Reviewerly/exHarmony huggingface dataset:
Pixel-Level Kernel Estimation for Blind Super-Resolution

Throughout the past several years, deep learning-based models have achieved success in super-resolution (SR). The majority of these works assume that low-resolution (LR) images are ‘uniformly’ degraded from their corresponding high-resolution (HR) images using predefined blur kernels—all regions of an image undergoing an identical degradation process. Furthermore, based on this assumption, there have been attempts to estimate the blur kernel of a given LR image, since correct kernel priors are known to be helpful in super-resolution. Although it has been known that blur kernels of real images are non-uniform (spatially varying), current kernel estimation algorithms are mostly done at image-level, estimating one kernel per image. These algorithms inevitably become sub-optimal in handling scenarios where an image is degraded non-uniformly. A divide-and-conquer form of approach, dividing an image into several patches for individual kernel estimation and SR can be a simple solution for this matter. Nevertheless, this approach fails in practice. In this paper, we address this issue by pixel-level kernel estimation. The three main components for training a SR framework based on pixel-level kernel estimation are as follows: Kernel Collage — a method for synthesizing non-uniformly degraded LR images, the indirect loss — a novel loss for training the kernel estimator, and an additional optimization — a scheme to robustify the SR network. Extensive experiments show the superiority of pixel-level kernel estimation in blind SR, surpassing state-of-the-art methods in terms of quantitative and qualitative results.
Implementation of frequency encoded all optical reversible logic

Abstract Reversible gate has been one of the emerging research areas that ensure continual process of innovation trends that explore and utilizes the resources. Due to the increasing power consumption of electronic circuits, it has been observed that quantum computing is one of its latest applications. This technology can be utilized by reducing the energy consumption by preserving the bits of information that are still useful. A photon has zero rest mass, while an electron has a nonzero rest mass. These characteristics inspired the researchers to develop an all-optical Fredkin gate. The proposed gate design overcomes the shortcomings of conventional Fredkin gates and provides better performance.
DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature.

In this work, we present to the NLP community, and to the wider research community as a whole, an application for the diachronic analysis of research corpora. We open source an easy-to-use tool coined DRIFT, which allows researchers to track research trends and development over the years. The analysis methods are collated from well-cited research works, with a few of our own methods added for good measure. Succinctly put, some of the analysis methods are: keyword extraction, word clouds, predicting declining/stagnant/growing trends using Productivity, tracking bi-grams using Acceleration plots, finding the Semantic Drift of words, tracking trends using similarity, etc. To demonstrate the utility and efficacy of our tool, we perform a case study on the cs.CL corpus of the arXiv repository and draw inferences from the analysis methods. The toolkit and the associated code are available here: https://github.com/rajaswa/DRIFT.
Digital Education at University: The Process of Adjustment Under Turbulence

The experience of implementing a blended learning system at the Russian university under the Covid-19 pandemic is presented in this paper. Practical steps for integrating two digital tools, namely the Moodle platform and the MS Teams service, into the learning process are described. An algorithm for proctoring the education quality was developed by the university and the digital skills of teachers and students were assessed in the process of adapting to work in the remote synchronous and asynchronous interaction modes. To ensure effective interaction of learning actors within digital mode specific measures are proposed. The implementation of these measures allowed the university to maintain the quality level of the competencies formation that is determined by the federal state educational standard.
Mode-coupling Crosstalk Compensation through Nonlinear- optical Loop Mirror (NOLM)-based Reservoir Computing

We propose a nonlinear-optical loop mirror (NOLM)-based reservoir computing to deal with the collected data from the experiment of a 3-mode fiber system, enabling the mode-coupling crosstalk compensation with the Q 2 improvement of 11.95dB.
Smart City Construction Visual Simulation Application Based on Intelligent BIM Technology

This paper takes a project comprehensive office building project of Chaoyang financial center as an example, based on the construction visualization requirements of BIM technology, this paper analyzes the modeling requirements and Suggestions applicable to the project. With the help of Revit modeling software, the architecture and structure model of the whole drawing and three floors underground and two floors above ground electromechanical model are completed. There is a collision detection between the structural and electromechanical subject model that be established by Navisworks Manage 2016 software, also, the paper analyzes the causes of the collision and puts forward corresponding solutions, and find out the problems in the model and make changes in time, which prepare the model for the construction visualization simulation application. On this basis, the construction progress plan set up by Microsoft Project 2013 was carried out and related to the three-dimensional (3D) parameterized model components, which implement the visual simulation of the construction process of research and the 4D simulation of construction process of the project. What’s more, the application of visualization simulation based on BIM technology can greatly reduce the occurrence of changes and rework in actual construction, thus greatly improving the efficiency of construction work. 11
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias

Transformers have shown great potential in various computer vision tasks owing to their strong capability in modeling long-range dependency using the self-attention mechanism. Nevertheless, vision transformers treat an image as 1D sequence of visual tokens, lacking an intrinsic inductive bias (IB) in modeling local visual structures and dealing with scale variance. Alternatively, they require large-scale training data and longer training schedules to learn the IB implicitly. In this paper, we propose a novel Vision Transformer Advanced by Exploring intrinsic IB from convolutions, \ie, ViTAE. Technically, ViTAE has several spatial pyramid reduction modules to downsample and embed the input image into tokens with rich multi-scale context by using multiple convolutions with different dilation rates. In this way, it acquires an intrinsic scale invariance IB and is able to learn robust feature representation for objects at various scales. Moreover, in each transformer layer, ViTAE has a convolution block in parallel to the multi-head self-attention module, whose features are fused and fed into the feed-forward network. Consequently, it has the intrinsic locality IB and is able to learn local features and global dependencies collaboratively. Experiments on ImageNet as well as downstream tasks prove the superiority of ViTAE over the baseline transformer and concurrent works. Source code and pretrained models will be available at GitHub.
Caregiver development of activity-supporting services for smart homes

Older adults often need some level of assistance in performing daily living activities. Even though these activities are common to the vast majority of individuals (e.g., eating, bathing, dressing), the way they are performed varies across individuals. Supporting older people in performing their everyday activities is a major avenue of research in smart homes. However, because of its early stage, this line of work has paid little attention on customizing assistive computing support with respect to the specific needs of each older adult towards improving its effectiveness and acceptability. We propose a tool-based approach to allowing caregivers to define services in the area of home daily living, leveraging their knowledge and expertise on the older adult they care for. This approach consists of two stages: 1) a wizard allows caregivers to define an assistive service, which supports aspects of a daily activity that are specific to an older adult; 2) the wizard-generated service is uploaded in an existing smart home platform and interpreted by a dedicated component, carrying out the caregiver-defined service. Our approach has been implemented. Our wizard has been successfully used to define existing manually-programmed, activity-supporting services. The resulting services have been deployed and executed by an existing assisted living platform deployed in the home of community-dwelling individuals. They have been shown to be equivalent to their manually-programmed counterparts. We also conducted an ergonomics study involving five occupational therapists, who tested our wizard with clinical vignettes describing fictitious patients. Participants were able to successfully define services while revealing an ease of use of our wizard.
IoT-edge anomaly detection for covariate shifted and point time series health data

Abrupt distributional covariate shift and abnormal data points can cause anomaly in the time series dataset. Such issue can exaggerate in the internet of things (IoT)-based scenario where continuous data is generated from the low-cost health sensors. Existing anomaly detection techniques mostly rely on the machine learning aware strategies resulting processing and storage overhead in the IoT-based tool chain. In this paper, we perform two experiments on anomaly detection on univarite data such as, (i) anomaly detection of online covariate shift i.e. IoTSAnom method and (ii) seasonal-decomposition by the Loess i.e. STL and piecewise median schemes i.e. IoTSDA method. Firstly, we propose the IoTSAnom architecture to detect online coviarte shift in the univariate time series pulse rate data stream. We implement the ”IRKernel” to investigate the efficiency of exponentially weighted moving average (EWMA)-based light-weight statistical learning by using three variants e.g. probabilistic (P), shift-detect (SD), and two-stage shift-detect (TS-SD) over the EWMA algorithms. We perform evaluation of execution time of the algorithms in classical, incremental, and optimized modes while following the Kolmogorov–Smirnov hypothesis testing at the two-stage anomaly detection. Secondly, the IoTSDA scheme is implemented to support an instantaneous anomaly detection based on the interquartile range and generalized extreme studentized deviate statistics. Both the experimental results show that use of probability, shift-detection scheme, and time-series seasonal-decomposition can detect anomalies. Both the schemes can leverage the IoT-edge analytics for smart health care anomaly detection.
End-to-End Management System Framework for Smart Public Buildings

This paper presents a project aiming to design a complete framework to measure energy (electricity and gas) and water consumptions in a local Parish Council building and an adjacent Sports Hall located in the central part of Portugal. The goal is an end-to-end solution, from data acquisition to data analysis. Besides acquiring and storing the data, the aim is to make this information available and valuable to enhance better decisions in building management actions, to enable detection of situations of anomalous consumption and also to promote building users&#x2019; awareness. To pursue this goal, PLCnext technology solutions from Phoenix Contact are adopted. The system is based on a new generation industrial controller that communicates with energy and water meters distributed throughout the building using a standard Information Technology (IT) network. The solution explores Industry 4.0 concept, such as Cloud Data Management, Cybersecurity, and Machine Learning. With historic consumption records available, Machine Learning strategies are being used to predict load profiles in a short-term horizon and also planned to classify untypical consumption behaviors (for monitor and alarm purposes). This project is being deployed in partnership between Polytechnic of Leiria, EduNet International Education Network and involving the local Parish Council, owner of the monitored buildings.
Blockchain in Supply Chain Management: Characteristics and Benefits

The basis for application of new technologies in organizations is supported by the continuous analysis of data and information from multiple sources. Blockchain is one of the new technologies that, in association with the concepts and principles of Industry 4.0, could generate gains and improvements. Much has been said about the potential of blockchain technology, its benefits, and its disruptive impact in various areas, including supply chain management. In the academic field, however, the development of this topic is in full swing, as a growing number of scientific studies have been published without a theoretical convergence of their foundations, concepts, and authors being noted. Thus, considering the initial stage of the scientific debate of this object of analysis, this research aimed to understand the scenario of the adoption of blockchain in supply chain management based on academic publications evidencing its characteristics and benefits, through a systematic literature review. Due to profound analysis, characteristics and benefits of its application on supply chain management as transparency, confidence, information decentralization, and information security are disclosed in this study.
The space broker

The Internet of Things (IoT) is a major technological development likely to have a profound effect on all aspects of society. Among other things, it promises smooth and personalized interactions between people and the spaces they inhabit and visit. Unfortunately, we are not yet at the point of interacting with smart spaces per se; rather, we simply interact with collections of devices having different interfaces, offered by different manufacturers, living in different administrative domains, and using different apps. The research reported in this paper promises to take us a step closer to achieving the personalized interaction modalities the IoT technology is capable of offering. The starting point is to reimagine the smart space as being defined by spatial characteristics (e.g., illumination, security, temperature, etc.) with most devices receding from the user's explicit awareness. Users can specify their needs from the environment in terms of the abstract characteristics. Key to accomplishing this is the introduction of the concept of the Space Broker, a software agent that manages available devices so as to meet user requirements expressed in terms of spatial characteristics.
Utilising Machine Learning Against Email Phishing to Detect Malicious Emails

Phishing is an identity theft evasion strategy used in which consumers accept bogus emails from fraudulent accounts that claim to belong to a legal and real company in the effort to steal sensitive information of the client. This act places many users’ privacy at risk, and therefore researchers continue to work on identifying and improving current detection instruments. Classification is one of the machine learning methods that can be used to detect emails received. Different classification algorithms such as Naive Bayes and Support Vector Machine (SVM) are discussed and compared in the course of this study. In an integration of the monitored and unregulated strategies, a new method has been developed to detect phishing emails. The research also contrasts the collection classes for manual and automatic emails. Series of terms are used to acquire words to differentiate between malicious and non-malicious communications in this research. In predicting the class attribute, the exactness of the different classifiers has been compared. SVM approach has the most reliable classification and misclassification rates of malicious emails than the Naive Bayes method. To date, 98% precision was achieved, but if a researcher has a big corpus of training data, it can also be increased further. This research aims to investigate whether email phishing during a pandemic has been accelerated and the proposed research highlights that the phishing sensitivity is focused on the protocols utilised in this research. The key purpose is to express a technique or algorithm for the dissection of mailbox information in order to identify it as phishing or to include a genuine email. Machine Learning is a part of Artificial Intelligence (AI), which uses the knowledge mining method to recognise new or current trends (or highlights) of a data set which is then used for characterisation purposes. This study will discuss the advancement and types of phishing attacks. It will examine the Machine Learning techniques and methods which are currently being utilised. The researcher will further analyse a structure on how to avoid phishing as well as recommending methods which can be improved upon for email phishing. Furthermore, the important role of human behaviour is highlighted i.e., working from home during the pandemic.
Swedish Student-Teachers in Digital Activities: Digital Competence Through Development-Oriented Thesis Projects

This article defines attempts using development-oriented thesis projects to increase teachers’ and pupils’ digital capability. To offer a more practice-oriented focus in the teacher education, the elementary school student-teachers were stimulated to participate in thesis projects with the purpose of developing the school events.Thirteen of the development-oriented thesis projects carried out during 2015-2018 involved testing the student-teacher’s ability to study and formulate the competence needs regarding digital learning at the practicum-school, as well as the results of carrying out activities for increasing the digital competence. The investigation is based on a review of completed thesis projects, process journals, and presentations and discussions in subsequent reports. An initial analysis of the thirteen development projects reveals two clear goal directions. One focus is on traditional knowledge goals and the other on more social goals. The outcomes clearly show that development-oriented thesis projects can be an effective way to increase the digital skills of teachers and pupils. Projects with distinct goals for collaboration and shared learning have reached further goal attainment than the projects focused more on discrete instruction and learning.When digital tools were used as a means to work with another area, for example, physical activity or democracy issues, the developed competence in digital skills became more pronounced and lasting. Digital competence is an important development area for school activities, and this study shows that development-oriented thesis projects can be an effective means toward a successful project.
Corrigendum: Concise and tight security analysis of the Bennett-Brassard 1984 protocol with finite key lengths (2012 New J. Phys.14 093014)

Abstract The paper (Hayashi M and Tsurumaru T 2012 New J. Phys. 14 093014 ) contains an error at the definition of g t ( x ) in section 5.3 while g t ( x ) is used for the second order asymptotic expansion for the sacrifice bit. This corrigendum corrects this error and shows its derivation.
Application of Information Technology in English Network Blended Learning

Higher vocational education has a great influence on the development of our country's education. It is the core task of higher vocational colleges to train high-quality students. The educational level of our country's higher vocational colleges needs to be improved, and education informatization is an important factor in breaking the bottleneck of our country's educational reform and development. In higher vocational education, English subjects play an important role in improving students' comprehensive quality, lifelong career development, and being able to have an international perspective. Therefore, the main research content of this article is the application of informatization teaching in higher vocational English teaching. This article first conducts a quick analysis of the related use of information technology in various fields of society; then this article introduces constructivist learning theory in detail and constructivist learning theory for higher vocational English teaching the enlightenment of the reform; then use constructivism and information technology to establish a mixed vocational English course teaching model; and conduct experiments on 120 students in a domestic vocational college. The experimental results show that the mixed vocational English course teaching mode can improve students' English performance, and the students are still very satisfied with the new teaching mode.
A cross-linguistic investigation of acoustic voice spaces

This study examines the extent to which the phonological structure of a language impacts acoustic variation in voice spaces for individuals and populations of speakers. Our recent work on two typologically different languages, American English and Seoul Korean, showed striking similarities in the acoustic spaces derived from Korean and English voices, but also notable differences in the importance of F0 and variability in vowel quality that were related to phrasal intonation patterns present in the Seoul dialect, but not in English. The present study examined eight speakers (5F) of Hmong, a language with tone and phonation contrasts. We performed principal component analysis on scaled values of F0, formants, spectral noise, source spectral shape, and their variability, measured from vowels, and compared results to Korean and English (which lack tone and phonation contrasts). Results revealed both substantial similarities and differences across languages. F0 commonly emerged only for Hmong and Korean voices, and H1-H2 (correlated with phonation) accounted for substantial variation only in Hmong voices. Our findings suggest that both biologically and phonologically relevant factors shape acoustic voice spaces, and point to a potential mechanism for the “own language” advantage in speaker perception. [Work supported by NIH/NSF.]
INCREASING ACCESS TO TECHNOLOGY IN EDUCATION IN NICARAGUA

K. Christopher1, P. Lane 2, R. Dormus3 1Grand Valley State University (UNITED STATES) 2Grand Valley State University/UNAN Managua (UNITED STATES) 3FAREM Esteli of UNAN Managua (NICARAGUA)
Virtual Education of Students during Coronavirus 2019 Epidemic: Problems and solutions

The article's abstract is not available.
Finding "retro" places in Japan

Understanding the ambience of an area is essential for making various geographical decisions. This kind of ambience (e.g., "beautiful," "quiet," "happy," "retro," etc.) is due to not only physical features, such as scenery and functionality, but also subjective information based on people's perceptions and experiences. Although scholars in various fields have attempted to quantitatively measure subjective human experiences, the amount of location-based subjective data varies depending on the area. Thus, we have proposed a method to quantify individuals' perceptions of urban ambiences, as well as a means to anticipate these perceptions from landscape images. In this study, we have focused on the "retro" ambience of old Japanese cities. First, we used crowdsourcing to assign ambience scores to the cities of Nara and Kyoto, the ancient capitals of Japan, based on the level of perception of retro people in the landscape images. Further, we trained a deep learning model to estimate the ambience score of unknown landscape images using previously labelled data. Finally, we discussed the utility of our method for routing applications and exploring its generalizability across different cities.