Reviewerly/exHarmony huggingface dataset:
Improving the teleportation of quantum Fisher information under non-Markovian environment

Quantum teleportation is designed to send an unknown quantum state between two parties. In the perspective of remote quantum metrology, one may be interested in teleporting the information that is encoded by physical parameters synthesized by quantum Fisher information (QFI). However, the teleported QFI is often destroyed by the unavoidable interaction between the system and the environment. Here, we propose two schemes to improve the teleportation of QFI in the non-Markovian environment. One is to control the quantum system through the operations of weak measurement (WM) and corresponding quantum measurement reversal (QMR). The other is to modify the quantum system based on the monitoring result of the environment (i.e., environment-assisted measurement, EAM). It is found that, in the non-Markovian environment, these two schemes can improve the teleportation of QFI. By selecting the appropriate strengths of WM and QMR, the environment noise can be completely eliminated and the initial QFI is perfectly teleported. A comprehensive comparison shows that the second scheme not only has a higher probability of success than the first one, but also has a significant improvement of the teleported QFI.
Blockchain Search Using Searchable Encryption Based on Elliptic Curves

Nowadays there are more key technologies, such as cloud computing, big data, blockchain, and different techniques that process the data via a well-established purpose. The security of data should be a significant aspect of any type of system, therefore the systems should implement security techniques that solve this aspect. A relatively new encryption technique is searchable encryption, which enables the user to submit search queries based on chosen criteria (for example, keywords) to the cloud server on which the encrypted data (in the form of encrypted documents) is stored. Another emerging technology is blockchain, a decentralized approach for data sharing, based on a direct communication between two peers. To protect the data and its integrity, blockchain uses cryptographic mechanisms and strong mathematics computations as challenges that need to be solved by the nodes that adheres to the blockchain network. In this paper, we propose a searchable encryption scheme, which can be used in the cloud environment, based on elliptic curves and symmetric bilinear mapping for its algorithms and blockchain to store the indexes structure and to process the search query. Using elliptic curves, the proposed scheme is efficient, as it is proven in the literature that elliptic curve cryptography is usually faster than other encryption systems with the same level of security.
Scientific and Pedagogical Support for Future Primary School Teachers during Teaching Practice

Considering the current reality, when the state needs highly qualified and creative specialists, the problem of scientific and pedagogical support for future primary school teachers’ professional growth during teaching practice is becoming increasingly important.&#x0D; The purpose of the research is to substantiate theoretically the scientific and pedagogical support for future primary school teachers’ professional growth during teaching practice.&#x0D; Methodology. A retrospective analysis of the process of future primary school teachers’ professional growth has been used. We have found that the process of professional growth becomes more effective if certain pedagogical conditions of students’ teaching practice are created and implemented. They facilitate the development of future specialists’ desire and readiness for faithful implementation of their professional duties, professional competence and important personal qualities development, the ability to self-educate, etc. The basis for our research was scientific and pedagogical studies relevant to future primary school teachers’ professional growth during teaching practice. &#x0D; Results. The essence of the concepts “scientific and pedagogical support”, “professional potential” has been defined. The main tasks of future teachers’ preparation for future pedagogical activity with orientation on their professional growth as well as professional potential components and the factors having an impact on their formation and realization have been characterized. It has been determined that teaching practice creates ample opportunities for future teachers’ personal abilities purposeful development, the desire for creativity through their systematic involvement in the various activities of a modern primary school teacher.&#x0D; The directions in which scientific and pedagogical support for future primary school teachers is implemented during the experimental teaching practice “A Day at School” have been stated. It has been defined that the system of creative scientific and pedagogical support for future teachers’ practical training deals with an introduction to pedagogical activities, adaptation to the professional skill requirements since the 1st year of study, professional self-realization, and pedagogical credo approval in the final year of study.&#x0D; Conclusions. Future primary school teachers’ scientific and pedagogical support during teaching practice deals with preparation for practical pedagogical activities, providing scientific and methodological assistance (both University and school teachers). Teaching practice ensures future specialists deepen and expand professional knowledge and skills, revealing their professional potential and facilitating professional growth in general.
Design and practice of website group architecture and security based on data mining technology

research-article Share on Design and practice of website group architecture and security based on data mining technology Author: Wei Zhao Wuxi Vocational Institute of Commerce, China Wuxi Vocational Institute of Commerce, ChinaView Profile Authors Info & Claims ICASIT 2021: 2021 International Conference on Aviation Safety and Information TechnologyDecember 2021 Pages 915–919https://doi.org/10.1145/3510858.3511423Published:04 March 2022Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Personality Determinants of the Forms of Addictive Behavior of Internal Affairs Officers (on the Example of Urgency Addiction)

The article examines the professional activity of the internal affairs officers who often con-centrate on the imposed work schedule, getting out of synch with their inner (body’s) clock. Disturbed synchronisation with one’s natural order is accompanied by psychoemotional stress, the latter becomes chronical and habitual. The study aims at revealing personality determinants of addictive behavior of internal affairs officers. The theoretical conclusions are substantiated with the experimental part of the research during which the following has been accomplished: 1) Relationship between one’s personality determinants and one’s ur-gency addiction has been established; 2) Personality determinants contributing to preven-tion of urgency addiction have been identified. Hence the need for developing one’s own personality determinants that would be able to deal with emergence and development of urgency addiction (positive value orientations, positive view of the future, well-developed personality’s regulatory qualities (flexibility and self-reliance)) during one’s training and performance.
Extended depth of field reconstruction with complex field estimation

The contradiction between high resolution and large depth of field (DOF) of microscopic imaging limits its application in imaging 3D distributed samples. Here, we propose a new technique to achieve extended depth of field reconstruction in bright-field microscopy through complex field estimation. The complex field is estimated by a phase retrieval algorithm with the four illumination modulation patterns and the corresponding captured Fourier intensities as dual constraints. Then, the digital refocusing strategy based on Fresnel propagator is integrated into reconstruction framework to achieve the reconstruction of high-resolution complex-valued information for each part of the sample, which ensures an effective extension of the DOF. The feasibility of the proposed method to maintain high resolution over extended DOF was verified in actual experiments with a tilted USAF target, biological sample and microlens array (MLA). The extended DOF reconstruction provides a robust method for imaging in both industrial inspection and biomedical research.
Multilingual Topic Labelling of News Topics Using Ontological Mapping

The large volume of news produced daily makes topic modelling useful for analysing topical trends. A topic is usually represented by a ranked list of words but this can be difficult and time-consuming for humans to interpret. Therefore, various methods have been proposed to generate labels that capture the semantic content of a topic. However, there has been no work so far on coming up with multilingual labels which can be useful for exploring multilingual news collections. We propose an ontological mapping method that maps topics to concepts in a language-agnostic news ontology. We test our method on Finnish and English topics and show that it performs on par with state-of-the-art label generation methods, is able to produce multilingual labels, and can be applied to topics from languages that have not been seen during training without any modifications.
PMAR: Multi-aspect Recommendation Based on Psychological Gap

Review-based recommendations mainly explore reviews that provide actual attributes of items for recommendation. In fact, besides user reviews, merchants have their descriptions of the items. The inconsistency between the descriptions and the actual attributes of items will bring users psychological gap caused by the Expectation Effect. Compared with the recommendation without merchant’s description, users may feel more unsatisfied with the items (below expectation) or be more impulsive to produce unreasonable consuming (above expectation), both of which may lead to inaccurate recommendation results. In addition, as users attach distinct degrees of importance to different aspects of the item, the personalized psychological gap also needs to be considered. In this work, we are motivated to propose a novel Multi-Aspect recommendation based on Psychological Gap (PMAR) by modelling both user’s overall and personalized psychological gaps. Specifically, we first design a gap logit unit for learning the user’s overall psychological gap towards items derived from textual review and merchant’s description. We then integrate a user-item co-attention mechanism to calculate the user’s personalized psychological gap. Finally, we adopt Latent Factor Model to accomplish the recommendation task. The experimental results demonstrate that our model significantly outperforms the related approaches w.r.t. rating prediction accuracy on Amazon datasets.
Self-directed machine learning

Conventional machine learning (ML) relies heavily on manual design from machine learning experts to decide learning tasks, data, models, optimization algorithms, and evaluation metrics, which is labor-intensive, time-consuming, and cannot learn autonomously like humans. In education science, self-directed learning, where human learners select learning tasks and materials on their own without requiring hands-on guidance, has been shown to be more effective than passive teacher-guided learning. Inspired by the concept of self-directed human learning, we introduce the principal concept of Self-directed Machine Learning (SDML) and propose a framework for SDML. Specifically, we design SDML as a self-directed learning process guided by self-awareness, including internal awareness and external awareness. Our proposed SDML process benefits from self task selection, self data selection, self model selection, self optimization strategy selection and self evaluation metric selection through self-awareness without human guidance. Meanwhile, the learning performance of the SDML process serves as feedback to further improve self-awareness. We propose a mathematical formulation for SDML based on multi-level optimization. Furthermore, we present case studies together with potential applications of SDML, followed by discussing future research directions. We expect that SDML could enable machines to conduct human-like self-directed learning and provide a new perspective towards artificial general intelligence.
Face mask detection using OpenCV

This paper proposes a Face Mask Detection Using OpenCV. This pandemic is causing an overall crisis in medical services. This infection especially spreads through beads which rise out of somebody contaminated with Covid and represents a danger to other people. The danger of transference is most elevated in gathering. one of the agreeable lifestyle choices protected from getting kindled is conveying a facial covering in open domains as proposed by world heath association on this pandemic. We will fabricate a continuous framework to distinguish regardless of whether the individual on the webcam is wearing a mask. we will prepare the facial covering finder model utilizing keras and openCv. A boundary container drawn over the face of the individual portrays weather the man or lady is conveying a mask or not. Assuming an individual's face is saved inside the data set, it distinguishes the name of the individual that isn't conveying facial coverings and an email may be shipped off that singular wariness them that they are not wearing a veils as a method for avoiding potential risk.
Toward Detail-Oriented Image-Based Virtual Try-On with Arbitrary Poses

Image-based virtual try-on with arbitrary poses has attracted many attentions recently. The purpose of this study is to synthesize a reference person image wearing a target clothes with a target pose. However, it is still a challenge for the existing methods to preserve the clothing details and person identity while generating fine-grained try-on images. To resolve the issues, we present a new detail-oriented virtual try-on network with arbitrary poses (DO-VTON). Specifically, our DO-VTON consists of three major modules: First, a semantic prediction module adopts a two-stage strategy to gradually predict a semantic map of the reference person. Second, a spatial alignment module warps the target clothes and non-target details to align with the target pose. Third, a try-on synthesis module generates final try-on images. Moreover, to generate high-quality images, we introduce a new multi-scale dilated convolution U-Net to enlarge the receptive field and capture context information. Extensive experiments on two famous benchmark datasets demonstrate our system achieves the state-of-the-art virtual try-on performance both qualitatively and quantitatively.
Facial Expression Recognition Using Convolutional Neural Network

Facial expression conveys the emotional state of human beings. Facial expressions are a common form of non-verbal communication that helps to transfer necessary information or data from one person to another. However, in today’s world with increasing demand for artificial intelligence, recognition of facial expressions is a challenging task in solving problems related to artificial intelligence, machine learning, and computer vision. In this paper, we present an approach that helps to classify different types of facial expressions using Convolutional Neural Network (CNN) algorithm. The proposed model is a Neural Network architecture that is based on sharing of weights and optimizing parameters using CNN algorithm. Two Models are designed using this algorithm which is named Simple CNN and Improved CNN models having different convolution layers. Architecture designs of these two models are different from each other. The input of our system is grayscale images which consist of expressions of different faces. Using Input as grayscale images, both CNN models are trained and parameters optimized in neural network. Output of system is seven common facial expressions such as happy, anger, sad, surprise, fear, disgust, and neutral. To achieve better experimental results of designed model, graph of loss and accuracy is plotted for both of the above models. The overall simulation results prove that Improved CNN model improves the accuracy of facial expression recognition as compared to Simple CNN model. As result, analysis of confusion matrix is also obtained for Simple CNN and Improved CNN model.
Artificial Intelligence with Enhanced Prospects by Blockchain in the Cyber Domain

The various modern state-of-the-art applications with increasing utility of artificial intelligence (AI) have enabled the human lives to thrive in a prevalent and convenient digital world. It has also led to usage of huge amount of sensors data emanated out of different sources. A precise and scalable data analysis performed by AI to examine the colossal amount of data in a cyber space has potential to open yawing inroads for many future applications. At the same time, blockchain being another promising technology can support a secure decentralized data communication network comprised of diverse independent nodes in a cyber domain. It has potential to overcome prevalent associated challenges of AI through resourceful data sharing. This paper brings out the specific facets of AI in a cyber domain facilitating IoT after giving brief overview on AI and blockchain. A marked progress in cryptographic techniques with strong support of AI in a cyber domain has opened the new avenues in digital applications. This paper aims in exploring viable data communication architecture with support of blockchain and AI by proposing it to support an extensive range of existing and upcoming smart applications. Both qualitative and quantitative examinations have been carried out to deal with computational complications and to evaluate the system performance to support the proposal. Lastly, the paper discusses about the future prospects and the possible challenges to be confronted in the cyber domain over an AI and blockchain integrated data communication network architecture.
Using data visualizations to study digital public spaces

This article reviews the history and current data visualizations in studying digital public spaces. I will discuss the recent development of visualizing raw data numerically, relationally, spatially, and textually. Each method involves different visual representations to integrate data collection with analysis and presentation of results. Through a case study of global Web use, this article also demonstrates a thinking process and analytical workflow to incorporate data visualizations when studying digital public spaces, particularly in the midst of a global crisis.
Reshaping the Smart Home Research and Development in the Pandemic Era: Considerations around Scalable and Easy-to-Install Design

Smart home research has traditionally included visiting participants' homes to build testbed environments and evaluate their experience. However, in-person home deployment poses limitations around scalability and is not a feasible method in the context of the COVID-19 pandemic. The smart home research community is now facing the need to reshape and innovate research methods and design approaches. This study introduces a scalable smart home platform prototype that demonstrates possible solutions to address issues and limitations posed by the pandemic, such as improving package design, enabling user-driven installation, and facilitating remote evaluation and maintenance. The prototype uses off-the-shelf products with specially designed packaging to ensure interoperability as well as ease of shipping and installation. In this study, the prototype kits were shipped to participants' homes to understand and evaluate user perceptions and experiences around installation and initial use. Responses to a post-installation questionnaire and remote monitoring of system status showed that the participants easily completed their self-installation of the prototype without any on-site support. The study also showed potential for a scenario-based evaluation of the prototype using a remote, contactless research procedure.
Object-Centric Unsupervised Image Captioning

Image captioning is a longstanding problem in the field of computer vision and natural language processing. To date, researchers have produced impressive state-of-the-art performance in the age of deep learning. Most of these state-of-the-art, however, requires large volume of annotated image-caption pairs in order to train their models. When given an image dataset of interests, practitioner needs to annotate the caption for each image in the training set and this process needs to happen for each newly collected image dataset. In this paper, we explore the task of unsupervised image captioning which utilizes unpaired images and texts to train the model so that the texts can come from different sources than the images. A main school of research on this topic that has been shown to be effective is to construct pairs from the images and texts in the training set according to their overlap of objects. Unlike in the supervised setting, these constructed pairings are however not guaranteed to have fully overlapping set of objects. Our work in this paper overcomes this by harvesting objects corresponding to a given sentence from the training set, even if they don’t belong to the same image. When used as input to a transformer, such mixture of objects enables larger if not full object coverage, and when supervised by the corresponding sentence, produced results that outperform current state of the art unsupervised methods by a significant margin. Building upon this finding, we further show that (1) additional information on relationship between objects and attributes of objects also helps in boosting performance; and (2) our method also extends well to non-English image captioning, which usually suffers from a scarcer level of annotations. Our findings are supported by strong empirical results. Our code is available at https://github.com/zihangm/obj-centric-unsup-caption .
Optimizing Power Saving in Cloud Computing Environments Through Server Consolidation

In cloud computing, surroundings power saving is a multifaceted challenge, as raising the system reliability, which can directly decrease the carbon dioxide emission and the in-use cost (Parthasarathi and Pamela in IJLTE 017–025 [1]). This paper suggests ways for saving the power through server consolidation in a cloud computing environment by minimizing the unused number of resources. Sever consolidation is a resourceful method that increases the utilization of system resource. Here, two types of energy-aware heuristics are analysed for consolidating the tasks. In this paper, an algorithm called bi-objective server consolidation, combining both heuristics, is suggested in order to increase the energy-efficient consolidation of the server. This method increases the efficiency of energy without reducing the performance of the server.
PetroBERT: A Domain Adaptation Language Model for Oil and Gas Applications in Portuguese

This work proposes the PetroBERT, which is a BERT-based model adapted to the oil and gas exploration domain in Portuguese. PetroBERT was pre-trained using the Petrolês corpus and a private daily drilling report corpus over BERT multilingual and BERTimbau. The proposed model was evaluated in the NER and sentence classification tasks and achieved interesting results, which shows its potential for such a domain. To the best of our knowledge, this is the first BERT-based model to the oil and gas context.