Reviewerly/exHarmony huggingface dataset:
Design Avionic Learning Media - VHF-HF Communication Based on Multimedia Animation on the ATKP

The importance of learning media and the development of technology is well-used Medan ATKP to continue to improve the quality of learning. The problem often encountered in avionic learning is the limited resource available because to access avionic learning the cadets must access from the LBT CBT and cannot be from elsewhere. This is because Avionic software is only installed in the lab and cannot be learned from outside the lab. The purpose of this study is to improve the digital avionic-VHF-HF Communication learning process that is packaged in multimedia animation to make it easier for cadets to learn avionic VHF-HF Communication without having access to a laboratory. The method used in this research is to use the Multimedia Development Life Cycle method, namely the Concept, Design, Material Collecting, Assembly, Testing, and Distribution. The resulting output is an animated video production and publication to journal.
SECOND LANGUAGE ACQUISITION BASED ON GRAMMATICAL RULE FOR THE FIRST SEMESTER OF TADRIS BAHASA INGGRIS DEPARTMENT AT STAI MEMPAWAH

Language acquisition is very similar to the process students  use in acquiring first and second languages. It requires meaningful interaction in the target language natural communication--in which speakers are concerned not with the form of their utterances but with the messages they are conveying and understanding. Error correction and explicit teaching of rules are not relevant to language acquisition (Brown, 1970), native speakers can modify their utterances addressed to acquirers to help them understand. Grammatical functions are assigned directly to the underlying representation in a more or less across the board fashion, only taking into consideration the language dependent semantic function hierarchy. This approach bypasses a number of constraints on subject assignment that may be gathered from typological data, and observed from the actual behaviour of speakers. We propose a treatment of Subject assignment on the basis of a combination of semantic factors of the relevant referents and other functional aspects of underlying representations. In Subject (and Object) assignment are now located in the daily diologue, in Mackenzie, J (2004).
AN IMPROVED SINGLE IMAGE DEHAZING ALGORITHM BASED ON DOWNSAMPLING

Now-a-days our environment is full of pollution. Pollution includes particles, water droplets, fog, smoke due to atmospheric absorption and scattering. Due to this the outdoor images suffer degradation, which is termed as Haze. The removal of this Haze is called Dehazing. In this paper, we propose a fast haze removal method, which is an improved version of an existing method using dark channel prior method proposed by He et al. [1]. In this method, we reduced the computational time by improving the method to estimate dark channel. Our proposed method uses a downsampled image and do not need a soft – matting process [2]. Experiments have proved that our proposed method is faster than existing He’s dark channel method with acceptable image quality.
PENINGKATAN MOTIVASI BELAJAR SISWA MELALUI PENERAPAN METODE HYPNOTEACHING PADA MATA PELAJARAN IPS SISWA KELAS V SD NURUL IJTIHAD PUJUT LOMBOK TENGAH

This research aims to improve the learning motivation students through the application of hpnoteaching methods on social studies fifth grade of Nurul Ijtihad Pujut elementary school even semester 2016-2017. The type of this research is classroom action research with Kurt Lewinâ€™s model. This research consisted of two cycles, and each cycle is made up of one meeting, data collection techniques were observation, questionnaires, and documentation. The observation used to see the activities of the teacher and the students while the questionnaires used to see student learning motivation. Data analysis was using techniques analysis quantitative descriptive. The object of this study is the fifth grade of elementary school Nurul Ijtihad Pujut. The results showed that the implementation of hpnoteaching method could improve student learning motivation. It can be seen from achieving category values, in the first cycle, the average score of learning motivation is 28 with a percentage of 40% with quite high category, and 60% high category. In the second cycle, the average score of learning motivation is 41.6 with a percentage of 30% high category and 70% very high category.Â
LibGuides: Remote Library Services for Online Learning: Library Help

This guide offers links and answers questions for the online learning shift taking place in response to the COVID-19 outbreak.
LibGuides: AU Libraries Remote Support: Home

The AU Libraries will continue supporting the faculty and students of Alfred University during our response to COVID-19.
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Objective: This study tried to find out if a teacher education program that supports professionalism for beginning child care teachers in toddler...
Holding scientific conferences in a foreign language among students of technical specialties as the main means of interdisciplinary integration

The article discusses the features of holding a scientific conference in a foreign language among students of technical specialties as the main means of interdisciplinary integration in the educational sphere for the continuous and holistic self-development of a future specialist in the technical field.Attention is focused on the need for holistic application by students of a technical university of knowledge from different fields of science, as well as their ability to transfer basic ideas from one field of science to another.
Single Trace Analysis against HyMES by Exploitation of Joint Distributions of Leakages

Beginning with the proposal of the McEliece cryptosystem in 1978, code-based cryptography has positioned itself as one of main categories in post-quantum cryptography (PQC). To date, the algebraic security of certain variants of McEliece cryptosystems has been challenged many times, although some of the variants have remained secure. However, recent studies on code-based cryptography have focused on the side-channel resistance since previous studies have indicated that the existing algorithms were vulnerable to side-channel analysis. In this paper, we propose the first side-channel attack on the Hybrid McEliece Scheme (HyMES) using only a single power consumption trace. HyMES is a variant of the McEliece system that provides smaller keys, along with faster encryption and decryption speed. By exploiting joint distributions of nonlinear functions in the decryption process, we were able to recover the private key of HyMES. To the best of our knowledge, this is the first work proposing a side-channel analysis based on a joint distribution of the leakages on the public-key system.
Structure Growth for Small-Footprint Speech Recognition

Modern speech recognition (ASR) is based on large-scale deep neural nets (DNNs) with various architectures. For small-footprint applications running on low-power chips, however, the size of the DNNs must be extremely constrained. In this case, training a generalizable acoustic model is not feasible, especially when the acoustic conditions are diverse. Most of existing approaches to small-footprint networks start from a large net and reduce its scale by pruning. In this paper, we investigate a reverse idea: starting from a small net and increasing it gradually. This structure-growth approach follows a `general to specific' principle and grows the net gradually. We start from the AdaBoost algorithm that builds specific nets for error-prone data, and then propose a new ConBoost that builds specific nets for specific conditions. Our experiments on a small-footprint ASR task demonstrated that both AdaBoost and ConBoost outperform the baseline and other comparative methods including bagging and double-net retraining. Furthermore, ConBoost performs better than AdaBoost.
Efforts by The Japanese Association for Experimental Animal Technologists (JAEAT) : Scheduled technical training courses and their future development

We, laboratory animal technicians, are always the closest to laboratory animals. We always have practiced breeding management, pre- and post-operative care, experimental treatment etc. by making the best use of our knowledge and techniques. That bring accurate experimental results. It is our significance.
OBJECT DETECTION AND TRACKING USING DEEP LEARNING ALGORITHMS

An object needs to be detected. This is done by the use of first and foremost Computer vision Technology YOLO i.e. you Look Only Once. It finds several applications due to its tremendous speed in detecting objects. These object needs to be tracked down, also using YOLO algorithm. YOLO object detection in images means not only to identify what kind of object is included, but also localize it inside the co-ordinates of the bounding box containing the Object. YOLO uses deep learning and Convolution Neural Networks (CNN) for object detection. It needs to see the image only once and identifies the image. It creates a unique ID for each of initial detections. And then tracking each of the objects as they move around the frames in a video, maintaining the assignment of Unique ID's. This project will be robust to occlusion. It can also be used to detect missing object that has been lost in between frames also.
CEK PLAGIASI ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI TINGKAT PENDAPATAN PEDAGANG PASCA RELOKASI PASAR BARU KECAMATAN PANYABUNGAN KABUPATEN MANDAILING NATAL TAHUN 2013-2015

Similarity Found: 20% Statistics: 1625 words Plagiarized / 7992 Total words Remarks: Medium Plagiarism Detected - Your Document needs Selective Improvement. ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI TINGKAT PENDAPATAN PEDAGANG PASCA RELOKASI PASAR BARU KECAMATAN PANYABUNGAN KABUPATEN MANDAILING NATAL TAHUN 2013-2015 Sulhana Lely Lubis Dosen DPK niversitas Graha Nusantara Padangsidimpuan Telah Terbit pada https://jurnal.ugn.ac.id/index.php/jurnalLPPM/article/view/287
STATISTICAL APPROACH TO STUDY THE LITHOSTRATIGRAPHIC SEQUENCE IN THE PROTEROZOIC KOLHANS

Lithofacies succession in the Proterozoic Kolhan Group  has been studied statistically using modified Cross-Association Analysis, Markov chain model and Entropy function. The lithofacies analysis based on the field descriptions and their vertical packaging has been done for assessing the sediment depositional framework and the environment of deposition. Six lithofacies arranged, in two genetic sequences, have been recognized within the succession. The result of Markov chain and cross-association analysis indicates that the deposition of the lithofacies is in NonMarkovian and non cyclic process and represents asymmetric fining- upward. The chi-square test has been done to test of randomness in hypotheses for lithofacies transition at confidence level of 95%. The entropy analysis has been done to evaluate the randomness of occurrence of lithofacies in a succession. Two types of entropies are related to every state; one is relevant to the Markov matrix expressing the upward transitions (entropy after deposition), and the other, relevant to the matrix expressing the downward transitions (entropy before deposition). The total energy regime calculated from the entropy analysis showing maximum randomness, suggests that changing pattern in deposition has been a result of rapid to steady flow. This results a change in the depositional pattern from deltaic to lacustrine deposit and sediment bypassing that finally generated non-cyclicity in the sequence.Keywords: - cyclicity,assymetricity,fan-delta, braided-ephemeral
Determinants of blockchain adoption and perceived benefits in food supply chains

Purpose: Although some practitioners and academics emphasise the opportunities that Blockchain offers in supply chains, it is not yet widely adopted. The purpose of this paper is to develop a conceptual framework in order to determine the factors influencing supply chain managers’ decision to adopt Blockchain and to explore the perceived benefits from its adoption.Research approach: The research design adopted follows two sequential phases of data collection: systematic literature review and interview with experts and practitioners across the food supply chain.Findings and Originality: Drawing from the technological, organisational, environmental (TOE) framework, a conceptual framework proposed that integrates various advantages of the Blockchain technology and several risks; these factors are used to explain supply chain managers intention to use Blockchain. The Blockchain adoption will in turn impact the level of perceived benefits such as supply chain transparency.Research Impact: Regarding Blockchain, studies in the field of supply chain management focus on the identification of drivers, challenges and its applications but there is currently no holistic evaluation of the determinants of Blockchain adoption as well as the implications on supply chain performance. This study therefore presents a more holistic assessment of Blockchain adoption and its impacts on supply chain performance.Practical Impact: This study produces a comprehensive set of benefits and risks to be considered by supply chain managers during the Blockchain adoption decision. Practitioners considering adoption of the technology could also justify potential efficiency and effectiveness related benefits.Keywords: Blockchain, Food supply chain, Supply chain Performance, Technology adoption
ATTENTION DEFICIT HYPERACTIVITY DISORDER (ADHD) IN ROBIN ROE’S NOVEL A LIST OF CAGES

This thesis entitled "Attention Deficit Hyperactivity Disorder (ADHD) in Robin Roe's Novel A List of Cages". This thesis discussed about the symptoms Attention Deficit Hyperactivity Disorder (ADHD) in the novel A List of Cages by Robin Roe. This thesis aimed to find out how the symptoms of ADHD are described through the leading character in the novel. This thesis used to describe the core symptoms of ADHD, namely: Attention, Impulsivity, and Hyperactivity. There are symptoms of ADHD that Adam has, namely: Attention symptom is easily bored; Impulsivity symptom is agitated; and the Hyperactivity symptom is: do excessive motor activities. This thesis used Russell Barkley's theory to analyze the symptoms in a novel using Literary Psychology. In analyzing this thesis, the writer used a qualitative descriptive method that presents all data in the form of words and sentences and uses library research as a way to collect data by reading and selecting quotations from the novel A List of Cages. From this research it is found that the leading character in the novel experienced all the three core symptoms of ADHD.
Features of the organization of project activities in the conditions vocational training at a technical university

The article reveals the features of the transprofessional training of future engineers by using the project method in a constantly changing social-economic environment.
A Super-Learning Machine for Predicting Economic Outcomes

We present a Super-Learning Machine (SLM) to predict economic outcomes which improves prediction (i) by cross-validated optimal tuning, (ii) by comparing/combining results from different learners. Our application to a labor economics dataset shows that different learners may behave differently. However, combining learners into one singleton super-learner proves to preserve good predictive accuracy lowering the variance more than stand-alone approaches.
Domain-adversarial neural network for improved generalization performance of Gleason grade classification

When training a deep learning model, the dataset used is of great importance to make sure that the model learns relevant features of the data and that it will be able to generalize to new data. However, it is typically difficult to produce a dataset without some bias toward any specific feature. Deep learning models used in histopathology have a tendency to overfit to the stain appearance of the training data { if the model is trained on data from one lab only, it will usually not be able to generalize to data from other labs. The standard technique to overcome this problem is to use color augmentation of the training data which, artificially, generates more variations for the network to learn. In this work we instead test the use of a so called domain-adversarial neural network, which is designed to prevent the model from being biased towards features that in reality are irrelevant such as the origin of an image. To test the technique, four datasets from different hospitals for Gleason grading of prostate cancer are used. We achieve state of the art results for these particular datasets, and furthermore for two of our three test datasets the approach outperforms the use of color augmentation.
Intellectual Property Rights in Nigeria: Issues and Challenges

This study is on issues and Challenges facing intellectual Property right in Nigeria.Although intellectual Property right laws exist in Nigeria, it is observable that the enforcement of this aspect of law is not taken seriously compared to that of real property.This problem has caused a decrease in the economic value of the productive sector, especially the creative industry, in Nigeria, and a corresponding decrease in our Gross Domestic Product (GDP).In view of this problem, the study aims at investigating the issues and challenges affecting intellectual property right in Nigeria, with a view to enlightening the public on the availability and role of intellectual property laws in our Jurisdiction and to proffer solutions on how to achieve more effective enforcement.The study employs a qualitative research approach, while information was sourced primarily through oral source mainly interviews with learned Jurists and practitioners in the creative industry.Secondary sources of information used comprise written sources which include decided cases, Books, Journals, Newspapers and dissertations/project.The study revealed that if intellectual properties are properly protected and the applicable laws enforced, Nigeria will witness a phenomenal rise in the economic value of most productive sector in Nigeria and a corresponding increase in Gross Domestic Product.The study will recommend among others, to improve on legal education in Nigeria by generating best practices and approaches that can be employed to redesign legal education to make it responsive to contemporary realities and needs of the society.
RANCANG BANGUN SISTEM PENGELOLAAN DATA ADMINISTRASI PERUSAHAAN MENGGUNAKAN PHP DAN MYSQL (STUDI KASUS : PT. GAPURA NIRWANA AGUNG CONSULTANT)

Use of the system is currently the most important thing in a company. Any company or institution in both the small and large scale certainly needs a system that can be used to manage large amounts of data. As in the current data management processes and administration of the company PT. Gapura Nirwana Agung Consultant still has not done well and controlled. The data is stored on a storage medium (folder) are different, because it could have a folder that contains important data are deleted or missing due to various things. To overcome this, it is necessary to design a data management system that can help companies in the management of administrative data and in accordance with the times. The aim in this thesis is to produce a system of administration of enterprise data management applications using php and mysql. As a result of system design built applications that generate employment report project data, employee reports, and generating paychecks for each personnel or employee
Experimental digital Gabor hologram rendering of C. elegans worms by a model-trained convolutional neural network (Conference Presentation)

Digital magnitude image rendering in Gabor holography can be performed by a convolutional neural network trained with a fully synthetic database formed by image pairs generated randomly. These pairs are linked by a numerical model propagation of a scalar wave field from the object to the sensor array. The synthetic database is formed by generating images made from source points at random locations with random brightness on a black background. Successful prediction of experimental Gabor holograms of microscopic worms by a UNet trained with 50,000 random image pairs is achieved, and a classifier-based regularization for twin-image removal is investigated.
Comparative Study of Data Mining Classifiers with Different Attributes and Different Databases Domain

In this paper, an attempt is made to identify and cross validate with five different classification methods in terms of precision, accuracy and kappa statistics calculated and visualized with different sets of database collected from different domain.This research paper has been implemented in R language environment and the obtained results show that which classifier is the most robust classifier method.The Accuracy based comparison of different classification for different datasets have been showed.By confusion matrix sensitivity, specificity, accuracy, true positive rate and false positive rate of different classifier for all four datasets are calculated and comparison of Kappa Statistics is also performed.The present work is about to analyze the effectiveness of the most popular classification techniques.According to the Experimental results, the Support Vector Machine model proved to have the best performance.It performed better of all datasets used.Naive Bayes Classifier, Decision Tree and Random Forest also performed well.The true positive rate and false positive rate table represent above 80% True Positive Rate and less than 20% False Positive Rate for all four datasets.Kappa Statistics basically performs the analysis between different classes.This shows the comparative analysis of different classification under the kappa statistics.Higher Value of kappa statistic is considered as good.
The business case for blockchain in health care — part I: Evaluating emerging technology: a solution framework and the potential business value

While an organization might want to focus on a specific emerging technology to solve a business probelm, strong organizations focus on an honest evaluation, internally and externally, to assure the timing and the business value for using the technology is in their best interest.  This requires a solution set of knowledge, people, and methodologies coupled with products datas and tools.
A Scenario Information Space Model Construction Method Based on Grid Operation Scenario

In the power operation scenario, user information, power equipment, environment, tasks, time and other data are collected through sensors, mobile terminals, wearable devices, GPS/Beidou/UWB positioning system, and computer input. The information space model is constructed from five dimensions: people, equipment, environment, task, time, etc., to realize the logical correspondence between physical space and information space. In the power operation scenario, the user wears the wearable device to automatically perceive the power operation scene information, and the information space model provides target positioning, path planning, navigation, electronic fence, audio and video conference, intelligent interaction, etc. as needed to realize the power field operation process precise guidance.