Reviewerly/exHarmony huggingface dataset:
Ship Image Classification Using Deep Learning Method

Ship image classification in ocean background is of great significance for military and civilian domains which will further improve the automation in ship identification and naval domain perception. Deep stacked layers of neurons are extensively employed in recent years due to their ability to recognize the high-level features from an image in a hierarchal way. However, CNN lacks the capability of dealing with global rotation in an image of large size. This limits the accuracy of the CNN algorithm. Therefore, we have proposed a deep learning framework which uses a few layers of AlexNet for initial feature extraction and subsequently, KNN classifiers have been utilized for measuring the accuracy in classifying ships according to various categories.
Expression Recognition Method of Fusion Gabor Filter and 2DPCA Algorithm

In recent years, with the continuous development of artificial intelligence technology, expression recognition has become a hot topic in human-computer interaction research. Expression recognition is an important branch of face recognition. Compared with the amount of information conveyed by language, the amount of information conveyed by facial expression is higher. In daily life, we can analyze a person's view of a thing or a person's inner activities through expression. Expression recognition is used in many fields such as machine vision, human-computer interaction, neural analysis, etc. Because of the large dimension of facial expression features, it affects the efficiency of facial expression recognition. In this paper, Gabor filter and 2DPCA algorithm are combined to propose expression recognition method, which can achieve high accuracy and recognition speed.
SIMPLEX-PB 2.0: A Reliable Dataset for Lexical Simplification in Brazilian Portuguese

Most research on Lexical Simplification (LS) addresses non-native speakers of English, since they are numerous and easy to recruit. This makes it difficult to create LS solutions for other languages and target audiences. This paper presents SIMPLEX-PB 2.0, a dataset for LS in Brazilian Portuguese that, unlike its predecessor SIMPLEX-PB, accurately captures the needs of Brazilian underprivileged children. To create SIMPLEX-PB 2.0, we addressed all limitations of the old SIMPLEX-PB through multiple rounds of manual annotation. As a result, SIMPLEX-PB 2.0 features much more reliable and numerous candidate substitutions to complex words, as well as word complexity rankings produced by a group underprivileged children.
Nature-inspired hybrid deep learning for race detection by face shape features

Purpose The humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue. Design/methodology/approach This paper intends to propose a new race detection model using face shape features. The proposed model includes two key phases, namely. (a) feature extraction (b) detection. The feature extraction is the initial stage, where the face color and shape based features get mined. Specifically, maximally stable extremal regions (MSER) and speeded-up robust transform (SURF) are extracted under shape features and dense color feature are extracted as color feature. Since, the extracted features are huge in dimensions; they are alleviated under principle component analysis (PCA) approach, which is the strongest model for solving “curse of dimensionality”. Then, the dimensional reduced features are subjected to deep belief neural network (DBN), where the race gets detected. Further, to make the proposed framework more effective with respect to prediction, the weight of DBN is fine tuned with a new hybrid algorithm referred as lion mutated and updated dragon algorithm (LMUDA), which is the conceptual hybridization of lion algorithm (LA) and dragonfly algorithm (DA). Findings The performance of proposed work is compared over other state-of-the-art models in terms of accuracy and error performance. Moreover, LMUDA attains high accuracy at 100th iteration with 90% of training, which is 11.1, 8.8, 5.5 and 3.3% better than the performance when learning percentage (LP) = 50%, 60%, 70%, and 80%, respectively. More particularly, the performance of proposed DBN + LMUDA is 22.2, 12.5 and 33.3% better than the traditional classifiers DCNN, DBN and LDA, respectively. Originality/value This paper achieves the objective detecting the human races from the faces. Particularly, MSER feature and SURF features are extracted under shape features and dense color feature are extracted as color feature. As a novelty, to make the race detection more accurate, the weight of DBN is fine tuned with a new hybrid algorithm referred as LMUDA, which is the conceptual hybridization of LA and DA, respectively.
A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In this paper, we summarize the results of Locatello et al., 2019, and focus on their implications for practitioners. We discuss the theoretical result showing that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases and the practical challenges it entails. Finally, we comment on our experimental findings, highlighting the limitations of state-of-the-art approaches and directions for future research.
Krill Herd Algorithm

In this chapter, a short description of the krill herd optimization algorithm (KH) and, the basic procedure of KH algorithm is discussed through a step-by-step pseudo-code is presented. Source-codes of the original version of KH in both Matlab and C++ programming language will also be presented. For better understanding of the algorithm basis, a step by step solution of a simple case study for one iteration of this algorithm will be provided. At the end of this chapter, the conclusion of the study will be discussed.
Breaking simple quantum position verification protocols with little entanglement

Instantaneous nonlocal quantum computation (INQC) evades apparent quantum and relativistic constraints and allows to attack generic quantum position verification (QPV) protocols (aiming at securely certifying the location of a distant prover) at an exponential entanglement cost. We consider adversaries sharing maximally entangled pairs of qudits and find low-dimensional INQC attacks against the simple practical family of QPV protocols based on single photons polarized at an angle $\theta$. We find exact attacks against some rational angles, including some sitting outside of the Clifford hierarchy (e.g. $\pi/6$), and show no $\theta$ allows to tolerate errors higher than $\simeq 5\cdot 10^{-3}$ against adversaries holding two ebits per protocol's qubit.
Data-driven confidence bands for distributed nonparametric regression

Gaussian Process Regression and Kernel Ridge Regression are popular nonparametric regression approaches. Unfortunately, they suffer from high computational complexity rendering them inapplicable to the modern massive datasets. To that end a number of approximations have been suggested, some of them allowing for a distributed implementation. One of them is the divide and conquer approach, splitting the data into a number of partitions, obtaining the local estimates and finally averaging them. In this paper we suggest a novel computationally efficient fully data-driven algorithm, quantifying uncertainty of this method, yielding frequentist $L_2$-confidence bands. We rigorously demonstrate validity of the algorithm. Another contribution of the paper is a minimax-optimal high-probability bound for the averaged estimator, complementing and generalizing the known risk bounds.
Crow Search Algorithm

Crow Search Algorithm (CSA) was introduced in 2016 and it is based on the behavior of the intelligent crows that store the excess food in different hiding places and retrieve it when they need it. The chapter has the following structure: the first section presents the CSA, the second section presents the pseudo code of the algorithm and describes the algorithm in more details, the third section presents the Matlab version of the source code, the fourth section presents the C++ version of the source code, the fifth section presents a step-by-step numerical example and the last section presents the conclusions.
A case study of Food Production Using Artificial Intelligence

In these days, artificial intelligence (AI) finds applications in many different domains, including art creation such as painting and music. However, there is lack of know-how involving AI in developing food products. In this case, we analyzed a large number of Japanese newspaper articles and developed a chocolate that represents the mood of each year. Specifically, the mood was expressed by predicting the taste of words by machine learning. This paper describes how to create software that converts human sensation into taste, and how people develop products based on it, and how to evaluate the resulting product. In the result, we introduce a product developed with the support of artificial intelligence that has an effect of stimulating curiosity in consumers and can lead developers to new discoveries and perspectives. In the future, we aim to create a system that supports collaborative recipe development involving AI, developers, and consumers.
A Gradient Condition for the Arrival Cost in Moving Horizon Estimation

Within the Moving Horizon Estimation (MHE) formulation, the contributions of measurements outside the horizon are summarized by the arrival cost term. We propose a gradient condition for the arrival cost which ensures that the state estimate does not change if it perfectly explains new measurements. Furthermore, the updated arrival cost locally approximates the arrival cost, which would be obtained by an exact arrival cost update, if the gradient condition is satisfied. Based on the proposed gradient condition, we derive formulas for updating quadratic arrival cost terms from one MHE iteration to the next.
The Modal Square of Opposition and the Mental Models Theory

The mental models theory is a current approach trying to account for human thought and hence communication by highlighting the action of semantics and ignoring, to a large extent, syntax. However, it has been argued that the theory actually contains an underlying syntax related to any kind of modal logic. This paper delves into this last idea and is intended to show that the concepts of possibility and necessity as understood in it fulfill the basic requirement that, according to Fitting and Mendelsohn, every modal logic has to meet: to satisfy the relationships provided by the Aristotelian modal square of opposition.
Penerapan Media Pembelajaran Interaktif Pelajaran Bahasa Indonesia Berbasis Android Untuk Kelas VII Madrasah Tsanawiyah Nahdlatul Wathan Ketangga Sebagai Upaya Untuk Peningkatkan Minat Belajar Siswa

In today's modern era, the use of computer devices, laptops and smartphones is something that can be said to be a necessity, because the functions of these devices are very helpful in daily activities. In the world of education the role of these devices is very important in the development of better education, with these tools, teaching and learning activities become easier and more enjoyable. The Media of Learning is a medium which was used in a delivery process carried out by someone to another person or between educator and to student. Initially the educator still used the lecture and whiteboard method to be able to deliver Indonesian material to student. This method was an irrelevant method in the sense that the method used was a large amount of energy in delivering an Indonesian language material to students and experiencing saturation by conveying material like this. This method is used before the existence of a computerized or mobile learning media is developing at this time. Therefore the author provides a solution or way to make a computerized or mobile learning media application that is an interactive Indonesian learning media application for Android-based for student of seventh graders which was interesting and fun for the educator and student. DOI : 10.29408/jit.v2i2.1437
Sign Language Translation System Using Convolutional Neural Networks Approach

Sign language is one of the ways to communicate by not making sound, as it is communicated by using hands and expressions. Sign language does not get much attention from the public because it is used by the minority of the society, hence the limited resources available for learning them. This paper proposes a sign translation system called ReadMe that uses deep learning approach, specifically the Convolutional Neural Networks (CNNs) to train the recognition model. However, training ReadMe using the CNNs revealed a low accuracy of 39.8% due to small size of training dataset, hence testing is aggravating. In order to increase the recognition accuracy in the future, CNNs algorithm in ReadMe will be trained using a larger dataset from both American Sign Language (ASL) and Malaysian Sign Language (BIM). In addition, the system is also hoped to enable users to train new gestures. Only by means of crowdsourcing that the system able to expand its vocabulary without facing the knowledge engineering bottleneck.
Experimental studies and model development of flow over Arched Labyrinth Weirs using GMDH method

Arched labyrinth weir is a special type of weir in which the crest length is increased by arch action with/without providing notches. Effects of various parameters on the coefficient of discharge of the arched labyrinth weir are established using experimental data obtained in the present study. A comparative study of discharge for different crest lengths of weir has also been presented. A new model is developed for coefficient of discharge of the arched labyrinth weir using the non-linear regression method (R = 0.8951, MAPE = 7.6037 AAD = 7.5997 and RMSE = 0.0509). Generalized models for predicting the coefficient of discharge using GMDH network have also been developed. The prediction based on the GMDH model (R = 0.9691, MAPE = 3.9453.6037 AAD = 3.7035 and RMSE = 0.0283) is more satisfactory than those given by traditional regression equations. Flow pattern in the vicinity of main channel of upstream of the arched labyrinth weir is also observed.
DEVELOPING COMMUNICATION SKILLS THROUGH AUGMENTED AND VIRTUAL REALITY APPS ON SMART DEVICES

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 3387-3394ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.0962Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
A NEW MODEL FOR ASSESSING THE VOICE OF THE STUDENT IN A MOBILE APP DESIGN STAGE

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 2094-2103ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.0659Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
Encrypted-Input Program Obfuscation: Simultaneous Security Against White-Box and Black-Box Attacks

We consider the problem of protecting cloud services from simultaneous white-box and black-box attacks. Recent research in cryptographic program obfuscation considers the problem of protecting the confidentiality of programs and any secrets in them. In this model, a provable program obfuscation solution makes white-box attacks to the program not more useful than black-box attacks. Motivated by very recent results showing successful black-box attacks to machine learning programs run by cloud servers, we propose and study the approach of augmenting the program obfuscation solution model so to achieve, in at least some class of application scenarios, program confidentiality in the presence of both white-box and black-box attacks.We propose and formally define encrypted-input program obfuscation, where a key is shared between the entity obfuscating the program and the entity encrypting the program's inputs. We believe this model might be of interest in practical scenarios where cloud programs operate over encrypted data received by associated sensors (e.g., Internet of Things, Smart Grid).Under standard intractability assumptions, we show various results that are not known in the traditional cryptographic program obfuscation model; most notably: Yao's garbled circuit technique implies encrypted-input program obfuscation hiding all gates of an arbitrary polynomial circuit; and very efficient encrypted-input program obfuscation for range membership programs and a class of machine learning programs (i.e., decision trees). The performance of the latter solutions has only a small constant overhead over the equivalent unobfuscated program.
Asking why: moving beyond error detection to failure mode and effects analysis

Abstract: Laboratory medicine has made tremendous strides in establishing safety and quality practices as part of our culture. In the past, most of the efforts of the profession have focused on error detection, classification, peer comparison and compliance. However, laboratory errors that significantly affect patient care can still evade detection. With increasing automation in the laboratory, there has been a gradual decline in the technical knowledge of the laboratory staff. This is in part contributed by the general lack of access to instrument raw data and low transfer for technical knowledge and troubleshooting expertise from the instrument manufacturer to the laboratory end user. Failure mode and effects analysis (FMEA) is a quality tool that aims to: (I) identify potential failures, including their cause and effect; (II) evaluate and prioritize the failure modes; and, (III) propose actions to eliminate or reduce the chance of failure. Drawing from recent lessons learned from the Boeing 737 Max tragedies, we suggested that laboratory medicine as a community should adopt FMEA to better understand the root cause of laboratory errors to improve preventive and troubleshooting efforts. Manufacturers have important roles to play, both to share instrument data and technical expertise, as well as actively engage in meaningful conversation to improve product design.
The Mediation Effect of Career Decision-Making Self-Efficacy on the Relationship between Career Stress and Career Preparation Behavior of Kinesiology Majors

Purpose: The purpose of this study is to examine the mediating effect of career decision-making self-efficacy on relations between career stress and career preparation behavior for the students majoring in physical education.<BR> Method: For the study, Kinesiology majors (N=465) were selected using convenience sampling. Career stress, career preparation behavior, and career decision-making self-efficacy were utilized after their verification of validity and reliability were conducted. The confirmatory factor analysis, reliability analysis, correlation analysis, and structure equation modelling were performed using SPSS 21.0 program and AMOS 21.0 program.<BR> Results: The results are as follows. First, career stress had a negative effect on career preparation behavior. Second, career stress had a negative effect on career decision-making self-efficacy. Third, career decision-making self-efficacy had a positive effect on career preparation behavior. Finally, the relationships between career stress and career preparation behavior were mediated by career decision-making self-efficacy<BR> Conclusion: In conclusion, career decision-making self-efficacy is a mediator leading to career stress and career preparation behavior for kinesiology majors.
Teaching Decision Tree Using a Practical Example

The positive experience with student gathering data in the Data Mining course is mentioned in this article. The inspiration for a practice lecture was a literature example of a decision tree for classification of sex from the weight and height of persons. Therefore, students fill anonymously simple questionnaire with personal weight, height, and sex. The data set was used as training data for the construction of a decision tree. Decision tree as supervised learning produces rules for classification of sex based on the input attributes. The final decision tree as a result of the training phase was used also for prediction of class (sex) on newly collected testing data. Both parts – construction of a decision tree and prediction was practically demonstrated. The data mining software Orange was selected for practical lectures. The Orange advantages are intuitiveness and easy design of workflow. The article shows result decision trees and results of prediction on real data. Teacher final finding is that the active collecting data make students more involved in the topic and assure a deep understanding of the lectured topics like decision trees.
Future of Finance

Financial regulators regulate financial intermediaries and activities to achieve their regulatory goals and, in so doing, address various market failures. These objectives are needed in the social interest regardless of the technologies used by the financial system. The blockchain-based financial system, however, has characteristics that could undermine the ability of regulators to achieve regulatory goals. Thus, financial regulators must discover ways to continue to achieve regulatory goals. This situation is similar to the case of telecommunication regulators during the rise of the Internet. In the face of such difficulties in cyberspace, it was suggested to invoke not just law but also social norms, market mechanisms, and architecture to achieve a certain level of oversight. Now, G20 financial regulators recognize the need for cooperation with other stakeholders. Because code embedded in a blockchain system could determine the level of oversight within a blockchain-based financial system, regulators should consider ways to cooperate with engineering communities to develop codes that appropriately facilitate mechanisms to achieve regulatory goals and must empower society to use such codes in order to achieve regulatory goals, which requires consideration on alignment with social norms and market competitiveness. Thus, regulators must cooperate with other stakeholders, including businesses and users.