Reviewerly/exHarmony huggingface dataset:
A Five-Year (2015 to 2019) Analysis of Studies Focused on Breast Cancer Prediction Using Machine Learning: A Systematic Review and Bibliometric Analysis

The objective 1 of this study was to investigate trends in breast cancer (BC) prediction using machine learning (ML) publications by analysing country, first author, journal, institutional collaborations and co-occurrence of author keywords. The objective 2 was to provide a review of studies on BC prediction using ML and a blood analysis dataset (Breast Cancer Coimbra Dataset [BCCD]), and the objective 3 was to provide a brief review of studies based on BC prediction using ML and patients’ fine needle aspirate cytology data (Wisconsin Breast Cancer Dataset [WBCD]). The design of this study was as follows: for objective 1: bibliometric analysis, data source PubMed (2015-2019); for objective 2: systematic review, data source: Google and Google Scholar (2018-2019); for objective 3: systematic review, data source: Google Scholar (2016-2019). The inclusion criteria for objective 1 were all publication results yielded from the searches. All English papers that had a ‘PDF’ option from the search results were included for objective 2. A sample of the ‘PDF’ English papers were included for objective 3. All 116 female patients from the BCCD, consisting of 64 positive BC patients and 52 controls were included in the study for objective 2. For the WBCD, all 699 female patients comprising of 458 with a benign BC tumour and 241 with a malignant BC tumour were included for objective 3. All 2928 publications were included for objective 1. The results showed that the United States of America (USA) produced the highest number of publications (n=803). In total, 2419 first authors contributed towards the publications. Breast Cancer Research and Treatment was the highest ranked journal. Institutional collaborations mainly occurred within the USA. The use of ML for BC screening and detection was the most researched topic. A total of 19 distinct papers were included for objectives 2 and 3. The findings from these studies were never presented to clinicians for validations. In conclusion, the use of ML for BC screening and detection is promising.
Is 42 the Answer to Everything in Subtitling-oriented Speech Translation?

Subtitling is becoming increasingly important for disseminating information, given the enormous amounts of audiovisual content becoming available daily.Although Neural Machine Translation (NMT) can speed up the process of translating audiovisual content, large manual effort is still required for transcribing the source language, and for spotting and segmenting the text into proper subtitles.Creating proper subtitles in terms of timing and segmentation highly depends on information present in the audio (utterance duration, natural pauses).In this work, we explore two methods for applying Speech Translation (ST) to subtitling: a) a direct end-to-end and b) a classical cascade approach.We discuss the benefit of having access to the source language speech for improving the conformity of the generated subtitles to the spatial and temporal subtitling constraints and show that length 1 is not the answer to everything in the case of subtitlingoriented ST.
Turku Enhanced Parser Pipeline: From Raw Text to Enhanced Graphs in the IWPT 2020 Shared Task

We present the approach of the TurkuNLP group to the IWPT 2020 shared task on Multilingual Parsing into Enhanced Universal Dependencies.The task involves 28 treebanks in 17 different languages and requires parsers to generate graph structures extending on the basic dependency trees.Our approach combines language-specific BERT models, the UDify parser, neural sequence-to-sequence lemmatization and a graph transformation approach encoding the enhanced structure into a dependency tree.Our submission averaged 84.5% ELAS, ranking first in the shared task.We make all methods and resources developed for this study freely available under open licenses from https://turkunlp.org.
Hasil belajar IPS menggunakan Kolaborasi Model Discovery Learning Berbasis Media Animasi

Learning social studies in elementary school should be taught by focusing on students, but the learning process was still focused on the teacher and used lecturing method. Therefore, the aim of this research was to analyze the use of discovery learning models based on animation media. This research was quasi experimental research with non equivalent control group design. The population of this research was all fourth-grade elementary school students which were consisted of 160 students. The samples were taken by random sampling technique. The sample consisted of 31 students in experimental group and 30 students in control group. Data collection was done by using test in the form of multiple-choice test. The data were analyzed by using t-test. The results of data analysis obtained t-count = 3.302 at a significance level of 5% and dk = 59, the value obtained t-table = 2,000 so that t-count = 3.302&gt; t-table = 2,000. So, the use of discovery learning model based on animation media would have an impact on social studies learning outcomes.
Psychological and pedagogical conditions for the formation of value-semantic sphere of students of technical specialties

The article considers the psychological and pedagogical conditions necessary for the formation of the value-semantic sphere of students technical specialties. The article is devoted to the theoretical aspects of the value-semantic approach in modern education. The problem is considered through the prism of concepts that consider the valuesemantic sphere, the problem of meaning, factors and conditions that affect the process of forming meaning-life and value orientations in youth. The degree of formation of the value-semantic sphere affects the quality of life, self-determination, and overall satisfaction with life. Various approaches to the formation of meaning-life and value orientations are considered.
Comparison of state-of-the-art deep learning APIs for image multi-label classification using semantic metrics

Image understanding heavily relies on accurate multi-label classification. In recent years, deep learning algorithms have become very successful for such tasks, and various commercial and open-source APIs have been released for public use. However, these APIs are often trained on different datasets, which, besides affecting their performance, might pose a challenge to their performance evaluation. This challenge concerns the different object-class dictionaries of the APIs’ training dataset and the benchmark dataset, in which the predicted labels are semantically similar to the benchmark labels but considered different simply because they have different wording in the dictionaries. To face this challenge, we propose semantic similarity metrics to obtain richer understating of the APIs predicted labels and thus their performance. In this study, we evaluate and compare the performance of 13 of the most prominent commercial and open-source APIs in a best-of-breed challenge on the Visual Genome and Open Images benchmark datasets. Our findings demonstrate that, while using traditional metrics, the Microsoft Computer Vision, Imagga, and IBM APIs performed better than others. However, applying semantic metrics also unveil the InceptionResNet-v2, Inception-v3, and ResNet50 APIs, which are trained only with the simple ImageNet dataset, as challengers for top semantic performers.
Practical security analysis of a continuous-variable quantum random-number generator with a noisy local oscillator

A quantum random-number generator (QRNG) can theoretically generate unpredictable random numbers with perfect devices and is an ideal and secure source of random numbers for cryptography. However, the practical implementations always contain imperfections, which will greatly influence the randomness of the final output and even open loopholes to eavesdroppers. Recently, Thewes et al. experimentally demonstrated a continuous-variable eavesdropping attack, based on heterodyne detection, on a trusted continuous-variable QRNG in Phys. Rev. A 100, 052318 (2019), yet like in many other practical continuous-variable QRNG studies, they always supposed the local oscillator was stable and ignored its fluctuation, which might lead to security threats such as wavelength attack. In this work, based on the theory of the conditional min-entropy, imperfections of the practical security of continuous-variable QRNGs are systematically analyzed, especially the local oscillator fluctuation under imbalanced homodyne detection. Experiments of a practical QRNG based on vacuum fluctuation are demonstrated to show the influence of local oscillator fluctuation on the total measurement noise variances and the practical conditional min-entropy with beam splitters of different transmittances. Moreover, a local oscillator monitoring method is proposed for the practical continuous-variable QRNG, which can be used to calibrate the practical conditional min-entropy.
Learning Optimal Decision Trees using Constraint Programming (Extended Abstract)

Decision trees are among the most popular classification models in machine learning. Traditionally, they are learned using greedy algorithms. However, such algorithms have their disadvantages: it is difficult to limit the size of the decision trees while maintaining a good classification accuracy, and it is hard to impose additional constraints on the models that are learned. For these reasons, there has been a recent interest in exact and flexible algorithms for learning decision trees. In this paper, we introduce a new approach to learn decision trees using constraint programming. Compared to earlier approaches, we show that our approach obtains better performance, while still being sufficiently flexible to allow for the inclusion of constraints. Our approach builds on three key building blocks: (1) the use of AND/OR search, (2) the use of caching, (3) the use of the CoverSize global constraint proposed recently for the problem of itemset mining. This allows our constraint programming approach to deal in a much more efficient way with the decompositions in the learning problem.
Requet

As video traffic dominates the Internet, it is important for operators to detect video quality of experience (QoE) to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection--based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for RE al-time QU ality of experience metric detection for &lt;underline&gt; E &lt;/underline&gt;ncrypted T raffic— Requet —which is suitable for network middlebox deployment. Requet uses a detection algorithm that we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a machine learning algorithm to predict QoE metrics, specifically buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi and LTE network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12×, 1.53×, and 3.14×, respectively.
Introduction to AI Chatbots

The modern era of technology has a tremendous impact on the society.With the creation of the ultimate virtual assistants, chatbots have become a popular entity in the conversational services.Chatbots are software programs that use natural language understanding and processing.Chatbots are not just restricted to help the user to complete his tasks such as booking a movie ticket or finding the nearest restaurant, but they also provide a source of entertainment, play a major role in home automation projects, give business strategy tips and help in other ways.In this paper, we will provide an insight into what a chatbot is and the types of chatbots.We also propose a classification based on the current market trends, ease of usability and requirements.
Relating spin squeezing to multipartite entanglement criteria for particles and modes

Entanglement witnesses based on first and second moments exist in the form of spin squeezing criteria for the detection of particle entanglement from collective measurements, and in the form of modified uncertainty relations for the detection of mode entanglement or steering. By revealing a correspondence between them, we show that metrologically useful spin squeezing reveals multimode entanglement in symmetric spin states that are distributed into addressable modes. We further derive tight state-independent multipartite entanglement bounds on the spin squeezing coefficient and point out their connection to widely used entanglement criteria that depend on the state's polarization. Our results are relevant for state-of-the-art experiments where symmetric entangled states are distributed into a number of addressable modes, such as split spin squeezed Bose-Einstein condensates.
Hand gesture recognition using machine learning algorithms

Gesture recognition is an emerging topic in today’s technologies. The main focus of this is to recognize the human gestures using mathematical algorithms for human computer interaction. Only a few modes of Human-Computer Interaction exist, they are: through keyboard, mouse, touch screens etc. Each of these devices has their own limitations when it comes to adapting more versatile hardware in computers. Gesture recognition is one of the essential techniques to build user-friendly interfaces. Usually gestures can be originated from any bodily motion or state, but commonly originate from the face or hand. Gesture recognition enables users to interact with the devices without physically touching them. This paper describes how hand gestures are trained to perform certain actions like switching pages, scrolling up or down in a page.
Digital technology as a trigger for learning: promises and realities

In the last fitty years, the remarkable development of digital technology, that has permeated practically all social, economic and cultural, political and technological realms is producing several phenomena that have a direct impact in education. In this paper, I discuss first, the fact that more and more we refer to digital technology as just ‘technology”, as if the rest of the many organizational, symbolic, artefactual and biotechnological developments were something “natural”. Second, I refer to the rise and spread of technological solutionism in education and a growing discourse that sees every new digital technology as the arrival of the Promised Land, as the panacea to solve the problems of education. I go on analysing the collateral effects of this discourse in the educational practice, with an especial reference to persuasive technologies and Big Data. The article concludes with the request and the need for researchers, practitioners and education policy makers to avoid the temptation to solve a deeply "wicked" problem such as education with simple solutions.
Adaptive phase estimation through a genetic algorithm

Quantum metrology is one of the most relevant applications of quantum information theory to quantum technologies. Here, quantum probes are exploited to overcome classical bounds in the estimation of unknown parameters. In this context, phase estimation, where the unknown parameter is a phase shift between two modes of a quantum system, is a fundamental problem. In practical and realistic applications, it is necessary to devise methods to optimally estimate an unknown phase shift by using a limited number of probes. Here we introduce and experimentally demonstrate a machine learning-based approach for the adaptive estimation of a phase shift in a Mach-Zehnder interferometer, tailored for optimal performances with limited resources. The employed technique is a genetic algorithm used to devise the optimal feedback phases employed during the estimation in an offline fashion. The results show the capability to retrieve the true value of the phase by using few photons, and to reach the sensitivity bounds in such small probe regime. We finally investigate the robustness of the protocol with respect to common experimental errors, showing that the protocol can be adapted to a noisy scenario. Such approach promises to be a useful tool for more complex and general tasks where optimization of feedback parameters is required.Received 24 April 2020Accepted 24 June 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.033078Published 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 AreasMachine learningQuantum metrologyQuantum sensingQuantum Information
Personalized Pervasive Health

& OVER MORE THAN two decades, mobile, wear- able, and ambient sensor and interaction devices have grown into today's plethora of computing platforms and tools for pervasive health.Pervasive computing is now assimilating into medicine, from disease risk prevention to diagnostics, and from treatment support to chronic-care management.Unlike more traditional medical lab technology, pervasive computing systems can be continuously available, using unobtrusive sensors, actuators, computing, and interactive interfaces to liberalize access to information on basic body functions and processes in everyday life, e.g., physical activity and vital signs.Pervasive computing platforms now natively provide practitioners and researchers with a set of functionalities that allow for the development of applications and systems that are able to make sense of complex situations, e.g., daily routines, mood, and the user's physiology.With this potential at hand, pervasive computing systems will play a central role as enabling technology for healthcare.
VR Panoramic Technology in Urban Rail Transit Vehicle Engineering Simulation System

Rail transit is a kind of rail transit based on fixed lines, which is carried out through special vehicles, passengers, and goods. However, urban rail transit construction activities are mostly underground activities, the construction environment and construction technology are complex, and the potential disaster risks are different. At present, the traditional 3D simulation technology is lack of interactive experience, but with the development of VR technology, 3D panorama technology, as a branch of VR technology, is rapidly popularized with its interactive and immersive characteristics. VR reality technology is applied in aerospace, real estate, education, entertainment, games, and other fields. Therefore, authors propose the application of VR panoramic technology based on the Unity3D engine and HTC Vive interactive equipment in the urban rail transit vehicle engineering simulation system. Furthermore, the content construction scheme and the overall development process of the simulation system are given. The function of VR technology in the simulation system and the main technology of real-time interaction for vehicle maintenance are discussed. The methods of literature analysis, expert interviews, on-site data acquisition, and questionnaire surveys are applied. By determining the intensity of protection measures, improving the construction quality of urban rail transit, preventing and controlling risks, and making the best plan and measures from a systematic point of view. From the final results of this study, user can gradually change the traditional ideas and methods of urban rail transit disaster prevention and mitigation and implement VR technology in the urban rail transit vehicle engineering simulation system, which plays a very important role in improving the safety management efficiency of urban rail transit construction.
Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

Abstract Deep learning is a new branch of machine learning, which is widely used by researchers in a lot of artificial intelligence applications, including signal processing and computer vision. The present research investigates the use of deep learning to solve the hand gesture recognition (HGR) problem and proposes two models using deep learning architecture. The first model comprises a convolutional neural network (CNN) and a recurrent neural network with a long short-term memory (RNN-LSTM). The accuracy of model achieves up to 82 % when fed by colour channel, and 89 % when fed by depth channel. The second model comprises two parallel convolutional neural networks, which are merged by a merge layer, and a recurrent neural network with a long short-term memory fed by RGB-D. The accuracy of the latest model achieves up to 93 %.
A query‐based quantum eigensolver

Solving eigenvalue problems is crucially important for both classical and quantum applications. Many well-known numerical eigensolvers have been developed, including the QR and the power methods for classical computers, as well as the quantum phase estimation (QPE) method and the variational quantum eigensolver for quantum computers. In this work, we present a different type of quantum method that uses fixed-point quantum search to solve Type II eigenvalue problems. This method serves as an important complement to the QPE method, which is a Type I eigensolver. We show that the quantum oracle of our query-based method can be efficiently constructed from the QPE gate, which is crucial for analyzing the total gate complexity of our method. In addition, compared with the QPE method, our query-based method achieves a quadratic speedup in solving Type II problems. As two applications, we then discuss how to apply our method to solve Type II eigenvalue problems for the Heisenberg model and the hydrogen molecule.
Approach for the construction of non-Calderbank-Steane-Shor low-density-generator-matrix–based quantum codes

Quantum low-density generator matrix (QLDGM) codes based on Calderbank-Steane-Shor (CSS) constructions have shown unprecedented error correction capabilities, displaying much improved performance in comparison to other sparse-graph codes. However, the nature of CSS designs and the manner in which they must be decoded limit the performance that is attainable with codes that are based on this construction. This motivates the search for quantum code design strategies capable of avoiding the drawbacks associated with CSS codes. In this article, we introduce non-CSS quantum code constructions based on classical LDGM codes. The proposed codes are derived from CSS QLDGM designs by performing specific row operations on their quantum parity check matrices to modify the associated decoding graphs. The application of this method results in performance improvements in comparison to CSS QLDGM codes, while also allowing for greater flexibility in the design process. The proposed non-CSS QLDGM scheme outperforms the best quantum low-density parity check codes that have appeared in the literature.