Reviewerly/exHarmony huggingface dataset:
Simple circuit simulations of classical and quantum Turing machines

We construct reversible Boolean circuits efficiently simulating reversible Turing machines. Both the circuits and the simulation proof are rather simple. Then we give a fairly straightforward generalization of the circuits and the simulation proof to the quantum case.
Solving systems of Boolean multivariate equations with quantum annealing

Polynomial systems over the binary field have important applications, especially in symmetric and asymmetric cryptanalysis, multivariate-based postquantum cryptography, coding theory, and computer algebra. In this paper, we study the quantum annealing model for solving Boolean systems of multivariate equations of degree 2, usually referred to as the multivariate quadratic problem. We present different methodologies to embed the problem into a Hamiltonian that can be solved by available quantum annealing platforms. In particular, we provide three embedding options, and we highlight their differences in terms of quantum resources. Moreover, we design a machine-agnostic algorithm that adopts an iterative approach to better solve the problem Hamiltonian by repeatedly reducing the search space. Finally, we use D-Wave devices to successfully implement our methodologies on several instances of the multivariate quadratic problem.
Intelligent Gloves for Deaf and Dumb People

The main objective of this project is to help deaf and dumb people by removing communication barrier so that they are not restricted in a small social circle and are able to convey their feelings and emotions whenever they want. Also, it would be helpful in educational and health issues related to deaf and dumb people. The gloves contain sensors which when touched displays a message in the phone, thus helping the person to communicate with others.
A Voltage-Controlled Magnetic Anisotropy based True Random Number Generator

An emerging nanodevice called voltage controlled magnetic anisotropy magnetic tunnel junction (VCMA-MTJ) exhibits stochastic behavior that can be taken advantage of to produce random numbers at low power, low area, and high speed. In this paper, a 40MHz 104fJ/bit true random number generator (tRNG) was designed that works by applying 1V pulses to the VCMA-MTJ and then reading the resultant state, achieving 6800x the throughput of the most energy-efficient MTJ-based tRNG module designed for security while only consuming 5.21x more energy per bit. However, VCMA-MTJ based tRNG was also found to be very sensitive to variations in free-layer thickness, which can be solved by trading off an 8x increase in area and energy consumption.
The Application of Network Multimedia Technology in Vocal Music Teaching

As a part of modern information technology, multimedia network technology is an inevitable trend of education reform. The application of multimedia network technology in classroom teaching and the comprehensive integration with various disciplines are the requirements of classroom reform in the new era. Therefore, by actively exerting the teaching function of multimedia network technology, it is to build conditions suitable for the physical and mental development of students and create the unique information teaching method of the times. This article studies the application of network multimedia technology in VCT. It summarizes the characteristics of VCT in the literature and materials related to vocal music application teaching. Then, the advantages of network multimedia technology applied to VCT can break through the traditional VCT. Finally, the application status of network multimedia technology in VCT was investigated. Through the analysis of the results, relevant conclusions were drawn. The survey results concluded that the main problem in the application of network multimedia technology in VCT is the production form of courseware PPT. More complicated, accounting for more than 43%.
A plug-and-play demosaicing and denoising method on mobile cameras

Image demosaicing and denoising are two important processes in the ISP pipeline of mobile cameras, because almost all mobile cameras in use today require colorful images generated by demosaicing algorithm, and the small sensor area of mobile cameras triggers low signal-to-noise ratio. Over the years, a considerable number of sequential demosaicing and denoising methods have been proposed, while they suﬀer from estimating the noise distribution and adjusting the hyper-parameters in order to balance demosaicing and denoising. There exit simultaneous demosaicing and denoising methods solving these problems. But they lack guidelines designed for mobile cameras. We propose a Plug-and-Play (PnP) demosaicing and denoising method on mobile cameras. Our method is built on PnP demosaicing framework which is derived from variable splitting theory. Any color demosaicing algorithm (i.e., bilinear, Malvar) can be plugged into our framework. We novelly trained an ISO conditioned denoiser for the framework and iteratively apply the denoiser in it. The ISO conditioned denoiser not only removes noise from the demosaicing procedure itself but also noise from camera sensors. By introducing ISO settings to the denoiser, our method takes possession of the adaptability and robustness in various capturing environments under diﬀerent camera settings. Our method has only two hyperparameters to tune, which eases the hyper-parameter adjustment in sequential demosaicing and denoising methods. Extensive experiments on synthetic datasets show that our method performs better than sequential demosaicing and denoising methods and is practical for mobile cameras.
Expert-guided Symmetry Detection in Markov Decision Processes

Learning a Markov Decision Process (MDP) from a fixed batch of trajectories is a non-trivial task whose outcome's quality depends on both the amount and the diversity of the sampled regions of the state-action space. Yet, many MDPs are endowed with invariant reward and transition functions with respect to some transformations of the current state and action. Being able to detect and exploit these structures could benefit not only the learning of the MDP but also the computation of its subsequent optimal control policy. In this work we propose a paradigm, based on Density Estimation methods, that aims to detect the presence of some already supposed transformations of the state-action space for which the MDP dynamics is invariant. We tested the proposed approach in a discrete toroidal grid environment and in two notorious environments of OpenAI's Gym Learning Suite. The results demonstrate that the model distributional shift is reduced when the dataset is augmented with the data obtained by using the detected symmetries, allowing for a more thorough and data-efficient learning of the transition functions.
On the State Approach Representations of Convolutional Codes over Rings of Modular Integers

In this study, we prove the existence of minimal first-order representations for convolutional codes with the predictable degree property over principal ideal artinian rings. Further, we prove that any such first-order representation leads to an input/state/output representation of the code provided the base ring is local. When the base ring is a finite field, we recover the classical construction, studied in depth by J. Rosenthal and E. V. York. This allows us to construct observable convolutional codes over such rings in the same way as is carried out in classical convolutional coding theory. Furthermore, we prove the minimality of the obtained representations. This completes the study of the existence of input/state/output representations of convolutional codes over rings of modular integers.
Analysis of teacher self-efficacy and its relationship with the level of academic satisfaction in times of COVID-19

This work describes the characterization of teacher self-efficacy in remote education contexts due to the COVID-19 pandemic and its relationship with the level of student satisfaction with the courses received in the 2020–2 cycle at the Faculty of Engineering from a Cajamarca university. For this, the descriptive and correlational cross-sectional nonexperimental basic research first sought to characterize the level of teacher self-efficacy according to gender, career in which the teacher teaches classes, age and then analyze if there was a correlation with the level of satisfaction of the students with the courses received. For this, two instruments were used, the first the university teacher self-efficacy test, which had a high level of reliability equal to 0.996 and the rubric of satisfaction level of the courses taught by the teachers, typical of the university, which also presented high levels of consistency, demonstrated by a Cronbach's Alpha equal to 0.997. After using descriptive statistics and correlation analysis, it was determined that the teachers had high levels of self-efficacy, but this did not show a correlation with the level of satisfaction. Therefore, it's concluded that despite the different combinations carried out for the correlational analysis between the levels of self-efficacy and the level of student satisfaction, there is no relationship between these variables. Despite being in a remote education context, similar behaviors continue to occur as in face-to-face education.
Generation of spin cat states in an engineered Dicke model

We study trajectories of collective spin states of an ensemble of spinors. The spinors considered here are either trapped ions in free space or atoms confined in a cavity, both systems of which are engineered through their interactions with light fields to obey an effective Dicke model. In an appropriate limit of the Dicke model, one obtains one-axis twisting dynamics of the collective spin and evolution after a finite time to a spin cat state or, in the long-time limit, the Dicke state ${|S,0\ensuremath{\rangle}}_{x}$, conditioned upon there being no photon emissions from the system (i.e., no quantum jumps). If there is a jump, however, the system evolves probabilistically into one of a finite number of entangled-state cycles, where the system then undergoes a persistent sequence of jumps between two Dicke-state superpositions in a rotated basis. The different cycles can be distinguished by the frequency at which jumps occur.
Optimizing Diffusion Rate and Label Reliability in a Graph-Based Semi-supervised Classifier

Semi-supervised learning has received attention from researchers, as it allows one to exploit the structure of unlabeled data to achieve competitive classification results with much fewer labels than supervised approaches. The Local and Global Consistency (LGC) algorithm is one of the most well-known graph-based semi-supervised (GSSL) classifiers. Notably, its solution can be written as a linear combination of the known labels. The coefficients of this linear combination depend on a parameter \(\alpha \), determining the decay of the reward over time when reaching labeled vertices in a random walk. In this work, we discuss how removing the self-influence of a labeled instance may be beneficial, and how it relates to leave-one-out error. Moreover, we propose to minimize this leave-one-out loss with automatic differentiation. Within this framework, we propose methods to estimate label reliability and diffusion rate. Optimizing the diffusion rate is more efficiently accomplished with a spectral representation. Results show that the label reliability approach competes with robust \(\ell _1\)-norm methods and that removing diagonal entries reduces the risk of overfitting and leads to suitable criteria for parameter selection.
GUIDELINES, IDEAS AND APPROACHES FOR AI EDUCATION IN SCHOOL. KNOWLEDGE STRUCTURING AND SEMANTIC MODELING

The strategic documents at European and national level set as a main goal for education the introduction of elements of artificial intelligence in the various educational levels and forms of training in a way appropriate for the learners. In the last two years, experimental training in artificial intelligence in high school has been implemented in Bulgaria. The report examines some problems and challenges in organizing and conducting this training in different types of schools, specialties and classes. Some guidelines are outlined and ideas for creating an appropriate curriculum and learning resources in the field of knowledge structuring and semantic modelling are shared. An approach to teacher and student training is proposed. The authors share their experience in teaching teachers and students and analyse the results of surveys and tests.
A Survey of Optical Character Recognition Techniques on Indic Script

Optical Character Recognition (OCR) is a technique that converts printed text and images into a digitized form, which can be manipulated by a machine. It has many application sectors like Banking, Financial, Legal applications, etc. Initially researchers were addressed and proposed many algorithms in image processing for character recognition and mapping. Most of the researchers focused on the Latin script English as it was supported by the Encoding standard ASCII. Later, people start realizing that OCR techniques for other languages are also gaining momentum these days. With the advent of technology and Unicode revolution, native language-based OCR solutions started emerging. In this paper, we aim to focus on the latest machine learning techniques applied on OCR for the language English and two languages from Indian continent were presented. Out of the two Indian languages, one is the stroke-based language, i.e. Hindi, and the other being cursive script-based language Telugu.
Automating Test Case Identification in Java Open Source Projects on GitHub

Software testing is one of the very important Quality Assurance (QA) components. A lot of researchers deal with the testing process in terms of tester motivation and how tests should or should not be written. However, it is not known from the recommendations how the tests are written in real projects. In this paper, the following was investigated: (i) the denotation of the word "test" in different natural languages; (ii) whether the number of occurrences of the word "test" correlates with the number of test cases; and (iii) what testing frameworks are mostly used. The analysis was performed on 38 GitHub open source repositories thoroughly selected from the set of 4.3 M GitHub projects. We analyzed 20 340 test cases in 803 classes manually and 170 k classes using an automated approach. The results show that: (i) there exists a weak correlation (r = 0.655) between the number of occurrences of the word "test" and the number of test cases in a class; (ii) the proposed algorithm using static file analysis correctly detected 97 % of test cases; (iii) 15 % of the analyzed classes used ttmain() function whose represent regular Java programs that test the production code without using any third-party framework. The identification of such tests is very complex due to implementation diversity. The results may be leveraged to more quickly identify and locate test cases in a repository, to understand practices in customized testing solutions, and to mine tests to improve program comprehension in the future.
Development of Skin Sensing1 Module for Safety Control in Human-Robot Collaboration

In this study, a skin sensing module is designed for the robotic arm. Based on this module, the robotic arm can perceive physical contacts or collisions, and trigger its predefined safety mechanism. A solution is provided to deal with the hidden work safety issue arisen with the advancement of human-robot collaboration. The purpose of this work is to protect the machine operators and improve their working environment by providing the modularization of the signal transmission. According to the proposed architecture through sensitive pressure sensing technique and wireless transmission, the robotic arm in execution can respond in a timely manner for protection, and ensure the task execution and safety control of the robot arm.
Encoder-decoder models for chest X-ray report generation perform no better than unconditioned baselines

High quality radiology reporting of chest X-ray images is of core importance for high-quality patient diagnosis and care. Automatically generated reports can assist radiologists by reducing their workload and even may prevent errors. Machine Learning (ML) models for this task take an X-ray image as input and output a sequence of words. In this work, we show that ML models for this task based on the popular encoder-decoder approach, like 'Show, Attend and Tell' (SA&T) have similar or worse performance than models that do not use the input image, called unconditioned baseline. An unconditioned model achieved diagnostic accuracy of 0.91 on the IU chest X-ray dataset, and significantly outperformed SA&T (0.877) and other popular ML models (p-value < 0.001). This unconditioned model also outperformed SA&T and similar ML methods on the BLEU-4 and METEOR metrics. Also, an unconditioned version of SA&T obtained by permuting the reports generated from images of the test set, achieved diagnostic accuracy of 0.862, comparable to that of SA&T (p-value ≥ 0.05).
Design and Characteristics of CMOS Inverter based on Multisim and Cadence

Abstract Known as complementary symmetrical metal oxide semiconductor (cos-mos), complementary metal oxide semiconductor is a metal oxide semiconductor field effect transistor (MOSFET) manufacturing process, which uses complementary and symmetrical pairs of p-type and n-type MOSFETs to realize logic functions. CMOS technology is used to build integrated circuit (IC) chips, including microprocessors, microcontrollers, memory chips (including CMOS BIOS) and other digital logic circuits. CMOS technology is also used in analog circuits, such as image sensors (CMOS sensors), data converters, RF circuits (RF CMOS), and highly integrated transceivers for various types of communications. Based on multisim 14.0 and cadence, the characteristics and performance of CMOS inverter are studied by simulation.
A Design of Policy-Based Scheduling for Federated Multi-Clusters

Kubernetes, the open-source container-based cloud orchestration platform, is becoming the de-facto standard for automatically deploying and managing containerized applications in the cloud environment. Recently, A cluster in Kubernetes can be extended to multiple clusters and the federated Kubernetes architecture is proposed for managing these multi-clusters. In this paper, we propose policy-based scheduling architecture for multiple clusters. The proposed scheduling architecture determines the appropriate federated clusters to deploy workload and the number of replicas to each cluster. The policy engine is used to determines the appropriate federated clusters to deploy workload, the number of replicas to each cluster, according to the given policies.
Knowledge socialism

In this article, Michael Peters articulates knowledge socialism as an alternative orientation to the more well-known terms of knowledge economy and knowledge capitalism. In the course, Peters gives a holistic account of the history of knowledge capitalism and socialism, as seen in the context of higher education and university life over time. Knowledge capitalism for Peters reflects a profound transformation of knowledge in relation to the recent phase of information technology use and digital capitalism. Influenced by Jean-Francois Lyotard, knowledge socialism can be seen as an alternative view informed by ‘post-structural Marxism’. Knowledge socialism entails a critique of the ownership and privatisation strategies encouraged under knowledge capitalism. Knowledge cultures are significant in this context, as cultures under knowledge capitalism meet in the unequal setting of the global, technological information and knowledge marketplace. As Peters argues, knowledge socialism is fundamental to traditions of university life, which is marked by the free exchange of ideas, enhanced by peer review. Meanwhile, knowledge capitalism hides its exclusivity, and its implications of scarcity and competition, under the name of innovation, despite the limits exclusion places on innovation in idea creation. The essay thus gives a systematic view of the way economic and technological values intersect in the contemporary era, while reflecting on the state of current understanding of ideology, capitalism, socialism, and culture.
A confluência das tecnologias I.4.0 e novos modelos de negócio: uma revisão sistemática de literatura

Objective of the study: The study’s objective is to provide a systematic literature review on the coevolution of the Technologies associated to Industry 4.0 and business models.Methodology/approach: The research was built on conceptual blocs at the interface between Industry 4.0 (I 4.0), business models (BM) and digitalization /digital business models (DBM) in order to identify relevant trends. Through bibliometric techniques, 80 articles were selected from Scopus and WoS, using specialized software.Originality/ Relevance: Few studies exist on the interface under analysis. This article contributes to this emerging literature, providing a synthesis of such literature, including both a detailed descriptive component and an analysis of the main thematic clusters associated to this interface.Main results: The literature still needs to evolve into a framework of a 4.0 business model. The majority of the existing studies focus on business models to specific technologies associated to Industry 4.0. Among the 80 articles scrutinized, 56 were associated to business models and I4.0, and 24 linked business models and digitalization. Few really described a business model that could be characterized as an I4.0 business model.Theoretical/methodological contributions: Identification of key studies underlying the elaboration of an innovative business model associated to I4.0. An immediate and useful characterization of the literature. Identification of the main thematic clusters.Social/management contributions: To provide academics and professionals (especially in Business/Economics) with a detailed and thorough review of the most relevant literature, including the main aspects underlying the creation of innovative business models based on I4.0.