Reviewerly/exHarmony huggingface dataset:
Biometric Authentication Using Face Thermal Images Based on Neural Fuzzy Extractor

A method of biometric authentication based on the thermogram of the subject's face was proposed. This method allows you to associate a biometric image of a person with a cryptographic key or password, as well as protect the biometric image and key (password) from being compromised during storage and transmission over communication channels. This effect was achieved through the use of a fuzzy neural extractor trained according to the GOST R 52633.5 standard. The solution also uses a deep convolutional neural network for face detection and an Inception-Resnet network for feature embedding. RetinaFace, ResNet50 and VGG-Face were tested as alternatives to these neural network models. The best result achieved was EER = 4.91
A Survey Paper on Adenocarcinoma Detection Using Deep Learning Methods

Adenocarcinoma is a subdivision of carcinoma which grows in the glands that line inside our body organs. The paper reviews recently developed systems with deep learning methods using medical imaging modalities like Computer Tomography (CT) and Optical Coherence Tomography (OCT) for the diagnosis of Adenocarcinoma. The study is done on the papers proposed in the past 5 years. The study compares with existing system on various performance measurements. Transfer learning technique with ResNet and Inception-v3 CNN models provide good results. Generative Adversarial Network (GAN) attracts the current scenario of Deep Learning. A good segmentation method with a powerful classifier can be used for medical image analysis.
Empowering MBTI Personality Classification through Transformer-Based Summarization Model

The Myers-Briggs Type Indicator (MBTI) is a popular personality classification tool that utilizes the work of Carl Jung. With individuals increasingly expressing themselves online rather than in person, social media has become a promising platform for predicting personality. However, predicting personality from online behavior is a challenging task that requires extensive data processing and modeling. To tackle this challenge, a novel approach is proposed that leverages a transformer-based summarization model to summarize the dataset records before applying the DistilBERT base classification model. The proposed method improves the MBTI classification task by achieving an accuracy rate of 0.96, which demonstrates the efficacy and durability of the strategy. The study emphasizes the importance of transformer-based summarization in enhancing NLP tasks and the need for applying various optimization techniques to achieve optimal performance. The findings provide a foundation for future research in personality classification and NLP.
Real Estate Management System using Blockchain

Real Estate is the third largest asset class in the world only behind the stock market and the bond market. The entire real estate market is valued at over $7 trillion and is expected to cross $8 trillion by 2028. With an ever growing population and the land being limited in the world it would be safe to assume that this asset class keeps on growing according to the simple rules of law of Supply and Demand. So we have to come up with new methods to improve the process of purchasing real estate as well as eliminate the third parties in between. So in order to make these processes more efficient we can use the technology of blockchain in the process. We can improve the various issues involving transparency and also promote partial ownership of properties.
Survey On Next Word Prediction Techniques In Natural Languages

Next word prediction is the core problem of NLP, which has numerous applications, to predict which word will be said next. Researchers used several word-level statistical and deep learning models to generate the text. In the past, before the advancement of deep learning techniques, statistical techniques were used to predict the next word. These methods have the drawback of generating text without ensuring the syntactic accuracy of the sentences. Neural networks have also become more popular than traditional methods due to their accuracy in word prediction. Nowadays, word prediction is often done using LSTM, Bi-LSTM, and GRU. This paper provides a survey of different techniques with their classification for predicting the next word and evaluates them. Also, this work identified research gaps and provide some future directions based on the findings.
Decision Level Fusion of Transfer Learning based Models for Diagnosing Lung and Colon Cancer

Lung and colon cancers are one of the most known cancer types that cause death. However, in most cases, early detection of the disease has critical impact in limiting the spread of tumor cells. Therefore, the diagnosis of the disease is considered the first step for treatment to improve the chance of survival. Artificial intelligence provides great assistance for specialists in the field of diagnosing pathological tissue by saving effort and time with accurate decisions. In this study, the high potential of ensemble learning was adapted to diagnosing colon and lung cancers by establishing multiple deep networks based computer-aided diagnosis system for infected cancer cells. In the model ensemble, the individual classification results of DenseNet201, ResNet101 and EfficientNet-b0 models were combined in terms of hard voting idea. The individual classification accuracies of DenseNet201, ResNet101, and EfficientNet-b0 are resulted in 99.94%, 99.93%, and 98.64% for colon cancer and 99.05%, 98.93%, and 95.79% for Lung cancer, respectively. However, the ensemble approach increased the performances to 99.98% and 99.78% for colon and lung cancer datasets respectively.
Abstractive Arabic Text Summarization Based on MT5 and AraBart Transformers

Text summarization is essential in natural language processing because of the rapid growth of data. Therefore, the user needs to summarize this data into meaningful text quickly. There are two standard methods of text summarization: extractive and abstractive. There are many efforts to summarize Latin texts. However, summarizing Arabic texts is challenging for many reasons, including the language’s complexity, structure, and morphology. Also, there is a need for benchmark data sources and a gold-standard Arabic evaluation metrics summary. Thus, the contribution of this research is multi-fold: First, it introduces a new Arabic benchmark dataset, called the HASD, which includes 43k articles with their extractive and abstractive summaries. Second, it presents a new Arabic benchmark dataset called the AASD, which includes 150k articles with their abstractive summaries. Third, this work modifies the well-known extractive EASC benchmarks by adding to each text its abstractive summarization. Fourth, this paper proposes a new measure called the Arabic-rouge measure for the abstractive summary depending on structure and similarity between words. Finally, an investigation of the impact of using abstractive Arabic text summarization on different transformer models with different data sets. The model is tested using the proposed HASD, AASD, modified EASC benchmarks and evaluated using Rouge, Bleu, and Arabic Rouge. The experimental results show satisfactory results compared to state-of-the-art methods.
Application of PRA and Machine Learning Algorithm in Budget Data Acquisition and Processing System

How to use automation, optimize the comprehensive budget management system, and help the automatic collection of budget data and budget preparation has become a growing concern for enterprises. This paper combines IT technologies such as robot process automation (PRA) and machine learning algorithm with comprehensive budget management, optimizes the budget data collection process, conducts budget data mining and analysis, so as to help enterprises formulate budget plans, and puts forward implementation suggestions and safeguards.
The Design of English Translation Software Based on Machine Learning Technology

With the increasing frequency of our country’s international communication and the popularization and penetration of mobile Internet into people’s work and life styles in modern society, the level of social informatization has also increased. In order not to be eliminated by this era, people must follow the pace of development of this era, always keep an eye on and receive the latest information from all over the world. Most of these materials are published on the Internet in foreign languages. Therefore, language has become the biggest obstacle for people to obtain information. As machine translation technology is restricted in terms of convenience and cost control, people’s need for machine translation or machine translation technology has become more and more urgent. This paper studies the design of English translation software (ETS) based on machine learning technology (MLT). By introducing MLT into ETS, a new neural machine translation method is proposed, and related experiments are used to test the effectiveness of the translation method. The designed translation software has been evaluated for translation quality. The experimental results show that as the arc length distribution increases, the translation quality (TTQ) decreases. The English translation software designed in this paper is of great importance to the research and development of machine translation.
Enhancing Khalifa Satellite Imagery Resolution with AI-Powered Super Resolution Generative Adversarial Networks (SRGAN)

KhalifaSat is a multispectral remote sensing Earth observation satellite used for various analyses such as building detection and road extraction. However, the limited resolution of satellite images can result in blurry details. For this limitation to be removed, super resolution techniques have been introduced, with Generative Adversarial Networks (GANs) being a notable approach. This research proposes a novel algorithm called Hybrid SRGAN, which simplifies the Super-Resolution Generative Adversarial Network (SRGAN) model while maintaining high accuracy. The proposed modifications involve removing batch normalization layers and utilizing bicubic interpolation for resizing low-resolution images. By eliminating batch sampling, training speed and generalization across different image types are improved. To assess the performance of the suggested model by using the metrices like SSIM, PSNR, and BRISQUE, showcasing its superior reconstruction quality and overall performance, particularly in the luminance channel(YCbCr), when compared to cutting-edge algorithms.
Leveraging Attention to Achieve Generalization for Image Forgery Detection

Recently, image forgery has become an alarming trend with the growth of available easy-to-use editing and generation tools. Modern DeepFake methods have achieved extraordinary progress in realistic face manipulation, thus raising concerns among the public about the misuse of such technologies. Unfortunately, with the obnoxiously wide range of possible manipulation and artifact-covering methods, most existing state-of-the-art detection methods lack the generalization capability to handle the output variations. To address this issue, a noticeable shift towards using attention mechanisms has emerged using balanced portions of the latest challenging datasets to detect intra-and inter-spatial relations. Our paper provides a comprehensive analysis of modern deep learning-based methods, showing the benefits of the shift. In addition, we make propositions for future research directions and dataset-building methodology.
Elements that Influence Transparency in Artificial Intelligent Systems - A Survey

Artificial Intelligence (AI) models operate as black boxes where most parts of the system are opaque to users. This reduces the user’s trust in the system. Although the Human-Computer Interaction (HCI) community has proposed design practices to improve transparency, work that provides a mapping of these practices and interactive elements that influence AI transparency is still lacking. In this paper, we conduct an in-depth literature survey to identify elements that influence transparency in the field of HCI. Research has shown that transparency allows users to have a better sense of the accuracy, fairness, and privacy of a system. In this context, much research has been conducted on providing explanations for the decisions made by AI systems. Researchers have also studied the development of interactive interfaces that allow user interaction to improve the explanatory capability of systems. This literature review provides key insights about transparency and what the research community thinks about it. Based on the insights gained we gather that a simplified explanation of the AI system is key. We conclude the paper with our proposed idea of representing an AI system, which is an amalgamation of the AI Model (algorithms), data (input and output, including outcomes), and the user interface, as visual interpretations (e.g. Venn diagrams) can aid in understanding AI systems better and potentially making them more transparent.
Is Overreliance on AI Provoked by Study Design?

Recent studies found that humans tend to overrely on AI when making decisions with AI support. AI explanations were often insufficient as mitigation, and sometimes even increased overreliance. However, typical AI-assisted decision-making studies consist of long series of decision tasks, potentially causing complacent behavior, and not properly reflecting many real-life scenarios. We therefore raise the question whether these findings might be favored by the design of these studies. In a first step to answer this question, we compared different study designs in an experiment and found indications that observations of overreliance might indeed be favored by common study designs. Further research is needed to clarify to what extent overreliance can be attributed to study designs rather than more fundamental human-AI interaction issues.
AHO-Guide: Automatically Guiding the Head Orientation of a Local User in Augmented Reality to Realign the Field of View with Remote Users

Augmented Reality (AR) offer significant benefits for remote collaboration scenarios. However, when using a Head-Mounted Display (HMD), remote users do not always see exactly what local users are looking at. This happens when there is a spatial offset between the center of the Field of View (FoV) of the HMD’s cameras and the center of the FoV of the user. Such an offset can limit the ability of remote users to see objects of interest, creating confusion and impeding the collaboration. To address this issue, we propose the AHO-Guide techniques. AHO-Guide techniques are Automated Head Orientation Guidance techniques in AR with a HMD. Their goal is to encourage a local HMD user to adjust their head orientation to let remote users have the appropriate FoV of the scene. This paper presents the conception and evaluation of the AHO-Guide techniques. We then propose a set of recommendations from the encouraging results of our experimental study.
Research on Graded Chinese Text Corpus for International Chinese Language Education

A data-driven approach has proven to be one of the most important methodologies for applied linguistics research. Moreover, the construction of large-scale corpora is a prerequisite for such data-driven research. With the release of the Chinese Proficiency Grading Standards for International Chinese Language Education, the construction of a graded Chinese corpus has taken on prominent value. A graded Chinese corpus has a major impact on teaching, learning, and reading, which can advance the work of international Chinese language education. Given the practical significance of graded corpora, this paper reviews the graded corpora used in previous text grading research and demonstrates the necessity of the construction of a graded corpus for international Chinese education. A graded Chinese corpus is constructed under the guidance of a principle proposed in this paper, namely "grading standards + expert knowledge." This corpus is further expanded via modern text enhancement technology to obtain the final mega corpus. Finally, this paper demonstrates that a corpus constructed in such a manner provides fundamental data support for international Chinese language teaching and research.
Designing Black Children in Video Games

Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole. --- Sheena Erete, Editor
Extending User Interaction with Mixed Reality Through a Smartphone-Based Controller

A major concern in mixed-reality (MR) environments is to support intuitive and precise user interaction. Various modalities have been proposed and used, including gesture, gaze, voice, hand-recognition or even special devices, i.e. external controllers. However, these modalities may often feel unfamiliar and physically demanding to the end-user, leading to difficulties and fatigue. One possible solution worth investigating further is to use an everyday object, like a smartphone, as an external device for interacting with MR. In this paper, we present the design of a framework for developing an external smartphone controller to extend user input in MR applications, which we further utilize to implement a new interaction modality, a tap on the phone. We also report on findings of a user study (n=24) in which we examine performance and user experience of the suggested input modality through a comparative user evaluation task. The findings suggest that incorporating a smartphone as an external controller shows potential for enhancing user interaction in MR tasks requiring high precision, as well as pinpointing the value of providing alternative means of user input in MR applications depending on a given task and personalization aspects of an end-user.
Natural Evolutionary Gradient Descent Strategy for Variational Quantum Algorithms

Recent research has demonstrated that parametric quantum circuits (PQCs) are affected by gradients that progressively vanish to zero as a function of the number of qubits. We show that using a combination of gradient-free natural evolutionary strategy and gradient descent can mitigate the possibility of optimizing barren plateaus in the landscape. We implemented 2 specific methods: natural evolutionary strategy stochastic gradient descent (NESSGD) and natural evolutionary strategy adapting the step size according to belief in observed gradients (NESAdaBelief) to optimize PQC parameter values. They were compared with standard stochastic gradient descent, adaptive moment estimation, and a version of adaptive moment estimation adapting the step size according to belief in observed gradients in 5 classification tasks. NESSGD and NESAdaBelief demonstrated some superiority in 4 of the tasks. NESAdaBelief showed higher accuracy than AdaBelief in all 5 tasks. In addition, we investigated the applicability of NESSGD under the parameter shift rule and demonstrated that NESSGD can adapt to this rule, which means that our proposed method could also optimize the parameters of PQCs on quantum computers.
Improved Private Data Protection Scheme for Blockchain Smart Contracts

Data security and privacy protection are critical challenges that constrain the advancement of edge computing. Similarly, blockchain technology faces constraints in addressing security issues linked with edge computing due to its scalability limitations. To tackle these challenges and promote the development of blockchain technology, this paper presents a scheme that enhances privacy data protection in blockchain smart contracts using edge computing and a master-slave multichain architecture. Firstly, we propose a master-slave multichain architecture based on the traditional single chain and integrate it with a three-layer edge computing structure to address security issues on the edge side. We also design a signature authentication scheme utilizing ECC integrated with blockchain encryption technology. Secondly, we incorporate the role-based access control (RBAC) model with smart contracts to finely divide user privileges, construct an interdomain role-based access control (ID-RBAC) model, and provide detailed access authentication process designs for both within and between domains. Finally, experimental results demonstrate that our proposed scheme can effectively resist various attacks, significantly improve algorithm efficiency, and maintain a system overhead of less than 160 p, with a maximum transaction throughput of nearly 310 tx/s.
Means of Students’ Functional Literacy Formation at the Lesson and Extracurricular Activities in Mathematics

This article presents an analysis of psychological and pedagogical research and methodological developments devoted to the problem of the formation of functional literacy in the educational process. The authors focus on the importance of organizing the formation and evaluation of the functional literacy of schoolchildren, based on a competency-based approach.
TOKCER BANTALAN : Tongkat Cerdas Alat Bantu Jalan Penyandang Tunanetra Menggunakan Arduino Mega 2560 dengan Sensor Ultrasonic HC SR-04 dan Water Level

Technology can be used in various fields that can support research, of course the results of this research can be useful and beneficial as a form of community service. Even today, technology plays a role in helping people with special needs (disabilities) experience difficulties. . One example is blind people who experience difficulties due to limited vision so that sticks are used as a solution to be used as walking aids for people with disabilities in facilitating their daily activities. Therefore, the authors designed a tool called "TOKCER BANTALAN : A Smart Stick for the Blind Auxiliary Tool" with the research method being carried out starting from the stage of conducting a literature study which is a process of reviewing the literature on references related to the research being carried out. . After that, preparation includes technical and non-technical aspects such as basic preparation related to hardware devices, software tools that support research. Then proceed with the process of testing the tools including the program and analysis of the results of the research conducted. The results of this study are walking aids for the blind using the Arduino Mega 2560 microcontroller equipped with an ultrasonic sensor HC-SR 04 that can detect distances &lt; 50 cm, a water level sensor that can detect standing water with a distance of ≤ 40 mm, and is equipped with a panel solar power as input power to be able to run the tool. In addition, this tool will automatically issue sound notifications when it detects objects and puddles blocking the way for the blind. It is hoped that this research will make it easier for the blind to carry out daily activities
An Adaptive Cybersecurity Training Framework for the Education of Social Media Users at Work

Formalizing the approach towards risk management on social media is critical for organizations. Regrettably, a review of the state-of-the-art on cybersecurity training highlighted that the existing frameworks are either too generic or too cumbersome to be adapted to different organizations and needs. Thus, we developed the Adaptive Cybersecurity Training Framework for Social Media Risks (ACSTF-SMR), a framework that incorporates social media cybersecurity policies and best practices. The ACSTF-SMR enables organizations, trainers, and policymakers to address the challenges posed by social media in a way that satisfies employees’ training needs and adjusts to their preferences. We tested the ACSTF-SMR with 38 case studies. Employees’ behaviors, learning, and responses after training were assessed, and feedback was gathered to improve the framework. Interviews with policymakers were held to gain insight into the enforcement of social media policies. We conclude that the ACSTF-SMR is a reliable option to mitigate social media threats within organizations.