Reviewerly/exHarmony huggingface dataset:
TEE-based protection of cryptographic keys on embedded IoT devices

The Internet of Things (IoT) consists of billions of embedded devices connected to the Internet.Secure remote management of many of these devices requires them to store and use long-term cryptographic keys.In this work we propose to protect cryptographic keys in embedded IoT devices using a Trusted Execution Environment (TEE) which is supported on many embedded platforms.Our approach provides similar protection as secure co-processors, but does not actually require an additional secure hardware element.
Software similarity measurements using UML diagrams: A systematic literature review

Every piece of software uses a model to derive its operational, auxiliary, and functional procedures. Unified Modeling Language (UML) is a standard displaying language for determining, recording, and building a software product. Several algorithms have been used by researchers to measure similarities between UML artifacts. However, there no literature studies have considered measurements of UML diagram similarities. This paper presents the results of a systematic literature review concerning similarity measurements between the UML diagrams of different software products. The study reviews and identifies similarity measurements of UML artifacts, with class diagram, sequence diagram, statechart diagram, and use case diagram being UML diagrams that are widely used as research objects for measuring similarity. Measuring similarity enables resolution of the problem domains of software reuse, similarity measurement, and clone detection. The instruments used to measure similarity are semantic and structural similarity. The findings indicate opportunities for future research regarding calculating other UML diagrams, compiling calculation information for each diagram, adapting semantic and structural similarity calculation methods, determining the best weight for each item in the diagram, testing novel proposed methods, and building or finding good datasets for use as testing material.
On machine learning algorithms and compositional data

Predictive methods such as Lasso regression, partition trees and random forests (RF), artificial neural networks (ANN) and deep learning, or support-vector machines (SVM) and other kernel methods have become in the last years increasingly popular, also in the compositional data community. However, most of the contributions using machine learning algorithms on compositional data just applied the relevant method to an additive, centered or isometric log-ratio (alr, clr, ilr) transformed version of the training data, without caring about the properties of the construct. In this contribution we briefly review the fundamental construction of these methods, and check in which way can they be tweaked or adapted to account for the compositional scale of the data. As an example, a binary partition tree aims at constructing a hierarchy of classification, where each branch splits the data in two subgroups according to the one single covariable that provides highest purity of the two resulting subgroups; at the end of the hierarchy, all branches contain only data from one pure group. Random Forests Breiman (2001) were introduced to deal with the obvious over-fitting of partition trees, with a double randomisation strategy: first bootstrapping the number of observations, creating B different trees that form the forest; second, each branching of each tree is based not on the whole set of variables, but on a different random subset of them. The fact that at each branching only one variable is actively used makes the method non-invariant under the choice of possible log-ratio transformations. A way to allow for this one feature selection while keeping the relative nature of compositional information would be to build the trees on the set of pairwise log-ratios (pwlr). This applies to all kinds of tree-based methods with compositional covariables.
Effectiveness of protection motivation theory based: Password hygiene training programme for youth media literacy education

This study adopts an experimental design to investigate the effectiveness of a password hygiene training programme.The password hygiene training programme adopted the Protection Motivation Theory's framework in its development, and was delivered online to 84 students aged 13 to 16. Strength of password measures, such as time taken, and number of tries required, to crack the password, were administered pre and post intervention.The findings revealed that the password hygiene training programme was effective in changing actual password setting behaviour.This study also provided hints on which perceptual changes, based on the theory's framework, was most influential in the exercise.
SCADA-NLI: A Natural Language Query and Control Interface for Distributed Systems

Human-Computer natural language interaction is helpful to reduce the operation and maintenance cost of the SCADA system, and it is necessary to solve the complex natural language interface problem that supports data query and real-time control. According to the complexity of natural language instructions, a hierarchical classification of semantic parsing algorithm is adopted. Firstly, the KWECS method is used to classify the intent of natural language instruction, then the TF-IDF keyword extraction algorithm combined with the cosine similarity is used to structure the key-value of the classified natural language instructions which was used into SCADA control intermediate language and then formally converted into actual control or query instruction. If the analysis fails, the complex control and query instruction analysis are carried out according to the classification results, structuring instruction parsing based on dependency parsing and SQL natural language parsing based on deep learning are adopted respectively to implement real-time control interface and database query interface. Our experimental results show that the proposed hierarchical classification of natural language comprehensive query and control interface can better solve the problem of human-computer natural language interaction in the SCADA system, and the accuracy of intent recognition reaches 96.5%. In more detail, the accuracy, precision, recall, and F-score of instruction parsing reach 88.47%, 90.21%, 89.48%, and 89.72% respectively. Especially, it provides more convenient interactive means for industrial and agricultural information management and control.
Improving DCP Haze Removal Scheme by Parameter Setting and Adaptive Gamma Correction

Recently, single-image haze removal based on the dark channel prior (DCP), originally proposed by He et. al., has attracted much attention in the image restoration community. This dehazing algorithm, called the DCP scheme here, is well-known to have four main problems in its dehazed images: artifacts, hue distortion, color over-saturation, and halos. In this paper, an improved DCP (IDCP) is proposed to deal with the four aforementioned problems, by setting the model parameters, i.e. scaling factors and window size and smoothing factor of a guided image filter in the DCP scheme. Note that a dehazed image is generally dim and low in contrast. An adaptive gamma correction (AGC) is introduced for dehazed image enhancement. The proposed IDCP and AGC are used to create the IDCP/AGC scheme, in which the IDCP scheme performs haze removal and the AGC enhances the dehazed image. The IDCP/AGC scheme was justified through extensive experiments and compared with the DCP scheme, an optimization-based scheme, and two learning-based schemes on two datasets. The results indicated that the proposed scheme is subjectively and objectively superior to the comparison schemes.
Sequence-to-Sequence Acoustic-to-Phonetic Conversion Using Spectrograms and Deep Learning

Distinctive phonetic features (DPFs) abstractedly describe the place, manner of articulation, and voicing of the language phonemes. While DPFs are powerful features of speech signals that capture the unique articulatory characteristics of each phoneme, the task of DPF extraction is challenged by the need for efficient computational model. Unlike the ordinary acoustic features that can be directly determined form speech waveform using closed-form expressions, DPF elements are extracted from acoustic features using machine learning (ML) techniques. Therefore, for the objective of developing an acoustic-to-phonetic converter of high accuracy and low complexity, it is important to select the input acoustic features that are simple, yet carry adequate information. This paper examines the effectiveness of using spectrogram as the acoustic feature with DPFs modeled using two deep learning techniques: the deep belief network (DBN) and the convolutional recurrent neural network (CRNN). The proposed method is applied on Modern Standard Arabic (MSA). Multi-label modeling is considered in the proposed acoustic-to-phonetic converter. The learning techniques were evaluated by proper evaluation measures that accommodate the imbalanced nature of DPF elements. The results showed that the CRNN is more accurate in extracting the DPFs than the DBN.
A Heterogeneous Learning Framework for Over-the-Top Consumer Analysis Reflecting the Actual Market Environment

The over-the-top (OTT) market for media consumption over wired and wireless Internet is growing. It is, therefore, crucial that service providers and carriers participating in the OTT market analyze consumer traffic for pricing, service delivery, infrastructure investments, etc. The OTT market has many consumer groups, but the proportion of users is not consistent in each. Furthermore, as multimedia consumption has increased owing to the COVID-19 epidemic, the OTT market has changed rapidly. If this is not reflected, the analysis will not be accurate. Therefore, we propose a framework that can classify consumers well based on actual OTT market environment conditions. First, by applying our proposed conditional probability-based method to basic machine learning techniques, such as support vector machine, k-nearest neighbor, and decision tree, we can improve the classification performance, even for an imbalanced OTT consumer distribution. Then, it is possible to analyze the changing consumer trends by dynamically retraining the incoming OTT consumer data. Conventional methods result in low classification accuracy in low-number classes, but our method shows an improvement of 5.3–19.2% based on recall. Moreover, conventional methods have shown large fluctuations in performance as the OTT market environment has changed, but our framework consistently maintains high performance.
Human Activity Recognition in Daily Life and Sports Using Inertial Sensors

Human Activity Recognition (HAR) deals with the automatic recognition of physical activities and plays a major role in the health and sports sector. Knowledge about the performed activities can be used to monitor compliance regarding physical activity recommendations, investigate the causes of physical activity behavior, implement sport-specific training programs, and replicate the physical demands during sport competition. Currently available tools for HAR often rely on questionnaires which involve problems in the reliability when recalling activities.In this thesis, algorithms for HAR are introduced and evaluated which apply machine learning techniques to inertial sensor data. Daily as well as sport-specific activities are considered including sitting, washing dishes, climbing stairs, and kicking in soccer. Besides the development and implementation of algorithms, mandatory extensions regarding the design of HAR systems are further identified and future research directions are provided.
Linguistically manipulative, disputable, semantic nature of the community reddit feed post

The phenomenon of online community has been deployed in the paper. Its structure, functions and linguistic peculiarities have been regarded. The notion of linguistic manipulation with its classification and general principles has been outlined. The Reddit social virtual platform has served to be a research material. Manipulative nature, disputable features as well as semantic analysis of textual fragments of the Coronavirus Comment discussion section have been considered. Different social network rubrics have been taken into consideration. The Reddit disputable opinion rubric posts and Coronavirus Community comment section were under comparative analysis. The Reddit Coronavirus Community posts have been allocated. They were linguistically marked both implicitly and explicitly for further textual semantic analysis, which was conducted as manually as in a computerized way. The latter provided statistical base for the substantiated linguistic comments to the linguistic and interpretative analysis of the Coronavirus Comment section. The disputable situation analysis considering its conflict solving modes has been applied. The statistical comparison of fragment semantic analysis findings of one of the earlier papers regarding Reddit Coronavirus news comments and this of Reddit Coronavirus Community post has been provided and interpreted. © 2021 CEUR-WS. All rights reserved.
Personal motivational resource as a condition for the development of self-education competence in university students

Introduction. The relevance of the research subject was determined by the present-day higher-education requirements for students’ self-educational competence. One of the conditions for its development is the personal motivational resource which allows the student to actualise and reliably meet his/her need for self-education and self-development. Materials and methods. The research was carried out on the basis of the federal state autonomous institution of higher education North-Caucasus Federal University (N=200) with the use of the following methods: the authors’ questionnaire “Assessing the level of students’ self-education”, “Diagnostics of the personality’s motivational structure” (Milman), “Methodology for exploring the learning motivation” (Lukyanova, Kalinkina), adapted version of “Diagnostics of motivators of a person’s social and psychological activity” (Fetiskin). The study used as well the comparative methods of mathematical statistics (Mann Whitney U test; Wilcoxon signed-rank test). Results. It was revealed that 30% and 29% of students, respectively, demonstrated low and high levels of formed self-education competence. The students with the high level of self-education had a progressive profile of personal motivational structure for the most part (41.4%); a very high level of motivation for learning, development motives and expressed inclination to creative activity (48.3%) testifying to internal motivation and personality-inherent substantiation of learning; the need for success (48.2%) and the tendency towards affiliation (group recognition) (41.4%). The students with the low level of self-education are predominantly characterised by impulsive (26.6%) and regressive (23.3%) profiles of personal motivational structure; moderate and reduced levels of motivation for learning (50%); three groups of motives: achieving success – 30.0%; striving for power – 36.7%; tendency towards affiliation – 33.3%, which evidences the prevailing external motivation for learning. The intergroup differences according to the Mann Whitney U test are statistically significant (p&lt;0.05). The obtained results of the students’ personal motivational characteristics made it possible to identify the areas of content-specific support required for the development of self-education competence in undergraduate students. The experimental control-stage data showed positive dynamics in the development of the students’ self-education competence. A number of statistically significant differences according to the Wilcoxon signed-rank test were identified (p &lt;0.05; p &lt;0.01).
Cross-Listings of Blockchain-Based Tokens Issued Through Initial Coin Offerings: Do Liquidity and Specific Cryptocurrency Exchanges Matter?

Initial coin offerings (ICOs) represent a novel funding mechanism where digital tokens are issued on the blockchain and sold to investors. One major reason for the success of this financing model is the fact that the issued tokens can immediately be traded on secondary markets. This event study analyzes 250 exchange cross-listings of 135 different tokens issued through ICOs on 22 cryptocurrency exchanges. We find significant abnormal returns of 6.51% on the listing day and 9.97% over a seven-day window around the event. Further analysis shows that the results clearly differ for individual cryptocurrency exchanges, as listings on individual exchanges yield returns of up to 34% on the event day, while others are negligible. An investigation of liquidity-related metrics shows that lower prior trading volume and asset market capitalization have positive effect on listing returns. Investors use phases of high market liquidity to sell off positions around the period of cross-listing events. These first results on the cross-listing effects of ICOs may be of relevance to investors/traders, ICO projects, cryptocurrency exchanges and regulators.
Fair-Net: A Network Architecture for Reducing Performance Disparity between Identifiable Sub-populations

In real world datasets, particular groups are under-represented, much rarer than others, and machine learning classifiers will often preform worse on under-represented populations. This problem is aggravated across many domains where datasets are class imbalanced, with a minority class far rarer than the majority class. Naive approaches to handle under-representation and class imbalance include training sub-population specific classifiers that handle class imbalance or training a global classifier that overlooks sub-population disparities and aims to achieve high overall accuracy by handling class imbalance. In this study, we find that these approaches are vulnerable in class imbalanced datasets with minority sub-populations. We introduced Fair-Net, a branched multitask neural network architecture that improves both classification accuracy and probability calibration across identifiable sub-populations in class imbalanced datasets. Fair-Nets is a straightforward extension to the output layer and error function of a network, so can be incorporated in far more complex architectures. Empirical studies with three real world benchmark datasets demonstrate that Fair-Net improves classification and calibration performance, substantially reducing performance disparity between gender and racial sub-populations.
Blockchain Techniques for Secure Storage of Data in Cloud Environment

The need for Blockchain advancement, as well as the importance of its use, has fueled ongoing research in a variety of theoretical and practical fields. Even as it is still in its early stages of development, the blockchain is being seen as a forward-thinking approach to modern technology issues such as decentralization, identification, confidence, character, data ownership, and information-driven decisions. The blockchain breakthrough offers major feedback when effectively searching for the optimal solution to storing and accessing cloud data. This essay examines the use of blockchain technologies to secure cloud computing. This research paper also presents a framework for secure storage of data in cloud computing environment. This framework makes use of smart contracts and access list for ensuring data security.
A Comprehensive Survey on Security Threats and Countermeasures of Cloud Computing Environment

Cloud computing technology is one of the biggest breakthroughs in technology and business boom. This technology is service oriented that focuses on reducing costs, minimizing devices and paying for used service only. This paper surveys recent research on security threats and countermeasures of cloud computing environment. It begins by explaining cloud computing definition with its layers. In addition, it provides a brief explanation of the common characteristics of cloud computing service models and deployment models. Authentication and access control in cloud computing will be given at end of this work.
Prediction of intensive care unit readmission for critically ill patients based on ensemble learning

Objective To develop machine learning models for predicting intensive care unit (ICU) readmission using ensemble learning algorithms. Methods A publicly accessible American ICU database, medical information mart for intensive care (MIMIC)-Ⅲ as the data source was used, and the patients were selected by the inclusion and exclusion criteria. A set of variables that had the predictive ability of outcome including demographics, vital signs, laboratory tests, and comorbidities of patients were extracted from the dataset. We built the ICU readmission prediction models based on ensemble learning methods including random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT), and compared the prediction performance of the machine learning models with a conventional Logistic regression model. Five-fold cross validation was used to train and validate the prediction models. Average sensitivity, positive prediction value, negative prediction value, false positive rate, false negative rate, area under the receiver operating characteristic curve (AUROC) and Brier score were used as performance measures. After constructing the prediction models, top 10 predictive variables based on importance ranking were identified by the model with the best discrimination. Results Among these ICU readmission prediction models, GBDT (AUROC=0.858) had better performance than random forest (AUROC=0.827), and was slightly superior to AdaBoost (AUROC=0.851) in terms of AUROC. Compared with Logistic regression (AUROC=0.810), the discrimination of the three ensemble learning models was much better. The feature importance provided by GBDT showed that the top ranking variables included vital signs and laboratory tests. The patients with ICU readmission had higher mean arterial pressure, systolic blood pressure, diastolic blood pressure, and heart rate than the patients without ICU readmission. Meanwhile, the patients readmitted to ICU experienced lower urine output and higher serum creatinine. Overall, the patients having repeated admissions during their hospitalization showed worse heart function and renal function compared with the patients without ICU readmission. Conclusion The ensemble learning based ICU readmission prediction models had better performance than Logistic regression model. Such ensemble learning models have the potential to aid ICU physicians in identifying those patients with high risk of ICU readmission and thus help improve overall clinical outcomes.
Care management in coping with COVID-19 at a teaching hospital

to report the experience of implementing care management strategies in coping with the COVID-19 pandemic in a teaching hospital.this is an experience report of the managers who work at the largest public hospital in Paraná with functions as Head of the Care Management Division, Head of the Care Lines Management Sector, Head of the Infectiology Unit and support team.care management strategies were structured based on the service dynamics; physical structure; human Resources; professional and user safety.preparing for a pandemic involves measures that include modifying infrastructure and processes, managing employees and users, infection prevention strategies, and clinical recommendations. These measures are necessary to optimize the quality of care provided to users with COVID-19 and to reduce the risk of viral transmission to other users or health professionals.