Reviewerly/exHarmony huggingface dataset:
Continuous Variable Quantum Algorithms: an Introduction

Quantum computing is usually associated with discrete quantum states and physical quantities possessing discrete eigenvalue spectrum. However, quantum computing in general is any computation accomplished by the exploitation of quantum properties of physical quantities, discrete or otherwise. It has been shown that physical quantities with continuous eigenvalue spectrum can be used for quantum computing as well. Currently, continuous variable quantum computing is a rapidly developing field both theoretically and experimentally. In this pedagogical introduction we present the basic theoretical concepts behind it and the tools for algorithm development. The paper targets readers with discrete quantum computing background, who are new to continuous variable quantum computing.
"How to best say it?" : Translating Directives in Machine Language into Natural Language in the Blocks World

We propose a method to generate optimal natural language for block placement directives generated by a machine's planner during human-agent interactions in the blocks world. A non user-friendly machine directive, e.g., move(ObjId, toPos), is transformed into visually and contextually grounded referring expressions that are much easier for the user to comprehend. We describe an algorithm that progressively and generatively transforms the machine's directive in ECI (Elementary Composable Ideas)-space, generating many alternative versions of the directive. We then define a cost function to evaluate the ease of comprehension of these alternatives and select the best option. The parameters for this cost function were derived empirically from a user study that measured utterance-to-action timings.
국제중등과학 올림피아드(IJSO)의 화학 실험 문항 분석

In this study, the chemistry experiment questions for the 11 years of the International Junior Science Olympiad(IJSO) were analyzed based on trend, knowledge and inquiry elements. The topic of chemistry experiment had a high proportion of quantitative analyses such as acid-base titration and oxidation- reduction titration, and separation of the mixture also had a certain percentage. The chemistry experiment question is characterized by obtaining the measurement by experimenting in accordance with experimental procedures, and then calculating the mass of material using stoichiometry. Chemistry experiment questions include inquiry elements, such as data collection, processing, and interpretation, along with scientific knowledge and methods of inquiry at the level of middle and high schools. Data collection evaluates the selection and use of measurement tools, and data processing organizes data into tables and evaluates measurement accuracy and precision. The data interpretation had a high percentage of the questions in the form of comparing and explaining the results of the experiment. The educational implications are discussed.
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection

An innovations sequence of a time series is a sequence of independent and identically distributed random variables with which the original time series has a causal representation. The innovation at a time is statistically independent of the history of the time series. As such, it represents the new information contained at present but not in the past. Because of its simple probability structure, an innovations sequence is the most efficient signature of the original. Unlike the principle or independent component analysis representations, an innovations sequence preserves not only the complete statistical properties but also the temporal order of the original time series. An long-standing open problem is to find a computationally tractable way to extract an innovations sequence of non-Gaussian processes. This paper presents a deep learning approach, referred to as Innovations Autoencoder (IAE), that extracts innovations sequences using a causal convolutional neural network. An application of IAE to the one-class anomalous sequence detection problem with unknown anomaly and anomaly-free models is also presented.
Multi-Objective Dynamic Resource Scheduling Model for Allocating User Tasks in the Cloud Computing

Cloud computing is the efficient distributed platform which manages all types of resources in the virtual manner. The major functionality of the cloud computing is to schedule the user tasks to the virtual machines. Many algorithms are proposed by the researchers to address the scheduling in cloud. Though there is some research gap need to be focused in the cloud scheduling. Minimizing the cost, makespan and deadline violations are difficult when large volume of tasks are assigned to the cloud. The multi-objective mechanisms are needed to address the two more objectives in the cloud scheduling model. This paper proposed the multi-objective algorithm based on the non-dominated method and crowding distance method. The proposed method computes the quality of service for the virtual machines before allocating to tasks to fulfil the requirements of the users. The performance of the proposed multi-objective model is evaluated based on the makespan, deadline violations and cost. The results prove the efficiency of the multi –objective model.
LibGuides: Tails and Tales Make Your Own Bookmark: Home

A collection of directions, craft templates, and pictures for making your own creative bookmarks at home.
Research on the hyper-heuristic of Sub-domain Elimination Strategies based on Firefly Algorithm

Abstract In this study, a hyper-heuristic named Sub-domain Elimination Strategies based on Firefly Algorithm (SESFA) is proposed. First, a typical hyper-heuristic is usually using the high-level strategy selection or the combination of the low-level heuristics to obtain a new hyper-heuristic, each round of optimization process is carried out in the whole problem domain. However, SESFA evaluates the problem domain through the feedback information of the meta-heuristic at the lower level, eliminating the poor performance areas, and adjusting the underlying heuristic or adjusting the algorithm parameters to improve the overall optimization performance. Second, the problem domain segmentation function in SESFA can reduce the complexity of the objective function within a single sub-domain, which is conducive to improving the optimization efficiency of the underlying heuristic. Further, the problem domain segmentation function in SESFA also makes there is no direct correlation between different sub-domains, so different underlying heuristics can be adopted in different sub-domains, which is beneficial to the realization of parallel computing. Comparing SESFA with Firefly Algorithms with five standard test functions, the results show that SESFA has advantages in precision, stability and success rate.
An Initial Investigation of Non-Native Spoken Question-Answering

Text-based machine comprehension (MC) systems have a wide-range of applications, and standard corpora exist for developing and evaluating approaches. There has been far less research on spoken question answering (SQA) systems. The SQA task considered in this paper is to extract the answer from a candidate$\text{'}$s spoken response to a question in a prompt-response style language assessment test. Applying these MC approaches to this SQA task rather than, for example, off-topic response detection provides far more detailed information that can be used for further downstream processing. One significant challenge is the lack of appropriately annotated speech corpora to train systems for this task. Hence, a transfer-learning style approach is adopted where a system trained on text-based MC is evaluated on an SQA task with non-native speakers. Mismatches must be considered between text documents and spoken responses; non-native spoken grammar and written grammar. In practical SQA, ASR systems are used, necessitating an investigation of the impact of ASR errors. We show that a simple text-based ELECTRA MC model trained on SQuAD2.0 transfers well for SQA. It is found that there is an approximately linear relationship between ASR errors and the SQA assessment scores but grammar mismatches have minimal impact.
Creating an Electronic Course on Programming in Python for the Android Platform

The description and principles of developing a mobile application for the Android platform that provides free access to electronic courses on teaching the basic structures of the Python language and the construction of template programming algorithms based on them are presented. The content of the course is based on the principle of comparative analysis with the C++ language, one of the goals of which is to differentiate the tasks for which it is more efficient to use either the Python scripting language or the C++ compiler. The developed application is logically integral, allows the possibility of supplementing with new data — examples, types of algorithms — and, no less important, is free.
A sprinkling of hybrid-signature discrete spacetimes in real-world networks

Many real-world networks are embedded into a space or spacetime. The embedding space(time) constrains the properties of these real-world networks. We use the scale-dependent spectral dimension as a tool to probe whether real-world networks encode information on the dimensionality of the embedding space. We find that spacetime networks which are inspired by quantum gravity and based on a hybrid signature, following the Minkowski metric at small spatial distance and the Euclidean metric at large spatial distance, provide a template relevant for real-world networks of small-world type, including a representation of the internet's architecture and biological neural networks.
Space and Molecular Human in the Process of Social Systems Digital Transformation in Belarus and Russia

To develop a strategy for building the Union State of Russia and Belarus’ better future, it is necessary to understand the mechanisms and identify the main causes that govern the dynamics of this complex and specifi c social system. As the main factor determining the social dynamics of a given civilization cluster, we consider the competition of two basic sociotypes. They are conditionally designated as a molecular man and a space man. A molecular person is a consumer whose social behavior is based on the power of the selfi sh gene and consumer society’s ideals. A space person is passionate, driven by the creative imperative of cognition and synthesis of culture. In our opinion, it is this fundamental confl ict that forms the social dynamics of the civilization cluster of the Union State. The scenario of its future development will depend on the resolution of this confl ict. In order to better understand this process, we have developed a cellular-automate computer model of competition between the two sociotypes in the framework of a unifi ed social system. This computer model can be considered as a simplifi ed cognitive proto-construct of the social system dynamics. By examining it, one can transfer the properties and features of its behavior to an unknown object under study - a society.
SISTEM INFORMASI PENYEWAAN DAN PERBAIKAN ALAT BERAT DI CV. MITRA PERKASA BINTAN BERBASIS WEB PHP DAN MYSQL

CV. Mitra Perkasa Bintan is one of the contracting company that competes with other contracting companies in developing business in the field. CV. Mitra Perkasa Bintan serve some services are sales-natural materials, heavy equipment rental and leasing services of contractors. However, these companies have several problems in the transaction. All lease transactions is still conventional. Marketing of the rental of heavy equipment is also just around the location of the company so many people do not know the CV. Mitra Perkasa Bintan. Therefore need to be designed an application using Internet facilities that can be used customers to rent heavy equipment and save time and costs in conducting transactions between companies and customers. So CV. Mitra Perkasa Bintan requires a web-based online application to support the rental of heavy equipment as a container or an intermediary between company and customers in terms of trade are accompanied by pictures of heavy equipment to be leased. The purpose of this final project provides a solution to these problems so that customers can rent heavy equipment through the website. The system created is a web-based online applications using program PHP language and MySQL as database. With of building this web-based online application customers can rent heavy equipment through the website CV. Mitra Perkasa Bintan.
PELATIHAN PEMBUATAN MEDIA PEMBELAJARAN DALAM RANGKA MENEMUKAN KONSEP MATEMATIS

This activity aims to provide training to students at the Otto Iskandar Dinata Foundation, in making learning media use finding mathematical concepts. Through this training, it is hoped that students at the Orphanage of Iotto Iskandar Dinata Foundation will be able to make learning media and find mathematical concepts from these media. Participants in this training are children of the orphanage at the Wisma Karya Bakti Foundation, otto Iskandar Dinata, who are in the junior high school (SMP) level. The method used is in the form of material delivery and assistance in making learning media through group discussions and tutorials. The results of this training activity are in the form of students' motivation in learning, students are able to find mathematical concepts from the media they make. The results of the distributed response questionnaire showed that 90% of students understood the training material, 88% were satisfied with the guidance, and 89% stated that they were satisfied with the method used by the instructor to provide assistance. The obstacles faced are the limited training time, so that additional time is needed so that the results can be maximized. Key words: Learning media, Mathematical concepts
Matching a Desired Causal State via Shift Interventions

Transforming a causal system from a given initial state to a desired target state is an important task permeating multiple fields including control theory, biology, and materials science. In causal models, such transformations can be achieved by performing a set of interventions. In this paper, we consider the problem of identifying a shift intervention that matches the desired mean of a system through active learning. We define the Markov equivalence class that is identifiable from shift interventions and propose two active learning strategies that are guaranteed to exactly match a desired mean. We then derive a worst-case lower bound for the number of interventions required and show that these strategies are optimal for certain classes of graphs. In particular, we show that our strategies may require exponentially fewer interventions than the previously considered approaches, which optimize for structure learning in the underlying causal graph. In line with our theoretical results, we also demonstrate experimentally that our proposed active learning strategies require fewer interventions compared to several baselines.
Human-like Relational Models for Activity Recognition in Video.

Video activity recognition by deep neural networks is impressive for many classes. However, it falls short of human performance, especially for challenging to discriminate activities. Humans differentiate these complex activities by recognising critical spatio-temporal relations among explicitly recognised objects and parts, for example, an object entering the aperture of a container. Deep neural networks can struggle to learn such critical relationships effectively. Therefore we propose a more human-like approach to activity recognition, which interprets a video in sequential temporal phases and extracts specific relationships among objects and hands in those phases. Random forest classifiers are learnt from these extracted relationships. We apply the method to a challenging subset of the something-something dataset and achieve a more robust performance against neural network baselines on challenging activities.
Mutator VR at Centre Pompidou, Paris

William Latham and team showed their latest Mutator VR (developed at Christchurch Studios and Goldsmiths by William Latham, Stephen Todd, Peter Todd and Lance Putnam. HTC Vive Pro VR equipment provided by HTC VIVE Arts) experience at the Pompidou Centre in Paris.The Mutations/Creations 4 installation, part of the Neurones, les Intelligences Simulees (Neurons, simulated intelligence) exhibition, saw the Centre Pompidou transformed into a laboratory for creation and innovation at the frontiers of art, science and engineering.Each year, the programme brings together artists, engineers and scientists.Mutator VR immerses the viewer in a multitude of alien worlds with evolving forms resembling sea anemones, ancient ammonites and multi-horned organisms with which they can interact.Bred and crossbred in software from multiple parents, the bizarre forms are created by a process of artistic evolution driven by human aesthetics. The aim is to give viewers a surreal and immersive experience in which their own body movements shape the evolving virtual world around them.William Latham says: “In VR, the artist can put the viewer in the artist’s own personal immersive space, where the viewer is entirely focused on the artistic content, with no distractions. Once in that immersive space the artist can then play with the illusion of space, creating a synthetic reality for the viewer where the ‘surreal’ appears ‘real’. This enables rich interaction for the public including enabling them to play with the physics of the forms in ways not possible in reality.”
협동학습중심의 유아교과교육론 수업이 예비유아교사의 교수효능감과 수업기술에 미치는 효과

The aim of this study was to examine the effects of a cooperative learning class on the teaching efficacy and skills of pre-service early childhood teachers. It sought to offer basic materials for pre-service teachers to provide high-quality education in the field by proposing a cooperative teaching and learning model. This would promote the teaching efficacy and skills of pre-service early childhood teachers. The research participants were 45 students in the 3rd year of a degree in early childhood education at C University. The study took place in the 2nd semester of 2019. The results were: First, the cooperative learning class was effective in improving the teaching efficacy of pre-service early childhood teachers, and it was effective in improving both general teaching efficacy and personal teaching efficacy. Second, the cooperative learning class on teaching subjects in early childhood education was effective in improving the teaching skills of pre-service early childhood teachers, and it was effective in improving class design, class execution, and class evaluation ability. Therefore, it was suggested that various opportunities to acquire various teaching methods and skills through teacher education courses with cooperative learning were needed to foster teaching efficacy of pre-service early childhood teachers.
Fine-Grained AutoAugmentation for Multi-Label Classification

Data augmentation is a commonly used approach to improving the generalization of deep learning models. Recent works show that learned data augmentation policies can achieve better generalization than hand-crafted ones. However, most of these works use unified augmentation policies for all samples in a dataset, which is observed not necessarily beneficial for all labels in multi-label classification tasks, i.e., some policies may have negative impacts on some labels while benefitting the others. To tackle this problem, we propose a novel Label-Based AutoAugmentation (LB-Aug) method for multi-label scenarios, where augmentation policies are generated with respect to labels by an augmentation-policy network. The policies are learned via reinforcement learning using policy gradient methods, providing a mapping from instance labels to their optimal augmentation policies. Numerical experiments show that our LB-Aug outperforms previous state-of-the-art augmentation methods by large margins in multiple benchmarks on image and video classification.
Contactless Attendance System in COVID-19 scenario

In the COVID-19 situation, the physical interaction of humans with common devices for attendance can be life-threatening. In this situation, taking attendance in a classroom, Lecture Hall, Library or at the exam centre, for attendance of workers, employees at workplace are very difficult and time-consuming task, if recorded manually or by using any physical interactive biometric devices. Moreover, handling record is itself a tough task. Smart and automated attendance is managed by using various biometric systems like Thumb recognition, Face recognition, etc. In a computer vision system, authentication is a major issue. In most systems, the Human face is important for recognition and is widely used in many applications like human monitoring, attention analysis, human-machine interaction, etc. Here we tried to introduce a system that takes automatic attendance in a classroom, workplaces, etc by using a Face recognition system. The proposed system mainly is categorized into two steps i.e., Face detection and Face recognition. Viola-Jones algorithm is used to detect the faces from the current frame. This algorithm is used to differentiate the pattern of face and non-face. It will locate the faces in a frame. After detection of face, we used a face recognition concept to recognize the students/employees, where the Local Binary Pattern feature descriptor is used to identify the student/employee texture to recognize the ID. Using Face Recognition, the record of student/employee is updated in the database. Student/Employee records are stored in a database. When the face of the individual student/employee matches with one of the faces stored in the database then the attendance is recorded.
PENGEMBANGAN MODEL LITERASI DIGITAL DALAM PEMBELAJARAN KITAB KUNING DI MA’HAD AL-JAMIAH UIN MAULANA MALIK IBRAHIM MALANG

Development of digital literacy is needed not only in formal education, but also for non-formal education such as pesantren. Therefore this study aims to describe the pattern of digital literacy development among ma'had al-jamiah students of Maulana Malik Ibrahim State Islamic University of Malang, as well as to explain the effectiveness of the use of the yellow book digital literacy model. This study uses the modified Borg and Gall R&amp;D method, and the type of experimental research carried out to determine the effectiveness of product use. This research results in exposure to digital literacy in the ma'had that has been developed and the use of digital literacy of the yellow book through an android application that has a significant effect in increasing the ability of students to study the yellow book.