Reviewerly/exHarmony huggingface dataset:
ICT Tools and Perceived Benefits in the Learning Process of Korean Nursing Students

This research study, a one-shot survey design, conducted in December 2020, examined the utilization and the perceived benefits of Information and Communication Technology (ICT) Tools in the learning process of nursing students in a private university in South Korea. The top-rated technological tools are digital books, nursing software, simulation, and multimedia. Laptop, desktop, tablet, and mobile are highly utilized. Students find these to be quick ways to access nursing information. ICT utilization by the nursing students is similar regardless of sex and grade level. Significant differences in perceptions of ICT benefits were noted by grade level, with fourth and third-grade students expressing more benefits to the utilization of ICT tools than second and first-grade students. Collaboration and communication are enhanced in an ICT-based learning environment, and interactions are facilitated, as well as opportunities for growth and engagements are created. Higher ICT utilization in nursing education is greatly beneficial to students. A nurse&#x2019;s education must integrate technology to prepare them for work in an increasingly technologically-rich environment.
Information and logical model of digitalization of education process in universities

The article describes the teaching model in terms of digitalization of universities. Digitalization of universities in this case means the work of university personnel with automated digital algorithms in order to successfully solve modern educational problems. The principles that create such a model are indicated, the goals facing the teacher are formulated depending on the level of knowledge transmitted to students, as well as the conditions for the effectiveness of training, depending on the degree of individualization of the approach. The data at the initial level of student learning and, accordingly, the required level of acquired knowledge have been found important. When compiling the model, it was taken into account that a small level of individualization is inherent in activities in the form of demonstration and modeling, therefore, it implies the student's independent control of the objects and processes being demonstrated, in particular, change of their parameters, establishing different degrees of detail, and forming spatial-temporal modes. The scenario of interaction between the teacher and the student includes the following actions: transfer of control; assessment of the student's condition according to the given parameters and comparison of the given educational situation with the set of provided answers; analysis of the answers, requests or reactions of the student; providing assignments or messages; collection of educational information; creation and delivery of system comments. The result of the work is the author's model itself, which includes a scenario of interaction between a teacher and &#x0D; a student; a scheme for identifying the uniformity of signs and organization of teaching in the digitalization of universities; the components of the model of the object of study; automation of task development; the algorithm of the model infrastructure, consisting of the blocks Training, Final Control, Instructing, and a block of methodological consulting. This model will contribute to solving problems in the educational system that arise in connection with the factors leading to forced long interruptions in full-time education.
Statistical Racing Crossover Based Genetic Algorithm for Vehicle Routing Problem

Genetic algorithms are modular metaheuristics simulating the evolutionary process over a solution set. The optimization is very adaptive but slow, making statistical research difficult. In this paper an algorithm is proposed where different variants are racing against each other while statistics are gathered. Our results show that this algorithm is an efficient, standalone, and even more adaptive solution. Those variants that result in faster convergence lead the race, but get stuck in local minima. In these cases, the more agile combinations with slower convergence gain higher probability and find better solutions farther from the local minimum. The hybrid is capable of faster convergence with minimal additional runtime. We also provide complexity estimations for resource requirements.
SSAP: Storylines and Sentiment Aware Pre-Trained Model for Story Ending Generation

As an interesting but under-explored task, story ending generation aims at generating an appropriate ending for an incomplete story. The challenges of the task are to deeply understand the story context, mine the storylines hidden in the story, and generate rational endings in logic and sentiment. Although existing pre-trained approaches have been proven effective to this task, how to learn to generate endings with appropriate plots and sufficient sentimental information still remains a major challenge. One possible reason is that an over reliance on external commonsense knowledge beyond the storylines and sentimental trends information hidden in the story context could lead to generation deviating from the main theme. To address this issue, we propose a two-stage <b>S</b>troylines and <b>S</b>entiment <b>A</b>ware <b>P</b>re-trained model (SSAP) for generating sentimentally relevant story endings. We apply a classifier for discriminating the sentiment of the story, and then employ a pre-trained language model, combining with storylines information, to conditionally generate sentences that match both the logic and sentiment of the story. Automatic and manual evaluations show that, without integrating external knowledge, our model can produce more consistent and diverse story endings than state-of-the-art baselines.
Research on path planning of vehicle dynamic obstacle avoidance based on improved RRT algorithm

In view of the situation that the traditional path planning algorithms have the disadvantages of local optimal solution or the generated path is not smooth, on the one hand, this paper combines Artificial Potential Field with Rapidly Randomexploring Trees to improve the efficiency of path planning and solve the problem of local optimal solution; on the other hand, in order to ensure that the algorithm can be used in vehicle obstacle avoidance, the calculation formula of safe distance to obstacles is modified to make the obstacle avoidance process conform to vehicle driving behavior. At the meanwhile, in the exploring rules of RRT algorithm, vehicle dynamics constraints are added to make the path smooth and meet the maximum steering limit. In addition, driving distance information is added to each node of the path to deal with dynamic obstacles. Finally, the simulation results show that the path planned by this method can guide the vehicle to avoid dynamic obstacles smoothly.
Fault Detection Method of Railway Communication Signal Transmitting Equipment Based on a Wavelet Singular Transform

The traditional fault detection method of railway communication signal transmitting equipment has a poor fault fluctuation detection effect, resulting in an increase in detection error. To solve this problem, a fault detection method of railway communication signal transmitting equipment based on wavelet singular transform is designed. Analyze the communication signal of signal transmitting equipment, determine the fault detection singularity of communication signal, calculate the equipment fault function by wavelet singular transform, and carry out the singularity transform of Li index of fault equipment detection, to realize the fault detection of railway communication signal transmitting equipment. Comparative experiments show that the fault detection error of this method is small, which is of great popularization value.
Indexers

Indexers allow an object to be treated as an array. They are declared in the same way as properties, except that the this keyword is used instead of a name and their accessors take parameters. In the following example, the indexer corresponds to an object array called data, so the type of the indexer is set to object:
Based on Max-Min Framework Transferable Adversarial Attacks

Though deep neural networks perform challenging tasks excellently, they are susceptible to adversarial examples, which mislead classifiers by applying human-imperceptible perturbations on clean inputs. Under the query-free black-box scenario, adversarial examples are hard to transfer to unknown models, and several methods have been proposed with the low transferability. To settle such issue, we design a max-min framework inspired by input transformations, which are beneficial to both the adversarial attacks and defenses. Explicitly, we decrease loss values with inputs’ affine transformations as a defense in the minimum procedure, and then increase loss values with the momentum iterative algorithm as an attack in the maximum procedure. To further improve the transferability, we determine transformed values with the max-min framework. Extensive experiments on the ImageNet dataset demonstrate that our defense-guided transferable attacks achieve impressive promotion on transferability. Experimentally, we show that the attack success rate of our method reaches to 58.38% on average, which outperforms the state-of-the-art method by 12.1% on the normally trained models and 11.13% on the adversarially trained models. Additionally, we provide a novel insight on the improvement of transferability, and our method is expected to be a benchmark for assessing the robustness of deep models.
Cross-hospital Access of Electronic Medical Records on Blokckchain Platform

The situation that patients' electronic medical records are stored in different hospitals is a major barrier to speedy access of treatment history in emergency. In addition, traditional storage systems built on centralized databases suffer from unauthorized data access and tampering. Blockchain technology can provide a distributed and safe storage and secure data sharing platform for encrypted medical data with a specialized access control schema which is similar to the CP-ABE schema. This paper describes the preliminary work on a blockchain network design that can store and share electronic medical records with cross-hosnital access schema.
Papier-mash-é

A comment about the new environmentally sound packaging used to send copies of Physics World, which sadly failed the Storm Barra test.
A reinforcement learning framework for pooled oligonucleotide design

The goal of oligonucleotide (oligo) design is to select oligos that optimize a set of design criteria. Oligo design problems are combinatorial in nature and require computationally intensive models to evaluate design criteria. Even relatively small problems can be intractable for brute-force approaches that test every possible combination of oligos, so heuristic approaches must be used to find near-optimal solutions.We present a general reinforcement learning (RL) framework, called OligoRL, to solve oligo design problems with complex constraints. OligoRL allows 'black-box' design criteria and can be adapted to solve many oligo design problems. We highlight the flexibility of OligoRL by building tools to solve three distinct design problems: (i) finding pools of random DNA barcodes that lack restriction enzyme recognition sequences (CutFreeRL); (ii) compressing large, non-degenerate oligo pools into smaller degenerate ones (OligoCompressor) and (iii) finding Not-So-Random hexamer primer pools that avoid rRNA and other unwanted transcripts during RNA-seq library preparation (NSR-RL). OligoRL demonstrates how RL offers a general solution for complex oligo design problems.OligoRL and all simulation codes are available as a Julia package at http://jensenlab.net/tools and archived at https://archive.softwareheritage.org/browse/origin/directory/?origin_url=https://github.com/bmdavid2/OligoRL.Supplementary data are available at Bioinformatics online.
Quantum Optics

Basic concepts of quantum optics are introduced in this chapter. In the beginning, we give a review of the basic quantum mechanics. Then, we perform quantization of an electromagnetic field, which naturally introduces the concept of mode and quadrature. After that, we introduce basic quantum states of light and their phase space representation. Quantum operations in the continuous-variable system and their universality are also explained. To obtain the values of the physical quantities, we have to measure our quantum systems. In the optical continuous-variable system, homodyne measurement and photon number measurement are among the most common measurements that are also discussed here. Finally, we introduce the concept of quantum entanglement, which is one of the central concepts in quantum computation.
ON THE INFLUENCE OF GAIT ON TRACTION DYNAMICS OF WALKING ROBOTS

A research method of the effect gait of movers, discretely interacting with the supporting surface, on the energy characteristics of mobile robot motors is considered.
Decomposing and Recomposing Event Structure

Abstract We present an event structure classification empirically derived from inferential properties annotated on sentence- and document-level Universal Decompositional Semantics (UDS) graphs. We induce this classification jointly with semantic role, entity, and event-event relation classifications using a document-level generative model structured by these graphs. To support this induction, we augment existing annotations found in the UDS1.0 dataset, which covers the entirety of the English Web Treebank, with an array of inferential properties capturing fine-grained aspects of the temporal and aspectual structure of events. The resulting dataset (available at decomp.io) is the largest annotation of event structure and (partial) event coreference to date.
Application of Active Learning in Face Liveness detection

Face anti-fraud attack detection is mainly to help the face recognition system determine whether the collected face is the user's face or a printed photo, recorded video, 3D mask and other forgeries, so it is also called liveness detection. This technology is essential for the security of mobile phone unlocking, access control, and face payment. So how to automatically and efficiently distinguish the authenticity of images and resist spoofing attacks to ensure system security has become an urgent problem in face recognition technology. This article aims to study the application of active learning in face detection. Through the third-party library of python video processing, from six (three real faces, three fake faces) videos, three kinds of face images of different races and different ages are intercepted. Then through the CNN network, pool-based active learning was constructed, and it was run on the colab platform. By comparing the results obtained by selecting an example with a smaller LMU value and randomly selecting an example, the superiority of the LMU algorithm was shown, and after 350 epochs on the self-made data set, the accuracy rate is close to 90%, and as the training continues, the accuracy rate will be further improved.
Optimal Mining on Type Control policy for Cloud Environment

In order to strengthen the cloud system’s ability to type control data resources, The information flow control model can more effectively protect the confidentiality and integrity of users' data in the cloud environment, and can prevent system vulnerabilities or attacks by illegal users outside the cloud system. However, in the process of implementing the type control system, too much manpower is often required to formulate rules to divide user security type labels. Unreasonable security type label division will directly affect system security and usability. In order to solve the problem of information flow control security type label distribution in the process of authorization system migration in a scientific way. This chapter proposes a bottom-up information flow access control security type label optimization mining method. The category domain label mining algorithm based on Louvain community discovery algorithm and the secret level mining algorithm based on genetic algorithm are used to obtain the optimal approximate solution to Information flow control security type label optimization mining problem (IFCSLMP). The results show that the proposed scheme can effectively dig out the effective information flow control security type label from the access control matrix.
SENTIMENT ANALYSIS OF INDONESIA’S DIGITAL WALLET USING COMBINATION MACHINE LEARNING AND EMOTICON WEIGHT

Opinions on social media can be used to determine user sentiment by using sentiment analysis concept. Sentiment analysis requires several important stages, namely, preprocessing, feature extraction and classification method stages. The preprocessing stage was carried out to eliminate inconsistent data. In previous research, punctuation marks removal was applied at the preprocessing stage which can eliminate the emoticon position. Emoticons are a combination of punctuation marks. According to previous research, the emoticon feature has no contribution in sentiment analysis. There is another suggestion to maintain an emoticon position like converting an emoticon into a more relevant word such as :) into a “smile”. However, the feature of emoticon weights has not been considered in the sentiment analysis process. In order to consider the role of emoticons and to improve sentiment analysis performance, we propose using a combination of machine learning and emoticon weights. We perform emoticon weight based on probability and sentiment score. Each probability value and sentiment score of the emoticon will be normalized using the z-score method. There are several machine learning methods that have the best classification success rates, namely, Naïve Bayes and SVM. Based on the evaluation results of the proposed model, the best accuracy is 87% - 89% when using the combination of machine learning and emoticon sentiment score. Based on the results also show that the emoticon sentiment score has a significant effect on the accuracy of sentiment analysis.
On the Error Rate for Classifying Point Processes

Point process data is increasingly occurring in a wide range of applications including social media. But basic problems such as classifying point processes have not been addressed. Here we study the misclassification error of a point process Bayes rule/likelihood ratio classification rule in a binary classification problem. We first develop the Bhattacharya bound for the error rate for the time-varying Poisson. We then derive a tight computable bound on the Bhattacharya bound for the renewal process. We then develop fast methods for computing these bounds. We study the accuracy of the bounds with some comparative simulations.
Blended Learning: Design and Organization Features on the Basis of the Use of Online Training Technologies

In the context of the digitalization of the economy, fundamentally new approaches are required in the training of engineers based on the capabilities of both modern digital technologies and the digital environment. The modern volume and quality of digital technologies create conditions for the development of new ways of their use in education, as well as the application of world experience in innovative solutions in the field of digitalization of education. One of the key peculiarities of open education is the widespread use of modern digital technologies in the learning process, open educational resources (OER), massive open online courses (MOOCs). There are two trends in the implementation of the concept of open education. The first trend is to use the advantages of OER and MOOCs to maximize access to the world’s highest quality educational services. To solve this problem, it is necessary to develop methods for the use of OER materials in education. The second trend is to create conditions for the design and constant updating of the personal educational environment of teachers and students based on modern digital tools. The use of OER and online learning technologies in the educational process have not become widespread yet. In fact, there is not enough attention which is paid to methodological support for the integration of both educational and digital technologies. These problems can be solved using blended learning technology, which allows combining the advantages of digital and pedagogical technologies, overcoming their disadvantages. Thus depending on the tools used in the digital educational environment, the nature of their application in the educational process, as well as on the degree of restructuring of the educational process, various options for the design and organization of blended learning can be developed. The obtained results of the study allowed us to propose the following recommendations: conducting an information campaign to reveal the prospects, advantages, strategies in the use of OER and MOOCs in higher education; creation of conditions for the development of innovative approaches to the use of digital technologies, the creation of methodological support for blended learning models, the processes of integrating both educational and digital technologies; for the continuous development and renewal of the competencies of teachers and students in the field of using digital resources.
Privacy-preserving Motion Detection for HEVC-compressed Surveillance Video

In the cloud era, a large amount of data is uploaded to and processed by public clouds. The risk of privacy leakage has become a major concern for cloud users. Cloud-based video surveillance requires motion detection, which may reveal the privacy of people in a surveillance video. Privacy-preserving video surveillance allows motion detection while protecting privacy. The existing scheme [ 25 ], designed to detect motion on encrypted and H.264-compressed surveillance videos, does not work well on more advanced video compression schemes such as HEVC. In this article, we propose the first motion detection method on encrypted and HEVC-compressed videos. It adopts a novel approach that exploits inter-prediction reference relationships among coding blocks to detect motion regions. The partition pattern and the number of coding bits of each detection block used in prior art are also used to help detect motion regions. Spatial and temporal consistency of a moving object and Kalman filtering are applied to segment connected/merged motion regions, remove noise and background motions, and refine trajectories and shapes of detected moving objects. Experimental results indicate that our detection method achieves high detection recall, precision, and F1-score for surveillance videos of both high and low resolutions with various scenes. It has a similarly high detection accuracy on encrypted and HEVC-compressed videos as that of the existing motion detection method [ 25 ] on encrypted and H.264-compressed videos. Our proposed method incurs no bit-rate overhead and has a very low computational complexity for both motion detection and encryption of HEVC videos.