outlined. A method of diagnostic research of the level and features of readiness of future teachers to use AR in the educational process of PEI has been developed. Criteria and indicators are defined, the levels of development of the main components of the studied readiness (cognitive and active) and the indicated readiness as a whole are characterized. The following points are proved: insufficiency and deficit of its formation of future teachers of the field of preschool education; inconsistency between the peculiarities of future teacher training to use AR in professional activities and modern requirements for the quality of educational process in PEI; the need to develop and implement a model for the formation of the studied readiness of future teachers of the institution of higher pedagogical education. A step-by-step model of formation of readiness of future teachers to use AR in the educational process of PEI has been developed.
Virtual Menageries: Animals as Mediators in Network Cultures

A review of Jody Berland’s new book, Virtual Menageries: Animals as Mediators in Network Cultures (2019). Rather than simply serving as content for media, Berland argues that animals perform the work of mediation itself. Still situated within colonial formations, Berland posits animal bodies, energies, and affects continue to be used to facilitate connections at fledgling frontiers.
Reminiscing the legendary P. Ramlee through data visualization technique / Jawaheer Jaffar ... [et al.]

P. Ramlee is a nickname for Tan Sri Datuk Amar Teuku Zakaria bin Teuku Nyak Putih. He is an evergreen and legendary Malaysian film actor, director, singer, songwriter, composer, and producer. Although the films related to him are mostly black and white, they are still the favourite characters of today's youth. To this day, the jokes and the pieces of advice contained in his work are still relevant in the current society. Due to his great contribution to the film industry and music literature, he is regarded as a famous icon of Malaysia entertainment. Acknowledging his contributions, many research works have been conducted and reported in academic journals but most of these works concentrated on the literary perspective of his artefacts. On the contrary, this paper provides additional knowledge on interactive and dynamic visualization techniques of the P.Ramlee unstructured data. The output of the work presented in this paper provides a fundamental knowledge on the design and implementation for effective visual data analytics.
Integrated Information in Process Theories: Towards Categorical IIT

We demonstrate how integrated information and other key notions from Tononi et al.’s Integrated Information Theory (IIT) can be studied within the simple graphical language of process theories (symmetric monoidal categories). This allows IIT to be generalised to a broad range of physical theories, including as a special case the Quantum IIT of Zanardi, Tomka and Venuti, and sets the foundation for a categorical definition of IIT.
BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine.

Background Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structured, expert-curated verb-related knowledge have been shown to boost model performance in different Natural Language Processing (NLP) tasks where accurate handling of verb meaning and behaviour is critical. The costliness and time required for manual lexicon construction has been a major obstacle to porting the benefits of such resources to NLP in specialised domains, such as biomedicine. To address this issue, we combine a neural classification method with expert annotation to create BioVerbNet. This new resource comprises 693 verbs assigned to 22 top-level and 117 fine-grained semantic-syntactic verb classes. We make this resource available complete with semantic roles and VerbNet-style syntactic frames. Results We demonstrate the utility of the new resource in boosting model performance in document- and sentence-level classification in biomedicine. We apply an established retrofitting method to harness the verb class membership knowledge from BioVerbNet and transform a pretrained word embedding space by pulling together verbs belonging to the same semantic-syntactic class. The BioVerbNet knowledge-aware embeddings surpass the non-specialised baseline by a significant margin on both tasks. Conclusion This work introduces the first large, annotated semantic-syntactic classification of biomedical verbs, providing a detailed account of the annotation process, the key differences in verb behaviour between the general and biomedical domain, and the design choices made to accurately capture the meaning and properties of verbs used in biomedical texts. The demonstrated benefits of leveraging BioVerbNet in text classification suggest the resource could help systems better tackle challenging NLP tasks in biomedicine.
Speech Steganalysis of Least Significant Bits Based on the Percentage of Equal Adjacent Samples

The popularity of audio formats usually attracts the attention of intruders and criminals to use this medium as a cover for establishing their secret communications. The extensive use of this formats, along with       various modern techniques, designed for audio steganography, can cause the cyber spaces to be insecure environments. In order to deal with threats, some audio steganalysis techniques have been presented that statistically analyze various audio formats, such as music, MP3, and VoIP, efficiently. Among the presented approaches, combining the techniques of signal processing and machine learning has made possible the creation of steganalyzers that are highly accurate. However, since the statistical properties of audio files differ from purely speech ones, the current steganalysis methods cannot detect speech stego files,            accurately. Another issue is the large number of analysis dimensions which increase the implementation cost, significantly. As response to these issues, this paper proposes the percentage of equal adjacent      samples (PEAS) feature, as a one-dimensional feature for speech steganalysis. Using a classifier, based on the Gaussian membership function, on stego instances with 50% embedding ratio, the evaluation results for the designed steganalyzer, show a sensitivity of 99.82%. Additionally, it can efficiently estimate the length of a hidden message with the desirable accuracy. Also, the PEAS steganalysis was evaluated on a database, containing classic music instances, and the results show an 81.2% efficient performance.
A note of optimism in the melody of education

In the article, the author reflects on the nature of the changes in the field of education, which are associated with its active reform in the last two decades. The author insists that the development of education is the key to the development of science, and vice versa. And without science, it is impossible to build a great power and maintain its status. Being an academic scientist, the author demonstrates how the formation of the younger generation takes shape and shows the role of scientific journals in this process.
Performance Analysis of Median and Wiener Filters in Image Denoising

Images are likely to be degraded by the sensing environment when acquired through optical, electro optical or electronic means. The degradation may be in the form of sensor noise, blur due to camera misfocus, relative object-camera motion, random atmospheric turbulence, etc. For digitally acquired pictures, noise can be summarized as the visible effects of an electronic error in the ﬁnal image. Noise is a function of how well the image sensor and digital signal processing systems inside a digital camera are prone to and can cope with or remove these errors. Noise signiﬁcantly degrades the image quality and increases the difﬁculty in discriminating ﬁne details in the image. It also complicates further image understanding and low-level computer vision processing, such as image segmentation and edge detection. This work aims to understand the performance of various filters on different noises. The image under test was subjected to Gaussian noise, salt & pepper noise and Speckle noise to two input image samples. They were further analysed with respect to the performance of Median filter, Wiener filter and Order statistic filter in denoising the images. The experiments proved and concluded that Median filter is capable of removing salt & pepper noise in images to a greater extent, while other filters used in this work could not remove the same. In this work the parameter SNR (signal to noise ratio) was used to decide the effectiveness of filter. The SNR value for the salt and pepper noise after applying the median filter gave as high as 23, which proves the fact that median filter is most suitable to remove the salt and pepper noise.
The Human Side of Cyber Security Breaches

Years ago, tourism security experts worried about such micro-aggressions as: room invasions, acts of personal robbery, or tourist victimization due to acts of pick pocketing. These problems should not be minimized and in many parts of the world are still major issues. Nevertheless, these micro-aggressions have now morphed into macro-aggressions and their consequences vibrate throughout the world of tourism.
Shortcuts and Miscellaneous

You can certainly use notepad or base R to write your scripts, in the same sense as a dog could walk on his hind legs to get places. It works but not well. Rstudio is built for the purpose of efficient, effective, and beginner-friendly script coding.
Unlimited Length Random Passwords for Exponentially Increased Security

Presented herein is a new method of exponentially strengthening user defined passwords against cracking. The enhanced security is achieved by injecting random strings of random length at random positions in the password string before encrypting and passing the ciphertext resulting after encryption over a network to its destination. Discussed also in detail is how the randomly injected strings are separated and the original password is extracted from the ciphertext. Also explained is how the method can be applied to any other confidential information such as credit and debit card information and cryptocurrency data.
Employment of University Graduates in Modern China: Expectations and Reality

The employment of China's tertiary education system’s graduates is considered, on the one hand, as a prerequisite for the implementation of the strategic goal set by the Chinese leadership – turning the country into a global center for the development of new knowledge and technologies by 2035, and on the other, as a factor of ensuring social stability. The analysis shows that an increasingly serious problem is the contradiction between the prevailing ideas in Chinese society about importance of “high scholarship” and the structure of vacancies for graduates of universities and higher professional colleges in the labor market; between the ideas of graduates about what kind of knowledge and skills are in demand by employers, and the requirements imposed by employers on applicants while searching for qualiﬁed personnel.