Reviewerly/exHarmony huggingface dataset:
Assessing written language in Greek: Validity and reliability considerations

This study aims at contributing to the reading assessment tools for Greek speaking population by evaluating the validity and reliability of decoding and reading comprehension measures.We tested 173 monolingual Greek children (aged 7-10) from elementary school Grades 2, 3, and 4, which were divided into three age groups: (i) 7-8 years, (ii) 8-9 years, (iii) 9-10 years.We employed the following measures: Word decoding measures (WDM): (i) a word and a non-word reading test and (ii) a reading fluency test.Reading comprehension measures (RCM): (i) a standardized reading comprehension test and (ii) a reading comprehension test based on the French test ECoSSe (Epreuve de Compréhension Syntaxico-Sémantique [Test of syntactico-semantic comprehension]).We investigated the convergent validity of these measures and found significant and strong (S & S) correlations between all these measures for the 7-year-old children; for the 8-year-old and 9-year-old groups S & S correlations were only found within each measure category.Furthermore, to investigate the external consistency of these measures we performed test-retest reliability and administered them for a second time after two weeks to a smaller group of 30 individuals (10 for each grade) of this cohort.Correlation analysis indicated S & S correlation.We also investigated internal consistency by performing split-half reliability, which indicated very high estimate for all tests except the second RCM.These results show the interdependence of different aspects of reading abilities in the early stage of reading development.
IGA As a Method to Promote Students’ Speaking Ability

Speaking is one of productive skills that should be learnt by the students. Nevertheless,&#x0D; the students faced difficulties in speaking. Therefore, Information Gap Activity is offered&#x0D; to solve the students’ problem in speaking. This research focuses on implementing&#x0D; IGA in teaching speaking. It aims on describing the implementation of IGA in teaching&#x0D; speaking and discovering the strengths and weaknesses of IGA. This research&#x0D; employed classroom action research. The first researcher acted as a lecturer and&#x0D; the second as an observer. This research was conducted on two cycles; each&#x0D; cycle consisted of three meetings, one pre-test and post-test. The participants of&#x0D; the research are 40 students of first semester of IKIP Siliwangi. Observation sheet,&#x0D; speaking test, and interview were used to collect the data. The data, then, were&#x0D; analyzed qualitatively using descriptive statistic and data triangulation. Based on the&#x0D; result, the implementation of IGA could develop the students’ ability in speaking.&#x0D; Besides, there were three strengths and one weakness in implementing IGA; they&#x0D; are: IGA could motivate the students in learning better, it coud help the students&#x0D; to improve the score of speaking, and it activated the students to work in a pair&#x0D; well. In other word, this method created a chance for them to help each other.&#x0D; Besides, the students could solve the problem by exchanging information. However,&#x0D; the weakness found in this research was this method, IGA, needed much time to&#x0D; apply so that the researchers felt that it was hard to use perfectly due to the limited time.
Machine Learning Based Heuristic Search Algorithms to Solve Birds of a Feather Card Game

This research was conducted by an interdisciplinary team of two undergraduate students and a faculty to explore solutions to the Birds of a Feather (BoF) Research Challenge. BoF is a newly-designed perfect-information solitaire-type game. The focus of the study was to design and implement different algorithms and evaluate their effectiveness. The team compared the provided depth-first search (DFS) to heuristic algorithms such as Monte Carlo tree search (MCTS), as well as a novel heuristic search algorithm guided by machine learning. Since all of the studied algorithms converge to a solution from a solvable deal, effectiveness of each approach was measured by how quickly a solution was reached, and how many nodes were traversed until a solution was reached. The employed methods have a potential to provide artificial intelligence enthusiasts with a better understanding of BoF and novel ways to solve perfect-information games and puzzles in general. The results indicate that the proposed heuristic search algorithms guided by machine learning provide a significant improvement in terms of number of nodes traversed over the provided DFS algorithm.
Surrogate-based adaptive particle swarm optimisation

Aiming at the shortcomings of Particle Swarm Optimisation (PSO) in solving complex problems, such as large computation cost and long computation time, the paper proposes a Surrogate-based Adaptive Particle Swarm Optimisation (SAPSO). In the algorithm, PSO carries on the optimisation search through a global exploration population and a local exploitation population. At the same time, a global Gaussian process surrogate and a local one are built based on the history search data to evaluate the two populations approximately. Moreover, some adaptive optimisation strategies have been developed, including Latin Hyper-cube Sampling (LHS) based initial strategy, self-adapting local optimisation, EI sampling-based global optimisation and cooperative searching strategy, which can ensure the balance between the global exploration and the local exploitation validly. The experimental results on benchmark problems show that the proposed algorithm not only can decrease the evaluation costs of the functions validly, but also has good convergence and robustness.
Numerical Optimization to AI, and Back

The impact of numerical optimization on modern data analysis has been quite significant. Today, these methods lie at the heart of most statistical machine learning applications in domains spanning genomics, finance and medicine. The expanding scope of these applications (and the complexity of the associated data) has continued to raise the expectations of various criteria associated with the underlying algorithms. Broadly speaking, my research work can be classified into two AI categories: Optimization in ML (Opt-ML) and Optimization in CV (Opt-CV).
Implementasi Jaringan Syaraf Tiruan Untuk Pengenalan Pola Notasi Balok Menggunakan Metode Backpropagation

The beam notations is officially used as the standard of international music notation, and is often found in scores for both musical instruments and vocals. In Indonesia, the use of numerical notation is more widely used and understood, because the learning process of notation beams is not easy, and takes time for the introduction of each symbol and its meaning. The pattern recognition technology makes it possible to recognize the pattern of the beam notations. The software used for system development is Matlab, utilizing artificial neural network using backpropagation method to recognize the pattern of beam notation. Backpropagation is a supervised learning method, where the system will be given the training first, and then the system can understand and identify patterns based on the knowledge gained. The final result shows that the system is able to recognize patterns from notations that have been previously studied with the highest percentage of 91.20%.
Impact of Application of Data Mining in the Lebanese Banking Sector

This study aims at showing the level of implementing concepts and tools of data mining in the banking sector in Lebanon in addition to its constituent domains. The researchers adopt the descriptive-analytical method which is based on a questionnaire distributed on the society of the study. The researchers found that there is an interest in applying concepts of data mining in managing operations in the Lebanese banking sector. The researchers concluded that there is difference among the respondents&amp;rsquo; opinions regarding applying concepts of data mining in managing operations in Lebanese banks as related to difference in experience, in job levels and in qualifications.
Research on Dirichlet Process Mixture Model for Clustering

This paper aims to develop a clustering method that need not predefine the number of clusters or incur a high computing cost.For this purpose, Dirichlet Process Mixture Model (DPMM) which based on nonparametric Bayesian method was introduced.Three datasets, from simple to complex, were selected for experiment.The results of the first two datasets showed that the DPMM is highly flexible and reliable, because it did not need to know the number of clusters in advance and had robustness for different rational parameters.However, the DPMM failed to achieve desirable results in the third dataset.To overcome the limitation of one-time DPMM clustering on complex datasets, the notion of hierarchical clustering was adopted to form the hierarchical DPMM algorithm, which outputted better clustering results than DPMM.In this paper, the rules of selecting parameters and the algorithm of hierarchical DPMM are provided for the effective using of DPMM.
COMIC DEVELOPMENT BASED ON DISCOVERY LEARNING AS THE LITERATION INCREASING MEDIA FOR ELEMENTARY STUDENTS

Abstract : Comic Development Based On Discovery Learning As The Literation Increasing Media For Elementary Students. The purpose of this study is to develop and test the validity, practicality, and effectiveness of comic media with the basis of discovery learning on thematic learning in elementary schools. The research method used was R&amp;D and the ADDIE design which has 5 stages, namely Analysis, Design, Development, Implementation, and Evaluation. The validity of the product was tested by experts by using media, material, and learning expert validation instruments. The practicality of the product was seen from the questionnaire of student responses to the media. Product effectiveness was seen from the results of the assessment of the three aspects of literacy, namely the process of understanding, the purpose of reading, and the attitude of reading from a reading and comic. This research produces comic based discovery learning as a medium to improve the literacy of elementary school students. This comic proved to be valid, as seen from the expert media and material test. Comics proved to be practical, seen from student responses to media questionnaires. Comics proved to effectiveness, seen from the result of the assessment of the three aspects of literacy. Keywords: Comic based on Discovery Learning; Media; Literacy Abstrak : Pengembangan Komik Berbasis Discovery Learning Sebagai Media Meningkatkan Literasi Siswa Sekolah Dasar. Tujuan dari penelitian ini adalah untuk mengembangkan dan menguji kevalidan, kepraktisan, dan keefektifan dari media komik berbasis dicovery learning pada pembelajaran tematik di Sekolah Dasar. Metode penelitian ini adalah R&amp;D dan menggunakan desain ADDIE yang memiliki 5 tahap yaitu Analysis, Design, Development, Implementation, dan Evaluation. Kevalidan produk di uji oleh pakar dengan menggunakan instrumen validasi pakar media, materi, dam pembelajaran. Kepraktisan produk dilihat dari angket respon siswa terhadap media. Keefektifan produk dilihat dari hasil penilaian dari ketiga aspek literasi yaitu proses pemahaman, tujuan membaca, dan sikap membaca dari sebuah bacaan dan komik. Penelitian ini menghasilkan komik berbasis discovery learning sebagai media untuk meningkatkan literasi siswa Sekolah Dasar. Komik ini terbukti valid, dilihat dari uji pakar media dan materi. Komik terbukti praktis, dilihat dari angket respon siswa terhadap media. Komik terbukti efektif, dilihat dari hasil penilaian ketiga aspek literasi. Kata kunci : Komik Berbasis Discovery Learning; Media; Literasi
Kombinasi Arnold Cat Map dan Modifikasi Hill Cipher Menggunakan Kode Bunyi Beep BIOS PHOENIX

Safeguarding and securing a secret data is something that must be done either in personal, organizational, corporate or governmental data but not a few do not know how to secure data, especially in a message, so this research is conducted to provide a way to encrypt messages. In this research using a collaboration of the Hill Cipher algorithm and Arnold cat map, used to get maximum encryption results on data or messages. In its application, the Hill cipher algorithm will be modified using a bios phoenix sound code and image. Hill Cipher and Arnold Cat Map algorithms are suitable to be combined because it will produce strong enough encryption because the results of text-based encryption from the Hill Cipher algorithm will be encrypted again using the image-based Arnold Cat Map algorithm. This research resulted in the modification of a Hill Cipher algorithm and Arnold Cat Map in the encryption process and description to improve the security of data and information. Keywords — Hill Cipher, Arnold Cat Map, Cryptography, Steganography
Brigitte, a Bridge-Based Grid Path-Finder

We present BRIGITTE, a new path-finding algorithm for 8-connected grids. It is based on the notion bridge that we define here, i.e., a high-level description of paths between all pairs of points from two convex regions that allows fast distance query and fast generation of the prefix and suffix of these paths. BRIGITTE uses a pre-processing step to first partition the map into convex regions and then compute a sufficient set of bridges between every pair of regions. Path-finding is then performed by looking up the regions of the source and target cells and then iterating over the bridges of the pair of regions to determine which one yields the shortest path. BRIGITTE competes favourably compared to CH-SG-R and Copp, although this currently comes at a price of an extensive pre-processing.
Safe and Sample-Efficient Reinforcement Learning Algorithms for Factored Environments

Reinforcement Learning (RL) deals with problems that can be modeled as a Markov Decision Process (MDP) where the transition function is unknown. In situations where an arbitrary policy pi is already in execution and the experiences with the environment were recorded in a batch D, an RL algorithm can use D to compute a new policy pi'. However, the policy computed by traditional RL algorithms might have worse performance compared to pi. Our goal is to develop safe RL algorithms, where the agent has a high confidence that the performance of pi' is better than the performance of pi given D. To develop sample-efficient and safe RL algorithms we combine ideas from exploration strategies in RL with a safe policy improvement method.
Identification of Associated Factors to Ontologies in the Field of Knowledge in Software Engineering: Case Study for MOOC-Type Courses Factors For Ontologies in Software Engineering

In this paper, the findings obtained regarding the way in which systems engineering students interpret the field of knowledge in software engineering are presented. Based on a questionnaire on the interrelation of concepts, conceptual relationships are identified located in hierarchical clusters that allow ontologies to be established to interpret the way in which knowledge evolves in the training program. As a result, ten conceptual clusters are obtained which contrasted with the knowledge areas proposed by the theoretical referents, allow the identification of failures in the conceptual relationships in the Systems Engineering knowledge field for students from the first to the eighth semester in order to adjust MOOC-type courses to this needs.
Integration of Weighted Terminological Concepts and Vague Knowledge in Ontologies for Decision Making

A well-known family of logics for managing structured knowledge is Description logics (DLs).They form the basis for a wide variety of ontology languages.Experience with the use of DLs in applications has, however, shown that their capabilities are insufficient for some domains.In particular, the decision-making process requires the assessment of two, possibly contradictory, influences on decision factors.First, there are items belonging to certain classes or fulfillling certain roles within complex logical constructs, but these memberships are to some extent vague.Secondly, individual preferences may change depending on the person who controls the decision-making process.Therefore, the challenge in building a decision making framework is to appropriately account for these variable influences by depicting and incorporating both aspects.This paper shows how these influences can be best modeled using a combination of fuzzy description logic and weighted description logic.Fuzzy logic is used to represent vagueness and ambiguity in ontologies, weighted description logic expresses individual preferences.In addition, the paper shows how to engineer an appropriate architecture for the suggested model.
STAD STRATEGIES OF DEFENDING SELF IMAGE IN THE PUBLIC SPEAKING DELIVERED BY MR TOURISM

&lt;em&gt;This article aims to explain how public speaking strategies for Mr. Tourism Sumatera Barat 2018 as a form of defending self image. The strategy that can be used is the strategy of telling Brown and Levinson about positive and negative faces. As the center of attention, a speaker must be able to maintain the face for an audience (audience) with the material presented. Public speaking training can be given in the form of material presentation and training using STAD type cooperative learning model. By using STAD type model, it is expected that the training participants can learn in their respective groups with directed instructors for the preparation of individual presentation. The results of the activities show that STAD model is effectively used to practice speech or public speaking. This is evident from the activity of the trainees following the material and confidence in public speaking&lt;/em&gt;.
Opportunities With Archives West: How a Small Academic Archive Benefits From Membership

Like many smaller academic organizations, Southern Oregon University’s Hannon Library Special Collections and University Archives (SPEC) operates with a limited budget and minimal staffing. Membership to Archives West provides Hannon Library with a framework to enhance discovery, improve collection management, and promote growth and sustainability. The additional resources provided by Archives West and the Orbis Cascade Alliance allow SPEC to increase capacity and improve services to students and community researchers. The underlying framework consists of training sessions, best practices documents, affordable access to ArchivesSpace, and system support. While improved discoverability is often the primary motivator, libraries should not overlook the added value of improved workflows concerning collection management. Overall, the support provided by Archives West enables smaller archives to streamline their management activities while enhancing discoverability for the current users and reaching new users. Participation in Archives West also offers greater visibility, which facilitates the library’s outreach and advocacy efforts. The benefits of membership in Archives West are well worth the efforts inherent in implementing new software and altering workflows.
On Bayesian Estimation of Dirichlet Process Lognormal Mixture Models and Comparison of Treatments in Censoring

One current interest in medical research is the comparison of treatments in the analysis of survival times of patients.This is particularly problematic, especially for censored data, and when these data consists of several groups, where each group has distinct properties and characteristics but belong to the same distribution.There are various modeling schemes that have been contemplated to overcome these complexities inherent in the data.One such possibility is the Bayesian approach which integrates prior knowledge in analysis.In this paper, we focus on the use of Bayesian lognormal mixture model (MLNM) with related Dirichlet process (DP) prior distribution for estimating patient survival.The advances in the Bayesian paradigm have considerably bolstered the development and application of mixture modelling methodology in the field of survival analysis.The proposed MLN model is compared with the conventional parametric lognormal and the nonparametric Kaplan Meier (K-M) models used to estimate survival to establish model robustness.A simulation study that investigates the impact of censoring on these models is also described.Real data from past research is used to show the resulting Dirichlet process mixture model's robustness in the comparison of censored treatment.The results indicate that the proposed lognormal mixtures provide a better fit to complex data.Further, the MLN models are able to estimate various survival distributions and therefore appropriate to compare treatments.Clinicians will find these models useful especially when confronted with the obstacle of choosing a suitable therapy for a disease.
Women Directors and Corporate Social Performance Around the World

We examine the relationship between women directors and corporate social performance (CSP) by considering the contingency effects of firms’ home country informal institutions. Based on 14,182 observations for 2,578 firms across 32 countries, our multilevel analysis shows that the influence of women directors on CSP is positive, and significantly moderated by the four GLOBE cultural dimensions of gender egalitarianism, in-group collectivism, assertiveness, and humane orientation. These findings contribute to the debate about the role of women directors in the boardroom with regards to CSP and illustrate the relevance of the institution-based view for understanding cross-country variations in women directors’ influence.
A Novel Image Encryption using Memetic Differential Expansion based Modified Logistic Chaotic Map

Under this paper, the primary conditions of a modified logistic chaotic map are created with the help of memetic differential expansion.In the beginning, the color image is broken down into different channels like red, blue and green.Then the modified logistic chaotic map essential variables are enhanced with the execution of memetic differential expansion.The strength operation is employed by the association coefficient and Entropy function.The private keys are produced by the modified logistic chaotic map.The encoded image is acquired with a combination of different encoded color channels.The memetic differential expansion builds on image encryption and the previous image encryption techniques build with the different standard images play a vital role in carrying out the larger experiments.The evaluation of the outcome of the proposed technique gives better security and efficiency in contrast to all the previously implemented image encryption techniques.
ABSTRACT AND CONCRETE TEACHING METHODS IN IMPROVING PRE-LITERACY

The study aims to improve the skills of preschool children to identify the letters and to pronounce syllables using concrete letters. Furthermore, this study also aims to identify the problem of reading using letters in preschool children and identifies difficulties in reading syllable. This study is action research. The population of this study involved a total of 7 children from Tadika Keluarga Harmoni dan Indah aged 6 years old. All children in this study are normal children and can understand the Malay language. Instruments used are tests, observations, and checklists. Pre-test and post-test to identify and pronounce letters and syllables. The findings of the study have shown that the use of concrete letter techniques can increase the mastery of children towards literacy, especially the understanding of literacy and reading the syllabus. The achievement of both children in both tests has been well received and satisfactory. The findings also found that these concrete letters techniques can help children to understand what they read and at the same time improve their vocabulary. The role of the school in providing teaching aids is needed to improve the implementation of this method in preschool.