Reviewerly/exHarmony huggingface dataset:
Impact of Land Cover Land Use Changes (LCLUC) on Earth Resources &amp;amp; Human Life – A Literature Survey

Land cover data determines how much of a region/area is covered by forests, wetlands, impervious surfaces, agriculture, water include wetlands or open water and other land. It can be defined as controlling element for terrestrial ecosystem and environment. It is also easiest detectable factor that indicate humans’ positive & negative activities towards earth’s resources and environment. Land use shows how people use the landscape for development, conservation, or mixed uses. It refers to the purpose the land serves to the human life and human activities on surface of earth such as recreation, wildlife habitat, agriculture, forestry and building construction. These anthropogenic activities alter land surface processes including biogeochemistry, hydrology and biodiversity. And are responsible to contribute the economic factor, social factor to society & living human being to make their life comfortable and easy. It is important to study the pattern of land cover and land use changes to analysis and understand the relationship between people and various lands such as grazing, agriculture land, urban land, wet land, mining land, bare land, water land and so on.
A four-level atom enclosed in an optical cavity with multiphoton transition in the‏‏‎ ‎steady-state regime

The mechanism of a four-level atom confined in a single-mode optical cavity with multiphoton transition is theoretically investigated in the steady-state. The behavior of the atom-cavity system is delineated by the master equation which in order to solve this equation, a set of expected values of a series of operators has been used. To numerically solve this set of coupled expectation values, the matrix continued fractions method is used. How the changes of the physical quantities including the atomic population inversion, mean photon number and second-order correlation function have been studied for any transition. Finally, the process of converting the four-level atom to a three-level one under several specific conditions is discussed for each transition.
Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining

Many real-world applications involve the use of Optical Character Recognition (OCR) engines to transform handwritten images into transcripts on which downstream Natural Language Processing (NLP) models are applied. In this process, OCR engines may introduce errors and inputs to downstream NLP models become noisy. Despite that pre-trained models achieve state-of-the-art performance in many NLP benchmarks, we prove that they are not robust to noisy texts generated by real OCR engines. This greatly limits the application of NLP models in real-world scenarios. In order to improve model performance on noisy OCR transcripts, it is natural to train the NLP model on labelled noisy texts. However, in most cases there are only labelled clean texts. Since there is no handwritten pictures corresponding to the text, it is impossible to directly use the recognition model to obtain noisy labelled data. Human resources can be employed to copy texts and take pictures, but it is extremely expensive considering the size of data for model training. Consequently, we are interested in making NLP models intrinsically robust to OCR errors in a low resource manner. We propose a novel robust training framework which 1) employs simple but effective methods to directly simulate natural OCR noises from clean texts and 2) iteratively mines the hard examples from a large number of simulated samples for optimal performance. 3) To make our model learn noise-invariant representations, a stability loss is employed. Experiments on three real-world datasets show that the proposed framework boosts the robustness of pre-trained models by a large margin. We believe that this work can greatly promote the application of NLP models in actual scenarios, although the algorithm we use is simple and straightforward. We make our codes and three datasets publicly available\footnote{https://github.com/tal-ai/Robust-learning-MSSHEM}.
Fluid Inclusion Study of Epithermal Gold-Base Metal Mineralization System in the Shwebontha Prospect, Monywa Mining District, Central Myanmar Monywa

The Shwebontha Prospect area is one of prominent epithermal Au-Ag prospects in Monywa mining district, central Myanmar, characterized by the appearance of gold-bearing and base metal quartz veins with gold grade is around 3g/t -10.4g/t. The geology of the area consists of the volcanic and volcaniclastic rocks of Upper Oligocene-Middle Miocene Magyigon Formation that served as the host rock of the ore mineralization. This research focused on fluid inclusion study is aimed to know the characteristics of hydrothermal fluids during ore mineralization as well as the possible paleo- depth and temperature of formation of gold-bearing and base metal quartz veins. The mineralization styles are gold-bearing brecciated quartz veins and chalcedonic quartz veins where sulfides are clustered as well as disseminated both in quartz gangue and volcanic host rocks. Those quartz veins include pyrite, sphalerite, galena, chalcopyrite and gold (electrum). Fluid inclusion microthermometry indicates that the ore mineralization is characterized by the values of homogenization temperature range from 158°C to 310°C and salinities range from 0.35 to 2.41wt.% NaCl equiv. This temperature is consistent with the formation temperature of 250°C to 270 °C and also their estimate paleo-depth of formation is between 440m and 640m respectively. Microthermometric data indicates that fluid mixing and dilution were significant processes during ore mineralization and evolution of hydrothermal fluids. Based on the petrography of fluid inclusion, microthermometric measurements and ore minerals assemblage as well as estimation of paleo-depth from the Shwebontha Prospect imply that forming in under shallow level epithermal environment
Designing Information Systems for Payroll Process for National University-Based University Lecturers: Designing Information Systems for Payroll Process for National University-Based University Lecturers

Payroll is one of the important things in an organization. Salary is one of the factors that inspire employees. Therefore a timely payroll process will make employees more motivated to achieve organizational goals. The purpose of this research is to build a simple information system to process employee payroll with a case study at the National University. This information system was built using the programming languages PHP, CSS and Javascript as a user interface, and MySQL as a database. The method used in this study is the waterfall method. The stages of the method used are analysis, design, coding and testing
Detection of Abnormal Behavior with Self-Supervised Gaze Estimation

Due to the recent outbreak of COVID-19, many classes, exams, and meetings have been conducted non-face-to-face. However, the foundation for video conferencing solutions is still insufficient. So this technology has become an important issue. In particular, these technologies are essential for non-face-to-face testing, and technology dissemination is urgent. In this paper, we present a single video conferencing solution using gaze estimation in preparation for these problems. Gaze is an important cue for the tasks such as analysis of human behavior. Hence, numerous studies have been proposed to solve gaze estimation using deep learning, which is one of the most prominent methods up to date. We use these gaze estimation methods to detect abnormal behavior of video conferencing participants. Our contribution is as follows. i) We find and apply the optimal network for the gaze estimation method and apply a self-supervised method to improve accuracy. ii) For anomaly detection, we present a new dataset that aggregates the values of a new gaze, head pose, etc. iii) We train newly created data on Multi Layer Perceptron (MLP) models to detect anomaly behavior based on deep learning. We demonstrate the robustness of our method through experiments.
Pengaruh Pendidikan Pedoman Gizi Seimbang dengan Menggunakan Puzzle terhadap Pengetahuan Siswa SD Swasta Al Fitriah Medan

This research aims to determine the education effect of balanced nutrition guidelines using puzzle on Al Fitriah Private Elementary School Students, Medan. The type of research used is quasi experiment. The study was conducted on 45 students using a questionnaire before and after the intervention. The statistical test used is a paired t-test. The results showed an average score of respondents knowledge before being given nutrition education by 10.96 and increasing after being given nutrition education using puzzle media to 16.84. The results of the analysis using paired sample t test obtained a value of p = 0,000 where p &lt;0.005. So it can be concluded that there is a significant difference in the variable knowledge of fifth grade students of Al Fitriah Medan Private Elementary School before and after being given nutrition education using puzzle media.
Work in Progress -- Automated Generation of Robotic Planning Domains from Observations

In this paper, we report the results of our latest work on the automated generation of planning operators from human demonstrations, and we present some of our future research ideas. To automatically generate planning operators, our system segments and recognizes different observed actions from human demonstrations. We then proposed an automatic extraction method to detect the relevant preconditions and effects from these demonstrations. Finally, our system generates the associated planning operators and finds a sequence of actions that satisfies a user-defined goal using a symbolic planner. The plan is deployed on a simulated TIAGo robot. Our future research directions include learning from and explaining execution failures and detecting cause-effect relationships between demonstrated hand activities and their consequences on the robot's environment. The former is crucial for trust-based and efficient human-robot collaboration and the latter for learning in realistic and dynamic environments.
Comparative analysis of knowledge bases: ConceptNet vs CYC

Data summarization, question answering, text categorization are some of the tasks knowledge bases are used for. A knowledge base (KB) is a computerized compilation of information about the world. They can be useful for complex tasks and problems in NLP and they comprise both entities and relations. We performed an analytical comparison of two knowledge bases which capture a wide variety of common-sense information - ConceptNet and Cyc.Manually curated knowledge base Cyc has invested more than 1,000 man-years over the last two decades building a knowledge base that is meant to capture a wide range of common-sense skills. On the other hand, ConceptNet is a free multilingual knowledge base and crowdsourced knowledge initiative that uses a large number of links to connect commonplace items. When looking for common sense logic and answers to questions, ConceptNet is a great place to start.In this research, two well-known knowledge bases were reviewed - ConceptNet and Cyc - their origin, differences, applications, benefits and disadvantages were covered. The authors hope this paper would be useful for researchers looking for more appropriate knowledge bases for word embedding, word sense disambiguation and natural-language communication.
Joint Matrix Decomposition for Deep Convolutional Neural Networks Compression

Deep convolutional neural networks (CNNs) with a large number of parameters require intensive computational resources, and thus are hard to be deployed in resource-constrained platforms. Decomposition-based methods, therefore, have been utilized to compress CNNs in recent years. However, since the compression factor and performance are negatively correlated, the state-of-the-art works either suffer from severe performance degradation or have relatively low compression factors. To overcome this problem, we propose to compress CNNs and alleviate performance degradation via joint matrix decomposition, which is different from existing works that compressed layers separately. The idea is inspired by the fact that there are lots of repeated modules in CNNs. By projecting weights with the same structures into the same subspace, networks can be jointly compressed with larger ranks. In particular, three joint matrix decomposition schemes are developed, and the corresponding optimization approaches based on Singular Value Decomposition are proposed. Extensive experiments are conducted across three challenging compact CNNs for different benchmark data sets to demonstrate the superior performance of our proposed algorithms. As a result, our methods can compress the size of ResNet-34 by 22X with slighter accuracy degradation compared with several state-of-the-art methods.
AI-BASED VOICE ASSISTANCE USING AWS

Amazon Alexa is a voice-controlled pplication that is rapidly gaining popularity.In this paper user interactions with this technology, and focused on the types of tasks requested of Alexa, the variables that affect user behaviours with Alexa, and Alexa's alternatives.AIbased voice assistance using AWS is offering the users a way to acquire such competence.Particularly, we focus on developing skills for the Alexa assistant, as it is the most widespread.It's open a new world, a world where the user can talk to a machine as if it were a human and the machine will perform the work you request.Ideally, such conversations should be solely between the user and the voice assistance.For the hands-free feature that the user raved about and the other for speech recognition and understanding which is one key feature of the Echo.
Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints

Adaptive Computation (AC) has been shown to be effective in improving the efficiency of Open-Domain Question Answering (ODQA) systems. However, current AC approaches require tuning of all model parameters, and training state-of-the-art ODQA models requires significant computational resources that may not be available for most researchers. We propose Adaptive Passage Encoder, an AC method that can be applied to an existing ODQA model and can be trained efficiently on a single GPU. It keeps the parameters of the base ODQA model fixed, but it overrides the default layer-by-layer computation of the encoder with an AC policy that is trained to optimise the computational efficiency of the model. Our experimental results show that our method improves upon a state-of-the-art model on two datasets, and is also more accurate than previous AC methods due to the stronger base ODQA model. All source code and datasets are available at https://github.com/uclnlp/APE.
Computer Science With Sentiment Analysis

Our proposed system is computer science portal with sentiment analysis we are aiming to design an efficient portal where one can write article on various topic related to Computer Science field. It also consist of a forum where one can raise doubt. In Proposed System we are going to apply sentiment analysis on article’s which will give learner an idea which article is better. The article are going to be sorted based on positive sentiment. Organizations use surveys, opinion polls, and social media as a mechanism to obtain feedback on their products and services. Sentiment analysis or opinion mining is the computational study of opinions, sentiments, and emotions expressed in text
Penggunaan inovasi dgdocspace dalam pengurusan fail peribadi secara digital: kajian kes di Majlis Perbandaran Port Dickson (MPPD) (The use of dgdocspace in personal file management: a case study at Majlis Perbandaran Port Dickson (MPPD) / Rafidah Kamarudin … [et al.]

Hari Bertemu Pelanggan (HBP) is an essential event of the Port Dickson Municipal Council (MPPD) which is held twice a year for staff to review their Personal Files. The problem faced by the staff at the Human Resource Department (JPSM) MPPD is to handle staff Personal Files from the file room to the HBP location during the event. Risks such as loss and damage of Personal Files and the high cost in transferring staff Personal Files lead to the development of innovation known as dgDocSPACE. The implementation of the innovation has successfully reduced the operating cost, increased productivity, shortened time for reviewing Personal Files and increased customer satisfaction.
The Morphographemic Changes of Normal Students’ Sundanese-Language Handwritten Words in Pasirluhur Elementary School Bandung: Deletion, Metathesis, and Substitution

Sundanese language is one of Indonesian local languages that is usually used by Sundanese ethnic and/or by other ethnic speakers. Sundanese people acquire it as their first language. The research is about the morphographemic changes of normal students’ Sundanese-language handwritten words in Pasirluhur Elementary School Bandung: deletion, metathesis, and substitution. The research used descriptive-qualitative method. The aims of the research were to find the morphographemic change that was rarely found and the most frequently occurred. The result showed that metathesis is the one and only of morphographemic changes that was rarely found, while deletion and substitution were the most frequently occurred.
Process-optimized phase-covariant quantum cloning

After the appearance of the no-cloning theorem, approximate quantum cloning machines (QCMs) has become a well-studied subject in quantum information theory. Among several measures to quantify the performance of a QCM, single-qudit fidelity and global fidelity have been most widely used. In this paper we compute the optimal global fidelity for phase-covariant cloning machines via semidefinite programming optimization, thereby completing a remaining gap in the previous results on QCMs. We also consider optimal simulations of the cloning and transpose cloning map, both by a direct optimization and by a composition of component-wise optimal QCMs. For the cloning map the composition method is suboptimal whereas for the transpose cloning map the method is asymptotically optimal.
PENINGKATAN KEMAMPUAN MENYIMPULKAN ISI BERITA YANG DIBACAKAN MELALUI METODE DISKUSI DALAM PEMBELAJARAN DARING

This study aims to improve the ability to conclude the content of the news read through the technique of applying the online discussion method (in the network). The research method uses classroom action research (PTK). The research was carried out in the odd semester of the 2020/2021 school year for two cycles in October 2020 at Salman Al Farisi Junior High School, Bandung. Based on the findings and results of data analysis, it can be concluded that learning through the discussion method can improve students' ability to conclude news content. This is based on the following findings. Through the discussion method, students are more enthusiastic and enthusiastic in concluding the content of the news that is read. This can be observed based on the results of reflection activities in cycle I, from 25 students as many as 17 students stated that they were more enthusiastic about concluding news content using the discussion method, while in cycle II all students stated that they were more enthusiastic about concluding the content of the news using the discussion method.
Decision tree heuristics can fail, even in the smoothed setting

Greedy decision tree learning heuristics are mainstays of machine learning practice, but theoretical justification for their empirical success remains elusive. In fact, it has long been known that there are simple target functions for which they fail badly (Kearns and Mansour, STOC 1996). Recent work of Brutzkus, Daniely, and Malach (COLT 2020) considered the smoothed analysis model as a possible avenue towards resolving this disconnect. Within the smoothed setting and for targets $f$ that are $k$-juntas, they showed that these heuristics successfully learn $f$ with depth-$k$ decision tree hypotheses. They conjectured that the same guarantee holds more generally for targets that are depth-$k$ decision trees. We provide a counterexample to this conjecture: we construct targets that are depth-$k$ decision trees and show that even in the smoothed setting, these heuristics build trees of depth $2^{Ω(k)}$ before achieving high accuracy. We also show that the guarantees of Brutzkus et al. cannot extend to the agnostic setting: there are targets that are very close to $k$-juntas, for which these heuristics build trees of depth $2^{Ω(k)}$ before achieving high accuracy.
Fujitsu representatives with CISR staff

Left to right: Dr Don Faulkner (Mount Stromlo and Siding Spring Observatory), Mr S. Tomio (Oceania Marketing - Fujitsu Ltd), Mr H. Watanabe (Financial Controller - Fujitsu Australia Ltd), Dr Ted Stapinski (Auspace), Mr S. Tanahashi (General manager, Information Processing Division - Fujitsu Laboratories), Dr Michael McRobbie (Executive Director - Centre for Information Science Research, ANU), Mr M. Ogi (President of Fujitsu Laboratories), Professor Alec Rodgers (Mount Stromlo and Siding Spring Observatories), Professor Ian Ross (Pro Vice-Chancellor, ANU)
Smart Helmet / Nur Fatihah Mohd Yatim

The business that we do is based on our innovation and our first product is SMART HELMET.SMART HELMET is very beneficial to every people who is used a motorbike as the main transportation to go for work, class or anywhere. It is because it can help them from being wet when rain suddenly and they do not bring a rain coat on that time. On the SMART HELMET we also prepared a raincoat that is free size with bat cutting on the hand sleeve and a light and water resistance casing bag on the top of the helmet so that the person who wear the helmet can wear the raincoat less than 5 seconds at anywhere and anytime without need to stop anywhere to wear it. Hence it also can be easily fold after use.This SMART HELMET is unique, compact and portable.Besides that, we also use Portable concept so that our customer can bring this SMART HELMET anywhere.Our target market are household and worker persons that use a motorcycle as it main transportation to go to work or any where else.This product is easily reached and receptive because we sell this product online and also wholesale through manufacturers represented.For those customer who want to get this product, they can order through our official website or get directly from our company. The competitive advantage is a set of unique features of a company and its product that are perceived by the market as significant and superior to the competition. It is the reason behind brand loyalty. There are three different types of competitive advantages that our companies use. They are cost, product or service differentiation and niche strategies. Our management team include Chief Executive Officer, Chief Financial Officer, Chief Operating Officer, Chief Marketing Officer and Administration. They have different primary duties and responsibility. Lastly, we will expect that our business will become more developed in the near future because with the new innovation of our SMART HELMET concept and also based on our vision which is to become the leading motorbike accessories supplier in the international by 2030.