Reviewerly/exHarmony huggingface dataset:
Continuous digital zooming using generative adversarial networks for dual camera system

This paper presents a generative adversarial network (GAN) with patch match algorithm to realize a high-quality digital zooming using two camera modules with different focal lengths. In dual camera system, shorter focal length module produces the wide-view image with the low resolution. On the other hand, the longer focal length module produces the tele-view image via optical zooming. The long-focal image contains more details than short-focal image and can be used to guide short-focal image to reconstruct high frequency part. Firstly, a feature extraction block (FEB) is advanced to extract feature of long-focal image and short focal-image to reconstruct a wide-view image with different resolutions. Next, a patch match algorithm is integrated into convolution neural networks (CNN) to fuse information of long-focal with short-focal image and generate a new fused image. Finally, the fused image and short-focal image are merged with a feature fusion block (FFB) to predict high-resolution images. In addition, generative adversarial networks are used for filtering information integrated by previous network and output the zoomed image. Extensive experiments on benchmark datasets show that our algorithm achieves favorable performance against state-of-the-art methods.
Metric Identification for Random Value Density Distribution

Abstract The problem of metric identification of probability density distribution for a random value is considered. It is shown that the accuracy is determined both by the sample size and by the number of intervals into which the domain of its determining is divided.
Analysis of iris obfuscation: Generalising eye information processes for privacy studies in eye tracking.

We present a framework to model and evaluate obfuscation methods for removing sensitive information in eye-tracking. The focus is on preventing iris-pattern identification. Candidate methods have to be effective at removing information while retaining high utility for gaze estimation. We propose several obfuscation methods that drastically outperform existing ones. A stochastic grid-search is used to determine optimal method parameters and evaluate the model framework. Precise obfuscation and gaze effects are measured for selected parameters. Two attack scenarios are considered and evaluated. We show that large datasets are susceptible to probabilistic attacks, even with seemingly effective obfuscation methods. However, additional data is needed to more accurately access the probabilistic security.
Collaborative Multi-View Convolutions With Gating For Accurate And Fast Volumetric Medical Image Segmentation

Due to their high capacity in capturing 3D spatial information, 3D Fully Convolutional Neural Networks (3D FCNs), especially 3D U-Net, are prevalent for volumetric medical image segmentation. However, 3D convolutions are much more computationally complex than 2D convolutions and thus, are more prone to overfitting. This paper proposes Collaborative Multi-View convolutions (CMV convs) that can keep the model complexity similar to those employing 2D convolutions while capturing the 3D spatial context like 3D convolutions. Specifically, CMV convs simultaneously extract information from three orthogonal views with three parameter-shared 2D convolutions. A Global-Guided Gating mechanism (3G) is further designed that selectively passes information from CMV convs to the next stage. Combined with 3G, a CMV conv become a G-CMV conv that constitutes a plug-and-play module, which can be easily integrated into various 3D CNNs for image segmentation. Extensive experiments utilizing BraTS18 dataset have been conducted. Our method achieves competitive results compared to state-of-the-art methods with over 10× fewer parameters than 3D-UNet.
SeaTac man 'heartbroken' after thieves damage home during burglary

A SeaTac man is shaken after a trio of crooks broke into his home. Detectives with the King County Sheriff’s Office are actively searching for the suspects involved in the burglary.
Digital and Extended Selves in Cyberspace

Social media technologies (e.g., Internet, Twitter, texting, smartphones) have the potential to extend our cognitive, affective, and social processes beyond the wetware of our brains.
Smart Biometric Lock with Security Features

Things are getting smarter day by day. With the present conventional locks, there is a possibility of keys being misplaced or lost or may fall into the hands of strangers. With the advances taking place in technology, people are also giving high preference for the technology that is safe, secure and handsfree. This made to the development of a biometric lock using Bluetooth technology working through Android app. To operate this, a person needs to enrol initially and later he can just operate the door using the fingerprint ensuring safety. When the user wants to lock/unlock the door, he needs to place the finger in Android app available in their mobile unit. This data via Bluetooth of mobile unit will be received by Bluetooth module attached to the Arduino. Then the Arduino unlocks the lock through relay if the fingerprint matches. This smart biometric lock avoids carrying keys and robbery. It enhances the security of their houses by sending an alert message, in case the fingerprint is not matched or trying to break the lock to the concerned person. A buzzer is also used which alerts the neighbourhood people. As the biometric system proposed here works on Bluetooth, it does not have any Internet issues. More importantly the system proposed is using the inbuilt app for fingerprint recognition in the mobile rather than physical device that prevents the mechanism of creating duplicate fingerprints and operates the lock. This makes the system more secure, cost effective and simple to implement.
Sensor fusion with high‐order moments constraints using projection‐based neural network

The existing sensor fusion methods mainly follow two approaches, including Gaussian and Non-Gaussian-based sensor fusion approaches. In the first approach, fusion weights are determined based on the second moment. This approach is unable to account for high-order moments; thus, it is not accurate for non-Gaussian sensors. In the second approach, the fusion weights are determined using distribution functions of sensor data. Though this method is more accurate than Gaussian-based sensor fusion, it is a sophisticated method as it requires all moments information of each sensor, which is either not available or at least hard to be identified. Here, we propose an alternative way to determine the fusion weights by a limited number of n (>2) moment information of data. The proposed method makes trades off between accuracy and complexity. The other problem, which has not been studied in the literature, is existence of constraints on moments. The proposed method can address this problem as well. To do this, a projection-based neural network-based optimization method is used to calculate the optimal fusion weights that satisfy moment constraints. A practical application of the proposed sensor fusion method on predicting occupancy for heating, ventilation, and air conditioning (HVAC) is conducted.
Correlation of Bibliographic Records for OMNIS Project

The paper presents work on merging sets of bibliographic data between two largest libraries in Poland—National Library of Poland and Jagiellonian Library as part of a project aiming to provide a single point of access to library assets in Poland. Focus is put on presenting the problem and decisions taken to develop an algorithm for finding similarities between bibliographic entries.
Perception of E-Learning by Consumers of Educational Services

This paper attempts to assess the perception of online learning by consumers of educational services. The study is based on the cognitive approach, interpreting the person as a cognitive system, which is influenced by the communication arising in the learning environment, and education is defined as a process of communication, where the associated problems have a negative impact on the final educational product. We revealed the indicators which determine the perception of online education, among which we can name the following: the quality of communication, convenience of tools, students’ motivation and their ability to self-organization, responsiveness to innovations. The analysis of the scientific literature revealed that the works devoted to the perception of online education are of a private nature, depending on the specific country and its situation at the market of educational services. This puts the Russian scientific community in front of the need for scientific understanding of the perception of online education based on the situation observed in Russian higher education. It is concluded that the main problem of the image of Russian online education is an attempt of the Ministry of Education to combine two mutually exclusive approaches: to use online education as an innovative way to train specialists and the possibility to reduce the cost of the classical education. According to the author, online-education is not a competitor to classical education, because it is focused on a different audience and is based on different methodological and didactic principles. That is the reason why the work on improvement of perception of e-learning should begin with defining its place in the sphere of Russian education and the decision about it should be made by the Ministry of Education.
Advanced ATM Robbery Prevention using Neural Network and IoT Technologies

The basic idea behind the project is to develop a system which is automated and provides the most advanced security features in places like ATM. The project aims to give access to ATMâ€™s for only those who follow the rules of the ATM usage that is preventing access to ATM for those wearing helmet. CNN (Convolutional Neural Network) Algorithm is used for identification like carrying any weapon, wearing helmet and mask identifications. Weapon detection using Tensor Flow increases the safety of ATM users and also increases the safety to the machine itself. In addition to the present, security to the machine is provided using a vibration sensor that is used to detect the movement of the machine just in case machine theft or Broken. Upon detection of the theft, the siren will begin alerting the public and door will be closed. Within the ATM chamber, the Solenoid valve is employed to leak the gas (Chloroform) to hold the thief into the unconscious stage. In addition to this, features like buzzer and alert notification to relevant security personnel is provided to enhance the security. The stepper motor controls the opening and closing of the ATM door. With help of above-mentioned attributes, law enforcement department authorities can guess the person involved in crime scene. And those suspected person images are captured, who are carrying weapons in an ATM, sent by mail to the authorized person respectively.
An analysis of the locality of binary representations in genetic and evolutionary algorithms

Mutation and recombination operators play a key role in determining the performance of Genetic and Evolutionary Algorithms (GEAs). Prior work has analyzed the effects of these operators on genotypic variation, often using locality metrics that measure the sensitivity and stability of genotype-phenotype representations to these operators. In this paper, we focus on an important subset of representations, namely nonredundant bitstring-to-integer representations, and analyze them through the lens of Rothlauf’s widely used locality metrics. Our main research question is, does strong locality predict good GEA performance for these representations? Our main findings, both theoretical and empirical, show the answer to be negative. To this end, we define locality metrics equivalent to Rothlauf’s that are tailored to our domain: the point locality for single-bit mutation and general locality for recombination. With these definitions, we derive tight bounds and a closed-form expected value for point locality. For general locality we show that it is asymptotically equivalent across all representations and operators. We also reproduce three established GEA empirical results to understand the predictive power of point locality on GEA performance, focusing on two popular and often juxtaposed representations: standard binary and binary-reflected Gray.
Path-Based Degraded Network Interface Card Detection at Mapreduce Startup Phase

The use of distributed system nowadays has vastly increased because of the ability to utilize multiple nodes efficiently on big data processing. One of the widely used distributed system framework is Hadoop. For more than a decade, Hadoop had overcome a single point of failure (SPOF) by using speculative execution (Base SE). A recent study entitled Limplock exposed Base SE's vulnerability on handling node failure caused by damaged network interface card (NIC) in which results in more than 60x performance degradation. This problem is said to be solved with a new version of Base SE, that is Path Based Speculative Execution (PBSE). Our study, however, has found out that PBSE is still unable to detect the existence of nodes with slow NIC at Mapreduce startup and cleanup phase in which leads to up to 300% slowdown. This paper tries to improve the PBSE solution by creating a new node with a slow NIC detection mechanism in the Mapreduce startup phase. The solution is comprised of creation of a new heartbeat to monitor localization process during the Mapreduce startup, adopting path diversity mechanism by PBSE and implementing straggler node detection algorithm. There are still special cases that cannot be handled by this solution, however those special cases hardly occur in big clustered Hadoop. Furthermore, the performance cost of conducting additional this detection mechanism is negligible. Finally, the proposed solution has successfully improved the PBSE solution in detecting the nodes with slow NIC during the Mapreduce startup phase.
Ruolo del transfer e strategie di rifiuto in apprendenti ispanofoni di italiano L2 Uno studio esplorativo

This exploratory study examines strategies of refusals in two groups of peninsular and Ecuadorian Spanish learners of Italian, attending university courses in Italy. The aim of the investigation is to explore the type, the order and the frequency of refusals and the role of transfer in situations characterized by different role relationships among interactants. Data was collected using a Discourse Completion Task and a retrospective interview. Results show that Ecuadorian learners sometimes transfer their L1 patterns into the L2. Outcomes are discussed in light of studies on Politeness Theory.
Reflections on Proof Complexity and Counting Principles

This paper surveys the development of propositional proof complexity and the seminal contributions of Alasdair Urquhart. We focus on the central role of counting principles, and in particular Tseitin’s graph tautologies, to most of the key advances in lower bounds in proof complexity. We reflect on a couple of key ideas that Urquhart pioneered: (i) graph expansion as a tool for distinguishing between easy and hard principles and (ii) “reductive” lower bound arguments, proving via a simulation theorem that an optimal proof cannot bypass the obvious (inefficient) one.
Modeling Analysis of Diachronic Changes in Auxiliary Words in Novels

助詞は文章の表現特性・文体様式の特徴を把握するうえで重要な要素であり,その経時変化の考察は,言語及び文体の変遷過程の一端を明らかにすることにつながる.本研究では,百余年間にわたった近現代小説を分析対象とし,モデリングを通して助詞の経時変化を捉え,小説の言語表現及び文体との関わりについて考察を試みた.具体的には,1910年から2014年に出版された小説のコーパスを作成し,助詞の使用データ状況について計量分析を行った.計量分析には,まず系統樹分析を通して,助詞の使用に明らかな変化が発生していることを確認したうえで,主な変動要素を特定するため,elastic net回帰分析を用いて,助詞に関するモデルを作成し,モデル構築に大きく寄与する助詞項目を抽出し,分析を試みた.観察された結果は言語学や文体学に関わる問題を考えるヒントになり得ることが示された.
An Unsupervised Learning Method for Attributed Network Based on Non-Euclidean Geometry

Many real-world networks can be modeled as attributed networks, where nodes are affiliated with attributes. When we implement attributed network embedding, we need to face two types of heterogeneous information, namely, structural information and attribute information. The structural information of undirected networks is usually expressed as a symmetric adjacency matrix. Network embedding learning is to utilize the above information to learn the vector representations of nodes in the network. How to integrate these two types of heterogeneous information to improve the performance of network embedding is a challenge. Most of the current approaches embed the networks in Euclidean spaces, but the networks themselves are non-Euclidean. As a consequence, the geometric differences between the embedded space and the underlying space of the network will affect the performance of the network embedding. According to the non-Euclidean geometry of networks, this paper proposes an attributed network embedding framework based on hyperbolic geometry and the Ricci curvature, namely, RHAE. Our method consists of two modules: (1) the first module is an autoencoder module in which each layer is provided with a network information aggregation layer based on the Ricci curvature and an embedding layer based on hyperbolic geometry; (2) the second module is a skip-gram module in which the random walk is based on the Ricci curvature. These two modules are based on non-Euclidean geometry, but they fuse the topology information and attribute information in the network from different angles. Experimental results on some benchmark datasets show that our approach outperforms the baselines.
Interdisciplinary connections in teaching mathematical and environmental disciplines to students

This article deals with the implementation of interdisciplinary relations through the problems of applied nature of students studying in the specialty 23.05.04 Operation of railway doro on the example of the implementation of the discipline «Mathematics». The purpose of this article is to teach the student to solve problems of an applied nature that are of interest to the student. When using a subject-based learning system, interdisciplinary connections allow us to solve the existing contradictions between the disparate assimilation of knowledge and the need for their application, both holistic and complex, in the practical life of a person. As a result, knowledge becomes not only concrete, but also generalized, which gives students the opportunity to transfer this knowledge to new situations and apply it in practice. The article substantiates the need for interdisciplinary connections to reflect the integrity of nature in the content of educational material, to create a true system of knowledge and understanding of the world.
Method for the prediction of some time series using small sets of experimental samples

The paper cares with the tactic of prediction of your time series supported the concepts of system identification. The distinctive property of the tactic is that the use of small sets of experimental samples of knowledge . The latter create some basis for building so-called learning subsets, which are wont to construct particular prediction models. Values of variables predicted by different particular models allow calculating the specified variables by employing a batch voting technique. the tactic are often used for short-term prediction of knowledge values at future time instants supported the analysis of a quick history of the method into account . It are often useful in cases of processing very large arrays of knowledge samples, when the researcher has got to confirm his (her) attention to only alittle a part of the samples received at the last instants of your time , in sight of the limited memory of the pc or in cases when very slow processes are analyzed. A special place within the paper is given to the instance during which the computational aspects of the proposed method are considered intimately .
Operation system for home appliances with simple gestures at arbitrary position

In this paper, a novel system that remotely operates home appliances is proposed. The system is based on a command space associated with the operation of the home appliance. Thus, a user is able to control the system with simple hand gestures at an arbitrary position. In the system, a pseudo relative coordinate system which is a user-definable coordinate system is applied. The hand waving gesture is detected from the images acquired by multiple cameras installed in the room. Then, the position of hand waving gesture is calculated using the stereo measurement. Next, the pseudo relative coordinate system is defined according to the calculated position. Therefore, it is possible to operate home appliances freely at an arbitrary position chosen by the user. In addition, home appliances are operated using a hand positioning gesture that requires less operation time and less burden than a hand waving gesture. The hand positioning gesture is detected using the frame subtraction and the background subtraction. Experiments verify the operation accuracy and operation time for commands using the proposed system for detailed operations such as TV channel switching.
“Edmodo, generation Z and mathematics” significant achievement in high school in Peru

During the last years, a series of technological and virtual innovations has been implemented, especially in the education field, most of them have been implemented without the necessary studies. In this paper, we show an analysis of the justifications and the reasons why the educational platform “Edmodo” has been specifically created for the Z Generation. This tool is used by them when they start to pursue their university degree. We conclude that the educational virtual environments must be applied correctly to the corresponding generation.
Paesaggi linguistici e scrittura plurilingue Un’analisi di testi plurilingui di allievi/e delle scuole secondarie di secondo grado

The present research investigates linguistic landscaping in plurilingual written communication. Students were asked to write plurilingual texts, using all the languages available in their repertoire. This resulted in a systematic use of code-switching and translanguaging which both became part of the writing process. The data analysis showed that students used the different languages of their plurilingual repertoire to construct and vary meaning in the texts according to specific needs or requirements, thus creating subjective linguistic landscapes. On the basis of an analysis of 69 texts and 5 stimulated recalls held after the writing workshops, the following research questions were investigated: according to which criteria do students chose languages to communicate in writing? What type of attitudes and dispositions emerge? Which linguistic landscapes can be recognised?
THE ONLINE TEACHING VIS-À-VIS THE CLASSROOM TEACHING (ON THE BACKGROUND OF COVID-19)

The history of mankind will depict 2020 as the beginning of the new era – the era of the pandemic, which has invaded the world countries. The life on the earth has changed drastically. A partial or complete lockdown, numerous infected citizens, an increasing economic crisis, devaluation of a national currency, paralyzed cultural and social lives – this is a partial list of the challenges faced by the majority of the world countries that have made efforts to survive. The paper depicts the challenges of Georgia’s educational space in the environment of the pandemic. The major emphasis is put on the transition from a classroom teaching to an online lecturing as well as on the (dis)advantages of a face-to-face interaction and a virtual communication. Moreover, the paper presents some strategies of the “peaceful” transition (creation of a student-oriented virtual-classroom atmosphere, timely informativeness, usage of appropriate technological means, replacement of written-type exams with hybrid-type examinations, flexible site-oriented means of an extracurricular communication, updated criteria of assessment, etc.) elaborated at the Faculty of Humanities of Ivane Javakhishvili Tbilisi State University. The methodology of research includes observation, analysis and evaluation of the recent processes.