Reviewerly/exHarmony huggingface dataset:
Compact NbN resonators with high kinetic inductance*

We design and fabricate λ /2 coplanar waveguide NbN resonators, the thickness and length of which are only several nanometers and hundred microns, respectively. The quality factor of such compact resonators can reach up to 7.5 × 10 4 at single photon power level at 30 mK with the resonance frequency around 6.835 GHz. In order to tune the resonant frequency, the resonator is terminated to the ground with a dc-SQUID. By tuning the magnetic flux in the dc-SQUID, the effective inductance of the dc-SQUID is varied, which leads to the change in the resonant frequency of the resonator. The tunability range is more than 30 MHz and the quality factor is about 3 × 10 3 . These compact and tunable NbN resonators have potential applications in the quantum information processing, such as in the precision measurement, coupling and/or reading out the quantum states of qubits.
A Comparative Analysis of Similarity Measures in Memory-Based Collaborative Filtering

Recommendation Systems are powerful tools generating relevant suggestions for customers, as support in the decision-making process. The most sensitive step in the recommendation process is the choice of the similarity measure. The goal of this article is to present a detailed analysis of similarity measures applied to memory-based collaborative filtering techniques. Several experiments have been conducted, considering various similarity-based scenarios, to determine which measure fits best in the user-based or item-based context. Moreover, the characteristics of similarity measures and data sets (sparsity, dimensionality) are explored to determine their impact on the recommendation process. Besides, this study provides valuable information that can be used to sustain the choice of similarity measure, which can lead to improved performance of the recommendation system.
Device Type Identification via Network Traffic and Lightweight Convolutional Neural Network for Internet of Things

Device type identification (DTI) is one of the most important techniques for the management of Internet of things (IoT).Recently, deep learning (DL) has been considered as a powerful tools for classification or identification, and some researches have introduced DL into DTI for advanced performance.However, DL-based DTI generally has high computational complexity and large model sizes, which are unsuitable for IoT.Thus, we proposed a lightweight convolutional neural network-based DTI with low computational complexity and few model sizes.In detail, we remove the redundant fully-connected layers and replace common convolution with separable convolution.Simulation results demonstrate that lightweight CNN-based DTI just has less than 5% of computational complexity and model size of CNN-based DTI, though there are less than 0.5% performance gap between them.
Quantization for Uniform Distributions on Hexagonal, Semicircular, and Elliptical Curves

In this paper, first we have defined a uniform distribution on the boundary of a regular hexagon and then investigate the optimal sets of n-means and the nth quantization errors for all positive integers n. We give an exact formula to determine them, if n is of the form $$n=6k$$ for some positive integer k. We further calculate the quantization dimension, the quantization coefficient, and show that the quantization dimension is equal to the dimension of the object, and the quantization coefficient exists as a finite positive number. Then, we define a mixture of two uniform distributions on the boundary of a semicircular disc and obtain a sequence and an algorithm, with the help of which we determine the optimal sets of n-means and the nth quantization errors for all positive integers n with respect to the mixed distribution. Finally, for a uniform distribution defined on an elliptical curve, we investigate the optimal sets of n-means and the nth quantization errors for all positive integers n.
CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU

Homomorphic encryption (HE) and garbled circuit (GC) provide the protection for users' privacy.However, simply mixing the HE and GC in RNN models suffer from long inference latency due to slow activation functions.In this paper, we present a novel hybrid structure of HE and GC gated recurrent unit (GRU) network, CRYPTOGRU, for low-latency secure inferences.CRYPTOGRU replaces computationally expensive GC-based tanh with fast GC-based ReLU , and then quantizes sigmoid and ReLU to smaller bit-length to accelerate activations in a GRU.We evaluate CRYP-TOGRU with multiple GRU models trained on 4 public datasets.Experimental results show CRYPTOGRU achieves top-notch accuracy and improves the secure inference latency by up to 138× over one of the state-of-the-art secure networks on the Penn Treebank dataset.
A Performance Analysis of Adaptive Streaming Algorithms in 5G Vehicular Communications in Urban Scenarios

Adaptive streaming is a technique used in multimedia over computer networks where the source content is encoded to decide about which bit-rate segments to download, through algorithms. The contribution of this work is the investigation about which and why an adaptation algorithm can be running included as service, aiming to relax network congestion while improving the user experience at onboard entertainment services. The goals of this present paper are the multimedia service delivery analysis to improve its efficiency in 5G vehicular communications, also to evaluate the vehicular Internet-based video services traffic in urban scenarios with different vehicle densities, and still to perform the vehicle-communication simulations in the 5G network to estimate the metrics for the algorithm selection. In this proposal, the studied metrics are the number of playback interruptions, video quality, quality of transitions, average buffer level, average bit-rate and bit-rate switching frequency, throughput, unfairness, instability, and inefficiency. The results have shown PANDA as the best adaptive streaming algorithm to perform videos in vehicular communications in urban scenarios.
RETRACTED: Research on College Sports Training Based on Computer Virtual Reality Technology

Abstract First of all, teachers should fully understand the connotation and application advantages of computer virtual reality technology, and explore the goal combined with the network background in the new era, so as to effectively grasp the content of physical education in the new period. Therefore, it is necessary to optimize the design of physical education curriculum. This frame design should focus on the method of avoiding leg muscle strain, which can not only improve students’ core cognition of basketball skills, but also let students understand the special value of the main frame with empathy in space, thus improving students’ self-protection consciousness. Secondly, it is necessary to optimize the sports skills and projects, develop the corresponding hardware equipment in VR technology, so that students can watch the practice skills and action posture in different forms with the help of the equipment. At this time, teachers need to input relevant code programs effectively to clarify the comprehensive value of various images in virtual space technology, so as to gradually improve students’ perception and experience ability. Finally, teachers need to integrate the module attributes of different dimensions, integrate the framework model of physical education into the main database, and effectively formulate a more scientific physical education teaching system with the help of the analysis, integration and diagnosis system of the database.
SENTIMENT ANALYSIS TECHNIQUES TO ANALYZE HSE SITUATIONAL AWARENESS AT OIL AND GAS PLATFORMS USING MACHINE LEARNING

Health Safety & Environment (HSE) situational awareness is a very important aspect of any risky workplace.Negligence in complying with HSE policies and practices might lead to unwanted incidents, critical injuries, death, spread of diseases and environmental pollution.In most corporations, information on HSE related incidents is disseminated through formal channels such as reports.Employees on the other hand frequently use social media to share, complain and discuss HSE-related issues.The issues are discussed through an informal platform, it is difficult to analyze opinions for further action.Therefore, this study will investigate existing sentiment analysis models and formulate a suitable sentiment analysis model using machine learning technique.Through literature review, Naïve Bayes model was found to be the most efficient text classification in sentiment analysis.This technique still needs further enhancement as the accuracy is not within requirement.Upon enhancing the Naïve Bayes model, a better outcome can be attained.
Learning Short-Term Differences and Long-Term Dependencies for Entity Alignment

We study the problem of structure-based entity alignment between knowledge graphs (KGs). The recent mainstream solutions for it apply KG embedding techniques to map entities into a vector space, where the similarity between entities could be measured accordingly. However, these methods which are mostly based on TransE and its variants treat relation triples in KGs independently. As a result, they fail to capture some advanced interactions between entities that are implicit in the surrounding and multi-hop entities: One is the differences between the one-hop and two-hop neighborhood of an entity, which we call as short-term differences, while the other is the dependencies between entities that are far apart, which we call as long-term dependencies. Based on the above observations, this paper proposes a novel approach learning to capture both the short-term differences and the long-term dependencies in KGs for entity alignment using graph neural networks and self-attention mechanisms respectively. Our empirical study conducted on four couples of real-world datasets shows the superiority of our model, compared with the state-of-the-art methods.
Bibliometric study of global information literacy research during 2000-2019

The study examined global publications on information literacy research (9105) using data from the Scopus database during 2000-19. The global literacy research registered (22.17%) annual average growth and averaged (7.48) citations per paper. The 118 countries unevenly participated in global information literacy research and the top 10 countries accounted for (73.86% and 83.52%) share of the global publications and citations. A total of 589 organizations and 855 authors contributed to global information literacy research. The top 20 most productive organizations and authors together contributed (11.17% and 4.74%) share and (14.06% and 7.82%) share to global citations output. Purdue University, USA (111 papers) and the Queensland University of Technology, Australia (76 papers) are the most productive organizations in the world, University of Sheffield, U.K.(20.48 and 2.74) and, University of Arizona, USA (14.25% and 1.91%) are the most cited organization. M. Pinto (48 papers) and A. Lloyd (34 papers) are the most prolific authors and A. Lloyd (35.24% and 4.71%) and R. Hobbs (22.47% and 3%) are the most cited authors in the subject. Reference Services Review (271) and Journal of Adolescent & Adult Literacy (204 papers) were the leading journals publishing on this theme. Journal of Documentation (28.45%) and Journal of Academic Librarianship (19.93%) were the lead journals in terms of citation impact per paper.
GANalyze: Toward visual definitions of cognitive image properties

We introduce a framework that uses Generative Adversarial Networks (GANs) to study cognitive image properties (e.g., memorability, aesthetics, valence). Often, we do not have concrete visual definitions of what these properties entail. Starting from input noise, GANs generate a manifold of natural-looking images with fine-grained differences in their visual attributes. By navigating this manifold, we can visualize what it looks like for a particular GAN-image to become more (less) memorable. Specifically, we trained a Transformer module to learn along which direction to move a BigGAN-image’s corresponding noise vector in order to increase (or decrease) its memorability. Memorability was assessed by an off-the-shelf Assessor (MemNet). After training, we generated a test set of 1.5K “seed images”, each with four “clone images”: two modified to be more memorable (one and two “steps” forward along the learned direction) and two to be less memorable (one and two steps backward; examples in Supplemental). The assessed memorability significantly increased when stepping along the learned direction (β = 0.68, p < 0.001), suggesting training was successful. Through a behavioral repeat-detection memory experiment, we verified that our method’s manipulations indeed causally affect human memory performance (β = 1.92, p < 0.001). The seeds and their clones (i.e., "visual definitions") surfaced candidate image properties (e.g., “object size”, “colorfulness”) that may underlie memorability and were previously overlooked. These candidates correlated with the learned memorability direction. We furthermore demonstrate that stepping along a learned “object size” direction indeed increases human memorability, though less strongly (β = 0.11, p < 0.001). This showcases how the individual, causal effects of a candidate can be studied further using the same framework. Finally, we find that by substituting the Assessor, our framework can also provide visual definitions for aesthetics (β = 0.72, p < 0.001) and emotional valence (β = 0.44, p < 0.001).
KEY COMPETENCIES FORMATION FEATURES AMONG STUDENTS WITH DIFFERENT PROFESSIONAL DIRECTION

The key competencies are considered as an integrated indicator of personal and professional competence formation in future specialists (for example, doctors and employees of the penal enforcement system (UIS)). It is shown that the formation of key competencies in the training process is more effective if the student is characterized by a high professional orientation. Correlation of key competencies at different stages of specialists’ training can serve as an indicator related to the formation level of professional activity internal picture.
Comparison of Naïve Bayes and KNN Algorithms to understand Hepatitis

The heart is the most important organ for humans. The liver functions to neutralize toxins that are in the blood and regulate the composition of blood that contains fat, protein, sugar and other substances. The Hepatitis is the disease that attacks the liver caused by a virus. Hepatitis can be known by holding a laboratory test on the blood. The development of technology and information on hepatitis can be known by the classification and prediction methods. The purpose of this study was to improve the accuracy of the classification of naïve Bayes and KNN algorithms by taking public data from the UCI Repository with total of 155 data, having 19 attributes owned such as Age, Gender, Steroids, Antivirus, Fatigue, Malaise, Anorexia, Big Heart, Heart Company, Spleen, Spiders, Ascites, Varicose, Bilirubin, Alk Phosphate, Shot, Albumin, Protime, Histology, and Class (predictive attribute). Experiments use the confusion matrix to determine the value of accuracy, precision, and recall. The results obtained in experiments using Naïve Bayes algorithm are the level of accuracy of 74.19% and the average level of error 25.81% higher than the K-Nearest Neighbor algorithm the average value is 54.84% and the level of value an average error of 45.18%. From the results obtained that the K-Nearest Neighbor algorithm increases the value of accuracy and the average value of errors from previous studies.
Generating Expressive Correspondences: An Approach Based on User Knowledge Needs and A-Box Relation Discovery

Ontology matching aims at making different ontologies interoperable. While most approaches have addressed the generation of simple correspondences, more expressiveness is required to better address the different kinds of ontology heterogeneities. This paper presents an approach for generating complex correspondences that relies on the notion of competency questions for alignment (CQA). A CQA expresses the user knowledge needs in terms of alignment and aims at reducing the alignment scope. The approach takes as input a set of CQAs as SPARQL queries over the source ontology. The generation of correspondences is performed by matching the subgraph from the source CQA to the lexically similar surroundings of the instances from the target ontology. Evaluation of the approach has been carried out on both synthetically generated and real-word datasets.
Decision Support System for Direct Target Cash Recipients Using the AHP and K-Means Method

&lt;strong&gt;&lt;em&gt;In the Covid-19 pandemic situation, the government organized a Direct Cash Assistance as known as BLT for the people who were affected by Covid-19. The nominal amount of BLT received by the public is Rp. 600,000 per month. But in fact, the amount of BLT quota is not proportional to the list of BLT recipients submitted by regional officials, in this case, RT (Rukun Tetangga). So, to find out who is really suitable or appropriate, a decision-making system is needed so that the deposit of the BLT from the government can be right on target in accordance with the criteria set by the government. Stages of the study began with clustering of weights. After that, it is entered into the system to do a decision support process with AHP (Analytical Hierarchy Process). The target of this research is the application that can be used to assist the government in distributing BLT to be right on target. Based on the functional testing of the system, the functions in the system have been succeeded according to plan. This system has successfully applied the K-Means and AHP methods for decision making, to receive direct cash assistance from the government. This system has a 100% accuracy rate. Where the most important criteria in this system are income with a weight of 0.394142515, ownership of a house with a weight of 0.231035138, number of dependents with a weight of 0.190359096, age with a weight of 0.081077616, employment with a weight of 0.058111736, and weight with a weight of 0, 045273898&lt;/em&gt;&lt;/strong&gt;
Information and Communication Technology Recommendations for the Further Development of a Robust National e-Health Strategy for Epidemics and Pandemics

This paper aims to provide recommendations for information and communication technology strategy that can support government actions to overcome epidemics and pandemics in Indonesia. The method used in this research is a qualitative approach by conducting a narrative review and discussion with four experts in the field of information and communication technology. The results of this study are recommendations related to people, process, technology, and data. The recommendations are inspired by the success of Internet in establishing public and private collaboration. This research is intended to assist the government in formulating information and communication technology policies to support government and health organization actions to overcome epidemics and pandemics.
Exploration of End-to-End Synthesisers for Zero Resource Speech Challenge 2020

A Spoken dialogue system for an unseen language is referred to as Zero resource speech. It is especially beneficial for developing applications for languages that have low digital resources. Zero resource speech synthesis is the task of building text-to-speech (TTS) models in the absence of transcriptions. In this work, speech is modelled as a sequence of transient and steady-state acoustic units, and a unique set of acoustic units is discovered by iterative training. Using the acoustic unit sequence, TTS models are trained. The main goal of this work is to improve the synthesis quality of zero resource TTS system. Four different systems are proposed. All the systems consist of three stages: unit discovery, followed by unit sequence to spectrogram mapping, and finally spectrogram to speech inversion. Modifications are proposed to the spectrogram mapping stage. These modifications include training the mapping on voice data, using x-vectors to improve the mapping, two-stage learning, and gender-specific modelling. Evaluation of the proposed systems in the Zerospeech 2020 challenge shows that quite good quality synthesis can be achieved.
A Deep Learning Approach to Helmet Detection for Road Safety

The rapid growth in the commute and vehicles has made exponential growth in the progress of mankind. This growth besides its positive aspects comes with a concern of saving life on road due to accidents. And, hence the technological advancements in the field of machine learning are required to cope up with the challenges such as road safety and traffic rule violations. According to the survey the majority of the life lost in road accidents is due to the negligence of wearing a helmet on a two wheeler vehicle. The enforcement of the traffic rules regarding this violation proves to be a challenge due to dense population and low rate of detection which is primarily caused by the lack of an automated system to detect the violation and take the necessary action. The growing population and the growing number of vehicles cause the manual systems in place to fail in curbing the issue. The recent advancements in Deep Learning and Image Processing provide an opportunity to solve this problem. This manuscript presents the implementation of a system which detects three objects namely the vehicle, non-usage of a helmet and the number plate of the vehicle under consideration using Tensorflow. Deep learning using the SSD MobileNet V2 is the primary technique used to implement the system. The system has been tested under different use cases with successful results.
Semi-supervised Visual Tracking Based on Variational Siamese Network

For object tracking in a visual dynamic data-driven application systems (DDDAS) framework, visual appearance features can be extracted by the convolutional neural network, which has been shown to provide a robust feature representation. In this paper, a new semi-supervised learning framework, variational Siamese neural network, is developed for visual tracking by combining a Siamese network with a variational autoencoder, which supports both supervised and unsupervised training. The learned features are represented as Gaussian distributions in feature space, and the object is represented as a distribution in image space. The similarity between objects’ features is measured by an information theoretic distance. The tracking algorithm is based on the detection network’s detections to update the object state estimate. Experiment results show that the proposed visual tracking framework outperforms existing state of the art visual tracking approaches.
A New Feature Fusion Network for Student Behavior Recognition in Education

ABSTRACTBehavior recognition is a research hotspot in the field of computer vision and it also is a challenging task. In particular, student behavior analysis has an impact on the efficiency of classroom education. Aiming at the complex student behavior recognition problem in the video, we propose a new feature fusion network for student behavior recognition in education in this paper. The new feature fusion network contains two main stages: feature extraction and classification. First, we combine spatial affine transformation network with convolutional neural network to extract more detailed features. Then, the weighted sum method is adopted to fuse the spatial-temporal features, and the softmax classifier is improved for classification recognition to improve the final recognition result. Experiments are carried out on standard human behavior data HMDB51, UCF101 and real student behavior data. The results show that the proposed algorithm can achieve better recognition effect than other state-of-the-art recognition algorithms.