Reviewerly/exHarmony huggingface dataset:
Hybrid Automata: An Algorithmic Approach Behavioral Hybrid Systems

Hybrid automata strategies have advanced as a vital tool to design, check and direct the execution of hybrid systems. Any way they can – and we assume should – be utilized to communicate quantitative models about hybrid systems in different areas, for example, experimental sciences. Since the conventional design of hybrid automata compares well to consecutively integrate behavioral chains in living creatures, we look for a use of hybrid modeling procedures in the social sciences and, particularly, brain research. We attempt to address the question related to how human drivers move onto an expressway and simultaneously utilize this study as our test-bed for utilizing hybrid automata inside behavioral sciences. Hybrid automata give a language to displaying and exploring advanced and simple calculations in real-time systems. Hybrid automata are studied here from a dynamical systems point of view. Essential and adequate conditions for the presence and uniqueness of arrangements are inferred and a class of hybrid automata whose arrangements rely consistently upon the underlying state is described. The outcomes on presence, uniqueness, and progression fill in as a beginning stage for solid study. In this paper, we present the structure of hybrid automata as a model and detailed language for hybrid systems. Hybrid automata can be seen as a theory of timed automata, in which the behavior of factors is represented in each state by a bunch of differential conditions. We show that a large number of the models considered in the workshop can be characterized by hybrid automata. While the reachability issue is undecidable in any event, for extremely confined classes of hybrid automata, we present two semi-decision techniques for checking security properties of piecewise-straight hybrid automata, in which all factors change at steady rates. The two techniques are based, individually, on limiting and figuring fix points on for the most part endless state spaces. We show that if the end of the method, at that point they offer the right responses. We then show that for a significant number of the run of the mill workshop models, the strategies do end and hence give an algorithmic approach to confirming their properties.
Controllable continuous variable quantum state distributor

To scale quantum information processing, quantum state distributors are an indispensable technology in quantum networks. We present a universal scheme of a continuous variable quantum state distributor that performs point-to-multipoint distributions via quantum teleportation with partially disembodied transport. The fidelity of the state at the output nodes can be conveniently manipulated as needed by engineering the correlation noise of the Einstein–Podolsky–Rosen (EPR) beam. For a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>1</mml:mn> <mml:mo stretchy="false">→<!-- → --></mml:mo> <mml:mn>2</mml:mn> </mml:math> distributor, controllable distributions were demonstrated by manipulating the squeezing factor of EPR entanglement. The fidelities of the two receivers gradually changed from (2/3, 2/3) to (0.95, 0.17) corresponding to the transition from symmetric to asymmetric quantum cloning.
Near-MDS codes from elliptic curves

Abstract We provide a geometric construction of $$[n,9,n-9]_q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mn>9</mml:mn> <mml:mo>,</mml:mo> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> <mml:mn>9</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> <mml:mi>q</mml:mi> </mml:msub> </mml:math> near-MDS codes arising from elliptic curves with n $${\mathbb {F}}_q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>q</mml:mi> </mml:msub> </mml:math> -rational points. Furthermore, we show that in some cases these codes cannot be extended to longer near-MDS codes.
Waveform Domain Deep Learning Approach for RF Fingerprinting

With the widespread deployment of wireless sensor networks and the nascent Internet of Things (IoT), enabling devices to be connected in wider, and denser ecosystems, improved wireless security has become of paramount importance. Limited power and computational resources of these devices, however, render sophisticated algorithms and protocols not suitable for all the devices. Radio Frequency (RF) fingerprinting has the potential to enhance the security and with increasing popularity of deep learning, RF fingerprinting approaches have attracted attention with new techniques proposed. In this paper we present a novel waveform domain-based approach operating on images generated from captured raw samples for device identification. The use of images, as opposed to raw sample sequences, enables the capture of information from theoretically infinite number of raw samples without impacting the structure and the complexity of the subsequent deep learning processing. We use a simple Dense Neural Network (DNN) model which is implemented and trained on waveform images generated from the captured raw samples. The efficacy of the proposed approach is demonstrated using over-the-air signals captured from 12 Zigbee devices, with the proposed approach achieving near 99% identification accuracy.
Combination of deep neural networks and logical rules for record segmentation in historical handwritten registers using few examples

This work focuses on the layout analysis of historical handwritten registers, in which local religious ceremonies were recorded. The aim of this work is to delimit each record using few available training data. To this end, two approaches are proposed. Firstly, three state-of-the-art object detection networks are explored and compared. Further experiments are then conducted on Mask R-CNN, as it yields the best performance. Secondly, we introduce and investigate Deep&Syntax, a hybrid system that takes advantages of recurrent patterns to delimit each record, by combining u-shaped networks and logical rules. Finally, these two approaches are evaluated on 3708 French records (sixteenth–eighteenth centuries), as well as on the Esposalles public database, containing 253 Spanish records (seventeenth century). While both systems perform well on homogeneous documents, we observe a significant drop in performance with Mask R-CNN on more challenging documents, especially when trained on a small, non-representative subset. By contrast, Deep&Syntax relies on steady patterns and is therefore able to process a wider range of documents with less training data. When both systems are trained on 120 documents, Deep&Syntax produces 15% more match configurations and reduces the ZoneMap surface error metric by 30%. It also outperforms Mask R-CNN when trained on a database three times smaller. As Deep&Syntax generalizes better, we believe it can be used for massive parish register processing, as collecting and annotating a sufficiently large and representative set of training data is not always achievable.
A lightweight cryptographic algorithm for the transmission of images from road environments in self-driving

Abstract With the large-scale application of 5G in industrial production, the Internet of Things has become an important technology for various industries to achieve efficiency improvement and digital transformation with the help of the mobile edge computing. In the modern industry, the user often stores data collected by IoT devices in the cloud, but the data at the edge of the network involves a large of the sensitive information, which increases the risk of privacy leakage. In order to address these two challenges, we propose a security strategy in the edge computing. Our security strategy combines the Feistel architecture and short comparable encryption based on sliding window (SCESW). Compared to existing security strategies, our proposed security strategy guarantees its security while significantly reducing the computational overhead. And our GRC algorithm can be successfully deployed on a hardware platform.
MEDIA DIDACTIC SUPPORT OF THE PROCESS OF FUTURE PRIMARY SCHOOL TEACHERS’ FOREIGN LANGUAGE TRAINING

The article reveals the essence of media didactic support of future primary school teachers’ foreign language training. It consists in providing students with methodological support in order to form their foreign language communicative competence through completeing tasks, solving professional and life situations. Media didactic support guids in creating a foreign language communicative environment, which provides clearly defined tasks (situations, instructions) within the subject of the discipline of the foreign language unit and ways to analyze, evaluate foreign media, create own ones and perform manual actions using media tools. The paper presents some elements of the author's media didactic support of future primary school teachers’ foreign language training in the content of foreign language disciplines, which testify to the diversity of future primary school teachers’ foreign language training with didactic tools, methods and forms of foreign language teaching. Applying theoretical research methods (analysis, comparison, generalization, systematization, synthesis and specification), emphasis is placed on aspects of media didactics development in the process of future primary school teachers’ foreign language training, which allows integration of future school teachers’ foreign language communicative competence and their media literacy. It is established that alongside with traditional methods of foreign language teaching (conversation, explanation, demonstration, etc.) innovative ones are being actively used (the usage of computer programs, the usage of mobile applications, creative tasks, webquest, filming, audio recording, analysis of problem situations and others). Media tools (digital images and photos, collages, comics, screenshots, websites, blogs, infographics, clusters, audio recordings, podcasts, cartoons, audiobooks, online dictionaries, social networks, mobile applications for others) expand the didactic tools of foreign language teaching.
Commitment Levels of Health Care Providers in Using the District Health Information System and the Associated Factors for Decision Making in Resource-Limited Settings: Cross-sectional Survey Study

Background Changing the culture of information use, which is one of the transformation agendas of the Ministry of Health of Ethiopia, cannot become real unless health care providers are committed to using locally collected data for evidence-based decision making. The commitment of health care providers has paramount influence on district health information system 2 (DHIS2) data utilization for decision making. Evidence is limited on health care providers’ level of commitment to using DHIS2 data in Ethiopia. Therefore, this study aims to fill this evidence gap. Objective This study aimed to assess the levels of commitment of health care providers and the factors influencing their commitment levels in using DHIS2 data for decision making at public health care facilities in the Ilu Aba Bora zone of the Oromia national regional state, Ethiopia in 2020. Methods The cross-sectional quantitative study supplemented by qualitative methods was conducted from February 26, 2020 to April 17, 2020. A total of 264 participants were approached. SPSS version 20 software was used for data entry and analysis. Descriptive and analytical statistics, including bivariable and multivariable analyses, were performed. Thematic analysis was conducted for the qualitative data. Results Of the 264 respondents, 121 (45.8%, 95% CI 40.0%-52.8%) respondents showed high commitment levels to use DHIS2 data. The variables associated with the level of commitment to use DHIS2 data were found to be provision of feedback for DHIS2 data use (adjusted odds ratio [AOR] 1.85, 95% CI 1.02-3.33), regular supervision and managerial support (AOR 2.84, 95% CI 1.50-5.37), information use culture (AOR 1.92, 95% CI 1.03-3.59), motivation to use DHIS2 data (AOR 1.80, 95% CI 1.00-3.25), health needs (AOR 3.96, 95% CI 2.11-7.41), and competency in DHIS2 tasks (AOR 2.41, 95% CI 1.27-4.55). Conclusions In general, less than half of the study participants showed high commitment levels to use DHIS2 data for decision making in health care. Providing regular supportive supervision and feedback and increasing the motivation and competency of the health care providers in performing DHIS2 data tasks will help in promoting their levels of commitment that can result in the cultural transformation of data use for evidence-based decision making in health care.
Covid-19 and Universities in Latin America

It is necessary to know the state of the Universities in Latin America. The general objective was raised, to analyze the situation of Covid-19 and Universities in Latin America, considering different aspects. Methodology, the research presents a qualitative-interpretative design, documentary type, 30 documents have been selected, carried out in the period 2020 - 2021; including: scientific articles, review articles and information from websites of recognized organizations. The keywords used in the searches were: Covid-19, Latin America, universities in Latin America and Covid-19 universities in Latin America. Results, COVID-19 is keeping children and adolescents around the world out of schools and universities. Conclusions, the teachers and students of Latin American universities make permanent efforts to continue their academic and research activities. COVID-19 has exposed the shortcomings in terms of infrastructure and mastery of ICTs.
Practical verification protocols for analog quantum simulators

Abstract Analog quantum simulation is expected to be a significant application of near-term quantum devices. Verification of these devices without comparison to known simulation results will be an important task as the system size grows beyond the regime that can be simulated classically. We introduce a set of experimentally-motivated verification protocols for analog quantum simulators, discussing their sensitivity to a variety of error sources and their scalability to larger system sizes. We demonstrate these protocols experimentally using a two-qubit trapped-ion analog quantum simulator and numerically using models of up to five qubits.
A comparative research on clothing images classification based on neural network models

To make deep learning really work in real life, it is better for the model to be used with neural networks of small memory requirements and small computations. In this paper, four neural network models, i.e. Fully Connected Neural Network, CNN, MobileNetV1, and MobileNetV2 are applied to deal with the classification of clothing images in the Fashion-MNIST dataset and the results are compared. MobileNetV2, which performs best, is a deep learning network in the field of image classification and recognition. With the development of robots, smart auto, and augmented reality in real-world application, it is better to use a network such as MobileNet in a computationally limited platform. The experimental results show that 1) MobileNet is a more effective method of classification of clothing images compared to Fully Connected Neural Network and CNN, with an accuracy rate of 91%. 2) MobileNetV2 has greater improvement in accuracy than the previous model, which accuracy has reached to 93%. 3) MobileNet is simpler than Fully Connected Neural Network and CNN model structures, although training time has increased, it ensures higher accuracy.
Quantum computation and error correction based on continuous variable cluster states*

Measurement-based quantum computation with continuous variables, which realizes computation by performing measurement and feedforward of measurement results on a large scale Gaussian cluster state, provides a feasible way to implement quantum computation. Quantum error correction is an essential procedure to protect quantum information in quantum computation and quantum communication. In this review, we briefly introduce the progress of measurement-based quantum computation and quantum error correction with continuous variables based on Gaussian cluster states. We also discuss the challenges in the fault-tolerant measurement-based quantum computation with continuous variables.
Recent Advances in Video Question Answering: A Review of Datasets and Methods

Video Question Answering (VQA) is a recent emerging challenging task in the field of Computer Vision. Several visual information retrieval techniques like Video Captioning/Description and Video-guided Machine Translation have preceded the task of VQA. VQA helps to retrieve temporal and spatial information from the video scenes and interpret it. In this survey, we review a number of methods and datasets for the task of VQA. To the best of our knowledge, no previous survey has been conducted for the VQA task.
How Many Data Points is a Prompt Worth

When fine-tuning pretrained models for classification, researchers either use a generic model head or a task-specific prompt for prediction. Proponents of prompting have argued that prompts provide a method for injecting task-specific guidance, which is beneficial in low-data regimes. We aim to quantify this benefit through rigorous testing of prompts in a fair setting: comparing prompted and head-based fine-tuning in equal conditions across many tasks and data sizes. By controlling for many sources of advantage, we find that prompting does indeed provide a benefit, and that this benefit can be quantified per task. Results show that prompting is often worth 100s of data points on average across classification tasks.
The Theoretical and Experimental Analysis of the Maximal Information Coefficient Approximate Algorithm

Abstract In the era of big data, correlation analysis is significant because it can quickly detect the correlation between factors. And then, it has been received much attention. Due to the good properties of generality and equitability of the maximal information coefficient (MIC), MIC is a hotspot in the research of correlation analysis. However, if the original approximate algorithm of MIC is directly applied into mining correlations in big data, the computation time is very long. Then the theoretical time complexity of the original approximate algorithm is analyzed in depth and the time complexity is n 2.4 when parameters are default. And the experiments show that the large number of candidate partitions of random relationships results in long computation time. The analysis is a good preparation for the next step work of designing new fast algorithms.
Evaluating the Masked and Unmasked Face with LeNet Algorithm

Face recognition is a biometric technique that has been widely used in many fields. Most of the face recognition applications are used in the security and surveillance system. However, due to current pandemic, wearing facemasks is an obligation for everybody in public places. Hence, face recognition will encounter a problem such as extracting the facial features due to blockage caused by the facemasks. Thus, it will lower down the recognition rate level. The collected dataset consists of two categories which is masked face and unmasked face. This dataset called FaceMask Dataset was obtained from kaggle website. The Multi-Task Cascaded Neural Network (MTCNN) was used to find the face region in the dataset, and it will undergo image feature extraction and remove the undetected face as to prepare a proper training dataset before it can be trained by using LeNet algorithm. As the result, the categories in this work are fall into two classes, which masked face and unmasked face. The training accuracy was 98.21% and it will focus on the features on each result and will justify the difference between the accuracy for both classes. The proposed method was able to achieve 98.21% accuracy with LeNet algorithm as the face image was mainly focused on the face area without taking full size of the image.
Will face recognition revolutionise the shopping experience?

Today, facial recognition is a prevalent though controversial technology. It has become established as the most natural means of biometric identification, as it doesn’t require any contact or physical interaction by the end user – a feature that has become even more important during the coronavirus pandemic. Facial recognition is also one of the most adopted biometric modalities that's now being used for many and varied use-cases, such as in payments or loyalty experiences.
A Parametrizable High-Level Synthesis Library for Accelerating Neural Networks on FPGAs

Abstract In recent years, Convolutional Neural Network CNN have been incorporated in a large number of applications, including multimedia retrieval and image classification. However, CNN based algorithms are computationally and resource intensive and therefore difficult to be used in embedded systems. FPGA based accelerators are becoming more and more popular in research and industry due to their flexibility and energy efficiency. However, the available resources and the size of the on-chip memory can limit the performance of the FPGA accelerator for CNN. This work proposes an High-Level Synthesis HLS library for CNN algorithms. It contains seven different streaming-capable CNN (plus two conversion) functions for creating large neural networks with deep pipelines. The different functions have many parameter settings (e.g. for resolution, feature maps, data types, kernel size, parallelilization, accuracy, etc.), which also enable compile-time optimizations. Our functions are integrated into the HiFlipVX library, which is an open source HLS FPGA library for image processing and object detection. This offers the possibility to implement different types of computer vision applications with one library. Due to the various configuration and parallelization possibilities of the library functions, it is possible to implement a high-performance, scalable and resource-efficient system, as our evaluation of the MobileNets algorithm shows.
Integral Imaging Based Optical Image Encryption Using CA-DNA Algorithm

Under the computational integral imaging-based security system, the application of Deoxyribonucleic Acid (DNA) encoding algorithm will cause silhouettes in the cipher image, thereby reducing the security of the system. To solve this problem, we introduced a cellular automata-based DNA (CA-DNA) algorithm, which effectively hides the distribution information of the original scene. It can prevent attackers from obtaining any valid information based on the statistical characteristics of the image, which makes our encryption system more security. At the same time, an improved high-resolution reconstruction algorithm is applied to achieve a high-quality decrypted scene. We conducted relevant experiments to certificate the effectiveness of proposed method. Experimental results verify that the scheme has high security and robustness.
Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model

Studies on grammatical error correction (GEC) have reported the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time-consuming pretraining for GEC because of the size of the pseudodata. In this study, we explore the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC. With the use of this generic pretrained model for GEC, the time-consuming pretraining can be eliminated. We find that monolingual and multilingual BART models achieve high performance in GEC, with one of the results being comparable to the current strong results in English GEC. Our implementations are publicly available at GitHub (https://github.com/Katsumata420/generic-pretrained-GEC).