Reviewerly/exHarmony huggingface dataset:
SCAR

Spectral clustering is one of the most advantageous clustering approaches. However, standard Spectral Clustering is sensitive to noisy input data and has a high runtime complexity. Tackling one of these problems often exacerbates the other. As real-world datasets are often large and compromised by noise, we need to improve both robustness and runtime at once. Thus, we propose Spectral Clustering - Accelerated and Robust (SCAR), an accelerated, robustified spectral clustering method. In an iterative approach, we achieve robustness by separating the data into two latent components: cleansed and noisy data. We accelerate the eigendecomposition - the most time-consuming step - based on the Nyström method. We compare SCAR to related recent state-of-the-art algorithms in extensive experiments. SCAR surpasses its competitors in terms of speed and clustering quality on highly noisy data.
An Advanced Facial Expression Recognition System Using Novel Machine Learning Approach by Comparing CNN over LeNet

Aim:The primary goal of this study is to compare Convolutional Neural Networks (CNN) and LeNet for classification of facial emotion recognition with improved accuracy.Materials and Methods: The data of the facial expressions is taken from the FER2013 available on kaggle.The Convolutional neural network (CNN) is the algorithm most popular way of analyzing images with best accuracy and LeNet is also an algorithm used here to compare the accuracy of Novel image classification.Results: The Convolution neural network (CNN) produces 82.14% accuracy in predicting facial expressions on the dataset,whereas LeNet produces 77.54% accuracy.Convolutional neural network (CNN) is better than LeNet.With (p<0.05),there is a statistically significant difference between the research groups.Conclusion: Convolutional Neural Network provides better outcomes in accuracy rate when compared to LeNet for predicting facial expressions.
Towards a Fair Evaluation of Feature Extraction Algorithms Robustness in Structure from Motion

Structure from Motion is a pipeline for 3D reconstruction in which the true geometry of an object or a scene is inferred from a sequence of 2D images.As feature extraction is usually the first phase in the pipeline, the reconstruction quality depends on the accuracy of the feature extraction algorithm.Fairly evaluating the robustness of feature extraction algorithms in the absence of reconstruction ground truth is challenging due to the considerable number of parameters that affect the algorithms' sensitivity and the tradeoff between reconstruction size and error.The evaluation methodology proposed in this paper is based on two elements.The first is using constrained 3D reconstruction, in which only fixed numbers of extracted and matched features are passed to subsequent phases.The second is comparing the 3D reconstructions using size-error curves (introduced in this paper) rather than the value of reconstruction size, error, or both.The experimental results show that the proposed methodology is more transparent.
DIGITIZATION INNOVATION IN UNIVERSITY EDUCATION

Covid-19 pandemic and post-pandemic had led to the confrontation of higher education system with enormous challenges.That necessitated the urgent transition from face-to-face -teaching to online -teaching.The change was an innovation in higher education.A comparative study of digital education based on the survey in 2020-2021 in seven different countries was conducted at Shanghai University.The study was based on grey comprehensive evaluation model.In general, developed countries had high comprehensive evaluation value, while Finland, United States of America, South-Korea and Latvia had relatively low grey correlation coefficient in several certain indicators, leading to a sharp drop in the overall score.Romania ranked last while China ranked second as a developing country as well.The study launched a conclusion that research and development personnel, infrastructure funds and university financial investment in digital education had relatively more obvious effects on improving the innovations and quality of higher education system including the leadership system of universities.
Using Siamese Networks for Advertisements Classification

Given the constant growth of investment in online advertising, it is essential to have tools that provide complete, accurate, verified, and real-time monitoring of the ads companies place on different websites and social networks to sell their products through campaigns. For this reason, many advertisement companies provide a digital platform that allows easy and secure access to strategic information for advertising agencies, media, media centers, and advertisers so that they can verify that their advertising campaigns are being carried out successfully. However, a challenge that many companies face today is the lack of semi-automatic mechanisms to identify advertising from their clients and even detect competing advertisers who invest their money in other media. Currently, this identification is accomplished by many companies manually, representing a high cost in time and effort. In this context, we propose a siamese networks approach for automatically classifying online advertisements. We evaluated the approach's performance using promotional advertising data provided by leading advertisement company in Argentina. We found that the accuracy of the proposed method can achieve an outstanding performance of 90%.
Reinforcement Learning Accelerator using Q-Learning Algorithm with Optimized Bit Precision

This paper presents a Reinforcement Learning (RL) accelerator using Q-learning algorithm with optimized bit precision. In this work, we perform evaluation of the employed bit width of the data path subject to accuracy of the Q-values. The designed RL accelerator is implementing the Q-Learning algorithm that comprises several blocks: Q-Value memories, Q-Updater, Policy Generator and, Environment block. In addition, we also present the corresponding architecture and implement the design in the FPGA. Experimental results show that the number of bits can be reduced from 32 bits to 16 bits without sacrificing the accuracy. The accuracy can be maintained at around 88% when employing 16 bits data path with 10 bits fraction. Moreover, the designed 16 bits RL accelerator design size offers reduction of LUTs and FFs compared to 32 bits implementation by around 40% and 14 %, respectively. Hence, the optimized accelerator can be useful for low-complexity systems or limited resources such as in robot automation for smart navigation and smart mapping.
A política de acesso aberto nas revistas de humanidades

Open Science is being increasingly adopted in scientific journals through initiatives such as allowing free access to articles, data availability, preprints, among others. The present study aims to analyze the editorial policies presented in the websites of SciELO journals of the humanities, more specifically about open access to research data and preprints. A descriptive survey was used, which exposes in a contextualized and direct manner the mapping about the implementation of Open Science policies in these journals. It was possible to observe that among the 97 indexed journals, 22 have a well-developed policy, 15 only mention open access, and 81 do not mention the request for research data.
Automatic Component Prediction for Issue Reports Using Fine-Tuned Pretrained Language Models

Various issues or bugs are reported during the software development. It takes considerable effort, time, and cost for the software developers to triage these issues manually. Many previous studies have proposed various method to automate the triage process by predicting component using word-based language models. However, these methods still suffer from unsatisfactory performance due to their structural limitations and ignorance of the word context. In this paper, we propose a novel technique based on pretrained language models and it aims to predict a component of an issue report. Our approach fine-tunes the pretrained language models to conduct multilabel classifications. The proposed approach outperforms the previous state-of-the-art method by more than 30% with respect to the recall at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> on all the datasets considered in our experiment. This improvement suggests that fine-tuned pretrained language models can help us to predict issue components effectively.
Linear Fusion under Random Correlation of Estimation Errors

Linear fusion of estimates has been studied from the perspectives of known and unknown correlations of estimation errors. Whereas optimal linear combinations can be designed in the former case, a robust approach is usually chosen in the latter one. The loss of performance may be unacceptably high, which raises the need to find a middle ground. This paper reviews various approaches to information fusion, formulates the problem of random correlation and presents the solution. Monte Carlo verification of the results is discussed and an illustration is provided.
Strategies to Evolve ExM Notations Extracted from a Survey with Software Engineering Professionals Perspective

Contemporary complex systems often exhibit dynamic structures and behaviors, several components/systems involved, and multiple interoperability links. Those systems have been exposed to fragilities of traditional software specification languages (e.g. UML and SySML), since such languages were designed to document single (not multiple interoperating) systems. Those limitations can potentially further compromise the quality of the final software product. In this context, Executable Models (ExM) technology, such as simulation models, models@runtime and executable UML, satisfy the aforementioned requirements by supporting engineers with visualization of the system structures (still at design-time) and the ability to exercise their behaviors and interactions. In our prior study, we presented the results of an exploratory study on the perceptions of those professionals (from both industry and academia) regarding the use of ExM to solve problems in their current practice. We exposed 58 professionals (researchers and practitioners) to situations to solve problems using a specific type of ExM (DEVS simulation models), based on survey research. Responses were quantitatively and qualitatively analyzed. In this article, we extended the obtained results by analyzing and compiling a list of strategies to improve ExM notations to better address the needs of software engineering professionals. Later, we assessed those strategies with software engineering researchers to confirm the importance of the proposed strategies. Results revealed that executable languages still require advances to bring them even closer to the current software engineering practice and towards a more significant adoption in the future. The proposed strategies focus on improvements on the robustness of the ExM notations, visual representation of the models, the usability of the models, and user support.
On the Teaching Mode of “Multi-dimensional Interaction” in Calculus

Calculus is a compulsory course for freshmen. The cumulative number of students choosing courses each year accounts for about 95% of the total number of freshmen. It has the widest influence, the most class hours and the longest time. As freshmen keep their enthusiasm and habits in high school, with the new generation of Internet plus growth, accept new things on the internet faster and have a stronger ability to obtain information. Traditional teaching mode has been passively accepting blackboard instruction, and taking notes and participation exercises. This teaching mode is not conducive to stimulating students' enthusiasm for further learning. Moreover, it is not conducive to cultivating students' scientific thinking ability to analyze and solve problems and the habit of lifelong learning. Therefore, the course team puts forward the teaching innovation mode - “multi-dimensional interactive” teaching mode between teachers and students, which uses multi-dimensional interactive teaching from different angles, different levels and different methods to create classes and raise knowledge, thus we can solve the problems in the teaching of Internet plus era and improve the quality of teaching.
Loopless Top-K Planning

In top-k planning, the objective is to determine a set of k cheapest plans that provide several good alternatives to choose from. Such a solution set often contains plans that visit at least one state more than once. Depending on the application, plans with such loops are of little importance because they are dominated by a loopless representative and can prevent more meaningful plans from being found. In this paper, we motivate and introduce loopless top-k planning. We show how to enhance the state-of-the-art symbolic top-k planner, symK, to obtain an efficient, sound and complete algorithm for loopless top-k planning. An empirical evaluation shows that our proposed approach has a higher k-coverage than a generate-and-test approach that uses an ordinary top-k planner, which we show to be incomplete in the presence of zero-cost loops.
A Real-Time Fall Classification Model Based on Frame Series Motion Deformation

Fall is common among the elderly and patients with severe health conditions. It can be life threatening, especially if the person does not received the required help to recover from fall on time. Therefore, an automatic real-time fall detection system is very desirable and has been the focus of research in the last couple of years. Traditional computer vision (CV) based fall detection systems require less infrastructure, is cheaper and can be more efficient comparing to systems based on wearable sensors. The robustness and efficiency of CV techniques depend on the extracted feature set from the surveillance video sequences. Generally, the efficiency and accuracy of more recent learning based CV systems rely heavily on the statistical characteristics of training dataset. Acquiring a balanced, comprehensive and representative training data that covers all the necessary aspects of the problem, including viewing direction of the camera and illumination condition of the environment is quite challenging. The problem would be even more serious when the training dataset does not have representative features as the surveillance area. In this paper, we propose a robust, real-time, CV based fall detection technique that can work in different settings. The propose system requires only a single affordable RGB camera. The proposed method works at frame level and only uses two significant feature points for classification, therefore occlusion would not influence the system. We performed experiments on different publicly available datasets such as le2i, UR and multiple camera fall detection datasets. The result shows that the proposed technique can distinguish fall from everyday activities, e.g., sitting down and sleeping and has a higher accuracy, recall and specificity comparing to other methods. The proposed method performs well in indoor environments with different lighting conditions and different viewing directions of the camera.
Distant supervision relation extraction based on mutual information and multi-level attention

Distant supervision for relation extraction, an effective method to reduce labor costs, has been widely used to search for novel relational facts from text. However, distant supervision always suffers from incorrect labelling problems. Meanwhile, existing methods for noise reduction oftentimes ignore the commonalities in the instances. To alleviate this issue, we propose a distant supervision relation extraction model based on mutual information and multi-level attention. In our proposed method, we calculate mutual information based on the attention mechanism. Mutual information are used to build attention at both word and sentence levels, which is expected to dynamically reduce the influence of noisy instances. Extensive experiments using a benchmark dataset have validated the effectiveness of our proposed method.
Automated Temporal Verification for Algebraic Effects

Although effect handlers offer a versatile abstraction for user-defined effects, they produce complex and less restricted execution traces due to the composable non-local control flow mechanisms. This paper is interested in the temporal behaviors of effect sequences, such as unhandled effects, termination of the communication, safety, fairness, etc. Specifically, we propose a novel effects logic ContEffs, to write precise and modular specifications for programs in the presence of user-defined effect handlers and primitive effects. As a second contribution, we devise a forward verifier together with a fixpoint calculator to infer the behaviors of such programs. Lastly, our automated verification framework provides a purely algebraic term-rewriting system (TRS) as the back-end solver, efficiently checking the entailments between ContEffs assertions. To demonstrate the feasibility, we prototype a verification system where zero-shot, one-shot, and multi-shot continuations coexist; prove its correctness; present experimental results; and report on case studies.
Development and Implementation of Interactive e-Module using Microsoft Sway to Improve Disaster Literacy Skills

Disaster literacy needs to be introduced to provide understanding to students and minimize disaster risk. The purpose of this study was to determine the use of an interactive e-module based on Microsoft Sway which is valid and effective in increasing students’ literacy. The design of this study uses a 4D (four-D) Research dan Development (R&amp;D) model. The research sample consisted of control and experimental classes with instruments in the form of test instruments, questionnaires, and interviews. Data analysis was based on n-gain data, effect size, and descriptive percentages of teacher and student responses. The percentages of construct, content, and language validation obtained were 84.68%,80.58%, and 86% respectively, with all valid criteria. The study findings indicate that the use of an interactive e-module based on Microsoft Sway is valid and highly effective in improving students’ disaster literacy skills. Keywords: Interactive e-module, disaster literacy skills, disaster literacy, Microsoft Sway. Abstrak: Literasi bencana perlu diperkenalkan untuk memberikan pemahaman kepada siswa dan meminimalisir risiko bencana. Tujuan penelitian ini untuk mengetahui penggunaan e-modul interaktif berbasis Microsoft Sway yang valid dan memiliki efektivitas untuk meningkatkan literasi kebencanaan siswa. Desain penelitian ini menggunakan desain Riset and Development (R &amp; D) model 4D (four-D). Sampel penelitian terdiri dari kelas kontrol dan eksperimen dengan istrumen berupa instrumen tes, angket dan wawancara. Analisis data berdasarkan data n-gain, effect size dan persentase deskriptif tanggapan guru dan siswa. Persentase validasi konstruk, isi dan bahasa yang diperoleh secara berturut-turut adalah 84.68%, 80,58% and 86% dengan kriteria seluruhnya valid. Berdasarkan hasil pre-test and post-test didapatkan N-gain 0,66 dengan kriteria sedang untuk meningkatkan literasi kebencanaan siswa. Temuan penelitian menunjukkan bahwa penggunaan e-modul interaktif berbasis Microsoft Sway valid dan memiliki efektivitas tinggi untuk meningkatkan kemampuan literasi kebencanaan siswa Kata kunci: e-modul interaktif, keterampilan literasi kebencanaan, literasi kebencanaan, Microsoft Sway. DOI: http://dx.doi.org/10.23960/jpmipa/v23i2.pp854-871
Comparison of coarse and fine sampling resolutions in vowel analysis

There are many researcher degrees of freedom in phonetic analyses, especially in that of segments such as vowels. The focus of this study is one such degree of freedom, governing how frequently measurements are taken for an individual token in the corpus. Choices for this stage of analysis include averaging formants (single measurement), taking point measurements (multiple measurements), or sampling continuously across the token (many measurements). Less-frequent measurements are convenient techniques compatible with many common statistical methods, but risk oversimplifying the patterns in the data. In this project, I compare point and continuous measurements using Generalized Additive Mixture Models (GAMMs) on speech corpus data. GAMMs are a generalized model that can flexibly parameterize both curves and points based on a combination of random and fixed effects. To compare different measurement frequencies, I quantify the benefit of additional measurements in terms of information gain and variance explained. I also address the modeling of multidimensional measurements and identify the degree to which issues of multidimensionality such as collinearity can be accounted for in GAMMs.
Teachers' Self-efficacy on Integration of Augmented Reality in Teaching-learning activities: Comparison among Selected Schools in Nigeria

Digital technology is redefining teaching-learning engagements and challenging the professional teachers to match the advancement; such is integration of augmented reality (AR) in education. Apparently, change often encounters reluctance to embrace it. As a lot of teachers still battle phobia for computer technologies, the integration of sophisticated AR could be a problem. No matter the challenges, reliable data could assist in planning towards mitigating the problems. Pertinently, self-efficacy data on teachers as stake holders are relevant to assist in planning, to maximise innovative potentials of AR in teaching and learning. Therefore, this study investigated teachers' self-efficacy on integration of augmented reality in teaching-learning activities among selected schools in Nigeria. In this descriptive survey research, cluster and random sampling methods were used to select sample. The target population was all the secondary school teachers in Lagos state, Nigeria; the sample comprised 178 participants. Validated Teacher Self-efficacy Questionnaire (TSQ) on 4-Likert scale, of 0.89 reliability coefficient, was used to collect data. The data were computed using percentage, bar-charts and Chi-square. The results revealed that differences in the levels of self-efficacy are not enough to make the teachers in public schools perform differently when compared to counterparts in private schools, if AR is integrated into teaching methods. Based on the results, it was recommended that awareness seminars should be organised for the teachers on how to use AR technology in education. Moreover, to ensure sustainability, require software should be made available, and the teachers should be given competency based training to use it.
Process-Oriented Intents: A Cornerstone for Superimposition of Natural Language Conversations over Composite Services

Task-oriented conversational assistants are in very high demand these days. They employ third-party APIs to serve end-users via natural language interactions and improve their productivity. Recently, the augmentation of process-enabled automation with conversational assistants emerged as a promising technology to make process automation closer to users. This paper focuses on the superimposition of task-oriented assistants over composite services. We propose a Human-bot-Process interaction acts that are relevant to represent natural language conversations between the user and multi-step processes. In doing so, we enable human users to perform tasks by naturally interacting with processes.