Reviewerly/exHarmony huggingface dataset:
New Technologies for Storing and Transferring Personal Data

Medical information about a person’s state of health, data on the diagnostic evaluation of his symptoms and complaints, especially those related to the field of mental health, require a special reliability of the information storage system, since their disclosure often leads to stigmatization of the patient. The article proposes a new technology for storing and transmitting personal data based on “zeroing the LSB layers” (least significant bit (LSB) component of the Red, Green and Blue color image) and filling these layers with QR-codes containing AES-encrypted personal medical data of the patient. Specified the approach allows you to remove personal data from open access, since the presence of a barcode in the LSB layer practically does not change the image, therefore, without knowing about its presence, an attacker will not pay attention to it, moreover, using existing applications or scanners, it is impossible to read information, and decryption of information is hindered by the lack of an encryption key.
Migration Studies with a Compositional Data Approach: A Case Study of Population Structure in the Capital Region of Denmark

Data normalization for removing the influence of population density in Population Geography is a common procedure that may come with an unperceived risk. In this regard, data are constrained to a constant sum and they are therefore not independent observations, a fundamental requirement for applying standard multivariate statistical tools. Compositional Data (CoDa) techniques were developed to solve the issues that the standard statistical tools have with close data (i.e., spurious correlations, predictions outside the range, and sub-compositional incoherence) but they are still not commonly used in the field. Hence, we present in this article a case study where we analyse at parish level the spatial distribution of Danes, Western migrants and non-Western migrants in the Capital region of Denmark. By applying CoDa techniques, we have been able to identify the spatial population segregation in the area and we have recognized some patterns that can be used for interpreting housing prices variations. Our exercise is a basic example of the potential of CoDa techniques, which generate more robust and reliable results than standard statistical procedures, but it can be generalized to other population datasets with more complex structures.
Analysis of Cyber Security Knowledge and Skills for Capture the Flag Competition

CTF (Capture The Flag) competitions have emerged as a pivotal instrument in development of cyber security education over the past six years. It is imperative to ensure that the CTF competitions continue to develop to facilitate growth of cyber security education. To achieve the desired growth, it is necessary to analyze the progress achieved in the last six years the program has been implemented. This research collects and analyzes approximately 500 CTF solutions from the CTFtime platform. From the results of this analysis, we have identified that skills and techniques are needed in each category of CTF questions. We use the results of this analysis as a reference for questions on the CTF Platform that we created during PCT (Polibatam Cyber ​​Team) regeneration. PCT consists of a group of students from the Cyber ​​Security Engineering study program who are prepared to be at the forefront in participating in various activities related to Cyber ​​Security. The method we use is different from other CTFs, in that we use a skill-based and technique-based approach. We found that this approach was able to attract 80% of participants in solving and improving their skills in Cyber Security towards any challenges in a competitive environment.
Predictive Maintenance Experiences on Imbalanced Data with Bayesian Optimization Approach

Predictive maintenance solutions have been recently applied in industries for various problems, such as handling the machine status and maintaining the transmission lines. Industrial digital transformation promotes the collection of operational and conditional data generated from different parts of equipment (or power plant) for automatically detecting failures and seeking solutions. Predictive maintenance aims at e.g., minimizing downtime and increasing the whole productivity of manufacturing processes. In this context machine learning techniques have emerged as promising approaches, however it is challenging to select proper methods when data contain imbalanced class labels. In this paper, we propose a pipeline for constructing machine learning models based on Bayesian optimization approach for imbalanced datasets, in order to improve the classification performance of this model in manufacturing and transmission line applications. In this pipeline, the Bayesian optimization solution is used to suggest the best combination of hyperparameters for model variables. We analyze four multi-output models, such as Adaptive Boosting, Gradient Boosting, Random Forest and MultiLayer Perceptron, to design and develop multi-class and binary imbalanced classifiers. We have trained each model on two different imbalanced datasets, i.e., AI4I 2020 and electrical power system transmission lines, aiming at constructing a versatile pipeline able to deal with two tasks: failure type and machine (or electrical) status. In the AI4I 2020 case, Random Forest model has performed better than other models for both tasks. In the electrical power system transmission lines case, the MultiLayer Perceptron model has performed better than the others for the failure type task.
Methodology for Product Recommendation Based on User-System Interaction Data: A Case Study on Computer Systems E-Commerce Web Site

Within the scope of this study, we developed a product recommendation methodology for customers by analyzing shopping behaviors based on user-system interaction data collected on Casper Computer Systems’ website. To achieve the “right product to the right customer” objective, we predict customer interests using a collaborative filtering algorithm on collected data from previous customer activities. In turn, this minimizes prediction errors and enables better-personalized suggestions of computer system configuration. We took advantage of the implicit feedback approach while modeling customer behaviors if they liked or disliked a particular product. After customer behavior data is collected, we form the customer-product matrix and generate personalized product suggestions for each customer with the help of user-item-based collaborating filtering and item-item-based collaborating filtering algorithms. Customer-website interaction is considered a key input variable in creating personalized recommendations. Customers are supposed to use the website and leave interaction data regarding product configurations they’re interested in. To prove the efficiency of this methodology, we developed a prototype application. The product suggestion success rate of the application is tested on datasets generated from log data of the Casper website. Performance results prove that the developed methodology is successful.
A Computational Measure for the Semantic Readability of Segmented Texts

In this paper we introduce a computational procedure for measuring the semantic readability of a segmented text. The procedure mainly consists of three steps. First, natural language processing tools and unsupervised machine learning techniques are adopted in order to obtain a vectorized numerical representation for any section or segment of the inputted text. Hence, similar or semantically related text segments are modeled by nearby points in a vector space, then the shortest and longest Hamiltonian paths passing through them are computed. Lastly, the lengths of these paths and that of the original ordering on the segments are combined into an arithmetic expression in order to derive an index, which may be used to gauge the semantic difficulty that a reader is supposed to experience when reading the text. A preliminary experimental study is conducted on seven classic narrative texts written in English, which were obtained from the well-known Gutenberg project. The experimental results appear to be in line with our expectations.
Adding Material Embedding to the image2mass Problem

An agent has to form a team at run-time in a dynamic environment for some tasks that are not completely specified. For instance, the mass of an object may not be given. Estimating the mass of an object helps in determining the team size, which can then be used for team formation. It has recently been shown that the mass of an object can be estimated from its image. In this paper we augment the existing image2mass model with material embedding. The resulting model has been extensively tested. The experimental results indicate that our model has achieved some improvements on the existing state-of-the-art model for some performance metrics.
Natural Language Processing and Deep Learning Based Techniques for Evaluation of Companies’ Privacy Policies

Companies’ websites are vulnerable to privacy attacks that can compromise the confidentiality of data which, particularly in sensitive use cases like personal data, financial transaction details, medical diagnosis, could be detrimental and unethical. The noncompliance of companies with privacy policies requirements as stipulated by the various Data Protection Regulations has raised lot of concerns for users and other practitioners. To address this issue, previous research developed a model using conventional algorithms such as Neural Network (NN), Logistic Regression (LR) and Support Vector Machine (SVM) to evaluate the levels of compliance of companies to general data protection regulations. However, the research performance shows to be unsatisfactory as the model’s performance across the selected core requirements of the legislation attained F1-score of between 0.52–0.71. This paper improved this model’s performance by using Natural Language Processing (NLP) and Deep Learning (DL) techniques. This was done by evaluating the same dataset used by the previous researcher to train the proposed model. The overall results show that LSTM outperform both GRU and CNN models in terms of F1-score and accuracy. This research paper is to assist the Supervisory Authority and other practitioners to better determine the state of companies’ privacy policies compliance with the relevant data protection regulations.
Il Darkweb

The map shows the distribution of the offer of illicit products in one of the Darkweb markets.
The Occurrence of Dolomite in Carbonate Organofacies and its Relationship to Diagenesis and Catagenesis as Revealed by Mineral Maps Developed Using Energy Dispersive Spectroscopy and Back Scatter Electron

Journal Article The Occurrence of Dolomite in Carbonate Organofacies and its Relationship to Diagenesis and Catagenesis as Revealed by Mineral Maps Developed Using Energy Dispersive Spectroscopy and Back Scatter Electron Get access David Jacobi, David Jacobi Aramco Americas, Houston Research Center, Houston, Texas USA Corresponding author: david.jacobi@aramcoamericas.com Search for other works by this author on: Oxford Academic Google Scholar John Longo, John Longo Aramco Americas, Houston Research Center, Houston, Texas USA Search for other works by this author on: Oxford Academic Google Scholar Jennifer Rodriguez Jennifer Rodriguez Aramco Americas, Houston Research Center, Houston, Texas USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 2898–2899, https://doi.org/10.1017/S1431927622010881 Published: 01 August 2022
Quantitative Compositional Wavelength-Dispersive Mapping of Particles from the Moon.

Journal Article Quantitative Compositional Wavelength-Dispersive Mapping of Particles from the Moon. Get access E S Bullock, E S Bullock Earth and Planets Laboratory, Carnegie Science, Washington, DC, USA Corresponding Author: ebullock@carnegiescience.edu Search for other works by this author on: Oxford Academic Google Scholar C M Corrigan, C M Corrigan National Museum of Natural History, Smithsonian Institution, Washington, DC, USA Search for other works by this author on: Oxford Academic Google Scholar S N Valencia, S N Valencia NASA Goddard Space Flight Center, Greenbelt, MD, USA Search for other works by this author on: Oxford Academic Google Scholar N M Curran, N M Curran NASA Goddard Space Flight Center, Greenbelt, MD, USA Search for other works by this author on: Oxford Academic Google Scholar B A Cohen, B A Cohen NASA Goddard Space Flight Center, Greenbelt, MD, USA Search for other works by this author on: Oxford Academic Google Scholar The ANGSA Science Team The ANGSA Science Team Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 612–613, https://doi.org/10.1017/S1431927622002999 Published: 01 August 2022
Development of a Flexible Ensemble Classification System for Microscopy

Journal Article Development of a Flexible Ensemble Classification System for Microscopy Get access Tomas J McIntee, Tomas J McIntee Ion Innovations, Boone, NC, United States Search for other works by this author on: Oxford Academic Google Scholar Mathieu Therezien, Mathieu Therezien Ion Innovations, Boone, NC, United States Search for other works by this author on: Oxford Academic Google Scholar Zachary E Russell Zachary E Russell Ion Innovations, Boone, NC, United States Corresponding author: Zach.Russell@ion-innovations.com Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 3036–3038, https://doi.org/10.1017/S1431927622011345 Published: 01 August 2022
Wearables and Remote Monitoring

Sensors have become embedded in modern life—powering our personal electronics devices and environments. In this chapter, we examine how hybrid healthcare can leverage these sensors for a wide range of applications. We outline the trends and opportunities for these devices in healthcare and also the particular challenges imposed by the domain. We then review two key areas for healthcare using wearable and remote monitoring: activity recognition and profiling and apps for personalised medicine and lifestyle monitoring.
Hybrid Healthcare Enhanced by Blockchain

As hybrid healthcare organizations strive to combine traditional in-person treatment models with digital health technologies, there is a need to manage data and technologies more effectively and efficiently. Blockchain technology is increasingly implemented in healthcare and may enhance functions to allow healthcare providers more capabilities and flexibility as they desire to utilize more features of digital health. At its most basic, blockchain is a time-stamped sequential list of data linked together using cryptography and managed by a distributed cluster of computers: a “distributed ledger technology.” This chapter introduces blockchain concepts as they are applied to healthcare and the hybridization of digital health in interpersonal healthcare delivery. There is a detailed focus on blockchain features that can assist organizations in meeting the Final Rule of the 21st Century Cures Act for patient-centered access and value-based care information. This chapter also offers basic guidelines as to when a blockchain might best meet an organization’s needs, as well as step-by-step decisions organizations should consider when implementing blockchain. Healthcare leaders and providers are provided with a nuanced understanding of blockchain’s current potential and how this technology may assist with hybrid healthcare.
The Hyper Drumhead: A Musical Instrument For The Audio/Visual Manipulation Of Sound Waves

The Hyper Drumhead is a novel digital musical instrument that allows for the visualization and the manipulation of sound waves. At its core, a GPU-accelerated physical model computes in real-time the propagation of sound waves in two dimensions, allowing for the audio/visual simulation of massive domains. Every time the musician touches the surface of the instrument, an excitation signal is injected into the simulation domain, triggering wave propagation in all directions. By drawing boundaries in the domain and modifying the acoustic parameters of the simulated medium, the musician may generate and modulate reflections and resonances, effectively shaping the timbre of the resulting sound. In this short paper, we describe the main components of the instrument, including the control interface and the underlying numerical simulation.
Graph Neural Networks as Application of Distributed Algorithms

At first sight, distributed computing and machine learning are two distant areas in computer science. However, there are many connections, for instance in the area of graphs, which are the focus of my talk. Distributed computing has studied distributed graph algorithms for many decades. Meanwhile in machine learning, graph neural networks are picking up steam. When it comes to dealing with graphical inputs, one can almost claim that graph neural networks are an application of distributed algorithms. I will introduce central concepts in learning such as underreaching and oversquashing, which have been known in the distributed computing community for decades, as local and congest models. In addition I am going to present some algorithmic insights, and a software framework that helps with explaining learning. Generally speaking, I would like to present a path to learning for those who are familiar with distributed message passing algorithms. This talk is based on a number of papers recently published at learning conferences such as ICML and NeurIPS, co-authored by Pál András Papp and Karolis Martinkus.
Intelligence in security countermeasures selection

Identifying security risks in organizations and also determining their severity in order to select appropriate security countermeasures is of great importance in organizations. In the last two decades, a lot of work has been done to increase the accuracy of risk impact calculation as well as the right selection of countermeasures. Also, a variety of work has been proposed to select combined countermeasures instead of single ones. So there is a challenge to balance the cost of security with the improvement of the defense system. In this paper, a dataset that includes the organization business processes, security data, assets, vulnerabilities, and related security countermeasures is suggested for the first time. In the previous work, this chain of information from the content of the organization, which is definitely different from another organization, has not been considered for the analysis of the performance of countermeasures (success or failure). Based on the results of the countermeasures during the organization’s lifetime, more efficient countermeasures can be suggested for new or existing risks. Therefore, by intelligently selecting the security countermeasures presented in this paper, organizations will be able to identify ineffective countermeasures and prevent them from being re-selected to counter attackers. In this way, we can make our organization more resilient to attackers over time.
DIRE and its Data: Neural Decompiled Variable Renamings with Respect to Software Class

The decompiler is one of the most common tools for examining executable binaries without the corresponding source code. It transforms binaries into high-level code, reversing the compilation process. Unfortunately, decompiler output is far from readable because the decompilation process is often incomplete. State-of-the-art techniques use machine learning to predict missing information like variable names. While these approaches are often able to suggest good variable names in context, no existing work examines how the selection of training data influences these machine learning models. We investigate how data provenance and the quality of training data affect performance, and how well, if at all, trained models generalize across software domains. We focus on the variable renaming problem using one such machine learning model, DIRE . We first describe DIRE in detail and the accompanying technique used to generate training data from raw code. We also evaluate DIRE ’s overall performance without respect to data quality. Next, we show how training on more popular, possibly higher quality code (measured using GitHub stars) leads to a more generalizable model because popular code tends to have more diverse variable names. Finally, we evaluate how well DIRE predicts domain-specific identifiers, propose a modification to incorporate domain information, and show that it can predict identifiers in domain-specific scenarios 23% more frequently than the original DIRE model.
IBE-Signal: Reshaping Signal into a MITM-Attack-Resistant Protocol

The Signal Protocol is one of the most popular privacy protocols today for protecting Internet chats and supports end-to-end encryption. Nevertheless, despite its many advantages, the Signal Protocol is not resistant to Man-In-The-Middle (MITM) attacks because a malicious server can distribute the forged identity-based public keys during the user registration phase. To address this problem, we proposed the IBE-Signal scheme that replaced the Extended Triple Diffie–Hellman (X3DH) key agreement protocol with enhanced Identity-Based Encryption (IBE). Specifically, the adoption of verifiable parameter initialization ensures the authenticity of system parameters. At the same time, the Identity-Based Signature (IBS) enables our scheme to support mutual authentication. Moreover, we proposed a distributed key generation mechanism that served as a risk decentralization to mitigate IBE’s key escrow problem. Besides, the proposed revocable IBE scheme is used for the revocation problem. Notably, the IND-ID-CPA security of the IBE-Signal scheme is proven under the random oracle model. Compared with the existing schemes, our scheme provided new security features of mutual authentication, perfect forward secrecy, post-compromise security, and key revocation. Experiments showed that the computational overhead is lower than that of other schemes when the Cloud Privacy Centers (CPCs) number is less than 8.
Modeling and physically interpreting dissipative dynamics of a charge qubit–atom hybrid system under the Born–Markov limit

In this study, we model the dissipative dynamics of a charge qubit–atom hybrid model under the Born–Markov limit. Especially, we focus on the physical relation between spectral density and dissipative dynamics. Analytically, we show that, if spectral density in the dynamics is a nearly linear function, then relaxation and dephasing noises separately affect the gate capacitor and Josephson junction, respectively, but if the spectral density is a genuine-nonlinear function, then these two noises affect both the gate capacitor and Josephson junction. Further, we observe that in a numerical way, when the spectral density is a genuine-nonlinear function, there are some cases in which the corresponding environment dramatically breaks quantumness including purity and entanglement.