Reviewerly/exHarmony huggingface dataset:
Security and Privacy for Future Healthcare IoT

Computer networks are widely used in today's society, and we utilize the Internet to connect to our home networks. IoT networks are a brand-new category of these networks where we attempt to connect various household equipment and attempt to issue orders from a distance. Hash code or message authentication code (MAC) is used for authentication, and several encryptions and decryption techniques guarantee secrecy. The cost of using traditional cryptographic security techniques in terms of computing resources like memory, processor power, and power consumption is high. There is no proper model/ framework available to address the major security issues of IoT devices. The literature address that still several gaps exist regarding security issues. To secure and protect future healthcare IoT systems, this research will provide a model that combines shallow learning with deep learning.
AR Make-up Filter for Social Media using the HSV Color Extraction

Choosing the appropriate cosmetics is an arduous task. Because cosmetics are tested directly on the skin to ensure each personâ€™s preferences are met. The consumer repeatedly tries a sample and then discards it until he discovers one that meets his tastes. The cosmetics business and consumers are affected by this move. Companies can utilize Augmented Reality (AR) technology as an alternative to mass-producing cosmetic samples. The difficulty of deploying augmented reality is the difficulty of putting cosmetics into camera video streams. Each individual bears the burden of skin color and its effect on light. HSV Color Extraction was the method employed for this study. The application of augmented reality intends to enable consumers to test cosmetics with their chosen color and assist businesses in competing in the industry by promoting items and engaging customers. This work makes it easier to choose cosmetics using augmented reality and social media. AR simulates the usage of the desired color cosmetics, whereas social media allows users to obtain feedback on their color preferences. The outcomes of this study indicate that augmented reality (AR) apps can display filters in bright, dim, and even wholly dark lighting conditions. This research contributes originality that cosmetic firms can utilize to market their products on social media.
Application of Digital Images and Corresponding Image Retrieval Paradigm

We live in a world where digital images are constantly generated during our daily activities, whether private or business. They play an important role in our private life, showing important moments, people, places, or events and keeping their memory. Images are unavoidable in business, especially in digital marketing, web sales, social networks, medicine, security, and education. In general, images contribute to a better understanding of the message, increase the attractiveness of textual content, provide a better user experience, and can convey emotion quickly. The key advantage of the image is that very often, even a cursory glance at the image is enough to convey a message and arouse emotion and interest. But with the increase in digital image numbers, storage, organization, and retrieval problems arise. The paper describes the importance of images in different areas of application and different image retrieval paradigms that include text-based, content-based, and combined approaches. Also, the most popular image search tools and cloud storage services are compared and discussed. The conclusion comments on the applicability of existing approaches to image searches in different application domains and highlights the advantages and disadvantages of each of the approaches.
Sedition Hunters: A Quantitative Study of the Crowdsourced Investigation into the 2021 U.S. Capitol Attack

Social media platforms have enabled extremists to organize violent events, such as the 2021 U.S. Capitol Attack. Simultaneously, these platforms enable professional investigators and amateur sleuths to collaboratively collect and identify imagery of suspects with the goal of holding them accountable for their actions. Through a case study of Sedition Hunters, a Twitter community whose goal is to identify individuals who participated in the 2021 U.S. Capitol Attack, we explore what are the main topics or targets of the community, who participates in the community, and how. Using topic modeling, we find that information sharing is the main focus of the community. We also note an increase in awareness of privacy concerns. Furthermore, using social network analysis, we show how some participants played important roles in the community. Finally, we discuss implications for the content and structure of online crowdsourced investigations.
Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software

Randomized numerical linear algebra - RandNLA, for short - concerns the use of randomization as a resource to develop improved algorithms for large-scale linear algebra computations. The origins of contemporary RandNLA lay in theoretical computer science, where it blossomed from a simple idea: randomization provides an avenue for computing approximate solutions to linear algebra problems more efficiently than deterministic algorithms. This idea proved fruitful in the development of scalable algorithms for machine learning and statistical data analysis applications. However, RandNLA's true potential only came into focus upon integration with the fields of numerical analysis and "classical" numerical linear algebra. Through the efforts of many individuals, randomized algorithms have been developed that provide full control over the accuracy of their solutions and that can be every bit as reliable as algorithms that might be found in libraries such as LAPACK. Recent years have even seen the incorporation of certain RandNLA methods into MATLAB, the NAG Library, NVIDIA's cuSOLVER, and SciKit-Learn. For all its success, we believe that RandNLA has yet to realize its full potential. In particular, we believe the scientific community stands to benefit significantly from suitably defined "RandBLAS" and "RandLAPACK" libraries, to serve as standards conceptually analogous to BLAS and LAPACK. This 200-page monograph represents a step toward defining such standards. In it, we cover topics spanning basic sketching, least squares and optimization, low-rank approximation, full matrix decompositions, leverage score sampling, and sketching data with tensor product structures (among others). Much of the provided pseudo-code has been tested via publicly available MATLAB and Python implementations.
Exploring the Morphologies of High Redshift Galaxies with Machine Learning

The morphology of a galaxy has been shown to encode the evolutionary history and correlates strongly with physical properties such as stellar mass, star formation rates and past merger events. While the majority of galaxies in the local universe can be classified on the Hubble sequence, little is known about the different types of galaxies we observe at high redshift. The irregular morphology of these galaxies makes visual classifications difficult, and with the future of astronomy consisting of many "Big Data" surveys we need an efficient, and unbiased classification system in place. In this work we explore the use of unsupervised machine learning techniques to preform feature extraction from galaxy images to separate high redshift galaxies into different morphological types based on the machine learning clusters. We expand on previous work by addressing observation biases such as the orientation, apparent size of the galaxies and noise before extracting features, thus reducing the number of clusters and forcing the network to learn meaningful features. We then compare the extracted clusters' physical properties, to investigate the separation between the groups.
A survey on techniques, methods and security approaches in big data healthcare

A huge percentage of people especially in developed countries spend a good chunk of their wealth in managing their health conditions. In order to adequately administer healthcare, governments and various organizations have embraced advanced technology for automating the health industry. In recent past, electronic health records have largely been managed by Enterprise Resource Planning and legacy systems. Big data framework steadily emerge as the underlying technology in healthcare, which offers solutions that limits capacity of others systems in terms of storage and reporting. Automation through cloud services supported by storage of structured and unstructured health data in heterogeneous environment has improved service delivery, efficiency, medication, diagnosis, reporting and storage in healthcare. The argument support the idea that big data healthcare still face information security concern, for instance patient image sharing, authentication of patient, botnet, correlation attacks, man-in-the-middle, Distributed Denial of Service (DDoS), blockchain payment gateway, time complexities of algorithms, despite numerous studies conducted by scholars in security management for big data in smart healthcare. Some security technique include digital image encryption, steganography, biometrics, rule-based policy, prescriptive analysis, blockchain contact tracing, cloud security, MapReduce, machine-learning algorithms, anonymizations among others. However, most of these security solutions and analysis performed on structured and semi-structured data as opposed to unstructured data. This may affect the output of medical reporting of patients’ condition particularly on wearable devices and other examinations such as computerized tomography (CT) Scans among others. A major concern is how to identify inherent security vulnerabilities in big healthcare, which generate images for transmission and storage. Therefore, this paper conducted a comparative survey of solutions that specifically safeguards structured and unstructured data using systems that run on big data frameworks. The literature highlights several security advancements in cryptography, machine learning, anonymization and protocols. Most of these security frameworks lacks implementation evidence. A number of studies did not provide comprehensive performance metrics (accuracy, error, recall, precision) of the models besides using a single algorithm without validated justification. Therefore, a critique on the contribution, performance and areas of improvements discussed and summarized in the paper.
Comparison of Tools for Web-Application Brute Forcing

&#x0D; &#x0D; &#x0D; For a long time, threat of web-application authentication break remained a problem not only for users but also for business. This threat still exists, since broken authentication provides a blackhat with full access to accounts of users and business data. This article analyzes software tools for breaking authentication as well as it defines time required for breaking-in depending on different conditions. Based on the results of the analysis, the fastest tool was determined. In order to complete this analysis and determine the fastest tool, own web-application was created.&#x0D; &#x0D; &#x0D;
Futuristic study of a criminal facial recognition system using open-source images

Face recognition provides an uncertainty challenge to criminal face identification. Thus, this recognition has many uses, from security to entertainment. Even if other bio-metric characteristics like iris and fingerprints are reliable, a person must be present to use them. Therefore, they are not the best tool for thieves to use for authentication. Anyone can recognize a criminal in an image by using face recognition technology in conjunction with a criminal database that contains the individual's personal information. In addition to needing great accuracy, a criminal recognition system also needs to be flexible to numerous variations in illumination, occlusion, maturity level, postures, etc. We use the Haar cascade and Histograms of oriented gradients (HOG) recognition learning systems discussed and contrasted in this study. We assessed models and HOG outperformed the criminal recognition system. We also go through several practical uses for this criminal recognition.
Deep Generative Models: A Review

Objectives: To provide insight into deep generative models and review the most prominent and efficient deep generative models, including Variational Auto-encoder (VAE) and Generative Adversarial Networks (GANs).Methods: We provide a comprehensive overview of VAEs and GANs along with their advantages and disadvantages.This paper also surveys the recently introduced Attention-based GANs and the most recently introduced Transformer based GANs.Findings: GANs have been intensively researched because of their significant advantages over VAE.Furthermore, GANs are powerful generative models that have been widely employed in a variety of fields.Though GANs have a number of advantages over VAEs, but, despite their immense popularity and success, training GANs is still difficult and has experienced a lot of setbacks.These failures include mode collapse, where the generator produces the same set of outputs for various inputs, ultimately resulting in the loss of diversity; non-convergence due to oscillatory and diverging behaviors of the generator and discriminator during the training phase; and vanishing or exploding gradients, where learning either ceases to occur or occurs very slowly.Recently, some attention-based GANs and Transformer-based GANs have also been proposed for high-fidelity image generation.Novelty: Unlike previous survey articles, which often focus on all DGMs and dive into their complicated aspects, this work focuses on the most prominent DGMs, VAEs, and GANs and provides a theoretical understanding of them.Furthermore, because GAN is now the most extensively used DGM being studied by the academic community, the literature on it needs to be explored more.Moreover, while numerous articles on GANs are available, none have analyzed the most recent attention-based GANs and Transformer-based GANs.So, in this study, we review the recently introduced attention-based GANs and Transformer-based GANs, the literature related to which has not been reviewed by any survey paper.
A Comparison Between the Performance of Features Selection Techniques: Survey Study

Feature selection is one of the most popular and crucial methods of data processing used in different machine learning and data mining approaches to avoid high dimensionality and increase classification accuracy. Additionally, attribute selection aids in accelerating machine learning algorithms, improving prediction accuracy, data comprehension, decreasing data storage space, and minimizing the computational complexity of learning algorithms. For this reason, several feature selection approaches are used. To determine the essential feature or feature subsets needed to achieve classification objectives, several feature selection techniques have been suggested in the literature. In this research, different widely employed feature selection strategies have been evaluated by using different datasets to see how efficiently these techniques may be applied to achieve high performance of learning algorithms, which improves the classifier's prediction accuracy.
Limited commercialization of artificial intelligence technology in the diagnosis of pigmented lesions despite explosive growth in published programs

To the Editor: Publications of artificial intelligence programs designed for diagnosing pigmented lesions and melanoma have skyrocketed as reports suggest they produce similar results to clinical assessments. 1 Brinker T.J. Hekler A. Enk A. et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer. 2019; 113: 47-54https://doi.org/10.1016/j.ejca.2019.04.001 Abstract Full Text Full Text PDF PubMed Scopus (270) Google Scholar ,2 Haenssle H.A. Fink C. Schneiderbauer R. et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018; 29: 1836-1842https://doi.org/10.1093/annonc/mdy166 Abstract Full Text Full Text PDF PubMed Scopus (824) Google Scholar Several smartphone applications have also been developed to classify these lesions. However, their consistency has been called into question. 3 Freeman K. Dinnes J. Chuchu N. et al. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ. 2020; 368: m127https://doi.org/10.1136/bmj.m127 Crossref PubMed Scopus (114) Google Scholar With all the buzz, the question remains as to why commercial applications have been so limited.
Enabling privacy by anonymization in the collection of similar data in multi-domain IoT

The efficiency of Internet of Things (IoT) systems heavily relies on possessing trustworthy and up-to-date data. Thus, data collection represents a key feature in IoT applications and services that rely on data-streams. Reusing the same data-stream for distinct queries and services represents a promising way to save energy and reduce operational cost for the deployment. However, acquiring data from different owners through distinct providers incurs privacy issues. Indeed, data crosses the borders of different systems, and the end consumer may get knowledge on the original producer. In-network data aggregation enables the compression of information and improves the privacy while reducing the network load. However, the same data should not be aggregated several times, to provide correct and consistent operations. In this paper, we provide a pub/sub routing scheme for IoT systems that respects privacy constraints. The routers publish offers that describe the data they can export: they may aggregate several offers to respect the privacy constraints as defined by the producers. Each producer has a disposable ID, only used to detect and avoid overlaps in the data-streams. Our performance evaluation highlights the efficiency of our pub-sub routing solution to construct a valid aggregation (without overlap) distributively, while respecting privacy constraints.
Learning by Demonstration of Coordinated Plans in Multiagent Systems

One of the significant challenges in Multiagent Systems (MAS) is the creation of cooperative plans to deal with the different scenarios that present themselves in a dynamic, real-time environment composed of teams of mobile robots. This work involves capturing human knowledge to demonstrate how robot teams can better cooperate in solving the problem they must solve. The research used the environment RoboCup 3D Soccer Simulation (3DSSIM) and the collection of human demonstrations were carried out through a set of tools developed from adapting existing solutions in the RoboCup community using a crowdsourcing strategy. In addition, fuzzy clustering was used to gather human demonstrations (setplays) with the same semantic meaning, even with minor differences. With the data organized, this work used a reinforcement learning mechanism to learn a classification policy that allows agents to decide which group of setplays is best suited to each situation that presents itself in the environment. The results show the ability of the robot team to evolve, from learning the suggested setplays and their use in an appropriate way to the skills of each robot.
Depok Eco-Friendly Library as an Educational Tourism Destination

Purpose: This study is to design a library in Depok City in West Java, Indonesia, which is a Central Government which is adjacent to Jakarta (the nation’s Capital) as well as to be able to support the City of Depok as an education city, and infiltration area.&#x0D; Research methods: It uses a descriptive method and divides into three stages: (1) data exploration by collecting related data and existing site conditions, (2) site and building analysis, (3) building design and making 3d model’s design.&#x0D; Findings: Understanding of surrounding conditions will be very helpful in designing eco-friendly building. Like wise with the building and site design that adapts to local environmental conditions will also provide comfort for the users themselves and even the surrounding residents.&#x0D; Implications: Creating an environmentally friendly library design by implementing the library tourism attractiveness index system will certainly make people interested in visiting continuously and getting positive benefits. The local and wider community not only gain knowledge but also freshness and happiness.
Why are Vietnamese people susceptible to cryptocurrency Ponzi schemes? Findings from using the PLS-SEM approach

Purpose This paper aims to explore factors influencing Vietnamese people’s susceptibility to fraud through cryptocurrency Ponzi schemes. Design/methodology/approach This study uses the gullibility theory, the theory of planned behavior theory, the traditional theory of finance and the theory of financial behavior, to develop a questionnaire which is then sent to respondents who are Vietnamese individuals. Subsequently, the partial least squares structural equation modeling approach (PLS-SEM) is used to analyze 370 collected responses. Findings This research shows the important roles that trust, risk appetite and knowledge of Ponzi play in respect of fraud susceptibility, among which trust has the highest positive impact. Moreover, there is no evidence of relationships between Vietnamese people’s susceptibility to fraud via cryptocurrency Ponzi schemes and attitudes toward investment scams, knowledge of investment or knowledge of Ponzi schemes. Research limitations/implications This paper collects only 370 valid responses, which raises some questions regarding the diversity and representativeness of the survey sample. Practical implications This study provides evidence on factors affecting Vietnamese people’s fraud susceptibility to cryptocurrency Ponzi schemes, which helps both authorities and individuals to be vigilant against investment scams. Social implications This research proposes several recommendations to prevent investment scams in cryptocurrency trading, from the perspective of state regulators and individuals. Originality/value This working paper provides a new approach using PLS-SEM to build a theoretical framework for the possibility of becoming victims of investment scams in Vietnam using a combination of different theories from criminology and finance.
UniXGen: A Unified Vision-Language Model for Multi-View Chest X-ray Generation and Report Generation

Generated synthetic data in medical research can substitute privacy and security-sensitive data with a large-scale curated dataset, reducing data collection and annotation costs. As part of this effort, we propose UniXGen, a unified chest X-ray and report generation model, with the following contributions. First, we design a unified model for bidirectional chest X-ray and report generation by adopting a vector quantization method to discretize chest X-rays into discrete visual tokens and formulating both tasks as sequence generation tasks. Second, we introduce several special tokens to generate chest X-rays with specific views that can be useful when the desired views are unavailable. Furthermore, UniXGen can flexibly take various inputs from single to multiple views to take advantage of the additional findings available in other X-ray views. We adopt an efficient transformer for computational and memory efficiency to handle the long-range input sequence of multi-view chest X-rays with high resolution and long paragraph reports. In extensive experiments, we show that our unified model has a synergistic effect on both generation tasks, as opposed to training only the task-specific models. We also find that view-specific special tokens can distinguish between different views and properly generate specific views even if they do not exist in the dataset, and utilizing multi-view chest X-rays can faithfully capture the abnormal findings in the additional X-rays. The source code is publicly available at: https://github.com/ttumyche/UniXGen.