Reviewerly/exHarmony huggingface dataset:
Grayscale visual cryptography scheme based on cheating prevention

To solve the problems of computing in decoding and cheating by a distributor or a participant of grayscale visual cryptography (VC), grayscale visual cryptography based on cheating prevention was proposed. We chose two grayscale images; one of the two images was a secret image and the other was a verification image. In the encoder, the input grayscale images were transformed into multitone images via multitoning first. Based on the unequal relationship between dots per inch (DPI) and pixels per inch (PPI), we got a group of sub-modes that contained the different numbers of print points. Two multitone images were transformed into two groups of bit-planes via sub-modes. For each secret bit-plane, we operated (k, n) -VC encoding and got secret bit-plane shares; for each verification bit-plane, we operated (2, n+1) -VC encoding and got verification bit-plane shares. To prevent cheating, we randomly chose n verification bit-plane shares and connected them with n secret bit-plane shares to obtain n composite secret bit-plane shares. The remaining verification bit-plane share was connected with a blank image of the same size as the secret bit-plane share to obtain a composite verification share. In cheating prevention, a participant can judge the authenticity of the secret bit-plane shares held in his hand through the cheating prevention module. In the decoder, we operated OR operations on composite secret bit-plane shares of the same level and obtained reconstructed bit-planes. Only the reconstructed bit-planes were stacked to obtain the original secret image. Experiments prove the feasibility and advantages of this scheme.
A Triangular Actuating Device Stand that Dynamically Adjusts Mobile Screen’s Position

This demo presents a triangular actuating device stand that can automatically adjust the height and tilt angle of a mounted mobile device (e.g., smartphone) to adapt to the user’s varying interaction needs (e.g., touch, browsing, and viewing). We employ a unique mechanism to deform the stand’s triangular shape with two extendable reel actuators, which enables us to reposition the mobile screen mounted on the hypotenuse side. Each actuator is managed by the mobile device and controls the height and base of the stand’s triangular shape, respectively. To demonstrate the potential of our new actuating device stand, we present two types of interaction scenarios: manual device repositioning based on the user’s postures or gestures captured by the device’s front camera and automatic device repositioning that adapt to the on-screen contents the user will interact with (i.e., touch-based menus, web browsers, illustrator, and video viewers).
The importance of security for the crypto industry

Cyber attacks are becoming an increasingly important issue in the financial industry. The recent shutdown of the darknet marketplace ‘Hydra Markets’ in April 2022, firmly established cyberthreats as a critical topic within the world of finance. Hydra was a platform that had generated a revenue of €1.2bn by 2020 from illegal business on the Internet. So how strongly is the sensitive environment of the crypto industry affected by cyber attacks, especially DDoS?
An Approach to Data Mining of Software Repositories in Terms of Quantitative Indicators of the Development Process and Domain Features

A modern large software organization has significant experience in the design and development of software systems. That experience was obtained in the process of working on previous (completed) projects. Software project repositories contain the experience and knowledge of a large number of highly qualified specialists for many years of their work. Software project repositories contain heterogeneous unstructured data. It becomes difficult to find the necessary information in the process of new project development. So the following problems exist: the experience of previous projects remains unclaimed, the time of the development process increases, and the quality of the final product decreases. So it is fundamentally important to use the experience of the previous projects at the initial stages of new projects development. The usage of intelligent methods and algorithms for software project repositories analyzing is the solution to this problem. This article proposes an approach to the data mining of software project repositories based on features of the domain and the development process dynamic. This approach allows to reduce the search space and speed up the process of the required knowledge searching to the new project development.
A Combined Approach of Steganography and Cryptography with Generative Adversarial Networks: Survey

Secure transformation of data over public networks like the Internet is nothing but achieving authenticity, secrecy, and confidentiality in secure data transmission is now the primary concern. These issues may be solved by using data hiding techniques. Steganography, cryptography, and watermarking techniques are used to hide data and ensure its security during transmission. Objective of this submission is to analyze and examine several methods of deep learning in image cryptography and steganography. The hidden message is revealed via steganography, but its format is altered through cryptography. Steganography and cryptography are both essential and robust techniques. This paper’s primary goal is to explore several integrating steganography with encryption to create a hybrid system. In addition, specific differences were also given between cryptography and steganographic approaches. This paper aims to help other researchers’ summaries current trends, problems, and possible future directions in this area.
FAIR4Chem Award – der FAIRste Datensatz der Chemie

Nachrichten aus der ChemieVolume 70, Issue 11 p. 99-99 GDCh FAIR4Chem Award – der FAIRste Datensatz der Chemie First published: 31 October 2022 https://doi.org/10.1002/nadc.20224132322Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume70, Issue11November 2022Pages 99-99 RelatedInformation
Sustained Silent Reading as a Significant Approach in the Intensification of the Writing Skills of English Language Learners

This study was conducted to find out the remarkable difference between the pretest and post-test results of the writing skills of Grade 9 learners and the pre-experimental research design specifically the pretest-posttest design was utilized. With the analysis and interpretation of data, this pre-experimental study used statistical tools such as Mean, Standard Deviation, and T-Test. Hypotheses were tested, and the findings revealed the following information; there is no significant difference between the pretest and post-test results of the writing skills of Grade 9 learners. This research proved that sustained silent reading directly affects the writing skills of Grade 9 learners. This method can be applied and helpful to both parents and teachers. Having all those findings, this study recommends that since sustained silent reading is a remarkable method of teaching so educators should be accommodated in training on how to develop and carry out the program in a classroom environment, teachers may be sensitive enough to observe the urgent need of learners, thereby, being resourceful and patient, parents may be so inclined in pursuing their children’s education and help them achieve their goals. They may possess a positive attitude toward reading and encourage their children to study hard, specifically struggling learners. Learners may be responsible enough to enhance their performance by developing a habit of reading to improve their writing skills, and future researchers may conduct a similar study to see the benefits of sustained silent reading and thus improve the learners’ writing skills. Keywords: sustained silent reading, writing skills, English language learners, pleasurable, cognitively undemanding, informative.
Exploring the Data Integrity Ordinance in Pharmaceutical Domain

Published on: October 2022Journal of Young Pharmacists, 2022; 14(4):377-381Review Article | doi:10.5530/jyp.2022.14.76 Authors:Darshil B Shah1,*, Jignesh S Shah2, Dilip G Maheshwari11Department of Quality Assurance, L J Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, INDIA.2Department of Regulatory Affairs, L J Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, INDIA. Abstract:With the revolutionary expedition of technologies being used in pharmaceutical world to collect and process data, the honesty of data is predominant factor and subject to preservation. If the data collection of the products soon to be marketed, is adulterated it would not only be a potential harm to general public but also cause of economical and reputational damage for manufacturers. To secure the value of crucial data, regulatory authorities has been producing guidance regarding the integrity of data. Data integrity guarantees that data is not tampered with be it paper or electronic. Protecting the integrity, whether electronic or handwritten, is essential for making sound business decisions. In attempt of introducing aspects of data, the FDA first developed the ALCOA principles which are still the first line defence of integrity breaches in pharmaceutical companies. With the changing times of technologies, FDA evolved the data integrity principles into requirements and specifications of current good manufacturing practice answering questions directly from manufacturers. To understand the basic yet complex terms and conditions, this article strives to outline the Data Integrity requirements of USFDA specified in its cGMP guidance for data integrity specifications.Keywords: Pharmaceutical Data Integrity, ALCOA, U.S. Food and Drug Administration (FDA).
The Effect of Environmental Metrics on Software Fault Prediction

In this study, besides the software metrics, the environmental metrics such as experience of software engineer, similar project experience, size of the project, programming language, time spent on analysis and development are also explored to see whether they also affect the results of software fault prediction and what would be the success rates. The dataset for this study was generated from combining various data from 10 projects. A total of 36 metrics and 6676 test cases were evaluated. The errors occurred in the test cases are not just considered as an error, their priority and cases that cannot be tested are also taken into consideration. Nine fault levels are employed in models. Models are created with four different algorithms which have achieved a success rate of; 76% by the decision tree algorithm, 94% by the nearest neighbors algorithm, 90% by the random forests algorithm and 73% by the Adaboost Classifier Algorithm. It was observed that environmental metrics are indeed effective in software fault prediction and when applied with machine learning algorithms a high rate of success can be achieved.
Internet of things implementation and challenges during COVID-19 in healthcare industries

Internet of things (IoT) plays a significant role in the medical and healthcare industry. Integrating powerful technologies like 5G, artificial intelligence (AI), machine learning (ML), and big data with IoT will certainly revolutionize the healthcare industry. IoT has gained maximum attention from healthcare service providers. The correct diagnosis and treatment is a costly service that will be offered by IoT at minimum cost. The right diagnosis through IoT helps in reducing the requirement for hospitalization. There are many benefits of IoT in health care and some of them are: end-to-end connectivity and affordability, remote medical assistance, tracking and alerts, etc. Some of the challenges seen with IoT in healthcare are data security and privacy, accuracy, cost, integration, etc. Some of the IoT applications in healthcare are minimizing the emergency state wait time, tracking patients, doctors, staff, and inventory, and improved drug management. By implementing IoT in the healthcare sector, healthcare providers get vital data through sensors, which provide awareness in the symptoms and pattern of the disease, health condition monitoring, early diagnosis while promoting remote treatment. The introduction of IoT in healthcare, however, remains a challenge. Many healthcare providers are either reluctant or unable to participate because of lack of requisite healthcare infrastructure to support IoT implementation, some of them are uncertain about the implementation process. But in future, IoT will surely revolutionize the healthcare industry by bridging the gaps.
Effect of Virtual Exhibition Characteristics on the Exhibitors’ Perceived value and Re-participation Intention

This study attempted to understand the characteristics of virtual exhibitions, attracting attention as a new form of trade shows, from a theoretical level and to understand how the perceived value of companies participating in virtual exhibitions affects their re-entry intention. The primary research purpose of this study was to suggest implications for what marketing methods organizers can use to hold them effectively. Questionnaires were distributed to companies with experience in virtual exhibitions as a methodology. Of the 205 collected questionnaires, 200 valid samples, excluding five invalid sample responses, were analyzed using SPSS 25.0 and Smart PLS 3.0. The characteristics of the virtual exhibition derived from the exploratory factor analysis were classified into four categories: convenience, information, technology, and interactivity, and the perceived value of exhibitors was classified into economic and social value. First of all, in terms of economic value, convenience, technology, and interactivity were found to affect the perceived economic value of exhibitors, whereas information was analyzed not to have a significant effect. In the case of social value perceived by participating companies, the three factors of information, technology, and interactivity were found to have a substantial impact. Still, convenience was analyzed to have no effect on perceived social value. Finally, it was found that all of the exhibitors' perceived value had a significant effect on their intention to re-participate. This study not only looked at the characteristics of virtual exhibitions academically in the exhibition industry that had to be switched to non-face-to-face due to COVID-19, but also reviewed the intention to re-participate through the perceived value by exhibitors in virtual exhibitions as practical implications.
A Hierarchical Approach to Conditional Random Fields for System Anomaly Detection

Anomaly detection to recognize unusual events in large scale systems in a time sensitive manner is critical in many industries, eg. bank fraud, enterprise systems, medical alerts, etc. Large-scale systems often grow in size and complexity over time, and anomaly detection algorithms need to adapt to changing structures. A hierarchical approach takes advantage of the implicit relationships in complex systems and localized context. The features in complex systems may vary drastically in data distribution, capturing different aspects from multiple data sources, and when put together provide a more complete view of the system. In this paper, two datasets are considered, the 1st comprising of system metrics from machines running on a cloud service, and the 2nd of application metrics from a large-scale distributed software system with inherent hierarchies and interconnections amongst its system nodes. Comparing algorithms, across the changepoint based PELT algorithm, cognitive learning-based Hierarchical Temporal Memory algorithms, Support Vector Machines and Conditional Random Fields provides a basis for proposing a Hierarchical Global-Local Conditional Random Field approach to accurately capture anomalies in complex systems across various features. Hierarchical algorithms can learn both the intricacies of specific features, and utilize these in a global abstracted representation to detect anomalous patterns robustly across multi-source feature data and distributed systems. A graphical network analysis on complex systems can further fine-tune datasets to mine relationships based on available features, which can benefit hierarchical models. Furthermore, hierarchical solutions can adapt well to changes at a localized level, learning on new data and changing environments when parts of a system are over-hauled, and translate these learnings to a global view of the system over time.
fauci-email: a json digest of Anthony Fauci's released emails

A collection of over 3000 pages of emails sent by Anthony Fauci and his staff were released in an effort to understand the United States government response to the COVID-19 pandemic. We describe how this email data was translated into a resource consisting of json files that make many future studies easy. Findings from our processed data include (i) successful organizational partitions using the simple mincut techniques in Zachary's karate club methodology, (ii) a natural example where the normalized cut and minimum conductance set are extremely different, and (iii) organizational groups identified by optimum modularity clusters that illustrate a working hierarchy. These example uses suggest the data will be useful for future research and pedagogical uses in terms of human and system behavioral interactions. We explain a number of ways to turn email information into a network, a hypergraph, a temporal sequence, and a tensor for subsequent analysis as well as a few examples of such analysis.
Neural Inspired Ant Lion Algorithm for Resource Optimization in Cloud

There are various task scheduling models that are in use currently, but the one which we will be focusing on is the ANN (Artificial Neural Network) based model. This model was set up to estimate the task execution status for resource allotment among the candidates. An ANN-based model makes use of various scheduling algorithms to find the best results possible in terms of quality of service (QoS), total cost, service satisfaction, etc. Through our paper, we are simulating various task scheduling algorithms in a virtual environment and comparing their efficiency based on the results we obtain from these simulations. While our focus will be on an emerging meta-heuristic optimization algorithm called the Ant lion Algorithm, we are also running simulations for the Whale Optimization algorithm, and the Genetic Algorithm. For the prediction and allocation of cloud resources we use the Ant Lion Optimization Algorithm. Artificial Neural Network (ANN) is used for resource allocation. We discuss the results that depicts we get better results compared to the existing methods with proper allocation of resources and minimal cost.
How Values Shape the Machine Learning Opacity Problem

One of the main worries with machine learning model opacity is that we cannot know enough about how the model works to fully understand the decisions they make. But how much is model opacity really a problem? This chapter argues that the problem of machine learning model opacity is entangled with non-epistemic values. The chapter considers three different stages of the machine learning modeling process that corresponds to understanding phenomena: (i) model acceptance and linking the model to the phenomenon, (ii) explanation, and (iii) attributions of understanding. At each of these stages, non-epistemic values can, in part, determine how much machine learning model opacity poses a problem.
Changes in the proverb formula in Finnish Bibles from 1642 to 1992

Abstract The article studies a subtype of the Finnish generalizing relative clause, referred to as the proverb formula . A generalizing relative clause refers to any person who fills the described condition. The proverb formula is used frequently in the Finnish translations of the Bible in the Book of Proverbs. The study examines two aspects that vary in this structure in Finnish editions from 1642 to 1992: the head pronoun of the relative clause and the order of the relative clause and the main clause. In the oldest of the studied translations, the most frequent one variant or the proverb formula begins with the relative clause, and the relative clause head in the main clause is a personal pronoun ( hän ). For the order of the structure, a clear model is found in the source texts of the translation. In later editions, personal pronoun heads were eliminated. Interestingly, they are not replaced with demonstrative heads, which would follow the most frequent proverb formula structure in Finnish vernacular and would also be recommenced by grammar guides. Instead, a variant with omission of the head pronoun gains in frequency. Variations within the proverb formula increase when the order with the main clause preceding the relative clause gains in frequency. The preference for omission of the head pronouns is not easily explainable. The article proposes that this variant was a conscious stylistic choice for solemn, biblical language.
Understanding from Deep Learning Models in Context

This chapter places into context how the term model in machine learning (ML) contrasts with traditional usages of scientific models for understanding and we show how the direct analysis of an estimator’s learned transformations (specifically, the hidden layers of a deep learning model) can improve understanding of the target phenomenon and reveal how the model organizes relevant information. Specifically, three modes of understanding will be identified: the difference between implementation irrelevance and functionally approximate irrelevance will be disambiguated, and how this distinction impacts potential understanding with these models will be explored. In addition, by distinguishing between empirical link failures and representational ones, an ambiguity in the concept of link uncertainty will be addressed, thus clarifying the role played by scientific background knowledge in enabling understanding with ML.
Effects of Socio-Economic Determinants on Educational Outcomes in Nigeria

The study examined the effects of socioeconomic determinants on educational outcomes in Nigeria. This study was done against the backdrop that the education sector of Nigeria has failed to produce quality educational outcomes needed for the growth of the nation’s economy despite enormous works on the topic by most scholars who failed to account for the combination and extent of the effect of infrastructure, ICT, per capita income, and corruption on education outcome in Nigeria. The study was carried out to investigate the effect of infrastructure, ICT, per capita income, and corruption on educational outcomes in Nigeria using time series data covering a period of 21 years (2000- 2020). The study employed the Autoregressive Distributed lag (ARDL) bounds testing approach to ascertain the relevant relationship while the ECM captured the speed of short-run adjustment to long-run equilibrium. The findings reveal that infrastructure and per capita income have a positive effect on education outcomes (with literacy rate as a proxy) in Nigeria with only infrastructure being statistically significant. The study also found that ICT and corruption negatively and insignificantly affect education outcomes in Nigeria. The study, therefore, recommends that emphasis should be shifted from education for all to learning for all, improvement in infrastructure, and incorporation of ICT in education to aid teaching and learning.
Heating Fluid Dynamics in Chaotic Advection Regim

Heating Fluid Dynamics in Chaotic Advection Regim