Reviewerly/exHarmony huggingface dataset:
An IoT and Deep Learning-Based Smart Healthcare Framework for Thyroid Cancer Detection

A world of healthcare possibilities has been opened with the development of the Internet of Medical Things and related machine learning, deep learning, and artificial intelligence approaches. It has a broad range of uses: when linked to the Internet, common medical equipment and sensors may gather important data; deep learning and artificial intelligence algorithms use this data to understand symptoms and patterns and allow remote healthcare. There are a large number of people affected by thyroid disorders across the world. The ultrasound-based thyroid nodule detection using traditional methods increased the burden on the expertise. Therefore, alternate methods are required to overcome this problem. In order to facilitate early thyroid disorder detection, this research aims to offer an IoT-based ensemble learning framework. In the proposed ensemble model, three pre-trained models DeiT, Mixer-MLP and Swin Transformer, are used for feature extraction. The mRMR technique is used for relevant feature selection. A total of 24 machine learning models have been trained, and weighted average ensemble learning is employed using the Improved Jaya optimization algorithm and Coronavirus Herd Immunity optimization algorithm. The ensemble model with the improved Jaya optimization algorithm achieved excellent results. The best value for accuracy, precision, sensitivity, specificity, F2-score and ROC-AUC score are 92.83%, 87.76%, 97.66%, 88.89%, 0.9551 and 0.9357, respectively. The main focus of this research is to increase the specificity. A poor value of specificity can lead to a high false positive rate. This situation can increase anxiety and emotionally weaken the patient. The proposed ensemble model with the Improved Jaya optimization algorithm outperformed state-of-the-art techniques and can assist medical experts.
Cloud Service Broker Using Ontology-Based System

Cloud computing offers more advantages to clients and associations regarding capital uses and working cost investment funds. This study gives an ontological model of the cloud fabricating space to help with the data trade between the cloud-producing assets. The ideas of the proposed model depend on a writing survey of models of the cloud and models of assembling. In the research article, the problem addressed is how cloud brokers are providing cloud services in an efficient way to cloud users. It is the main prologue to an ontology-based, process-situated, and specialist framework that is autonomous of a society that permits most associations to utilize it. The rising number of cloud providers, the nonappearance of interoperability, and the heterogeneity in current open cloud stages lead to the requirement for creative frameworks to track down the foremost fitting cloud resource plan as successfully and mechanized as may be anticipated. In this paper, we depicted the building arrangement of a cloud organization made of two agreeable modules. The Cloud Agency’s objective is to naturally secure assets from suppliers on the premise of SLA evaluation rules and find the foremost reasonable cloud supplier that fulfills users’ prerequisites, and the Semantic Motor’s objective is to make a rationalist depiction of assets based on users’ benefit prerequisites and a brokering framework.
A Secure Framework for Communication and Data Processing in Web Applications

Web applications are widely used, and the applications deployed on the web do not always satisfy all the security policies. This may arise due to less secure configurations, less knowledge in security configurations, or due to insecure coding practices. Even though a lot of practices are available, a lot of security loopholes are still available for hackers to steal information. A secure web application framework is discussed here which incorporates solutions to major security loopholes that attackers may use for stealing information or compromising systems. The security framework proposed here ensures an encrypted data transfer making the data safe and server-side vulnerability detection and avoidance for major attacks like SQLinjection (SQLi) and Cross Site Scripting (XSS). The client side of the framework is responsible for validations, encryption, and session management through a JavaScript module. The server side of the framework is responsible for decryption and validation, data management, and URL management. The framework deployed with PHP showed a good outcome when tested with the Arachni web application security scanner. The framework will be further studied for performance with huge workloads. Further, the work will be extended to cover other attacks.
Robust JPEG steganography based on the robustness classifier

Abstract Because the JPEG recompression in social networks changes the DCT coefficients of uploaded images, applying image steganography in popular image-sharing social networks requires robustness. Currently, most robust steganography algorithms rely on the resistance of embedding to the general JPEG recompression process. The operations in a specific compression channel are usually ignored, which reduces the robustness performance. Besides, to acquire the robust cover image, the state-of-the-art robust steganography needs to upload the cover image to social networks several times, which may be insecure regarding behavior security. In this paper, a robust steganography method based on the softmax outputs of a trained classifier and protocol message embedding is proposed. In the proposed method, a deep learning-based robustness classifier is trained to model the specific process of the JPEG recompression channel. The prediction result of the classifier is used to select the robust DCT blocks to form the embedding domain. The selection information is embedded as the protocol messages into the middle-frequency coefficients of DCT blocks. To further improve the recovery possibility of the protocol message, a robustness enhancement method is proposed. It decreases the predicted non-robust possibility of the robustness classifier by modifying low-frequency coefficients of DCT blocks. The experimental results show that the proposed method has better robustness performance compared with state-of-the-art robust steganography and does not have the disadvantage regarding behavior security. The method is universal and can be implemented in different JPEG compression channels after fine-tuning the classifier. Moreover, it has better security performance compared with the state-of-the-art method when embedding large-sized secret messages.
Supervised Sentiment Analysis of Indirect Qualitative Student Feedback for Unbiased Opinion Mining

In the education domain, the significance of student feedback and other stakeholders for raising educational standards has received more attention in recent years. As a result, numerous instruments and strategies for obtaining student input and assessing faculty performance, as well as other facets of education, have been developed. There are two main methods to collect feedback from students, as follows: the direct and indirect methods. In the direct method, feedback is collected by distributing a questionnaire and taking their responses. The limitation of this method is that the true experience of students is not revealed, and there is room for bias in the collection and assessment of such a questionnaire. To overcome this limitation, the indirect method can be followed where social media posts can be used to collect feedback from students as they are active on social media and use it to express their opinions as posts. To address the problem of the manual annotation of large volumes of data, this paper proposes a machine learning method that uses the sentiment 140 dataset as the training set to automate the process of annotations of tweets. The same method can be used to label any qualitative data. In total, 5000 tweets were scraped and considered for this study. Various pre-processing methods, including byte-order-mark removal, hashtag removal, stop word removal, and tokenization, were applied to the data. The term frequency-inverse document frequency (TF-IDF) trigrams technique was then used to process the cleaned data. The TF-IDF technique using trigrams captures negation for sentiment analysis. The vectorized data are then processed using various machine learning algorithms to classify the polarity of tweets. Performance parameters such as the F1-score, recall, accuracy, and precision are compared. With a 94.16% F1-score, 94% precision, 94% recall, and 95.16% accuracy, the Ridge Classifier performed better than the others.
A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging

Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging. CNNs are particularly adept in automatically identifying complex patterns and features in pictures like X-rays, CT scans, and MRIs. They accomplish this by capturing hierarchical information utilizing layers of convolutional and pooling processes. By enabling precise disease diagnosis, anatomical structure segmentation, and even patient outcomes’ prediction, CNNs have transformed medical imaging. In this review paper, we examine how crucial CNNs are for improving diagnostic effectiveness and efficiency across a range of medical imaging applications. This review details how Convolutional Neural Networks (CNNs) are used, focusing on the development and use of 3D CNNs for processing and categorizing multidimensional and moving images. The paper discusses how critical 3D CNNs are in areas like analyzing surveillance videos and, especially, in the field of medical imaging to find pathological tissues. With this method, pathologists can segment the layers of the bladder with a lot more accuracy, which cuts down on the time they have to spend looking over them by hand. CNNs use specific filters to find spatial and temporal relationships in images, making understanding and interpreting them easier. CNNs are better at fitting image datasets because they have fewer parameters and weights that can be used more than once. This makes the network better able to understand complex images. This thorough review shows how 3D CNNs could improve the speed and accuracy of processing and analyzing medical images and how far they have already come.
Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images

Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (for example,. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies.
Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention

In response to the challenges of personal privacy protection in the dialogue models of the information era, this study introduces an innovative privacy-preserving dialogue model framework. This framework seamlessly incorporates Fully Homomorphic Encryption (FHE) technology with dynamic sparse attention (DSA) mechanisms, aiming to enhance the response efficiency and accuracy of dialogue systems without compromising user privacy. Experimental comparative analyses have confirmed the advantages of the proposed framework in terms of precision, recall, accuracy, and latency, with values of 0.92, 0.91, 0.92, and 15 ms, respectively. In particular, the newly proposed DSA module, while ensuring data security, significantly improves performance by up to 100 times compared to traditional multi-head attention mechanisms.
Parallel-Based Corpus Annotation for Malay Health Documents

Named entity recognition (NER) is a crucial component of various natural language processing (NLP) applications, particularly in healthcare. It involves accurately identifying and extracting named entities such as medical terms, diseases, and drug names, and healthcare professionals are essential for tasks like clinical text analysis, electronic health record management, and medical research. However, healthcare NER faces challenges, especially in Malay, in which specialized corpora are limited, and no general corpus is available yet. To address this, the paper proposes a method for constructing an annotated corpus of Malay health documents. The researchers leverage a parallel source that contains annotated entities in English due to the limited tools available for the Malay language, and it is very language-dependent. Additional credible Malay documents are incorporated as sources to enhance the development. The targeted health entities in this research include penyakit (diseases), simptom (symptoms), and rawatan (treatments). The primary objective is to facilitate the development of NER algorithms specifically tailored to the healthcare domain in the Malay language. The methodology encompasses data collection, preprocessing, annotation of text in both English and Malay, and corpus creation. The outcome of this research is the establishment of the Malay Health Document Annotated Corpus, which serves as a valuable resource for training and evaluating NLP models in the Malay language. Future research directions may focus on developing domain-specific NER models, exploring alternative algorithms, and enhancing performance. Overall, this research aims to address the challenges of healthcare NER in the Malay language by constructing an annotated corpus and facilitating the development of tailored NER algorithms for the healthcare domain.
WRITTEN GAMBLING DISCOURSE IN SPANISH

The aim of our studies is to suggest the definition of gambling discourse, to define its types and to describe lexico-grammar, sintactic and graphic peculiarities of the texts included in gambling discourse. The studied discourse belongs to institutional and ritual discourse because gambling games possess rules which the gamblers should follow obligatorily. Gambling discourse is represented by oral and written form. We determine various types of texts belonging to the gambling discourse: advertising texts in the sphere of gambling; specialized texts of gambling discourse; texts of gamblers’ communication during the game and outside the game. Among lexical resources of gambling discourse we single out gambling terms, words of positive evaluation, metaphors, metonymy, hyperbole, comparison. Grammar and syntactic resorces include nominative and imperative sentences, rhetoric questions which communicate a dialogue character to written gambling discourse and motivate potential gamblers to play. Graphic means are characterized by the use of symbols, numbers, verbal and non verbal resources, such as change of colors and sizes, capitalization, animation, outlined text, links to other web pages dedicated to gambling. The range of lexical, grammar, syntactical and graphic means represent a positive image of a game and incent the gamblers to play. The texts of gambling discourse perform nominative, informative and expressive functions.
Optimal Control for a Three-Rotor Unmanned Aerial Vehicle in Programmed Flights

In this study, we propose a new approach to selecting PID controller parameters for a UAV tricopter during programmed flights. The approach uses optimization techniques to determine the optimal values of the PID controller parameters based on the desired performance of the UAV. The proposed method is particularly effective for repeated programmed flights in which the UAV follows a defined flight path. The use of a PID control provides a reliable and robust control system that can cope with various disturbances and uncertainties in UAV dynamics. The proposed method provides an alternative to adaptive control, which requires a significant amount of system identification and parameter tuning. The effectiveness of the proposed method has been verified in simulation studies, and the results show its ability to achieve satisfactory performance with low tracking error and fast response time. The result of the work is an improvement in control for a specific object at a specific mission. We present how the recipients can perform this procedure for their object and their mission to be able to improve the control gain in the physical controller.
A Close Observation of the Dynamic Inspiration for Interactive Jewelry

Interactive jewelry and kinetic jewelry are emerging concepts in the field of jewelry design which overlaps with each other. However, interactive jewelry lacks specific parameters and a standard definition, which hinders early research progress and designers’ idea construction. To address this challenge, based on the idea of taking ambiguity as a design strategy, the fuzzy definition boundary between kinect jewelry and interactive jewelry makes the design features of kinect jewelry have certain enlightening significance for the interactive jewelry. This paper aims to collect design pictures related to kinetic jewelry using a Python model, analyze the collected pictures, and obtain current mainstream design parameters to reference for interactive jewelry design. The data will show details such as the interaction mode, wearing part, and materials used of kinect jewelry, thus providing valuable suggestions for interactive jewelry design. Additionally, the paper considered the possibility of how to leverage ambiguity in design as a resource.
Furry - Design of Augmented Reality Blind Box

Furry fandom becomes an influential sub-culture in social media among the young generation. More and more people love anthropomorphic animal characters and like to engage in magical thinking, the fantasy activities like a furry animal. However, the furry content or activities are mainly based offline, on the website, or scatter on social media. It is hard to find a mobile-based platform for furry fans to create, share, and exchange. In addition, the blind box industry develops quickly, which grabs the imagination of young consumers and raises incredible social excitement. However, the physical version of the blind box has various limitations, such as delivery, space occupation, expense, etc. Inspired by the needs of furry fans and the concept of a blind box, we develop a platform named ‘FURRY’ - a creative, fantasy, and interactive platform for the creation, sharing, and monetization of the animal-themed virtual blind box based on augmented reality technology and social media. Users can collect random parts of a virtual animal from the blind box and wear them to become an anthropomorphic animal character they prefer. In this paper, the whole structure of the platform is discussed. The four sections of FURRY including obtaining, playing, building fursona, and mine are introduced in detail. Then the markerless AR applied in the project is discussed. Two stages preprocessing and real-time processing are introduced to create an interactive user experience. The results of the present research are expected to help vitalize and expand the practice of AR-based culture content.
A Study on GML-Based Encryption Technology for Open-Source Software License and Service Structure Analysis in Cloud-Based Micro Service Architecture Environment

Micro-service architecture (MSA) refers to a structure in which several software with different roles are bundled into one service, rather than one or a small number of software when building a service. A lot of software is used to build a service with an MSA structure, and recently, open-source software is being actively used. Open-source software is not always available free of charge as each license exists. However, since commercialized MSA-structured cloud-based software does not provide execution code to consumers, it is difficult to check whether open-source software is used without permission. Therefore, even if the licenses granted by open-source software are violated or open-source software contains potential risks, it is very difficult for consumers to identify these issues. To solve this problem, a graph modeling language (GML) can be used as a method for analyzing the structure of software operating based on the cloud MSA environment and determining the open-source license included in the software. However, since GML contains a lot of information such as software structure and information, it can be abused by analyzing the software structure when GML is leaked. To solve this problem, in this study, we conduct research on how to encrypt GML and perform analysis in the encrypted state.
Core Values for a Mixed Reality Software Development Kit: A Qualitative Study Among Main SDK Tools for XR Development

In early 2022, the UX Research team from Sidia’s Research and Development Institute, located at Manaus/Brazil, received a requirement from one of its projects to conduct a comparative study on the usage of different Software Development Kits (SDKs) for Mixed Reality (XR) applications. The study aims to compile developer’s necessities and difficulties while using common available SDKs in the market. This qualitative study is about the core SDK values pointed out by participants related to the development of XR applications. We have selected developers based on availability to conduct a study for a month. With the help of Microsoft Office Forms, we have structured a series of surveys about the main usage activities of five important SDK tools to develop XR experiences. The selected SDKs were: ARCore, AR Foundation, Vuforia, Spark AR (Scripting API), and A-Frame. The surveys have been structured obeying the flow of activities of an XR application development. The defined flow, based on interview with the candidates, consisted of main phases: Installation e Update, Development, Tests and Deploy. The survey, compiled in a detailed report, exposes the collected data and share some of the participant’s opinion to provide context and exemplify the exposed use cases. In this report, we observe that the characteristics and needs of the participants varies according to which phase of the activity flow they are executing. For some stages, users demanded agility and frictionless processes. At other times, the flexibility and customization of the SDK behaviors were widely punctuated by participants.