Reviewerly/exHarmony huggingface dataset:
Numerical Discrimination of the Generalisation Model from Learnt Weights in Neural Networks

This research demonstrates a method of discriminating the numerical relationships of neural network layer inputs to the layer outputs established from the learnt weights and biases of a neural network's generalisation model. It is demonstrated with a mathematical form of a neural network rather than an image, speech or textual translation application as this provides clarity in the understanding gained from the generalisation model. It is also reliant on the input format but that format is not unlike an image pixel input format and as such the research is applicable to other applications too. The research results have shown that weight and biases can be used to discriminate the mathematical relationships between inputs and make discriminations of what mathematical operators are used between them in the learnt generalisation model. This may be a step towards gaining definitions and understanding for intractable problems that a Neural Network has generalised in a solution. For validating them, or as a mechanism for creating a model used as an alternative to traditional approaches, but derived from a neural network approach as a development tool for solving those problems. The demonstrated method was optimised using learning rate and the number of nodes and in this example achieves a low loss at 7.6e-6, a low Mean Absolute Error at 1e-3 with a high accuracy score of 1.0. But during the experiments a sensitivity to the number of epochs and the use of the random shuffle was discovered, and a comparison with an alternative shuffle using a non-random reordering demonstrated a lower but comparable performance, and is a subject for further research but demonstrated in this decomposition class architecture.
CLASSIFICATION OF SIGNATURES USING TEMPLATE MATCHING METHOD

Template Matching is one of the methods used for digital image processing, usually used to recognize the shape or pattern of an image. The shape or pattern that is often used to be recognized is in the form of character images, letters, numbers, or fingerprints. In the research conducted, signature pattern recognition was made using Template Matching for signature classification. Signature is chosen in research conducted with the aim of knowing whether the signature can be recognized using the Template Matching in addition to character images of letters, numbers, or fingerprints. Template Matching works by matching each pixel in the image matrix that has been digitally processed with the reference image (template) and because Template Matching is an applied method of convolutional technique, Template Matching combines two numbers to produce a third number series, so that the correlation coefficient (r) of the Template Matching will be obtained between -1 and +1. The results of the trials carried out show that the signature pattern recognition with Template Matching can recognize the signature image tested with a recognition accuracy rate of 96% with as many as 100 signature images.
Moving Beyond Content‐Specific Computation in Artificial Neural Networks

A new wave of deep neural networks (DNNs) have performed astonishingly well on a range of real‐world tasks. A basic DNN is trained to exhibit, in parallel, a large collection of different input‐output dispositions. While this is a good model of the way humans perform some tasks automatically and without deliberative reasoning, more is needed to approach the goal of human‐like artificial intelligence. Indeed, DNN models are increasingly being supplemented to overcome the limitations inherent in dispositional‐style computation. Examining these developments, and earlier theoretical arguments, reveals a deep distinction between two fundamentally different styles of computation, defined here for the first time: content‐specific computation and non‐content‐specific computation. Deep episodic RL networks, for example, combine content‐specific computations in a DNN with non‐content‐specific computations involving explicit memories. Human concepts are also involved in processes of both kinds. This suggests that the remarkable success of recent AI systems, and the special power of human conceptual thinking are both due, in part, to the ability to mediate between content‐specific and non‐content‐specific computations. Hybrid systems take advantage of the complementary costs and benefits of each. Combining content‐specific and non‐content‐specific computations both has practical benefits and provides a better model of human cognitive competence.
ASSESSING THE EFFECTIVENESS OF COLLABORATIVE LEARNING DURING SOPHOMOE ENGLISH CLASSROOM: THE CASE OF SECOND YEAR ENGLISH MAJOR STUDENTS AT BULE HORA UNIVERSITY

The main purpose of the study was to increase students’ participation through collaborative learning during Sophomore English classroom in case of second year English Major Students at Bule Hora University. The study used a descriptive survey method that involves quantitative and qualitative methods. Questionnaire and observation were used to gather the necessary data for the study.38 second year English Major Students and 3 English language teachers were used as the target population of the study. Purposive sampling technique was employed to select teachers. Comprehensive sampling method was also used to select students. Thus, descriptive statistics such as frequencies and percentages were employed in analyzing the quantitative data. The qualitative data were analyzed and reported essay form. The findings of the study showed that students haven't a good participate and interest in collaborative learning, lack of motivation, lack of self- esteem and confidence, students haven't smooth relation with the others. Based on the finding, it is recommended that create smooth relation with others, they should develop the interest of effective collaborative and to make students effectiveness in collaborative learning, teachers should design regular programs for group practice.
Kata kunci : Android, Layanan Ja Rancang Bangun Sistem Layanan Jasa Otomotif

Based on a survey conducted on 35 private vehicle users, it’s known that many problems occur suddenly in their vehicles while traveling, such tire leaks, engine failure and running out of fuel. So motorists have to look for automotive service places such as repair shops and fuel stalls. However, they don’t know the automotive service establishment and find difficult to find one. Another problem is the difficulty in dividing the time for drivers to wash their vehicles, because they have find the doorsmeer and bring their vehicles to the doorsmeer places and wait for the washing process. Therefore, research was conducted, namely the development of automotive service system using Location Based Service (LBS) technology to find the location of the automotive service business from the location of the driver. To make it easier to order automotive services, system was created that could order repair shop services, doorsmeer and purchase fuel with an Android smartphone. Where the officer will be sent to the location of the driver. Based on usability testing with 5 indicators, the efficiency indicator has a greater percentage of 90%, which indicates that the system can help motorists find automotive service locations and make ordering automotive services easier.
Data Analysis with Pandas

Modern life challenges require fast-paced solutions. These days, we need to evaluate tons of information in real time. Python is fast, but if you need to go faster, there is Pandas. Pandas is a core Python library for accelerated data manipulation. Originally developed for Wall Street professionals, it quickly became popular among people who crunch numbers, analyze big data, and simply want to switch from spreadsheets to the powerful and more efficient Python programming language. The name Pandas stands for panel data. Panel data is an econometric term for multidimensional data set. Pandas has many useful functions to make a data analysis process more efficient.
HOLISTICS JOURNAL HOLISTICS JOURNAL HOSPITALITY AND LINGUISTICS: An Investigation of the Students’ Satisfaction with the Academic Services Given

This study was aimed at finding out the students’ satisfaction with the academic services given by the Faculty of Teacher Training and Education, Faculty of Teacher Training and Education, Sriwijaya University in the aspects of Tangibles, Reliability, Responsiveness, Assurance, and Empathy. The samples were the students of English Education Study Program in the third, fifth, and seventh semesters of Inderalaya Palembang campuses. The data were collected using questionnaires and documentation. The data were analyzed using simple statistic computation. The results of the analysis were then described. It was found that the students of English Education Study Program were satisfied with the academic services given by the Faculty of Teacher Training and Education, Sriwijaya University. The results showed that there was no student who fell into the UNSATISFACTORY category. 96% students were in SATISFIED category and only 4 % were in QUITE SATISFIED category. This group can also be categorised as SATISFIED. This finding shows that the students were satisfied with the academic services given by the Faculty of Teacher Training and Education, Sriwijaya University.
Artificial Neural Networks, Postdigital Infrastructures and the Politics of Temporality

This essay examines the infrastructures and temporalities of modern AI technology based on artificial neural networks (ANN) and aims to contribute to a more substantial understanding of its political challenges. In order to unlock the different temporalities of ANN, a theoretical framework for the relationship of media and infrastructures is suggested that also might help to distinguish between the different levels of analysis related to specific steps and aspects of the machine learning process (the collection and production of learning data, the training of AI models etc.). An important reference point for the following considerations is ethnographic research conducted at TwentyBN,1 a Toronto and Berlin based AI company specialized in ANN and computer vision that just recently developed an app for the fitness market.
Object Tracking Based on Global Context Attention

Previous research has shown that tracking algorithms cannot capture long-distance information and lead to the loss of the object when the object was deformed, the illumination changed, and the background was disturbed by similar objects. To remedy this, this article proposes an object-tracking method by introducing the Global Context attention module into the Multi-Domain Network (MDNet) tracker. This method can learn the robust feature representation of the object through the Global Context attention module to better distinguish the background from the object in the presence of interference factors. Extensive experiments on OTB2013, OTB2015, and UAV20L datasets show that the proposed method is significantly improved compared with MDNet and has competitive performance compared with more mainstream tracking algorithms. At the same time, the method proposed in this article achieves better results when the video sequence contains object deformation, illumination change, and background interference with similar objects.
Developing a Gesture Library for Working in a Virtual Environment

From year-to-year virtual reality conquers more and more IT market share and according to the forecasts, it will continue further. Except for virtual reality itself, there is also a need for the creation of new interaction ways. The specific feature of the VR requires special methods and tools that will provide users with comfort, efficiency, fast response. Also, it is important to make these tools native and understandable to answer the needs of consumers.In the following paper, the authors present an analysis of works that suggest new types of human-computer interaction. The necessity in searching for new methods in communication between users and the virtual environment, and the need for their inclusivity and accessibility are shown.Besides, the self-developed product and the process of a gesture library creation are reflected in the paper. It is assumed that the approach will improve the perception of information, shorten the training time for the user, which together will increase the efficiency of the application in which the product will be implemented.
Systematic Review of Research- Driven Pedagogy In New Normal Modalities

This investigation presents a fundamental examination project intended to create a hypothesis by utilizing an inductive methodology that would clarify the marvel of instructing research in the new typical modalities in senior secondary school level for the school year 2020- 2021. The analyst used the meta-union methodology through a twenty (20) survey of explores identified with the science and specialties of educating research. The means include: outlining of the inquiry for a survey, distinguishing important work, evaluating the nature of studies, summing up the proof, and deciphering the discoveries. For the subjective papers, an agenda from Basic Examination Abilities Program (CASP) was utilized to survey the nature of each paper from various information bases with its general strength of proof. From the led topical examination, the investigation yields three subjects: (1) Reflectivity on the exploration cycle; (2) Teaching with compassion; and (3) Pedagogical culture is all that matters. From the analysis of themes, three propositions were formulated: (1) Students can adapt to their learning environment with the guidance of the research teacher; (2) Students’ learning styles are influenced by their research teachers openness in technology utilization; and (3) Outcomes of learning justifies the type of learning modalities employed by the research teachers. The generated theory from analyzing the propositions state: Teachers can make a difference to their students’ capabilities along academic evolution and steadfast development on the basics of applied research. Finally, the constructs of the theory justifiably espouse self-learning activities specifically for Grade 11 students who will be taking up Practical Research 1 in qualitative designs.
Autonomous Vision-Based UAV Landing with Collision Avoidance Using Deep Learning

The autonomous vision-based Unmanned Aerial Vehicles (UAVs) landing is an adaptive way to land in special environments such as the global positioning system denied. There is a risk of collision when multiple UAVs land simultaneously without communication on the same platform. This work accomplishes vision-based autonomous landing and uses a deep-learning-based method to realize collision avoidance during the landing process. Specifically, the landing UAVs are categorized into Level I and II. The YoloV4 deep learning method will be implemented by the Level II UAV to achieve object detection of Level I UAV. Once the Level I UAV’s landing has been detected by the onboard camera of Level II UAV, it will move and land on a relative landing zone beside the Level I UAV. The experiment results show the validity and practicality of our theory.
Shelftalkers: Empowering student voice

This article was presented at the IASL conference in July 2021. It describes SLAVs new initiative – Shelftalkers. This initiative is a website that publishes 100-word student reviews of texts they are reading or have been sent by arrangement with publishers for review. The process is facilitated by the school library and is open to students of any age. The project aims to give students a voice, give school library staff and publishers an insight into student views, and allows participating school libraries to be central players in the literacy development and reading culture of their respective schools.
Anomaly Detection for Health Monitoring of Heavy Equipment Using Hierarchical Prediction with Correlative Feature Learning

Data-based diagnostics and condition prediction research is actively underway in the heavy equipment industry, including construction machinery. However, it is practically difficult to obtain anomalous data and predict maintenance in most industrial equipment. Also, several problems occur with missing sensors and abnormal conditions of equipment in various forms. In this paper, a realistic industrial problem is dealt with an anomaly detection approach based on prediction of multi-sensor data collected under normal conditions. We propose a multiple feature extraction model to discriminate at least four different anomaly types, and a prediction model based on normal state to diagnose abnormalities according to the difference between the predicted and the actual values. The proposed method uses a hierarchical model that predicts missing sensor information and predicts global sensor information. Extensive experiments have shown that the proposed model improves robustness and detection accuracy. Our model generates missing sensor data with about 90% accuracy and detects anomalies with about 85% accuracy, even if part of the sensor is missing or the device has been changed.
COMPLIANCE OF THE RUSSIAN EDUCATION SYSTEM WITH THE REQUIREMENTS OF THE DIGITAL ECONOMY

Improving the education system provides the digital economy with competent personnel, contributes to a painless transformation of the labor market, forms a system of motivation for the development of the necessary competencies and the participation of personnel in the development of the digital economy of Russia. Such a significant influence that the education system has as an investment in human capital leads to the need to consider this element in the surrounding cultural environment as a key factor in both national and international business. Comparative data with foreign markets can help in understanding, for example, the level of accessibility of education and its impact on lifelong learning and the establishment of market relations.
ONTOLOGY MERGING USING PROTÉGÉ – A CASE STUDY

The exponential growth of data and the boom of online businesses necessitates the need for data to be machine-readable, as humans are no longer able to manually manage the vast amounts of data. Ontologies can define concepts and relations that are amenable to processing by machines. Ontologies are created in silos and pockets of domains, and the need to merge these resources is key to universal access to multi-domain knowledge. Merging of ontologies has been explored to an extent over the last two decades, and this paper explores the extent of the tools and techniques available with a case study of merging two ontologies which are publicly available, the Person ontology and Institutional ontology, using the latest tools available on the most popular ontology editor, Protégé. It is found that automated merging tools have not been improved much over the last two decades, and the most current merging tools provided combine the two ontologies into one but do not unite or merge any of the classes or axioms which are equivalent. This can be seen in the axiom count, which does not decrease in the merged ontology, showing that no similar classes or actual axioms were merged. Protégé plugins which used to provide the semi-automatic mapping of similar classes to assist the merging process were found to be no longer available, and manual mapping by the knowledge engineer was required. This supports further research in automated ontology merging techniques.
VALIDATION OF THE E-LEARNING PRACTICES INSTRUMENT

The success of the Malaysian Education Blueprint 2015-2025 could be achieved by the effective practices of e-learning. E-learning is not just an option today but has become a necessity when the pandemic covid-19 strikes the world. Therefore, this study aims to validate an instrument of an e-learning practice among student teachers in a public university. 139 respondents are randomly selected from undergraduate students from one of the public universities in Malaysia. In order to measure the suitability of each item and validate the instrument, exploratory factor analysis (EFA) is used. The findings of the study show that all load factor values exceed 0.5 so all items are accepted for real study. This instrument has a high degree of validity and reliability to determine the e-learning practices among university students.
A Progressive Search Algorithm for the Minimum Hitting Set Problem

Given a finite universe and a collection of the subsets of the universe, the minimum hitting set of the collection is the smallest subset of the universe that has non-empty intersection with each set in the collection. Finding the minimum hitting set is an NP-Hard problem that has many real world applications. In this study, we propose a progressive search-based approach to find the minimum hitting set of a given collection. The algorithm starts searching for the hitting sets of size 1 and increase the expected size of the minimum hitting set by a factor of d. After each unsuccessful search, it increases the expected size by d and generate the candidate sets with the expected size. After each successful search, the algorithm takes the average of last unsuccessful and successful searches and continue the searching with the new expected size. The algorithm terminates when the detected upper bound coincides with the detected lower bound. The effect of different values for d on the performance of the algorithm has been experimented on various data sets. Experimental results reveal that the proposed method effectively computes the minimum hitting set on real-world data and random dataset.
Web Based Framework for Automated Prognosis of Specific Learning Disabilities in Children Using Machine Learning Model.

Specific Learning disorder is a medical disorder that includes difficulties in reading, spellings, following instructions , understanding, comprehending, writing. 10% of school going children have difficulties in learning varying from negligible to significant. Students with SLD  suffer Less self-esteem . Diagnosing their difficulties and assisting them has become a tedious job. This article outlines web based  framework to diagnose SLD using  machine learning .To easy the process and make the diagnosis automated a web based frame work is proposed.”mylab”   app designed to check their learning ability .This Web app is designed based on GLAD “Grade level assessment Device” which  is an assessment tool to acess the students learning difficulties. “MyLab”  is used to determine the learning difficulties in language processing-Dyslexia , arithmetic -dyscalculia and dysgraphia  writing difficulties . The app collects the data as voice clipping,images, quiz score and based on glad scoring data set are generated and using CNN and ML algorithms like SVM,KNN and classifies students as negligible , mild or significant SLD.
The Big-Rip A Quantum Study of the Universe

In cosmology, the Planck era refers to the period in the history of the Universe during which the four fundamental interactions (electromagnetism, weak interaction, strong interaction and gravitation) were unified, that is to say -to say that they applied at the same time, which prevents it from being described using general relativity or quantum physics, since these theories are incomplete and are only valid when gravitation and quantum effects can be studied separately.