Reviewerly/exHarmony huggingface dataset:
Quantum Anonymous Private Information Retrieval for Distributed Networks

Quantum cybersecurity is the study of all facets regarding the security of communication and computation in a distributed network. Significant developments in quantum technologies have outclassed their classical counterparts, thus envisioning the realization of a quantum internet. However, such quantum resources, in the hands of an adversary, can jeopardize network security. In this paper, we study two secure network connectivity concerns, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">privacy</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">anonymity</i> , in quantum information retrieval systems. To this end, we propose a state-of-the-art single-server multi-user quantum anonymous private information retrieval (QAPIR) protocol. To actualize this, we utilize anonymous entanglement as a quantum resource. We show that the QAPIR protocol not only provides privacy but also introduces anonymity as an added layer of security in quantum networks. Furthermore, we also detail a comparative security analysis that establishes the desirable properties of our proposal.
Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes

In this paper, we study an important problem of parsing contextual information from user reviews for recommendation purposes. First, we study the ways contextual information is expressed in user reviews and obtain novel insights about it. Among other things, we demonstrate that such type of information tends to appear at the beginning of the review, in longer sentences, in the sentences written in the past tense or using gerund form, and in the sentences referring to some points in time. Second, we propose a novel context parsing method for systematically extracting contextual information from user-generated reviews that rely on the insights obtained in our study. We apply the proposed method to three different Yelp applications (restaurants, hotels, and beauty &amp; spas) and demonstrate that it works well and leads to better recommendation performance than the baseline approaches. Our method systematically extracts more comprehensive sets of relevant contextual variables and corresponding phrases than the baselines. Our analysis also shows the importance of the newly discovered contextual information for recommendation purposes. The obtained results and the proposed method can help to get more comprehensive knowledge about contextual variables in a given application that leads to better recommendations.
Trapped Ion Quantum Computing Using Optical Tweezers and Electric Fields

We propose a new scalable architecture for trapped ion quantum computing that combines optical tweezers delivering qubit state-dependent local potentials with oscillating electric fields. Since the electric field allows for long-range qubit-qubit interactions mediated by the center-of-mass motion of the ion crystal alone, it is inherently scalable to large ion crystals. Furthermore, our proposed scheme does not rely on either ground state cooling or the Lamb-Dicke approximation. We study the effects of imperfect cooling of the ion crystal, as well as the role of unwanted qubit-motion entanglement, and discuss the prospects of implementing the state-dependent tweezers in the laboratory.
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training

Recent years have witnessed increasing interest in code representation learning, which aims to represent the semantics of source code into distributed vectors.Currently, various works have been proposed to represent the complex semantics of source code from different views, including plain text, Abstract Syntax Tree (AST), and several kinds of code graphs (e.g., Control/Data Flow Graph).However, most of them only consider a single view of source code independently, ignoring the correspondences among different views.In this paper, we propose to integrate different views with the naturallanguage description of source code into a unified framework with Multi-View contrastive Pre-training, and name our model as CODE-MVP.Specifically, we first extract multiple code views using compiler tools, and learn the complementary information among them under a contrastive learning framework.Inspired by the type checking in compilation, we also design a fine-grained type inference objective in the pre-training.Experiments on three downstream tasks over five datasets demonstrate the superiority of CODE-MVP when compared with several state-of-the-art baselines.For example, we achieve 2.4/2.3/1.1 gain in terms of MRR/MAP/Accuracy metrics on natural language code retrieval, code similarity, and code defect detection tasks, respectively.
Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems

User simulation has been a cost-effective technique for evaluating conversational recommender systems. However, building a human-like simulator is still an open challenge. In this work, we focus on how users reformulate their utterances when a conversational agent fails to understand them. First, we perform a user study, involving five conversational agents across different domains, to identify common reformulation types and their transition relationships. A common pattern that emerges is that persistent users would first try to rephrase, then simplify, before giving up. Next, to incorporate the observed reformulation behavior in a user simulator, we introduce the task of reformulation sequence generation: to generate a sequence of reformulated utterances with a given intent (rephrase or simplify). We develop methods by extending transformer models guided by the reformulation type and perform further filtering based on estimated reading difficulty. We demonstrate the effectiveness of our approach using both automatic and human evaluation.
Recognizing Students and Detecting Student Engagement with Real-Time Image Processing

With COVID-19, formal education was interrupted in all countries and the importance of distance learning has increased. It is possible to teach any lesson with various communication tools but it is difficult to know how far this lesson reaches to the students. In this study, it is aimed to monitor the students in a classroom or in front of the computer with a camera in real time, recognizing their faces, their head poses, and scoring their distraction to detect student engagement based on their head poses and Eye Aspect Ratios. Distraction was determined by associating the students’ attention with looking at the teacher or the camera in the right direction. The success of the face recognition and head pose estimation was tested by using the UPNA Head Pose Database and, as a result of the conducted tests, the most successful result in face recognition was obtained with the Local Binary Patterns method with a 98.95% recognition rate. In the classification of student engagement as Engaged and Not Engaged, support vector machine gave results with 72.4% accuracy. The developed system will be used to recognize and monitor students in the classroom or in front of the computer, and to determine the course flow autonomously.
Detection of Counterfeit Indian Currency Note Using Image Processing

Most of the country suffering from the problem of counterfeit note. Indian is also part of such problem. As advance in technology, it is possible for anyone to print counterfeit note. Such counterfeit notes are produced without the legal sanction of Government and production of such notes degrades the economy of any country. After demonetization in India Newly 500 and 2000 currency notes are launched but due to misuse of advanced technology, fake currency of such notes are produced by counterfeiter due to its high value and circulated in most of the part of our country. With the production and circulation of such counterfeit notes, it becomes difficult for common people to differentiate whether the currency is real or fake as they differentiate on the basis of physical appearance which effects the economy of our country. Here a system is proposed to differentiate between real and fake note which is based on the image processing technique and implemented in MATLAB. In this technique, first the image of 500 and 2000 rupee note is captured through the digital camera and perform preprocessing in order to remove noise and then mean intensity of RGB channels of image is calculated and further the three different features including Latent image, RBI Logo and denomination numeral with Rupee symbol are extracted to differentiate between real and fake note. The performance of the proposed system achieves good accuracy rate.
An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction

Target-oriented opinion words extraction (TOWE) (Fan et al., 2019b) is a new subtask of target-oriented sentiment analysis that aims to extract opinion words for a given aspect in text.Current state-of-the-art methods leverage position embeddings to capture the relative position of a word to the target.However, the performance of these methods depends on the ability to incorporate this information into word representations.In this paper, we explore a variety of text encoders based on pretrained word embeddings or language models that leverage part-of-speech and position embeddings, aiming to examine the actual contribution of each component in TOWE.We also adapt a graph convolutional network (GCN) to enhance word representations by incorporating syntactic information.Our experimental results demonstrate that BiLSTM-based models can effectively encode position information into word representations while using a GCN only achieves marginal gains.Interestingly, our simple methods outperform several state-of-the-art complex neural structures.
O enfrentamento da covid-19 na atenção primária em saúde: uma experiência em Natal-RN

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Stress Amongst Novice Information Security Risk Management Practitioners

Incident prioritization is nowadays a part of many approaches and tools for network security and risk management.However, the dynamic nature of the problem domain is often unaccounted for.That is, the prioritization is typically based on a set of static calculations, which are rarely adjusted.As a result, incidents are incorrectly prioritized, leading to an increased and misplaced effort in the incident response.A higher degree of automation could help to address this problem.In this paper, we explicitly consider flaws in the prioritization an unalterable circumstance.We propose an adaptive incident prioritization, which allows to automate certain tasks for the prioritization model management in order to continuously assess and improve a prioritization model.At the same time, we acknowledge the human analyst as the focal point and propose to keep the human in the loop, among others by treating understandability as a crucial requirement.
Marathi Poem Classification using Machine Learning

Poem a piece of writing in which the expression of feelings and ideas is given intensity by particular attention to diction (sometimes involving rhyme), rhythm, and imagery. It is used for showing different views. Every poet writes a poem with a different intention and different views. In the proposed system we have classified the poem according to its sentiments by using words of different categories. Machine learning algorithm SVM classifier is used for differencing the class of the poem. This system also enables the user to search the poem based on the poet name and poet type. For 341 poems of five categories 'Friend', 'Prem', 'Bhakti', 'Prerna' and 'Desh' accuracy achieved is 93.54%.
An Improved Local Binary Patterns Histograms Technique for Face Recognition for Real Time Applications

Recently, face recognition and its applications has been considered as one of the image analysis most successful applications, especially over the past several years. Face Recognition is a unique system that can be used by using unique facial features for identification or verification of a person from a digital image. In a face recognition system, there are many technique that can be used. This paper provides an efficient of the Local Binary Patterns Histograms (LBPH) based technique provided by OpenCV library which is implemented in Python programming language which is well suitable for realistic scenarios. In this paper we also provide visual qualitative outcome with existing algorithm (Haar-cascade classifier and Local Binary Patterns Histograms (LBPH)). As a result, the proposed technique outperform better in terms of visual qualitative analysis.
Real-time estimation of hospital discharge using fuzzy radial basis function network and electronic health record data

Hospital resources are scarce and should be properly distributed and justified. Information about how long patients stays in critical intensive care units can provide significant benefits to hospital management resources and optimal admission planning. In this paper, we propose an approach for estimating intensive care unit length of stay using fuzzy radial basis function neural network model. The predictive performance of the model is compared to others using data collected over 13,587 admissions and 54 predictive factors from five critical units with discharges between 2001 and 2012. The proposed model compared to others demonstrated higher accuracy and better estimations. The three most influential factors in predicting length of stay at the early stage of pre-admission were demographic characteristics, admission type, and the first location within the hospital prior to critical unit admission. We have found about 63% of patients with multiple chronic conditions, stayed significantly longer in hospital. Enabling the proposed prediction model in clinical decision support system may serve as reference tools for communicating with patients and hospital managers.
An Efficient Technique for Detection of Fake Currency

During the past years, some of the researchers are using the matching techniques for identification of the fake currency either by using the Mathematical formulation or by using the readymade simulation tools. A lot of methods namely edge detection, segmentation, feature extraction, pattern matching has been used for finding and identification of the fake currency. In the present work, Principal Component Analysis (PCA) is used to detect the feature of currency through modeling and a proposed algorithm is elaborated to recognize the fake currency in the form of note Rs 2000 of Indian currency. Graphs are also designed to justify the present approach along with the comparison of results.
Penggunaan Situs Liveworksheets untuk Mengembangkan LKPD Interaktif di Sekolah Dasar

Interactive student worksheets are a very important part of learning media to support the learning process to achieve the expected goals. The use of the liveworksheets site is one way to develop interactive worksheets so that online learning can run well. The purpose of this community service is to provide knowledge to teachers to take advantage of the free liveworksheets site to develop interactive worksheets so that the obstacles to teaching and learning activities during the Covid-19 pandemic can be overcome. The method of implementing this service begins with observations to partner schools before the implementation of training activities to discuss the time and need for facilities and infrastructure. Then during the training, the activity begins with the provision of material related to interactive student worksheets and how to create interactive student worksheets using the live worksheet site. Furthermore, assistance is provided during practice for teachers or training participants. The results of this service can be said to be successful, as seen from the enthusiasm of the teacher listening to the material and the practice of making interactive worksheets questions. The types of questions that were successfully practiced by the participants were the types of multiple-choice questions, short entries, descriptions, join arrows, dropdowns, drag and drop, checkboxes, word search, and listening and speaking questions. Therefore, with the increasing ability of teachers to create interactive worksheets, it is hoped that teachers can develop more perfect interactive worksheets that are made according to the needs of students. In addition, the knowledge that has been obtained from this training is expected for teachers to share their knowledge and knowledge with other teachers.
A survey on the use of association rules mining techniques in textual social media

The incursion of social media in our lives has been much accentuated in the last decade. This has led to a multiplication of data mining tools aimed at obtaining knowledge from these data sources. One of the greatest challenges in this area is to be able to obtain this knowledge without the need for training processes, which requires structured information and pre-labelled datasets. This is where unsupervised data mining techniques come in. These techniques can obtain value from these unstructured and unlabelled data, providing very interesting solutions to enhance the decision-making process. In this paper, we first address the problem of social media mining, as well as the need for unsupervised techniques, in particular association rules, for its treatment. We follow with a broad overview of the applications of association rules in the domain of social media mining, specifically, their application to the problems of mining textual entities, such as tweets. We also focus on the strengths and weaknesses of using association rules for solving different tasks in textual social media. Finally, the paper provides a perspective overview of the challenges that association rules must face in the next decade within the field of social media mining.
Implementation of Virtual Reality in Health Professional Higher Education: Protocol for a Scoping Review

Background The use of virtual reality in higher education show great potential to promote novel and innovative learning experiences. Until recently, virtual reality has mostly been used in technical higher education, but lately medical education programs have begun using virtual reality. Virtual reality for health professional education improves the knowledge and skills of health professionals compared with traditional or other digital education initiatives. However, the implementation of technology in higher education is slow because of barriers to technology use and innovative and successful practices are not shared. It is, therefore, of great interest to explore how virtual reality is implemented in higher health professional and continuing education. Objective The aim of this scoping review is to identify studies that reported implementation of virtual reality in higher health professional education, to identify barriers and facilitators for implementation, and to highlight research gaps in this area. Methods The scoping review will be conducted according to JBI Evidence Synthesis methodologies. CINAHL, the Academic Search Elite and Education Source electronic databases, and Google Scholar will be searched for studies published between 2017 and 2022. In addition, manual searching of key items, reference tracking, and citation tracking will be performed. Searches for white papers will also be manually conducted. All authors will independently extract data from full-text papers. We will use qualitative content analysis to abstract the findings. Results The literature searches were conducted in January and February 2022. The review is expected to be completed by fall 2022, after which time it will be submitted for publication. Conclusions We anticipate that, from the review, we will be able to coordinate recommendations for and present the challenges of virtual reality initiatives in health professional education programs. We will present recommendations for future research. International Registered Report Identifier (IRRID) DERR1-10.2196/37222
Constrained Attitude Maneuvering via Modified-Rodrigues-Parameter-Based Motion Planning Algorithms

The attitude dynamics and control of a spacecraft becomes complex in the presence of constraints in its orientation. Such constraints consist in not pointing sensitive payload toward bright objects in space or, vice versa, keeping the same bright objects within some instrument’s field of view. This paper investigates the applicability of nonsingular attitude motion planning algorithms to navigate a three-dimensional grid in the Modified Rodrigues Parameter configuration space. The aim is to compute a constraint-compliant reference trajectory in Modified Rodrigues Parameter space that can be tracked to reorient a spacecraft from an initial attitude to a final attitude while also attempting at minimizing the required control effort. Nonzero initial and final angular rates are considered to yield a general solution. The path is constructed with B-spline curves that pass through each attitude waypoint. Further, a constant angular maneuver rate is imposed, which influences the speed at which the reference attitude traverses the B-spline solution. Simulations illustrate that constraint-compliant, control-torque-minimizing paths are found if the grid spacing is chosen to be fine enough.
A simple but accurate two-state model for nowcasting PV power

A two-state model for short-term forecasting of PV plants output power (further referred as PV2-state) is proposed. PV2-state connects in an inventive way an empirical estimator for clear-sky photovoltaic (PV) power output with a statistical predictor for the sunshine number, a binary indicator stating whether the Sun shines or not. PV2-state shows remarkable features: (1) accessibility (only data series resulted from the PV plant monitoring are processed), (2) universality (no physical models for PV plant components are required) and (3) high-performance (due to a continuous adaptation to the actual atmospheric and PV modules conditions). Based on different error metrics, the model performance is investigated from three perspectives: forecast accuracy, forecast precision and the response to the variability in the state-of-the-sky. The study was conducted with high-quality data collected from a fully monitored experimental micro-PV plant developed on the Solar Platform of the West University of Timisoara, Romania. By processing information about the actual performance of the PV plant, PV2-state proves a notable advance in the forecast precision, becoming a robust competitor in the race for high-accuracy in intra-hour forecast of PV power.
Non-Markovian Quantum Dynamics in Strongly Coupled Multimode Cavities Conditioned on Continuous Measurement

An important challenge in non-Markovian open quantum systems is to understand what information we gain from continuous measurement of an output field. For example, atoms in multimode cavity QED systems provide an exciting platform to study many-body phenomena in regimes where the atoms are strongly coupled amongst themselves and with the cavity, but the strong coupling makes it complicated to infer the conditioned state of the atoms from the output light. In this work we address this problem, describing the reduced atomic state via a conditioned hierarchy of equations of motion, which provides an exact conditioned reduced description under monitoring (and continuous feedback). We utilise this formalism to study how different monitoring for modes of a multimode cavity affects our information gain for an atomic state, and to improve spin squeezing via measurement and feedback in a strong coupling regime. This work opens opportunities to understand continuous monitoring of non-Markovian open quantum systems, both on a practical and fundamental level.