Reviewerly/exHarmony huggingface dataset:
Image Blocks Encrypted then Rotated: A New Pixel-Level Scrambling Method Based Logistic Map for IOT

recently, grayscale images have been used in many areas such as healthcare or Internet Of Things (IOT), and securing these images has become important and necessary. Encryption is one of the most reliable and effective ways. Scrambling image contents adds an extra level of image security. This paper proposes a simple new method for scrambling gray images using a Chaotic Logistic Map and pixel-level rotation. In the proposed method, the gray image is encrypted first and then divided into several N×M non-overlapped blocks. The image blocks will be rotated according to the encryption key with angles of 90, 180 and 270 degrees. In the same way, the decryption process will reverse the image block’s rotation angles first, then retrieve the original image by applying the XORing operation with the same key. The efficiency of the proposed method was evaluated and analyzed by a histogram, Number of Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI), information entropy, Peak Signal to Noise Ratio (PSNR), correlation coefficient, and Adjacent Pixels Correlation Coefficient (APCC). A comparison with other scrambling methods was carried out and the results boosted the proposed method.
User Centred design Methods in Animal Centred Design: A systematic review

Animals play a critical and nuanced part in our lives today. Animal Centred Design (ACI) is heavily influenced by the discipline of human-computer interaction (HCI). The field of ACI focuses on investigating the interactive technologies affected by individual animals and how/what technologies could be developed to improve animals’ welfare, support their activities, and foster positive interspecies relationships. The purpose of this research is to review and understand the use of the User-Centred Design (UCD) to develop a framework for the best practices of using the UCD methods in ACD research. The research data will be generated through a systematic literature review to find the most common UCD methods used on ACI. The review summarised the results of lessons, experiences, and contradictory issues, and how to better cooperate to solve these problems in future research. This research will help us to understand the best way to design human-animal interactive technologies with a focus on mobile technology.
Design of Rooftop Panel Performance Monitoring System using Power and External Parameter

In maintaining the maximum capability of rooftop solar panels, requires quite expensive costs, the cost of installing solar panels is not small, it requires the durability of the life of the solar panels the old one, then a monitoring system is needed to see how these solar panels are running properly. In this study, we used the PZEM 004T Module as a reading of output parameters, namely voltage, current, power, power factor, frequency, and also Kwh whose data will be processed in ESP32 microcontrollers as brains or processor units that control inputs, processes, and outputs on solar panels. Based on the test results, it is known that the monitoring system can work well to display measurement results in real-time through the blynk application. From the results of the analysis on this tool, an error value was obtained in this tool for voltage measurement, which is 0.05%, and for current measurement, which is 0.06%. In the future, it is hoped that the results of this study can be the basis for analysis to determine the service life of rooftop solar panels
Windows-based Instant Messaging (IM) Application for Educational Institution

Communication from educational institutions to lecturers, students, employees, and communities generally uses SMS, telephone call, or announcement on the portal. This method has several obstacles, such as expensive cost for frequently SMS and telephone, difficulty in getting the latest contacts, and unread announcements or messages. Meanwhile, the current instant messaging (IM) has not been able to meet some of the communication and operational needs of educational institution, or quite expensive if customized. This study aims to develop an EM system to meet the needs of educational institution. The system will be developed on the Windows platform and as a continuation of previous research that has been implemented on the Android platform. The system is developed using the Rational Unified Process (RUP) method which consists of inception, elaboration, construction, and transition phases. The results obtained from this study are an IM system with features of personal chat, announcement, broadcast message, and group chat. The conclusion generated through this research is that this system increases the ease of communication activities in educational institution.
Cervical Cancer Identification using Deep Learning Approaches

The presence of cervical cancer is not apparent as its incubation period is long. A pap smear screening is the only diagnostic method; examining the pap smear slides uses a microscope. However, problems happen where humans make mistakes during the diagnostic process, causing inaccurate results and delaying the individual who needs to receive comprehensive treatments. With hopes to improve the current situation, assisting cervical cancer diagnosis with artificial intelligence techniques is suggested. This paper will use ResNet101v2, an upgraded residual network from ResNet, to develop a cervical cancer detection model to predict the severity of cervical cells. The Herlev dataset distributed 70% into training and 30% into validation; remaining 98 unique images will be used during the testing stage. Transfer learning techniques were introduced to develop the model. Using the testing images, the model reached 71.4% accuracy contributing better accuracy compared to other research studies in predicting the cervical cells using 7 classification classes. The model shows potential for clinical cytotechnologists’ during the pap smear diagnosis.
A Secure and Authentication Scheme to Preserve the Privacy of Electronic Health Records in the Healthcare System

Electronic Healthcare Records (EHRs), networks, and cloud computing have significantly revolutionized the traditional healthcare system. Secure remote user authentication is an important component of the healthcare system for authenticating an individual's identification via an unsecure communication network. The roles are distributed among the system's users, contrasting between administrator and data entry within certain rules and policies. It is possible to restrict access to key user activities (addition, deletion, and modification) based on a user's position in the healthcare system. As a result, security issues, hostile attacks, and privacy concerns are viewed as key challenges in the healthcare system. Using role-based access control and smart multi-factor authentication, this research provides a privacy-preserving solution to solve the issues in earlier approaches. The proposed scheme is based on crypto hash functions and asymmetric cryptosystems by using chameleon digital signatures. The proposed scheme carried out two exhaustive formal security proofs for the work that was provided. Using scyther, a formal security tool, we present a secure user authentication technique in the healthcare system that overcomes the familiar security problems and ensures the security of the proposed scheme and informal analysis. Our proposed scheme focuses on two main components: users and administrators. The administrator is considered to be the heart of the system. Therefore, any information that is stolen from them poses a threat to the entire system and all its parts. Compared to others, the proposed scheme more secure. By the way, our work gains many potentials to protect users account gains malicious attacks.
A Phono- Pragmatic Analysis of Threatening and Warning Phrases in Three Selected Speeches of Trump In 2020

Former President Donald Trump is one of the most political figures to join the United States presidential election. He was known for his use of warning and threatening phrases. In the broader domain of verbal expression in speech, intonation’s function goes beyond the speaker’s level of phrases or sentences. A speaker forms an utterance taking into consideration that it is performed through an expression’s communicative force. In this analysis, a descriptive qualitative methodology is employed to achieve the objectives of the study. The selected speeches are analyzed following Speech Act Theory (SAT) and Brazil’s model to understand the intended meaning of the words. Other researchers in this field of phonology and pragmatics will benefit from this analysis because it clarifies the role of commissive acts and intonation in political speeches. The findings of this study substantiate the importance of Brazil’s model in analyzing tones pragmatically. SAT was utilized to critically evaluate the effectiveness of verbal communication in conveying the speaker’s intention. Based on the study results, it is evident that commissive acts, particularly threats and warning, are present in the three speeches expressed in an indirect illocutionary front.
On the Equivalence of Normal and Extensive Form Representations of Games

In this chapter I examine the traditional position in game theory concerning the adequacy of the normal form representations of extensive form games. According to this traditional view, an extensive form game can be reduced to a normal form game without loss of strategically relevant information about the structure of the game. Many game theorists have already raised questions about such a reduction from a game in extensive form to strategic form by considering solution concepts that explicitly use the extensive form structure of the game. In the same vein, I evaluate the traditional view of the equivalence of extensive and normal form games in a systematic way. Within the traditional game-theoretic framework, I show that two extensive form games with the same reduced normal form may lead to different outcomes, and thus are not strategically equivalent. In addition, I demonstrate that the reduced normal form of an extensive form game cannot sufficiently represent certain reasonable strategic reasoning in the game of extensive form.
The Enrichment Of MVSA Twitter Data Via Caption-Generated Label Using Sentiment Analysis

Due to the widespread use of Twitter data and the diversity of scientific research, it is a goldmine of data. Besides the emergence of new areas, including visual sentiment analysis, it was necessary to develop datasets with distinct requirements that would meet the purpose of this area. To compensate for the lack of annotated data, data enrichment is commonly used to generate new instances by leveraging newly available information or relying on other types of knowledge. This study will enrich and augment a dataset of images and captions with a sentiment analysis-generated label. A method has been put forth to determine the ideal sentiment. This technique uses the caption feature to implement six different sentiment analysis techniques. Finalizing the classification of each dataset item based on the percent of positive, negative, and neutral polarities extracted for each caption has been determined. Apart from that, the classification will have the largest percentage of polarities. Results from experiments run on the Multi-View Sentiment Analysis (MVSA) Twitter dataset, which contains pairs of images and textual image captions. All images within the dataset will be labelled, introducing a new feature to this dataset. The polarity of the generated sentiment is identical to the polarity of the objective text perspective. Note that the results were exceptional in every way.
SRA System Design: Using Deep Learning to Analyse Experimental data for Speech Researchers

Speech researchers expend tremendous effort when measuring and analyzing audio data from experimental participants and evaluating large amounts of audio data to aid speech research. In traditional assessment methods, researchers listen verbatim to long audio files from participants to find the keywords needed for the assessment. This approach is very tedious and time intensive for speech researchers. To improve research efficiency and efficacy, we designed a system called SRA (Speech Recognition Assistant) based on the DeepSpeech model. SRA effectively supports speech researchers in the evaluation of participant experimental audio data. The purpose is to achieve the following: (i) simplify the workflow for analyzing data and (ii) reduce the time cost for researchers to extract data; (iii) provide a framework with potential for adaptation for use in other fields, such as speech science, audiology, and hearing science.
Texture Feature Based Colonic Polyp Detection And Classification Using Machine Learning Techniques

Having a high mortality rate, colorectal cancer (CRC) is the second most prevalent disease in women and the third most prevalent cancer in males. The majority of CRCs are derived from intestinal or colonic polyps. Colorectal cancer, commonly referred to as bowel cancer, is a type of cancer that arises when cells in the colon, rectum, or appendix grow out of control. Early colonic polyp detection and excision is the most effective strategy to prevent CRC. In the proposed study, feature extraction is carried out using local feature descriptors. The feature selection is carried out using the correlation filter method- and wrapper-based techniques and then applied to machine learning algorithms that can be utilized to identify and classify polyps. The Kvasir [1] and CVC Clinic dataset is used in the proposed effort to categorize the polyps. Before feature selection, features are extracted using local feature descriptors such as the Gray Level Co-occurrence Matrix (GLCM) [2], Local Binary Pattern (LBP) [3], and Histogram of Oriented Gradients (HOG) [4] we get 1789 features. Then correlation-based feature selection (CFS) [5] is used to find the relevant features. After that apply a wrapper-based strategy to locate the most relevant feature by using the Naive Bayes classifier. The model performances are assessed using Bayes Network, Naive Bayes, Stochastic Gradient Descent, Support Vector Machine, Random Forest, and Logistic Model Tree. The three types of polyps can be classified in the proposed model. They are large polyps, normal polyps, and small polyps. It is evaluated by using the evaluation metrics such as Recall, Precision, Accuracy, Specificity, F-Score, Area under the Curve and Kappa score. The Bayes Network is the best classifier and shows an accuracy of 99.3%.
User Experience (UX): A Comparative Study Between Public and Private Institution Library Website In Indonesia and Malaysia

The evolution of Internet is seen well progressed. From Internet 1.0 to now known as human intelligent. Every website will ensure their pages is suit and able to cater their users’ needs and functions. User Experience (UX) is one of the approaches that most developer will take into consideration when developing the website. It is crucial to measure the acceptance of the users’ so that improvement can be made. Apart from that, the users’ engagement is also seen important in ensuring the website is continuous use by the users. To students and researchers, library website is one of the most and common places they visited in completing their assignment, assessment and/or research. To get know the users experience in using their university library website, this study intended to measure to what extend their liking in using it. AttrakDiff questionnaire is used due to its effectiveness in measuring the UX. The study used 4 dimensions which are Pragmatic Quality (PQ), Hedonic—Identity Quality (HQ-I), Hedonic—Stimulation Quality (HQ-S), and Attractiveness (ATT). The study is using SPSS Version 26 to analyse the data and the results show that there are no significant differences among both local and private university in Malaysia and Indonesia. It is hope that the instrument of this study can be used as a checklist or assessment to web administrator to measure their website performance.
About the method of increase of accuracy of measurements characteristics of complexsystems at presence duplicating measuring devices

The method of increase of accuracy of quantitative estimations of characteristics of complex systems is considered at use of duplicating sources of the information.
Attribute Analysis

Attribute analysis is a methodological approach that divides an assemblage or a sample of stone tools into individual lithic artefacts and lithic artefacts into individual elements (attributes or traits), with the latter constituting the basic analytical units. Attributes, which capture the complex morphology and metrics of stone artifacts, are individually defined, measured, addressed, and analyzed. The approach emphasizes standardized, quantitative collection of data on large samples, transparency and comparability between analysts, use of databases, and statistical analysis of data. The goal of attribute analysis is to reconstruct the techniques and methods of stone artifact production based on the quantitative evaluation of individual attributes, to generate testable conclusions about reduction sequences and techno-economic behavior, and to test specific hypotheses.
Spill-Over Effects of Cryptocurrencies Price Volatilities in Financial Markets: An Empirical Estimation during Global Health Crises

This study is conducted to investigate the potential volatilities and interdependencies among cryptocurrencies during global health crises, i.e., during COVID-19. The top five cryptocurrencies have been selected to assess their interdependencies. These currencies have been ranked as the top five due to their highest market capitalization. These top five currencies are Bitcoin, Ethereum, Tether, USD coin, and BNB. Monthly data for these currencies from the first month of 2019 to eighth month of 2022 is taken from online sources by investing.com. Squared deviations from mean values have been taken as measures of volatilities. These measures are simple but more precise to capture the possibility of potential interrelations in the volatilities of cryptocurrencies through regression analysis. The results of this study showed that the volatility of each cryptocurrencies involved is interlinked with the volatility of at least one other cryptocurrencies.
Dilation and Informativeness

One sometimes sees it suggested that the phenomenon of dilation is a problem for sets of probabilities, since your belief state becomes “less informed” on learning new evidence, and that is something that should not happen. This article explores several ways to make precise the idea of “informativeness” for a set of probabilities, and finds that this criticism of imprecise probability does not stand up to scrutiny.
Clustering-Based Collaborative Recommender System Using a Nature-Inspired Algorithm

The recommender system is a filtering technique that tries to reduce the available selections for users by finding the relevant items that satisfy their desires. Collaborative filtering is the most simple and efficient recommender system approach, as it makes use of the similarity among users’ preferences to generate recommendations. However, this approach suffers from some issues such as data sparsity and scalability, which make the process of identifying user’s neighbourhood a challenging task. This paper proposes a clustering-based collaborative filtering recommender system using the k-means algorithm along with a nature-inspired algorithm to improve the clustering process, which leads to a better recommendation quality. The Artificial Fish Swarm Algorithm (AFSA) has been employed to determine the optimal initial centroid for the clustering task. MovieLens 100K dataset was used to evaluate the proposed system’s performance in terms of precision, recall, and mean absolute error. The dataset was divided into 80% and 20% randomly for training and testing, respectively. Python 3.9.12 programming languages was used to implement the proposed system. The experimental results confirmed over the different number of clusters and the value of mean absolute error decreased from 0.75 to 0.67 as the number of clusters increased from 5 to 35 clusters. The obtained results are compared against other recent nature-inspired algorithms, which proved that the proposed recommender system outperforms the considered methods.
Image-Based Person Re-identification Using Omni-Scale Feature Learning

Nowadays, person re-identification (re-id) has received a large interest from the research community. It has been considered the heart of the studies of computer vision that plays a vital role in security applications to prevent terrorist attacks and track robberies. This paper highlights the design of the recently presented network Omni-Scale Network in image-based person re-id which has fewer parameters than the best-performing Residual-based Network methods. Then, views some datasets that have been used with such a network as well as our custom-created dataset which is better in performance than some well-known small datasets. The custom-created dataset contains 256 images collected using mobile phone cameras. The results of the different datasets applied to the proposed network are compared and evaluated.
Analyzing the Differences of Female/ Male Faces on Recognition Accuracy

This paper provides a comprehensive analysis of the differences between men and women on recognition accuracy. Proposed methodology depends on standard (ArcFace) face matcher , which was trained with Residual Network (ResNet-100) depending on MS1MV2 dataset images. Differences between male/female faces are analyzed to inspect the effects on recognition results. It is shown that recognition accuracy is lower for female faces due to set of factors. Yet, the deviation in the genuine distribution for female face tends toward lower scores of similarity. Accuracy difference is also obvious over face subsets that have minimum possible pitch angle (close to zero). Even by excluding images with partial forehead obstruction by hat or hair, accuracy differences persist for the juggler/genuine distributions. It is also witnessed that the genuine distribution was enhanced where only women's images without makeup were used; however, the female juggler distribution is also degraded as well. Finally, the results show that differences in recognition accuracy are witnessed even if (standard adopted) the deep learning techniques are trained on a face dataset with an explicitly balanced number of images and subjects for female and male faces.
Implementation of an Expert System in Diagnosing Obstetrical Health in Pregnant Women using Fuzzy Algorithms and Certainty Factor

The maternal mortality rate (MMR) in Indonesia is still very high and the highest among ASEAN countries. In 1990, the MMR was at a rate of 390 per 100,000 live births, and a survey in 2002-2003 yielded an estimate of 307 per 100,000 live births. However, the analysis concludes that the situation is very concerning for Indonesian women. An expert system is needed to help pregnant women find out quickly the health condition of the womb in pregnant women based on the symptoms that appear. Not only types of disease, this system also informs how to handle pregnant women whose health conditions are problematic. The method used is the fuzzy logic method and the certainly factor method is used to predict the health condition of pregnant women based on age and temperature, then the certainly factor method is used to predict the health of pregnant women based on the symptoms suffered. The results of this study are to make it easier for pregnant women to obtain information about the health of the womb in pregnant women who are suffering, as well as get solutions for handling it and make it easier for pregnant women to diagnose the health of pregnant women. The analysis of the maternal health diagnosis system has provided convenience and can be used as an alternative to diagnose other diseases, because it is able to diagnose accurately according to the symptoms felt, so that it can help make it easier to get accurate diagnostic results.