Reviewerly/exHarmony huggingface dataset:
Robust Multi-Class Classification Using Linearly Scored Categorical Cross-Entropy

In this paper, we investigate the robust single-label multi-class classification problems in machine learning using the proposed linearly scored categorical cross-entropy for training data with wrong class labels. Deep neural networks are constructed and trained using different loss functions with various noise levels. CIFAR10 and CIFAR100 image datasets are used to calculate the estimated classification accuracy performances via 10-fold cross validation. From the simulation results, the proposed loss function may actually provide a promising alternative for robust multi-class classification problems.
All-Russian Competition of Scientific Works in the area of Library Science, Bibliography and Book Science as the Reflection of Research Potential of the Scientific Branch

The article considers the history of the All-Russian competition in library science, bibliography and book science. The article gives the historical background of founding of the Competition and the main normative legal acts that laid down the procedure for the organization of scientific research in the library sector. Active participation in the competition of federal libraries, central libraries of the Russian Federation, universities of culture, libraries and organizations of other departments allows us to consider it as a basis for institutional and content analysis of the flow of domestic scientific publications in the field of librarianship. The article analyses the content of the competition winners’ publications in accordance with the evolution of scientific research in the field of librarianship. The set of submitted works is considered as reflection of scientific potential in the library sphere. The authors highlight organizations and scientists who have received the highest professional recognition as winners and who have contributed to the development of library science, bibliography and book science. The resumption of the Competition in 2021 is considered as one of the mechanisms for scientific support of librarianship.
Las redes sociales digitales: una valoración socioeducativa. Revisión sistemática.

The future and already current industrial revolution 4.0 demand the introduction of a digital transformation in the higher education contributing to the formation of competent professionals, for which, they are evidenced in the pedagogies and emergent technologies, an increase of didactic experiences in the use of the digital social networks. In this research a systematic review to identify current trends in the use of online social networks for educational purposes are performed. PRISMA protocol was used and analyzed 79 studies present in the database Scopus. In the systematic review, the following questions are answered: What types of designs predominate in the scientific literature? What is the dependentindependent pairs of variables? And What are the current trends in the use of digital social networks for educational purposes? As a result of this research, Facebook is reaffirmed as the social network most used by educators and the need for the integrated and varied use of these networks. It concludes with the defense of the following trends: university institutional communication policies from the curricular levels; the development of creativity, cultural convergence and media diversification; educational innovation; media culture and academic digital identity
An improved projector calibration method for structured-light 3D measurement systems

Abstract In a structured-light three-dimensional measurement system, understanding the optical configuration of the projector and suppressing the eccentricity error caused by the camera perspective projection are critical to realize high-precision measurement. In this paper, we analyze the special offset optical structure in commercial projectors, where a larger diameter lens is used to ensure the quality of the projected image, and the position of the principal point has been shifted. Meanwhile, a projector calibration strategy that makes the camera’s optical axis perpendicular to the target plane is proposed to avoid the pollution of perspective projection distortion. The sub-pixel correspondence based on homography transformation is performed by relying on the phase value of the fringe, and bundle adjustment optimization is used to further improve the accuracy and robustness of projector calibration. The experimental results demonstrate that the proposed calibration method can improve the calibration accuracy by 52.52%.
Designing for Pandemics - a Design Concept based on Technology Mediated Nudging for Health Behavior Change

This paper addresses the Covid-19 pandemic and the need to find innovative approaches to fight transmission when societies open up.Throughout the pandemic, a number of countries have released mobile applications for contact tracing which has sparked a debate about privacy and ethics.To complement existing solutions, this paper proposes a different approach.This paper presents a design concept for an application promoting health behavior change based on Bluetooth proximity estimation and nudging theory.The approach is underpinned by current understanding of the main transmission routes, the risk of asymptomatic spreaders, and evidence of physical distancing to reduce transmission risk.The aim of this mobile system is to promote physical distancing, in line with public health guidelines promoted all over the globe.The concept stems from design thinking and a shift in perspective: from solutions focused on tracking infections to solutions focused on primary prevention by supporting human behavior.
Practical Multiauthority Attribute-Based Access Control for Edge-Cloud-Aided Internet of Things

With the assistance of edge computing which reduces the heavy burden of the cloud center server by using the network edge servers, the Internet of Things (IoTs) architectures enable low latency for real-time devices and applications. However, there still exist security challenges on data access control for the IoT. Multiauthority attribute-based encryption (MA-ABE) is a promising technique to achieve access control over encrypted data in cross-domain applications. Based on the characteristics and technical requirements of the IoT, we propose an efficient fine-grained revocable large universe multiauthority access control scheme. In the proposed scheme, the most expensive encryption operations have been executed in the user’s initialization phase by adding a reusable ciphertext pool besides splitting the encryption algorithm to online encryption and offline encryption. Massive decryption operations are outsourced to the near-edge servers for reducing the computation overhead of decryption. An efficient revocation mechanism is designed to change users’ access privileges dynamically. Moreover, the scheme supports ciphertext verification. Only valid ciphertext can be stored and transmitted, which saves system resources. With the help of the chameleon hash function, the proposed scheme is proven CCA2-secure under the q-DPBDHE2 assumption. The performance analysis results indicate that the proposed scheme is efficient and suitable in edge computing for the IoT.
Real-Time Data Cleaning in Traffic Sensor Networks

Through deploying Internet of Things (IoT) technologies, many aspects of the urban environment can be monitored in real-time. Mobility, pollution, parking, waste, lighting can be controlled and managed in an intelligent city thanks to a low-cost sensor network. Such big data streams generated in realtime by sensors need to be handled with appropriate techniques to detect erroneous measurements instantly. In this paper, we implement a fast data cleaning process to remove traffic sensor faults. Then, we present a traffic model that takes advantage of the detection of anomalous data measured by traffic sensors. Experiments on a real case scenario have demonstrated that anomaly detection can further improve the performance of a traffic model in emulating the real urban traffic.
Dot-science top level domain: Academic websites or dumpsites?

Dot-science was launched in 2015 as a new academic top-level domain (TLD) aimed to provide 'a dedicated, easily accessible location for global Internet users with an interest in science'. The main objective of this work is to find out the general scholarly usage of this top-level domain. In particular, the following three questions are pursued: usage (number of web domains registered with the dot-science), purpose (main function and category of websites linked to these web domains), and impact (websites' visibility and authority). To do this, 13,900 domain names were gathered through ICANN's Domain Name Registration Data Lookup database. Each web domain was subsequently categorized, and data on web impact were obtained from Majestic's API. Based on the results obtained, it is concluded that the dot-science top-level domain is scarcely adopted by the academic community, and mainly used by registrar companies for reselling purposes (35.5% of all web domains were parked). Websites receiving the highest number of backlinks were generally related to non-academic websites applying intensive link building practices and offering leisure or even fraudulent contents. Majestic's Trust Flow metric has been proved an effective method to filter reputable academic websites. As regards primary academic-related dot-science web domain categories, 1,175 (8.5% of all web domains registered) were found, mainly personal academic websites (342 web domains), blogs (261) and research groups (133). All dubious content reveals bad practices on the Web, where the tag 'science' is fundamentally used as a mechanism to deceive search engine algorithms.
Fast Estimation of Outcome Probabilities for Quantum Circuits

We present two classical algorithms for the simulation of universal quantum circuits on n qubits constructed from c instances of Clifford gates and t arbitrary-angle Z-rotation gates such as T gates. Our algorithms complement each other by performing best in different parameter regimes. The Estimate algorithm produces an additive precision estimate of the Born-rule probability of a chosen measurement outcome with the only source of run-time inefficiency being a linear dependence on the stabilizer extent (with scaling approximately equal to 1.17t for T gates). Our algorithm is state of the art for this task: as an example, in approximately 13 h (on a standard desktop computer), we estimate the Born-rule probability to within an additive error of 0.03, for a 50-qubit, 60 non-Clifford gate quantum circuit with more than 2000 Clifford gates. Our second algorithm, Compute, calculates the probability of a chosen measurement outcome to machine precision with run time O(2t−rt), where r is an efficiently computable, circuit-specific quantity. With high probability, r is very close to min{t,n−w} for random circuits with many Clifford gates, where w is the number of measured qubits. Compute can be effective in surprisingly challenging parameter regimes, e.g., we can randomly sample Clifford+T circuits with n=55, w=5, c=105, and t=80T gates, and then compute the Born-rule probability with a run time consistently less than 10 min using a single core of a standard desktop computer. We provide a C+Python implementation of our algorithms and benchmark them using random circuits, the hidden-shift algorithm, and the quantum approximate optimization algorithm.4 MoreReceived 26 June 2021Revised 24 March 2022Accepted 9 May 2022DOI:https://doi.org/10.1103/PRXQuantum.3.020361Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum simulationQuantum Information
3D pointing gestures as target selection tools: guiding monocular UAVs during window selection in an outdoor environment

Abstract Firefighters need to gain information from both inside and outside of buildings in first response emergency scenarios. For this purpose, drones are beneficial. This paper presents an elicitation study that showed firefighters’ desires to collaborate with autonomous drones. We developed a Human–Drone interaction (HDI) method for indicating a target to a drone using 3D pointing gestures estimated solely from a monocular camera. The participant first points to a window without using any wearable or body-attached device. Through the drone’s front-facing camera, the drone detects the gesture and computes the target window. This work includes a description of the process for choosing the gesture, detecting and localizing objects, and carrying out the transformations between coordinate systems. Our proposed 3D pointing gesture interface improves on 2D interfaces by integrating depth information with SLAM and solving ambiguity with multiple objects aligned on the same plane in a large-scale outdoor environment. Experimental results showed that our 3D pointing gesture interface obtained average F1 scores of 0.85 and 0.73 for precision and recall in simulation and real-world experiments and an F1 score of 0.58 at the maximum distance of 25 m between the drone and building.
Explainable Tree-Based Predictions for Unplanned 30-Day Readmission of Patients With Cancer Using Clinical Embeddings

PURPOSE Thirty-day unplanned readmission is one of the key components in measuring quality in patient care. Risk of readmission in oncology patients may be associated with a wide variety of specific factors including laboratory results and diagnoses, and it is hard to include all such features using traditional approaches such as one-hot encoding in predictive models. METHODS We used clinical embeddings to represent complex medical concepts in lower dimensional spaces. For predictive modeling, we used gradient-boosted trees and adopted the shapley additive explanation framework to offer consistent individualized predictions. We used retrospective inpatient data between 2013 and 2018 with temporal split for training and testing. RESULTS Our best performing model predicting readmission at discharge using clinical embeddings showed a testing area under receiver operating characteristic curve of 0.78 (95% CI, 0.77 to 0.80). Use of clinical embeddings led to up to 23.1% gain in area under precision-recall curve and 6% in area under receiver operating characteristic curve. Hematology models had more performance gain over surgery and medical oncology. Our study was the first to develop (1) explainable predictive models for the hematology population and (2) dynamic models to keep track of readmission risk throughout the duration of patient visit. CONCLUSION To our knowledge, our study was the first to develop (1) explainable predictive models for the hematology population and (2) dynamic models to keep track of readmission risk throughout the duration of patient visit.
Effective feature representation using symbolic approach for classification and clustering of big data

The tremendous growth in the technology has led to the accumulation of enormous Big Data. Techniques that efficiently analyse this Big Data are in great demand. Tweets from Social media and Sensor data are some of the most common forms of Big Data. Machine learning algorithms pave way for researchers to analyze Big Data. Most Machine learning algorithms depend on efficient feature extraction and feature selection for its success. Here, we explore feature selection methods like entropy and Rough set on the sensor data. Also a symbolic approach of feature extraction is proposed which represents both sensor and twitter data efficiently for further data analysis. Some popular classifiers like Naïve Bayes, K Nearest Neighbour, Support Vector Machine and Decision Tree are used for validating the efficacy of the features selected. An ensemble classifier technique is also proposed which is compared with various state of the art ensemble classifiers. Symbolic features perform better than both entropy and Rough set features for sensor data and improves the clustering efficiency of twitter data. The proposed ensemble weighted average classifier on Symbolic features outperform all the other ensemble classifiers and independent classifiers. The results obtained from these methods have the potential to aid the public health surveillance.
Negative correlations can play a positive role in disordered quantum walks

We investigate the emerging properties of quantum walks with temporal disorder engineered from a binary Markov chain with tailored correlation, $C$, and disorder strength, $r$. We show that when the disorder is weak -- $r \ll 1$ -- the introduction of negative correlation leads to a counter-intuitive higher production of spin-lattice entanglement entropy, $S_e$, than the setting with positive correlation, that is $S_e(-|C|)>S_e(|C|)$. These results show that negatively correlated disorder plays a more important role in quantum entanglement than it has been assumed in the literature.
The linear codes of t-designs held in the Reed-Muller and Simplex codes

A fascinating topic of combinatorics is the study of t-designs, which has a very long history. The incidence matrix of a t-design generates a linear code over GF(q) for any prime power q, which is called the linear code of the t-design over GF(q). On the other hand, some linear codes hold t-designs with t ≥ 1. The purpose of this paper is to study the linear codes of t-designs held in the Reed-Muller and Simplex codes. Some general theory for the linear codes of t-designs held in linear codes is presented. Several open problems are also presented.
The Impact of eLearning as a Knowledge Management Tool in Organizational Performance

This paper aims to understand the impact of eLearning capabilities on organizational performance.It also addresses the obstacles of organizational learning using eLearning methods and highlighting some emerging trends and technologies that will impact the eLearning experience in organizations.It examines a brief history of knowledge management and how it is related to learning, organizational learning, and performance.It also explores different eLearning technologies and trends.A systematic literature review was used to examine previous papers between 2016-2020.Results show eLearning can impact organizational performance in many ways, and human factors can be one of the most challenging obstacles in deploying eLearning solutions in organizations, and many emerging eLearning trends were explored including open educational resources, gamification, flipped classrooms, and many others.
Evaluation of Deep Learning YOLOv3 Algorithm for Object Detection and Classification

You Only Look Once version 3 (YOLOv3) is a deep learning model for object detection and classification. It is a single neural network architecture model that uses features from the feeding images and predicts bounding box for all classes of image simultaneously. This paper descript an experimental work for train the deep learning model based on YOLOv3 architecture implemented using Tensor Flow as a deep learning framework. The training process had been done using the data-set PASCAL VOC 2007 and data-set PASCAL VOC 2012 and using The Adaptive Moment Estimation Optimizer (ADM optimizer). The trained model is then tested by using the VOC 2007 test data-set. The final results evaluate the YOLOv3 deep learning model performance for object detection and classification.
Client-driven animated GIF generation framework using an acoustic feature

Abstract This paper proposes a novel, lightweight method to generate animated graphical interchange format images (GIFs) using the computational resources of a client device. The method analyzes an acoustic feature from the climax section of an audio file to estimate the timestamp corresponding to the maximum pitch. Further, it processes a small video segment to generate the GIF instead of processing the entire video. This makes the proposed method computationally efficient, unlike baseline approaches that use entire videos to create GIFs. The proposed method retrieves and uses the audio file and video segment so that communication and storage efficiencies are improved in the GIF generation process. Experiments on a set of 16 videos show that the proposed approach is 3.76 times more computationally efficient than a baseline method on an Nvidia Jetson TX2. Additionally, in a qualitative evaluation, the GIFs generated using the proposed method received higher overall ratings compared to those generated by the baseline method. To the best of our knowledge, this is the first technique that uses an acoustic feature in the GIF generation process.
Distributed Event-Triggered Algorithms for Finite-Time Privacy-Preserving Quantized Average Consensus

In this article, we consider the problem of privacy preservation in the average consensus problem when communication among nodes is quantized. More specifically, we consider a setting where nodes in the network can be curious, while certain nodes in the network want to ensure that their initial states cannot be inferred exactly by these curious nodes. Curious nodes are not malicious, i.e., they try to identify the initial states of other nodes based on the data they receive during their operation (and some of them might even collude) but do not interfere in the computation in any other way. Each node in the network (including curious nodes) can opt to execute a privacy-preserving algorithm (so as not to reveal the initial state it contributes to the average calculation) or its underlying (plain) average consensus algorithm (if privacy is not a concern). We propose two privacy-preserving event-triggered quantized average consensus algorithms. Under certain topological conditions, both the proposed algorithms allow the nodes that adopt privacy-preserving protocols to preserve their privacy and at the same time to obtain, after a finite number of steps, the exact average of the initial states while processing and transmitting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quantized</i> information. We also present illustrative examples and comparisons of our algorithms against other algorithms in the existing literature and discuss a motivating application in which smart meters in a smart grid collect real-time demands in a privacy-preserving manner.