Reviewerly/exHarmony huggingface dataset:
Collaborative mind mapping in RICOSRE learning model to improve students’ information literacy

&lt;span lang="EN-US"&gt;This research aimed to determine how the reading, identifying problem, constructing solution, solving problem, reviewing and extending problem solution (RICOSRE) learning model integration with collaborative mind mapping affects students’ information literacy in higher education institutions. This quasi-experimental study used a pretest-posttest non-equivalent control group design. There were 40 multiple-choice questions selected to assess student information literacy. All instruments have been validated and are reliable. The data were collected from 100 participants who were college students enrolled in the environmental pollution course of the biology education degree program at Siliwangi University, Indonesia. To analyze the research data, analysis of covariance (ANCOVA) was used at a 5% level of significance, followed by the least significance difference (LSD) test. The ANCOVA results showed that the RICOSRE-CMM learning model impacted students’ information literacy. LSD scores were significantly different in terms of students’ information literacy. It can be concluded that the RICOSRE-CMM model could enhance each component of information literacy as these components have been integrated into the learning model syntax, which involves a series of information discovery and analysis processes during group work to find a solution to a problem.&lt;/span&gt;
Challenges encountered by newly arrived Thai students when learning Indonesian as a foreign language

&lt;span lang="EN-US"&gt;This study aimed to determine the challenges faced by Thai international students when they first came to Indonesia to study Indonesian as a foreign language. There were nine Thai students studying in six different cities in Indonesia were recruited as participants. The thematic analysis revealed two major themes: academic challenges and technological issues. Both made it difficult for Thai students to master the Indonesian language effectively. The condition potentially hinders their academic success when studying in Indonesia. However, the researchers found that despite the language barrier, Thai students still tried to speak Indonesian with Indonesian students and residents. It indicates that Thai students in this study were highly determined, friendly, and open to new cultures. Furthermore, the researchers advise teachers and program organizers to pay attention to these challenges to maximize the learning output.&lt;/span&gt;
Assessing numeracy skills on flat shapes and scaffolding forms in junior high school

&lt;span lang="EN-US"&gt;Based on The Regulation of The Minister of Education, Culture, Research and Technology No.17 of 2021, cognitive learning outcomes consist of literacy and numeracy as measured through a minimum competency assessment (MCA) with grade 5, 8, and 11. The subjects in this study are 42 students of grade VIII (second grade) from state Junior High School 4 Cirebon and State Junior High School 2 Cirebon, Indonesia. The instruments were tests of numeracy literacy skills, documentation, and interviews. Purposive sampling was used to determine the subject of analysis of test results and interviews. The data were analyzed descriptively through the following stages: data reduction, data presentation, and conclusions drawing. The results showed that the research subjects are still not familiar with numeracy literacy questions which have the characteristics of always being preceded by texts, not understanding what is known and asked in the questions so they tend to blindly guess the answers and needed to be reminded of the prerequisite material. Based on the results of this study, teachers can develop learning media, project-based learning, scaffolding, and teaching materials that accommodate numeracy skills or differentiated learning.&lt;/span&gt;
Stakeholders’ awareness and acceptance of the vision, mission, goals, and outcomes statements of a teacher education college

&lt;span lang="EN-US"&gt;This study examined the awareness and acceptance of the stakeholders of a teacher education college in the Philippines of their university vision and mission and the college goals, and outcomes statements (VMGOs). Of the 700 target respondents, 406 stakeholders responded to the survey transcribed in Google Form. They were 42 school personnel, 106 parents, and 258 students; 301 females and 105 males. The five-point rating scales asked the respondents to assess their awareness and acceptance of 16 statements including one vision, one mission, five institutional outcomes (IO), two college goals, and seven program outcomes (PO). The respondents as a whole indicated they were very aware of the VMGOs and these were very acceptable for them. However, the school personnel were consistently more aware than the students and the parents/officials; while the parents/officials consistently indicated the lowest awareness and acceptability scores for all the statements. The results further revealed a significant acceptable relationship between awareness and acceptability scores. The variables share a variance ranging from 51% to 61% confirming that as awareness increases, acceptance rate also increases. Various implications of these findings are discussed.&lt;/span&gt;
Portrait of students’ language politeness in elementary school

&lt;span lang="EN-US"&gt;This study described students’ language impoliteness during the Indonesian language learning process taking place at &lt;/span&gt;&lt;span lang="EN-US"&gt;elementary school in the Kediri Regency, East Java Province, Indonesia.&lt;/span&gt;&lt;span lang="EN-US"&gt; This study used a qualitative approach to the type of qualitative descriptive research. The research participant of this study were elementary school educators and students in Kediri, East Java, Indonesia, who have a rough and smooth Javanese language culture when communicating with teachers during the learning process in class. The results of the study showed that all politeness maxims were violated in the learning process. Language politeness must continue to be implemented in elementary schools because it is still being discovered violation of the maxim of wisdom is shown when students give long-winded statements to educators. Violation of the maxim of generosity is shown when students give emotional statements to educators. Violation of the maxim of praise is shown when students express criticism at will, to both educators and friends. Violation of the maxim of politeness is shown when students make statements that demean the speech partner. Violation of the maxim of agreement can be seen when students are giving very harsh statements and ignoring the rules of the speech partners. The violation of the maxim of sympathy is shown when students do not have sympathy for the speech partner who is having difficulty. Language politeness is very important for the world of education as an effort to familiarize students as the nation’s next generation with polite language.&lt;/span&gt;
The study of digital literacy components for youths during the COVID-19 pandemic in Bangkok, Thailand

&lt;span lang="EN-US"&gt;This research paper investigates digital literacy components for youths in Bangkok. The first phase used a qualitative method. The key informants were eight experts in information and communication technology (ICT), human resource (HR), and education. The second phase was to develop the measurement of digital literacy components using the confirmatory factor analysis (CFA) approach with a sample consisting of 1,362 youths in Bangkok. Data collection was conducted through a questionnaire with 52 questions. The results revealed that digital literacy criteria for youths in Bangkok consisted of four factors containing 13 indicators. The first factor and its related access components was three subcomponents, which are i) use of tools and devices; ii) data collection in the cloud; and iii) internet connection. The second factor was understanding components consisting of evaluation, ethics, and legal literacy. The third factor was use of components consisting of safeguarding, search, sharing, and innovation. The fourth factor was creating components consisting of creating weblogs/applications, presenting on website/application, and safeguarding. CFA was employed to test the construct validity of the research latent variables that revealed the harmony correlation of empirical data contained in this research model. These results were employed to develop a digital literacy for youth during the COVID-19 pandemic in Bangkok, Thailand.&lt;/span&gt;
TLFS23 Tamil Language Fingerspelling Dataset

Tamil is one of the oldest existing languages, spoken by around 65 million people across India, Sri Lanka and South-East Asia. Countries such as Fiji and South Africa also have a significant population with Tamil ancestry. Tamil is a complex language and has 247 characters. A labelled dataset for Tamil Fingerspelling named TLFS23 has been created for research related to vision-based Fingerspelling translators for the Speech and hearing Impaired. The dataset would open up avenues to develop automated systems as translators and interpreters for effective communication between fingerspelling language users and non- users, using computer vision and deep learning algorithms. One thousand images representing each unique finger flexion motion for every Tamil character was collected overall constituting a large dataset with 248 classes with a total of 2,55,155 images. The images were contributed by 120 individuals from different age groups. The dataset is made publicly available at : https://data.mendeley.com/datasets/39kzs5pxmk/2
3-D Shape Measurement of Translucent Objects Based on Fringe Projection

Fringe projection profilometry (FPP) has been widely used in various fields because of its advantages of non-contact, non-destructive, high-precision, and high-speed. However, due to the subsurface scattering effect, FPP encounters severe challenges when performing three-dimensional (3D) measurements of translucent objects. Based on FPP, this paper proposes a novel 3D measurement method for translucent objects using periodic line-fringe patterns and complementary Gray code patterns. By photographing a set of periodic line-fringe patterns with different phases and performing multi-Gaussian sum fitting on the grayscale values of the same pixel position in different images, the wrapping phase of the fringe corresponding to the maximum fitted grayscale value can be obtained. Furthermore, a novel complementary Gray code method is proposed to solve the absolute phase, which avoids phase unwrapping errors caused by subsurface scattering. Comparative experimental results show that the point cloud of translucent objects obtained using the proposed 3D reconstruct method is more complete, denser, and smoother, and more accuracy. The fitting diameter errors of the reconstruction results of the translucent nylon sphere and PP sphere are 0.025 mm and 0.044 mm respectively. In addition, our method can use the conventional binocular stereo vision system based on grating projection, without modifying the hardware system.
A Tourism Dataset from Historical Transaction for Recommender Systems.

The tourism industry has currently grown in various aspects, including the types of attractions, their quantity, and the number of tourist visits in various regions, contributing positively to both regional and global economies. Historical transactions are essential for developing recommender systems, utilizing techniques such as Collaborative Filtering and Demographic Filtering. TripAdvisor is a reputable website providing a wide range of accessible tourism information, including attractions, user profiles, and ratings. However, this unstructured raw data requires processing to create an adequate dataset for recommender systems. This study conducted a series of data processing steps on the raw data, including data restructuring, validation, content addition, integration with Google Maps, normalization, and modeling. This study successfully produced an original dataset comprising User Transaction, Item or Attraction, Attraction Type, Continent, Region, Country, City, and Visiting Mode. It also includes an entity relational model for tourism in Indonesia, particularly in Bali, Malang, and Yogyakarta regions, based on various global user experiences. This dataset is adequate and essential for developing various models of tourism recommender systems such as using Collaborative Filtering.
Balancing exploration and exploitation: Unleashing the adaptive power of automatic cuckoo search for meta-heuristic optimization

Meta-heuristic optimization algorithms are versatile and efficient techniques for solving complex optimization problems. When applied to clustering algorithms, these algorithms offer numerous advantages over traditional optimization methods, including global search capabilities, iterative refinement processes, robustness to initial conditions, and flexibility in handling diverse clustering objectives and constraints. Employing meta-heuristic optimization in clustering algorithms leads to improved accuracy, scalability, robustness, and flexibility in finding optimal or near-optimal clustering solutions. These algorithms generate new individuals iteratively using nature-inspired operations to obtain high-quality results. However, they often suffer from slower convergence and lack guarantees of finding the best solution for every problem, posing ongoing challenges in algorithm development. This study focuses on addressing the issue of premature convergence in metaheuristic algorithms by introducing an automatic cuckoo search (AuCS) algorithm. The AuCS algorithm aims to strike a balance between exploration and exploitation by dynamically updating the step size in each generation, thereby avoiding premature convergence. To evaluate the effectiveness of the proposed algorithm, experiments were conducted on 13 standard benchmark functions and 14 CEC 2005 benchmark functions. In overall performance, AuCS has the best optimum value in 72.22% of cases. This demonstrates the efficacy of the proposed algorithm in achieving improved clustering accuracy and minimizing intra-cluster distance. The proposed AuCS algorithm was applied to data clustering and compared with four swarm optimization algorithms. Here, AuCS outperforms these well-known algorithms in 5 out of 7 datasets. The experimental evaluations in both benchmark functions and clustering problems confirm the promising results of the proposed algorithm, suggesting that AuCS could be considered as a potential improvement over the cuckoo search algorithm.
Learning image representations for anomaly detection: Application to discovery of histological alterations in drug development

We present a system for anomaly detection in histopathological images. In histology, normal samples are usually abundant, whereas anomalous (pathological) cases are scarce or not available. Under such settings, one-class classifiers trained on healthy data can detect out-of-distribution anomalous samples. Such approaches combined with pre-trained Convolutional Neural Network (CNN) representations of images were previously employed for anomaly detection (AD). However, pre-trained off-the-shelf CNN representations may not be sensitive to abnormal conditions in tissues, while natural variations of healthy tissue may result in distant representations. To adapt representations to relevant details in healthy tissue we propose training a CNN on an auxiliary task that discriminates healthy tissue of different species, organs, and staining reagents. Almost no additional labeling workload is required, since healthy samples come automatically with aforementioned labels. During training we enforce compact image representations with a center-loss term, which further improves representations for AD. The proposed system outperforms established AD methods on a published dataset of liver anomalies. Moreover, it provided comparable results to conventional methods specifically tailored for quantification of liver anomalies. We show that our approach can be used for toxicity assessment of candidate drugs at early development stages and thereby may reduce expensive late-stage drug attrition.
Genetic Programming for Document Classification: A Transductive Transfer Learning System

Document classification is a challenging task to the data being high-dimensional and sparse. Many transfer learning methods have been investigated for improving the classification performance by effectively transferring knowledge from a source domain to a target domain, which is similar to but different from the source domain. However, most of the existing methods cannot handle the case that the training data of the target domain does not have labels. In this study, we propose a transductive transfer learning system, utilizing solutions evolved by genetic programming (GP) on a source domain to automatically pseudolabel the training data in the target domain in order to train classifiers. Different from many other transfer learning techniques, the proposed system pseudolabels target-domain training data to retrains classifiers using all target-domain features. The proposed method is examined on nine transfer learning tasks, and the results show that the proposed transductive GP system has better prediction accuracy on the test data in the target domain than existing transfer learning approaches including subspace alignment-domain adaptation methods, feature-level-domain adaptation methods, and one latest pseudolabeling strategy-based method.
Updating of higher education curriculum for future engineers during the COVID-19 pandemic

Higher education is immersed in unpredictable environments and is facing challenges during the Coronavirus Disease 2019 (COVID-19) pandemic. It needs to adapt to external changes constantly, especially to the level of development of science and technology as the most important component of education courses for future engineers are facing development difficulties within achieving their goals as the core of higher education. Curriculum serves as basis for realization of goals of higher education and curriculum for future engineers needs updating. The paper analyzes dilemma of updating of higher education curriculum for future engineers within its content and structure. During the learning process students participated in curriculum renewal and accelerated the upgrading of curriculum content and structure. Finishing the course, students conducted reflection, improved their understanding during the reflection process continuously that helped them to develop the habit of lifelong learning. The authors propose to expand teaching methods for the courses, to adapt engineering courses to the needs of economic development during the COVID-19 pandemic, to strengthen students’ interaction and participation, to supply multiple participation in curriculum “customization” countermeasures to update higher education curriculum for future engineers.
The role of gamification implementation in improving quality and intention in software engineering learning

Gamification can make learning more fun and engaging for students. Software engineering can utilize gamification to help students learn and improve their skills from the complexity of software engineering. This study used quantitative research to examines perceived ease of use, student satisfaction, and perceived usefulness to measure gamification quality, which can have an impact on software engineering intention, namely intention, loyalty, and participation in following and understanding software engineering materials. The data was collected based on an online questionnaire survey, 90 data were collected and then measured and analyzed using SmartPLS 3. The results showed that perceived ease of use, student satisfaction, and perceived usefulness have a significant influence on gamification quality, which also leads to a positive impact on software engineering intention. This research guides teachers and educational institutions that gamification is very successful as a learning medium to simplify complex information to be more interactive.
Corrections to “Model-Based Design of the COAST Guidewire Robot for Large Deflection”

Presents corrections to the article “Model-Based Design of the COAST Guidewire Robot for Large Deflection.
TestFit: A plug-and-play one-pass test time method for medical image segmentation

Deep learning (DL) based methods have been extensively studied for medical image segmentation, mostly emphasizing the design and training of DL networks. Only few attempts were made on developing methods for applying DL models in test time. In this paper, we study whether a given off-the-shelf segmentation network can be stably improved on-the-fly during test time in an online processing-and-learning fashion. We propose a new online test-time method, called TestFit, to improve results of a given off-the-shelf DL segmentation model in test time by actively fitting the test data distribution. TestFit first creates a supplementary network (SuppNet) from the given trained off-the-shelf segmentation network (this original network is referred to as OGNet) and applies SuppNet together with OGNet for test time inference. OGNet keeps its hypothesis derived from the original training set to prevent the model from collapsing, while SuppNet seeks to fit the test data distribution. Segmentation results and supervision signals (for updating SuppNet) are generated by combining the outputs of OGNet and SuppNet on the fly. TestFit needs only one pass on each test sample—the same as the traditional test model pipeline—and requires no training time preparation. Since it is challenging to look at only one test sample and no manual annotation for model update each time, we develop a series of technical treatments for improving the stability and effectiveness of our proposed online test-time training method. TestFit works in a plug-and-play fashion, requires minimal hyper-parameter tuning, and is easy to use in practice. Experiments on a large collection of 2D and 3D datasets demonstrate the capability of our TestFit method.
Heterogeneous Sensor Fault Detection for Networked Systems Based on a Graph Transformer

Networked systems like fuel, petrochemical, and water supply systems use various sensors for monitoring and control. Due to harsh environmental conditions, sensors are prone to failure and pose a significant threat to the stability of the system, making the accurate identification of such anomalies crucial. In recent years, data-driven sensor fault detection methods utilized sensor data correlations, spatial and temporal features, but they haven’t fully harnessed the physical structure relation of sensors in real system. Our study introduces a novel sensor fault detection method that integrates graph neural networks and transformers. The model efficiently uses the interdependence among various sensors for information transmission and aggregation, relying on the physical struction of networked system. To enhance the information exchange among identical sensor, the model devides the graph neural network into subgraphs based on sensor types. Within each subgraph, the attention mechanism of transformers is employed to capture sensor features and distributions, thereby improving the learning of the sensor node representation vector. Finally, a decoder with reconstructed input is utilized for sensor fault detection. The experiments demonstrate better fault detection accuracy in both real networked systems and four heterogeneous sensor datasets. Across all experimental cases, the F1 score of our method is 95.79%, and the AUC value is 98.83%. Finally, through ablation study, the effectiveness of each component of the model is confirmed.