Reviewerly/exHarmony huggingface dataset:
Old Bailey event sees young people debate pros and cons of votes at 16

Children and Young People NowVol. 2019, No. 6 AnalysisOld Bailey event sees young people debate pros and cons of votes at 16Derren HayesDerren HayesSearch for more papers by this authorDerren HayesPublished Online:5 Jul 2019https://doi.org/10.12968/cypn.2019.6.13AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 June 2019Volume 2019Issue 6ISSN (print): 1755-8093ISSN (online): 2515-7582 Metrics History Accepted 1 June 2019 Published online 5 July 2019 Published in print 2 June 2019 Information© MA Education LimitedPDF download
Research on Safety Risk Assessment of Long-span Suspension Bridge Construction in Deep Valley in Mountainous Area Based on Cloud Model

Based on the fuzziness and randomness of cloud model, it is applied to the construction safety risk assessment of long-span suspension bridge in deep valley in mountainous area.According to the situation of Jiangdi River Bridge project, the cloud model evaluation method is adopted to add two indexes of wind load condition and import and export construction condition to the index system.The index weight is calculated by the maximum deviation weighting method, the comprehensive determination degree is calculated and the construction safety risk level is determined.By comparing the standard evaluation results of construction Safety Risk Assessment Guide for Highway Bridges and Tunnels (Trial) and the cloud model evaluation results, it is shown that the evaluation index system is constituted by adding corresponding special risk factors to the actual project and cloud model calculation is more reasonable and accurate than the traditional risk evaluation results.
Fairness Aware Swarm-based Machine Learning for Data Streams

Machine learning has been widely applied to extract insights from streaming data. However, ethical issues such as fairness have emerged related to these decision-support systems. Feature engineering methods have shown potential in representing and learning of fairness learning. However, these techniques have not been applied to streaming data. In this paper, we proposed a fairness-aware swarm-based machine learning for streaming data. The novelty of this algorithm is in the utilisation of two swarms, one for classification by building a network of prototypes and one for discrimination mitigating using feature weighting. Experiments with well-known datasets in fairness learning show that the proposed methods can improve fairness while maintaining the classification performance.
Fact-Checking Politicians

We investigate the reaction of Italian Members of Parliament to a rigorous fact-checking of their public statements. Our research design relies on a novel randomized field experiment in collaboration with the leading Italian fact-checking company. Our results show that politicians are responsive to negative fact-checking. Specifically, we observe a significative reduction in the number of incorrect statements made by politicians after being treated. This effect persists for at least two months. We also observe a reduction in the probability of politicians making verifiable statements, suggesting that fact-checking may also increase the ambiguity of politiciansâ€™ statements.
Analisis Kebutuhan Pengembangan Media Pembelajaran Berbasis Teknologi Informasi dengan Permainan Tradisional di Masa Pandemi pada Materi Sistem Ekskresi

The emergence of the COVID-19 virus pandemic in Indonesia at the beginning of 2020 made the government issue a policy that the learning process was carried out online. The learning process is not effective because the teacher does not master information technology well. The online learning system that has been implemented makes students unable to learn optimally such as learning that is applied directly in the classroom. In addition, it consumes a lot of internet quota. The existence of internet quota assistance from the government for teachers and students is a bit of a solution to this. However, this is not possible for students who are constrained by internet access, so a new breakthrough is needed by utilizing the increasingly developing information technology, namely by using interactive learning media. This research is important in order to inventory the types of needs for learning media needed for teachers and students and as a basis for research on the development of learning media. The purpose of this study was to determine the types of learning media needed in the learning process during a pandemic for teachers and students of class VIII. The research method used a questionnaire consisting of 15 questions given to respondents, namely 3 Natural Science teachers and 10 grade VIII students at SMP Negeri 1 Mempawah Hulu. The results showed that all respondents revealed that it was necessary to develop information technology-based multimedia accompanied by traditional games.
Message from the ISMAR 2022 Workshop and Tutorial Chairs

We are very proud and pleased to introduce the 21st IEEE International Symposium on Mixed and Augmented Reality workshops and tutorials. After two years in which ISMAR has been held virtually, we have a renewed opportunity to interact face-to-face this year in Singapore. This will be the first time ISMAR will be held in Southeast Asia. Considering that the global COVID-19 situation has become more stabilized and cross-border travel restrictions are much lifted, we decided to organize ISMAR - and its associated workshops and tutorials - in hybrid mode. Though we encourage people to attend the conference in person, we will provide the online platform to those who could not attend physically. Despite the uncertain circumstances, we are very excited to announce that the ISMAR community has come forward to offer a slate of nineteen workshops and eleven tutorials, spanning various cutting-edge topics in XR.
Results and Evaluation

The educational community and the whole society are moving away from examination and test procedures. The strengths and significant deficiencies of a person are identified on a digital platform in the process of education and application of its results. They become the basis for decision-making by an educational organization or employer. This does not exclude the possibility of demonstrating the highest achievement in an exam or educational contests which may be the main argument in promoting a person.
Soft Decision Tree for Regression

Decision tree (DT) plays an important role in pattern recognition and machine learning, which is widely used for regression tasks because of its natural interpretability. Nevertheless, the traditional decision tree is constructed by recursive Boolean division. The discrete decision-making process in DT makes it non-differentiable, and causes the problem of hard decision boundary. To solve this problem, a probability distribution model — Staired-Sigmoid is proposed in this paper. The Staired-Sigmoid model is used to differentiate the decision-making process, by which the samples can be assigned to two sub-trees more finely. Based on Staired-Sigmoid, we further propose the soft decision tree (SDT) for regression tasks, where the samples are assigned to different sub-nodes according to a continuous probability distribution. This process is differentiable, and all parameters in SDT can be optimized by gradient descent algorithms. Owing to its constructing rules, SDT is more stable than decision tree, and it is easier to overcome the problem of overfitting. We validate SDT on several datasets obtained from UCI. Experiments demonstrate that SDT achieves better performance than decision tree, and it significantly alleviates the overfitting.
Designing Smart Energy Contract with Blockchain Technology

Recently, the disruption of blockchain technology has changed the business world, including supply and use of electrical energy. The article addresses the advantages of blockchain technology to a national utility company to act both roles as producer and consumer (prosumers) of electricity to maximize all electricity capacity. The prosumer role encourages the collaboration of independent small and medium utility producers in a business ecosystem to assist managing national wide electricity distribution. The article uses the Blockchain 2.0 framework that links all parties in prosumer role in smart contract mechanism. Participants in the system enable to exchange their data transactions in open platform collaboration easily and securely. The outcome of the article is expected to invite more startups to participate to assist national utility company in efficiently manage electricity investment and distribution.
USING VIOLA-JONES AND K NEAREST NEIGHBORS METHODS FOR FACE DETECTION AND RECOGNITION

This paper discusses and analyzes algorithms and methods of detecting faces in a video stream and direct recognition of found faces.
A study of machine learning algorithms to measure the feature importance in class-imbalance data of food insecurity cases in Indonesia

The development of various machine learning algorithms on supervised models has become one of the issues in selecting a suitable algorithm. The black box of machine learning requires a technique that can be used to interpret the feature importance using the SHAP in order to obtain predictors. The class-imbalance problem in real cases is another challenge in improving the performance of minority class predictions. This study uses a food insecurity dataset, one of the SDG's important indicators to study to achieve zero hunger. The machine learning algorithms studied consisted of Random Forest, XGBoost, SVM, and NN. Meanwhile, the study of the effect of class-imbalance used three treatments: without handling, SMOTE-N, and ADASYN-N. Twelve models are built based on a combination of four algorithms and three treatments to study the performance models and their feature importance. The SMOTE-N and ADASYN-N were able to increase the sensitivity value up to 0.48 units higher when compared to without handling data. The agreement level on without handling data has a low value, indicated by the 0.736 ICC value, while on SMOTE-N and ADASYN-N, it is higher, indicated by the 0.925 and 0.919 ICC values, respectively. This study dataset is more suitable for using SMOTE-N. It is based on the higher ICC and superior AUC performance. The relatively high ICC value indicates that the use of machine learning algorithms does not influence the agreement level on the feature importance score. Therefore, the choice of a machine learning algorithm can refer to a measure of its performance. Random Forest produced the best performance (AUC and sensitivity). Therefore, the Random Forest SMOTE-N is the best model in this study. It produces food insecurity household characteristics with household conditions having poor water, a small house size, low household head education, few/no savers, and cement or tile flooring.
Adaptive Weight Structure Representation for Multi-view Subspace Clustering

Traditional subspace clustering method based on self-representation has been widely applied in learning similarity matrix. Existing self-representation based methods treat all features equally in the process of learning similarity matrix, which makes redundant features in learning stage may have a certain negative impact on the final representation and even the representation of other non-redundant features. To solve the above problems, this paper proposes Adaptive Weight Low-Rank Representation (AWSLRR) algorithm. AWSLRR uses firstly nested structure to learn more clean and reasonable similarity matrix, then applied weight constraints to the reconstruction corruption which is generated during the reconstruction process. The adaptive weight matrix imposes a small weight coefficient on the larger corruption value by imposing constraints on the corruption term during the self-representation process, and vice versa. Finally, the experimental results on five real datasets validate the competitiveness of proposed algorithm.
Intelligent Recognition Method of Multitype Human Posture Based on Deep Learning

Human posture recognition is widely used in intelligent monitoring, human-computer interaction, video retrieval, virtual reality and so on.So it has been an active research direction in the field of computer vision.Firstly, the edge of human posture is detected, and the key feature points of the target image are calibrated.Then the human posture image is mapped by convolution neural network.Experimental results show that the proposed method has some advantages in both recognition rate and recognition time.
Analysis of Poor Population in DKI Jakarta Regions using Fuzzy C-Means and K-Means Algorithms

The government of DKI Jakarta have been struggling to lower the number of poverties in its region. The first step that the government need to do is analyzing the number of poverty itself. The information is important to identify the effective way to solve the problem based on the priority of the region that have most poverty number. By now, the government has formulated the data collection by weighting the 15 indicators into 3 groups. The large number of data and indicators may cause difficulties in its manual implementation, make the process become ineffective and less objective. Therefore, automation is needed in the process of clustering poverty data. This study aims to analyze the performance of the FCM algorithm (Fuzzy C-Means) and K-Means which is implemented on poverty data in Central and categorize it into 3 clusters. There are several steps that must be carried out before clustering step: 1) preprocessing, namely data cleaning and data transformation, 2) clustering is carried out using the two algorithms (FCM and K-Means). The calculation results are used to compare between two algorithms. Based on the comparison result, it is obtained that FCM is better than K-Means algorithm, which has an accuracy rate of only 83.33%, which better than the K-Means algorithm of 50%.
An analytical Evaluation of Secondary State School ELT Coursebook: Sunrise (9)

The main focus of the paper is to appraise Sunrise (9) to discover the strengths and weaknesses in this coursebook.Mixed methodological approaches were used in this evaluative study.The data were gathered numerically by rating the checklist scale to value each criterion.The instrument which was used to collect data was a evaluative checklist because it is a systematic and include all criteria for investigating.The findings of this applied linguistic study illustrates that Sunrise (9) can be communicative textbook since it applies the key tents of CLT paradigm.It may fit Kurdish learners, might have has attractive physical appearance and supplementary aids.In Addition, it is possible that the textbook has a good balance and integration of skills.On the other hand, the textbook do not cover enough topics about Kurdish culture as it is written especially for Kurdish leaners.Furthermore, it lacks videos which are essential visual elements.As well as, the most of tasks of the coursebook look they are not interesting.Consequently, the pros of the textbook can outweigh the cons of it.
The role of the rare books collection in forming the regional cultural space

The article reviews the experience of the Altai Regional Universal Scientific Library named after V. Ya. Shishkov in organizing the system of cultural and educational events to promote book and reading with the use of the Rare Books Department collection.The author gives examples of specific forms and lines of action: exhibitions and excursions; development of digital collections; book studies and research on the history of librarianship and dissemination of book use in the region; implementation of publishing projects; formation of compatriots’ private libraries; cooperation with mass media, and others.Making rare books a part of major library activities: exhibitions, presentations and cultural events helps to extend the library reputation as a custodian of the book heritage and a place of intellectual leisure.Creation of informational resources – electronic collections makes combines the efforts of various organizations and individuals to preserve history and culture and form the image of the region in the global information space. Promotion of rare books in the mass media ensures targeting a wider audience of potential readers.Local history cultural and educational informing is realized through researches, disclosing the history of book existence in the region and books publishing provides an opportunity to access historical sources freely for everyone. Modern approaches to replenishing rare books collections – the formation of personal libraries, studying them and presenting the results in the field of literary and historical research is a significant contribution to the further development of bibliology. The author comes to the conclusion that the library makes active use of the opportunities for disclosing and promoting the rare books collection with the aim of forming the regional cultural space.
Markovian Model-Based Safety Analysis in Perception Systems Inside Self-Driving Cars

At present, the development of self-driving car systems has been increasing. The need for man to control all possible scenarios has led to the inclusion of theories such as human perception. This means identifying how the human brain recognizes its environment and translating it into data that a machine can learn and make decisions. For this, great doubts have been generated concerning safety; in the present work, the Markovian model is implemented as a stochastic method in a constantly changing system. The model shows possible forms, future states' transitions rate of changes, and probabilities without depending on past states. Markovian models can also recognize patterns, make predictions, and learn sequential statistics.
Planetary Rover Path Planning Method with Robustness against Digital Elevation Model's Uncertainty

Planetary rovers need to plan a safe route to ensure the success of their mission. Many missions to the Moon and Mars use existing remote sensing data to plan their exploration routes before launch under the assumption that the data is correct. For example, digital elevation models (DEMs) are biased in their accuracy depending on the density of their source data. Most studies of advance path planning using DEMs do not account for that uncertainty. Therefore, the path planned in advance is not necessarily safe for the actual rover. In this paper, we propose a path planning method that is robust against the uncertainty of terrain data generated from remote sensing data. Simulations using real data confirmed that the route planned by this method is safer than these two conventional methods: 1) route planning that assumes the topographic data is correct, and 2) route planning that adds Gaussian noise to the DEM to generate multiple DEMs and treats their variance as uncertainty.
CONTRIBUTIONS OF AUGMENTED REALITY IN ARCHITECTURAL PROJECT REVIEW

Already present in the fields of medicine and video games, augmented reality is now being employed in architectural design.We explore here the role that this new digital tool can play, as a means of interaction, in the review of projects.To do so, we work through experiments that immerse designers in three different environments: (1) a 2D environment, composed of plans and sections, (2) an augmented reality environment on a tablet and, finally, (3) an environment combining the two approaches.We can thus dissect how augmented reality can be a relevant tool and which functionality(ies) can enrich its current potentialities in architectural design.
Modelling Emotions Recognition from Facial Expression using Vision Transformer with IMED Dataset

Humans use facial expressions to express their emotions. Facial expressions can convey hidden feelings that are not verbalized. This research aims to find out, implement, and fine-tune Vision Transformer architecture to recognize basic emotions through human faces using the Indonesian Mixed Emotion Dataset (IMED). The research methods used are literature review, performance comparison alongside similar models that used the same dataset, and interviewing with a psychology student, concluding with Fear and Disgust being the hardest to predict as both have similarities in eye, mouth, or eyebrows, and develop an application using Flutter framework. The experiment results show that the proposed model of this research predicts emotions faster than most models by 21.5% on average. Furthermore, it produces 98.9% test accuracy and can compete against state-of-the-art models such as CNN. YOLOv3, and ARCH-I.
Capacitação à distância no modelo híbrido sobre Open Journal Systems

Since 2005, one of the most visible manifestations of Ibict's support for OJS has been scientific editor training. Version 3 offered improvements in system design and editorial flow, which poses a challenge to many journals; this underscores the necessity for scientific editors who utilize the platform to be trained. This study is to describe the findings of research undertaken in giving OJS 3 hybrid training from 2019 to August 2022. Contribute to debates concerning the availability of OJS 3 training while also exhibiting Ibict's initiatives in promoting the software in Brazil.
PCA Method in Facial Features Extraction for Attendance Tracking Prototype

Facial recognition has long become a way to identify an individual-it represents an identity of a person. For this study, an automated students' attendance tracking prototype based on facial recognition was proposed, and later developed. Facial recognition systems can be extremely helpful in real-world applications, particularly in security control systems. For this study, firstly video framing was prepared by activating the camera via a user-friendly interface. Using the Multi-Task Cascaded Convolutional Neural Network (MTCNN) algorithm, the face Region of Interest (ROI) was then identified, and segmented from the obtained video frames. In order to minimize loss of information, the images were resized, cropped, and RGB-greyed at the pre-processing step. Principal Component Analysis (PCA) was then used to extract the characteristics from facial images during the face recognition stage. The extracted features from the test images were then compared to the extracted features from the training images. The facial images were then categorised, and identified using the PCA algorithm. Finally, the identified student's attendance will be recorded for tracking purposes, and stored in a local database. One of the main advantages of the prototype is the automated registration of new students where Adhoc registration are allowed using the prototype. The study managed to achieve the best recognition accuracy of 98.7%. For future works, it is suggested that a higher number of images per individual to be used, with different pre-processing techniques.