Reviewerly/exHarmony huggingface dataset:
An Engineer’s Brief Introduction to Microwave Quantum Optics

Classical microwave circuit theory is incomplete in that it is incapable of fully modeling some phenomena at the quantum level. Various theoretical frameworks can be employed to incorporate single-photon statistical effects in the treatment of microwave networks. Such methods include quantum input-output network (QION) theory and SLH theory. A synthesis of the quantum and classical circuit treatments requires a description of second quantization within classical microwave theory. In order to make these topics understandable to an electrical engineer, we demonstrate some underpinnings of quantum optics in terms of microwave engineering. For example, we relate traveling-wave phasors for transmission-lines, such as voltage and current, to bosonic field operators. The second quantization and necessary components for a quantum treatment of microwave circuit theory are summarized in a table that maps microwave scattering parameters to bosonic operators in a transmission-line. To illustrate the need for second quantization, we use the results of second quantization along with first principles of quantum input-output network theory to determine a state-space representation and a transfer function of a single port quantum network. The same results could be obtained from SLH theory, and this serves as a case study for applying microwave theory to open quantum systems. The results of this work could be used to treat NISQ and other superconducting hardware. Additionally, these results could be incorporated into an applied curriculum, assisting in the engineering education of the future quantum workforce.
Finitary Type Theories With and Without Contexts

Abstract We give a definition of finitary type theories that subsumes many examples of dependent type theories, such as variants of Martin–Löf type theory, simple type theories, first-order and higher-order logics, and homotopy type theory. We prove several general meta-theorems about finitary type theories: weakening, admissibility of substitution and instantiation of metavariables, derivability of presuppositions, uniqueness of typing, and inversion principles. We then give a second formulation of finitary type theories in which there are no explicit contexts. Instead, free variables are explicitly annotated with their types. We provide translations between finitary type theories with and without contexts, thereby showing that they have the same expressive power. The context-free type theory is implemented in the nucleus of the Andromeda 2 proof assistant.
Automating Normative Control for Healthcare Research

There is an increasing need for norms to be embedded in technology as the widespread deployment of big data analysis applications increases. However, existing methodologies do not provide automated policy enforcement mechanisms especially for policies derived from legislation and contractual agreements. Consequently, data access is hindered and collaborations derailed due to fear data misuse and high non-compliance fees. This research aims to automate normative controls in healthcare, such as data sharing agreements, and ultimately, enforce these policies for compliant data usage and access which encourages collaboration and facilitates research outcomes while maintaining accountability. This paper outlines the PhD research questions, current approaches and preliminary results.
Research of App Exercises After Class Effect on College English Teaching

English teaching includes several parts such as self-study instruction, cooperative inquiry, intensive lecture and after class practice. In this paper, the role of extracurricular application on the effectiveness of college English teaching is explored through questionnaires, teaching experiments and interviews. The experimental design and study of college English teaching in the first year of Nanning University, using R language to analyze the experimental data. The conclusion is that extracurricular application has positive effect on college English teaching. Moreover, it is found that although students have different attitudes and opinions, some of them also affirm the positive effect of after class application. At the same time, in the process of teaching implementation, we should pay attention to the difficulty of practice content and the variety of practice methods, but we should not excessively occupy after class time of students. The most importance is to provide students with feedback and summary of after class practice in time. The research shows that after class application can improve the effectiveness of classroom teaching when it is well integrated with classroom teaching.
THE EFFICIENCY OF POLYMOTIVATION IN STUDYING RUSSIAN AS A FOREIGN LANGUAGE IN THE COURSE OF INTENSIVE PREPARATION FOR ADMISSION TO A UNIVERSITY

The article is devoted to the urgent problem of modern education - increasing the motivation of students, the influence of various motivational components on the effectiveness of intensive study of a foreign language in the framework of pre-university training, which implies the study of complex grammatical and lexical material in a short time period and requires increased motivation from students. The relevance of the research is due to the importance of a thorough scientific study of the influence of students' motivation on learning outcomes and the study of methods and techniques for increasing both motivation and the successful implementation of the anthropocentric, communicative-cognitive and personality-oriented paradigm of teaching Russian as a foreign language (Russian as a foreign language). The purpose of the study can be defined as the systematization of existing pedagogical techniques to increase the motivation of students and the search for new ones, as well as the analysis of factors that reduce the motivation of students. The main research method is scientific analysis, observation and generalization. The key results of the research are the introduction of the term "polymotivation", its definition and role in the teaching process; presentation of the experience of increasing polymotivation among foreign students undergoing training at the preparatory department. It has been concluded that motivation, when divided into external and internal, cannot be considered and applied separately, as a self-sufficient unit; the term "polymotivation" is introduced as a sublimation of the types of motivation used in the teaching process; the methods and techniques of increasing polymotivation in the classroom in Russian as a foreign language have been analyzed.
Research on the Application and Practice of Blended Teaching Mode in Big Data Era

With the development of information technology, the traditional teaching model has emerged problems such as simply imparting knowledge and failing to meet students' personalized learning. Big data technology will provide richer learning resources, teaching methods and learning styles to drive new changes in education. In the era of big data, the blended teaching mode makes full use of the information-based teaching platform to break the drawbacks of the traditional teaching mode, which is of great value and significance to the reform of China's teaching mode. This paper will explore the specific application of blended teaching based on Chaoxing in practical teaching. As a blended teaching platform and analysis tool, Chaoxing can not only grasp students' learning in time and accurately complete learning evaluation, but also optimize teaching design and expand the time and space for teaching and learning. on this basis, this paper also proposes the application strategies of blended teaching mode such as increasing the strength and depth of integration of information technology and curriculum teaching in the era of big data to realize resource integration and make full use of information technology, strengthening teacher training and enhancing information technology application ability, so as to improve the quality of classroom teaching.
An FPGA-Based Optimizer Design for Distributed Deep Learning with Multiple GPUs

Since deep learning workloads perform a large number of matrix operations on training data, GPUs (Graphics Processing Units) are efficient especially for the training phase. A cluster of computers each of which equips multiple GPUs can significantly accelerate the deep learning workloads. More specifically, a back-propagation algorithm following a gradient descent approach is used for the training. Although the gradient computation is still a major bottleneck of the training, gradient aggregation and optimization impose both communication and computation overheads, which should also be reduced for further shortening the training time. To address this issue, in this paper, multiple GPUs are interconnected with a PCI Express (PCIe) over 10Gbit Ethernet (10GbE) technology. Since these remote GPUs are interconnected with network switches, gradient aggregation and optimizers (e.g., SGD, AdaGrad, Adam, and SMORMS3) are offloaded to FPGA-based 10GbE switches between remote GPUs; thus, the gradient aggregation and parameter optimization are completed in the network. The proposed FPGA-based 10GbE switches with the four optimizers are implemented on NetFPGA-SUME board. Their resource utilizations are increased by PEs for the optimizers, and they consume up to 56% of the resources. Evaluation results using four remote GPUs connected via the proposed FPGA-based switch demonstrate that these optimizers are accelerated by up to 3.0x and 1.25x compared to CPU and GPU implementations, respectively. Also, the gradient aggregation throughput by the FPGA-based switch achieves up to 98.3% of the 10GbE line rate.
Research on network news information dissemination planning path based on Knowledge Map

Today’s society has entered the information age, the traditional way of news communication is not consistent with the development of the times, the relevant departments must actively apply new technology. In order to solve the above problems, this paper studies the network news information dissemination planning path based on knowledge map. Starting from the overview of knowledge mapping, this paper introduces its definition, characteristics and digital media; At the same time, it analyzes the change of news communication under the knowledge map, and lists the promotion path of news communication under the knowledge map; This paper expounds the application significance of network information in news communication, and puts forward effective strategies for news communication, so as to provide reliable reference for relevant departments.
Feature Engineering for Motion Classification in Machine Vision

Abstract With the most advanced classification algorithms in the technological platform, the computational power requirement is on the surge. The paper hereby presents computationally trivial algorithms to simplify the process of computational intensive classifications techniques, especially in the Motion Classification arena. The proposed methods prove crucial in acting as a lightweight and computationally fast stepping stone to a fundamentally more significant application of Motion indexing and classification, Action recognition, and predictive analysis of motion energy. The algorithms classify the motions into linear, circular, or periodic motion types by following an appropriate execution order. They consider the tracked motion path of the object of interest as a sequence and use it as a starting point to perform all operations, resulting in a feature that can be classified into separate classes. Using a single parameter for classifying the motion engenders a faster and relatively more straightforward route to motion identification and elicits the algorithm’s uniqueness. Two algorithms are proposed, namely, Angle Derivative Technique and Determinant Method for classifying the motion into two classes (linear &amp; circular). On the other hand, a different algorithm identifies periodic motion using the principle of correlation on the motion sequences. All the algorithms show an average accuracy of over 95%. It also elicited an average processing time of 15.6 ms and 19.86 ms for Angle Derivative Method and Determinant Method, respectively, and 31.2 ms for periodic motion on Intel(R) Core(TM) i3-5005U CPU @ 2.00 GHz and 8GB RAM. A dataset of camera-captured videos consisting of three motion types is used for testing while the proposed methods are trained on a dataset of motion described by mathematical equations with added 3 σ noise levels.
Research on reversible ciphertext field information hiding algorithm based on data stream

At present, the reversible information hiding algorithm in ciphertext domain preprocesses the data stream according to the length of the data stream, which leads to the low information entropy of the hidden data stream. According to the length of data stream and transmission protocol, the data stream information transmission model is established; The length of data stream and packet loss rate are taken as the basis to preprocess the data stream; The mapping matrix of data stream is generated, the bit string of data stream is determined, and the encryption and decryption algorithms of data stream are designed to realize reversible information hiding in ciphertext domain of data stream. From the two aspects of algorithm implementation and performance comparison, a comparative experiment of reversible information hiding algorithm in ciphertext domain is designed. Experimental results: the data frequency difference between the hidden data stream and the original data stream is 0.05. Compared with the two methods selected in this experiment, the information entropy of data stream is 1 and 0.5 higher respectively.
A Portable GPS Signal Simulator Design Based on ZYNQ

Aiming at the problems of a large volume, high price, and complex operation of traditional global positioning system (GPS) signal simulator, a low-cost and portable GPS signal simulator based on ZYNQ is designed, which can simulate and generate multiple satellite signals. Compared with the common digital signal processing (DSP) and field programmable gate array (FPGA) combination technical solutions, ZYNQ adopts ARM + FPGA mode. In this mode, ARM and FPGA can achieve efficient coupling through an advanced extensible interface (AXI) on-chip bus interface, with a faster communication speed and more stable data transmission. The ARM module is responsible for receiving the scene information sent by the host computer as the information processing part, and generating the navigation message in the real time. The FPGA module is used as the signal processing part to process the data of multiple satellites in parallel. The intermediate frequency (IF) signal is modulated by the direct digital synthesis (DDS) technology and sent to the digital-analog converter (DAC) to generate the analog IF signal. The experimental results show that the GPS LI frequency satellite signal is successfully simulated, and the error between the positioning coordinates and the preset coordinates is within a reasonable range, which verifies the feasibility of the design scheme.
E xploration E xploitation Problem in Policy Based Deep Reinforcement Learning for Episodic and Continuous Environments

Reinforcement learning is an artificial intelligence paradigm that enables intelligent agents to accrue environmental incentives to get superior results. It is concerned with sequential decision-making problems which offer limited feedback. Reinforcement learning has roots in cybernetics and research in statistics, psychology, neurology, and computer science. It has piqued the interest of the machine learning and artificial intelligence groups in the last five to ten years. It promises that it allows you to train agents using rewards and penalties without explaining how the task will be completed. The RL issue may be described as an agent that must make decisions in a given environment to maximize a specified concept of cumulative rewards. The learner is not taught which actions to perform but must experiment to determine which acts provide the greatest reward. Thus, the learner has to actively choose between exploring its environment or exploiting it based on its knowledge. The exploration-exploitation paradox is one of the most common issues encountered while dealing with Reinforcement Learning algorithms. Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. We describe how to utilize several deep reinforcement learning (RL) algorithms for managing a Cartpole system used to represent episodic environments and Stock Market Trading, which is used to describe continuous environments in this study. We explain and demonstrate the effects of different RL ideas such as Deep Q Networks (DQN), Double DQN, and Dueling DQN on learning performance. We also look at the fundamental distinctions between episodic and continuous activities and how the exploration-exploitation issue is addressed in their context.
A Typical Practical Team Structure and Setup in Agile Software Development

Agile software development provides a different approach to Agile projects. This approach emphasizes employing experienced and committed individuals. In this approach, team management is different from what the disciplined methods defined. In this condition, team organization, team members’ roles, and responsibilities, and project control methods need to be defined in a new form. Although some Agile methods define particular roles, it seems that team structure in Agile projects in real projects is different from what is expected. This paper aimed to propose a common team structure and setup for Agile teams in practice. Differences in the Agile and plan-driven approaches in software development lead to define new concepts such as team self-organization, on-site customer, leadership, and collaboration in Agile teams. Also, some new roles and responsibilities have been defined in Agile teams, including Technical Lead, Product Owner, Architecture Owner, Developer, etc.
Modulation Recognition Algorithm of Radar Signal Based on ICanny-CNN

Aiming at the low recognition rate on the composite signal modulation in low Signal to Noise Ratio (SNR), this paper proposes a modulation recognition algorithm based on ICanny-CNN. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.
Mixed Reality in STEM Didactics: Case Study of Assembly Drawings of Complex Machines

Mixed Reality (MR) could help students in the understanding of complex concepts as well as increase their motivation in the learning process. In this work, our aim is to propose a MR application for the support of engineering students in the understanding of assembly drawings of complex machines. We presented the application of our design methodology for this case study. Then, based on the results of a user study with a sample of students, we tried to improve the usability and the user experience of the MR application, proposing an updated version. The usability of the revised application was in the range “good-excellent” (mean SUS score 77.0). We also presented the lessons learned in this case study, that can be a starting point for a renewal of consolidated didactic processes aiming at future application of MR in other STEM courses.
Anomaly Detection in Brazilian Federal Government Purchase Cards Through Unsupervised Learning Techniques

The Federal Government Purchase Card (CPGF) has been used in Brazil since 2005, allowing agencies and entities of the federal public administration to make purchases of material and provision of services through this method. Although this payment system offers several advances, in the technological and administrative aspect, it is also susceptible to possible cases of card misuse and, consequently, waste of public funds, in the form of purchases that do not comply with the terms of the current legislation. In this work, we approach this problem by testing and evaluating unsupervised learning techniques on detecting anomalies in CPGF historical data. Four different methods are considered for this task: K-means, agglomerative clustering, a network-based approach, which is also introduced in this study, and a hybrid model. The experimental results obtained indicate that unsupervised methods, in particular the network-based approach, can indeed help in the task of monitoring government purchase card expenses, by flagging suspect transactions for further investigation without requiring the presence of a specialist in this process.
Social distance measurement for indoor environments

Social distancing is a suggested solution by many scientists, health care providers and researchers to reduce the spread of COVID-19 in public places. Over a year ago most countries have closed their borders, put people under lockdown, and have been suspending people from work and travel. However, there are still many organizations that need to operate, especially hospitals, services industry, governments, etc. However, people cannot maintain social distancing which includes staying at least 1.5 2 meters from other people because they need to communicate with each other. As a result, this increases the infection of Covid-19. This work proposes a social distancing tracking tool in offices or indoor places. We propose a YOLOv5-based Deep Neural Network (DNN) model to automate the process of monitoring the social distancing via object detection and tracking approaches. We detect office objects of known size and use it to estimate the social distance in real-time with the bounding boxes in indoor environments.
Moderate Deviation Analysis for Quantum State Transfer

Quantum state transfer involves two parties who use pre-shared entanglement and noiseless communication in order to transfer parts of a quantum state. In this work, we quantity the communication cost of one-shot state splitting in terms of the partially smoothed max-information. We then give an analysis of state splitting in the moderate deviation regime, where the error in the protocol goes sub-exponentially fast to zero as a function of the number of i.i.d. copies. The main technical tool we derive is a tight relation between the partially smoothed max-information and the hypothesis testing relative entropy, which allows us to obtain the expansion of the partially smoothed max-information for i.i.d. states in the moderate deviation regime. This then also establishes the moderate deviation analysis for other variants of state transfer such as state merging and source coding.
Intuitionistic Neuro-Fuzzy Optimization in the Management of Medical Diagnosis

Diabetes has become a major concern nowadays and its complications are affecting various organs of a diabetic patient.Therefore, a multi-dimensional technique including all parameters is required to detect the cause, its proper diagnostic procedure and its prevention.In this present work, a technique has been introduced that seeks to build an implementation for the intelligence system based on neural networks.Moreover, it has been described that how the proposed technique can be used to determine the membership together with the non-membership functions in the intuitionistic environment.The dataset has been obtained from Pima Indians Diabetes Database (PIDD).In this work, a complete diagnostic procedure of diabetes has been introduced with seven layered structural frameworks of an Intuitionistic Neuro Sugeno Fuzzy System (INSFS).The first layer is the input, in which six factors have been taken as an input variable.Subsequently, a neural network framework has been developed by constructing IFN for all the six input variables, and then this input has been fuzzified by using triangular intuitionistic fuzzy numbers.In this work, we have introduced a novel optimization technique for the parameters involved in the INSFS.Moreover, an inference system has also been framed for the neural network known as INFS.The results have also been given in the form of tables, which describe each concluding factor.