Reviewerly/exHarmony huggingface dataset:
The Application of geographic information system (GIS) for shop houses data storage at Jalan Kesuma 3/6 and Jalan Kesuma 3/7, Bandar Tasik Kesuma, Semenyih, Selangor Darul Ehsan / Muhamad Husaini Muhamad Mokhtar

Now days , each area must have same problems which is vacant shop houses and don't have a complete data storage about shop houses. Geographic Information System (GIS) is the fast and effective search information is important for systems that involve large data. To address the problem of shop houses data storage which is using geographic information system (GIS) commercial activities management system at shop houses involving dozens or hundreds of shop houses. So without using the GIS it very difficult to find or search the specific information of shop houses and don't have or can't show the data visually. This system focuses on finding the shop houses information using geographical position. The new system also provides mapping and deployment functions, panning, data identification, buffer requests to facilitate the management of manipulating existing data. The use of data disclosure in Geographic Information System (GIS) can help management get faster and more interactive data.
KNOWLEDGE MANAGEMENT ENABLER ON SERVICE DELIVERY AT NATIONAL TRANSPORT AND SAFETY AUTHORITY, KENYA

This research sought to analyse knowledge management enablers and service delivery at National Transport and Safety Authority. Specifically, this study sought to examine the role of organization’s culture on service delivery at NTSA and to establish the role of human resource on service delivery at National Transport and Safety Authority. A descriptive research design was adopted as the study design and the population targeted was staff working with the National Transport and safety Authority. The sampling method used was stratified random sampling that divides the population into strata or groups that included the different departments. This researcher interviewed 90 participants whose selection was done randomly across existing departments. Questionnaires were used to collect primary information. Descriptive tools of statistics aided in data description and determination of the level employed. A simple regression model was employed to determine how much the independent variables influence the dependent variable. The study established that NTSA had developed a culture of knowledge sharing and the adopted organization’s culture had a positive significant impact on service delivery at NTSA. The study also revealed that human resource impacted positively on service delivery at National Transport. NTSA should therefore continue propagating practices that promote a culture of knowledge sharing. Such may include offering rewards to employees who demonstrate absolute willingness to water these values within the organisation. Human resource managers must lead by example; they must develop strategic measures that are to be followed in realisation of all organisational values projected. Also Human resource managers should ensure that the HR policies are organised in a way that promotes information sharing values. Key Words: Organizational Culture, Human Resource, Service Delivery CITATION: Kihia, P. W., &amp; Thairu, R. W. (2021). Knowledge management enabler on service delivery at National Transport and Safety Authority, Kenya . The Strategic Journal of Business &amp; Change Management, 8 (1), 302 – 314.
On-Site Solar Power Forecasting Using Sky-Images

The variability of solar power generation introduces significant challenges to power system operation. Accurate irradiance forecasts will help to overcome these challenges. Therefore, onsite-irradiance forecasting technique was proposed by utilizing a wide-angle camera. The proposed model predicts solar power by several minutes in advance via extracting the cloud motion vectors from sky images. Individual cloud movement was tracked by cross-correlation and motion vectors were created for each cloud. A separate model for cloud deformation and a dynamic model for cloud movement were constructed. For each cloud, cloud deformation over time was identified, and it was embedded into the cloud motion-based forecasts. To improve forecasting results, the cloud motion-based forecasts and time series forecasts were merged. The results were obtained for 1 min, 2min, and 5min horizons with one-minute granularity. The MBE percentages of 11%, 10% and 12% were achieved. The effects of seasonal variations on image-based forecasting models were discussed at the end.
Beyond GNNs: A Sample Efficient Architecture for Graph Problems

Despite their popularity in learning problems over graph structured data, existing Graph Neural Networks (GNNs) have inherent limitations for fundamental graph problems such as shortest paths, k-connectivity, minimum spanning tree and minimum cuts. In all these instances, it is known that one needs GNNs of high depth, scaling at a polynomial rate with the number of nodes n, to provably encode the solution space. This in turn affects their statistical efficiency thus requiring a significant amount of training data in order to obtain networks with good performance. In this work we propose a new hybrid architecture to overcome this limitation. Our proposed architecture that we call as GNNplus networks involve a combination of multiple parallel low depth GNNs along with simple pooling layers involving low depth fully connected networks. We provably demonstrate that for many graph problems, the solution space can be encoded by GNNplus networks using depth that scales only poly-logarithmically in the number of nodes. This significantly improves the amount of training data needed that we establish via improved generalization bounds. Finally, we empirically demonstrate the effectiveness of our proposed architecture for a variety of graph problems.
Progressive Deep Network with Channel Back-Projection for Hyperspectral Recovery from RGB

Hyperspectral images are useful in a variety of fields such as remote sensing, medical diagnosis, and agriculture. But it requires very expensive professional equipment and a lot of time to obtain. In this paper, we propose a deep learning architecture that reconstructs hyperspectral images from RGB images that are easy to acquire in real time. Hyperspectral reconstruction is inherently difficult because much information is lost when hyperspectral bands are integrated into three RGB channels. To effectively overcome the problem of hyperspectral restoration, we design a neural network with the following three basic principles. First, it adopts a method in which channels are gradually increased through several steps to restore hyperspectral images. Second, it is learned on a group basis for efficient restoration. Hyperspectral bands are divided into three groups: R, G, and B. Finally, the concept of channel back projection is newly proposed. In the process of gradually performing hyperspectral reconstruction, the reconstructed image is refined by repeatedly projecting the reconstructed hyperspectral to RGB. In the experimental results, these three principles proved the performance that exceeds the state-of-theart methods.
Automatic Text Summarization of Patent Documents

This thesis investigates Automatic Text Summarization and how it can be used to generate summaries of patent documents. The purpose of the generated summary was to convey the patent document’s subject so that the reader could decide whether the patent document is relevant and should be read in full or could be discarded. This, in order to reduce the time patent attorneys need to spend reading full patent documents in their daily work. A summarizing tool using Extraction-based summarization, and a Graph-based ranking method was implemented and tested. Summaries were generated from ten patent descriptions using the implemented tool. Each summary was also evaluated using human evaluation. The method showed very promising results in summarizing patent descriptions. In addition, the results highlighted some areas of improvement for future work. (Less)
RETRACTED: Research on the Application Analysis of Four-dimensional Teaching Method in Higher Vocational English Teaching Based on Big Data Analysis

Abstract In order to train English talents better, in the process of English teaching in higher vocational colleges, it is necessary to reform its teaching mode constantly so as to promote the quality of English teaching. However, according to the analysis of big data, in the current situation, there are still many problems in English teaching in China, so we should make full use of the advantages of four-dimensional teaching method to improve the current difficulties in English teaching. Therefore, this article is mainly to explore four-dimensional teaching methods based on the current problems in English teaching of higher vocational education, and with a view to providing reference for the follow-up English teaching.
The DANS services for sharing, cataloguing and archiving your health data

DANS (Data Archiving and Networked Services) is the Netherlands institute for permanent access to digital research resources. DANS encourages researchers to make their research data and related digital outputs Findable, Accessible, Interoperable and Reusable (FAIR). To realise our mission, DANS provides expert advice and certified services. DataverseNL is the DANS service for short‐term data management, EASY our long‐term data archive, and NARCIS the national catalogue service for scholarly information. Training and consultancy services are provided for generic Research Data Management and Data Management Planning. More specific training sessions focus on repository certification, metadata standards, software sustainability and knowledge organisation systems. The (coordinating) activities of DANS in (inter)national projects and networks, ensure constant innovation and a state‐of‐the‐art knowledge on infrastructural data developments. Although the roots of DANS are within the humanities and social sciences, most DANS services are generic services relevant for nearly all scientific disciplines, including the life and health sciences. As part of the Dutch national e‐infrastructure for research data, DANS is involved in several projects and initiatives around health data, often acting at the cross roads between the life and social sciences. Also, the DANS training activities touch upon the developments around health data. Cataloguing the Dutch “zorggegevens” in NARCIS, or the DANS training modules in the Helis Academy FAIR data stewardship course, are examples of specific DANS contributions to the life and health sciences. The DANS poster presentation provides an overview of the DANS services of interest to the owners and custodians of health data, including examples of relevant recent projects. DANS invites participants of the Health‐RI 2020 conference to probe how DANS could support the sharing, cataloguing and archiving of their health data.
LibGuides: New Non-Fiction Shelves: 100s - Self Help

Includes - General Knowledge, Encyclopedias, Associations & Museums, Journalism, Quotations, Library Science and Rare Books
Understanding Classifiers with Generative Models

Although deep neural networks are effective on supervised learning tasks, they have been shown to be brittle. They are prone to overfitting on their training distribution and are easily fooled by small adversarial perturbations. In this paper, we leverage generative models to identify and characterize instances where classifiers fail to generalize. We propose a generative model of the features extracted by a classifier, and show using rigorous hypothesis testing that errors tend to occur when features are assigned low-probability by our model. From this observation, we develop a detection criteria that we test against different sources of classification mistakes: mistakes made on the test set due to poor model generalization, adversarial samples and out-of-distribution samples. Our approach is agnostic to class labels from the training set which makes it applicable to models trained in a semi-supervised way.
LibGuides: Streaming Video Collections: Troubleshooting Off Campus Access

Through its streaming video collections, the library provides access to thousands of videos online. This guide offers instructions on how to provide access to these collections for teaching purposes.
Dual Adversarial Training for Unsupervised Domain Adaptation

Deep neural networks obtain remarkable achievements in diverse real-world applications. However, their success relies on the availability of large amounts of labeled data. A trained model may fail to generalize well on a domain whose distribution differs from the training data distribution. Collecting abundant labeled data for all domains of interest are expensive and time-consuming, sometimes even impossible. Domain adaptation sets out to address this problem, aiming to leverage labeled data in the source domain to learn a good predictive model for the target domain whose labels are scarce or unavailable. A mainstream approach is adversarial domain adaptation, which learns domain invariant-features by performing alignment across different distributions. Most domain adaptation methods focus on reducing the divergence between two domains to make the improvement. A prerequisite of domain adaptation is the adaptability, which is measured by the expected error of the ideal joint hypothesis on the source and target domains, should be kept at a small value in the process of domain alignment. However, adversarial learning may degrade the adaptability, since it distorts the original distributions by suppressing the domain-specific information. In this paper, we propose an approach, which focuses on strengthening the model's adaptability, for domain adaptation. Our proposed dual adversarial training (DAT) method introduces class-invariant features to enhance the discriminability of the latent space without sacrificing the transferability. The class-invariant features, extracted from the source domain, can play a positive role in the classification on the target domain. We demonstrate the effectiveness of our method by yielding state-of-the-art results on several benchmarks.
Sampling Policy Design for Tracking Time-Varying Graph Signals with  Adaptive Budget Allocation

There have been many works that focus on the sampling set design for a static graph signal, but few for time-varying graph signals (GS). In this paper, we concentrate on how to select vertices to sample and how to allocate the sampling budget for a time-varying GS to achieve a minimal tracking error for the long-term. In the Kalman Filter (KF) framework, the problem of sampling policy design and budget allocation is formulated as an infinite horizon sequential decision process, in which the optimal sampling policy is obtained by Dynamic Programming (DP). Since the optimal policy is intractable, an approximate algorithm is proposed by truncating the infinite horizon. By introducing a new tool for analyzing the convexity or concavity of composite functions, we prove that the truncated problem is convex. Finally, we demonstrate the performance of the proposed approach through numerical experiments.
STUDENTS’ SELF CONFIDENCE IN SPEAKING ENGLISH

This research intended to analyze factors of students’ lack of self confidence in speaking English. It was also aimed to present possible strategies used to overcome students’ lack of self confidence in speaking English. The type of this research was qualitative. This research was conducted in SMA Negeri 4 BauBau. This research focused on obtaining data from the English teacher and students at eleventh grade of SMA Negeri 4 BauBau in the academic year 2018/2019 as the subjects of the research. In collecting data, the researchers used observation, interview. The technique used in analyzing data were three steps, those were data reduction, data display, and conclusion drawing. The result of this research showed that the factors which cause students’ lack of self confidence were anxiety, shyness, fear of making mistakes, and lack of vocabulary. Meanwhile, the possible strtaegies to overcome students’ lack of self confidence in speaking English were lowering students’ anxiety in classroom, improving students’ vocabulary, boosting students’ self-confidence, and forming group discussion.
Library Guides: Collection Development Committee (CDC): Home

Previously the Collections and Online Resources Working Group – The CDC has been recently established and is composed by all faculty with liaison responsibilities.
Learning Disentangled Representations for Image Translation

Recent approaches for unsupervised image translation are strongly reliant on generative adversarial training and architectural locality constraints. Despite their appealing results, it can be easily observed that the learned class and content representations are entangled which often hurts the translation performance. To this end, we propose OverLORD, for learning disentangled representations for the image class and attributes, utilizing latent optimization and carefully designed content and style bottlenecks. We further argue that the commonly used adversarial optimization can be decoupled from representation disentanglement and be applied at a later stage of the training to increase the perceptual quality of the generated images. Based on these principles, our model learns significantly more disentangled representations and achieves higher translation quality and greater output diversity than state-of-the-art methods.
Joint Descent: Training and Tuning Simultaneously

Typically in machine learning, training and tuning are done in an alternating manner: for a fixed set of hyperparameters y, we apply gradient descent to our objective f(x,y) over trainable variables x until convergence; then, we apply a tuning step over y to find another promising setting of hyperparameters. Because the full training cycle is completed before a tuning step is applied, the optimization procedure greatly emphasizes the gradient step, which seems justified as first-order methods provides a faster convergence rate. In this paper, we argue that an equal emphasis on training and tuning lead to faster convergence both theoretically and empirically. We present Joint Descent (JD) and a novel theoretical analysis of acceleration via an unbiased gradient estimate to give an optimal iteration complexity of O(κnylog⁡(n/ϵ)), where κ is the condition number and ny is the dimension of y. This provably improves upon the naive classical bound and implies that we essentially train for free if we apply equal emphasis on training and tuning steps. Empirically, we observe that an unbiased gradient estimate achieves the best convergence results, supporting our theory.
Library Guides: Communications & Marketing Committee (CMC): Home

Sets guidelines for coordinated internal and external communications that follow the GS Marketing and Communications guidelines and procedures.
Κατασκευή εφαρμογής Chatbot με σύγχρονη πλατφόρμα

For centuries, having a realistic and meaningful conversation with a form of artificial intelligence has been a dream of human kind. Today, the closest application to fulfilling that dream is the chatbot, which is also the subject of this thesis. More specifically, the aim is to find an appropriate console for developing an English and a Greek chatbot. Furthermore, we try to advance the Greek one in order to manage having an interesting conversation regarding a specific field of discussion.In the beginning, a research on the most useful free platforms for creating bots is presented along with analysis of their basic features. We choose Synthetic Intelligence Markup Language (SIML) for chatbot development, as well as .NET Framework and C language for further advancement.The Greek chatbot is further worked upon, due to the lack of chatbots communicating in the Greek language. We analyze various methods for overcoming obstacles regarding the greek syntax and grammar, which results in an interesting conversation on a certain chosen topic (Greek cinematography).To summarize, the conclusion is that a well-defined programming language specialized on developing chatbots, combined with a framework with heavy integrating potential, help the developer create an interesting and customizable form of AI in any language needed.
ЭКОНОМИЧЕСКАЯ ГРАМОТНОСТЬ И ЕЕ СТРУКТУРА

Analyzing educational resources over the past two years, we can conclude that the greatest increase in interest in the formation of the foundations of financial literacy of schoolchildren. Thanks to the joint project of the Ministry of Finance of the Russian Federation and the world Bank Promotion of financial literacy and development of financial education in the Russian Federation, it became possible to teach schoolchildren through online lessons. Whether these lessons are the best tools for the formation of financial and economic literacy of schoolchildren is the main question of our research.The results obtained in the course of the work allowed us to determine the structure of economic literacy, as well as criteria that reflect the level of its formation.
Five Seconds of Fame

Smartphones are seemingly everywhere but the variety of ring and alert tones used are often limited to a standard number of preset options. New tones can be purchased from commercial websites but it is possible for anyone to create their own tone. In this project, students create their own custom tones to use on their smartphones. First, they compose short original compositions in a DAW and then upload them to their phones as custom ringtones or alert tones. The DAW Ableton Live Intro is used, which has a free trial available for educational institutions. Three 45-minute lessons are allotted to complete the project, but there is scope for increased depth of musical skills depending on the student’s previous experience with composing. Smartphone technology and operating systems change frequently, so please be prepared to check this module for any technical changes before you begin and adjust the lesson plan accordingly.
A modified image steganographic method based on chaotic text encryption using Lifting Wavelet Transform

Steganography is an art of concealing text into cover image over a covert channel, enabling covert communication. In this paper, a modified steganographic approach using lifting wavelet transform (LWT) is proposed. The key for encrypting the text are generated randomly using chaotic maps. The concept of confusion and diffusion is applied using one of the widely used dynamic systems namely Lorenz, Chen and Lu. By applying chaos strategy effectively in secret communication, the strength of the anticipated algorithm is improved to a significant level. The encrypted text can be concealed into the cover image using adaptive least significant bit (ALSB) algorithm. The stego image obtained at the end of data embedding process undergoes few security statistical analyses such as peak signal to noise ratio (PSNR), mean square error (MSE), elapsed time and correlation calculations. With the significant knowledge of above parameters, it is observed that the suggested methodology proves to have advantages over existing ones, providing higher level of security against some existing attacks.
RETRACTED: Experimental Research on Dance Teaching Based on Computer Multimedia Technology

Abstract Along with the progress of the society and the development of science and technology, computer multimedia technology has been widely used to the life of people, in the development of the connotation of modern teaching practice work, the new multimedia technology guide is very important, quality education is becoming more and more urgent, sports dance teaching also gradually be taken seriously, sports education gradually toward diversification direction. With the main role of Xing Xing multimedia, the further development of the dance project in Figure 1 is promoted. The application practice of new multimedia technology in sports dance is comprehensively discussed, and the technology-oriented mode and the factors of teachers’ role are pointed out, so as to realize the utilization of technical advantages in education and teaching.