Reviewerly/exHarmony huggingface dataset:
Data Mining Optimization Software and Its Application in Financial Audit Data Analysis

The scope of finance is very wide, data also plays a very important role in the financial industry, a small data change, and it may have a great impact on the economy. Therefore, the author proposes data mining optimization software and its application in financial audit data analysis. First, discuss the decision tree method, the main function module design of the system software, the financial analysis software method of weighted multiple random decision trees is described. To conduct verification experiments, the decision-making effect of constructing 10 random decision trees is the best. So, the author constructed a total of 10 random decision trees to analyze the data, since the tree is constructed using a random method, in order to verify the stability of decision tree classification, a total of 5 experiments were carried out, the training data set for each experiment, randomly select 1200 pieces of data from the original data set as training data, the tree is constructed by randomly selecting 12 attributes from 24 attributes. The remaining 300 pieces of data are used as verification data. As can be seen from the results, the accuracy of the random decision tree method is about 10% higher than that of C4.5. In order to improve the accuracy rate of high risk, 300 pieces of high-risk data were added to the training data set. To change the original random sampling into stratified sampling, according to the high, medium, and low risk, the original data is stratified; random sampling is used for each layer, thereby ensuring the amount of training data with high risk. The accuracy of decision tree classification is related to the number of samples of the training data, the larger the number of samples, the more accurate the classification of the constructed decision tree.
Internal Communications and Organization Performance in Zanzibar Public Institutions

Today’s competitive business environment has witnessed the significance of effective internal communication and its flow as crucial variables impacting collaboration among workers and allowing the organization to function successfully. This paper seeks to assess the contribution of internal communication to the organization’s level of performance by specifically identifying tools used by the employees in the organization to communicate internally. In addition, the study aims at determining the perceptible effects of internal communication to on organizational performance. A qualitative survey questionnaire with a size of 150 questionnaires was conducted together with interviews and observations and the data obtained were analyzed using the Software Package for Social Sciences. The results have validated the connection between the internal communication tools used and the performance of the organization. Organizations found to be using multiple tools of internal communication that produce a positive contribution for better performance and with the presence of scatted employees work station as in the municipal case, face to face communication through conference and mobile telephone were mostly preferable than an internal memo and email address due to poor Information and Communication and Technology (ICT). The employees sighted the benefits of internal communication in their organization performance since their activities were planned and implemented by internal communication and hence employees were able to remind each other of the working agreement as the principle to achieve organization plan and objectives. Internal communication in the public institution has several positive contributions to better organizational performance and the organization may quickly employ its activities with accurate performance when internal communication is well utilized. However, some public organizations like the council faced same challenges such as late timing, information leakage among staff, poor effective internal communication strategy, communication breakdown and poor channel of communication.
EPOS Thematic Core Service Anthropogenic Hazards in the operational phase

The problem of hazards induced by the exploitation of geo-resources focuses growing interest of science, industry, public administration, non-governmental organisations and the general public. Anthropogenic seismicity, i.e. the undesired dynamic rock mass response to geo-resources exploitation, is one of the examples of unwanted by-products of the technological operation of humans. The socio-economic impact of the induced seismicity is very significant. Induced earthquakes can cause material loss, injuries and even fatalities. Restricted access to data constitutes a barrier to assessing and mitigating the associated hazards. To respond to the need of the scientific community the Thematic Core Service Anthropogenic Hazards (TCS AH) has been created within the framework of the European Plate Observing System, a solid earth science European Research Infrastructure Consortium (ERIC). TCS AH is an open consortium of 13 European institutions. TCS AH provides access to a novel e-research infrastructure, the EPISODES Platform (former name: IS-EPOS platform) to foster both research and training on induced seismicity and geo-hazards related to the exploration and exploitation of geo-resources. The EPISODES Platform is connected to international data nodes which offer open access to multidisciplinary datasets, called episodes. Episodes comprise geoscientific and associated data from industrial activity along with a large set of embedded applications for their efficient data processing, analysis and visualization. The EPISODES Platform opens also the possibility to create new applications and combine implemented applications with the user's codes. The team-working features of the EPISODES Platform facilitate collaborative and interdisciplinary scientific research, public understanding of science, citizen science applications, knowledge dissemination, and the teaching of anthropogenic hazards related to geo-resource exploitation. This study presents the current results of the TCS AH research infrastructure integration and also indicates the benefits of their usage for science, education, and innovation.
Runs and RSK tableaux of boolean permutations

We define and construct the "canonical reduced word" of a boolean permutation, and show that the RSK tableaux for that permutation can be read off directly from this reduced word. We also describe those tableaux that can correspond to boolean permutations, and enumerate them. In addition, we generalize a result of Mazorchuk and Tenner, showing that the "run" statistic influences the shape of the RSK tableau of arbitrary permutations, not just of those that are boolean.
Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information Fusion

Attention-based models such as transformers have shown outstanding performance on dense prediction tasks, such as semantic segmentation, owing to their capability of capturing long-range dependency in an image. However, the benefit of transformers for monocular depth prediction has seldom been explored so far. This paper benchmarks various transformer-based models for the depth estimation task on an indoor NYUV2 dataset and an outdoor KITTI dataset. We propose a novel attention-based architecture, Depthformer for monocular depth estimation that uses multi-head self-attention to produce the multiscale feature maps, which are effectively combined by our proposed decoder network. We also propose a Transbins module that divides the depth range into bins whose center value is estimated adaptively per image. The final depth estimated is a linear combination of bin centers for each pixel. Transbins module takes advantage of the global receptive field using the transformer module in the encoding stage. Experimental results on NYUV2 and KITTI depth estimation benchmark demonstrate that our proposed method improves the state-of-the-art by 3.3%, and 3.3% respectively in terms of Root Mean Squared Error (RMSE). Code is available at https://github.com/ashutosh1807/Depthformer.git.
Self-Development of Professional Competencies of a Novice Teacher in the Process of Enriching the Natural Science Subject-Spatial Environment

The purpose of the study is to evaluate the possibilities of self-development of professional competencies of a young (novice) teacher in the process of research activities in order to enrich the natural science subject-spatial environment. The article presents the opinions of scientists E.I. Petrenko, L.V. Ignatieva about the role and place of research activity of a young teacher – a former student who graduated from a higher educational institution or a trainee student who began teaching as a student, who knows many teaching methods, has studied various scientific disciplines in his field, or a teacher who has worked for up to 5 years. In their opinion, the competence of a graduate is manifested in the willingness (motivation and personal qualities) to apply knowledge, skills and experience for successful professional or other activities in the presence of problems and resources. The creation of methodological resources begins with a bachelor student at a university and is the result of research activity of a young teacher in school. Young teacher often experiences difficulties while choosing material for &amp;quot;Environmental Studies&amp;quot; with the purpose of junior schoolers achieving different results. Scientific novelty is the idea of creating innovative methodological projects which enrich subject-spatial environment of elementary school aimed at development of natural subjects and research skills of students that took &amp;quot;Environmental Studies' subject. The author managed to define methodological innovations presented as guidance manual which were created while writing a diploma then were approbated under the conditions of educational space of school. &amp;quot;Geographer's diary&amp;quot;, &amp;quot;Society city&amp;quot; interactive game, researcher's handbook &amp;quot;Way to health&amp;quot;, &amp;quot;Briefcase museum&amp;quot; call for analyzing of different topics of scientific topic, creation of which requires research competence of a teacher. The resources provided directed at enriching school's subject-spatial environment since work with the resources means usage of subject equipment. It was proved that having learned basic professional competence in university young teacher can develop them in school's educational space. Basic directions of activities on development of professional competences are: methodological work, innovative activity, project-research teacher's activity, Young specialist can approbate created methodological materials under actual conditions of school thus enriching audience's subject environment, providing individual educational requirements of all subject of educational process.
Human Gesture Recognition using Hidden Markov Models and Sensor Fusion

Considering the continued drive of human needs besides the constant improvement of technology, it is convenient to develop techniques that enhance the communication between computers and humans in the most intuitive ways as possible. The possibility to automatically recognize human gestures using artificial vision among other kind sensorsallows to explore a whole range of interaction applications to control and interact with environments. Nowadays, most of approaches for gesture recognition using sensors agree in the use of vision, myography and movement devices applied to robotic, medical and industrial applications. In the context of this work, we study the principles of using both vision andbody contact sensing applied to automatic classification of a human gesture set. For this, two different approaches are evaluated: Feed-forward Neural Networks and Hidden Markov Models. These models are studied and implemented for the recognition up to eight different human hand gestures commonly applied in collaborative robotics tasks. In our tests,we conclude the effectiveness of combining the information of two different sort of devices for human gesture recognition reaching accuracy rates up to 95.05% for a whole proposed Hand-gesture set.
Deep learning for the development of an OCR for old Tibetan books

Motivation and Goals:The funds of the Center of Oriental Manuscripts and Xylographs of the Institute for Mongolian, Buddhist and Tibetan Studies of the Siberian Branch of the Russian Academy of Sciences have the richest Buddhist heritage collections of manuscripts and woodblock printings, numbering about 100 thousand books in Tibetan and Mongolian.The manual processing of these writings and their translation into a machine-readable form is tedious and time-consuming [1].Automation of optical character recognition or OCR allow us to save cultural treasures for the future generation.Methods and Algorithms: We use computer vision and deep learning advances to apply the OCR for old Tibetan books.Experts in Tibetan studies at the Institute for Mongolian, Buddhist and Tibetan Studies of the Siberian Branch of the Russian Academy of Sciences annotated a dataset of scanned rare woodblock printings, namely Jone (Chone) Kangyur of early 18 th century.These works have been chosen because they represent the woodcutting and printing quality typical for most Tibetan books.The dataset includes 500 images of book pages.Each image has text annotations.We split the dataset into 420 training images and 30 testing images.The RGB image width is 2048 pixels and the average height is 650 pixels.Each image contains 8 text lines on average.Each line is framed with bounding box, and the corresponding text annotation is transliterated in Latin characters.The text length per line is 300 characters on average.First, we detect rectangular bounding boxes with text in image.After we crop out the boxes from image and extract the text using OCR model.Results: We evaluate the performance of the baseline model on validation dataset, consisting of 30 high-resolution images each containing 8 text lines.We use standard metrics for OCR.The model reaches: character-by-character recall (char_recall): 0.9495, character-by-character precision (char_precision): 0.9539, normalized edit distance (1-N.E.D): 0.9397. Conclusion:We have created a full-featured system for OCR of the Tibetan script.
Set Representation for Rule Generation Algorithms

The task of mining the association rule has become one of the most widely used discovery pattern methods in Knowledge Discovery in Databases (KDD). One such task is to represent the itemset in the memory. The representation of the itemset largely depend on the type of data structure that is used for storing them. Computing the process of mining the association rule im- pacts the memory and time requirement of the itemset. With the increase in the dimensionality of data and datasets, mining such large volume of datasets will be difficult since all these itemsets cannot be placed in the main memory. As representation of an itemset greatly affects the efficiency of the rule mining association, a compact and compress representation of an itemset is needed. In this paper, a set representation is introduced which is more memory and cost efficient. Bitmap representation takes one byte for an element but the set representation uses one bit. The set representation is being incorporated in Apriori Algorithm. Set representation is also being tested for different rule generation algorithms. The complexities of these different rule generation algorithms using set representation are being compared in terms of memory and time execution.
The Role of Media Literacy in Online Information Searching Strategies

Along with the spread of Web 2.0 technologies, individuals' habits such as learning, socializing, and getting information have changed rapidly. A lot of information, the accuracy of which cannot be trusted, is available in the web, and it becomes difficult to choose useful, relevant, and accurate information. This pollution is also present in the media. The abilities to choose messages in the media, to look at these messages critically, and to produce your own messages are considered among the 21st-century skills. These reasons bring media literacy (ML) and online information searching strategies (OISS) to the agenda. The processes of ML and OISS have interrelated features. Therefore, it is important and necessary to examine these concepts together. Based on this necessity, the aim of the study was to determine the role of ML in OISS. To this end, the data were collected from 1809 pre-service teachers using the OISS inventory and the ML level determination scale. Data were analyzed by descriptive statistics, MANOVA, and multiple regression analysis. According to the results, pre-service teachers' ML and OISS levels were above the moderate level. ML and OISS vary significantly according to the type of websites. In conclusion, ML was revealed as a predictor variable that could explain OISS at a rate of 33.2%.
Model bimbingan dan konseling menggunakan cinematherapy untuk mereduksi keyakinan negatif

Negative beliefs cause many problems in living life so that it can prevent oneself from developing healthy self-esteem, prevent oneself from feeling fulfilled in life, prevent individuals from developing healthy relationships. Negative beliefs can limit what is seen and lead to actions against the best interests of our loved ones and ourselves. Guidance and Counseling Using Cinematherapy can be used to reduce these negative beliefs. Cinematherapy is defined as an umbrella term for a therapeutic process that involves the assignment of films to clients in order to promote and enhance therapeutic benefits. This study uses a single case experimental designs using the A-B-A design with one subject. From the hypothesis test, the result of the Asmyp probability number is obtained. Sig.(2-tailed) is 0.068, or the probability is above alpha 0.05 (0.001 &lt; 0.05). From these results, Ho is rejected and H1 is accepted. From the results of the research conducted, it can be concluded that cinematherapy is effectively used to reduce negative beliefs
Linked Data for the Historic Environment

This article discusses the outcomes of research undertaken by the Hypermedia Research Group at the University of South Wales in collaboration with the OASIS team at the Archaeology Data Service in the Linked Data for the Historic Environment (LD4HE) project. The new OASIS system allows stakeholders to record information/events accurately and consistently, using established heritage and spatial vocabularies, including those currently available via the Heritage Data platform. The LD4HE project explores one avenue of enhancing the potential re-use of information recorded by OASIS and making connections with other online data collections. LD4HE enables the creation and export of RDF from the new OASIS V system, a major step towards the production of Linked Data. The main outcomes comprise a conceptual mapping between mandatory OASIS fields and the standard CIDOC-CRM ontology, together with a template-based tool to convert records exported from OASIS to a corresponding RDF representation. A set of SPARQL queries demonstrates the outcomes of the data conversion. New specialised vocabularies required by OASIS have been published as linked data on the Heritage Data platform. The methods are described and illustrated with examples. Reflections on the case study and cost/benefit considerations for Linked Data conversion are discussed, together with possible strategies for reducing the costs of producing Linked Data.
Multilingual Speech to Text using Deep Learning based on MFCC Features

The proposed methodology presented in the paper deals with solving the problem of multilingual speech recognition. Current text and speech recognition and translation methods have a very low accuracy in translating sentences which contain a mixture of two or more different languages. The paper proposes a novel approach to tackling this problem and highlights some of the drawbacks of current recognition and translation methods. The proposed approach deals with recognition of audio queries which contain a mixture of words in two different languages - Kannada and English. The novelty in the approach presented, is the use of a next Word Prediction model in combination with a Deep Learning speech recognition model to accurately recognise and convert the input audio query to text. Another method proposed to solve the problem of multilingual speech recognition and translation is the use of cosine similarity between the audio features of words for fast and accurate recognition. The dataset used for training and testing the models was generated manually by the authors as there was no pre-existing audio and text dataset which contained sentences in a mixture of both Kannada and English. The DL speech recognition model in combination with the Word Prediction model gives an accuracy of 71% when tested on the in-house multilingual dataset. This method outperforms other existing translation and recognition solutions for the same test set. Multilingual translation and recognition is an important problem to tackle as there is a tendency for people to speak in a mixture of languages. By solving this problem, the barrier of language and communication can be lifted and thus can help people connect better and more comfortably with each other.
Vehicle Detection and Count in the Captured Stream Video Using Opencv in Machine Learning

The technology of detection within the captured video has implementation within the sort of fields. This emerging technology when implemented over the real-time video feeds could even be beneficial. The supreme good thing about vehicle detection within the real-time streaming video feed is to trace vehicles in busy roads or Bridges like Padma or Jamuna Bridge. An accidents occurred anywhere which may rather be detected. Vehicle detection also called computer vision beholding, basically the scientific methods and ways of how machines see instead of human eyes. This chapter aims to explore the prevailing challenging issue within the planet of unsupervised surveillance and security, Helps traffic police, Maintaining records and Traffic surveillance control. The detection of vehicles is implemented with enhanced algorithms and machine learning libraries like OpenCV, TensorFlow, and others. The varied approaches are accustomed identify and track the particular object through the trained model from the captured video.
Analysis and Evaluation of Capture the Flag Challenges in Secure Mobile Application Development

Capture the Flag (CTF) challenges are frequently used as cybersecurity learning environments to engage students in cybersecurity education activities and learning, focusing on technical concepts. CTF challenges cover various learning topics. However, they do not always maintain a clear learning outcome. In this paper, we present a systematic approach to study and evaluate CTF challenges, then apply the evaluation methodology in two CTF challenges that relate to the development of secure mobile applications. For this proof of concept, we used the National Initiative for Cybersecurity Education (NICE) which is a cybersecurity educational framework published by the National Institute of Standards and Technology (NIST). Additional information was used for the evaluation process which included threat, vulnerability, and weakness taxonomies proposed by Open Web Application Security Project® (OWASP) and Mitre Corporation (MITRE). The evaluation methodology could be used to assess and determine the learning outcomes of other existing or upcoming CTF challenges, including though not limited to secure mobile application development.