Reviewerly/exHarmony huggingface dataset:
PELAPORAN JUMLAH PENDUDUK DESA KEMBANGSERI KECAMATAN BERMANI ILIR KABUPATEN KEPAHIANG

ABSTRACT Villagers are a group of relatively small areas within an area that are bound by applicable rules and interact with each other continuously or continuously. Population development will be influenced by the number of babies born, the number of deaths, out-migration (moving) and in-migration (migrants). The problem in this research is how to report the number of population, deaths, births, out-migration (moving) in-migration (migrants) using a programming language. The benefits of research are to facilitate reporting of population, make it easier for village officials to record population numbers, births, deaths, out-migration (moving) and in-migration (newcomers) quickly and effectively, making it easier for people to get information on population numbers, births, deaths, and out-migration ( moving) and in-migration (migrants) The purpose of this research is to create new technology in the world of village government with a special programming language (Visual Basic.Net). in the village government of Kembang Seri, Bermani Ilir District, Kepahiang Regency, Bengkulu Province. The research method is planning, needs analysis, hardware and software preparation, programming, system testing, evaluation, implementation. In carrying out public services to the community, a system that can help local governments, especially in the Kembangseri Village, Bermani Ilir District, Kepahiang Regency, Bengkulu Province, is needed in terms of reporting population numbers, births and deaths, out-migration (moving) and in-migration (newcomers) using programming languages. specifically (Visual Basic.Net). Keywords : Birth, death, outgoing migration and incoming migration
Learning Transferable Convolutional Proxy by SMI-Based Matching Technique

Domain-transfer learning is a machine learning task to explore a source domain data set to help the learning problem in a target domain. Usually, the source domain has sufficient labeled data, while the target domain does not. In this paper, we propose a novel domain-transfer convolutional model by mapping a target domain data sample to a proxy in the source domain and applying a source domain model to the proxy for the purpose of prediction. In our framework, we firstly represent both source and target domains to feature vectors by two convolutional neural networks and then construct a proxy for each target domain sample in the source domain space. The proxy is supposed to be matched to the corresponding target domain sample convolutional representation vector well. To measure the matching quality, we proposed to maximize their squared-loss mutual information (SMI) between the proxy and target domain samples. We further develop a novel neural SMI estimator based on a parametric density ratio estimation function. Moreover, we also propose to minimize the classification error of both source domain samples and target domain proxies. The classification responses are also smoothened by manifolds of both the source domain and proxy space. By minimizing an objective function of SMI, classification error, and manifold regularization, we learn the convolutional networks of both source and target domains. In this way, the proxy of a target domain sample can be matched to the source domain data and thus benefits from the rich supervision information of the source domain. We design an iterative algorithm to update the parameters alternately and test it over benchmark data sets of abnormal behavior detection in video, Amazon product reviews sentiment analysis, etc.
LibGuides: Movies & More Guide: Puzzles

Need a break? The Movies & More Guide helps you find online movies, literature, art, museums, animal webcams, and puzzles from LUC, public libraries, and Internet sources.
Strategies for integrating Open Access Resources (OAR) into libraries collections: A study

The study’s general purpose is to assist both management and collection development practitioners in adopting appropriate strategies for integrating OA materials into libraries' collections. The study was designed to specifically examine the challenges to the integration of OAR into libraries' collections and to explore relevant strategies for the integration. The study utilized an online survey questionnaire sent to Seventy-Eight collections development staff drawn from academic libraries, Library Boards, and special libraries for completion. Sixty-Two (62) however, were completed and returned. This implies a 79.4% return rate. All the respondents surveyed are members of the Nigerian Library Association (NLA) and are certified by the Librarians Registration Council of Nigeria (LRCN). The respondents were drawn from Federal University Libraries (7), State university libraries (6), private university libraries (22), federal polytechnic libraries (5), state polytechnic libraries (12), college libraries (5), special libraries (2), library boards (3). The survey instrument included Eleven (11) items. Data were analyzed using graphical charts. The findings indicate that majority of the library does not acquire OAR but have plans to make them part of their regular library acquisition workflows; majority of them strongly agree that collection development policy is key to OAR integration, but they don’t have the document for now, neither are their staff trained to manage OA effectively. Their major challenge to OAR integration includes poor electricity supply, lack of knowledge and skills required for OA integration, and lack of support from the head librarian, lack of collection development policy document. The recommendations include that collection development policy should be put in place; library staff should be equipped with relevant skills through training and retraining; libraries should rather than purchasing expensive books and journals download OAR relevant to them, process them, and make them available to their users as part of their collections; Libraries should be provided with uninterrupted fast speed internet access and power supply from solar power.
Longitudinal study of voice recognition in children

Speaker recognition as a biometric modality is on the rise in the consumer marketplace for banking, online services, and personal assistant services with a potential for wider application areas. Most current applications involve adults. One of the biggest challenges in speaker recognition for children is the change in the voice properties as a child age. This work proposes a baseline longitudinal dataset from the same 30 children in the age group of 4 to 14 years over a time frame of 2.5 years and evaluates speaker recognition performance in children with the available speaker recognition technology.
Quantum Mechanics in Wavelet Basis

We describe a multi-scale resolution approach to analyzing problems in Quantum Mechanics using Daubechies wavelet basis. The expansion of the wavefunction of the quantum system in this basis allows a natural interpretation of each basis function as a quantum fluctuation of a specific resolution at a particular location. The Hamiltonian matrix constructed in this basis describes couplings between different length scales and thus allows for intuitive volume and resolution truncation. In quantum mechanical problems with a natural length scale, one can get approximate solution of the problem through simple matrix diagonalization. We illustrate this approach using the example of the standard quantum mechanical simple harmonic oscillator.
APLIKASI ALGORITMA FEED FORWARD BACKPROPAGATION UNTUK BEBAN LISTRIK HARI LIBUR PADA TIPE BEBAN ANOMALI

Load pattern for holiday have significant differenct from weekday day, so that require special prediction. This research have purpose to test the performance feed forward backpropagation to know accuracy level of load forecasting for holiday anomalous load. Learning algorithm used feed forward backpropagation from artificial neural network with matlab. Load forecasting optimization is change learning rate and some of learning input. The reasult of this reasult showed learning input give best reasult on mean but difference from that value is small, so learning rate variation small affected at learning
THE ROLE OF COMMUNITY EDUCATION IN IMPROVING THE LITERACY OF ELEMENTARY SCHOOL CHILDREN: A CASE STUDY OF SMALL GROUP READING COMMUNITY IN CENTRAL LOMBOK

Literacy is not only the responsibility of formal education institutions. One form of education in society is a community library. This study aimed to describe the roles and problems faced by community library forums in enhancing literacy culture. The subject in this study was a community reading garden as a part of a community library forum. Data were collected in Kabupaten Lombok Tengah. This study applied a qualitative research design with a case study approach. Data collection was conducted through surveys, observation, and in-depth interviews. Then, the data were presented in the form of tables, diagrams, and word descriptions. The researchers extended the observation time in order to achieve the validity of findings. The main problems found in this study included: what are the challenges faced by community library forums? What is the role of the community library forum in enhancing the literacy culture of children in Lombok Tengah? The results revealed that the challenges faced by community library forums included problems of regeneration, inadequate facilities, lack of book collections, low literacy culture, and non-optimal support from the government. The role of libraries in enhancing literacy culture was as a facilitator, motivator, creator, and pioneer of the literacy movement in rural areas. With the presence of the community library, the children felt more excited about reading.
Uncertainty-aware Contact-safe Model-based Reinforcement Learning

This letter presents contact-safe Model-based Reinforcement Learning (MBRL) for robot applications that achieves contact-safe behaviors in the learning process. In typical MBRL, we cannot expect the data-driven model to generate accurate and reliable policies to the intended robotic tasks during the learning process due to sample scarcity. Operating these unreliable policies in a contact-rich environment could cause damage to the robot and its surroundings. To alleviate the risk of causing damage through unexpected intensive physical contacts, we present the contact-safe MBRL that associates the probabilistic Model Predictive Control's (pMPC) control limits with the model uncertainty so that the allowed acceleration of controlled behavior is adjusted according to learning progress. Control planning with such uncertainty-aware control limits is formulated as a deterministic MPC problem using a computation-efficient approximated GP dynamics and an approximated inference technique. Our approach's effectiveness is evaluated through bowl mixing tasks with simulated and real robots, scooping tasks with a real robot as examples of contact-rich manipulation skills. (video: https://youtu.be/sdhHP3NhYi0)
Data Management at CEDA

&amp;lt;p&amp;gt;Sending data to a secure long-term archive is increasingly a necessity for science projects due to the funding body and publishing requirements. It is also good practice for long term scientific aims and to enable the preservation and re-use of valuable research data. The Centre for Environmental Data Analysis (CEDA) hosts a data archive holding vast atmospheric and earth observation data from sources including aircraft campaigns, satellites, pollution, automatic weather stations, climate models, etc. The CEDA archive currently holds 14 PB data, in over 250 millions of files, which makes it challenging to discover and access specific data. In order to manage this, it is necessary to use standard formats and descriptions about the data. This poster will explore best practice in data management in CEDA and show tools used to archive and share data.&amp;lt;/p&amp;gt;
Contact Tracing, Intrastate and Interstate Quarantine, and Isolation

Contact tracing, quarantine, and isolation are core communicable disease control measures used by public health departments as part of a comprehensive case ascertainment and management strategy. These are practices with historic roots enabled by state laws and policies and have been used by other countries to slow and stop the spread of COVID-19. To date, their implementation as part of U.S. response efforts at the national, state, and local levels has been confounded by the scale of the COVID-19 outbreak; lack of a systemic infectious disease response; insufficient and fragmented funding streams; low levels of public accountability; and concerns about the impact of such efforts on individual privacy, liberty, and travel rights, as well as the financial and personal costs that may arise out of a positive diagnosis. Recommendations have been offered by expert groups on both the scaling up of contact tracing and ensuring ethical implementation of such measures. One state has passed legislation establishing an oversight framework for state contact tracing and associated data collection and use. Legal challenges to interstate quarantine rules have, thus far, been unsuccessful. Recommendations include: appropriating federal funding adequate to mount and sustain rapid, comprehensive, culturally-appropriate state and local testing, treatment, contact tracing, and supported quarantine and isolation service efforts; building contact tracing systems that cover social as well as health care supports for those affected; and, to bolster trust and participation in public health efforts, implementing contact tracing-related health communication efforts targeted to reach the diverse array of communities affected by the epidemic.This paper was prepared as part of Assessing Legal Responses to COVID-19, a comprehensive report published by Public Health Law Watch in partnership with the de Beaumont Foundation and the American Public Health Association.
Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter

Understanding predictions made by Machine Learning models is critical in many applications. In this work, we investigate the performance of two methods for explaining tree-based models- Tree Interpreter (TI) and SHapley Additive exPlanations TreeExplainer (SHAP-TE). Using a case study on detecting anomalies in job runtimes of applications that utilize cloud-computing platforms, we compare these approaches using a variety of metrics, including computation time, significance of attribution value, and explanation accuracy. We find that, although the SHAP-TE offers consistency guarantees over TI, at the cost of increased computation, consistency does not necessarily improve the explanation performance in our case study.
Eliciting semantic properties: methods and applications

Asking subjects to list semantic properties for concepts is essential for predicting performance in several linguistic and non-linguistic tasks and for creating carefully controlled stimuli for experiments. The property elicitation task and the ensuing norms are widely used across the field, to investigate the organization of semantic memory and design computational models thereof. The contributions of the current Special Topic discuss several core issues concerning how semantic property norms are constructed and how they may be used for research aiming at understanding cognitive processing.
Meta-learning the Learning Trends Shared Across Tasks.

Meta-learning stands for 'learning to learn' such that generalization to new tasks is achieved. Among these methods, Gradient-based meta-learning algorithms are a specific sub-class that excel at quick adaptation to new tasks with limited data. This demonstrates their ability to acquire transferable knowledge, a capability that is central to human learning. However, the existing meta-learning approaches only depend on the current task information during the adaptation, and do not share the meta-knowledge of how a similar task has been adapted before. To address this gap, we propose a 'Path-aware' model-agnostic meta-learning approach. Specifically, our approach not only learns a good initialization for adaptation, it also learns an optimal way to adapt these parameters to a set of task-specific parameters, with learnable update directions, learning rates and, most importantly, the way updates evolve over different time-steps. Compared to the existing meta-learning methods, our approach offers: (a) The ability to learn gradient-preconditioning at different time-steps of the inner-loop, thereby modeling the dynamic learning behavior shared across tasks, and (b) The capability of aggregating the learning context through the provision of direct gradient-skip connections from the old time-steps, thus avoiding overfitting and improving generalization. In essence, our approach not only learns a transferable initialization, but also models the optimal update directions, learning rates, and task-specific learning trends. Specifically, in terms of learning trends, our approach determines the way update directions shape up as the task-specific learning progresses and how the previous update history helps in the current update. Our approach is simple to implement and demonstrates faster convergence. We report significant performance improvements on a number of FSL datasets.
Two Stay Two Stray (TSTS) Learning Model Assisted with Mind Mapping Improving Students’ Social Studies Learning Outcomes

This research aimed to analyze the effect of the Two Stay Two Stray (TSTS) learning model assisted with mind mapping media toward social studies learning outcomes in Grade V. The type of this research was a quasi-experimental research design with nonequivalent post-test only controls group design. The population of this research was all of the fifth-grade elementary schools in Cluster I Bangli District, which totaled 7 classes with 138 students. The samples were determined using a simple random sampling technique, totaling 20 students as the experimental group and 30 students as the control group. Data analysis techniques in this research used descriptive statistical analysis techniques and data analysis prerequisite tests. Hypothesis analysis used the t-test polled variance test. The results of the calculation of the average social studies learning outcomes of the experimental group students were 23.15 included in the very high category, while students in the control group had an average value of 17.23 including the moderate category. Based on calculations with the t-test obtained a t-count of 4.89 is greater than the t-table value of 2.010 with a dk of 48, at a significance level of 5%. From the results of the analysis, it can be concluded that the Two Stay Two Stray (TSTS) learning model assisted with mind mapping media has a significant effect on social studies learning outcomes for fifth-grade elementary school students. Therefore, the TSTS learning model assisted with mind mapping media is suitable to be applied to improve students' social studies learning outcomes
Further results on uniform mixing on abelian Cayley graphs

In the past few decades, quantum algorithms have become a popular research area of both mathematicians and engineers. Among them, uniform mixing provides a uniform probability distribution of quantum information over time which attracts a special attention. However, there are only a few known examples of graphs which admit uniform mixing. In this paper, a characterization of abelian Cayley graphs having uniform mixing is presented. Some concrete constructions of such graphs are provided. Specifically, for cubelike graphs, it is shown that the Cayley graph ${\rm Cay}(\mathbb{F}_2^{2k};S)$ has uniform mixing if the characteristic function of $S$ is bent. Moreover, a difference-balanced property of the eigenvalues of an abelian Cayley graph having uniform mixing is established. Some nonexistence results of uniform mixing on abelian Cayley graphs are presented also. Notably, for a linear abelian Cayley graph $\Gamma$ over $\mathbb{Z}_n^r$, it is proved that uniform mixing occurs on this graph only if $n=2,3,4$ which confirms a long-standing conjecture.
Teachersâ€™ Strategies in Preparing Online Learning Digital Media for Developing Childrenâ€™s Literacy Skills

The era of 4.0, following the advancement of technology especially digital media, has challenged the existed learning activity of kindergarten students. Due to the pandemic situation, learning activity must be conducted from home. Hence, this research aims to observe the teachersâ€™ strategies in preparing digital media to develop childrenâ€™s literacy skills during online learning. This research used a quantitative approach using the survey method. Findings showed that 90.48% of teachers have planned their strategies properly to develop childrenâ€™s literacy skills using digital media. Meanwhile from the implementation, 45.24% was not conducted optimally, and from the evaluation, only 53.28% of the children have developed their literacy skills using digital media in online learning. The benefit of this research is to provide an explanation that education in the pandemic era must prioritize coordination between teachers, parents and their children.Â
Perancangan Game “IM HERE” Berbasis Augmented Reality dan Location Based Sebagai Media Promosi

On a business there must be advertise for a product, services, and also for entertainment. Advertisement activities has been changed from door to door advertisement to technological advertisement. Technology that used for advertisement are televisions, web and social media. One of the new technology and media that use for advertisement are games. Games as advertisement tool use ads via monetizing and promote services. Some company use augmented reality and location based technology which new technology as advertisement enhancement to attract costumer. Augmented reality is technology that combine real world object and game object, and location based is technology that determine user location in the point of world as coordinate or place descriptions. Augmented reality and location based can be implemented using Unity3D algorithm through accessing user camera and gps. Location based can be implemented as marker for some feature to active if company that want advertise their product or place, user must get profit from activating the feature. Using augmented reality and location base as gameplay may give new experience for user and attract new user or customer.
Usage of social networks sites and level of awareness in cyber security: A study of college students in Chennai City

Sharing of information among student’s community is an important avenue. Social networks play a significant role in sharing of information rapidly. In this exploratory study, different types of college students were interviewed to assess the impact of use of smart phone, Facebook, Twitter, LinkedIn and Whatsapp. The general awareness in use of software and installation is also surveyed. Security measures in system usage and mail usage are also examined. The worth of user-generated informationdiffersconsiderably from tremendous to abuse and spam. As the availability of such informationraises, the target of identifying highquality information in sites based on user contributions-social media sites-becomes even more significant.