Reviewerly/exHarmony huggingface dataset:
Pengaruh Model Student Teams Achievement Division (STAD) dengan Media Video terhadap Kemampuan Menulis Teks Eksplanasi Siswa Kelas XI SMA UISU Medan

The study population was all grade XI students North UISU senior High School, totaling 70 people, While the research class XI Science and Social Sciences, which compares two classes where class XI Science as an Experiment class uses the learning model of Student Teams Achievement Division (STAD) with media in the form of video and social science class as a class control without using the learning model Student Teams Achievement Division (STAD) using video media. Experiment obtained the value Lhitung &lt; Ltable (0.88 &lt; 0.137) proves that the variable X data is normally distributed. in the Control class ,the value of that Lhitung &lt; Ltable (0.97 &lt; 0.161) proves that the variable Y data is normally distributed. Based on homogeneity that has been done, then (1.4 &lt; 3.98). This proves that the sample of researchers comes from a homogeneous population. Value of t(table) = 1.99 with t_count = 9.26. then the alternative hypothesis is accepted it can be concluded that the learning model of Student Teams Achievement Division (STAD) with media in the form of video has a significant effect in increasing the ability to write explanatory texts.
The Design and Implementation of the University Network Education Information Monitoring System Based on Big Data Technology

Online teaching is not simply “Internet + traditional teaching”, but is based on the Internet, reshaping traditional teaching contents, reconstructing traditional teaching structures, rebuilding traditional teaching processes and innovating traditional teaching methods, and the development of big data technology brings opportunities and challenges for teaching quality monitoring. It is necessary to clarify the scope and content of quality monitoring, build a quality monitoring system for online teaching, and continuously improve the effectiveness and quality of online teaching. Big data technology is a complete “data + business + requirements” solution. This paper analyses the process of online education information monitoring system and designs the Apriori algorithm process. The implementation process requires strengthening the collection, processing and analysis of data, forming an efficient teaching feedback and improvement mechanism, and improving the online teaching effect from multiple perspectives.
FAM: Visual Explanations for the Feature Representations from Deep Convolutional Networks

In recent years, increasing attention has been drawn to the internal mechanisms of representation models. Traditional methods are inapplicable to fully explain the feature representations, especially if the images do not fit into any category. In this case, employing an existing class or the similarity with other image is unable to provide a complete and reliable visual explanation. To handle this task, we propose a novel visual explanation paradigm called Fea-ture Activation Mapping (FAM) in this paper. Under this paradigm, Grad-FAM and Score-FAM are designed for vi-sualizing feature representations. Unlike the previous approaches, FAM locates the regions of images that contribute most to the feature vector itself. Extensive experiments and evaluations, both subjective and objective, showed that Score-FAM provided most promising interpretable vi-sual explanations for feature representations in Person Re-Identification. Furthermore, FAM also can be employed to analyze other vision tasks, such as self-supervised represen-tation learning.
Design of Hazard Management Method Based on Blockchain

This paper proposes a blockchain-based hazard management method. Firstly, the data structure and operation types of hazards in civil aviation risk management are analyzed in detail. Secondly, a blockchain is designed to maintain all the data items of hazards, including but not limited to state data and operation data. In addition, the latest state of hazards is stored in a global hash list for quick query. Finally, a hazard management method based on the above blockchain is proposed.
Implementing COCOMO II as personnel direct cost in an owner estimate cost model for software project

COCOMO II is a method to get effort, time (schedule), and cost for software development. In this study, COCOMO II was used to obtain personnel direct costs of a software development project. Personnel direct costs were one of the three components that compose the owner estimate cost. The purpose of this study was to propose a conceptual model to determine the owner estimate cost that was formed from three components, namely: personnel direct cost, non-personnel direct costs, and taxes. In this study, it was explained the stages of using the model to determine the owner estimate cost for software development projects.
Fully Deep Simple Online Real-time Tracking: Efficient Re-Identification by Attention without Explicit Similarity Learning

Most existing Multi-Object Tracking methods consider detection and re-identification as two distinct steps. As a result, the re-identification cannot leverage object location and is only based on appearance, thus leading to ID merges when dealing with highly similar objects. The few works that combine detection and re-identification still generate an appearance descriptor for similarity computation. However, since the detection task conflicts with the tracking task, the network privileges the former and generates similar descriptors for objects of the same class, especially when class instances have a strong visual similarity. Besides, when using a motion model or a motion prediction recurrent neural network to delimit the search area and overcome the problem of ID merges, the rise of uncertainty occurring when those models are not updated often leads to ID switches. In this paper, we tackle these issues and propose to use the same model for detection and re-identification by leveraging attention between features of two frames. By doing so, the network can make motion predictions without providing any appearance descriptor and without computing any learned similarity, thus eliminating the need for any motion prediction model and making the tracking trainable end-to-end. Our experimental results support our main contributions and show that our fully DeepSORT significantly reduces the number of ID switches and merges, even when using non-class-agnostic non-maximum suppression. Besides, our model is more resistant to variations in time lapses between two images, leading to improved tracking results.
EFEKTIFITAS PENGGUNAAN APLIKASI DARING DAN VIDEO CONFERENCE MATERI SYSTEM DESIGN AND ANALISYS METHOD (SDAM) MASA PANDEMI

The phenomenon of the Covid-19 virus that spreads to Indonesia which causes the death rate to increase, Covid-19 has an impact on the application of learning patterns in the world of education. This also occurs in the course of the study program system design and analysis method (SDAM) for Computer and Information Engineering Education, Tarbiyah, IAIN Bukittinggi. The process of delivering lecture material switches from face-to-face (offline) to fully digital (online) by utilizing educational-based applications, namely Scoology and Zoom Meetings. The purpose of this study was to determine the level of understanding of students in using the application. The research method used is a survey method which was conducted online by taking samples of students in the System Design and Analysis Method (SDAM) course. The expected result of this research is that it can be used as a benchmark in the development of the online teaching and learning process in the coming semester
CONCEPTUAL DESIGN IMPACTS IN NEW NORMAL ERA: THE USE OF ARTIFICIAL INTELLIGENCE (AI) AND INTERNET OF THINGS (IOT) (CASE STUDIES: CLASS ROOM AND RESTAURANT)

The Covid-19 pandemic that has lasted until now has forced changes in various sectors of life, for example, the use of masks on a daily basis has brought changes in the fashion sector, namely the addition of mask accessories to clothing where previously there were none. Likewise in the field of planning and building design which must adjust to the “new normal era”, so that it can fulfill the function of space according to the needs of the “New Normal Era”. The use of information technology in this case IoT (Intenet of Things) and AI (Artificial Intelligence) is needed to support these changes, especially planning and building design will function more optimally if equipped with these technologies and take a role in responding to the needs of building users to feel safer, comfortable and guaranteed health. Some examples of previous research using IoT are the use of automation in architectural concepts and using AI to detect human presence in a room or building. Building design in the “New Normal Era” supported by the use of IoT and AI is needed so that the building meets the health protocol standards issued by the Ministry of Health, but still has to meet the aesthetic and beauty concepts as well as the function of the building. IoT which is a concept that uses connectivity from the internet network as a communication medium for all connected objects into a single unit of an integrated system and similarly the use of AI for artificial intelligence applied to these objects. This research aims to discuss how it is possible to use IoT and AI in building design in the “New Normal Era” as a result of the Covid 19 pandemic. The methodology used in this research is to use a descriptive qualitative methodology with the aim of being able to understand and then provide alternative building design solutions with the concept of automation using IoT and AI. This research resulted in scientific studies related to building designs that use IoT and AI which can be input for alternative design solutions in the “New Normal Era”. This research is expected to answer the question of how the application of health protocols such as social distancing and hand washing that must be applied affects the design of buildings and the use of IoT and AI to support this. The limitation of the research study is on the design of the building with the concept of automation using IoT and AI in Indonesia. The existence of building locations in Indonesia also greatly influences differences in building design solutions, where the Health Protocol reference used can of course differ from one country to another, even though international standards can also be used in general. The concept of building design with the use of IoT and AI technology automation in buildings in Indonesia which is very widespread in the “New Normal Era” and is urgently needed can provide space design solutions that provide security, comfort and “health” for space users.
Information Classification Algorithm based on Project-based Learning Data-driven and Stochastic Grid

The adaptive partitioning algorithm of information set in simulation laboratory based on project-based learning data-driven and random grid is studied to effectively preprocess the information set and improve the adaptive partitioning effect of the information set.Using the improved fuzzy C-means clustering algorithm driven by project-based learning data, the fuzzy partition of information set in simulation laboratory is carried out to complete preprocessing of information set; The pre-processing information set space is roughly divided by the grid partitioning algorithm based on the data histogram; A random mesh generation algorithm based on uniformity is used to finely divide the coarse mesh cells; Taking the representative points of grid cells as the clustering center, the pre-processing information set is clustered by the density peak clustering algorithm to complete the adaptive partitioning of the information set in simulation laboratory.Experimental results show that this algorithm can effectively preprocess and adaptively partition the information set of simulation laboratory; For different dimension information sets, the evaluation index values of Rand index, Purity, standard mutual information, interval and Dunn index of the algorithm are all high, and the evaluation index values of compactness and Davidson's banding index are all low, so the algorithm has a high accuracy of adaptive partitioning of information sets.
Current Situation, Problems and Countermeasures of Professional Development of Middle School English Teachers in Rural Areas of Western China

In order to explore the current situation of rural middle school teachers' training and professional development in the county, this paper carries out the corresponding research on the form and professional development of primary middle school teachers in the county.Then, combined with SPSS25 and Excel software for data statistics and analysis.The study found that: in the professional development of rural middle school teachers, the development status in three dimensions from high to low is: professional philosophy and ethics, professional ability and professional knowledge.Among them, the development of teachers' attitude and behavior in education and teaching, general knowledge, teaching implementation and teaching evaluation is not ideal.Finally, this study puts forward relevant suggestions to the state, training institutions and middle schools from the macro, meso and micro perspectives.
The value of trust in the digital economy: The role of data sharing clubs

The existing studies posit that open data could unlock more than $3 trillion in additional value worldwide across different application domains. At the same time, it is unclear how to increase data donation for value realisation and societal benefit. A Data Sharing “club” is a business model in which organisational or personal data can be shared and potentially re-used for the benefit of its members. This study aims at disclosing the important role of data sharing clubs, as “social” communities driving trust to data donation and reuse, which supports the operation of platform business models, such as data marketplaces and data aggregators in converting private data into a private or public good. We contribute to the business model innovation literature by describing the need for communities for value realisation in the data-driven business models. We exemplify their operation for food supply chains. Future research should investigate the inter-sectoral differences in data sharing clubs.
Testing Pyrrhotite Trace Element Chemistry as a Vector Towards the Mineralization in the Sullivan Deposit, B.C.

The Sullivan deposit, located
Bayesian Knowledge Tracing based on Transformer

Knowledge tracing (KT) is a crucial technique for modelling learners in intelligent education. Deep learning has been applied to knowledge tracing to improve the tracing accuracy of the model substantially. However, compared with traditional knowledge tracing methods, deep knowledge tracing lacks effective feedback to teachers or students. The connection between the input data features and the output tracing results cannot be well explained. It is challenging to predict students' performance in the future period based on a small number of samples in practice. In this paper, we propose a deep knowledge tracing model based on Bayesian inference (TBKT). Specifically, a priori data subsets are formed by sampling the training data, and data augmentation is performed on the data subsets by Bayesian regression. The model is trained to approximate the Bayesian posterior distribution based on a small subset combined with query data. The model is trained on the ASSISTments2009 dataset with interpretability and achieves an AUC metric similar to the original transformer.
Design of a Lightweight MOOC Teaching System for Online Learning in Colleges and Universities

In order to improve the teaching function and operation performance of the MOOC teaching system in colleges and universities, under the condition of online learning, a lightweight MOOC teaching system in colleges and universities is designed from three aspects: hardware, database and software. In terms of hardware, the program operation controller, teaching data acquisition and transmission module of the system are mainly modified and optimized. Collect the user and related teaching resource data in the teaching system, install it in a certain format to form a database table, and obtain the design result of the system database through the logical relationship between the database tables. This process keeps the system running in a lightweight state. With the support of hardware devices and databases, the identity of the users entering the system is determined, and students and teachers are given different functional rights. The software teaching function of the system is realized through the design and development of functional modules such as interactive practice of lightweight MOOC teaching content, uploading MOOC teaching resources, online testing and score management in the course management module. Through the system test experiment, it is concluded that the functional operation success rate of the designed system is higher than 99%, and the operation performance of the system in the two aspects of response speed and concurrency has been significantly improved.
Room Management based Machine Learning and Data Analytics: Concept Overview

In managing room management, it is often done traditionally based on human observation and experience. Still, nowadays, technologies such as Machine Learning, Artificial Intelligent, Data Analytics, robotics, Augmented Reality/Virtual Reality, and automation has rapidly changed the way people do work. The future of work is a rethinking of how work can be done. This is a fundamental shift in the work model to a model that encourages collaboration between machines and people, thereby enabling new skills and work experiences and supporting an environment that is not limited by time and physical room. This study aims to create a concept in building a room management/scheduling system assisted by Machine Learning algorithms to produce web-based centralized room management equipped with a feature of rarely used or even empty room recommendations. The method used in this study is a qualitative method because it can directly present the relationship between researchers and respondents more sensitively. It is hoped that with a measurable and directed concept, the system can be realized precisely.
Development of Airport Terminology based Synthetic Animated Indian Sign Language Dictionary

In the current era of computerization, the development of a synthetic animated Indian Sign Language (ISL) dictionary could prove very beneficial for deaf people to share their ideas, views and thoughts with hearing people. Although many human based video dictionaries are available, no ISL synthetic animated dictionary solely for public places is developed yet. The development of an ISL dictionary of 1200 words using synthetic animation for airports terminology is reported in this article. The most frequently used words at airports in ISL are categorized and then are translated into Signing Gesture Markup Language (SiGML) which generates the signs utilizing synthetic animations through a virtual avatar. The developed ISL dictionary can be used for automatic sign translation systems at airports animating signs from written or spoken announcements. This ISL dictionary is used in the development of airport announcement system for deaf that is capable of displaying spoken airport announcements in ISL using synthetic animations. Moreover, the developed dictionary can prove very beneficial for educating deaf people and for assisting while visiting public places.
Analisis Faktor-faktor Kesulitan Membaca Permulaan Siswa Kelas II SD Negeri Poris Pelawad 2

This research is a descriptive qualitative research to examine and analyze the data objectively according to the data found in the field. Then the data collected from the research results are described in the form of words. Informants in this study were grade II teachers and grade II students at SD Negeri Poris Pelawad 2 Tangerang City. Data collection techniques used are observation, interviews, tests, and documentation studies. The results of this study aim to find out what factors are the origin of reading difficulties in grade II students at SD Negeri Poris Pelawad 2 Tangerang City. Based on the results of research that has been done by researchers that there are problems that become factors of early reading difficulties, namely the omission of words or letters, pronunciation of words with the help of the teacher, not paying attention to punctuation, hesitation, stuttering, retreats, and self-correction. Therefore, researchers can draw conclusions from the results of research that has been studied that there are still students who are not fluent in reading the beginning, this is caused by lack of motivation to learn, unavailability of family environmental factors, and unavailability of reading materials.
Project activity based on blog technologies for engineering training programs

Currently, engineering education is the largest subsystem of higher professional education in Russia and abroad. However, despite the scale of the engineers training, there remains a shortage of specialists in the field of knowledge-intensive and intellectual-intensive technologies. In this regard, there is an increase in demand among young people for engineering education programs, as well as the need for higher professional competence of graduates in these programs in accordance with the requirements of scientific and technological development. In this situation, the hands-on approach of the individual to his development, and in particular, to his own self-actualization, increases. In this regard, we have undertaken a study of the formation the level of personal self-actualization of engineering students based on blog technologies under study of a foreign language in the course of project work. The following methods were chosen as specific research methods: pedagogical testing, pedagogical experiment, methods for studying collective phenomena, analysis and description of quantitative and qualitative results of experimental teaching. The platform for the implementation of our research was a blog, on the basis of which students created a mobile blogging community studying foreign languages professionally, with the goal of developing and improving the communicative and information competence of a language personality capable of independent self-development and self-actualization. As a result of the study, the positive dynamics of the personal qualities formation, which are the component of students self-actualization based on blog technology, was identified.
Cnosso, a Novel Method for Business Document Automation Based on Open Information Extraction

The state-of-the-art in automated processing of unstructured business documents has evolved from manual labor to advanced AI systems in the span of mere decades. Such systems involve learning techniques, rule or clause sets, neural models — either used alone or in combination — for the extraction to work. As an example, rule-based processes operate on a perceived layout or positioning of the informations, whereas model-based frameworks adopt a semantical, and often uninspectable, approach. Verb-Based Semantic Role Labeling (VBSRL) is a novel system presented in a former paper that uses a hybrid foundation to inform the extraction phase via a set of rules modeling natural language. We propose a new VBSRL-based document processing method, aided by valuable and innovative architectural choices, which has been implemented for the Italian language and experimented upon with promising results. Even in its infancy, in fact, the first implementation of this system shows better results than comparable IE solutions, obtaining an aggregate, average F-measure of nearly 79%.
Characterization of pulmonary function impairment in a pediatric sickle cell anemia cohort using electrical impedance tomography

Introduction Sickle cell disease (SCD) is a common autosomal recessive hemoglobinopathy, being endemic in large parts of Africa, the Middle East, India, and in some areas of Turkey, Greece and Italy.