Reviewerly/exHarmony huggingface dataset:
Teaching Quality Evaluation System for College Teachers Based on Artificial Intelligence Facial Expression Recognition Technology

Artificial intelligence is gradually entering the field of education, and evaluating and improving teachers' classroom performance has become an important content in the education industry. To evaluating teachers' teaching quality scientifically and objectively is also an important foundation in the field of education, with reference to long way of evaluation, such as questionnaire survey, classroom observation and other solutions are empirical, not comprehensive, such as defects, to a large extent affect the authenticity of the teacher's teaching quality evaluation, for the improvement of teaching quality also cannot provide effective data support. However, the development of information science, psychology and other disciplines has brought new solutions to this problem, and the use of the data of facial expressions collected in class to study the teaching situation has become a way to be considered. In the existing intelligent monitoring equipment design of classroom management system, based on the use of artificial intelligence technology of face detection and face recognition to automatic tracking and analyzing the change of the mood and behavior of students in the process of teaching, through to the students of class status (for example: confusion, curiosity, exclusion, drowsiness, understanding, etc.) of the intelligent analysis, establish a more objective, scientific and precision level of teacher's teaching quality evaluation system, so that the teaching mode of evaluation, monitoring and improvement to provide corresponding reference.
High-throughput error correction for continuous-variable quantum key distribution systems based on shuffled iterative decoding

Throughput of error correction is the bottleneck of the postprocessing for continuous-variable quantum key distribution system. In this paper, a shuffled iterative decoding method is proposed to reduce the number of iterations for error correction. For three typical code rate, i.e., 0.1, 0.05, 0.02, our results show that the maximum decoding speed is up to 72.86 Mbits/s, 53.96 Mbits/s and 42.45 Mbits/s, respectively, which significantly improves the real-time processing capacity of continuous-variable quantum key distribution system.
Label Embedding Enhanced Multi-label Sequence Generation Model

Existing sequence generation models ignore the exposure bias problem when they apply to the multi-label classification task. To solve this issue, in this paper, we proposed a novel model, which disguises the label prediction probability distribution as label embedding and incorporate each label embedding from previous step into the current step’s LSTM decoding process. It allows the current step can make a better prediction based on the overall output of the previous prediction, rather than simply based on a local optimum output. In addition, we proposed a scheduled sampling-based learning algorithm for this model. The learning algorithm effectively and appropriately incorporates the label embedding into the process of label generation procedure. Through comparing with three classical methods and four SOTA methods for the multi-label classification task, the results demonstrated that our proposed method obtained the highest F1-Score (reaching 0.794 on a chemical exposure assessment task and reaching 0.615 on a clinical syndrome differentiation task of traditional Chinese medicine).
Design of Circuit Patrol Monitor Module Based on ARM Processor

Based on the theory and technology of embedded system, a circuit patrol monitor module based on 32-bit ARM processor SGC2102 is designed. The paper specifically introduces the implementation of the circuit patrol monitor module, while describing detaily the related software and hardware functions. The effective result of the circuit patrol monitor module is verified by the commissioning, production and pilot operation.
Analyzing the effectiveness of metamorphosing images using color maps

Ensuring the image privacy, storing, and securely retaining them is a challenge in the present world. In this work, a method for metamorphosing the image is studied. For these images from the dataset METUSURV used for studying the pseudo coloring method for implementing image privacy and obtaining the real picture from this disguised image [1]. The pseudo coloring performed with different color maps and motion blurring. The effectiveness of color maps producing disguised images is analyzed and selected best result producing maps for creating a disguised image. Joint photographic expert group (JPEG) compression, Zlib compression, advanced encryption standard (AES) are incorporated in this proposed research work. The quality of the retained image is measured using SSIM and PSNR. The characteristics of the method are examined and used with other privacy ensuring methods. This extended study shows the quality and effectiveness of the method.
Violence and discrimination against healthcare workers in times of new coronavirus

to reflect about the increase in violence and discrimination against healthcare workers due to the new coronavirus pandemic Method: theoretical reflection based on reading, analyzing and interpreting news, scientific articles and reports on violence and discrimination to healthcare workers who combat the novel coronavirus Results: as it is a new disease, there are still many uncertainties around it which can lead to violence and prejudice That's exactly what happened when the population mistakenly began to see the healthcare worker as a spreader of diseases International organizations call on governments around the world to take action to protect health teams that have become victims of violence and discrimination Conclusions: hospital administrators and governments need to create measures to stop these aggressions and discrimination reflexionar sobre el aumento de la violencia y la discriminacion contra los personales de salud debido a la pandemia de nuevo coronavirus Metodo: reflexion teorica basada en la lectura, analisis e interpretacion de noticias, articulos cientificos e informes sobre violencia y discriminacion a profesionales que trabajan para combatir el nuevo coronavirus Resultados: como se trata de una enfermedad nueva, todavia hay muchas incertidumbres al respecto, lo que puede conducir a la aparicion de violencia y prejuicios Esto es exactamente lo que sucedio cuando la poblacion comenzo a ver por error al personal de salud como un difusor de enfermedades Las organizaciones internacionales hacen un llamado a los gobiernos de todo el mundo para que tomen medidas para proteger a los equipos de salud de violencia y discriminacion Conclusiones: los administradores de hospitales y los gobernantes deben crear medidas para evitar estas agresiones y discriminaciones refletir sobre o aumento da violencia e da discriminacao deferidas aos profissionais de saude em razao da pandemia do novo coronavirus Metodo: reflexao teorica baseada na leitura, analise e interpretacao de reportagens, artigos cientificos e relatorios sobre violencia e discriminacao aos profissionais que atuam no combate ao novo coronavirus Resultados: por ser uma doenca nova, ainda existem muitas incertezas a seu respeito, o que pode levar ao surgimento de violencia e preconceito aos profissionais de saude Foi justamente o que aconteceu, quando a populacao erroneamente comecou a ver o profissional de saude como um disseminador de doencas Organizacoes internacionais pedem que governantes de todo mundo tomem providencias, procurando proteger as equipes de saude de se tornarem vitimas de violencia e discriminacao Conclusoes: administradores hospitalares e governantes precisam criar medidas, visando impedir que essas agressoes e discriminacoes ocorram
Emerging Intention Mining Inspired by Semantic Reconstruction

Emerging intention mining is the process of identifying the inputs that differ in some respect from the training samples. With the development of artificial intelligence, more and more attention has been paid to data mining. Therefore, methods which can efficiently identify the emerging intentions are needed. However, due to the lack of data for emerging intention, training an end-to-end deep network is a cumbersome task. In this paper, we propose an end-to-end architecture for emerging intention mining which inspired by the success of generative adversarial networks and conditional variational autoencoder (CVAE). Our architecture tries understand the underlying concept of the inputs and then reconstruct them through semantic information. There are two deep networks by Convolutional Neural Networks (CNN). One network works as the semantic information extraction and the other works as the novelty detector. The first network supports the second by enhancing the inlier samples and distorting the outliers. The proposed framework applies to different datasets. The results illustrate that our proposed method learns the target class effectively and is superior to the baseline and state-of-the-art methods.
Variational Regularized Single Image Dehazing

Dehazing from a single hazy image has been a crucial task for both computer vision and computational photography applications. While various methods have been proposed in the past decades, estimation of the transmission map remains a challenging problem due to its ill-posed nature. Moreover, because of the inevitable noise generated during imaging process, visual artifacts could be extremely amplified in the recovered scene radiance in densely hazy regions. In this paper, a novel variational regularized single image dehazing (VRD) approach is proposed to accurately estimate the transmission map and suppress artifacts in the recovered haze-free image. Firstly, an initial transmission is coarsely estimated based on a modified haze-line model. After that, in order to preserve the local smoothness property and depth discontinuities in the transmission map, a novel non-local Total Generalized Variation regularization is introduced to refine the initial transmission. Finally, a transmission weighted non-local regularized optimization is proposed to recover a noise suppressed and texture preserved scene radiance. Compared with the state-of-the-art dehazing methods, both quantitative and qualitative experimental results on both real-world and synthetic hazy image datasets demonstrate that the proposed VRD method is capable of obtaining an accurate transmission map and a visually plausible dehazed image.
Penerapan Sistem Informasi Akuntansi pada Perusahaan Event Organizer menggunakan Metode Prototyping Berbasis Web (Studi Kasus : CV.Chitra Pesta)

CV.Chitra Pesta is a company engaged in Event Organizer and Wedding Organizer services such as weddings, birthday parties, product launches, thanksgiving events and others. All the recording of financial transactions is still done manually so that for making financial reports takes a long time. In addition to financial statement problems, the recording of inventory data and recording of employee recitation has not been recorded properly and is not regular. Based on the existing problems, the accounting information system is built aimed at helping companies to process financial statements until recording inventory data and recording employee recitation so that it can help companies in the future to optimize business processes. The system to be built is Web-based with the PHP programming language, the system development method used prototyping so that in the future it can be developed and MySQL as a database. And the system will be tested using the blackbox method and testing the User Acceptance Test (UAT) which is done objectively to the user whether it is in accordance with the wishes.
Reinforcement Learning for Named Entity Recognition from Noisy Data

Named entity recognition (NER) is an important task in natural language processing, and is often formalized as a sequence labeling problem. Deep learning becomes the state-of-the-art approach for NER, but the lack of high-quality labeled data remains the bottleneck for model performance. To solve the problem, we employ the distant supervision technique to obtain noisy labeled data, and propose a novel model based on reinforcement learning to revise the wrong labels and distill high-quality data for learning. Specifically, our model consists of two modules, a Tag Modifier and a Tag Predictor. The Tag Modifier corrects the wrong tags with reinforcement learning and feeds the corrected tags into the Tag Predictor. The Tag Predictor makes the sentence-level prediction and returns rewards to the Tag Modifier. Two modules are trained jointly to optimize tag correction and prediction processes. Experiment results show that our model can effectively deal with noises with a small number of correctly labeled data and thus outperform state-of-the-art baselines.
AFPun-GAN: Ambiguity-Fluency Generative Adversarial Network for Pun Generation

Automatic pun generation is an interesting and challenging text generation task. In this study, we focus on the task of homographic pun generation by given a pair of word senses. Current efforts depend on templates or laboriously annotated pun source to guide the supervised learning, which is lack of quality and diversity of generated puns. To address this, we present a new text generation model, called Ambiguity-Fluency Pun Generative Adversarial Network (AFPun-GAN) for pun genration. This model is composed of a pun generator to produce pun sentences by a hierarchical on-lstm attention model, and a pun discriminator to distinguish the generated pun sentences and real sentences with word senses of target pun word. The proposed model assigns a hierarchical low reward to train the pun generator via reinforcement learning, encouraging the pun generator to produce the ambiguous and fluent pun sentences that can better support two word senses. The experimental results on pun generation task demonstrate that our proposed AFPun-GAN model is able to generate pun sentences that are more ambiguous and fluent in both automatic and human evaluation.
Learning from Rankings with Multi-level Features for No-Reference Image Quality Assessment

Deep neural networks for image quality assessment have been suffering from a lack of training data for a long time, as it is expensive and laborious to collect sufficient subjective mean opinion scores (MOS). The Siamese network (learning from rankings) makes it possible to use images with only rough labels, such as the relative quality. On the other hand, features from intermediate layers, which possess local information highly sensitive to the quality degradation, are overlooked in the direct use of DNNs. In light of these findings, in this paper, we propose a framework for NR-IQA based on transferring learning from the Siamese network to the traditional CNNs by exploiting features from multiple layers. Specifically, we first train a Siamese network on a large artificially generated dataset. Then, we fine-tune the network with a small number of MOS-labeled images to match the perceptual scaling of human beings. In both steps, features from several layers are combined before being fed into the regression layer for the final score. Experimental results are presented, validating the effectiveness of this transfer-learning framework when considering multi-level information. Furthermore, performance comparable to state-of-the-art NR-IQA approaches on standard IQA datasets is achieved.
A General Approach to Robust QR Codes Decoding

With the continued proliferation of smart mobile devices, Quick Response (QR) code has played an important role in daily life. They may be distorted and partially invisible due to bright spots, folding and stains When they are printed on soft materials such as plastic bags. Existing scanners may fail in detecting and decoding QR codes due to distortion. In this paper, we propose a simple but effective approach to decoding distorted and partial QR codes. First, we improve an existing QR code detection algorithm to extract QR codes. Then based on the structural features of QR codes that white and black modules are staggered, we propose a novel distortion correction mechanism that uses an adaptive window to match each module. In order to tackle the problem of invisibility, we print multiple QR codes and capture them in an image. Considering confidence of each module in separate, we reconstruct a relatively complete QR code. Extensive experiments have been conducted to evaluate the performance of our approach. The results show that our approach improves the decoding rate by 50% – 60% compared to the other two baselines.
Generation Method of Hierarchical Pronunciation Font for Hearing-impaired Children Based on Artificial Intelligence

Setting up personalized hierarchical pronunciation font for hearing-impaired children is one of the most important segments when designing individualized rehabilitation teaching. Creating an individual Chinese pronunciation training word stock is a big challenge in speech rehabilitation training for children with hearing impairment. This research applies the speech recognition technology and Chinese syllable classification theory, and proposes a convenient method of automatic generation of personalized hierarchical word base. The method includes first analyzing the initials and vowel of children's test pronunciation by calling the API of artificial intelligence speech recognition. Subsequently, on the basis of the established multi-dimensional information table of pronunciation classification of Mandarin initials and vowel, the multi-level pronunciation ability of the initials and vowel tested by children is rated. As a result, the datasets of initials and vowel of different levels are obtained. and according to the combination of initials and vowel of different levels, it is convenient to generate a personalized graded Chinese character library. Finaly, The effectiveness of this method is proved by a typical example.
Design of Real-Time Vehicle Tracking System for Drones Based on ROS

When using vehicles to hunt down criminal vehicles, the difficulty of chasing criminals is increased due to factors such as vision, surrounding environment and road conditions. In order to solve this problem, we have designed a real-time vehicle tracking system based on ROS. The system uses Pixhawk as the flight control platform in the hardware part. Its internal integrated attitude control module and altitude control module are mainly responsible for controlling the attitude of the aircraft to achieve the effect of stable flight of the aircraft. The GPS module is responsible for acquiring coordinate position data, so that the aircraft can achieve the fixed point effect. This system solves the problem that a processor has insufficient resources when processing relatively large data. Compared with using vehicles to chase criminal vehicles, this system can solve the problem of increasing the difficulty of chasing criminals due to factors such as visual field, surrounding environment and road conditions. This system greatly improves the pursuit efficiency.
Evaluation of TDM and ADM Construction Using SIMECK 32/64 based on Cryptographic Randomness Test

This study aims to infer the cryptographic strength of double-block-length schemes Tandem Davies-Meyer (TDM) and Abreast Davies-Meyer (ADM) using SIMECK 32/64 with Cryptographic Randomness Test (CRT). CRT is a form of statistical randomness test that consists of SAC Test, Linear span Test, Collision Test, and Coverage Test which can be used to evaluate the collision resistance property of a hash function. The SIMECK 32/64 algorithm is applied to the TDM hash with a 32-bit block size and 64-bit key size. Both hash function constructs have IVG and IVH of fixed values 0000 0000H and hash values of 64 bits. In this study, the test ware performed for 220 trials for each test. The observed values obtained in the test were evaluated using a chi-square goodness of fit test with a significance level of 0.01 and a degree of freedom of 4 for SAC Test, Collision Test, Coverage Test and of 2 for Linear Span Test so as to produce a p-value. Hypothesis testing with significance level of 0.01 on the obtained CRT results procured the p - value ≥ 0.01, hence H0 was accepted, it is means the hash functions being tested exhibited random mappings. On the tests performed on both hash schemes, p-value ≥ 0.01 was obtained for each test. It can be conclude that both hash function schemes passed the CRTs, therefore it can be stated that both of the hash function schemes have good collision resistance property based on the criteria of a random mapping.
Geometric Refactoring of Quantum and Reversible Circuits: Quantum Layout

With the advent of gated quantum computers and regular structures of the qubit layout, methods for placement, routing, noise estimation and logic to hardware mapping become imminently required. In this paper, we propose a method for quantum circuit layout that is intended to solve such problems when mapping a quantum circuit to a quantum computer. The proposed method starts by building a Circuit Interaction Graph (CIG) that represents the ideal hardware layout minimizing the distance and path length between the individual qubits. The CIG is also used to introduce a qubit noise model. Once constructed, the CIG is iteratively reduced to a given architecture (qubit coupling model) specifying the neighborhood, qubits, priority and qubits noise. The introduced constraints allow to additionally reduce the graph according to preferred weights of desired properties. The proposed method is verified and tested on a set of standard benchmarks.
DPoC: Distributed Proof of Correctness for Blockchain in the Internet of Things

In recent years, digital currency has become one of the hottest topics in both academia and industry. However, the trust issue still exists at the data source, as the data may be falsified before recorded on the chain. In this paper, the blockchain system architecture and the consensus mechanism is well designed, with the proposed idea of distributed verification. Performance evaluation is also provided in this paper to demonstrate the performance improvement with the proposed mechanism.
Technique for Information Security Based on Controls Established by the SysAdmin Audit, Networking and Security Institute

Information security has become a worldwide trend; however, there is little or no mention of how the incorporation of security-related concepts should be initiated from the network design phase. The lack of a secure network means that losses amount to millions of dollars from cybercrime, much more if this same scenario were taken into account in the globalized environment. The need to evaluate the method by which the designs are executed becomes crucial, a before and after of the networks designed based on security. The present work proposes an alternative in information security with the methodology of design of communication and telecommunication networks, implementing 2 security controls, the 8 and 12, malware defense and perimeter respectively. These controls are listed by the SysAdmin Audit, Networking and Security Institute (SANS), evaluating the compliance with the recommendations that this institute delivers and demonstrating at the end that a functional and secure network can be obtained. The applicability of the described is measured in the implementation of the mentioned controls, since the antimalware provided adequate protection, administration and configuration in all the endpoints, thus avoiding zero-day threats. While the configuration at the perimeter level has allowed blocking of attacks, web filtering with new generation security parameters, maintenance of operational business roles and protected information.