Reviewerly/exHarmony huggingface dataset:
Knowledge Extraction and Quality Inspection of Chinese Petrographic Description Texts with Complex Entities and Relations Using Machine Reading and Knowledge Graph: A Preliminary Research Study

(1) Background: Geological surveying is undergoing a digital transformation process towards the adoption of intelligent methods in China. Cognitive intelligence methods, such as those based on knowledge graphs and machine reading, have made progress in many domains and also provide a technical basis for quality detection in unstructured lithographic description texts. (2) Methods: First, the named entities and the relations of the domain-specific knowledge graph of petrography were defined based on the petrographic theory. Second, research was carried out based on a manually annotated corpus of petrographic description. The extraction of N-ary and single-entity overlapping relations and the separation of complex entities are key steps in this process. Third, a petrographic knowledge graph was formulated based on prior knowledge. Finally, the consistency between knowledge triples extracted from the corpus and the petrographic knowledge graph was calculated. The 1:50,000 sheet of Fengxiangyi located in the Dabie orogenic belt was selected for the empirical research. (3) Results: Using machine reading and the knowledge graph, petrographic knowledge can be extracted and the knowledge consistency calculation can quickly detect description errors about textures, structures and mineral components in petrographic description. (4) Conclusions: The proposed framework can be used to realise the intelligent inspection of petrographic knowledge with complex entities and relations and to improve the quality of petrographic description texts effectively.
How to Choose the Best Media to Implement the Choosing by Advantages (CBA) Tabular Method

Choosing by Advantages (CBA) applications in the construction industry have been growing over time and teams need to decide which tools they will use to facilitate and document a CBA decision.This paper aims to determine which criteria should be considered when deciding on the tool/media to apply the CBA Tabular method?Researchers analyzed four different tools: (1) analog, (2) spreadsheet, (3) digital whiteboard, and (4) CBA decision-making software.These four tools were selected based on direct experience of the authors on four case studies.Researchers also conducted a survey to expand experiences, collect information on alternatives, and identify factors and define criteria to help users to select the tool.The conclusion is that there is no-one-size fits-all solution, and the authors therefore encourage teams to choose the best tool that suits their context.This paper aims to help teams be aware of multiple alternatives and of the consequences that come with each tool.
A Survey of Smart Classroom: Concept, Technologies and Facial Emotions Recognition Application

Technology has transformed traditional educational systems around the globe; integrating digital learning tools into classrooms offers students better opportunities to learn efficiently and allows the teacher to transfer knowledge more easily. In recent years, there have been many improvements in smart classrooms. For instance, the integration of facial emotion recognition systems (FER) has transformed the classroom into an emotionally aware area using the power of machine intelligence and IoT. This paper provides a consolidated survey of the state- of-the-art in the concept of smart classrooms and presents how the application of FER systems significantly takes this concept to the next level.
Retrieval and Recommendation of English Learning Resources Based on Knowledge Correlation

&#x0D; The design of retrieval and recommendation method of personalized learning resources based on students’ cognition level of professional knowledge and the correlation degree of the knowledge is of certain practical significance. In reality, students’ learning preferences and needs will change with the passing of time and the upgrading of learning stages, so giving learning resource recommendations without considering the time series features of students’ needs for learning resources cannot guarantee students' learning efficiency or the effect of personal knowledge system perfection. To solve this problem, this paper took online English learning as an example to study the retrieval and recommendation of English learning resources based on knowledge correlation. At first, the paper gave the relationship between the knowledge points of English learning resources, and gave judgements on students’ mastery degree of knowledge points. Then, the influence of the semantics and relationships of knowledge points involved in English learning was fully considered, and the closeness degree of the correlation of learning resources was calculated. At last, this paper introduced a hybrid attention mechanism into the learning resource recommendation model, and gave the process for calculating the weight of hybrid attention. The effectiveness of the constructed model was verified by the experimental results.
Bibliometric Analysis on Research Trends of Digital Literacy in Higher Education from 2012 to 2021

Digital literacy is a multidimensional concept that encompasses a complex integration of knowledge, skills, attitudes, and cognition dimensions, which gains increased attention in the context of higher education. The objective of this study is to analyze the scientific production of digital literacy in higher education from 2012 to 2021 based on the Scopus database, and reveal the current research trends on this subject. To this end, a bibliometric study was carried out, which first investigated some variables that addressed different features, from the distribution of publications to the most productive countries/regions and the prolific publication venues, and continued with the analysis of the authors and the keywords co-occurrence through VOSviewer software. The results show that the most frequently explored themes in the area of digital literacy can be detected, which focused on the integration of digital technology, ontology research and the educational practices of digital literacy, and the effectiveness evaluation of digital literacy in the time of the COVID-19 pandemic. The findings of this study are beneficial for researchers to gain a comprehensive understanding of digital literacy in the domain of higher education, and provide new directions for future research.
YOLO-Based Efficient Vehicle Object Detection

&lt;p&gt;Vehicle detection is one of the key techniques of intelligent transportation system with high requirements for accuracy and real-time. However, the existing algorithms suffer from the contradiction between detec-tion speed and detection accuracy, and weak generalization ability. To address these issues, an improved vehicle detection algorithm is presented based on the You Only Look Once (YOLO). On the one hand, an efficient feature extraction network is restructured to speed up the feature transfer of the object, and re-use the feature information extracted from the input image. On the other hand, considering that the fewer pixels are occupied for the smaller objects, a novel feature fusion network is designed to fuse the seman-tic information and representation information extracted by different depth feature extraction layers, and ultimately improve the detection accuracy of small and medium objects. Experiment results indicate that the mean Average Precision (mAP) of the proposed algorithm is up to 93.87%, which is 11.51%, 18.56% and 20.42% higher than that of YOLOv3, CornerNet, and Faster R-CNN, respectively. Furthermore, its detection speed can meet the real-time requirement of practical application basically with 49.45 frames per second.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
A Complexity-Reducing HEVC Intra-Mode Method Based on VGGNet

&lt;p&gt;High-efficiency video coding (HEVC) has improved the coding performance by 50% compared with the previous H.264 coding standard. However, it has also introduced an extremely high coding complexity. The quad-tree partition used by the coding unit (CU) is one of the key factors leading to the increase in complexity. Therefore, this paper proposes a CU partition method based on a convolutional neural net-work (CNN). Aiming at the complex recursive calculation of CU partition, an improved VGGNet network structure is proposed to replace the brute-force search strategy, which effectively reduces the computa-tional complexity of intra frame coding. Finally, to enhance the effectiveness of the network model in this paper, the feature pyramid network is added to the CNN model to improve the accuracy of feature extraction. The experimental results show that the proposed method can reduce the intra coding time by 59.71% while maintaining the coding performance.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
An Efficient Tasks Scheduling Algorithm for Drone Operations in the Indoor Environment

This research proposes an efficient algorithm that can be applied to drones to transport materials in indoor environment. This algorithm optimizes the time and reduces energy consumption during sharing and completing tasks between different drones. In this research, the results will be achieved based on the "Earliest Time Algorithm". We have modified this algorithm, where we have reached to get much better results in terms of saving time while performing various tasks from the drone. The performance of the algorithm is tested and analyzed for three different types of tasks and depending on the weight the drone carries.
Feature Selection Based on Multi-Filters for Classification of Mammogram Images to Look for Signs of Breast Cancer

The accuracy of classification results on mammogram images has a significant role in breast cancer diagnosis. Therefore, many stages consider finding the model has a high level of accuracy and minimizing the computing load, one of which is the accuracy in using the best feature. This needs to be prioritized considering that mammogram image has many features resulting from the mammogram extraction process. Our research has four stages: feature extraction, feature selection-multi filters, classification, and performance evaluation. Thus, in this research, we propose algorithms that can select the features by utilizing multiple filters simultaneously on the filter model for feature selection of mammogram images based on multi-filters/FSbMF. There are six feature selection algorithms with a filter approach (information gain, rule, relief, correlation, gini index, and chi-square) used in this research. Based on the testing result using 10-fold cross-validation, the features resulting from the FSbMF algorithm have the best performance based on the accuracy, recall, and precision from 72,63%, 70,38%, 75,01% to be 100%. Furthermore, the number of resulting features is the minimum because it results from intersection operation from the feature subsets resulting from the multi-filter.
Analysis of the Role of Parents in Online Learning in the Covid-19 Pandemic

AbstractDuring the Covid-19 pandemic in Indonesia, the learning system in schools offline was changed to an online learning system, commonly called Distance Learning. This online learning not only involves educators and students but also involves parents. Because parents directly accompany their children while studying at home. For this reason, there is a need for assistance from parents to children in the online learning process. But of course, there are several positive and negative causes in this online learning process. Because the things parents feel are different, students and educators are foreign. Some feel effective with, and some feel less effective with this online learning.Keywords: Role o f Parents, Online Learning, Covid-19 Pandemic AbstrakDi masa pandemi Covid-19 di Indonesia, sistem pembelajaran di sekolah offline diubah menjadi sistem pembelajaran online yang biasa disebut Pembelajaran Jarak Jauh. Pembelajaran online ini tidak hanya melibatkan pendidik dan siswa tetapi juga melibatkan orang tua. Karena orang tua langsung mendampingi anaknya selama belajar di rumah. Untuk itu perlu adanya pendampingan dari orang tua kepada anak dalam proses pembelajaran online. Namun tentunya ada beberapa penyebab positif dan negatif dalam proses pembelajaran online ini. Karena hal yang dirasakan orang tua berbeda, siswa dan pendidik menjadi asing. Ada yang merasa efektif dengan, dan ada yang merasa kurang efektif dengan pembelajaran online ini.Kata kunci: Peran Orang Tua, Pembelajaran Online, Pandemi Covid-19
Think About Making Your Signal Processing Projects Compatible With Sustainable Development [From the Editor]

As humans, we cannot be indifferent to the increasing number of dramatic events taking place in the world: fires, tornadoes, floods, and—recently—the collapse of a huge block of the Marmolada glacier in the Italian Alps. All are clear evidence to the global warming of the Earth. As scientists in signal and image processing and in the data sciences, domains that are marked by great economic growth and a high consumption of energy and Earth resources, we must take into account these events and ponder the utility for and impact on humans of the research work we are doing.
The Lingering Challenges of Underdevelopment in Africa’s and Nigeria: The Root Causes, Impacts and Panacea

The underdevelopment of Africa’s and Nigeria is neither natural nor solely caused by internal contradictions, such as corruption, bad leadership, embezzlement of public funds as argued by some political science scholars even though the aforementioned internal factors plays a very devastating roles on the issue of underdevelopment in Africa. However, Andre G. Frank argued that the Europe and Africa’s were developing at a different pace, until the Europe came and distorted the development of Africa, the idea which Frank called development of underdevelopment in Africa. Nevertheless, it is germane to expose the fact that the underdevelopment of Africa’s and Nigeria was caused or could be traced to internal and external factors such as bad leadership, corruption and highhandedness etc. as the internal factors on one hand, and slavery, colonialism and imperialism, deceptive politics of exploitation of Africa’s surplus values as the external factors on the other hand. However, this paper examined the external causes of underdevelopment in Africa’s and Nigeria, and examined the pattern or process through which the Europe underdeveloped Africa’s and Nigeria. This paper adopted the relative deprivation theory as a model to explaining how Africa’s and Nigeria were deprived of her development, and how her resources were exploited by the Europe, hence, underdevelopment of Africa’s and Nigeria. This paper also adopted the secondary methods of data gathering, as valuable data were retrieved from articles, textbooks, newspapers and internets materials, as those data collected are used to validate the arguments of this study. This paper finally proffers sustainable solutions on how to fall out of the devastating challenges of underdevelopment ravaging Africa’s and Nigeria.
A Novel Framework to Detect Irrelevant Software Requirements Based on MultiPhiLDA as the Topic Model

Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within a SRS. A previous study had attempted to detect noise in SRS, in which noise was considered as an outlier. However, the resulting method only demonstrated a moderate reliability due to the overshadowing of unique actor words by unique action words in the topic-word distribution. In this study, we propose a framework to identify irrelevant requirements based on the MultiPhiLDA method. The proposed framework distinguishes the topic-word distribution of actor words and action words as two separate topic-word distributions with two multinomial probability functions. Weights are used to maintain a proportional contribution of actor and action words. We also explore the use of two outlier detection methods, namely Percentile-based Outlier Detection (PBOD) and Angle-based Outlier Detection (ABOD), to distinguish irrelevant requirements from relevant requirements. The experimental results show that the proposed framework was able to exhibit better performance than previous methods. Furthermore, the use of the combination of ABOD as the outlier detection method and topic coherence as the estimation approach to determine the optimal number of topics and iterations in the proposed framework outperformed the other combinations and obtained sensitivity, specificity, F1-score, and G-mean values of 0.59, 0.65, 0.62, and 0.62, respectively.
The Effectiveness of Learning to Read and Write Arabic Letters in Early Children at Taman Pendidikan Quran (TPQ)

The purpose of this study is to describe process of learning to read and write Arabic letters in early childhood at Taman Pendidikan Quran (TPQ) Nurul Muttaqin 4, the students' interest in learning, positive impacts or benefits for students, opportunities in learning, and challenges faced during learning. The research method used is qualitative with the subject of early childhood aged 4-8 years at TPQ Nurul Muttaqin 4. Data collection techniques by observing the teaching and learning process of students and interviews with one of the teachers and students concerned. The research results obtained include: 1. The process of learning to read and write Arabic letters for early childhood at TPQ Nurul Muttaqin 4 using the iqro' method for reading and writing 2. The students' interest in learning is very large in learning because the parents of the students support it. and very open so that students are very interested in this learning. 3. The positive impact or benefits obtained by the students vary after participating in learning using the iqro' method, because each child has a different level of ability. There are children who are proficient in reading but lacking in writing Arabic letters. Vice versa. 4. The opportunities for learning are very good because the surrounding community is supportive and open with evidence that their children are entrusted to study at this TPQ. 5. The challenge faced during learning is the feeling of drowsiness that comes to students during learning, which makes it difficult for students to concentrate. The solution that can be done is that teachers can make learning as interesting as possible so that it makes students enthusiastic and interested in learning.
PENGEMBANGAN VIDEO PEMBELAJARAN ANIMASI BERBASIS ANIMAKER MATERI GARIS DAN SUDUT UNTUK MENINGKATKAN MINAT BELAJAR SISWA SMPN 1 GEGER DI MASA PANDEMI COVID-19

Education that is carried out remotely results in a decrease in students' interest in learning. This study aims to develop and produce learning video media that can be used remotely. Media development uses a 4D development model to the third stage, namely defining, planning, and developing. The results of the research are 1) Animaker-based Animated Learning Video Media is declared quite valid with an average assessment result by the validator of 79.52%. 2) Animaker-Based Animation Learning Video Media is declared practical based on the average value obtained from the student response questionnaire sheet of 89.26%. 3) Animaker-based Animation Learning Video Media is declared to be effective in learning with the average results obtained from student learning outcomes tests of 84.95%. mathematics on Lines and Angles.
Research on Emotion Recognition and Mental State Prediction of College Students Based on Deep Neural Network

Since entering the new century, people’s living standards have steadily improved, and their living conditions have also continuously improved. The improvement in living standards has brought greater pressure to people. The recognition of emotion and psychological state has become one of the research hotspots. Because of excessive work pressure, many students now suffer from depression. In this paper, we use a large number of research data and comparative charts to facilitate the analysis of the collected data. At the same time, we use LDA topic classification and NLDA neural network algorithm for refining and concise data processing. The research shows that students’ psychological state will affect their academic achievements. Students should try their best to manage their mental state, learn to know their emotional state, and ensure a good mental state. Emotional leadership is very important for students’ physical and mental health. Emotional leadership is a very important task for students and schools. Schools and students should actively cooperate to improve ’students’ ability to manage emotions, especially their ability to self-regulate emotions, and to jointly help build a harmonious campus. Because of the contradiction between the complexity of interpersonal relationship and society and the simplicity of students, students are often frustrated in interpersonal communication. The biggest reason for students’ psychological state is that they have no courage to express themselves in public, cannot communicate with others, and are interested in and concerned about the failure of various activities.
Homomorphically counting elements with the same property

We propose homomorphic algorithms for privacy-preserving applications where we are given an encrypted dataset and we want to compute the number of elements that share a common property. We consider a two party scenario between a client and a server, where the storage and computation is outsourced to the server. We present two new efficient methods to solve this problem by homomorphically evaluating a selection function encoding the desired property, and counting the number of elements which evaluates to the same value. Our first method programs the homomorphic computation in the style of the the functional bootstrapping of TFHE and can be instantiated with essentially any homomorphic encryption scheme that operates on polynomials, like FV or BGV. Our second method relies on new homomorphic operations and ciphertext formats, and it is more suitable for applications where the number of possible inputs is much larger than the number of possible values for the property. We illustrate the feasibility of our methods by presenting a publicly available proof-ofconcept implementation in C++ and using it to evaluate a heatmap function over encrypted geographic points.
On the Challenges of Developing a Concise Questionnaire to Identify Privacy Personas

Concise instruments to determine privacy personas – typical privacy-related user groups – are not available at present. Consequently, we aimed to identify them on a privacy knowledge–privacy behavior ratio based on a self-developed instrument. To achieve this, we conducted an item analysis (N = 820) and a confirmatory factor analysis (CFA) (N = 656) of data based on an online study with German participants. Starting with 81 items, we reduced those to an eleven-item questionnaire with the two scales privacy knowledge and privacy behavior. A subsequent cluster analysis (N = 656) revealed three distinct user groups: (1) Fundamentalists scoring high in privacy knowledge and behavior, (2) Pragmatists scoring average in privacy knowledge and behavior and (3) Unconcerned scoring low in privacy knowledge and behavior. In a closer inspection of the questionnaire, the CFAs supported the model with a close global fit based on RMSEA in a training and to a lesser extent in a cross-validation sample. Deficient local fit as well as validity and reliability coefficients well below generally accepted thresholds, however, revealed that the questionnaire in its current form cannot be considered a suitable measurement instrument for determining privacy personas. The results are discussed in terms of related persona conceptualizations, the importance of a methodologically sound investigation of corresponding privacy dimensions and our lessons learned.