Reviewerly/exHarmony huggingface dataset:
Combination rules for homoscedastic and heteroscedastic MANOVA models from multiply imputed datasets

Classical MANOVA tests do not pose any difficulty when the assumptions on which they are based are satisfied, while the modified Brown-Forsythe (MBF) procedure has low sensitivity to the lack of multivariate normality and homogeneity of covariance matrices. Both methods assume complete data for all subjects. In this paper, we present combination rules for the MANOVA and MBF procedures with multiply imputed datasets. These rules are illustrated by pooling the results obtained with a two-factor multivariate design after applying the two approaches to each of the imputed datasets when the covariance matrices were equal (MI-MANOVA) and when the covariance matrices were unequal (MI-MBF). A Monte-Carlo study was carried out to compare the proposed solution, in terms of type I error rates and statistical power, with the MANOVA and MBF approaches without missing data, and with listwise deletion of missing data followed by the MANOVA approach (LD-MANOVA) and listwise deletion followed by the MBF procedure (LD-MBF). Simulations showed that the type I error rates in all analyses on datasets with missing values (with or without imputation) were well controlled. We also found that the MI-MANOVA approach was substantially more powerful than LD-MANOVA. Moreover, the power of the MI-MANOVA was generally comparable to that of its complete data counterpart. Similar results were obtained for the MI-MBF procedure when covariance matrices were unequal. We conclude, based on the current evidence, that the solution presented performs well and could be of practical use. We illustrate the application of combination rules using a real dataset.
The Development of the Curriculum Fidelity Scale

The purpose of this study is to develop a five-point likert scale called 'Curriculum Fidelity'.For this purpose, a draft scale consisting of 65 items have been represented to the expert opinion firstly and it has been decided that 15 items should be excluded.The remaining 50 items have been given to 249 teachers and the maximum likelihood has been used in the factor analysis of the scale in order to examine the structural validity.The remaining 28 items have been grouped into 3 factors.The explained variance is 62,753 of the total variance.The Cronbach's Alpha value is 0.94.Furthermore, item-total, item-remaining and item discrimination have been found significant.After exploratory factor analysis, it has been found that goodness of fit indexes are acceptable according to the results of confirmatory factor analysis (RMSEA= .075;CFI= .90;RMR= .08;GFI= .
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation

Classifiers in machine learning are often brittle when deployed. Particularly concerning are models with inconsistent performance on specific subgroups of a class, e.g., exhibiting disparities in skin cancer classification in the presence or absence of a spurious bandage. To mitigate these performance differences, we introduce model patching, a two-stage framework for improving robustness that encourages the model to be invariant to subgroup differences, and focus on class information shared by subgroups. Model patching first models subgroup features within a class and learns semantic transformations between them, and then trains a classifier with data augmentations that deliberately manipulate subgroup features. We instantiate model patching with CAMEL, which (1) uses a CycleGAN to learn the intra-class, inter-subgroup augmentations, and (2) balances subgroup performance using a theoretically-motivated subgroup consistency regularizer, accompanied by a new robust objective. We demonstrate CAMEL's effectiveness on 3 benchmark datasets, with reductions in robust error of up to 33% relative to the best baseline. Lastly, CAMEL successfully patches a model that fails due to spurious features on a real-world skin cancer dataset.
Initial Centroid Selection Method for an Enhanced K-means Clustering Algorithm

Clustering is an important method to discover structures and patterns in high-dimensional data and group similar ones together. K-means is one of the most popular clustering algorithms. K-means groups observations by minimizing distances between them and maximizing group distances. One of the primordial steps in this algorithm is centroid selection, in which k initial centroids are estimated either randomly, calculated, or given by the user. Existing k-means algorithms uses the ‘k-means++’ option for this selection. In this paper, we suggest an enhanced version of k-means clustering that minimize the runtime of the algorithm using ‘Ndarray’ option. Experiments have shown that if the first choice of centroids is close to the final centers, the results will be quickly found. Thus, we propose a new concept that provides one of the best choices of starting centroids that reduces the execution time by ≈80% on average for UCI, Shape and Miscellaneous datasets.
Automatic Extractive Text Summarization for Indonesian News Articles Using Maximal Marginal Relevance and Non-Negative Matrix Factorization

The rapid development of the internet has led to the creation of many online news. Competition between the publishers usually will lead to the making of interesting and often misleading titles that are not conveyed the content of the news itself. To overcome this problem, we have developed a text summarization that extracts the keyword from the content, then used that keyword to produce a summary. This research combines Maximal Marginal Relevance (MMR) and Nonnegative Matrix Factorization (NMF) to build automatic extractive text summarized. MMR used to summarize the text automatically and NMF is used to extract the keyword to form the query for MMR. Based on our experiment, the performance of the summarizer system that combines NMF as the keyword generator produce better performance in ROUGE-1 and ROUGE-2 compared to systems that only use a title with MMR. We also perform an experiment to see the impact on the number of a keyword in the summary result. We also found that 8 keywords are the ideal number of keywords to represents the idea of the main text.
Impact of Total Quality Management on Business Value Excellence: A Field Study

This study aimed to analyse the elements of total quality management (TQM) and its impact on achieving business value excellence.The study aimed to determine the level of managers' awareness regarding various elements of TQM.The study population consists of 14 Royal Medical Services (RMS) hospitals in Jordan.The sample consisted of 31 respondents who are working in a quality department in RMS .The study used the analytical descriptive research method and a questionnaire as a tool for data collection.The collected data were analysed using Statistical Package for the Social Sciences via descriptive and simple regression analysis.The study recommended RMS to sustain the implication of TQM by using empowerment, customer focus and scientific approach to decision making and problem solving.In addition, the study embraced the possibility of future improvements to use measurements in determining the level of managers' awareness of TQM and business value excellence.
E-Mufradat: A Digital Game Application for Learning Arabic Vocabulary among Non-native Arabic Speakers

A good educational digital game should be error free and engaging to ensure it can increase students' learning motivation.While there is an abundant of educational digital games available in the market, many seem to not fulfil players' need and some contain technical or content errors, which will defy their purpose as education games.Thus, this study aims to validate e-Mufradat which is an educational digital-game application for learning Arabic vocabulary.This study is a qualitative study and the data were collected through semi-structured interviews with nine experts in instructional design and technology and Arabic as a second language.The subject matter experts were selected purposively from various universities in Malaysia.The finding indicates that e-Mufradat is aligned with multimedia learning theory and embedded digital game-based learning principles.
Simulating Power Generation from Photovoltaics in the Polish Power System Based on Ground Meteorological Measurements—First Tests Based on Transmission System Operator Data

The Polish power system is undergoing a slow process of transformation from coal to one that is renewables dominated. Although coal will remain a fundamental fuel in the coming years, the recent upsurge in installed capacity of photovoltaic (PV) systems should draw significant attention. Owning to the fact that the Polish Transmission System Operator recently published the PV hourly generation time series in this article, we aim to explore how well those can be modeled based on the meteorological measurements provided by the Institute of Meteorology and Water Management. The hourly time series of PV generation on a country level and irradiation, wind speed, and temperature measurements from 23 meteorological stations covering one month are used as inputs to create an artificial neural network. The analysis indicates that available measurements combined with artificial neural networks can simulate PV generation on a national level with a mean percentage error of 3.2%.
An Answer Set Programming Approach to Argumentative Reasoning in the ASPIC+ Framework

A major research direction in AI argumentation is the study and development of practical computational techniques for reasoning in different argumentation formalisms. Compared to abstract argumentation, developing algorithmic techniques for different structured argumentation formalisms, such as assumption-based argumentation and the general ASPIC+ framework, is more challenging. At present, there is a lack of efficient approaches to reasoning in ASPIC+. We develop a direct declarative approach based on answer set programming (ASP) to reasoning in an instantiation of the ASPIC+ framework. We establish formal foundations for direct declarative encodings for reasoning in ASPIC+ without preferences for several central argumentation semantics, and detail ASP encodings of semantics for which reasoning about acceptance is NP-hard in ASPIC+. Empirically, the ASP approach scales up to frameworks of significant size, thereby answering the current lack of practical computational approaches to reasoning in ASPIC+ and providing a promising base for capturing further generalizations within ASPIC+.
Penerapan Metode Certainty Factor Dan Naïve Bayes Untuk Mendiagnosa Penyakit Akibat Gigitan Nyamuk

Health is the main point for the expensive human life. However, many stakeholders ignore their health, which in the end the disease they suffer is too late to be diagnosed, thus reaching a chronic stage that makes it difficult to treat. Same is the case with mosquito bites. The poor behavior of stakeholders toward healthy living habits, especially littering in gutters or in rivers, causes mosquitoes to form colonies by making their nests and environment dirty, dirty and unsightly. The initial symptoms that often arise from the bite of a dengue mosquito, malaria and chikungunya are generally the same and difficult to distinguish. To overcome the above problems, it is necessary to build a system where the system can help stakeholders to diagnose diseases caused by mosquito bites on android. The research method used in this research is the Research and Development method, because the final result of this research is to produce a product in the form of an expert system application software to diagnose diseases caused based mosquito bites by android. The results obtained in this study are by applying the certainty factor method in the application of this expert system is able to provide the percentage calculation results in detecting diseases caused by mosquito bites, while the naïve bayes method is able to detect the type of mosquito. The accuracy of the expert system application that has been made is 90% in diagnosing diseases caused by mosquito bites.
Diagnóstico de las necesidades de capacitación de docentes de biología, química, física y matemática, en áreas disciplinares, pedagógicas, y uso de las tecnologías para la promoción de habilidades de pensamiento científico

This study aims to better understand training secondary and high school teachers require to develop scientific thinking skills in the areas of natural science (Biology, Chemistry, and Physics) and Mathematics. The study focuses on teachers of these disciplinary areas from public schools with an academic curriculum of the Heredia District of Education in Costa Rica. The study worked with 72 teachers of Mathematics and 88 teachers of natural sciences. The research used a naturalistic paradigm. The experimental design used a dominant qualitative approach, combined with a phenomenological study type. The study developed a questionnaire-type instrument to gather the information needed. This instrument consisted of a total of 12 questions in the following areas: pedagogical knowledge and use of technologies, and exploration of the degree of interest that teaching staff has in receiving training in disciplinary knowledge. The five categories used to analyze the teachers’ answers to the instrument were the following: general aspects of the practicing teacher, training interests in the disciplinary and pedagogical field, use of technologies, and challenges expressed by the teaching staff. Among the main findings of the study, the interest of the teaching staff to participate in updating processes in topics such as planning, mediation, and evaluation was evident, as well as training in the use of information and communication technologies. Also, it was determined that training in disciplinary areas should focus on the design of mediation strategies that allow the contents of the programs to be covered in an interdisciplinary and contextualized way. The teachers also showed interest in receiving training related to the design of evaluative processes that allow giving appropriate feedback to students, in terms of the degree of appropriation of scientific and mathematical thinking skills.
Designing an Open Source Virtual Assistant

A chatbot is a type of agent that allows people to interact with an information repository using natural language. Nowadays, chatbots have been incorporated in the form of conversational assistants on the most important mobile and desktop platforms. In this article, we present our design of an assistant developed with open-source and widely used components. Our proposal covers the process end-to-end, from information gathering and processing to visual and speech-based interaction. We have deployed a proof of concept over the website of our Computer Science Faculty.
EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors

Gradient-based planners are widely used for quadrotor local planning, in which a Euclidean Signed Distance Field (ESDF) is crucial for evaluating gradient magnitude and direction. Nevertheless, computing such a field has much redundancy since the trajectory optimization procedure only covers a very limited subspace of the ESDF updating range. In this paper, an ESDF-free gradient-based planning framework is proposed, which significantly reduces computation time. The main improvement is that the collision term in the penalty function is formulated by comparing the colliding trajectory with a collision-free guiding path. The resulting obstacle information will be stored only if the trajectory hits new obstacles, making the planner only extract necessary obstacle information. Then, we lengthen the time allocation if dynamical feasibility is violated. An anisotropic curve fitting algorithm is introduced to adjust higher-order derivatives of the trajectory while maintaining the original shape. Benchmark comparisons and real-world experiments verify its robustness and high-performance. The source code is released as ROS packages.
Protecting ECC Against Fault Attacks: The Ring Extension Method Revisited

Abstract Due to its shorter key size, elliptic curve cryptography (ECC) is gaining more and more popularity. However, if not properly implemented, the resulting cryptosystems may be susceptible to fault attacks. Over the past few years, several techniques for secure implementations have been published. This paper revisits the ring extension method and its adaptation to the elliptic curve setting.
A Concept-based Sentiment Analysis Approach for Arabic

Concept-Based Sentiment Analysis (CBSA) methods are considered to be more advanced and more accurate when it compared to ordinary Sentiment Analysis methods, because it has the ability of detecting the emotions that conveyed by multi-word expressions concepts in language. This paper presented a CBSA system for Arabic language which utilizes both of machine learning approaches and concept-based sentiment lexicon. For extracting concepts from Arabic, a rule-based concept extraction algorithm called semantic parser is proposed. Different types of feature extraction and representation techniques are experimented among the building prosses of the sentiment analysis model for the presented Arabic CBSA system. A comprehensive and comparative experiments using different types of classification methods and classifier fusion models, together with different combinations of our proposed feature sets, are used to evaluate and test the presented CBSA system. The experiment results showed that the best performance for the sentiment analysis model is achieved by combined Support Vector Machine-Logistic Regression (SVM-LR) model where it obtained a F-score value of 93.23% using the Concept-Based-Features+Lexicon-Based-Features+Word2vec-Features (CBF+LEX+W2V) features combinations
Your Age Revealed by Facebook Picture Metadata

Facebook users unknowingly reveal personal information that may help attackers to perpetrate malicious actions. In this paper, we show how sensitive age information of a given target user can be predicted from his/her online pictures. More precisely, we perform age inference attacks by leveraging picture metadata such as (i) alt-texts automatically generated by Facebook to describe the picture content, and (ii) picture reactions (comments and emojis) of other Facebook users. We investigate whether the target’s age affects other users’ reactions to his/her pictures. Our experiments show that age information can be inferred with AUC of 62% by using only alt-texts and with AUC of 89% by using combination of alt-texts and users’ reactions. Additionally, we present a detailed analysis of spearman correlation between reactions of Facebook users and age.
Weakly-supervised 3D Shape Completion in the Wild

3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned. Different from previous methods, we address the problem of learning 3D complete shape from unaligned and real-world partial point clouds. To this end, we propose a weakly-supervised method to estimate both 3D canonical shape and 6-DoF pose for alignment, given multiple partial observations associated with the same instance. The network jointly optimizes canonical shapes and poses with multi-view geometry constraints during training, and can infer the complete shape given a single partial point cloud. Moreover, learned pose estimation can facilitate partial point cloud registration. Experiments on both synthetic and real data show that it is feasible and promising to learn 3D shape completion through large-scale data without shape and pose supervision.
Research on Practice Teaching System of Micro-enterprise Training Center for Electronic Information Specialty

Under the reform of China's education system, the construction and education development model of "school-enterprise co-construction" is put forward to promote the construction and development of universities. Through the mutual cooperation between universities and enterprises, the improvement and innovation of universities' infrastructure and education base are promoted. This paper mainly discusses the research on the practice teaching system of micro-enterprise training center under the condition of "Schools and enterprises to build together" of electronic information major in Beihai campus of Guilin university of electronic science and technology.
Energy-Guided Temporal Segmentation Network for Multimodal Human Action Recognition

To achieve the satisfactory performance of human action recognition, a central task is to address the sub-action sharing problem, especially in similar action classes. Nevertheless, most existing convolutional neural network (CNN)-based action recognition algorithms uniformly divide video into frames and then randomly select the frames as inputs, ignoring the distinct characteristics among different frames. In recent years, depth videos have been increasingly used for action recognition, but most methods merely focus on the spatial information of the different actions without utilizing temporal information. In order to address these issues, a novel energy-guided temporal segmentation method is proposed here, and a multimodal fusion strategy is employed with the proposed segmentation method to construct an energy-guided temporal segmentation network (EGTSN). Specifically, the EGTSN had two parts: energy-guided video segmentation and a multimodal fusion heterogeneous CNN. The proposed solution was evaluated on a public large-scale NTU RGB+D dataset. Comparisons with state-of-the-art methods demonstrate the effectiveness of the proposed network.
FPGA Implementation of A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msup><mml:mrow /><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:math> Algorithm for Real-Time Path Planning

The traditional A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msup><mml:mrow /><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:math> algorithm is time-consuming due to a large number of iteration operations to calculate the evaluation function and sort the OPEN list. To achieve real-time path-planning performance, a hardware accelerator’s architecture called A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:msup><mml:mrow /><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:math> accelerator has been designed and implemented in field programmable gate array (FPGA). The specially designed 8-port cache and OPEN list array are introduced to tackle the calculation bottleneck. The system-on-a-chip (SOC) design is implemented in Xilinx Kintex-7 FPGA to evaluate A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:msup><mml:mrow /><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:math> accelerator. Experiments show that the hardware accelerator achieves 37–75 times performance enhancement relative to software implementation. It is suitable for real-time path-planning applications.