Reviewerly/exHarmony huggingface dataset:
Online Risk-Averse Submodular Maximization

We present a polynomial-time online algorithm for maximizing the conditional value at risk (CVaR) of a monotone stochastic submodular function. Given T i.i.d. samples from an underlying distribution arriving online, our algorithm produces a sequence of solutions that converges to a (1−1/e)-approximate solution with a convergence rate of O(T −1/4 ) for monotone continuous DR-submodular functions. Compared with previous offline algorithms, which require Ω(T) space, our online algorithm only requires O( √ T) space. We extend our on- line algorithm to portfolio optimization for mono- tone submodular set functions under a matroid constraint. Experiments conducted on real-world datasets demonstrate that our algorithm can rapidly achieve CVaRs that are comparable to those obtained by existing offline algorithms.
Independent Task Scheduling in Cloud Computing using Meta-Heuristic HC-CSO Algorithm

Cloud computing is a vital paradigm of emerging technologies.It provides hardware, software, and development platforms to end-users as per their demand.Task scheduling is an exciting job in the cloud computing environment.Tasks can be divided into two categories dependent and independent.Independent tasks are not connected to any type of parent-child concept.Various meta-heuristic algorithms have come into force to schedule the independent tasks.In this, paper a hybrid HC-CSO algorithm has been simulated using independent tasks.This hybrid algorithm has been designed by using the HEFT algorithm, Self-Motivated Inertia Weight factor, and standard Cat Swarm Optimization algorithm.The Crow Search algorithm has been applied to overcome the problem of premature convergence and to avoid the H-CSO algorithm getting stuck in the local fragment.The simulation was carried out using 500-1300 random lengths independent tasks and it was found that the H-CSO algorithm has beaten PSO, ACO, and CSO algorithms whereas the hybrid algorithm HC-CSO is working fine despite Cat Swarm Optimization, Particle Swarm Optimization, and H-CSO algorithm in the name of processing cost and makespan.For all scenarios, the HC-CSO algorithm is found overall 4.15% and 7.18% efficient than the H-CSO and standard CSO respectively in comparison to the makespan and in case of computation cost minimization, 9.60% and 14.59% than the H-CSO and the CSO, respectively.
A survey of smartphone-based Face Recognition systems for Security Purposes

Abstract: Smart devices, such as smartphones and tablets, are frequently used to store and send confidential and private information. The disadvantages of traditional methods of recognition have led researchers to explore biometrics to obtain accurate and convenient security of mobile devices, taking advantage of image detection technologies that capture environmental, positional and interactive information. The face is our main focus of concentration and represents an important role in the transmission of identity and emotions. In this paper, face recognition biometric researches to mobile device security are surveyed. The discussion of various feature extractions algorithms, along with the advantages and research challenges associated with face modality have reviewed.Abstract: Smart devices, such as smartphones and tablets, are frequently used to store and send confidential and private information. The disadvantages of traditional methods of recognition have led researchers to explore biometrics to obtain accurate and convenient security of mobile devices, taking advantage of image detection technologies that capture environmental, positional and interactive information. The face is our main focus of concentration and represents an important role in the transmission of identity and emotions. In this paper, face recognition biometric researches to mobile device security are surveyed. The discussion of various feature extractions algorithms, along with the advantages and research challenges associated with face modality have reviewed.
Analisa Perbandingan Rumus Haversine Dan Rumus Euclidean Berbasis Sistem Informasi Geografis Menggunakan Metode Independent Sample t-Test

Comparative Analysis of the Haversine Formula and the Euclidean Formula based on Geographical Information Systems which aims to analyze and compare the measurement results of the two formulas whether there are significant differences to the distance measured based on geographic information systems. Research objects used to collect distance data are several bank offices in the city of Solo. The method used in this research is to create a geographic information system application using web-based programming using the PHP (Pearl Hypertext Prepocessor) programming language, with the MySQL database. Measurements were carried out on two formulas, namely the Haversine formula and the Euclidean formula and obtained data in the form of distances in kilometers (km) which were obtained from the program system, then the data was processed using Statistical Product and Service Solution (SPSS) 23 software. is the Independent Sample t-test method. From the data analysis, the conclusion is that there is no significant difference in the calculation of the distance between the Haversine formula and the Euclidean formula. The Independent Sample T Test method is able to analyze the comparison of distance measurements using the Haversine formula and the Euclidean formula does not have a significant difference, meaning that the variants of the two formulas are the same.&#x0D; &#x0D; Kata Kunci : Haversine formula, Euclidean formula , geographic &#x0D; information system, Independent Sample t-Test Method
Perspectivas de jóvenes universitarias durante la pandemia de COVID-19

This paper aims to describe the perceptions of undergraduate female students from public and private institutions about distance education during confinement due to COVID-19 in three cities in the state of Yucatan. The methodology used was mixed research methods, based on semi- structured interviews and data analysis from national and international sources. Among the findings the challenges of both groups of students in the implementation of online education during the first months of the pandemic in 2020 were recorded. This paper's distinctiveness is that it contributes to research on the advancement of girls’ education, through the collection of primary information, in order to make suggestions that allow them to continue in the national educational system.
An Information-Theoretic Approach on Causal Structure Learning for Heterogeneous Data Characteristics of Real-World Scenarios

While the knowledge about the structures of a system’s underlying causal relationships is crucial within many real-world scenarios, the omnipresence of heterogeneous data characteristics impedes applying methods for causal structure learning (CSL). In this dissertation project, we reduce the barriers for the transfer of CSL into practice with threefold contributions: (1) We derive an information-theoretic conditional independence test that, incorporated into methods for CSL, improves the accuracy for non-linear and mixed discrete-continuous causal relationships; (2) We develop a modular pipeline that covers the essential components required for a comprehensive benchmarking to support the transferability into practice; (3) We evaluate opportunities and challenges of CSL within different real-world scenarios from genetics and discrete manufacturing to demonstrate the accuracy of our approach in practice.
LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event Extraction

Most existing medical event extraction methods have primarily adopted a simplex model based on either pattern matching or deep learning, which ignores the distribution characteristics of entities and events in the medical corpus. They have not categorized the granularity of event elements, leading to the poor generalization ability of the model. This paper proposes a diagnosis and treatment event extraction method in the Chinese language, fusing long short-level semantic dependency of the corpus, LSLSD, for solving these problems. LSLSD can effectively capture different levels of semantic information within and between event sentences in the electronic medical record (EMR) corpus. Moreover, the event arguments are divided into short word-level and long sentence-level, with the sequence annotation and pattern matching combined to realize multi-granularity argument recognition, as well as to improve the generalization ability of the model. Finally, this paper constructs a diagnosis and treatment event data set of Chinese EMRs by proposing a semi-automatic corpus labeling method, and an enormous number of experiment results show that LSLSD can improve the F1-value of event extraction task by 7.1% compared with the several strong baselines.
Image Classification of Beef and Pork Using Convolutional Neural Network in Keras Framework

Beef is a food ingredient that has a high selling value. Such high prices make some people manipulate sales in markets or other shopping venues, such as mixing beef and pork. The difference between pork and beef is actually from the color and texture of the meat. However, many people do not understand these differences yet. In addition to socialization related to understanding the differences between the two types of meat, another solution is to create a technology that can recognize and differentiate pork and beef. That is what underlies this research to build a system that can classify the two types of meat. Convolutional Neural Network (CNN) is one of the Deep Learning methods and the development of Artificial Intelligence science that can be applied to classify images. Several regularization techniques include Dropout, L2, and Max-Norm were applied to the model and compared to obtain the best classification results and may predict new data accurately. It has known that the highest accuracy of 97.56% obtained from the CNN model by applying the Dropout technique using 0.7 supported by hyperparameters such as Adam's optimizer, 128 neurons in the fully connected layer, ReLu activation function, and 3 fully connected layers. The reason that also underlies the selection of the model is the low error rate of the model, which is only 0.111.Keywords: Beef and Pork, Model, Classification, CNN
On Prices of Privacy Coins and Bitcoin

Since the inauguration of cryptocurrencies, Bitcoin has been under pressure from competing tokens. As Bitcoin is a public open ledger blockchain coin, it has its weaknesses in privacy and anonymity. In the recent decade numerous coins have been initiated as privacy coins, which try to tackle these weaknesses. This research compares mostly mature privacy coins to Bitcoin, and comparison is made from a price perspective. It seems that Bitcoin is leading privacy coins in price terms, and correlation is typically high and positive. From the earlier crypto market peak of 2017–18, only a very small number of coins are showing positive returns in 2021. It is typical that many privacy coins have lost substantial amounts of their value (ranging 80–90%) or that they do not exist anymore at all. Only Horizen and Monero have shown long-term sustainability in their value; however, their price changes follow that of Bitcoin very closely. The role of privacy coins in the future remains as an open issue.
Dual Active Learning for Both Model and Data Selection

To learn an effective model with less training examples, existing active learning methods typically assume that there is a given target model, and try to fit it by selecting the most informative examples. However, it is less likely to determine the best target model in prior, and thus may get suboptimal performance even if the data is perfectly selected. To tackle with this practical challenge, this paper proposes a novel framework of dual active learning (DUAL) to simultaneously perform model search and data selection. Specifically, an effective method with truncated importance sampling is proposed for Combined Algorithm Selection and Hyperparameter optimization (CASH), which mitigates the model evaluation bias on the labeled data. Further, we propose an active query strategy to label the most valuable examples. The strategy on one hand favors discriminative data to help CASH search the best model, and on the other hand prefers informative examples to accelerate the convergence of winner models. Extensive experiments are conducted on 12 openML datasets. The results demonstrate the proposed method can effectively learn a superior model with less labeled examples.
A Systematic Literature Review of the Types of Authentication Safety Practices among Internet Users

The authentication system is one of the most important methods for maintaining information security in smart devices.There are many authentication methods, such as password authentication, biometric authentication, signature authentication, and so on, to protect cloud users' data.However, online information is not yet effectively authenticated.The purpose of this systematic literature review is to examine the current types of authentication methods as a safety practices for information security among Internet users.The PRISMA method was adopted to present a systematic literature review of 28 articles from three main databases (20 articles from Scopus, one article from Google Scholar, and seven articles from Dimension).This study used the Prediction Study Risk of Bias Assessment Tool to appraise the quality of the included studies.From the findings of the study, a total of three main themes were identified: password authentication, biometric authentication, and multiple-factor authentication.Multiple-factor authentication was found to be the most secure and most frequently recommended authentication method.It is highly recommended to implement three-factor authentication and multi-biometric model in the future, as it provides a higher surveillance level in terms of information security among cloud computing users.
Machine Learning of Ambiguous Sentences and Analysis of relation between Ambiguous Sentences and Diagrams in Technical Manual for Tacit Knowledge Acquisition

Many companies create manuals to inherit the technology. Manuals contain a lot of information, but few are focused on tacit knowledge. Against this background, this research has two purposes, targeting manuals. First, it is to judge ambiguous sentences for mining tacit knowledge. Second, it is to clarify relation between ambiguous sentences and diagrams for mining tacit knowledge. This study was conducted with maintenance of railway rolling stock as the subject. At first, to confirm the hypothesis that ambiguous sentences include tacit knowledge, we compared result of mining by human and by machine learning. We obtained the results that we can recognize the ambiguous sentences by machine learning with high accuracy. The most striking observation to emerge from this data analysis was that ambiguous sentences can identified not only by adjectives and adverbs but also nouns, post positional particle, or conjunctions. Secondly, since many diagrams are used in the manual, it is thought that there were clues to mining tacit knowledge in the diagrams. As a result, it was found that there is a correlation between ambiguous sentences and diagrams.
HATE SPEECH AND ETHNO-RELIGIOUS CONCLICTS IN NIGERIA: Implications for Political Stability

While freedom of expression or free speech is a fundamental human right of all, hate speech heralds danger for a country. Using a qualitative research approach, this study examines the connection between hate speech, ethnoreligious conflicts, and political stability in Nigeria. This study argues that the threat to internal security in Nigeria is a resultant effect of social injustice in the country. In Nigeria, hate speech has been on the increase instigating ethnic and religious sentiment, mistrust, and conflicts. The study suggests that peace social justice and political inclusion should be adopted in running the affairs of the nation.
Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features

This study investigates the sound of Hanacaraka in Javanese to select the best frame feature in checking the reading sound. Selection of the right frame feature is needed in speech recognition because certain frames have accuracy at their dominant weight, so it is necessary to match frames with the best accuracy. Common and widely used feature extraction models include the Mel Frequency Cepstral Coefficient (MFCC). The MFCC method has an accuracy of 50% to 60%. This research uses MFCC and the selection of Dominant Weight features for the Javanese language script sound Hanacaraka which produces a frame and cepstral coefficient as feature extraction. The use of the cepstral coefficient ranges from 0 to 23 or as many as 24 cepstral coefficients. In comparison, the captured frame consists of 0 to 10 frames or consists of eleven frames. A sound sampling of 300 recorded voice sampling was tested on 300 voice recordings of both male and female voice recordings. The frequency used is 44,100 kHz 16-bit stereo. The accuracy results show that the MFCC method with the ninth frame selection has a higher accuracy rate of 86% than other frames.
Enhancing Students’ Mobile-Assisted Language Learning through Self-Assessment in a Chinese College EFL Context

The rapid development of mobile communication technology, high penetration of mobile devices as well as the increasing number of mobile English learning applications all together facilitate the feasibility and popularity of mobile-assisted English learning among Chinese college students. Self-assessment is always recommended as a powerful strategy to enhance learning by helping learners regulate their study and improve their motivation and engagement, so it may also offer the potential to evaluate students’ mobile autonomous learning in the new era. This study investigates the current condition of Chinese college students’ mobile-assisted English learning, and which self-assessment strategies are employed during the process. Data collected from the questionnaire, which surveyed about 300 undergraduates from 7 Chinese public and private universities, show the popularity of mobile-assisted English learning among Chinese undergraduates. However, in terms of self-assessment strategies, the results are less optimistic, for only about half of the respondents use it with a medium to high frequency. The reasons, analyzed through a follow-up interview, mainly lie in the lack of strong will and motivation, the loneliness of learning alone and the difficulty in finding the perfect application. Suggestions for strengthening self-assessment include improving knowledge of what self-assessment is and its benefits, planning one’s study properly, improving collaboration with classmates, involving teachers as supporters and paying more attention to subsequent reflection.
Impact of Design Principles and Patterns on Software Flexibility: An Experimental Evaluation Using Flexible Point (FXP)

Software flexibility is a crucial factor in designing and developing software as it reflects its capability to adapt to changes.It is a topic that has been discussed for a very long time which indicates its importance in software development.However, it is not easy to produce a flexible software design.Design principles provide fundamental concepts in designing good software.On the other hand, design patterns are proven solution to recurring problem.When used correctly, design principles and patterns can be used to improve software flexibility.However, it is necessary to evaluate its impact on software flexibility.For this purpose, this research will conduct an experiment by developing a simple application using Object-Oriented Programming (OOP) based on a case study.Based on the requirements of the case study, two SOLID design principles are chosen such as Single Responsibility Principle (SRP) and Open Closed Principle (OCP) while Strategy and Decorator for the patterns from the GoF.Then, Flexible Points (FXP) is used to measure the differences in software flexibility by comparing the solutions developed before and after applying design principles and patterns.This is aimed to prove that the chosen design principles and patterns have positive impact on software flexibility.Lastly, the result analysis shows that the use of the chosen design principles and patterns indeed improve the flexibility of the software.Therefore, the authors highly recommend adopting design principles and patterns in software development.
Modified Bag of Visual Words Model for Image Classification

Image classification is acknowledged as one of the most critical and challenging tasks in computer vision. The bag of visual words (BoVW) model has proven to be very efficient for image classification tasks since it can effectively represent distinctive image features in vector space. In this paper, BoVW using Scale-Invariant Feature Transform (SIFT) and Oriented Fast and Rotated BRIEF(ORB) descriptors are adapted for image classification. We propose a novel image classification system using image local feature information obtained from both SIFT and ORB local feature descriptors. As a result, the constructed SO-BoVW model presents highly discriminative features, enhancing the classification performance. Experiments on Caltech-101 and flowers dataset prove the effectiveness of the proposed method.
Unsupervised Cosegmentation Model based on Saliency Detection and Optimized Hue Saturation Value Features of Superpixels

Segmenting out the foreground from a single image remains a challenge in computer vision.Image co-segmentation has been used recently to alleviate the single image segmentation by exploiting the information of the common object to be segmented from a group of images having the same object.This research proposes an unsupervised co-segmentation technique based on saliency detection and optimized features of the histogram of Hue, Saturation and Value (HSV) of the superpixels.The proposed method is formulated as the conventional Markov Random Field (MRF) segmentation model with an added co segmentation constraint.First, an initial segmentation is extracted based on a saliency technique.Afterward, a Particle Swarm algorithm (PSO) is utilized to select, iteratively, some superpixels on the inner and outer boundary of the initial segmentation to be a foreground or a background according to the optimized energy function.PSO is guided by the HSV dominant colors of the image class and the superpixels around the foreground to decide if a certain superpixel is related to the foreground or the background.The proposed method is evaluated by two datasets: iCoseg and MSRC, along with comparisons to the results of using ten conventional methods based on the Intersection over Union (IoU) metric.The experimental results demonstrate that the proposed method can segment the object successfully and accurately more than the traditional cosegmentation methods, even with a cluttered background.
Hierarchical Class-Based Curriculum Loss

Classification algorithms in machine learning often assume a flat label space. However, most real world data have dependencies between the labels, which can often be captured by using a hierarchy. Utilizing this relation can help develop a model capable of satisfying the dependencies and improving model accuracy and interpretability. Further, as different levels in the hierarchy correspond to different granularities, penalizing each label equally can be detrimental to model learning. In this paper, we propose a loss function, hierarchical curriculum loss, with two properties: (i) satisfy hierarchical constraints present in the label space, and (ii) provide non-uniform weights to labels based on their levels in the hierarchy, learned implicitly by the training paradigm. We theoretically show that the proposed hierarchical class-based curriculum loss is a tight bound of 0-1 loss among all losses satisfying the hierarchical constraints. We test our loss function on real world image data sets, and show that it significantly outperforms state-of-the-art baselines.