Reviewerly/exHarmony huggingface dataset:
Design-based research on gamified outdoor social enquiry learning with context-aware technology: integration of teacher facilitation for advancing the pedagogical effectiveness

GAMES (Gamified Authentic Mobile Enquiry in Society) is a context-aware mobile application that we developed to support students in conducting gamified social enquiry learning in outdoor environments. This paper focuses on discussing the latter part of a piece of 2-cycle Design-Based Research (DBR) conducted in the context of formal curriculum learning and teaching in Hong Kong, reporting on the work that we carried out in the second research cycle where teacher facilitation was integrated into the pedagogical implementation of GAMES for advancing its effectiveness. The entire DBR involved 9 teachers (from 9 different high schools in 3 different academic categories) and their Grade-10 classes in two consecutive school years (with a total of 555 students). Upon the different academic settings, we (researchers) collaborated with the teachers (practitioners) to derive and enact what and how they could do in order to optimise their students' social enquiry experience in the course of gamified, technology-enhanced outdoor learning. This study not only reveals the importance of teacher facilitation in the process of gamified mobile learning, but it also sheds light on how to design and implement the articulated pedagogical, administrative and technical facilitation acts in the context of formal education.
Realtime Face Recognition for Intelligent Door System

This paper focus on in-deep analysis of the majority of both hand-craft based and CNN-based that proposed in states-of-the-art for face detection and face recognition method.This issue has been researched for recent decades as it enjoys many practical applications such as entertainments, security, tracking, human-machine interaction and so on.However, this problem has been faced to many challenges such as complex background, illumination, face pose, states of face, fake face.Which lead to high time cost, and low accuracy system.In recent times, thanks to the cutting-edge technique that give impressive performance on both hand craft-based and deep learning-based method.There have been improvement in detecting face exploiting feature vectors.Therefore, in this research, we comparing face detection model such as Haar Cascade[6], Dlib [19] and YOLO [27].Then, we consider and compare affects of methods when extract feature vectors.Besides, we try to examine on various classifications to obtain the suitable system.Evaluation results confirm that the best accuracy rate achieves at 97.83%.The proposed method suggests a feasible solution addressing technical issues in using hand craft-based face recognition.Moreover, we deploy a realtime intelligent door system that utilize face recognition results to automatically control door.
DEVELOPING SPEAKING SKILL OF GRADE VIII STUDENTS AT SMP NEGERI 1 PALU THROUGH INTERVIEW

This research aims at developing Speaking Skill through Interview. Its method was quasi-experimental research non-equivalent control group design. Its population was the students of grade VIII at SMP Negeri 1 Palu, in which consists of thirteen parallel classes. The experimental class consists of 29 students and control class consists of 30 students. It was done in eight meetings. The variables of this research were speaking skill and interview. The samples of this research were students of grade VIII C and VIII D, selected through cluster sampling technique. Its results indicate that the application of interview has overcome the students’ problem. After analyzing them, the result of t-counted was 10.01. By applying degree of freedom (df) 57(29+30-2), and the level significant 0.05, the value of t-table was 2.00. So, the value of t-counted was higher than t-table. It means that the hypothesis of this research was accepted. Thus, the interview can be applied to develop speaking skill of students grade VIII at SMP Negeri 1 Palu.
Learning to Find Good Correspondences Based on Global and Local Attention Mechanism

We are committed to designing a method for establishing a set of reliable correspondences between two images in this paper. Previous work proposes an outlier removal network based on global and local attention mechanism. It normalizes the feature map under the guidance of attention weight, as a result the robustness to outliers is improved and inliers are better preserved. However, element-wise multiplication of these two different attentions makes them unable to perform their original functions. In order to overcome this limitation, a network based on attention mechanism has been designed in our work. Firstly, global attention is employed to compute a global weight which can help normalization focus on inliers and reduce the influence of outliers on context information. Secondly, to extract channel information, we use local attention to learn a channel weight for each correspondence. Finally, the above structures and residual network modules are combined for correspondence establishment. Experimental results show that our method is better than other SOTA methods.
A Hadoop-based Co-occurrence Pattern Mining Model on AIS data

AIS is a tracking and self-reporting system used by maritime vessels to exchange information with other ships, AIS base stations, and satellites.Co-occurrence mining in AIS data can measure the proximity of ships in space and time and can be used in maritime traffic monitoring or other security purpose.Most of the current existing approaches can not meet the practical needs of large-scale ship trajectory data mining due to the lack of designing on parallel computing architecture and are insensitive to the spatial data characteristics.A model on co-occurrence pattern mining based on Hadoop is presented in this paper.By using parallel partitioning on the original data set, the mining in ship trajectory data is implemented on an extended MapReduce architecture.The experiments on real AIS data sets show that the large-scale ship trajectory data can be processed effectively , and the efficiency and correctness are maintained.
Sierra Leone

Actions by the new government since taking office in April 2018 helped to stabilize macroeconomic conditions, but the situation remains challenging. Overall growth remained subdued. While elevated, inflation is tracking down. Program performance is broadly on track, though progress on structural measures has been slower than anticipated. Healthy revenues and significant underspending resulted in a lower-than-programed fiscal deficit. All quantitative targets were met, except the end-December performance criterion on net domestic assets (NDA) of the central bank and the end-March indicative target on poverty-related spending.
Classification of Sentiment Based on Movie Feedback Given By Audiences

The need for generating automated sentiment on audience feedbacks has been the need of the hour. Manually going through the entire movie feedback becomes tedious therefore an attempt to predict the polarity of a movie based on the reviews using machine learning models is done. Usage of the IMDB movie reviews dataset has been done for training and testing. In this study we also try to depict the real-life problems of class imbalance and train-test splits, hence obtaining solutions for the same. The problem of class imbalance in today’s world has affected a large amount of predictive applications such as cancer detection , fraudulent transactions in banks etc, hence this study is an attempt to perform a solution to solve the class imbalance problem. Use of the undersampling method has been done in this study to improve the accuracy of an imbalanced class. Feature extraction methods such as Bag of Words and Term Frequency Inverse document Frequency have been used to generate features from the reviews. The Logistic regression and SVM classifiers have been used in the study to measure the accuracy. Along with the accuracy the Confusion Matrix has also been calculated to showcase the class imbalance taking its effect on the accuracy.
Scene Labeling using H-LSTM by Predicting the Pixels using Various Functions

Scene Labeling plays an important role in Scene understanding in which the pixels are classified and grouped together to form a label of an image. For this concept, so many neural networks are applied and they produce fine results. Without any preprocessing methods, the system works very well compared to methods which are using preprocessing and some graphical models. Here the neural network used to extract the features is Hierarchical LSTM method, which already gives greater result in Scene parsing in the existing method. In order to reduce the computation time and increase the Pixel accuracy H-LSTM is used with Makecform and Softmax functions were applied. The color transformation is applied using the Makecform function. The color enhancement of images has given object as input to H-LSTM function to identify the objects based on the referential shape and color. H-LSTM constructs the neural network by taking the reference pattern and the corresponding label as input. The pixels present in the neighbourhood identified with the help of neural network. In this method, the color image is converted into greyscale and then the Hierarchical LSTM method is applied. Therefore, this method gives greater results when it is implemented in Matlab tool, based on pixel accuracy and computation time when compared to other methods.
Designing an Architecture for Delivering Multilingual Interactive Voice Information Services

In this paper, we address the issue of how to design multilingual market information service delivery interactive voice response system (MMI-IVR) architecture to serve hundreds of thousands of low literacy target groups to access market information simply and independently using their mobile and fixed line telephones network for three mostly spoken Ethiopian local languages Amharic, Afaan Oromo and Tigrigna.The proposed system architecture uses threetier architecture model, which segments into three tiers of services.The top layer of the presentation logic is service delivery which is mainly used as an interface to farmers and traders, which are located at distant locations, via the telephone network.The second level of the architecture is middle tiers that accepts telephone digit as inputs from the user over the telephone network and provide required information back to the caller via voice messages.The data tier is the database containing market price information.When market information is requested by the caller, the presentation logic reads the information from the database and uses its template files to construct on voice message.To accomplish this, it uses the pre-recorded template files, and dynamically generated information which basically done in this tier by text to speech engine.To illustrate the breadth of the MMI-IVR architecture prototype is developed using PHP programming language PHPAGI script to interface with PBX as software (Asterisk server) and emulated by telephone X-lite soft phone as phone interface.
An Efficeint Feature Selection from Hetrogenous Data with Reduced Data Complexity

Highlight choice might be significant as data is made ceaselessly and at a consistently developing charge, it decreases the extreme dimensionality of certain issues. Highlight decision as a pre-preparing venture to gadget acing, is ground-breaking in bringing down repetition, getting rid of unessential records, developing picking up learning of exactness, and improving final product fathom ability. This work offers far reaching strategy to work decision inside the extent of classification issues, clarifying the principles, genuine application issues, etc inside the setting of over the top dimensional records. To begin with, we consideration on the possibility of trademark decision gives an examination on history and essential standards. We advocate quick sub sampling calculations to effectually rough the most extreme shot gauge in strategic relapse. We initially build up consistency and asymptotic ordinariness of the estimator from a well known sub sampling calculation, and afterward determine choicest sub sampling probabilities that limit the asymptotic suggest squared blunder of the subsequent estimator. An open door minimization standard is additionally proposed to additionally diminish the computational esteem. The best sub sampling chances rely on the all out data gauge, so we increment a - step set of guidelines to inexact the perfect sub sampling strategy. This arrangement of guidelines is computationally effective and has a gigantic markdown in figuring time contrasted with the entire insights technique. Consistency and asymptotic typicality of the estimator from a two-advance arrangement of principles are likewise mounted. Fake and real data units are utilized to assess the pragmatic generally execution of the proposed system.
Review for “Transverse jointing in foreland fold-and-thrust belts: a remote sensing analysis in the eastern Pyrenees” (# se-2020-70) by Stefano Tavani et al.

This manuscript deals with the character of jointing in the foreland of the Pyrenees through mapping their trend in aerial imagery.The manuscript describes a pronounced N-S trend of joints, i.e. perpendicular to the trend of the Pyrenees and provides an explanation for their formation.I navigated myself across this area with Google Earth and it is really fascinating to see the clarity and spatial extend of this joint system making also this
A comparative analysis of non­reference assessment methods

A comparative analysis of functions to assess image quality in the absence of a sample: no-reference (NR) measure or NR-type methods. The availability of NR-methods is very important for assessing the quality of streaming video such as television, game streaming, online conferences, web-chatting, etc. (because on the side of the recipient of the video there is no standard for quality comparison) and assessing the results of transformations aimed at improving video, and choosing the parameters of these transformations (brightness change, semitone and others). The human visual system (HVS) is able to visually assessing video quality, but If required to visually assess the quality of dozens or hundreds of videos or ranking them by quality level it will be needed a huge amount of time. Six types of experiments were performed to analyze the correlation of calculated quantitative estimates with visual assessments of the quality of the tested video files. Three of them are fundamentally new: comparing video after gamma correction and changing the contrast with different parameters, as well as blurring, which may be the result of defocusing the camcorder. A hybrid method (or reduced-reference (RR) measure) and a full-reference (FR) measure or FR-type method were also added for comparison. It has been experimentally shown that none of the studied non-reference methods of image quality assessment is universal, and the calculated assessment cannot be converted into a quality scale without taking into account the factors influencing the distortion of image quality. Moreover, all NR-type methods could not cope with the experiment of changing the contrast, believing that the best result is the most contrasting image but the original. Instead, the reference methods showed an excellent result (except one, which showed partial ineffectiveness). Also, it has been shown performance comparison between methods. It is shown that most of the studied methods calculate local estimates for each frame, and their arithmetic mean value is an estimate of the quality of the entire video file. If the video is dominated by large areas of uniform evaluation, methods of this type may give incorrect quality evaluations that do not coincide with the visual evaluations.
Modifikasi enkripsi dan dekripsi AES menggunakan polybius chiper dalam pengamanan data

Data is a file that can be confidential so it requires a data security process to maintain confidentiality. Kripotgrrafi is a data security process that can be used based on the use of algorithms, one of which is AES. AES is a modern algorithm that can be modified to improve confusion and diffusion in cryptography. AES combination can be done using Polybius which has cryptographic diffusion properties. This study modified the AES using 6x6 and 10x10 polybius matrices that were performed on plaintext and plaintext and keys. Analysis was carried out based on the highest bit change rate found in modification II in the plaintext and 6x6 matrix keys, which amounted to 51.8% using the avalanche effect test. The results of the AE compared to the expected results using chi square with the modified AES results can increase the AE by 5% with the real level is 0,05 and the degree of freedom is 4 . Execution time was tested in this study with the results of the AES modification time longer than the standard AES because the complexity of the algorithm affects both encryption and decryption time.Abstract Data is a file that can be confidential so it requires a data security process to maintain confidentiality. Kripotgrrafi is a data security process that can be used based on the use of algorithms, one of which is AES. AES is a modern algorithm that can be modified to improve confusion and diffusion in cryptography. AES combination can be done using Polybius which has cryptographic diffusion properties. This study modified the AES using 6x6 and 10x10 polybius matrices that were performed on plaintext and plaintext and keys. Analysis was carried out based on the highest bit change rate found in modification II in the plaintext and 6x6 matrix keys, which amounted to 51.8% using the avalanche effect test. The results of the AE compared to the expected results using chi square with the modified AES results can increase the AE by 5% with the real level is 0,05 and the degree of freedom is 4 . Execution time was tested in this study with the results of the AES modification time longer than the standard AES because the complexity of the algorithm affects both encryption and decryption time.
The Code Switching of Singkil Language in Indonesian Language Learning Process at Junior High School of Aceh Singkil and Its Usefulness as Student Reading Material

In teaching learning process at SMP Darulmuta'alimin Tanah Merah Aceh Singkil, code switching and code mixing are useful in some events. This does not only happen to students, but also teachers who have the same background (mother tongue) as students. A teacher at SMP Darulmuta'alimin Tanah Merah Aceh Singkil uses the local language or mother tongues so that the students are being taught understand. In accordance with the explanation of Article 36, Chapter XV, of the 1945 Constitution, it can be seen that the language of instruction is used in the teaching and learning process in the Indonesian class. This type of research is qualitative analytical descriptive, which seeks to describe the characteristics of the data in accordance with the actual situation. The description is described as it is systematically, factually, and accurately regarding the data and characteristics of the phenomena under study. The data shows that the code switching event in Indonesian language learning in grade VII-A SMP Darulmuta'alimin Tanah Merah Aceh Singkil, which is from the Singkil language to Indonesian. Since the beginning of the conversation in the learning process, the teacher and students communicate using Singkil and the end of their conversation using Indonesian.
Actibles: design and development of a new platform for active tangibles

In this thesis, we present a new kind of active tangible call an Actible. Actibles are an open source hardware/software platform for designing and implementing active tangibles for Tangible Embodied Interaction (TEI) applications. Web technologies and a smartwatch core are leveraged for ease of development, and enables the active tangibles to act coupled to a server, or act as their own server. This kind of connectivity enables the use of the Actibles independently or in combination with other devices and displays ubiquitously. We derived an expanded set of input and output interactions based on previous work on active tangibles, including tilting, shaking, neighboring, stacking, on-screen gestures, and integrated LED feedback. We describe the design and technical implementation of these interactions, and demonstrate it's use in a multitude of published example applications.
Cloud Security Risk Analysis Method based on Fuzzy Logic

This paper proposes a cloud security risk assessment index system through the study of cloud computing structure and services and quantifies the risk through entropy weight and fuzzy theory. This method can determine the value of risk from the three essential attributes of assets, threats, and vulnerability, and it can be applied to the risk assessment of various types of cloud services.
Final Simplest Model of Smallest Particles and Possibly Developed Directions of Particle Physics

First, so far the high energy experiments in the past sixty years have shown that the smallest mass fermions are proton, electron, neutrino and photon, which form the simplest model of particles. These fermions seem to be inseparable truth “atoms” (elements), because further experiments derive particles with bigger mass. They correspond to four interactions, and are also only stable particles. Next, the final simplest theory is based on leptons (e- e ν ) and nucleons (p-n) or (u-d) in quark model with SU(2) symmetry and corresponding Yang-Mills field. Other particles and quark-lepton are their excited states. Their spectrum is mass formula and symmetric lifetime formula. Some applications are discussed. Further, the simplest interactions and unification of weak-strong interactions by QCD are discussed. We research opposite continuous separable models. Finally, we propose some possibly developed directions of particle physics, for example, violation of basic principles, in particular, the uncertainty principle, and precision and systematization of the simplest model, etc.
Performance Evaluation of a Big Data Application on Apache Spark

Apache Spark enables a big data application—one that takes massive data as input and may produce massive data along its execution—to run in parallel on multiple nodes. Hence, for a big data application, performance is a vital issue. This project analyzes a WordCount application using Apache Spark, where the impact on the execution time and average utilization is assessed. To facilitate this assessment, the number of executor cores and the size of executor memory are varied across different sizes of data that the application has to process, and the different number of nodes in the cluster that the application runs on. It is concluded that different pairs (data size, number of nodes in the cluster) require different number of executor cores and different size of executor memory to obtain optimum results for execution time and average node utilization.
Sequential Monte Carlo methods for multi-sensor tracking with applications to radar systems

The aim of this thesis is to explore specific sequential Monte Carlo (SMC) methods and their application to the unique demands of radar and bearing only tracking systems. Asynchronous radar networks are of special interest and a novel algorithm, the multiple imputation particle filter (MIPF), is formulated to perform data fusion and estimation using asynchronous observations. Convergence analysis is carried out to show that the algorithm will converge to the optimal filter. Simulations are performed to demonstrate the effectiveness of this filter. Next, the problem of multi-sensor bearing only tracking is tackled. A particle based tracking algorithm is derived and a new filter initialization scheme is introduced for the specific task of multi-sensor bearing only tracking. Simulated data is used to study the efficiency and performance of the initialization scheme.