Reviewerly/exHarmony huggingface dataset:
Imbalanced Seismic Event Discrimination Using Supervised Machine Learning

The discrimination between earthquakes and artificial explosions is a significant issue in seismic analysis to efficiently prevent and respond to seismic events. However, the discrimination of seismic events is challenging due to the low incidence rate. Moreover, the similarity between earthquakes and artificial explosions with a local magnitude derives a nonlinear data distribution. To improve the discrimination accuracy, this paper proposes machine-learning-based seismic discrimination methods—support vector machine, naive Bayes, and logistic regression. Furthermore, to overcome the nonlinear separation problem, the kernel functions and regularized logistic regression are applied to design seismic classifiers. To efficiently design the classifier, P- and S-wave amplitude ratios on the time domain and spectral ratios on the frequency domain, which is converted by fast Fourier transform and short-time Fourier transform are selected as feature vectors. Furthermore, an adaptive synthetic sampling algorithm is adopted to enhance the classifier performance against the seismic data imbalance issue caused by the non-equivalent number of occurrences. The comparisons among classifiers are evaluated by the binary classification performance analysis methods.
Memory Efficient Federated Recommendation Model

Federated Learning (FL) is rapidly gaining traction in the field of Machine Learning (ML) to train models collaboratively in a decentralized fashion due to increasing privacy concerns. One domain where FL shows promising improvements over centralized ML solutions is in developing Recommendation Systems (RS), as they can learn faster to provide better recommendations to new users and for new items without taking data out of user devices. Training FL based RS on edge devices at large-scale comes with its own challenges where the numbers of users and items are indefinitely large. This paper introduces a novel model framework for FL based RS which employs a fixed size encoding scheme for items and users, which is independent of number of items/users, therefore memory efficient and well suited for RS in large-scale applications. The proposed framework is explored through a use-case of Next App Recommendation, which is shown to have comparable recommendation performance with much lower memory consumption compared with SOTA solutions.
A Multi-Attribute Approach for Cyber Threat Intelligence Product and Services Selection

Cyber Threat Intelligence (CTI) is a significant field in Cyber Security research.It enables organizations to share threat data and allow a proactive defence against sophisticated intrusion attempts.The wide variety in the CTI products and services offered by different providers from the market, makes it difficult for the security experts to decide which CTI provider is the most suitable according to their security program requirements.CTI products and services provider selection is a complex decision-making problem that involves multiple criteria.The aim of the present paper is to propose a multiattribute approach based on the VIKOR method for CTI providers ranking and selection, according to a set of criteria.A case study based on the users' evaluations reviews about the security threats intelligence providers is studied.The impact of the VIKOR user parameter variation on the CTI providers ranking is analysed.The proposed approach is a support tool for the security program leaders faced with the decision of selecting the CTI providers.It also helps the CTI service providers to improve the quality of their products and services.
Current State and Future Directions of Technologies for Music Instrument Pedagogy

Technological advances over the past 50 years or so have resulted in the development of a succession of hardware and software systems intended to improve the quality and effectiveness of Western music instrument pedagogy during classroom instruction or individual study. These systems have aimed to provide evaluation or visualization of single or combined technical aspects by analyzing performance data collected in real time or offline. The number of such educational technologies shows an ever-increasing trend over time, aided by the wide diffusion and availability of mobile devices. However, we believe there are unrealized opportunities for modern technologies to help music students in their technical development and assist them during their practice sessions in between visits to their teachers. The ubiquity of PCs and mobile devices with built-in microphones, speakers, and cameras has inspired the development of media technologies in support of music pedagogy. They offer an attractive potential for implementing audio signal processing algorithms addressing different technical skills of the performer, providing real-time feedback, collecting data over time, and applying statistical models. Despite this potential, most available software for music instrument pedagogy remains very limited in functionality. This study provides a survey of music edTech software available, together with the methods of use, addressed technical skills, commonalities, and limitations. Results show that most current software is based on the metronome and tuner, with only a few systems that have limited abilities to follow a performance in real-time and compare it to a given score to monitor correctness of notes, intonation, and rhythm. The survey also highlights a high and under-exploited potential regarding the monitoring of other more specific technical skills, which are more instrument-dependent, but no less important, such as the control of dynamic range and clarity of the attack. This article ends with a discussion of possible directions for future development of technologies to support the practice of music students at different levels, with some consideration for the corresponding signal processing methods that can be utilized or that need advancement. By helping students to more efficiently achieve a high level of proficiency of their instruments with assistive technologies, we hope to minimize stress and afford better enjoyment of the music performance experience for all.
Contextual Mixing Feature Unet for Multi-Organ Nuclei Segmentation

Nuclei segmentation is fundamental and crucial for analyzing histopathological images. Generally, a pathological image contains tens of thousands of nuclei, and there exists clustered nuclei, so it is difficult to separate each nucleus accurately. Challenges against blur boundaries, inconsistent staining, and overlapping regions have adverse effects on segmentation performance. Besides, nuclei from various organs appear quite different in shape and size, which may lead to the problems of over-segmentation and under-segmentation. In order to capture each nucleus on different organs precisely, characteristics about both nuclei and boundaries are of equal importance. Thus, in this article, we propose a contextual mixing feature Unet (CMF-Unet), which utilizes two parallel branches, nuclei segmentation branch and boundary extraction branch, and mixes complementary feature maps from two branches to obtain rich and integrated contextual features. To ensure good segmentation performance, a multiscale kernel weighted module (MKWM) and a dense mixing feature module (DMFM) are designed. MKWM, used in both nuclei segmentation branch and boundary extraction branch, contains a multiscale kernel block to fully exploit characteristics of images and a weight block to assign more weights on important areas, so that the network can extract discriminative information efficiently. To fuse more beneficial information and get integrated feature maps, the DMFM mixes the feature maps produced by the MKWM from two branches to gather both nuclei information and boundary information and links the feature maps in a densely connected way. Because the feature maps produced by the MKWM and DMFM are both sent into the decoder part, segmentation performance can be enhanced effectively. We test the proposed method on the multi-organ nuclei segmentation (MoNuSeg) dataset. Experiments show that the proposed method not only performs well on nuclei segmentation but also has good generalization ability on different organs.
PHP Framework for Web Application Development

PHP is one of the widely used language for web development.However, due to the unstructured development model, the code of data access, process of business logic and web presentations were mixed.It confuses and causes problems for the web developers.On contrary, PHP framework provide basic structure for building web applications thereby helping developer in promoting rapid application development (RAD).It helps to save time, reduce repetitive code and the application can be built with more stability.This is due to the fact that the framework uses the concept called Model View Controller (MVC) to overcome the issue of plain PHP.The paper presents basic concepts of PHP frameworks and also the overview of top 5 PHP framework based on the usage stats.
Multiple Context Learning Networks for Visual Question Answering

A novel Multiple Context Learning Network (MCLN) is proposed to model multiple contexts for visual question answering (VQA), aiming to learn comprehensive contexts. Three kinds of contexts are discussed and the corresponding three context learning modules are proposed based on a uniform context learning strategy. Specifically, the proposed context learning modules are visual context learning module (VCL), textual context learning module (TCL), and visual-textual context learning module (VTCL). The VCL and TCL, respectively, learn the context of objects in an image and the context of words in a question, allowing object and word features to own intra-modal context information. The VTCL is performed on the concatenated visual-textual features that endows the output features with synergic visual-textual context information. These modules work together to form a multiple context learning layer (MCL) and MCL can be stacked in depth for deep context learning. Furthermore, a contextualized text encoder based on the pretrained BERT is introduced and fine-tuned, which enhances the textual context learning at the feature extraction stage of text. The approach is evaluated by using two benchmark datasets: VQA v2.0 dataset and GQA dataset. The MCLN achieves 71.05% and 71.48% overall accuracy on the test-dev and test-std sets of VQA v2.0, respectively. And an accuracy of 57.0% is gained by the MCLN on the test-standard split of GQA dataset. The MCLN outperforms the previous state-of-the-art models and the extensive ablation studies examine the effectiveness of the proposed method.
Antisocial Behavior Monitoring Services of Indonesian Public Twitter Using Machine Learning

Antisocial behavior is a personality disorder that has characteristics such as repetitive actions that violate social norms, deceit and lying, impulsiveness, irritability and aggression, reckless disregard for the safety of oneself and others, consistently irresponsible, and lack of remorse. The cause can be from various factors, including genetics, psychological conditions, interactions in the environment, and wrong parenting. The impact of antisocial behavior on social life can cause people to tend to be aggressive and take it into action by not having feelings of guilt for their actions. Thus, a monitoring of antisocial behavior disorders is needed so that it can be a warning for the public to be more concerned about the difficulties experienced by each other. The potential gained from the availability of tweet data access from the Twitter API opens up opportunities for monitoring antisocial behavior. By utilizing traditional machine learning and deep learning methods, it can be an opportunity to automate labeling on Twitter data that contains elements of antisocial behavior. Based on the description of the problems and opportunities found, this study proposes a multi-class classification monitoring service to identify public antisocial behavior on Twitter Indonesia using machine learning.
Exploring the Effect of Science Teachers’ Age Group on Technological Knowledge, Technological Content and Pedagogical Knowledge in Augmented Reality

This study describes the effect of science teachers’ cognizance of technological knowledge, technological content and pedagogical knowledge towards augmented reality across five age groups. This pilot study involved 44 science teachers from Penang, Malaysia using a 5-point Likert scale instrument. The administered survey instruments consisted of 18 questions on technological knowledge; technological content knowledge; and technological pedagogical knowledge. The analyses showed that generally teachers had good technological knowledge but their technological content and pedagogical knowledge seemed to be less. The results also showed that two items had significant differences using the Kruskal Wallis non-parametric test. The findings would be used as a tool to revise the servicing teachers’ technological practices using augmented reality incorporated in their daily teaching and learning sessions to obtain better learning outcomes. The research findings could also be used for further research exploring other variables affecting teachers’ technological knowledge. The intervention may be used in teachers’ training curriculum and continuing professional development in terms of determining their level of technological content and pedagogical knowledge that would increase the interest of students in learning and exploring science using Augmented Reality.
Sentiment Analysis using Improved Novel Convolutional Neural Network (SNCNN)

Sentiment Analysis is an important method in which many researchers are working on the automated approach for extraction and analysis of huge volumes of user achieved data, which are accessible on social networking websites. This approach helps in analyzing the direct falls under the domain of SA. SA comprises the vast field of effective classification of user-initiated text under defined polarities. The proposed work includes four major steps for solving these issues: the first step is preprocessing which holds tokenization, stop word removal, stemming, cleaning up of unwanted text information like removing of Ads from Web pages, Text normalization for converting binary format. Secondly, the Feature extraction is based on the Bag words, Word2Vec and TF-ID which is a Term Frequency-Inverse Document Frequency. Thirdly, this feature selection includes the procedure for examining semantic gaps along with source features using teaching models and this involves target task characteristic application for Improved Novel Convolutional Neural Network (INCNN). The Feature Selection accompanies the procedure of Information Gain (IG) and PCC which is a Pearson Correlation Coefficient. Finally, the classification step INCNN gives out sentiment posts and responses for the user-based post aspects which helps in enhancing the system performance. The experimental outcome proposes the INCNN algorithm and provides higher performance rather than the existing approach. The proposed INCNN classifier results in highest accuracy.
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices

The prediction accuracy of deep neural networks (DNNs) after deployment at the edge can suffer with time due to shifts in the distribution of the new data. To improve robustness of DNNs, they must be able to update themselves. However, DNN adaptation at the edge is challenging due to lack of resources. Recently, lightweight prediction-time unsupervised DNN adaptation techniques have been introduced that improve prediction accuracy of the models for noisy data by re-tuning the batch normalization parameters. This paper performs a comprehensive measurement study of such techniques to quantify their performance and energy on various edge devices as well as find bottlenecks and propose optimization opportunities.
Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction

We propose GLassoformer, a novel and efficient transformer architecture leveraging group Lasso regularization to reduce the number of queries of the standard self-attention mechanism. Due to the sparsified queries, GLassoformer is more computationally efficient than the standard transformers. On the power grid post-fault voltage prediction task, GLasso-former shows remarkably better prediction than many existing benchmark algorithms in terms of accuracy and stability.
TANet: Thread-Aware Pretraining for Abstractive Conversational Summarization

Although pre-trained language models (PLMs) have achieved great success and become a milestone in NLP, abstractive conversational summarization remains a challenging but less studied task.The difficulty lies in two aspects.One is the lack of large-scale conversational summary data.Another is that applying the existing pre-trained models to this task is tricky because of the structural dependence within the conversation and its informal expression, etc.In this work, we first build a large-scale (11M) pretraining dataset called RCSUM, based on the multi-person discussions in the Reddit community.We then present TANET, a thread-aware Transformerbased network.Unlike the existing pre-trained models that treat a conversation as a sequence of sentences, we argue that the inherent contextual dependency among the utterances plays an essential role in understanding the entire conversation and thus propose two new techniques to incorporate the structural information into our model.The first is thread-aware attention which is computed by taking into account the contextual dependency within utterances.Second, we apply thread prediction loss to predict the relations between utterances.We evaluate our model on four datasets of real conversations, covering types of meeting transcripts, customer-service records, and forum threads.Experimental results demonstrate that TANET achieves a new state-of-the-art in terms of both automatic evaluation and human judgment.
Gerlach and Ely’s Learning Model: How to Implement It to Online Learning for Statistics Course

Statistics is a compulsory subject that is very useful for students, so, in online learning, lecturers need to determine a suitable learning model. Lecturers can apply specific learning models that are adapted to the situation. The existence of characters in Gerlach and Ely's complete learning model can be a choice for lecturers in carrying out their learning so that even though learning is carried out online, the needs needed by students are still met. The purpose of the research is to describe the design of the Gerlach and Ely learning model, which is carried out online in the statistics course. The research method is field research with data obtained through observational studies of the learning process, interviews with students taking Statistics courses, and documentation of the learning process and the results of student assignments. The data analysis used Spradley Model. The results of this study are that students and lecturers can do online learning by maximizing some of the available applications with improvements to the media used. The media must be easily accessible to support fluency when learning. The lecturer prepares all the learning needs in the stages of Gerlach and Ely's learning activities. The advantages of this applied model are that it provides detailed stages in learning to determine learning objectives to the analysis stage of the results of the feedback provided by students. It can guide learning to achieve the objectives of the planned learning. The details of Gerlach and Ely's learning model need to be emphasized so that all stages can be carried out according to the systematics in its application.
Factors Affecting Senior High School Students' Willingness to Communicate in English in the EFL Classroom

This study aims at revealing factors affecting senior high school students’ willingness to communicate (WTC) in English in the classroom. This study employed a descriptive research design, and was situated in an Indonesian state senior high school in Sumenep, Madura. The data were collected from 57 students by using an adapted questionnaire taken from Riasati &amp; Rahimi (2018), and the result was analyzed using descriptive statistics. The findings revealed that the students’ WTC in English in class were affected by both individual and situational factors. The individual factors here include L2 self-confidence, L2 self-perceived communication competence, L2 anxiety, and fear of making mistakes. Meanwhile, the situational factors consist of task types, interlocutors’ effects, topics of discussion, and seating locations. Thus, it was suggested that the English teachers should not only encourage the students’ self-confidence in speaking English, or convince them that making mistakes is part of learning, but also should be careful in deciding the type of task given, topic to be discussed, and even the seating arrangements for their students, since all of these affect the students’ WTC in English in class.
GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering

Content-based collaborative filtering (CCF) predicts user-item interactions based on both users' interaction history and items' content information.Recently, pre-trained language models (PLM) have been used to extract highquality item encodings for CCF.However, it is resource-intensive to train a PLM-based CCF model in an end-to-end (E2E) manner, since optimization involves back-propagating through every content encoding within a given user interaction sequence.To tackle this issue, we propose GRAM (GRadient Accumulation for Multi-modality in CCF), which exploits the fact that a given item often appears multiple times within a batch of interaction histories.Specifically, Single-step GRAM aggregates each item encoding's gradients for back-propagation, with theoretic equivalence to the standard E2E training.As an extension of Single-step GRAM, we propose Multistep GRAM, which increases the gradient update latency, achieving a further speedup with drastically less GPU memory.GRAM significantly improves training efficiency (up to 146×) on five datasets from two task domains of Knowledge Tracing and News Recommendation.Our code is available at https://github.com/yoonseok312/GRAM.
Ridge-Regression-Induced Robust Graph Relational Network

Graph convolutional networks (GCNs) have attracted increasing research attention, which merits in its strong ability to handle graph data, such as the citation network or social network. Existing models typically use first-order neighborhood information to design specific convolution operations, which aggregate the features of all adjacent nodes. However, such models ignore the high-order spatial relationship among neighboring nodes in noisy data due to its modeling complexity. In this article, we propose a novel robust graph relational network to address this issue toward modeling high-order relationships in noisy data for graph convolution. Our key innovation lies in designing a generic relation network layer, which is used to infer the underlying relations among adjacent noisy nodes. Specifically, a fixed number of adjacent nodes for each node is chosen by solving the ridge regression problem, in which the regression coefficients are used to rank the adjacent nodes of each node in a graph. Furthermore, to mine the rich features, we extract high-order information from the nodes to significantly enhance the representation ability of the GCNs for extensive applications. We conduct extensive semisupervised node classification experiments on the noisy benchmark datasets, which clearly show that our model is superior to the existing methods and can achieve state-of-the-art performance.