Reviewerly/exHarmony huggingface dataset:
THE EFFECT OF COMIC MEDIA ON MATHEMATICS LEARNING OUTCOMES FOR CLASS VI STUDENTS ON THE CONCEPT OF FACTORS AND NUMBER MULTIPLES AT SDN TELUK SANTONG

This study aims to determine whether the use of comics in Mathematics learning for grade VI elementary school on factors and multiples of numbers helps students learn and understand difficult concepts. This research was also conducted to see whether learning by using comics does not make students feel difficult in learning Mathematics because the material will be changed in the form of conversation. The type of research used in this study is a quasi-experimental research. The treatment that will be given to the control class is in the form of comic media. While the experimental class does not use comics media, but the treatment for the experimental class is learning as usual in class. This research was conducted at SDN Teluk Santong. The number of subjects in this study were 39 students from class VI A and VI B SDN Teluk Santong. Based on the results of research, data analysis, and learning, it can be said that the learning outcomes achieved before the treatment was given to the two sample groups (Pre-Test) were lower than the treatment (Post-test) so that it could be said that there was an influence in the application of comic media. on the results of learning mathematics in the material of factors and multiples of class VI SDN Teluk Santong for the 2021/2022 academic year.
Signal demodulator based on in-phase and quadrature interference-robust feature

In this letter, the issue of mitigating strong co-channel interference (CCI) in communication systems is addressed. Unlike conventional model-based methods, a novel data-driven scheme is proposed. A recurrent neural network (RNN) is trained to directly demodulate the desired signal under strong CCI. Instead of inputting the original received signal, in-phase and quadrature interference-robust features (IRF) are extracted through preprocess. The RNN is then trained offline to implement sequence labelling, with the IRF sequences and known code sequences of the desired signal as inputs and ground-truth labels. Meanwhile, a guard zone is introduced when loading the IRF sequences to enable better contextual information exploitation by the RNN demodulator. Online tests validated the low bit error rate (BER) of the RNN demodulator, under strong CCI. Moreover, the proposed scheme outperformed existing model-based and data-driven interference mitigation schemes in terms of the BER, especially in low signal-to-interference ratio region. Inspiringly, the proposed data-driven scheme generalized well to varied unseen test conditions.
Perancangan Sistem Keamanan Kain Tradisional Lampung Berbasis Sensor PIR

Abstract--The museum is a place to store historical relics that are very important as evidence that these relics are true. Museums are also a means for a country to show the public about the relics of historical objects which are treasures and must be preserved. Lampung Province State Museum, is one of the museums in Indonesia. One of the relics that must be preserved in this museum is the traditional Lampung cloth which is still there and is a legacy for the people of Lampung. The Lampung Museum has a collection of traditional Lampung fabrics whose safety conditions are still a problem for the Lampung Museum management, where there is a lack of care and it is easy for visitors to touch and change the layout of these items. This can cause damage and changes to the collectible. Therefore, the Passive Infra Red (PIR) sensor-based Security System is a tool designed to maintain the security system of the Lampung Province State Museum object "Ruwa Juwai", by using a PIR sensor which can detect the presence of infrared rays which will be detected by the sensor and then will activate the buzzer and LED as output. The design of the PIR sensor-based security system tool is carried out by observing the infrared sensor detected on the PIR sensor and the output on the security system tool in the form of a buzzer and LED.Keywords--Lampung Museum, Security System, Passive Infra Red Sensor&#x0D;
COGNITIVE SCIENCE - Epistemological construct configuration

The development of Cognitive Science configures a strange scenario.In just over 40 years of official existence, it has a huge spread.While always emphasizing its interdisciplinary project, this new Science was always marked by an oscillation between the study of the brain as opposed to the study of the mind.An oscillation generated the corollary of the predominance of a discipline or of a specific perspective in the way it architected its investigation and its proposal of interdisciplinarity.In the first decades of its history, Cognitive Science bet on the analogy between minds and computers, between thought and symbols.The mind would be the brain's software and the bet on the possibility of simulating it through computer programs made Computing occupy a privileged place in this initial scenario.However, it is necessary to elaborate a concept for this discipline.
Enhanced Content-Based Image Retrieval with Trio-Deep Feature Extractors with Multi-Similarity Function

Introduction of the World Wide Web (WWW) and progressions in the field of multimedia and computer technology have enlarged the amount of image collections and databases including art galleries, digital libraries, and medical imageries that extend to millions of images.From these large-scale datasets, the retrieval procedure of images is carried out by classical approaches like chi square distance, colour histogram and text-based image retrieval, which however requires more time for getting the desired images.Thus, there is a need of proposing an effective retrieval system for images by handling these large numbers of images.For this reason, a new Content-Based Image Retrieval (CBIR) system has been implemented in this paper for retrieving the desired images of users from the collected images.Initially, different kinds of images like medical images, texture images, and environmental images etc., are collected from the standard image database.The deep feature extraction using Visual Geometry Group network (VGG-16), Inception v3, and Xception are used to get the features of all the images in the database.The parameters in these three deep learning techniques are tuned with an enhanced optimization algorithm of Modified Bypass-Rider Optimization Algorithm (MB-ROA).The features from VGG16, Inception and Xception are used for optimal weighted fused feature selection using enhanced ROA under training phase.During testing with query images, it undergoes deep feature extraction with the same set of techniques and, then fed into the optimal weighted fused feature selection to get the features of query image.Then, the multi-similarity function considering the cosine, Euclidean and Jaccard similarity are checked between the optimal features of database images and query image.The database images with the minimum similarity with the query image are retrieved from the database.The overall precision and recall to retrieve the top ten images related to the provided query image from the Corel dataset are 77.25% and 76.89%, respectively, while 70.15% and 70.26%, respectively from the VisTex dataset.The experimental findings show that the recommended model is effective at retrieving images from the database.
Arbitrary Oriented Scene Text Recognizer (AOTR)

Recognizing arbitrary oriented text has grabbed researchers' attention to develop several algorithms due to its high complexity in scene images and real-time applications like language translators, reading text for blind people, navigation systems, and smart parking.Converting text in natural scene images into strings has extended its application to Natural Language Processing (NLP)-based applications like named entity recognition.There are only three methods for text recognition: Optical Character Recognition (OCR), conventional methods, and Neural Network (NN) models.OCR is only known to be a successful text recognizer for scanned images, and in recent years, NN models have outperformed traditional methods for scene text recognition.It is necessary to create an optimal model to address the issue of scene text irregularity.We present the NN and customized OCR models, which we tailor for arbitrarily oriented text recognition, thereby avoiding scene text irregularity.An Orientation Correction Model (OCM) was introduced to improve the recognition model.In place of the recognition model, we used OCR.Alternatively, we created another model that reads corrected text images and extracts low-level features using the convolutional neural network layer.A recurrent neural network then uses these features to recognize text.Experiments were conducted on ICDAR2015, Total Text, Art19, and Cute80 benchmark datasets.It is observed that the proposed model obtains 79.5 % accuracy and hence increases the result by 1.9 % compared to the existing method after adding the orientation correction model.Similarly, results are promising on other datasets compared to existing algorithms.
A Hybrid approach for Image Acquisition Methods based on Feature Based Image Registration (FBIR)

Abstract In this paper, the problem of Feature-Based Image Registration (FBIR) has been addressed using a novel hybrid feature detector. To prove the efficiency of the proposed hybrid feature detector, its performance is compared with that of the state-of-the-art feature detectors like BRISK, FAST, ORB, Harris, MinEigen and MSER. Three different image acquisition methods are choosen for comparison. They are (i) Rotation transformation (ii) Scene to model transformation and (iii) Scaling transformation. Taking into consideration the above three types of image acquisition methods, a FBIR method is proposed for different types of remote sensing images. This paper proposed a hybrid feature detection algorithm, that consumes less time to detect the feature key points and it is also rotation and scale invariant. In addition to this the proposed algorithm performs well in terms of match points and match rate. This paper also focuses on a comparative analysis of BRISK, FAST, Harris, MinEigen, MSER and ORB with the proposed algorithm in terms of time to detect feature keypoints. It can be observed from the results and tables that in the case of hybrid feature detectors,It takes less time to detect the feature key points.
Medical Image Encryption Using Bit Plane Slicing, Dynamic Chaos, and Hash Function

Telemedicine is a communication in the medical field that requires privacy and confidentiality, so encryption is needed as a security method.The development of encryption methods must accompany technological developments.This research proposes a cryptography method developed and tested on medical images.This method consists of bit plane slicing, system chaos, and hash function.Bit plane slicing to break up the image bits so that they can be scrambled independently and dynamically with the chaos method.The hash function encrypts key 1, which is user input.The chaos method is run with dynamic parameters generated with a hash function to perform the confusion process on each bit plane.Then bit-plane join is performed, followed by the diffusion process using a logistic map with the second key for the parameter.The method is sensitive to slightly modified plain images or keys.Several measurement tools use to prove the method's performance.The result is a mean value are 7.999 for entropy information, 99.6027 for the number of pixels change rate (NPCR), 33.0925 for unified average changing intensity (UACI), 0.0021 for correlation coefficient, 231.8679 for chi-square and histogram, 50.0550 for avalanche effect, and perceptual analysis have proven the proposed method has satisfactory performance.The decryption is also done perfectly, as verified by the structural similarity index (SSIM)=1, peak signal-to-noise ratio (PSNR) is ∞, and bit error ratio (BER) is 0. Finally, this method can also work very fast in less than one second, so it can be implemented in real-time encryption.
Fragile Image Watermarking Based on Bidiagonal SVD-LSB for Tamper Detection and Localization

Tamper detection and localization are essential things in fragile image watermarking to carry out the authentication process.The tampering method continues to evolve from general and complex tampering.This study proposes a tamper detection and localization technique that can withstand a variety of standard and complex attacks with a combination of bidiagonal singular value decomposition, blockwise and group block for authentication.The least significant bit method is used in the embedding process to increase imperceptibility.By combining these methods, fragile image watermarking is produced that is robust against various common tampers such as type I and type II copypaste, rotate, text addition, and noise addition, as well as various complex attacks such as vector quantization, collage, content only and constant feature.The tamper detection rate is more than 0.99, and the average PSNR value is 51.85 dB.This shows that the proposed method is robust against various tampers and has excellent imperceptibility.
Kinematics of a Platform Stabilization using 3-PRS Parallel Manipulator

Abstract In this paper, a 3-PRS (prismatic, revolute, and spherical) parallel manipulator for platform stabilization is designed. The main purpose of this device is to stabilize visual equipment, which is placed on top of a car to inspect electrical transmission cables as part of routine maintenance. Due to the bulky and heavy infrared cameras used during inspections, a stabilizer platform has been designed to handle the weight of camera equipment up to 10 kg. This device consists of two major mechanisms. The first mechanism is able to adjust the angle of the camera. Thus, the user can focus the camera along the electric transmission lines. The second mechanism is for stabilization. The mechanism serves to stabilize the orientation and position of the camera in roll, pitch, and heave directions. To test the performance of the stabilization mechanism, the device is fed with the known value of the angle as regards input. As such, the device is trying to compensate for the change in angle. Results show that errors between input angles and compensated angles are in the range of 0.4-3%. Errors are seen to be within an acceptable range. It is significant that the resultant errors do not affect the orientation of the camera.
Image Improvement via Fuzzy Inference Centered Contextual-Dissimilarity Histogram Equalization

To solve the drawbacks of existing picture enhancement methods which include the error that occur in the pixel intensity expression, a fuzzy inference-centered contextual-dissimilarity histogram equalization (FICDHE) approach is suggested.Proposed system has three components.The first module generates intensity membership functions based on predicted intensity intervals.Second, fuzzy inference techniques are constructed and contextualdissimilarity of every pixel is determined.In the last module, dissimilarity histograms are cut and leveled.The fuzzy system in this study not only addresses the uncertainty source of pixel gray level expression, but is also adaptable.In this approach, the fuzzy inference system membership function parameter is automatically selected depending on picture pixel gray.Its versatility creates an algorithm that is more accessible.Two typical medical photos from BrainWeb are used in experiments.Performance of the suggested technique was compared against a number of enhancement algorithms based on subjective evaluation and picture quality measurement indexes.Experiments show that the proposed method is superior to the other algorithms in terms of improving the contrast improvement index (CII) (03.603), the peak signal-to-noise ratio (PSNR) (22.877), the entropy (E) (06.781), the enhancement measures (EME and EMEE) (56.688 and 3344.63 respectively), the quality index based on local variance (QILV) (00.965), and the feature similarity index (FSIM Index) (00.905).
Secure Adaptive Cluster based Routing Using Multi Objective-Trust based Hybrid Optimization Algorithm for CPS

Cyber physical system (CPS) is an engineered system that integrates the physical world with the cyber world through enhanced computation, communication and control (3C) abilities.But, the CPS's sensors are susceptible to malicious threats due to its dynamic topology and open medium of the network.Therefore, an effective secure adaptive cluster-based routing is required to be developed for improving the security of CPS.In this paper, the multi objective-trust based hybrid optimization algorithm (MO-THOA) is proposed to improve CPS security.The MO-THOA is the combination of trust based flower pollination algorithm (TFPA) and trust based ant colony optimization (TACO).In that, the TFPA is used to select a secure adaptive cluster head (ACH) and TACO is used to identify the secure routing path via ACHs.Therefore, the proposed MO-THOA improved the security against malicious attacks while broadcasting the data.The performance of the MO-THOA is evaluated using packet delivery ratio (PDR), average end to end delay (AEED) and normalized routing overhead (NRO).The existing methods namely grey-wolf updated whale optimization algorithm (GU-WOA), moving centroid based routing protocol (MCRP) and multiobjective ant colony optimization (MOACO) are used to evaluate the MO-THOA method.The PDR of the MO-THOA for 20 nodes is 0.981, it is high when compared to the GU-WOA, MOACO and MCRP.
Registration System with Midtrans Payment Gateway using Waterfall Method

The Airlangga international conference is an event organized by the student executive body of Airlangga University which is engaged in youth for international issues which then comes up with ideas to solve these social issues. To join this activity, you need to fill in your personal data and then upload the article. A registration system that requires a lot of end-user time because it has to go through the admin approval stage manually. The confirmation process that must upload proof of transaction data is quite consuming business processes and involves many actors to provide confirmation to end users. In solving this problem, this research implements a payment gateway as automation in the payment process in the registration information system. This research using the PHP programming language which is integrated with the midtrans payment gateway. This research also uses the waterfall method in the application development process, then the blackbox method for testing and also codeigniter as a framework for development. This registration information system has business processes ranging from creating an account, selecting a category, filling out forms, making payments, and uploading articles. With the implementation of a payment gateway to the information system, it is hoped that it will gain validity in the registration process as well as help ease the burden on the committee in preparing financial reports.
Impact of physiotherapy on the quality of life of depressive people: a literature review

Introduction: Depression is considered a mental health problem. It is a pathology with multifactorial causes whose symptoms are considered complex. Objective: To investigate, through a literature review, the effects of physical therapy modalities on the quality of life of depressive patients. Methodology: This was a literature review with a qualitative approach carried out in January/2019. The selected material corresponds to articles available in the SCIELO – BR, PubMed, PEDro and Cochrane databases. To operationalize the search for articles, the following DeCS descriptors were used followed by the Boolean marker and: depression and quality of life and physical therapy. After searching the databases, 19 articles were found. 3 surveys were excluded. Results: the bibliographic material used as a substrate of analysis for this literature review study consisted of 16 scientific articles read in full. Thus, 9 observational studies and 7 experimental studies were analyzed that aimed to evaluate the impact of Physical Therapy on the quality of life of depressive patients. Final Considerations: Realized that the exercise used as a therapeutic proposal can be effective in the adjuvant treatment of depressive patients recovering, among other benefits, their quality of life. In addition, the release of endorphin and serotonin can provide a feeling of pleasure and well-being to the individual. It is also concluded that more research should be carried out on this topic to support the physical therapist professional in this outcome.
Users’ trust in black-box machine learning algorithms

Purpose This paper aims to investigate whether professional data analysts’ trust of black-box systems is increased by explainability artifacts. Design/methodology/approach The study was developed in two phases. First a black-box prediction model was estimated using artificial neural networks, and local explainability artifacts were estimated using local interpretable model-agnostic explanations (LIME) algorithms. In the second phase, the model and explainability outcomes were presented to a sample of data analysts from the financial market and their trust of the models was measured. Finally, interviews were conducted in order to understand their perceptions regarding black-box models. Findings The data suggest that users’ trust of black-box systems is high and explainability artifacts do not influence this behavior. The interviews reveal that the nature and complexity of the problem a black-box model addresses influences the users’ perceptions, trust being reduced in situations that represent a threat (e.g. autonomous cars). Concerns about the models’ ethics were also mentioned by the interviewees. Research limitations/implications The study considered a small sample of professional analysts from the financial market, which traditionally employs data analysis techniques for credit and risk analysis. Research with personnel in other sectors might reveal different perceptions. Originality/value Other studies regarding trust in black-box models and explainability artifacts have focused on ordinary users, with little or no knowledge of data analysis. The present research focuses on expert users, which provides a different perspective and shows that, for them, trust is related to the quality of data and the nature of the problem being solved, as well as the practical consequences. Explanation of the algorithm mechanics itself is not significantly relevant.
Antoinette Handley. Business and Social Crisis in Africa. Cambridge: Cambridge University Press, 2020. xii + 219 pp. List of Figures. List of Tables. Acknowledgements. List of Abbreviations. Bibliography. Index. £ 19,99. Paper. ISBN: 978-1-108-44503-0.

Antoinette Handley. Business and Social Crisis in Africa. Cambridge: Cambridge University Press, 2020. xii + 219 pp. List of Figures. List of Tables. Acknowledgements. List of Abbreviations. Bibliography. Index. £ 19,99. Paper. ISBN: 978-1-108-44503-0.