Reviewerly/exHarmony huggingface dataset:
Politeness Speech of Students and Lecture in Online Learning Process at A Polytechnic in Manokwari

In the Covid-19 pandemic situation, the online learning process took over the previous mode of face-to-face meeting.By using Zoom meeting and WhatsApp, the lecture invited the students to join the online class.This research focused on the students from the third semester of the development of agriculture polytechnic in Manokwari, West Papua.In Politeness principles, Leech (2014) provided six maxims were tact, generosity, approbation, modesty, agreement, and sympathy.Qualitative design with observation as the primary research instrument.In WhatsApp sessions, students waited for the lecture's instructions delivered through speech and command.Besides, the student did not have space to communicate with their friends.In the zoom meeting class, students had the opportunity to speak with their peers, From the using of those two platforms it was found that both the students and lecture involved six maxims of the language politeness in their interactions.The principles using was to build effective communication between the two without having to eliminate the value of tolerance, respect, and appreciation for the speaker who has a higher status both in terms of age and educational status.Besides, among fellow students or the peer still used a natural language that was supposed as a politeness speech.
Deep Appearance Model and Crow-Sine Cosine Algorithm-Based Deep Belief Network for Age Estimation

Age estimation has been paid great attention in the field of intelligent surveillance, face recognition, biometrics, etc. In contrast to other facial variations, aging variation presents several unique characteristics, which make age estimation very challenging. The overall process of age estimation is performed using three important steps. In the first step, the pre-processing is performed from the input image based on Viola-Jones algorithm to detect the face region. In the second step, feature extraction is done based on three important features such as local transform directional pattern (LTDP), active appearance model (AAM), and the new feature, deep appearance model (Deep AM). After feature extraction, the classification is carried out based on the extracted features using deep belief network (DBN), where the DBN classifier is trained optimally using the proposed learning algorithm named as crow-sine cosine algorithm (CS).
Attention Based Video Summaries of Live Online Zoom Classes.

This paper describes a system developed to help University students get more from their online lectures, tutorials, laboratory and other live sessions. We do this by logging their attention levels on their laptops during live Zoom sessions and providing them with personalised video summaries of those live sessions. Using facial attention analysis software we create personalised video summaries composed of just the parts where a student's attention was below some threshold. We can also factor in other criteria into video summary generation such as parts where the student was not paying attention while others in the class were, and parts of the video that other students have replayed extensively which a given student has not. Attention and usage based video summaries of live classes are a form of personalised content, they are educational video segments recommended to highlight important parts of live sessions, useful in both topic understanding and in exam preparation.The system also allows a Professor to review the aggregated attention levels of those in a class who attended a live session and logged their attention levels. This allows her to see which parts of the live activity students were paying most, and least, attention to. The Help-Me-Watch system is deployed and in use at our University in a way that protects student's personal data, operating in a GDPR-compliant way.
Credit Platform Construction of Vocational Education Group Based on Blockchain

To establish the vocational education personal learning account and achieve the traceability, query and conversion of learning results, the paper proposes a credit platform of vocational education chain. The technical characteristics of block chain decentralization, bookkeeping, asymmetric encryption, distributed, smart contract and consensus mechanism are used to construct the platform. In system design and system logic design, we focus on how to evaluate students more objectively, completely and accurately, how to make the platform more secure, public and shared, and how to make colleges, students and enterprises more trustworthy and prefer to use credit platforms. In view of the difficulties in the construction of credit platform nodes in polytechnic education chain, such as credit issuance and acquisition, credit query and so on, this paper puts forward some ideas and carries out prototype design. As a result, the credit platform is made more suitable for the development of educational alliance, collectivization, diversification and Internet +, open, shared and lifelong education.
Secure Data Communication With Cryptography and Steganography

The study says that the double security layer with image encryption and decryption technique is used for watermarking the important details or images being hacked or theft. Initially in order to improve the security and invisibility of the robust watermarked image the adaptive based watermarking algorithm is used which uses chaos security layer. But this layer can be hacked or broken easily due to lack of blocks in it. In order to overcome that the Discrete cosine transform (DCT) is used will be used for encrypting the robust image with the Arnold transform and Chaos encryption for the security level. The input image will be divided into non overlapping 8*8 blocks and then the DCT will be applied for encrypting the secured image in an watermarked manner. Then the Arnold based double layer security using chaotic encryption will be done to secure the data with key. The Inverse Discrete cosine transform (IDCT) will be used to decrypt the secured image from the security layer with help of key. This technique show more efficiency with robustness in the image with high security system. It will be used in the health care application to hide the details of the patient and also in the e commerce system with high security process.
Improved algorithm for matched-pairs selection of informative features in the problems of recognition of complex system states

The problem of computer diagnostics of complex systems is one of the non-trivial tasks of modern information technology. Such systems are, for example, computer networks, automatic and/or automated control systems for complex technological objects, including related to complex problems of environmental protection, biology, etc. In pattern recognition, one of the major problems is forming subspaces of informative features, which only in the «ensemble» allow diagnosing the states of such systems with a high degree of reliability. An effective approach to solving this problem based on the principles of inductive modeling of complex systems is proposed. The quality criterion for recognizing classes of patterns is formulated, which also makes it possible to evaluate the quality of the constructed ensemble of informative features. As an example, the problem of constructing an ensemble of informative features represented by a binary code based on the data of an experiment to determine the hazard levels of some plant protection products is considered. Real primary data on plant protection products used in practice were applied to recognize the effect of certain characteristics on the so-called integrated «hazard indicator». Comparative numerical estimates of the effectiveness of the proposed approach are given. In this case, there can be a fivefold gain in the amount of computations for a relatively small number of input features equal to 5 compared to the known algorithms of the class considered in the paper. It is shown that, from a practical point of view, the described algorithm has advantages over the known algorithms with brute-force search of feature subspaces in pattern recognition problems.
Image Segmentation by Relaxed Deep Extreme Cut with Connected Extreme Points

In this work, we propose a hybrid method for image segmentation based on the selection of four extreme points (leftmost, rightmost, top and bottom pixels at the object boundary), combining Deep Extreme Cut, a connectivity constraint for the extreme points, a marker-based color classifier from automatically estimated markers and a final relaxation procedure with the boundary polarity constraint, which is related to the extension of Random Walks to directed graphs as proposed by Singaraju et al. Its second constituent element presents theoretical contributions on how to optimally convert the 4 point boundary-based selection into connected region-based markers for image segmentation. The proposed method is able to correct imperfections from Deep Extreme Cut, leading to considerably improved results, in public datasets of natural images, with minimal user intervention (only four mouse clicks).
Efficient Correlation Power Analysis (CPA) Focusing on Byte-Wise Calculation Points

Correlation Power Analysis (CPA) uses a lot of computational resources for deploying side-channel analysis to power traces.For every guessing key, it searches all data points and all power traces to reveal the secret key implemented in the encryption module.In this work, we propose a novel technique to narrow down the target region for applying CPA to the power traces.As byte-wise operations occur in every clock period, the assumed amplitude value based on the power consumption model and the actual amplitude are most correlated at the points where the peaks are observed.We extract the calculation points of each power trace using signal processing and machine learning techniques, and select the region where the CPA will be applied to.Our proposed approach achieves an approximately 20% improvement in accuracy and requires less computational resources compared to conventional CPA.
Privacy-Preserving Optimal Insulin Dosing Decision

Precision diagnosis and treatment are blending outcomes of machine learning and the Internet of Medical Things (IoMT). In the diabetes treatment, a medical center acts as a medical service provider (MSP) with patients data from IoMT devices. The MSP calculates the accurate dosage by importing the health index data into a corresponding decision-making model. However, the outsourcing unprotected patient data directly to the MSP suffers privacy leakage. In this paper, we propose a privacy-preserving optimal insulin dosing decision in the IoMT system (PIDM) to assist doctors in their decision-making with the patients privacy. To achieve practicality and confidentiality simultaneously, we design a series of secure and efficient interactive protocols depending on additive secret sharing to perform in one stage of DQN, namely, optimal decision making. Contrasted to the most relevant schemes, no additional trusted party is needed in our PIDM, which makes our system more practical and efficient. The security of PIDM is testified, meanwhile, the system effectiveness, and the overall efficiency of PIDM is demonstrated through theoretical analysis and simulation experiments.
MagnifierNet: Learning Efficient Small-scale Pedestrian Detector towards Multiple Dense Regions

Despite the success of pedestrian detection, there is still a significant gap in the performance of the detection of pedestrians at different scales. Detecting small-scale pedestrians is extremely challenging due to the low resolution of their convolution features which is essential for downstream classifiers. To address this issue, we observed pedestrian datasets and found that pedestrians often gather together in crowded public places. Then we propose MagnifierNet, a simple but effective small-scale pedestrian detector towards multiple dense regions. MagnifierNet uses our proposed sweep-line based grouping algorithm to find dense regions based on the number of pedestrians in the grouped region. And we adopt a new definition of small-scale pedestrians through grid search and KL-divergence. Besides, our grouping method can also be used as a new strategy for pedestrian data augmentation. The ablation study demonstrates that MagnifierNet improves the representation of small-scale pedestrians. We validate the effectiveness of MagnifierNet on CityPersons and KITTI datasets. Experimental results show that MagnifierNet achieves the best small-scale pedestrian detection performance on CityPersons benchmark without any external data, and also achieves competitive performance for detecting small-scale pedestrians on KITTI dataset without bells and whistles.
Stroke Based Posterior Attention for Online Handwritten Mathematical Expression Recognition

Recently, many researches propose to employ attention based encoder-decoder models to convert a sequence of trajectory points into a LaTeX string for online handwritten mathematical expression recognition (OHMER), and the recognition performance of these models critically relies on the accuracy of the attention. In this paper, unlike previous methods which basically employ a soft attention model, we propose to employ a posterior attention model, which modifies the attention probabilities after observing the output probabilities generated by the soft attention model. In order to further improve the posterior attention mechanism, we propose a stroke average pooling layer to aggregate point-level features obtained from the encoder into stroke-level features. We argue that posterior attention is better to be implemented on stroke-level features than point-level features as the output probabilities generated by stroke is more convincing than generated by point, and we prove that through experimental analysis. Validated on the CROHME competition task, we demonstrate that stroke based posterior attention achieves expression recognition rates of 54.26% on CROHME 2014 and 51.75% on CROHME 2016. According to attention visualization analysis, we empirically demonstrate that the posterior attention mechanism can achieve better alignment accuracy than the soft attention mechanism.
Productive collocation knowledge and advanced CEFR-levels in Swedish as a second language: A conceptual replication of Forsberg Lundell, Lindqvist &amp; Edmonds (2018)

This study constitutes a conceptual replication of Forsberg Lundell et al. (2018), who investigated whether productive collocation knowledge – a linguistic feature known to be indicative of high-level L2 proficiency – developed between the B2 and C1 levels of the Common European Framework of Reference for Languages scale in second-language (L2) French. The results showed significant development. The present study set out to replicate these findings in L2 Swedish, in order to investigate whether the reported development would stand cross-linguistic validation. To this end, a test of productive collocation knowledge in L2 Swedish was developed based on 22 separate computerized newspaper corpora of Swedish, searchable via the corpus tool <em>Korp</em> at SpråkbankenText (Swedish Language Bank). The method of the item selection was identical to that of the Forsberg Lundell et al., but the replication could only be conceptual since the reference corpora are different, from different languages. The test was conducted comparing participants from B2 and C1 levels in Swedish (<em>N</em> = 60). The results replicated the original study, confirming a significant difference in productive collocation knowledge between the B2 and C1 levels. Furthermore, in addition to the replication, the study explored frequency and Mutual Information score as potential factors for collocation item difficulty. The study found no significant effects for frequency or for Mutual Information score. Finally, the impact of cross-linguistic similarity was also investigated, grouping the results for participants with Germanic and non-Germanic first languages. This analysis did not point to any noteworthy effects.
Research and Practice of Computer English Assisted Learning System

The development of society and the advancement of science and technology have made new technologies in the computer field emerge in an endless stream. For computer-related majors, in order to improve learning efficiency and master new technologies, it is necessary to improve the level of computer English. Therefore, we should combine information technology and English teaching to design a computer-assisted English learning system. In this system, the learner's interest increases and the learning effect is improved. This article discusses how to design each module of the computer-assisted English learning system.
Management of Teachers’ Creative Activity Development

The paper justifies the management of teachers' creative activity development based on best practices described by Seit Kaskabasov. It analyzes and generalizes the theoretical positions of the modern state of the problem of teachers' creative activity development based on available psychological and pedagogical scientific research. It also contains the description of the creation and experimental testing of a creative activity development model applied to university teachers. The results of the study are recommended for practical use in universities to improve the effectiveness of the managerial qualities of the teaching staff.&#x0D; &#x0D; Received: 8 December 2020 / Accepted: 22 March 2021 / Published: 10 May 2021
Do I Speak Anxiously? A Correlation of Self-Efficacy, Foreign Language Learning Anxiety and Speaking Performance of Indonesian EFL Learners

Background:&#x0D; Self-efficacy and foreign language learning anxiety are already known as two importantly affective variables in learning a foreign language. However, little is known about the relationships between the two affective variables with learnersâ€™ speaking performance. Therefore, this present study seeks to figure out: the EFL learnersâ€™ self-efficacy, foreign language learning anxiety (FLLA), speaking performance, and the correlation between their speaking performance and self-efficacy, speaking performance and FLLA, and self-efficacy and FLLA.&#x0D; Methodology:&#x0D; A correlational design was selected to answer the research questions. Seventy students of the sixth semester got involved in this study. Three research instruments were applied, namely: a self-efficacy questionnaire, a FLLA questionnaire, and one speaking test used to measure studentsâ€™ speaking performance.&#x0D; Findings:&#x0D; Based on these results, it can be concluded that the null hypothesis (Ho) was rejected, while the alternative hypothesis (Ha) was accepted. Therefore, there was a significantly positive correlation between speaking performance and self-efficacy and a negative correlation between speaking performance and FLLA, and between self-efficacy and FLLA. It means that student with high-level speaking performance will have high-level of self-efficacy. Then, the higher studentsâ€™ speaking performance is, the lower their FLLA becomes. It is in line with the correlation between studentsâ€™ self-efficacy and FLLA, that student with high-level of self-efficacy would has low FLLA.
Improved Deep Distributed Light Field Coding

Light fields enable increasing the degree of realism and immersion of visual experience by capturing a scene with a higher number of dimensions than conventional 2D imaging. On another side, higher dimensionality entails significant storage and transmission overhead compared to traditional video. Conventional coding schemes achieve high coding gains by employing an asymmetric codec design, where the encoder is significantly more complex than the decoder. However, in the case of light fields, the communication and processing among different cameras could be expensive, and the possibility of trading the complexity between the encoder and the decoder becomes a desirable feature. We leverage the distributed source coding paradigm to effectively reduce the encoder's complexity at the cost of increased computation at the decoder side. Specifically, we train two deep neural networks to improve the two most critical parts of a distributed source coding scheme: the prediction of side information and the estimation of the uncertainty in the prediction. Experiments show considerable BD-rate gains, above 59% over HEVC-Intra and 17.45% over our previous method DLFC-I.
Robust Three Stage Central Difference Kalman Filter for Helicopter Unmanned Aerial Vehicle Actuators Fault Estimation

This paper proposes state and fault estimations for uncertain time-varying nonlinear stochastic systems with unknown inputs. we suppose, the information about the fault and unknown inputs is not perfectly known. For this purpose, in this manuscript, we developed a robust three-stage central difference Kalman filter (RThSCDKF). We used RThSCDKF for model-based fault detection and identification (FDI) in nonlinear hover mode of helicopter unmanned aerial vehicle (HUAV) in the presence of external disturbance. In this system, actuator faults are affected by each other. The proposed method estimates and decouples actuator faults in the presence of external disturbances. This model can detect stuck and floating faults that are important to detect. At the end, this method is compared with the three-stage extended Kalman filter (ThSEKF). Simulation results show the effectiveness of the proposed robust method for detection and isolation of various actuator faults and also this shows more accuracy with respect to ThSEKF.
Enhanced Cloud Data Storage Security by Using Hadoop

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Dual-Channel Hybrid Neural Network for Modulation Recognition

In the blind recognition of wireless communication modulation based on deep learning, how to further improve the recognition accuracy has become a key research issue. In this work, we propose a dual-channel hybrid model termed as CLDR (convolutional long short-term deep neural and residual network). The CLDR consists of the convolutional long short-term deep neural network (CLDNN) and the residual network (ResNet), where CLDNN is to reduce the variations in the spectrum and time, ResNet is to avoid gradient vanishing or exploding. In addition, we design an exponential curve decay adaptive cyclical learning rate method to decrease the training time cost of the neural network model. This method eliminates the need to experimentally search the optimal learning rate as with the fixed learning rate policy. It also avoids the slow convergence of the model due to the excessive attenuation amplitude as with the triangular learning rate policy. We test the feasibility of the CLDR model and discuss the influence of the exponential decay cyclical learning rate on the training of CLDR model based on the RadioML2016.10b public dataset. Simulation results show that the CLDR model yields a recognition accuracy of 93.1% at high SNRs, which effectively reduces the influence of external environment such as noise and fading on recognition accuracy. The training time cost of CLDR using exponential cyclical learning rate is reduced by 14.6% and 32.1% compared with triangular and fixed methods. Therefore, the exponential cyclical learning rate policy effectively reduces the training time cost of the model in the same classification accuracy.