Reviewerly/exHarmony huggingface dataset:
Novel Perspectives and Applications of Knowledge Graph Embeddings: From Link Prediction to Risk Assessment and Explainability

Knowledge graph representation is an important embedding technology that supports a variety of machine learning related applications. By learning the distributed representation of multi-relational data, knowledge embedding models are supposed to efficiently deal with the semantic relatedness of their constituents. However, failing in the fundamental task of creating an appropriate form to represent knowledge harms any attempt of designing subsequent machine learning tasks. Several knowledge embedding methods have been proposed in the last decade. Although there is a consensus on the idea that enhanced approaches are more efficient, more complex projections in the hyperspace that indeed favor link prediction (or knowledge graph completion) can result in a loss of semantic similarity. We propose a new evaluation task that aims at performing risk assessment on domain-specific categorized multi-relational datasets, designed as a classification problem based on the resulting embeddings. We assess the quality of embedding representations based on the synergy of the resulting clusters of target subjects. We show that more sophisticated embedding approaches do not necessarily favor embedding quality, and the traditional link prediction validation protocol is a weak metric to measure the quality of embedding representation. Finally, we present insights about using the synergy analysis to provide risk assessment explainability based on the probability distribution of feature-value pairs within embedded clusters.
Pelatihan dan Pendampingan Penulisan Artikel Ilmiah Bagi Guru SD di SDN Margorejo VI Surabaya

The legal foundation in Indonesia requires a teacher to be able to carry out his duties professionally. One of the professional development activities is to carry out scientific writing / paper (KTI) activities in the field of education. Law No.14 of 2005 on teachers and lecturers states that professional teachers have proven ability in writing scientific papers as a requirement for promotion and position. However, the reality shows that many teachers including elementary school teachers at SDN Margorejo VI Surabaya do not yet have the skills to write articles for scientific journals. One solution is training and assistance in writing scientific articles. This activity is expected to increase knowledge, understanding, skills as well as awareness of the importance of writing scientific articles and producing scientific articles that are ready for publication. In order to achieve these targets, the implementation of this community service program is divided into three stages: (1) the preparation stage, (2) the implementation stage, and (3) the monitoring and evaluation stage. This service activity is well organized and runs smoothly in accordance with the activity plan that has been prepared even though not all training participants have mastered the material presented properly. This activity received a very good response as evidenced by the active participation of the participants in participating in the activity by not leaving before the training time ended. The mentoring activity went well so that there were several teachers who produced articles that were ready to be published.
Design of New Media Data Processing System

This paper is mainly for the design of new media data processing system. The research of new media data processing shows that the traditional database processing new media data is more and more huge, which causes the operating system to do a lot of output and input operations (I / O) when querying, and the processing speed is slow. Through the analysis, the main reason that affects the new media data processing is the large amount of data. In this paper, Bayesian algorithm classifier model is used, and Map/Reduce function is used in the parallel computing framework. Multi computing resource nodes are used for parallel processing to effectively improve the speed of new media data processing.
Prednosti in zahteve izvedbe kreditno ovrednotene obštudijske dejavnosti na primeru usposabljanja v potapljanju

In this article, we presented an example of how we prepared a diving course with autonomous diving equipment at UM FOV as a student optional possibility for a credit-evaluated extracurricular activity. Theoretical and legal basis for applying for the credit-evaluated curriculum are presented. Different types of diving and international schools that provide training are presented. The advantages of PADI education system and the requirements for creating a curriculum are given. In conclusion, we presented the anticipated positive effects of the implementation of such extracurricular activities.
Behavioral Analysis of Neural Network Using Various Visualization Strategies

Currently extensive researches are focusing on understanding Machine Learning models mainly Deep Learning ones because of their black-box nature. Convolutional neural network (CNN) architectures used in Deep Learning have made their way into computer vision. Saliency maps or attribution maps are mainly used to find the most important features which in turn help us predicting results in the model. In this paper we have worked on different visualization techniques like Gradient Class Activation Map (Grad-CAM), Grad-CAM++, Score-CAM, and Faster Score-CAM on various architectures like VGG16, ResNet-based architectures, and pre-trained models and further investigated their results. Three different datasets (Plant village, Internet augmented, Real-world augmented) have been used and experimented on. The core processes comprise of image capturing, study and implementation of image pre-processing, testing on different neural network architecture, and assessment of data visualization. All of the key steps required to implement the model are detailed throughout the document.
NVivo Data Curation Primer

This work was created as part of the “Specialized Data Curation” Workshop #2 held at Johns Hopkins University in Baltimore, MD on April 17-18, 2019.
Quantum states associated to mixed graphs and their algebraic characterization

Graph states are present in quantum information and found applications ranging from quantum network protocols (like secret sharing) to measurement based quantum computing. In this paper, we extend the notion of graph states, which can be regarded as pure quantum graph states, or as homogeneous quadratic Boolean functions associated to simple undirected graphs, to quantum states based on mixed graphs (graphs which allow both directed and undirected edges), obtaining mixed quantum states, which are defined by matrices associated to the measurement of homogeneous quadratic Boolean functions in some (ancillary) variables. In our main result, we describe the extended graph state as the sum of terms of a commutative subgroup of the stabilizer group of the corresponding mixed graph with the edges' directions reversed.
LibGuides: Creating Maps with HistoryPin: Create a Collection & Pin Things

Create interactive digital maps with pinned archival or open web images, video, and text. Digital maps; digital tours; digital collections. Great for class and group assignments.
Artificial Intelligence as a Public Service: Learning from Amsterdam and Helsinki

In September 2020, Helsinki and Amsterdam announced the launch of their open AI registers—the first cities in the world to offer such a service. The AI registers describe what, where, and how AI applications are being used in the two municipalities; how algorithms were assessed for potential bias or risks; and how humans use the AI services. Examining issues of security and transparency, this paper discusses the potential for implementing AI in an urban public service setting and how this might be harnessed to improve services and citizens’ experiences.
The Effect Of English Grammar Test Application On Students’ Grammar Mastery Of Simple Past Tense At The Eighth Grade Of Junior High School

The objective of this study is to find out whether there is a significant effectof English Grammar Test (EGT) application on students’ grammar mastery ofsimple past tense at the eighth grade of Junior High School. Quantitative researchmethod with quasi-experimental design was used in this research. It was conductedat the eighth grade of MTs. Islamiyah Perkebunan Tanjung Kasau. The populationconsisted of 60 students from two classes. Using saturation sampling method, thetwo classes namely VIII-A and VIII-B were selected as the sample of this studywith each class consisted of 30 students. Furthermore, VIII-A class was selected ascontrol class and VIII-B class as experimental. In collecting the data, multiplechoice and “fill in the blank” test forms were used in this study. The tests were thendistributed in two ways; 1. Pre-test was distributed before the treatment: 2. Posttest was distributed after the treatment. Furthermore, the data was analyzed by usingstatistical formulation and Microsoft Excel 2013 with 0,05 significance level. Afterthe data was analyzed, it can be found that the value of tobserved was higher than ttable.It can be seen from tobserved (3,839) > ttable (2,001) at the degree of freedom (df) = 58.It means that the alternative hypothesis (Ha) was accepted and null hypothesis (H0)was rejected. Therefore, it can be concluded that English Grammar Test applicationgives significant effect on students’ grammar mastery of simple past tense at theeighth grade of MTs. Islamiyah Perkebunan Tanjung Kasau.
Estimating Missing Data with Bayes Bootstrap Regression Imputation

This research is about estimating missing data when dependent variable Y is correlated with independent variable X, and X and Y are distributed as normal . The proposed method for estimating missing data is Bayes - Bootstrap regression imputation method ( BRI ) that is compared with regression imputation method ( RI ) and distance regression imputation method ( DRI ) . The measurement criteria is mean absolute error ( MAE ). Comparing of estimating missing data used the Monte Carlo simulation technique . The results of study indicate that BRI and RI are more accuracy than DRI for all cases, but BRI presents the lowest mean absolute error in some case . Therefore, researchers introduce the BRI method for estimating missing data when the correlation coefficient between dependent variable Y and independent variable X is known and both variable distributions are normal distributions . Keywords : : missing data ;  Bayes-Bootstrap regression imputation ; Distance regression imputation ;  regression imputation
Diff-ResNets for Few-shot Learning - an ODE Perspective.

Interpreting deep neural networks from the ordinary differential equations (ODEs) perspective has inspired many efficient and robust network architectures. However, existing ODE based approaches ignore the relationship among data points, which is a critical component in many problems including few-shot learning and semi-supervised learning. In this paper, inspired by the diffusive ODEs, we propose a novel diffusion residual network (Diff-ResNet) to strengthen the interactions among data points. Under the structured data assumption, it is proved that the diffusion mechanism can decrease the distance-diameter ratio that improves the separability of inter-class points and reduces the distance among local intra-class points. This property can be easily adopted by the residual networks for constructing the separable hyperplanes. The synthetic binary classification experiments demonstrate the effectiveness of the proposed diffusion mechanism. Moreover, extensive experiments of few-shot image classification and semi-supervised graph node classification in various datasets validate the advantages of the proposed Diff-ResNet over existing few-shot learning methods.
LibGuides: Guide for MAC Students: APA Style Guide

This guide provides students in the MAC program with a description of how to use library resources and services. Use this guide to help when you are writing papers for your counseling classes.
Poizì Kazarss: traduzione di un sentimento

This work deals with Giovanni Belluscio's translation in his own variety, Italoalbanian, spoken in San Basile - Shin Vasil, of Pasolini's Poesie a Casarsa, written in a Friulian dialect. This translation is neither a philological nor a purely literary one, as he says. More properly, it is both a tribute to a great intellectual figure and a reminder of his dead brother, who died far away from his homeland. A similar experiment concerns, on one hand, the linguistic level and the complexity of the codes involved, their role in the different repertoires, and, on the other, the problem of translatability considered from an ethnolinguistic and cognitive point of view.
Subject Guides: Printing in the Innovation & Research Commons: I&RC Printing

Information about printing in the I&RC, printer types, costs, and policies. Large format printing for posters and other large works.
基於 LLVM 的二元碼轉譯器框架，以 RISC-V 為範例

In order to share software components, such as optimization passes, among several binary translators, we need to establish a convention, such as the calling convention, for the generated LLVM IR. % from binary. A common convention can make the development of frontends for different ISAs independent of that of the backends.We first defined a convention. Based on this convention, we designed and implemented an LLVM-based binary translator framework called Rabbit. Rabbit contains a friendly frontend template. It uses the frontend plugins to support different source ISAs. The Rabbit framework can support both DBT and SBT. Furthermore, we proposed a Division method (see section 6.1) which can reduce the optimization time significantly (see Fig. 8.3).
VALIDITAS DAN RELIABILITAS INSTRUMEN PENILAIAN PROGRAM BAHASA INGGRIS GRUP 5 FAKULTAS EKONOMI BISNIS ISLAM PUSAT PENGEMBANGAN BAHASA IAIN PADANGSIDIMPUAN T.P. 2018/2019

This research is aimed to describe validity and reliability of instrument on English assessment at Language Centre of IAIN Padangsidiimpuan Program. The descriptive quantitative is kind of research method that used on this research. Subject of this research is the group 5 of Economy of Islamic Business Faculty at IAIN Padangsidimpuan that consisted with 24 students. Data collected from result of reading, writing, listening, speaking, and structure of the students. Method of analysis data is taken by using validity and reliability tests through software SPPS version 16. The result showed that all of indicators on mid-semester test were valid and the instrument is reliable meanwhile in the final-semester test showed reading, writing, listening, speaking were not valid and the structure one was valid and the instruments were not reliable.
A Shadow Based Low-Cost Hand Movement Recognition System for Human Computer Interaction

TIn this paper, we propose and implement a real-time hand motion detection system which uses shadows projected by hand and detected by low cost Light Dependent Resistor (LDR). The main advantage of the shadow approach is real-time recognition and its application in Post COVID 19 world where contactless interactions are required. Our proposed approach uses 2D hand motion shadows to determine sequence of light blocking the LDRs and thus making the system less complex and less compute intensive compared to 3D image recognition based systems. The accuracy of the proposed system is tested under various lighting conditions and 82-95\% accuracy is observed under normal lighting conditions.
Ninja Codes: Exploring Neural Generationof Discreet Visual Codes

In this paper we report the results of our early explorations regarding Ninja Codes, a new class of visual codes intended to be used in a variety of interactive applications including augmented reality, motion/gesture control, contactless data transfer, robotics, etc. By harnessing the power of adversarial examples, Ninja Codes can be rendered discreet, concealed to human eyes but easily recognizable to detectors based on deep neural networks. The paper will provide a high-level overview of Ninja Codes, and describe an initial, proof-of-concept implementation built on top of existing face detection software. We see this work as a promising step toward a new family of methods by which digital information can be seamlessly encoded into real-world objects and environments.
Use cases and best practices for map-based energy data visualizations

Author(s): Salmon, KIernan; Sanguinetti, Angela | Abstract: Across a variety of energy conservation programs and tools, energy data visualizations are common components used to inform and influence behavior. Several studies have explored map-based energy data visualizations and found that they compare favorably to conventional charts and graphs, but all generated more unanswered questions about how, when, and why map-based visualizations work well. This research reviews the current state of knowledge and adds to the limited empirical work on map-based energy displays in order to articulate best practices and support broader use of maps to visualize energy data. Two online experiments were conducted with over 830 participants from Amazon Mechanical Turk to assess and compare the usability of multiple versions of map-based energy displays and bar charts. Results were consistent with past research findings that map-based energy data visualizations are more interesting and enjoyable than more basic displays (bar charts). These findings support a general use case for map-based energy displays when trying to engage a broad audience, including those unfamiliar with energy data, by telling a richer story and elevating the data to be more than a set of metrics. Results also highlight the importance of interactivity for map-based energy displays; participants who used interactive features were more accurate in interpreting energy data than those who did not. Other best practices for map-based energy data visualizations are discussed, including considerations for single-variable heat maps, proportional symbol maps, and dual-encoded proportional symbol maps.n
LibGuides: Social Sciences Education: Referencing

This guide will help students to easily access information that is relevant to the social sciences subject.
Joint Optimization for Visual Data Transmission in the Resource Constraint 5G IoT

With the integration of 5G and Internet of Things (IoT), the application of large-scale IoT devices networking is increasingly extensive, such as building management, property maintenance, autonomous vehicles, healthcare, and shopping to tourism. In these scenes, the volume of data transmission is quite large, especially visual data (image, video, et al). However, due to the limited resource of IoT devices, such as battery, power, bandwidth, visual data transmission is complex to optimize, single objective optimization is difficult to insure the optimal latency, throughput and power at the same time. In this paper, we propose a method to jointly optimize the resource allocation of visual data transmission in resource constraint 5G IoT. Instead of single objective optimization, we combine the bandwidth, power consumption and latency into a hybrid model, then propose a low-complexity algorithm to solve this multiple objective optimization problem. The simulation results demonstrate that the proposed method increases the comprehensive utility of visual data transmission in the resource constraint 5G-IoT by 35%-48% compared with existing approaches.
Ensemble Learning Boosting Model of Improving Classification and Predicting

Artificial Intelligence Engineering is an important topic and has been studied extensively in variousfields. Machine learning is part of Artificial Intelligence that has been used to solve predictionproblems and financial decision making. An effective prediction model is one that can provide ahigher prediction accurate, that is the goal of prediction model development. In the previousliterature, various classification techniques have been developed and studied, which by combiningseveral classifier approaches have shown performance over a single classifier. In building aboosting ensemble model, there are three critical issues that can affect model performance. Firstare the classification techniques actually used; the second is a combination method for combiningseveral classifiers; and all three classifiers to be combined. This paper conducts a comprehensivestudy comparing the ensemble boosting classifier and three widely used classification techniquesincluding AdaBoost, Gradient boosting, XGB Classifier. The results of the experiment with twofinancial ratio datasets show that the Ensemble Boosting Classifier has the best performance withan accurate value of 98%, while AdaBoost is 96%, Gradient_boosting is 98%, and XGB Classifieris 98%. Ensemble Boosting matches all available data, so the predict () function can be called tomake predictions on new data.
Comparison and Choice of Computational Architectures Based on Cost-Value Approach

Software engineers make use of several computational architectures (CA) to host an application, such as desktop, web, and cloud computing architectures. As the requirements vary according to the desired CA, developers may face two problems: determining which requirement better fulfills a CA and determining which CA fulfills a given set of requirements. This paper presents a new approach based on the Cost-Value Approach (CVA). We have slightly modified the CVA method (SCVA method) and also developed a new method for choosing the most appropriate CA (MMACA method). Our results provide a set of requirements ordered by priority for each CA. Finally, we discuss the current and most appropriate CA for a real project solution.
Organization of work of educational organizations to promote employment of graduates with disabilities: issues of theory and practice

This article deals with issues related to the activities of an educational organization to promote employment of graduates of educational organizations that belong to the category of persons with disabilities and persons with disabilities. Attention is paid to the problems of formation and functioning of specialized structural divisions in higher education institutions, the structure and requirements for the personnel of such a division, the system of building an individual career trajectory and promoting employment, working with a psychologist in self-determination and building an individual career, organizing work practices for people with disabilities, working with potential employers.
Real-time automatic tracking of hand motion in RGB videos using local feature SIFT

This paper proposes a method for real-time visual tracking of moving hand in RGB videos without any segmentation process and background subtraction. We have used YCgCr converted version of YCbCr colour space for a more compact representation of the initial region of moving hand and then local feature SIFT to detect and track hand simultaneously. YCgCr has a high tendency for skin colour accretion and can effectively discriminate between the skin and non-skin colour regions. The approach demonstrates that using local features (SIFT) of only active region reduces the computation as well as make the method free from the challenges of freedom factor of hand and thus the methodology can detect the hand of any shape and size without being affected by background conditions. In general, researchers avoid using a normal camera for applications based on hand tracking, as RGB images are sensitive to illumination. Our work exhibits that the combination of YCgCr and two-stage feature matching through SIFT algorithm is successful in tracking non-rigid objects with less computation. The methodology is further evaluated with Kalman tracking in hand gesture recognition and is also compared with contemporary works.