Reviewerly/exHarmony huggingface dataset:
Pedagogical practices of the grade 6 &amp; grade 7 teachers in teaching reading on the blended learning modality

This study dealt with the pedagogical practices of the Grade 6 &amp; Grade 7 teachers of Digos City National High School and Rizal Central Elementary School from the Schools Division Office of Digos City, Davao del Sur in teaching reading on the blended learning modality. Participants of the study were purposively chosen. Open-ended interview questions related to pedagogical practices were formulated. A qualitative research design, using a phenomenological method on exploring the pedagogical practices of teachers teaching reading in English was utilized. Based on the result of the study, there are three basic components that describe the pedagogical practices of reading teachers which are pre-reading activities, during reading activities, and post-reading activities. Reading teachers used these activities as they worked to overcome the difficulties of teaching reading during the epidemic. Lack of support, lack of technology, and lack of motivation were the challenges that reading teachers encountered upon teaching reading to Grades 6 and 7. Finally, collaboration is the leading ingredient of the reading model that directs the actions of the three elements: the parents, teachers, and students. To make the model functional, these elements should collaboratively work. It is hereby recommended that in order to enhance teachers’ capacity to teach reading, continuous training may be conducted to supplement teachers’ knowledge and capacitate them in teaching reading, teacher-parent dialogue may be conducted to help parents fully understand their roles in the success of their children and the further enhancement of the reading model by conducting research relevant to the subject being studied.
Retraction: Speaking system for speechless people using Flex sensors (J. Phys.: Conf. Ser. 1916 012201)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
A NUMA-Aware Parallel Truss Decomposition Algorithm for Large Scale Graphs

Truss decomposition algorithm is to decompose a graph into a hierarchical subgraph structure. A k-truss ( $$k \ge 2$$ ) is a subgraph that each edge is in at least $$k-2$$ triangles. The existing algorithm is to first compute the number of triangles for each edge, and then iteratively increase k to peel off the edges that are not in the ( $$k+1$$ )-truss. Due to the scale of the data and the intensity of computations, truss decomposition algorithm on the billion-side graph may take more than hours on a commodity server. In addition, today, more servers adopt NUMA architecture, which also affects the scalability of the algorithm. Therefore, we propose a NUMA-aware shared-memory parallel algorithm to accelerate the truss decomposition for NUMA systems by (1) computing different levels of k-truss between each NUMA nodes (2) dividing the range of k heuristically to ensure load balance (3) optimizing data structure and triangle counting method to reduce remote memory access, data contention and data skew. Our experiments show that on real-world datasets our OpenMP implementation can accelerate truss decomposition effectively on NUMA systems.
A weakly supervised object detection method based on attention mechanism

Object detection is one of the basic problems in computer vision. Currently, the detection models based on fully supervised learning which demand fine labeled data such as bounding box annotated images are the mainstream of this research field. However, obtaining high-precision tagged images usually costs huge time and human labor. To lighten the restriction for training data of detection models, we propose an attention-based weakly supervised object detection model which can be trained only using image-level annotated images. The weakly supervised object detection model consists of two stages. In the first stage, an attention-based convolutional neural network (CNN) is designed to enhance the localization ability of CNN and generate coarse detection results. In the second stage, a neural network for edge detection is utilized to get the fine results based on the coarse results in stage one. Tested on PascalVOC 2007 and 2012, the proposed weakly supervised learning detection model achieves 53.4mAP and 48.9mAP in these two datasets, respectively, which is competitive with the state-of-the-art weakly supervised learning detection models and reduces the gap with the fully supervised learning detection models.
Preface

XVIII International Conference of Students and Young Scientists “Prospects of Fundamental Sciences Development” (PFSD 2021) April 27–30, 2021, Tomsk, Russia conf-prfn.org The XVIII International Conference of Students and Young Scientists “Prospects of Fundamental Sciences Development” (PFSD 2021), held on 27-30 April 2021 in Tomsk, Russia, is an annual multidisciplinary conference gathering together students, young researchers, post-docs, and experienced researchers working in the different research areas in order to share scientific results and ideas for a better collaboration. PFSD 2021 was dedicated to the 125th anniversary of Tomsk Polytechnic University and was held within the framework of the Year of Science and Technology in Russia. Five Tomsk universities hosted the conference. 467 young researchers applied for the participation in the conference from 67 universities and academic institutes of Russia and foreign countries. The conference presented 20 keynote speakers, 10 interactive masterclasses and competitions for participants. This year the conference was organized partially by personal attendance of participants (mainly from Tomsk city), and many foreign participants took part in conference virtually using Internet communication technologies (Zoom, Adobe Connect). The conference attendance was organized in compliance with all sanitary standards and safety requirements. The Organizing Committee was mainly located in Tomsk. List of PFSD 2021 PROGRAM COMMITTEE includes, ORGANIZING COMMITTEE are available in this pdf
Basics of Distributed Machine Learning

Extending the gradient optimization of a machine learning model, which is originally serial, to a distributed setting is nontrivial. Several fundamental techniques are involved in meeting the characteristics of distributed environments. In this chapter, we first conduct an anatomy of distributed machine learning, with which we understand the indispensable building blocks in designing distributed gradient optimization algorithms. Then, we provide an overview for each technique individually and present the state-of-the-art approaches.
Retraction: A Novel Session Password Security Technique using Textual Color and Images (J. Phys.: Conf. Ser. 1916 012176)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
The complete collection assessment manual: a holistic approach

"The complete collection assessment manual: a holistic approach." Journal of the Australian Library and Information Association, 71(1), pp. 114–115
IDENTIFYING DIFFICULTIES IN THE KNOWLEDGE TRANSFER PROCESS IN GLOBAL SOFTWARE: A QUALITATIVE ANALYSIS

Due to increasing demand for new technologies, it is common in software projects to be necessary to incorporate new team members.However, when new hires join a company, they should learn about working process, organization structure, and company corporate culture.Therefore, companies should be adopting strategies to manage and transfer knowledge to better support newcomers.Sidia R&D Institute had to hire new members to increase our Project Leader team, consequence of a massive project demand.As we work in a distributed environment, we use Wiki as an alternative tool for sharing knowledge and for helping newcomers learn about our software development process.However, newer members reported great difficulty during Wiki usage.This paper reports results of a qualitative study aimed to identify the difficulties faced by new team members during wiki usage at Sidia R&D Institute, as well as identify actions that are often adopted to mitigate these difficulties.The study reveals that despite of wiki being important to improve knowledge transfer in Sidia team, newly PLs had difficulties to access wiki information caused by usability issues and lack of adequate knowledge management.
Extension of Bat Algorithm on Standard Benchmark Functions

Meta heuristics are superior methods of finding, producing and even modifying heuristics that are able to solve various optimization problems. All Meta-heuristic algorithms are influenced by the nature. These types of algorithms tend to mimic the behaviour of biotic components in nature and are emerging as an effective way of solving global optimization algorithms. We have reviewed that no any algorithm is best for all applications due to lack of generality (no. of parameters), non-dynamic input values. So, this paper studied BAT algorithm deeply and found weakness in terms of non-dynamic pulse rate and loudness. In order to avoid being trapped into local optima these inputs are made dynamic with inclusion of levy Flight too. Performance of this proposed Modified BAT approach is evaluated using few standard benchmark functions. For justifying the superiority of Modified BAT, its performance has been compared with standard Bat algorithm too. From simulation it is found that dynamic pulse rate and dynamic loudness improve the performance of Bat algorithm in terms of results without being stuck at local optima and is more general.
Genetic Feature Construction: a parallel implementation of a genetic programming tool for feature construction

&#x0D; In this paper a parallel implementation of a recently introduced method for feature construction is described. This technique utilizes parallel genetic algorithms along with RBF neural networks to create new features from the original ones by discovering the hidden relations between patterns. The method is tested on series of classification problems from a variety of areas and the results are reported. The accompanied software is written entirely in ANSI C++ using the well established MPI library for parallelization.&#x0D;
Introduction

Machine learning (ML) has shown its ability to extract buried knowledge from data that is hard for humans to understand. To obtain a satisfying machine learning model, we often use some optimization algorithms to train the model to converged. Among these optimization algorithms, a class of gradient-based optimization algorithms is widely used due to their simple formulations and convergence properties. Since the big data era arrived following the prosperity of data-intensive applications, a new challenge arises when data volume is beyond the capability of one physical machine. To address this scalability problem, distributed machine learning techniques are proposed to train the models with several physical machines. In particular, designing gradient optimization algorithms has become a hot topic in distributed machine learning. In this chapter, we introduce the background of this book, following which we describe the concepts of distributed machine learning and gradient optimization. We finally discuss the challenges and open problems arising recently.
Retraction: Predicting Supervised Machine Learning Performances for Sentiment Analysis Using Contextual Based Approaches (J. Phys.: Conf. Ser. 1916 012117)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Retraction: Security Aware High Scalable paradigm for Data Deduplication in Big Data cloud computing Environments (J. Phys.: Conf. Ser. 1916 012097)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Retraction: A Multifunctional RFID enabled Shopping Tramcar (J. Phys.: Conf. Ser. 1916 012071)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Addition of Criteria for the Drought Level Vulnerability to the Reservoir Location Priority

Blora regency is mostly a water critical region during dry seasons due to its porous soil characteristic. In order to overcome this condition, in 2006, Indonesian Ministry of Public Works and Housing, through Mettana Engineering Consultant, conducted a Study of Identification and Basic Design Development of Reservoirs in Blora Regency. It determined 28 future reservoir locations by considering their technical, economical and socio-environmental aspects. However, for the reservoirs to be the most effective, it is necessary to include regional drought vulnerability aspect into consideration to determine their location priority. The main purpose of this research, to conduct an analysis of meteorogical drought vulnerability in Blora regency region.In more detail the main goal is to determine reservoir location priority in Blora regency region by considering meteorogical drought vulnerability aspect. Drought Index was calculated using SPI (Standardized Precipitation Index ) method using 2009-2018 monthly precipitation data obtained from 15 precipation stations. Mapping of drought vulnerable regions was done using GIS (Geographic Information System) technology using spatial interpolation approach. This research determined new priority, different from that determined by Mettana Engineering Consultant. Priority 1 formerly consisted of Suruhan, Kedungwungu, Bangsri, Sambong and Kalisari reservoirs while the new one consisted of Polaman 1, Polaman 2, Suruhan and Jurangjero reservoirs. Suruhan reservoir appeared in both versions. Kedungwungu, Bangsri, Sambong and Kalisari reservoirs moved to priority 2 in the new priority since they were located in low drought level regions while Polaman 1, Polaman 2 and Jurangjero reservoirs moved to priority 1 since they were located in moderately high to moderate drought level regions. It indicated that is necessary to include drought vulnerability aspect into consideration in determining reservoir location, along with technical, economical and socio-enviromental aspects. The government then may employ this aspect as an additional criteria in developing future reservoir construction program to be more effective that targets drought vulnerable regions
Weather Determination Prediction Using Expert Fuzzy Logic Mamdani Method

The current climate and weather patterns are very extreme. This kind of weather condition can harm many people. In recent years, heavy rains have resulted in flooding. So far, computers can be used to help people solve problems. The smarter the system and the higher the level of information handling, the more active the role played by the Weather computer is the condition of the air at a certain time and in a certain area that is relatively narrow and in a short period of time. The weather is formed from a combination of weather elements and the weather period can only be a few hours. For example morning, afternoon, or evening, and the situation can be different for each place and every hour. The purpose of this research is to help predict the weather as information. This research uses the Mamdani method. This Mamdani method uses 4 stages to produce an output value, namely, determining the input value (fuzzification), determining the value of x (implication function application), Combination of Rules (Rules), and finally determining the final value or (Defuzzification). This research produces an output value of 35 which is located in the Panas range.
Neural Network based End-to-End Query by Example Spoken Term Detection

This paper focuses on the problem of query by example spoken term detection (QbE-STD) in zero-resource scenario. State-of-the-art approaches primarily rely on dynamic time warping (DTW) based template matching techniques using phone posterior or bottleneck features extracted from a deep neural network (DNN). We use both monolingual and multilingual bottleneck features, and show that multilingual features perform increasingly better with more training languages. Previously, it has been shown that the DTW based matching can be replaced with a CNN based matching while using posterior features. Here, we show that the CNN based matching outperforms DTW based matching using bottleneck features as well. In this case, the feature extraction and pattern matching stages of our QbE-STD system are optimized independently of each other. We propose to integrate these two stages in a fully neural network based end-to-end learning framework to enable joint optimization of those two stages simultaneously. The proposed approaches are evaluated on two challenging multilingual datasets: Spoken Web Search 2013 and Query by Example Search on Speech Task 2014, demonstrating in each case significant improvements.
Performance Analysis of the Libra Blockchain: An Experimental Study

Since Bitcoin was first introduced in 2008, many types of cryptocurrencies have been proposed based on blockchain. However, the performance of permissionless blockchains restricts the widespread of cryptocurrency. Recently, Libra was proposed by Facebook based on a permissioned blockchain, i.e. the Libra blockchain. The vision of Libra is to become a global currency supporting financial applications, but it is doubted whether the performance of the Libra blockchain is able to support frequent micropayment scenarios. In this paper, we propose a methodology to evaluate the performance of blockchain platforms and conducted an experimental study on the Libra blockchain. The results show that the Libra blockchain can only process about one thousand transactions per second at most, and the performance drops significantly as the number of validators increases. Although it outperforms permissionless blockchain platforms, the performance of the Libra blockchain is still unsatisfactory compared to other permissioned blockchains like Hyperledger Fabric and needs to make effective improvements in order to support global micropayment in the future.
Neural data-to-text generation: A comparison between pipeline and  end-to-end architectures

Traditionally, most data-to-text applications have been designed using a modular pipeline architecture, in which non-linguistic input data is converted into natural language through several intermediate transformations. In contrast, recent neural models for data-to-text generation have been proposed as end-to-end approaches, where the non-linguistic input is rendered in natural language with much less explicit intermediate representations in-between. This study introduces a systematic comparison between neural pipeline and end-to-end data-to-text approaches for the generation of text from RDF triples. Both architectures were implemented making use of state-of-the art deep learning methods as the encoder-decoder Gated-Recurrent Units (GRU) and Transformer. Automatic and human evaluations together with a qualitative analysis suggest that having explicit intermediate steps in the generation process results in better texts than the ones generated by end-to-end approaches. Moreover, the pipeline models generalize better to unseen inputs. Data and code are publicly available.