Reviewerly/exHarmony huggingface dataset:
Security Requirements for Store-on-Client and Verify-on-Server Secure Biometric Authentication

The Fast IDentity Online Universal Authentication Framework (FIDO UAF) is an online two-step authentication framework designed to prevent biometric information breaches from servers. In FIDO UAF, biometric authentication is firstly executed inside a user’s device, and then online device authentication follows. While there is no chance of biometric information leakage from the servers, risks remain when users’ devices are compromised. In addition, it may be possible to impersonate the user by skipping the biometric authentication step. To design more secure schemes, this paper defines Store-on-Client and Verify-on-Server Secure Biometric Authentication (SCVS-SBA). Store-on-client means that the biometric information is stored in the devices as required for FIDO UAF, while verify-on-server is different from FIDO UAF, which implies that the result of biometric authentication is determined by the server. We formalize security requirements for SCVS-SBA into three definitions. The definitions guarantee resistance to impersonation attacks and credential guessing attacks, which are standard security requirements for authentication schemes. We consider different types of attackers according to the knowledge on the internal information. We propose a practical concrete scheme toward SCVS-SBA, where normalized cross-correlation is used as the similarity measure for the biometric features. Experimental results show that a single authentication process takes only tens of milliseconds, which means that it is fast enough for practical use.
"Continuous Pedagogical Education In North-Eastern Federal University: Vectors Of Development "

The article discusses the topical issues of developing continuing professional education in North-Eastern Federal University in conditions of ongoing changes and in line with the global trends. The global trends today include the accelerating rate of social development, globalisation, total digitalisation, increased competition, increasing mobility of people and expanding opportunities, massification of education (growing share of learners in universities in all age categories, regions and countries), cognitive revolution (growing scale of intellectual activity), creation of new ways of thinking and creativity. The distinctive feature that can ensure the competitiveness of continuing professional education in NEFU is its focus on learners and professional context within the framework of introduction of the National System of Qualifications. Today it is possible to say with confidence that people understand and are aware of the objective necessity and subjective need for lifelong education. However, for Yakutia, with its vast territory and unique geographic and climatic conditions, the vital issue of improving the quality and accessibility of continuing professional education for adults, including various diverse categories such as idle and unemployed citizens, requires further research and a search for solution.
Development and Performance Analysis of An Automated Attendance System Based on Faced Detection

An Automated Attendance System based on face detection are focused on developing an attendance system by using a face detection. The face detection program develops using Python include with an open source algorithm for face detection, OpenCV. The program sends the data to the database as the face detected and the system will mark the attendance for the person in database as attend. The attendance can be verified through HTML output interface. The performance analysis is based on the time taken to detect number of faces and the accuracy of the face detection.
Comparative Convolutional Neural Network for Younger Face Identification

We consider the problem of determining whether a pair of face images can be distinguishable in terms of age and if so, which is the younger of the two. We also determine the degree of distinguishability in which age differences are categorized into large, medium, small and tiny. We propose a comparative convolutional neural network combining two parallel deep architectures. Based on the two deep learnt face features, we introduce a comparative layer to represent their mutual relationships, followed by a concatenatation implementation. Softmax is adopted to complete the classification task. To demonstrate our approach, we construct a very large dataset consisting of over 1.7 million face image pairs with young/old labels.
Enhancing Model Performance for Fraud Detection by Feature Engineering and Compact Unified Expressions

The performance of machine learning models can be improved in a variety of ways including segmentation, treating missing and outlier values, feature engineering, feature selection, multiple algorithms, algorithm tuning/compactness and ensemble methods. Feature engineering and compactness of the model can have a significant impact on the algorithm’s performance but usually requires detailed domain knowledge. Accuracy and compactness of machine learning models are equally important for optimal memory and storage needs. The research in this paper focuses on feature engineering and compactness of rulesets. Compactness of the ruleset can make the algorithm more efficient and derivation of new features makes the dataset high dimensional potentially resulting in higher accuracy. We have developed a technique to enhance model’s performance with feature engineering and compact unified expressions for dataset of unknown domain using profile models approach. Classification accuracy is compared using well-known classifiers (Decision Tree, Ripple Down Rule and RandomForest). This technique is applied on fraud analysis bank dataset and multiple synthetic bank datasets. Empirical evaluation has shown that not only the ruleset size of training and prediction dataset is reduced but performance is also improved in other performance metrics including classification accuracy. In this paper, the transformed data is used for the experimental validation and development of fraud detection technique, but it can be used in other domains as well especially for scalable and distributed systems.
Essential maintenance of websites

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Spjallmennis ráðgjafi - Íba

Spjallmennisraðgjafinn Iba is a final project in BSc in Computer Science at University of Reykjavik, done in cooperation with Islandsbankinn. Iba is a text based conversational chatbot, created to aid customer service by answering common queries (such as currency exchange rates, opening hours, and location of branches). Iba aims to expedite service time for both bank users as well as customer service employees. The researchers conducted surveys to gather data about the viability of such a bot, which is entirely trained in the Icelandic language.
Summarizing ETDs with deep learning

Inspired by the millions of Electronic Theses and Dissertations (ETDs) openly available online, we describe a novel use of ETDs as data for text summarization. We use a large corpus of ETDs to evaluate techniques for generating abstractive summaries with deep learning. Using an extensive ETD collection of over 30,000 doctoral dissertations and master’s theses, we examine the quality of state-of-the-art deep learning summarization technologies when applied to an ETD corpus. Deep learning requires a large set of training data to produce satisfactory results. Finding suitable training data is especially difficult due to the widespread use of domain-specific jargon in ETDs, coupled with the wide-ranging breadth of subject matter contained in an ETD corpus. To overcome this significant limitation, we demonstrate the potential of transfer learning on automatic summarization of ETD chapters. We apply several combinations of deep learning models and training data to the ETD chapter summarization task and compare the outputs of the top performers.
보육교사의 영유아 권리인식과 임파워먼트가 교사-영유아 상호작용에 미치는 영향

The study was conducted by surveying 230 childcare teachers in Gyeonggi-do. The collected data were analyzed by independent sample t-test and one-way ANOVA using SPSS 24.0. Scheffe's post hoc test was conducted to examine the differences among groups. The study results are as follows. First, the following three factors respectively showed significant differences in teachers’ social conditions: childcare teachers' awareness in the rights of infants and toddlers, empowerment, and interaction between the teachers and infants and toddlers. Second, there was a positive correlation between the awareness of the rights in infants and toddlers and the interaction, the empowerment and interaction, and the awareness of the rights of infants and toddlers and empowerment. Third, childcare teachers’ awareness of the rights and empowerment had a positive effect on the interaction. In conclusion, growing teachers’ awareness of the rights of infants and toddlers in order to have high quality interaction is essential. Also, raising teachers’ empowerment with the management of an organization related to the education of infants and toddlers is needed.
장애를 다룬 아동/청소년 영문학을 이용한 영어 교육에 대한 고찰

Children’s literature is widely used in English language education in Korea. The main purpose of this paper is to call for a...
Trajectory Planning for Autonomous Vehicles for Optimal Exploration of Spatial Processes

Author(s): Song, Sisi | Advisor(s): Rodriguez, Abel | Abstract: Autonomous vehicles are becoming the platform of choice for large-scale exploration of environmental processes, owing to their low cost and dependability of sensors. Standard trajectory planning methods often preplan a trajectory in advance, or are based on information criteria, and do not use the observations taken at earlier locations on the trajectory to decide where the vehicle should go next. In this dissertation, we propose a framework for real-time, adaptive generation of trajectories that are optimal with respect to some exploration goal, with a focus on smooth continuous trajectories for nonholonomic vehicles. We develop an algorithm that enables the vehicle to gather data, reconstruct the environmental process, and generate piecewise optimal trajectory segments that use the data collected at previously visited locations. Our approach is based on Gaussian process priors and Bayesian optimal design. Gaussian processes provide a method to fuse any prior knowledge of the environmental process with the collected data to obtain the most updated estimation of the process. Through Bayesian optimal design, we develop reward functions that explicitly reflect the operational goal and naturally address the exploration-exploitation trade-off in a principled way. We include a number of empirical evaluations of the methodology and show the advantages of our algorithm on different archetypes of spatial processes. Field tests of the planner on an autonomous ground vehicle demonstrate the practical usefulness of our algorithm. We then extend the framework to incorporate supplementary information from different types of off-vehicle sensors. Three incorporation methods, depending on the nature of the supplementary source, are developed and tested, resulting in performance improvement, especially in the early stages of exploration. Finally, we turn to the case of multiple vehicles and develop an extension of our optimal trajectory algorithm that uses collective information to estimate the process and individual trajectory planning for each vehicle. As a result, computation time per vehicle does not increase as more vehicles are added, while the time required to achieve the exploration goal is reduced nearly linearly.
Task-Oriented Design Optimization for a Mobile Painting Robot*

Under the specific task requirement of mobile manipulators, the workspace analysis plays an indispensable role in the design and optimization of their geometric structure. To generate the most suitable workspace for mobile manipulators and cater to specific task requirement, the geometric structure parameter of mobile manipulators should be optimized and this need to consider the effective information from operated target. When deal with one specific operating task by painting one prototype room composed of vertical plane walls and ceilings, this research presents one novel method for designing and optimizing the structure parameter of mobile painting robot. To be specific, the feature extraction is executed for the operated target-prototype room, and the corresponding feature constraints function is established. Closely following this, under the given optimized goals, the optimized structure parameter of the mobile painting robot for specific operation will be obtained. What’s more, to verify the validation of obtained structure parameter, the painting simulation platform via V-REP software is established, and the painting path which is attached with painting effect for painting the whole prototype room is analyzed initially. Last but not least, the smooth implementation of simulation process demonstrates that the optimized structure parameter can fulfill the task requirement, and this has actually confirmed one feasible way of task-oriented structure optimization for mobile painting robot.
Good work so far. As you note

Good work so far. As you note, micro.blog already offers a lot of the IndieWeb building blocks. And it recently started to offer an option
On maximum additive Hermitian rank-metric codes

Inspired by the work of Zhou On equivalence of maximum additive symmetric rank-distance (2020) based on the paper of Schmidt Symmetric bilinear forms over finite fields with applications to coding theory (2015), we investigate the equivalence issue of maximum $d$-codes of Hermitian matrices. More precisely, in the space $\mathrm{H}_n(q^2)$ of Hermitian matrices over $\mathbb{F}_{q^2}$ we have two possible equivalence: the classical one coming from the maps that preserve the rank in $\mathbb{F}_{q^2}^{n\times n}$, and the one that comes from restricting to those maps preserving both the rank and the space $\mathrm{H}_n(q^2)$. We prove that when $d<n$ and the codes considered are maximum additive $d$-codes and $(n-d)$-designs, these two equivalence relations coincide. As a consequence, we get that the idealisers of such codes are not distinguishers, unlike what usually happens for rank metric codes. Finally, we deal with the combinatorial properties of known maximum Hermitian codes and, by means of this investigation, we present a new family of maximum Hermitian $2$-code, extending the construction presented by Longobardi et al. in Automorphism groups and new constructions of maximum additive rank metric codes with restrictions (2020).
Kernel of CycleGAN as a Principle homogeneous space.

Unpaired image-to-image translation has attracted significant interest due to the invention of CycleGAN, a method which utilizes a combination of adversarial and cycle consistency losses to avoid the need for paired data. It is known that the CycleGAN problem might admit multiple solutions, and our goal in this paper is to analyze the space of exact solutions and to give perturbation bounds for approximate solutions. We show theoretically that the exact solution space is invariant with respect to automorphisms of the underlying probability spaces, and, furthermore, that the group of automorphisms acts freely and transitively on the space of exact solutions. We examine the case of zero `pure' CycleGAN loss first in its generality, and, subsequently, expand our analysis to approximate solutions for `extended' CycleGAN loss where identity loss term is included. In order to demonstrate that these results are applicable, we show that under mild conditions nontrivial smooth automorphisms exist. Furthermore, we provide empirical evidence that neural networks can learn these automorphisms with unexpected and unwanted results. We conclude that finding optimal solutions to the CycleGAN loss does not necessarily lead to the envisioned result in image-to-image translation tasks and that underlying hidden symmetries can render the result utterly useless.
PENJERNIHAN AIR SUMUR UNTUK KEBUTUHAN SEHARI-HARI DI DAERAH KAMPUNG BYOBYOSI DISTRIK ARSO KABUPATEN KEEROM

Dedication is directly applied in the field with the management of using equipment that is cheap, simple, and affordable and can be directly benefited by the community of sarmi village. To overcome high water turbidity levels required a processing with the aim of reducing and even removing the level of turbidity and odor of the water. One of the treatment is filtration method using fibers, charcoal, sand, foam, and gravel as filter and absorbent material, as well as paralon pipes as medium where the processing place. The result of this water treatment is not prioritized for the purposes of drinking water, but for the purposes of bathing and washing. With the precense of groundwater processing, so in this case a little more the need for clean water for the community can be resolved, where the color and odor in the groundwater can be  neutralized to odorless and colorless, so the water is feasible to be used for everyday purposes in particular bathing and washing. Keywords : Physical properties of groundwater, treacment.
Variable selection in finite mixture of generalized estimating equations

This paper develops a new method to estimate the parameters in mixture models. Traditionally, the parameter estimation in mixture models is performed from a likelihood point of view by exploiting the expectation maximization (EM) method. In this paper, however, we utilize the Least Square Principle. Based on this principle, we propose an iterative algorithm called Iterative Weighted least Square (IWLS) to estimate the parameters. Through comparative study, we demonstrate the superiority of our method compared to EM method. We show that IWLS method outperforms EM in both accuracy and the number of iterations required for convergence.
Statistical Box Graph Method and Determination of Outlier Values by Meta Heuristic Algorithms

Detection of outliers in the context of multivariate data is an important issue for data mining. Most of recommended methods for solving this problem are based on mahalanobis distance, euclidean distance, which is the most common distribution of observations or it is based on projection pursuit approach. Metaheuristic methods automatically optimize distance parameters in applications where it is used to detect outliers, instead of manually adjusting them. In this study, metaheuristic algorithms were used to detect outliers in the context of multivariate data, which is an important problem of data mining. Detection of outliers and, if necessary, removal from the data is important both to improve the quality of the original data and to reduce the effects of ambiguous values in the database information discovery process. Outlier values obtained from 6 different data sets are presented in tabular form using meta algorithms in which particle swarm algorithm, genetic algorithm and differential evolution algorithm.
Eyematch: An Eye Localization Method For Frontal Face Images

In this paper, a novel approach to detect iris from images which contains frontal faces is proposed. The approach can be used for real time eye detection from cheap web cameras or other kind of cameras. First face region is detected by using haar cascades for frontal faces than possible eye region chosen from the face region. The eye regions reduced with the foreknowledge which is eyes are darker. Reduced image used with a tailored template matching algorithm to determine where eye really is.
Book Chapter: The ‘New(ish)’ Property, Informational Bodies and Postmortality

This chapter examines the concept of digital assets from an angle that has not yet been explored in legal scholarship around digital death and the transmission of digital assets on death. Digital death is conceived herein as the death of an individual who leaves behind various digital fragments of their identity, either in the form of digital assets broadly or as digital biographies, dossiers, autobiographies and archives. Digital death causes uncertainty as to what happens in this dispersed, interconnected and often unregulated digital space, which Kasket lucidly entitles The New Elysium.Most legal scholars have considered digital assets either from a perspective of ‘hard law’ of succession and probate or the intersection of property, contracts and intellectual property; sometimes referring to data protection, jurisdiction or cybercrime. The scholars have not ventured into exploring theory that goes beyond theories of property, intellectual property and privacy.The chapter begins by examining classical conceptualisations of digital assets as property and the ‘”new” new property’, exploring whether this is the correct way to perceive digital assets conceptually. It will then go on to examine post-mortem privacy in the context of digital assets, and introduce a novel link with the Floridian concept of informational bodies. In the attempt to offer a comprehensive framework and a more nuanced normative support for future policy and law, the chapter interrelates all of the concepts with the ideas of postmortal society, introducing a new concept of ‘postmortal privacy’. Finally, the author uses this conceptualisation to test some of the existing legal regimes in the area of the transmission of digital assets. Suggestions from this chapter remain mainly at an abstract level, due to the scope and the nature of the framework it introduces.
Automatic Identification of Political Ideology in Online News Articles

The following research discusses text analysis approaches to automatically categorize news articles based on their political ideology. In this case, ideology is defined as a writer expressing either a liberal or a conservative point of view. This classification is done at both the document and the phrase level, as previous research has indicated that doing so increases classifier performance over using a “bag of words” approach. Linguistic features related to lexical richness are extracted from the articles via Python, and features related to emotions and values are extracted via the Linguistic Inquiry and Word Count software. The machine learning software Weka is then used to apply various classification algorithms on the numeric features. Additionally, Amazon Mechanical Turk is used to measure human accuracy and inter-rater agreement on identifying the ideology of the same texts. In all, the trained classifiers perform well above the baseline and outperform the human annotators on the same tasks.
Feature-Based Patch Matching for Moving Object Detection

In this paper, a new background subtraction framework is proposed to deal with possible scenarios occurring in natural scenes. In this method, a combination of two feature descriptors, namely color information in HSV color format and global texture descriptor T, are introduced to effectively identify background points under varying conditions. Using these features, an adaptive background model is constructed to automatically adapt to scene changes. The proposed framework is evaluated on common change detection datasets, showing improved performance compared to three well-known methods.