Reviewerly/exHarmony huggingface dataset:
Penggunaan Media Big Book untuk Meningkatkan Kemampuan Berpikir Kritis Siswa di Kelas V Sekolah Dasar

This study aims to improve students' critical thinking skills by using big book media in class V of SD Negeri 13/1 Rengas Condong in the academic year 2017/2018. This research is a classroom action research (CAR), data taken through observation. This research was conducted in 4 stages, namely Planning, Implementation, Observation, and Reflection. During the process of learning activities using the big book media. The instrument used in this study was in the form of tests and observation sheets, while the benchmarks for the tests were critical thinking indicators, while the benchmarks for observation sheets were critical thinking features using the big book media. The results of this study indicate the use of big book media can improve the critical thinking skills of fifth grade students at SD Negeri 13/1 Rengas Condong. Based on the observation sheet and the test of students' critical thinking skills showed an increase, with the results of the observation sheet percentage of the first cycle of 61.4% with a fairly good category, on the second cycle of 77% with a good category, while the percentage of the results of the first cycle of the test was 45.5 %, in the second cycle increased by 81%. Based on the results of the study, it can be concluded that students' critical thinking skills by using big books in SD Negeri 13/1 Rengas Condong can increase after the use of big book media is applied
High-efficiency Low-latency NTT Polynomial Multiplier for Ring-LWE Cryptography

This paper presents a novel architecture to perform polynomial multiplication in ring learning with errors (ring-LWE) cryptosystems. By employing number theoretic transform (NTT) of the input polynomials simultaneously, the multiplication latency is significantly reduced. In addition, a multiple-path delay feedback (MDF) architecture is used to speed up the multiplication process. As a result, the proposed NTT multiplier offers a better value of area-latency product compared with that of previous studies. The simulation results for the security parameters n = 512 and q = 12,289 on Xilinx Virtex-7 FPGA show that the proposed multiplier uses only about 8.69% of the number of clock cycles required by previous works to completely perform the polynomial multiplication. Furthermore, the obtained area-latency product value of the proposed architecture is less than 45.3% of that of previous works.
On Deep Speech Packet Loss Concealment: A Mini-Survey

Packet-loss is a common problem in data transmission, using Voice over IP. The problem is an old problem, and there has been a variety of classical approaches that were developed to overcome this problem. However, with the rise of deep learning and generative models like Generative Adversarial Networks and Autoencoders, a new avenue has emerged for attempting to solve packet-loss using deep learning, by generating replacements for lost packets. In this mini-survey, we review all the literature we found to date, that attempt to solve the packet-loss in speech using deep learning methods. Additionally, we briefly review how the problem of packet-loss in a realistic setting is modelled, and how to evaluate Packet Loss Concealment techniques. Moreover, we review a few modern deep learning techniques in related domains that have shown promising results. These techniques shed light on future potentially better solutions for PLC and additional challenges that need to be considered simultaneously with packet-loss.
Development of teaching materials arithmetic sequence and series based on android for problem based learning

Abstract This research aims to produce the teaching materials based on Android which is valid and practical in order to support the senior high school students in learning arithmetic sequence and series for problem based learning. The subjects of this research were the eleventh and twelfth grade students of Senior High School Number 1 Indralaya Utara. This research is kind of development research by adapting model development of ADDIE. The stages of the research conducted consist of analyze, design, and development. Data collection techniques which were used in this research were by walkthrough, observation and questionaire. The result of this research is teaching materials of arithmetic sequence and series based on Android for problem based learning are valid and practical. Said to be valid because based on the assessment of experts who stated that in terms of content, construct and language in teaching materials had met the valid criteria. Whereas, based on the results of the questionnaire and observation it was concluded that the students think that this teaching materials is interesting, easy to use, easy to understand, can improve enthusiasm for learning and can help to improve concepts understanding of students.
Fashion Recommendation and Compatibility Prediction Using Relational Network

Fashion is an inherently visual concept and computer vision and artificial intelligence (AI) are playing an increasingly important role in shaping the future of this domain. Many research has been done on recommending fashion products based on the learned user preferences. However, in addition to recommending single items, AI can also help users create stylish outfits from items they already have, or purchase additional items that go well with their current wardrobe. Compatibility is the key factor in creating stylish outfits from single items. Previous studies have mostly focused on modeling pair-wise compatibility. There are a few approaches that consider an entire outfit, but these approaches have limitations such as requiring rich semantic information, category labels, and fixed order of items. Thus, they fail to effectively determine compatibility when such information is not available. In this work, we adopt a Relation Network (RN) to develop new compatibility learning models, Fashion RN and FashionRN-VSE, that addresses the limitations of existing approaches. FashionRN learns the compatibility of an entire outfit, with an arbitrary number of items, in an arbitrary order. We evaluated our model using a large dataset of 49,740 outfits that we collected from Polyvore website. Quantitatively, our experimental results demonstrate state of the art performance compared with alternative methods in the literature in both compatibility prediction and fill-in-the-blank test. Qualitatively, we also show that the item embedding learned by FashionRN indicate the compatibility among fashion items.
Parallel Batch-dynamic Trees via Change Propagation

The dynamic trees problem is to maintain a forest subject to edge insertions and deletions while facilitating queries such as connectivity, path weights, and subtree weights. Dynamic trees are a fundamental building block of a large number of graph algorithms. Although traditionally studied in the single-update setting, dynamic algorithms capable of supporting batches of updates are increasingly relevant today due to the emergence of rapidly evolving dynamic datasets. Since processing updates on a single processor is often unrealistic for large batches of updates, designing parallel batch-dynamic algorithms that achieve provably low span is important for many applications. In this work, we design the first work-efficient parallel batch-dynamic algorithm for dynamic trees that is capable of supporting both path queries and subtree queries, as well as a variety of non-local queries. To achieve this, we propose a framework for algorithmically dynamizing static round-synchronous algorithms that allows us to obtain parallel batch-dynamic algorithms with good bounds on their work and span. In our framework, the algorithm designer can apply the technique to any suitably defined static algorithm. We then obtain theoretical guarantees for algorithms in our framework by defining the notion of a computation distance between two executions of the underlying algorithm. Our dynamic trees algorithm is obtained by applying our dynamization framework to the parallel tree contraction algorithm of Miller and Reif, and then performing a novel analysis of the computation distance of this algorithm under batch updates. We show that $k$ updates can be performed in $O(k \log(1+n/k))$ work in expectation, which matches an existing algorithm of Tseng et al. while providing support for a substantially larger number of queries and applications.
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension

Natural Questions is a new challenging machine reading comprehension benchmark with two-grained answers, which are a long answer (typically a paragraph) and a short answer (one or more entities inside the long answer). Despite the effectiveness of existing methods on this benchmark, they treat these two sub-tasks individually during training while ignoring their dependencies. To address this issue, we present a novel multi-grained machine reading comprehension framework that focuses on modeling documents at their hierarchical nature, which are different levels of granularity: documents, paragraphs, sentences, and tokens. We utilize graph attention networks to obtain different levels of representations so that they can be learned simultaneously. The long and short answers can be extracted from paragraph-level representation and token-level representation, respectively. In this way, we can model the dependencies between the two-grained answers to provide evidence for each other. We jointly train the two sub-tasks, and our experiments show that our approach significantly outperforms previous systems at both long and short answer criteria.
Implicit relation-based question answering to answer simple questions over DBpedia

RDF-based question answering systems give users the capability of natural language querying over RDF data. In order to respond to natural language questions, it is necessary that the main concept of the question be interpreted correctly, and then it is mapped to RDF data. A natural language question includes entities, classes, and implicit and explicit relationships. In this article, by focusing on identification and mapping of implicit relationships in the question (in addition to the explicit relationships), the mapping step has been improved. In the proposed solution (IRQA), entities and implicit/explicit relationships are identified by means of the provided rules and then will be presented as a graph. In the next phase, according to the determined priority of graph nodes, unknown nodes and their adjacent relationships are mapped to their peers in the RDF dataset. The proposed solution is evaluated on the QALD-3 test set. The results prove that the proposed work has improved performance in mapping implicit relationships and reveals higher precision and F-measure values.
BAT.jl Upgrading the Bayesian Analysis Toolkit

In all but the simplest cases, performing data analysis based on Bayesian reasoning requires the use of advanced algorithms. The Bayesian Analysis Toolkit (BAT) provides a collection of algorithms and methods that facilitate the application of Bayesian statistics to user-defined problems of arbitrary complexity. With BAT.jl, we present a modern rewrite of BAT in the Julia programming language. Through the use of a modular software design that is capable of running parallel and distributed, and by extending the tool with new sampling and integration algorithms, BAT.jl is a high-performance framework for Bayesian inference, meeting the requirements of modern data analysis.
Object Detection with the Addition of New Classes Based on the Method of RNOL

Object detection plays an important role in many computer vision applications. Innovative object detection methods based on deep learning such as Faster R-CNN, YOLO, and SSD have achieved state-of the-art results in terms of detection accuracy. There have been few studies to date on object detection with the addition of new classes, however, though this problem is often encountered in the industry. Therefore, this issue has important research significance and practical value. On the premise that the old class samples are available, a method of reserving nodes in advance in the output layer (RNOL) was established in this study. Experiments show that RNOL can achieve high detection accuracy in both new and old classes over a short training time while outperforming the traditional fine-tuning method.
Interpreting Neural Ranking Models using Grad-CAM

Recently, applying deep neural networks in IR has become an important and timely topic. For instance, Neural Ranking Models(NRMs) have shown promising performance compared to the traditional ranking models. However, explaining the ranking results has become even more difficult with NRM due to the complex structure of neural networks. On the other hand, a great deal of research is under progress on Interpretable Machine Learning(IML), including Grad-CAM. Grad-CAM is an attribution method and it can visualize the input regions that contribute to the network's output. In this paper, we adopt Grad-CAM for interpreting the ranking results of NRM. By adopting Grad-CAM, we analyze how each query-document term pair contributes to the matching score for a given pair of query and document. The visualization results provide insights on why a certain document is relevant to the given query. Also, the results show that neural ranking model captures the subtle notion of relevance. Our interpretation method and visualization results can be used for snippet generation and user-query intent analysis.
PENGARUH SPEED READING TERHADAP KEMAMPUAN MEMBACA PEMAHAMAN SISWA MATA PELAJARAN BAHASA INDONESIA SD NEGERI 55 PAREPARE

This objective of this research was to determine the effect of speed reading on students reading comprehension on subjects Bahasa Indonesia in SD Negeri 55 Parepare. This research was Quasy Experimental Design with the form of none equivalent control group design. The sampling technique used was nonprobability with a type of purposive sampling technique and the samples was 50 students, they were the experiment group dan control group. The data collection technique used was a mupltiple choice test and documentation. Data analysis used descriptive statistical analysis techniques and inferential statistical analysis techniques. The results of this research is that speed reading can have a positive influence on improving students reading comprehension on subjects Bahasa Indonesia in class V SD Negeri 55 Parepare.
Covid-19: The US state copying a global health template for contact tracing success

Contact tracing is widely considered to be key to ending the pandemic. Tinker Ready visits the pioneering Massachusetts effort taking a leaf out of west Africa’s book
Ways to Improve the Professional Skills of Academics

The sphere of education is in urgent need of a professional academic, able to prepare students for their future profession while mastering new teaching technologies, pedagogical methods, and managerial tools. The present article focuses on the systemic changes needed in higher education. The methodological and theoretical framework of the present research included the concepts of the modern science on the dialectical nature of active cognitive activity as a basis for contextual training. The study was also based on the theoretical foundations of pedagogical education at university. Other fundamental principles included pedagogy and psychology of developing the pedagogical culture of future academics and their personality. The methods involved an analysis of documents and scientific publications on the reform of higher education and empirical experience connected with professional skills of academics. The authors believe that highly specialized professional skills are still valued on the labor market, yet they are gradually being displaced with quasi-professional skills that grant future employees a high level of adaptability, inter-sectoral mobility, and freedom of choice. A modern university has to provide its students with these skills, as well as with the ability of independent learning. As a result, a professional academic has to be able to teach students how to act in a changing environment. The required skills can be mastered by acquiring the culture of independent work based on the assimilation of social experience during practical activities. Students can obtain problem solving skills only via their own social experience. Thus, university education should provide students with experience of solving cognitive, ideological, moral, organizational, and communicative problems. Academics improve their professional skills by building up intellectual experience and professional pedagogical competence. The competence is based on knowledge, skills, life, and professional experience, strict pedagogical culture, and values.
Exploring Links Between Field Research and Information Design

Information design is the art of presenting information in a logically compatible way that enables the informationconsumers to assimilate the information by their brains. The term ‘Information design’ is broadly used for a specific area of graphic design related to information display. It is closely related to the field of data visualization and is often taught as part of graphic design courses. It simplifies complex data to cull out understandable information from it for a particular audience. The various domains of applications of information design have created some overlap in the definitions of communication design, data visualization and information architecture. Read more . . .
Pengembangan Modul Pembelajaran Kimia Berbasis Augmented Reality

The study is research and development that aims to develop augmented reality-based chemistry learning module which is valid, practical, and effective. Augmented reality-based chemistry learning module refers to the 4-D development model by Thiagarajan et. al., which includes the stages of defining, designing, developing and disseminating. This learning modules had been conducted the validation stage by the assessors or experts and gone through field trial, revised by the developer to produce learning module which is valid, effective, and practical. The results of the study reveal that: (1) the assessments of feasibility or validity of the media expert and a material expert are in the very valid category; (2) in the field trial stage to 33 students of grade XI MIA 1 at SMAN 10 Makassar obtained the mean score of 80.00 learning outcomes from an ideal score of 100 with a grade completeness of 82%; (3) the responses of students and teachers respectively with a percentage of 80.38% and 100%, so the module is stated as practical. Therefore, the conclusion of the study is the augmented reality-based chemistry learning module which had been developed using 4D model is valid, practical, and effective to be applied in learning.
Weakly Supervised Training of Hierarchical Attention Networks for Speaker Identification

Identifying multiple speakers without knowing where a speaker's voice is in a recording is a challenging task. In this paper, a hierarchical attention network is proposed to solve a weakly labelled speaker identification problem. The use of a hierarchical structure, consisting of a frame-level encoder and a segment-level encoder, aims to learn speaker related information locally and globally. Speech streams are segmented into fragments. The frame-level encoder with attention learns features and highlights the target related frames locally, and output a fragment based embedding. The segment-level encoder works with a second attention layer to emphasize the fragments probably related to target speakers. The global information is finally collected from segment-level module to predict speakers via a classifier. To evaluate the effectiveness of the proposed approach, artificial datasets based on Switchboard Cellular part1 (SWBC) and Voxceleb1 are constructed in two conditions, where speakers' voices are overlapped and not overlapped. Comparing to two baselines the obtained results show that the proposed approach can achieve better performances. Moreover, further experiments are conducted to evaluate the impact of utterance segmentation. The results show that a reasonable segmentation can slightly improve identification performances.
A Comparison of Software Defect Prediction Metrics Using Data Mining Algorithms

Data mining is an interdisciplinary field that uses methods such as machine learning, artificial intelligence, statistics, and deep learning.Classification is an important data mining technique as it is widely used by researchers.Generally, statistical methods or machine learning algorithms such as Decision Trees, Fuzzy Logic, Genetic Programming, Random Forest, Artificial Neural Networks and Logistic Regression have been used in software defect prediction in the literature.Performance measures such as Accuracy, Precision, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used to examine the performance of these classifiers.In this paper, 4 data sets entitled JM1, KC1, CM1, PC1 in the PROMISE repository, which are created within the scope of the publicly available NASA institution's Metric Data Program, are examined as in the other software defect prediction studies in the literature.These datasets include Halstead, McCabe method-level, and some other class-level metrics.Data sets are used with Wakiato Environment for Knowledge Analysis (WEKA) data mining software tool.By this tool, some classification algorithms such as Naive Bayes, SMO, K *, AdaBoost1, J48 and Random Forest were applied on NASA error datasets in PROMISE repository and their accuracy rates were compared.The best value among the accuracy rates was obtained in the Bagging algorithm in the PC1 data set with the values of %94.13.
A Review of Information Security: Issues and Techniques

Currently, companies are more into using distributed systems and relying on network and communication facilities for transmitting critical and important information that needs to be secured.Therefore, protecting companies information becomes more important, and information security is essential to maintain.Information security is defined as protecting the information, the system, and the hardware that use, store and transmit the information, to ensure integrity, confidentiality and availability of data and operation procedures are protected.In this paper, we illustrate the factors that impact information security in different fields; cyber security, Internet of Things and network security from various studies and outline the security requirements to reduce this impact.