Reviewerly/exHarmony huggingface dataset:
Intra-domain and cross-domain transfer learning for time series

&amp;lt;p&amp;gt;In this study, we compared the performance of machine learning models trained using transfer learning and those that were trained from scratch - on time series data. Four machine learning models were used for the experiment. Two models were taken from the field of seismology, and the other two are general-purpose models for working with time series data. The accuracy of selected models was systematically observed and analyzed when switching within the same domain of application (seismology), as well as between mutually different domains of application (seismology, speech, medicine, finance). In seismology, we used two databases of local earthquakes (one in counts, and the other with the instrument response removed) and a database of global earthquakes for predicting earthquake magnitude; other datasets targeted classifying spoken words (speech), predicting stock prices (finance) and classifying muscle movement from EMG signals (medicine).&amp;lt;br&amp;gt;In practice, it is very demanding and sometimes impossible to collect datasets of tagged data large enough to successfully train a machine learning model. Therefore, in our experiment, we use reduced data sets of 1,500 and 9,000 data instances to mimic such conditions. Using the same scaled-down datasets, we trained two sets of machine learning models: those that used transfer learning for training and those that were trained from scratch. We compared the performances between pairs of models in order to draw conclusions about the utility of transfer learning. In order to confirm the validity of the obtained results, we repeated the experiments several times and applied statistical tests to confirm the significance of the results. The study shows when, within the set experimental framework, the transfer of knowledge brought improvements in terms of model accuracy and in terms of model convergence rate.&amp;lt;br&amp;gt;&amp;lt;br&amp;gt;Our results show that it is possible to achieve better performance and faster convergence by transferring knowledge from the domain of global earthquakes to the domain of local earthquakes; sometimes also vice versa. However, improvements in seismology can sometimes also be achieved by transferring knowledge from medical and audio domains. The results show that the transfer of knowledge between other domains brought even more significant improvements, compared to those within the field of seismology. For example, it has been shown that models in the field of sound recognition have achieved much better performance compared to classical models and that the domain of sound recognition is very compatible with knowledge from other domains. We came to similar conclusions for the domains of medicine and finance. Ultimately, the paper offers suggestions when transfer learning is useful, and the explanations offered can provide a good starting point for knowledge transfer using time series data.&amp;lt;/p&amp;gt;
Pengembangan Media Pembelajaran Tebak Kata Pada Materi Bank Sentral, Sistem Pembayaran dan Alat Pembayaran

This research was conducted as an effort for students so that they can actively participate in learning. The learning media for guessing the word are presented in an attractive manner so that students can play while learning. The purpose of this research is to develop a product in the form of learning media for word guessing in order to improve learning outcomes. This type of research is development research using the 4D model which is limited to the develop stage. The data collection instrument used questionnaire guidelines by analyzing the validation results that were calculated using a Likert scale. The results of the test show that the media for guessing words on the material of the central bank, payment systems, and payment instruments have criteria that are suitable for use as a medium for economic learning. So that the media is declared feasible to be applied in class ten economic learning.
Joint Extraction of Triple Knowledge Based on Relation Priority

Triple knowledge extraction from texts is an important type of information extraction and plays a crucial role in such domains as knowledge base construction. To do so, most of existing methods usually first extract entities or entity pairs, which may lead to the redundant combination problem of entities and cannot tackle with the overlapping problem. To solve these problems, this paper proposes a new model, called RFTE, based on relation priority. The model first classifies texts according to the classification of relations to be extracted, then combines the predicted relations with texts to perform entity recognition based on the sequence labelling technique, and finally combines the extracted head and tail entities and the corresponding relations to obtain triple knowledge. Our model decouples entity recognition under different types of relations and significantly reduces the impact of the large search space. In addition, this paper also employs data augmentation and rules to further promote the performance of the model. Experiments on three benchmark datasets show that the proposed model successfully overcomes the overlapping problem and significantly outperforms the traditional methods.
On Geodesic Distances and Contextual Embedding Compression for Text Classification

In some memory-constrained settings like IoT devices and over-the-network data pipelines, it can be advantageous to have smaller contextual embeddings.We investigate the efficacy of projecting contextual embedding data (BERT) onto a manifold, and using nonlinear dimensionality reduction techniques to compress these embeddings.In particular, we propose a novel post-processing approach, applying a combination of Isomap and PCA.We find that the geodesic distance estimations, estimates of the shortest path on a Riemannian manifold, from Isomap's k-Nearest Neighbors graph bolstered the performance of the compressed embeddings to be comparable to the original BERT embeddings.On one dataset, we find that despite a 12-fold dimensionality reduction, the compressed embeddings performed within 0.1% of the original BERT embeddings on a downstream classification task.In addition, we find that this approach works particularly well on tasks reliant on syntactic data, when compared with linear dimensionality reduction.These results show promise for a novel geometric approach to achieve lower dimensional text embeddings from existing transformers and pave the way for dataspecific and application-specific embedding compressions.
E-Learning: Boon or Bane to Higher Education during COVID-19 at MENA Region

The sudden effect of the corona pandemic in many nations has seriously harmed the physical, social, and mental well-being and the global economy. Higher education is the bedrock of every nation that the Pandemic has ravaged. The entire educational system has unexpectedly moved to digital, creating a serious challenge for both students and teachers to adapt to. Is it a challenge, a boon, or a bane for counties in the MENA region to have their higher education systems turned upside down? The complexities of online education will be discussed in this report. The study employed qualitative techniques, which were accomplished through desktop review. The result indicates that eLearning is both boon and bane on higher education in countries in the MENA region. As a result, we can conclude that the eLearning education system should be geared up, and internet accessibility in the MENA region should be strengthened.
Explanation of Siamese Neural Networks for Weakly Supervised Learning

A new method for explaining the Siamese neural network (SNN) as a black-box model for weakly supervised learning is proposed under condition that the output of every subnetwork of the SNN is a vector which is accessible. The main problem of the explanation is that the perturbation technique cannot be used directly for input instances because only their semantic similarity or dissimilarity is known. Moreover, there is no an "inverse" map between the SNN output vector and the corresponding input instance. Therefore, a special autoencoder is proposed, which takes into account the proximity of its hidden representation and the SNN outputs. Its pre-trained decoder part as well as the encoder are used to reconstruct original instances from the SNN perturbed output vectors. The important features of the explained instances are determined by averaging the corresponding changes of the reconstructed instances. Numerical experiments with synthetic data and with the well-known dataset MNIST illustrate the proposed method.
Does Job Characteristics Predicted Employee Job Satisfaction

Employee job satisfaction is the important factor that will determine employee performance, which in turn will determine organizational performance. Employee job satisfaction influenced by various factors, such as personal and non-personal factors. The ability to understand what factors most determine employee job satisfaction is a challenge and an important task for managers. Among various non-personal factors, several previous studies found that job characteristics are of the important in determining employee job satisfaction. This research intended to understand better the interplay between job characteristics and job satisfaction. Based on studies and descriptions of various previous studies, this study proposes job characteristics positively and significantly effect job satisfaction.
The portfolio selection problem: Hybrid approach to solve the mean-variance rational model

In this paper, we propose to complete the Markowitz portfolio problem by a new rational constraint. Besides, the mean goal of this work is to use The Continuous Hopfield Networks (CHN) and the genetic algorithm to solve the proposed model. As know, (CHN) is a robust neural tool, which was widely used to solve combinatorial problems. For that, we propose an adequate energy function. The equilibrium points of our neural network represent the local’s minimums of this energy function. These solutions are not enough good in the general case. As the CHN is fast, we start from several initial points to construct a set of local solutions, this litter is given as the initial population to the genetic algorithm to construct better solutions. As it is showing in the experimental results, the proposed hybrid approach allows improving largely the CHN portfolio profile for the rational mean-variance model. © 2020 SEIO.
Mitigating Security Risks on Privacy of Sensitive Data used in Cloud-based ERP Applications

This paper explains the security risk of data being used in Cloud-based Enterprise Resource Planning (ERP) application and challenges of implementation in healthcare/hospitals. It further explains the challenges faced during and after implementation using SaaS (Software as a Service) and how to overcome these challenges. This study additionally provides a comparison before and after cloud implementation in hospitals and advantages/disadvantages about usage of Cloud-based ERP in the healthcare sector and how it can help to manage and care patients efficiently in an economical way. Survey methodology was used for data collection. Study of survey results proves that Cloud based ERP products are robust and provide secured storage for data and use high end encryption methods to handle data security and privacy. Cloud-based applications have changed the way organizations used to work and provided them the tools to be more focused on innovation and other areas and not bother on maintenance and upgrade of server and back-up servers (Disaster Server), Business Continue Planning (BCP servers and their OS (Operating System) along with other software. There are multiple benefits of Cloud-based applications though there are concerns related to data security about confidentiality and integrity considering data is critical and sensitive. This paper is addressing the issues during Cloud Based ERP implementation for an organization, e.g. data security and data privacy along with the ways to address them.
Despair – Destiny - Dearth: A Retrospective Sea Bajau Labyrinth Education Review

There is no way for children to be left behind in education. The difference growth in these children’s lives will lead to the future of a nation’s development. Sea Bajau is a group of individuals who still embrace the maritime and nomadic lifestyle, unidentified, and have no access to formal education. In particular, they are different in terms of socioeconomic status, place of residence, culture, historical origin, and citizenship. They have no chance of obtaining the education provided by the government and are spending time on a boat or a beach daily basis. This paper looks into an alternative education of the Sea Bajau children to have the right to formal education, regardless of whether children are registered or undocumented. In various ways, alternative education has many positive effects on students and communities. It helps develop the literacy of students who can read and write after attending alternative education classes.
Online Limited Memory Neural-Linear Bandits with Likelihood Matching

We study neural-linear bandits for solving problems where both exploration and representation learning play an important role. Neural-linear bandits leverage the representation power of Deep Neural Networks (DNNs) and combine it with efficient exploration mechanisms designed for linear contextual bandits on top of the last hidden layer. A recent analysis of DNNs in the infinite-width regime suggests that when these models are trained with gradient descent the optimal solution is close to the initialization point and the DNN can be viewed as a kernel machine. As a result, it is possible to exploit linear exploration algorithms on top of a DNN via the kernel construction. The problem is that in practice the kernel changes during the learning process and the agent's performance degrades. This can be resolved by recomputing new uncertainty estimations with stored data. Nevertheless, when the buffer's size is limited, a phenomenon called catastrophic forgetting emerges. Instead, we propose a likelihood matching algorithm that is resilient to catastrophic forgetting and is completely online. We perform simulations on a variety of datasets and observe that our algorithm achieves comparable performance to the unlimited memory approach while exhibits resilience to catastrophic forgetting.
Eshort Message Service Spam Detection and Filtering Using Machine Learning Approach

A robust and efficient spam filtering and detection system is important in today’s ever changing and growing world. While basic filters and detector are there most of them are not efficient. There is a scope of further development in the systems used. Hence, we are doing our work in way to provide and design a system more efficient and user friendly according to user preferences. Most of the work that has been carried out in this field has been done on data-sets sourced from European countries and mostly on European language. We would be designing a SMS spam filter, which will be universal in nature.As well as our model would be smart enough to distinguish whether a SMS from an operator will be useful to the user or not and rank them accordingly.
Anomaly Detection Using Support Vector Machines for Time Series Data

Research on anomaly detection is of great interest in machine learning and data mining.Detecting anomalies or finding outliers involves identifying abnormal or inconsistent patterns in a dataset.Abnormal data often results from unauthorized activity.Credit card fraud offers a well-known example.Transactions with a stolen or fake credit card can produce suspicious data.A fake card can be made by copying information from an authorized card and using it to create a new unauthorized one.Data such as personal identifying information may be obtained through phishing or from employees who work in credit card companies [1].Another source of abnormal data may derive from unauthorized intrusions in networks.Abnormal traffic or user actions are common signs of intrusions, which may occasion breaches of sensitive or confidential data.Intrusions may also cause sensor networks to generate erroneous data.When a sensor malfunctions, it is unable to capture data correctly and thus may produce anomalies.Abnormal changes in data sources may also result in anomalies [2].Anomaly detection typically uses data mining and machine learning methods for detecting abnormal activities in systems.Many anomaly detection techniques have been developed, including Support Vector Machines (SVM), which can solve classification and regression problems.The performance of SVM depends on the selection of kernel function and kernel parameters.The selection quality of SVM parameters and kernel functions has an effect on learning and generation performance.Appropriate kernel function and associated parameters should be selected to obtain optimal classification performance.When an appropriate kernel function and parameters are selected, the prediction error of SVM can be minimized.This paper reports on application of the support vector machine method to eight real world time series data sets to detect anomalies using three different kernels for analysis and prediction.In addition, SVM kernels are compared for effectiveness based on Area under the Curve (AUC), Precision, Recall, F1-Score, Specificity, and Jaccard index criteria.
Construction and Application of Online and Offline Blended Teaching Model in Colleges and Universities in Post-Epidemic Era

The online and offline Blended Teaching of "Language of Film and Television" is taken as research object to discuss its construction and application in colleges and universities in the context of "Internet plus" and postepidemic.The results show that Blended Teaching requires to break the traditional thinking of binary opposition, and to use complementary advantages to design teaching, so as to build a three-dimensional teaching field integrating situation, interaction, experience and reflection.
Weight-covariance alignment for adversarially robust neural networks

Stochastic Neural Networks (SNNs) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks. However, existing SNNs are usually heuristically motivated, and often rely on adversarial training, which is computationally costly. We propose a new SNN that achieves state-of-the-art performance without relying on adversarial training, and enjoys solid theoretical justification. Specifically, while existing SNNs inject learned or hand-tuned isotropic noise, our SNN learns an anisotropic noise distribution to optimize a learning-theoretic bound on adversarial robustness. We evaluate our method on a number of popular benchmarks, show that it can be applied to different architectures, and that it provides robustness to a variety of white-box and black-box attacks, while being simple and fast to train compared to existing alternatives.
Adversity and emotional quotients of public elementary school heads amidst the COVID-19

This study aimed to identify the relationship of adversity quotient and emotional quotient amongst public elementary school heads. The study employed a descriptive-correlation research design with the online survey as primary data collection tool. The respondents came from the 25 elementary schools in a Schools Division Office in Central Luzon. 25 elementary school heads participated in the online survey using universal sampling technique. Adapted questionnaires were used to gather data. For the statistical treatment of the study, mean was used for the responses of the school heads; for the relationship, the study used Pearson-r. This paper concluded that the school heads' Adversity Quotient (AQ) score falls within "above average," which indicates above normal capacity for challenges, difficulties, setbacks, and demands. In terms of emotional quotient, all factors involved in emotional intelligence like self-awareness, managing emotions, motivating oneself, empathy, and social skill all "apply" to school heads. In addition, there exists a significant relationship between adversity quotient and emotional quotient of public elementary school heads. Based on the aforementioned results, the researcher provided some important recommendations for the study.
Test Case Recording using Java Script for Automation Testing

Test automation is the usage of software to monitor the execution of experiments and the comparison of actual results with expected results, apart from the software being evaluated. In a formalized web testing method already in place, test automation may automatize certain routine but required tasks and carry out extra testing which can be troublesome to try and perform manually. There is a need to simplify the test case recording process as most of the methods used at present requires the testers to generate scripts using any coding language. Hence, a light weight script less model is developed which uses JavaScript to record mouse and keyboard actions. Selenium framework is used to induce the JavaScript in the browser. Therefore, this paper focuses on developing a lightweight script less model for recording test cases on browsers.
Attribute-Based encryption mechanism with Privacy-Preserving approach in cloud computing

Native hardware and code maintenance are not burdened by a cloud computing infrastructure that provides customers with the abilité to remote storage and use the cloud dominance applications on request. Although cloud storage draws additional exposure to those opportunities than ever before, it also introduces outsourced information of innovative and challenging security threats. Up to now an overly wide range of strategies has been projected to deal with this drawback to confirm info protection and credibility in cloud computing, but all of them have their own intrinsic limits. The structured Ciphers text-policy attributes model mostly based on the algorithmic rule for hidden writing (CP-ABE) with a data structure for improving quantifiable and adaptation will be provided during this project. In order to avoid unwanted data access and integrity, the database server is completely removed. The metrics of efficiency suggest that the theme is cost efficient in handling the information so that it is kept within the data storage servers and greatly decreases the time of processing.
Enhancing Students’ Oral Presentation Skills By Using Facebook

The rapid rise of the information and communication technology (ICT) has made a big impact in the field of education, which demands educators to learn new or add additional knowledge in order to keep themselves knowledgeable in this Digital Age.Educators need to improve on their teaching style by incorporating creative and fun way in order for the students to learn effectively.The study looked at the use of Facebook to enhance students' oral presentation skills at Seberang Perai Polytechnic.The first aim of this paper is to look at how Facebook can be used to improve students' presentation skills.Second, the researchers aim is to analyze students' perception of the use of Facebook in improving students' oral presentation skills.A questionnaire and interview sessions were conducted to examine students' views towards Facebook in improving students' performance during oral presentation tasks.Questionnaires and interviews were triangulated and analyzed using SPSS.This research employed a qualitative approach and supported by quantitative research methods.The findings of the research reflected students' perception of the use of Facebook to enhance oral presentation skills.