Reviewerly/exHarmony huggingface dataset:
The Effecr of RCRR (Read, Cover, Remember, Retell) Reading Strategy In Reading Comprehension for Seventh Grade at SMPN 2 Kota Jambi

This research was done aimed at investigating the effect of using RCRR reading strategy towards the students’ reading comprehension at the seventh grade of SMPN 2 Jambi City in academic year 2019/2020. This research carried out in a quantitative method and quasi-experimental design. The sampling technique used in this research was simple random technique. The population of this research was the seventh grades students. The sample was two classes which divided into two group assigned as experimental (VII C) and control class (VII D). The experimental class was taught using the RCRR strategy as a treatment, while the control class was not. To collect the data, the researcher conducted pre-test and post-test of both classes. The researcher used t-test to know the effect of RCRR strategy.After conducting the pre-test, and post-test to both classes and giving the treatment to the experimental class, the researcher analysed the data. The mean score of pre-test session in control class was 55,46 while the mean of experimental class was 56,09. This score was increased in the post-test, the mean score of control class was 69,37 while the mean score of experimental class was 76,25. Based on the calculation, the value of t-test observation was higher compared to the value of t-test table, 5,010 > 1,998 at the degree of freedom (df 32-1=31) and the level of significance 5% in which thealternative hypothesis was accepted. The researcher interpreted the result that the RCRR strategy had a significant effect toward the students’ reading comprehension of seventh grade at SMPN 2 Jambi City.   Key Words :  Read, Cover, Remember, Retell.
Trends and Countermeasures of Next Generation Telemetry Technology Innovation

With an analysis of new test requirements of next-generation aviation weapons and equipment, this article focus on summarising the current development of existing telemetry technology and shortcomings of flight test capability on new aviation weapons and equipment and comes up with a new generation telemetry system framework. In this article telemetry system networking, intelligent monitoring, and data fusion of new generation telemetry system are elaborated and trends of development of telemetry technique in flight tests are prospected.
PERAWATAN DAN PERBAIKAN INCINERATOR KAPAL MT. PRIBUMI UNTUK MENGURANGI PENCEMARAN SAMPAH DI LAUT PADA PT JANATA MARINA INDAH I SEMARANG

Incinerator is a garbage burner that is operated by using combustion technology at a certain temperature, so that the waste can burn out. Incineration and processing of other high temperature waste is defined as thermal processing. So much pollution in the sea caused by passing ships, forced the international body to deal with the problem of sea pollution to tighten regulations regarding the disposal of waste from ships. Through the laws listed in MARPOL (marine pollution) for each ship with a certain gross tonage and through certain waters it is required to be equipped with an incinerator. The aim to be achieved in this study is to obtain an overview of the maintenance and repair of incinerators to reduce pollution at PT Janata Marina Indah and reduce the volume of solid waste from dredgers. The method used is an experimental method by making a hydraulic press machine so that it can reduce the volume of waste produced from dredgers. The crew crew's indifference to handling solid waste on board ships as much as 75%, so by trying to use a press tool, the volume of solid waste will decrease. In addition, the press tool designed is also very easy to operate, high effectiveness, cheap and free of maintenance.Keywords: Incenerator, maintenance, repair, pollution
Meta-k: Towards Unsupervised Prediction of Number of Clusters

Data clustering is a well-known unsupervised learning approach. Despite the recent advances in clustering using deep neural networks, determining the number of clusters without any information about the given dataset remains an existing problem. There have been classical approaches based on data statistics that require the manual analysis of a data scientist to calculate the probable number of clusters in a dataset. In this work, we propose a new method for unsupervised prediction of the number of clusters in a dataset given only the data without any labels. We evaluate our method extensively on randomly generated datasets using the scikit-learn package and multiple computer vision datasets and show that our method is able to determine the number of classes in a dataset effectively without any supervision.
Manajemen Rantai Pasok

Abstract-. Supply Chain Management (SCM) is a process for integrating, coordinating and controlling the movement of raw materials into finished products and sending a product into the hands of end users and integrating all of the company's business partners, especially those related to the supply of materials needed in the production process. The company has seen the development of information technology more rapidly so that it encourages companies to implement information systems with a Supply Chain Management (SCM) approach. The development of this information system is taken through the Supply Chain Management concept which is focused on the Upstream, Middle Stream and Downstream sections using the Waterfall method. Along with the very high market demand, companies often experience problems, namely in the process of ordering raw materials to suppliers so that the impact on the warehouse which results in the production process is often stopped because of the availability of raw materials that are not controlled optimally. By using a supply chain management information system, the information flow can run well, procurement of raw materials can be planned in advance and can be predicted for manufacturing production even the use of supply chain management information systems can optimize the use of raw materials where raw materials used for the production process are not wasted and inventory usage can be reduced. Supply chain management information systems are also able to accelerate services ranging from purchasing raw materials, storing, producing and distributing finished products to customers. The main solution offered by the system is the existence of a user interface with functions that are interrelated to each other to help overcome various problems that often occur.Keywords - information systems; supply chain management; middle stream; upstream; downstream; waterfall;
SWOT Analysis of the Development of Guizhou Big Data Financial Industry

With the increasing advancement of science and technology, big data technology has become the new "oil" in the Internet era. Alvin Toeffler called it "the festivities in the third wave". As a new type of strategic resource, big data technology has been continuously innovated and developed by countries all over the world, and the most important part is the integration with the financial industry. This article uses the SWOT analysis method to analyze the advantages and disadvantages of the development of big data finance in Guizhou Province from both internal and external aspects, and puts forward relevant suggestions and measures for the development of big data finance in Guizhou Province, so that Guizhou Province can better use big data technology as a fulcrum. Economic transformation has stimulated the economic development of Guizhou Province.
Kanerva++: Extending the Kanerva Machine With Differentiable, Locally Block Allocated Latent Memory

Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems (McClelland et al., 1995) suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (semantic memory). In this work, we develop a new principled Bayesian memory allocation scheme that bridges the gap between episodic and semantic memory via a hierarchical latent variable model. We take inspiration from traditional heap allocation and extend the idea of locally contiguous memory to the Kanerva Machine, enabling a novel differentiable block allocated latent memory. In contrast to the Kanerva Machine, we simplify the process of memory writing by treating it as a fully feed forward deterministic process, relying on the stochasticity of the read key distribution to disperse information within the memory. We demonstrate that this allocation scheme improves performance in conditional image generation, resulting in new state-of-the-art likelihood values on binarized MNIST (≤41.58 nats/image) , binarized Omniglot (≤66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32×32.
Erasure-resilient sublinear-time graph algorithms

We investigate sublinear-time algorithms that take partially erased graphs represented by adjacency lists as input. Our algorithms make degree and neighbor queries to the input graph and work with a specified fraction of adversarial erasures in adjacency entries. We focus on two computational tasks: testing if a graph is connected or e-far from connected and estimating the average degree. For testing connectedness, we discover a threshold phenomenon: when the fraction of erasures is less than e, this property can be tested efficiently (in time independent of the size of the graph); when the fraction of erasures is at least e, then a number of queries linear in the size of the graph representation is required. Our erasure-resilient algorithm (for the special case with no erasures) is an improvement over the previously known algorithm for connectedness in the standard property testing model and has optimal dependence on the proximity parameter e. For estimating the average degree, our results provide an interpolation between the query complexity for this computational task in the model with no erasures in two different settings: with only degree queries, investigated by Feige (SIAM J. Comput. `06), and with degree queries and neighbor queries, investigated by Goldreich and Ron (Random Struct. Algorithms `08) and Eden et al. (ICALP `17). We conclude with a discussion of our model and open questions raised by our work.
PSYCHOLOGICAL BASES OF STUDYING THE KARAKALPAK LANGUAGE BY STUDENTS IN PRIMARY CLASSES

The relevance of the chosen topic is due to the need to pay attention to the psychological foundations of teaching students to make sentences in the lessons of the Karakalpak language. The psychological basis for mastering the skills of constructing sentences in a second language is teaching children activity in a second language, performing actions and speech operations to express their thoughts, bringing communication skills to automatism when performing exercises based on sensory perception, moving from visual-effective to visual - figurative and then to logical thinking by an oral of Russian-speaking students in the lessons of the Karakalpak language are performing exercises related to speaking.
Awareness of the concept of hibah as an alternative property management system among students in Diploma Muamalat, in UiTM Puncak Alam. / Arif Zafran Khairudin

Hibah is a contract used to show appreciation and love. The purpose of this study is to determine the level of awareness of Students in Diploma Muamalat Part 5, Academy of Contemporary Islamic Studies, UiTM Puncak Alam about the concept of hibah. This study focuses only on the semester 5 students. The total number of students involved is 110 peoples (population). The method used was quantitative through the set of questionnaires using google form help among students of the semester 5. The results showed that the semester 5 students of this program had a mid-level understanding of the concept of the hibah based on the objectives of the study. In conclusion, it still needs a lot of effort to increase level of understanding and knowledge about hibah concept, especially to the whole society.
Ανάλυση εικόνας με στόχο τον εντοπισμό καρκίνου με τεχνικές μηχανικής και εις βάθος μάθησης

Affecting roughly about 10% of women worldwide, Breast Cancer has been proven to be one of the most shocking and common cancers among women today. Although, there are available treatments for this cancer, the real issue we face is when we cannot properly detect the disease in its early stages, a problem which both first world and third world countries encounter constantly. Deep Learning while thriving at computer vision field has been shown to play a vital role in predicting such diseases. Methods of classifying and extracting data have so far proved to be reliable and effective. However, recent studies have shown, through multiple experiments and trials, that deep learning techniques have achieved the best results. In this study, we will be seeing the use of different depth-wise convolutional neural networks in medical imaging and compare them with a simple convolutional neural net on multiple datasets of breast cancer images. Performance comparisons are made between MobileNet, VGG16 and AlexNet on breast ultrasounds and breast histopathology images. Our primary goal is to demonstrate the efficiency and accuracy of such systems in classifying different kind of breast cancer images. The ultrasounds dataset is containing 250 images of benign and malignant cases, the second (3268 images) and third (10483 images) datasets contain images of breast histology with cases of Invasive Ductal Carcinoma (IDC) and cancer free tissue. Experimental evidence suggests that the proposed deep learning models may effectively classify ultrasounds and histopathology images. The performance of the most efficient Convolutional Neural Network model reached 96.82% accuracy on ultrasounds, 88.23% on the second dataset and 91.04% on the third. Therefore, we concluded based on our results, that deep-learning tenets in the diagnosis of breast cancer are indeed very promising and the world can benefit undoubtedly from the implementation of such systems.
An Interactive Video Search Tool: A Case Study Using the V3C1 Dataset

This paper presents a prototype of an interactive video search tool for the preparation of MMM 2021 Video Browser Showdown (VBS). Our tool is tailored to enable searching for the public V3C1 dataset associated with various analysis results including detected objects, speech recognition, and visual features. It supports two types of searches: text-based and visual-based. With a text-based search, the tool enables users for querying videos using their textual descriptions, while with a visual-based search, one provides a video example to search for similar videos. Metadata extracted by recent state-of-the-art computer vision algorithms for object detection and visual features are used for accurate search. For an efficient search, the metadata are managed in two database engines: Whoosh and PostgreSQL. The tool also enables users to refine the search results by providing relevance feedback and customizing the intermediate analysis of the query inputs.
Automatic language identification of short texts

The world is growing more connected through the use of online communication, exposing software and humans to all the world's languages. While devices are able to understand and share the raw data b ...
E-Learning Satisfaction Menggunakan Metode Auto Model

E-Learning just like learning media in general need to be evaluated to find out and measure how much effectiveness, efficiency and user satisfaction with the quality of the overall learning process. One effort that can be done to find out and evaluate the quality of a learning is to use satisfaction evaluation. Measurement of satisfaction requires data derived from questionnaires that are presented using a Likert scale. The data illustrates the perception of users who have uncertainty because it is very subjective so that it has the potential to cause misinterpretation. The auto model method can be used to evaluate e-Learning satisfaction because the auto model method has the advantage of solving a problem with the various models produced, which in this case are in accordance with the context of the satisfaction problem that is often presented in natural language that has uncertainty, such as "how satisfied? "," How efficient? "And" how much is user satisfaction. Based on the auto model method, the results of the satisfaction scores of each respondent, shown in the table above, are summed and the average is calculated. With the auto model, the results show that SVM is the best performance method with an acceleration rate of 90% and best gains with a value of 38.
LibGuides: OverDrive: Libby by OverDrive

This guide will take a look at OverDrive and give you a general understanding on how it works, what it is, and how you can use it.
Copywriting Competition | Apply Now | Vision One

Calling all copywriters. Were on the lookout for one or two talented wordsmiths from agencies, or freelancers, to help us with our marketing ...
LibGuides: Career Services: Career Exploration

This guide is designed to assist you through career planning with helpful links to online resources. The following sources in this guide are good places to begin your research. Explore skills/majors, test prep e-books, best jobs lists, videos, articles & books
Alignment and stability of embeddings: Measurement and inference improvement

Representation learning (RL) methods learn objects’ latent embeddings where information is preserved by distance. Since certain distance functions are invariant to certain linear transformations, one may obtain different embeddings while preserving the same information. In dynamic systems, a temporal difference in embeddings may be explained by the stability of the system or by the misalignment of embeddings due to arbitrary transformations. This study focuses on the embedding alignment problem to distinguish structural changes inherent to a system from arbitrary changes caused by representation learning methods, and quantify their magnitudes. In order to avoid any confusion due to the naming conventions in the literature, it should be noted that embedding alignment problems are different from graph matching/network alignment problems. In the representation learning literature, although the embedding alignment issue has been acknowledged, its measurement and empirical analysis have not received sufficient interest. In this work, we explore the embedding alignment and its parts, propose novel metrics to measure alignment and stability, and show their suitability through synthetic experiments. Real-world experiments show that both static and dynamic RL methods are prone to produce misaligned embeddings and such misalignment worsens the performance of dynamic network inference tasks. By ensuring alignment, the prediction accuracy raises by up to 90% in static and up to 40% in dynamic RL methods.
Algebraic geometric secret sharing schemes over large fields are asymptotically threshold

In Chen-Cramer Crypto 2006 paper \cite{cc} algebraic geometric secret sharing schemes were proposed such that the Fundamental Theorem in Information-Theoretically Secure Multiparty Computation by Ben-Or, Goldwasser and Wigderson \cite{BGW88} and Chaum, Crepeau and Damgard \cite{CCD88} can be established over constant-size base finite fields. These algebraic geometric secret sharing schemes defined by a curve of genus $g$ over a constant size finite field ${\bf F}_q$ is quasi-threshold in the following sense, any subset of $u \leq T-1$ players (non qualified) has no information of the secret and any subset of $u \geq T+2g$ players (qualified) can reconstruct the secret. It is natural to ask that how far from the threshold these quasi-threshold secret sharing schemes are? How many subsets of $u \in [T, T+2g-1]$ players can recover the secret or have no information of the secret? In this paper it is proved that almost all subsets of $u \in [T,T+g-1]$ players have no information of the secret and almost all subsets of $u \in [T+g,T+2g-1]$ players can reconstruct the secret when the size $q$ goes to the infinity and the genus satisfies $\lim \frac{g}{\sqrt{q}}=0$. Then algebraic geometric secret sharing schemes over large finite fields are asymptotically threshold in this case. We also analyze the case when the size $q$ of the base field is fixed and the genus goes to the infinity.
Building a free open source based automated system administration multi-threading and parallel programming performance

Building a system integrates the thoughts of security related issues and raises the concerns about the environment compatibility in order to provide secured environments. Administrating tasks stand behind complex dashboards and gives a view to all theoretical skills on field. Yet, limitations caused by constraints are faced, and to trespass those is a must through many different feasible ways; mainly the FOSS (Free Open Source Software) is a key for plenty of Information Technology security and also administration issues. This research covers specific outcomes to fulfill systems administration expertise along with the necessary security requirements.
LibGuides: Virtual and Augmented Reality: Google Cardboard

Learn about Virtual and Augmented Reality, and services available at the Nexus Collaborative Learning Lab!
Research on the Application of “Cloud Technology” in Piano Teaching of Colleges and Universities

In the environment of rapid development of “internet +” information technology, multimedia software and integrated system of educational resources are introduced to the teaching process of specialized courses of different colleges and universities. They are used to collect teaching resources in and out of class, make online teaching videos, convey theoretical knowledge, interact with students and guide practical teaching. This paper focuses on specialized piano course teaching in colleges and universities, uses big data and cloud computing service technology, builds cloud platform of network data mining and education informatization, strengthens knowledge impartation and instrumental performance of teachers in class, the exchange between teachers and students, exercise of students and some other teaching links, lays emphasis on cultivating students’ independent study awareness and practice ability and pushes forward the integrated development of specialized piano theory teaching and artistic practice.
The influencing factors of accessibility, culture and trust of customer adoption on education management information system (EMIS): a study at PPD Pasir Puteh / Qurratu `Izzati Mohamad Nizami

EMIS System stands for Education Management Information System that was develop by the Ministry of Education (MOE). This system is a tool for collecting the data for the purpose of the short and long planning also for the educational sources. . The school, district, state and MOE are the parties that involve with the four level of EMIS System. The purpose of this study was to identify the influencing factor of accessibility, culture and trust on customer adoption on Education Management Information System (EMIS) at PPD Pasir Puteh. There are three (3) independent variables that been studied, which were accessibility, culture and trust factor. This study also was conducted to examine the factor that of accessibility, culture and trust that will influence on consumer adoption. Moreover, questionnaire had been used in order to get feedback from respondents. The researcher had distributed 100 questionnaires. The sampling design used in this research is convenient sampling. Based on the finding, the result indicates that there is significant relationship in hypothesis testing between trust and customer adoption. This result shows that trust factor is a factor that can influence consumer adoption on EMIS System. Based on the result obtained in this study, the researcher makes some recommendation that can be used to help organization to improve usage of EMIS System among current employees