Reviewerly/exHarmony huggingface dataset:
Novel energy-aware approach to resource allocation in cloud computing

In this paper, we address the issue of resource allocation in a Cloud Computing environment. Since the need for cloud resources has led to the rapid growth of data centers and the waste of idle resources, high-power consumption has emerged. Therefore, we develop an approach that reduces energy consumption. Decision-making for adequate tasks and virtual machines (VMs) with their consolidation minimizes this latter. The aim of the proposed approach is energy efficiency. It consists of two processes; the first one allows the mapping of user tasks to VMs. Whereas, the second process consists of mapping virtual machines to the best location (physical machines). This paper focuses on this latter to develop a model by using a deep neural network and the ELECTRE methods supported by the K-nearest neighbor classifier. The experiments show that our model can produce promising results compared to other works of literature. This model also presents good scalability to improve the learning, allowing, thus, to achieve our objectives.
Machine Learning for Learning Analytics for Meta-cognitive Support

The motivation for this chapter is to show the evolutionary nature from earlier student tracking and 'intelligent tutoring systems' until the current stages of advanced learning analytics. This chapter's added value is its illustration of how learning analytics contributes to learners’ meta-cognitive awareness and how indirectly a teachers’ 'dashboard' allows teachers, parents, and remedial teachers to trace the more subtle cognitive entailments in the student’s mind. The sources for the chosen direction rest upon earlier research into media-support for conceptual learning. This chapter may elicit the discussion on how far technology-driven innovations can bypass the existing repertoire of earlier didactic solutions.
Implementation Jenkins Automation Deployment with Scheduler and Notification

Speed and customer satisfaction are the keys to the success of information technology companies engaged in software. Software development companies currently have several stages of the process, namely analysis, development, testing and deployment. The obstacle that is currently being found is the distribution of the website results that have been created because currently it is still manual and has not used Continuous Integration (CI). The problem that occurs is the time it takes to deploy for developers and sometimes forget to execute. Using CI greatly simplifies deployment and even manages to schedule deployments as desired. One of the popular CI software is Jenkins. With the many plugins Jenkins has, it is possible to build a feature-rich CI system. Based on the results of analysis and testing conducted by the company, the company is satisfied with using Jenkins because it helps speed up and simplify the process of developing Website software there.
Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary

Recent advancements in scientific equipment and the adaptation of electronics and the Internet of Things (IoT) in our everyday lives resulted in large and complex data production at a high rate. Making meaningful and timely knowledge discovery at a modest cost from this big data is difficult for computing power and storage limitations. Training deep learning models incrementally in a streaming setting can help us with overcoming these limitations. However, in a well-known phenomenon named catastrophic forgetting, incrementally trained models increasingly perform poorly on the past data. To mitigate catastrophic forgetting in training in a streaming setting, we propose constructing a historical summary over time and use the summary with newly arrived data during incremental training. We propose various data summarization techniques such as random sampling, micro clustering, coreset computation, and Auto Encoders to counteract catastrophic forgetting. We built a pipeline for incremental training with a historical summary for training deep learning models for streaming data. We demonstrate the effectiveness of historical summary in mitigating catastrophic forgetting using three case studies involving three different deep learning applications: an Artificial Neural Network (ANN) for classification task on MNIST dataset, a language model (RNN-LM) on the WikiText2 dataset, and a Convolutional Neural Network (CNN), ResNet50 to classify the ImageNet dataset. Through the training of the models, we observe that catastrophic forgetting is evident in ANN and CNN but not in an RNN. For the first task, our method recovers up to 47.9% lost accuracy due to catastrophic forgetting. For the third task, the historical summary recovers classification accuracy by up to 25%. For the second task, though there is not proof of catastrophic forgetting, the training performance (PPL) improves by up to 26% with historical summary.
Prediction of Missing Frequency Response Functions Through Deep Image Prior

Vibration analysis is crucial when designing and monitoring resonant structures. The characterization of vibrational properties in mechanical systems, e.g. machinery or musical instruments, can indeed detect noise sources and damages. Several methods can retrieve these parameters starting from a set of measurements. The level of detail in the estimate mostly depends on the amount and distribution of points acquired over space. A potential issue for these techniques consists in the presence of regions over the object where sensors cannot be attached. In this case, an interpolation scheme with a suitable prior of the data model should be devised. We propose here to predict the missing vibrational data within the framework of image inpainting and apply a fully data-driven method based on Deep Image Prior, which allows to capture the prior inside data without the need of a dataset. The performance is assessed in the case of violin top plates. The proposed method proved to better predict data, in particular resonances, for points close to the boundary, whereas a baseline based on Thin Plate Splines fails, due to the reduced number of available samples.
Keyword spotting in historical document collections withoutsegmentation using the Siamese Network

Keyword spotting is the method of estimating whether the text query occurs in the document or not. The query- by-example model is used in this paper to present an efficient segmentation-free keyword spotting approach that can be applied in historical document collections. For image de-noising and binarization, we use an autoencoder network in our approach. We are using a patch-based system to create patches for the binarized image, followed by a Siamese network. To determine the degree of similarity between two input word images, a Siamese network employs two identical convolutional networks. Once trained, the network can detect not only words from different writing styles and contexts, but also words that are not in the training set. The method proposed is evaluated on the Bengali Handwritten dataset.
Learning From Other Labels: Leveraging Enhanced Mixup and Transfer Learning for Twitter Sentiment Analysis

Twitter sentiment analysis has received interest in both industry and academia recently. It strives to analyze people’s emotions through tweets, which is an important part of natural language processing. However, it faces some special challenges such as short texts of tweets, informal expressions, and a lack of precisely labeled data. In this paper, we present a novel data augmentation method called Enhanced Mixup, which is able to generate additional labeled data by using the current instance and other instances of the original training data. Furthermore, we present a simple convolutional neural network that leverages Enhanced Mixup and Transfer Learning (termed as EMTCNN) for Twitter sentiment analysis. We also conducted a series of ablation experiments to verify the effectiveness of our proposed techniques. Finally, we evaluate our model on five standard public Twitter datasets. The results demonstrate that EMTCNN can reach new state-of-the-art in all of them without using any external lexicons or feature engineering.
ArrivieW2: an Android Application for General-Purpose Information Presentation Using Google Map API, Location and Azimuth Sensor

This paper describes an Android application for general-purpose information presentation. A researcher at Waseda University identified where all the 766 photos taken at the beginning of excavation in Tonozuka and Himezuka ancient tombs in Shibayama Town in 1956. In our laboratory, AR-TonoHime was developed that took advantage of its feature. It is an Android application by using an AR (augmented reality) technology that presents the photos, descriptions, and reading voices at the time of excavation at the exact location where the photos were taken. Later, by using location and azimuth data, the system was expanded to GPS-TonoHime which does not need markers. Although GPS-TonoHime directly embeds content such as photos and data such as location information in the application, we developed ArrivieW which can deal with some contents data on outside of the application. In this study, we present ArrivieW version 2 which uses Google Map API and sensor data of location and azimuth. It can show current location almost correctly. We confirmed that the application successfully presents photos, descriptive text, and read-aloud voice when the user visits the site and turns in a certain direction.
Examination of Feedback That Elementary School Teachers Given On Students' Mathematics Homework

This quantitative study ascertained the benefits of project VLOGI as an intervention to the eighth-grade students in resolving the usual unattained learning competencies in Probability and Statistics, especially in the last quarter of the school year. The researcher utilized the true-experimental research design using vlogs as an intervention to this study. There were two (2) randomly selected classes out of the six (6) heterogeneous classes in school as (1) the control group (prevailing method) and (2) the experimental group (project VLOGI) in teaching. Respondents underwent pre-test and post-test utilizing the quality assured 20-item multiple-choice type of questionnaires, reviewed and verified by an expert panel of evaluators. The researcher used descriptive and inferential analyses using the SPSS 2.0 tool to analyze and interpret the outcomes. The study's significant results showed that learners improved their academic performance in Probability and Statistics using these vlogs. Likewise, learners gained knowledge while working on their projects to create vlogs. Furthermore, by capturing learners' attention and engaging them in their learning through various social media platforms, project VLOGI was served as an alternative teaching method in any discipline. Hence, project VLOGI was strongly recommended for teachers as a replacement when they are out of class due to ancillary functions in school to foster unattained learning competencies before the school year ends.
Completing WordNets with Sememe Knowledge

WordNets organize words into synonymous word sets, and the connections between words present the semantic relationships between them, which have become an indispensable source for natural language processing (NLP) tasks. With the development and evolution of languages, WordNets need to be constantly updated manually. To address the problem of inadequate word semantic knowledge of “new words”, this study explores a novel method to automatically update the WordNet knowledge base by incorporating word-embedding techniques with sememe knowledge from HowNet. The model first characterizes the relationships among words and sememes with a graph structure and jointly learns the embedding vectors of words and sememes; finally, it synthesizes word similarities to predict concepts (synonym sets) of new words. To examine the performance of the proposed model, a new dataset connected to sememe knowledge and WordNet is constructed. Experimental results show that the proposed model outperforms the existing baseline models.
Financial Risk Assessment Model Based on Stochastic Forest and Decision Tree Hybrid Algorithm

The random forest model is a classification model whose underlying meta-classifier is the decision tree of the CART algorithm. The random forest model adopts sampling and putting back, that is, each training set is constructed based on the Bagging method and a new training set is randomly selected without additional pruning. In this paper, a financial risk assessment model based on the random forest is proposed. Firstly, six types of variables are set according to the characteristics of samples, and the outliers are eliminated by using the box graph model. After processing, the samples are modeled and tested by the cross verification method. The model adopts 600 decision trees, and the split node is 4. After calculation, the model precision is 82.27%, the recall rate is 83.55%, and the F1 value is 0.8196, which has good prediction accuracy. By comparing the accuracy of the random forest model with KNN and SVM models, it was found that the accuracy of the random forest model was 9.56% and 3.36% higher than the other two models respectively, which was obviously due to KNN and SVM in the accuracy of processing high-dimension big data samples.
Information and Communication Digital Environment to Strengthen Systematization of Engineers' Competencies Development

The information and communication digital environment is widely introduced in education today, and it often covers the educational discipline content considering its presentation possibilities. The use of the environment as structured content storage does not fully contribute to engineers&#x0027; competencies development. We believe the environment can offer such competitive advantages as the following qualities: adaptive, interdisciplinary, informative, and communicative. These qualities help develop a system of universal and professional engineers&#x0027; competencies. The adaptive environment enables personalized learning. The interdisciplinarity promotes competencies mastering using synthesized material of humanitarian and technical disciplines. Finally, the information and communication environment allows collected data processing and captures personalized interdisciplinary interaction between teachers and students. This paper presents the structure of adaptive interdisciplinary information and communication digital environment. The pedagogical experiment proved its effectiveness in engineers&#x0027; systematic competencies development.
Efficient and Large Scale Pre-training Techniques for Japanese Natural Language Processing

Pre-training in natural language processing greatly affects the accuracy of downstream tasks. However, pre-training is a bottleneck in the AI system development process because it takes a long time to train the neural network model by using large-scale input data. Our purpose in this paper is to obtain a highly accurate pre-training model in a short time using a large-scale computation environment. Since reducing the time per iteration is difficult even if using large number of computation nodes, it is necessary to reduce the number of iterations. Therefore, we focus on the learning efficiency per iteration and choice a dense Masked Language Model (MLM) task of pretraining in order to utilize a significant power of large-scale cluster. We implemented BERT-xlarge using the dense MLM on Megatron-LM and evaluated the improvement of learning time and learning efficiency for a Japanese language dataset using 768 GPUs on AI Bridging Cloud Infrastructure (ABCI). Our BERT-xlarge improves the learning efficiency per iteration 10 times and completes pre-training in 4.65 hours. This pre-training takes 4.9 months if we use a single GPU. We also evaluated two fine-tunings, JSNLI and Twitter evaluation analysis, to compare the accuracy of downstream tasks with our BERTs and other BERTs. As a result, our BERT-3.9b achieved 94.30% accuracy of JSNLI, and our BERT-xlarge achieved 90.63% accuracy of Twitter evaluation analysis.
‘Give the Money Where it’s Due’: The Impact of Knowledge-Sharing via Social Media on the Reproduction of the Academic Labourer

This paper addresses the impact that the ever-growing involvement of scientists in knowledge-sharing practices by means of social media might have on the material conditions under which academic labour-power reproduces itself. Science communication is advocated for on the basis of its supposedly altruistic character, so scientists are prevented from expecting any form of remuneration ensuing from it. However, as our Marxian-informed analysis of the determinations of the value of the commodity labour-power will elucidate, this novel duty involves the exertion of an additional quantity of work by academics, owing either to the relative complexation of the productive attributes requisite to use social media efficiently, or, particularly, to the accelerated pace at which scientists’ working capacity becomes productively consumed, both in absolute (extension of working hours) and in relative (work intensification) terms. It will be demonstrated that, as science communication via social media brings about an increase in the value of scientific labour power, wages must be raised so as to preserve science labourers in their normal condition. The article will further throw into relief that the political action of scientists themselves stands as the more general mediation for this innermost necessity of the capitalistic relationship of production to assert itself.
A Modified Markov-Based Maximum-Entropy Model for POS Tagging of Odia Text

POS (Parts of Speech) tagging, a vital step in diverse Natural Language Processing (NLP) tasks has not drawn much attention in case of Odia a computationally under-developed language. The proposed hybrid method suggests a robust POS tagger for Odia. Observing the rich morphology of the language and unavailability of sufficient annotated text corpus a combination of machine learning and linguistic rules is adopted in the building of the tagger. The tagger is trained on tagged text corpus from the domain of tourism and is capable of obtaining a perceptible improvement in the result. Also an appreciable performance is observed for news articles texts of varied domains. The performance of proposed algorithm experimenting on Odia language shows its manifestation in dominating over existing methods like rule based, hidden Markov model (HMM), maximum entropy (ME) and conditional random field (CRF).
Construction of MDS Matrices Based on the Primitive Elements of the Finite Field

With the advent of the information age, the protected objects and application environments vary greatly. Using the same standard algorithm with fixed logic is difficult to meet the increasingly diversified cryptographic application requirements of cloud computing, Internet of things and 5g network environment. Dynamic variable cryptographic algorithm can improve the security and adaptability of cryptographic algorithm, which is the focus of cryptographic algorithm research and design at present. The key to the research and design of dynamic variable cryptographic algorithms is to find dynamic variable cryptographic components with good cryptographic performance. MDS (Maximum Distance Separable) is often used in the diffusion layer design of block ciphers because of its optimal diffusion characteristics. Traditional MDS transformation generally adopts MDS matrices in the finite field. While using them in dynamic variable cryptographic algorithms, it is difficult to realize parametric application because of their logical differences and implementation imbalance. Aiming at the lightweight MDS variable design requirements of dynamic variable cryptographic algorithm, this paper first proposes a MDS parametric construction method based on block cyclic shift and XOR operation. Compared with the traditional MDS construction in the finite field, we obtain a class of MDS component clusters with larger scale which is convenient for software and hardware implementation. The main design idea is to convert the multiplication operation in the finite field into cyclic shift operation, and introduce parameters to participate in the MDS operation in the way of direct XOR. Based on the MDS matrix in AES diffusion layer, two kinds of variable MDS diffusion layers are given by this method. We adopt the consistent structure based on cyclic shift and XOR operation, and each parameter corresponds to a different MDS transformation. The free transformation of operations in different finite fields of traditional MDS matrix can be realized by adjusting the parameters, which can be used for the parametric design of diffusion layer of dynamic variable cryptographic algorithm.
Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks

18F-FDG positron emission tomography (PET) imaging of brain glucose use and amyloid accumulation is a research criteria for Alzheimer's disease (AD) diagnosis. Several PET studies have shown widespread metabolic deficits in the frontal cortex for AD patients. Therefore, studying frontal cortex changes is of great importance for AD research. This paper aims to segment frontal cortex from brain PET imaging using deep neural networks. The learning framework called Frontal cortex Segmentation model of brain PET imaging (FSPET) is proposed to tackle this problem. It combines the anatomical prior to frontal cortex into the segmentation model, which is based on conditional generative adversarial network and convolutional auto-encoder. The FSPET method is evaluated on a dataset of 30 brain PET imaging with ground truth annotated by a radiologist. Results that outperform other baselines demonstrate the effectiveness of the FSPET framework.
Named Entity Recognition of traditional architectural text based on BERT

Traditional architecture is an important component carrier of traditional culture. Through deep learning models, relevant entities can be automatically extracted from unstructured texts to provide data support for the protection and inheritance of traditional architecture. However, research on text information extraction oriented to this field has not been effectively carried out. In this paper, a data set of nearly 50,000 words in this field is collected, sorted out, and annotated, five types of entity labels are defined, annotation specifications are clarified, and a method of Named Entity Recognition based on pre-training model is proposed. BERT (Bidirectional Encoder Representations from Transformers) pre-training model is used to capture dynamic word vector information, Bi-directional Long Short-Term Memory (BiLSTM) module is used to capture bidirectional contextual information with positive and reverse sequences. Finally, classification mapping between labels is completed by the Conditional Random Field (CRF) module. The experiment shows that compared with other models, the BERT-BiLSTM-CRF model proposed in this experiment has a better recognition effect in this field, with F1 reaching 95.45%.