Reviewerly/exHarmony huggingface dataset:
Information Technologies in Evidence-Based Education

The development of information technologies has determined the possibility of developing evidence-based education. The goal of evidence-based education as in evidence-based medicine is to use such pedagogical technologies that would allow students to achieve maximum results in the learning process. The adoption of information technologies made it possible through the digital accumulation of various data about the subject of the educational process and the digital analysis of this data. There is a unique opportunity to personalize learning, to create individual learning paths for many learners, as opposed to the practices of mass education and unification of the industrial era. The authors turn to the study of the possibilities for applying the evidence-based approach in education based on the use of information technologies in educational organizations in order to create individual educational trajectories of students. The article considers the possibilities of creating a system of diagnostics, monitoring, adaptation, and correction of students' educational achievements in an educational organization based on the analysis of “digital traces” of the educational activity of the subject of the educational process.
Enhance Via Decoupling: Improving Multi-Label Classifiers With Variational Feature Augmentation

Multi-label classification remains a challenging problem due to the inherent label imbalance issue, which brings overfitting of minor categories to modern deep models. In this paper, to tackle this issue, we propose a novel method named Variational Feature Augmentation (VFA) to enhance the deep neural networks for multi-label classification. Our method decouples the feature vectors extracted by the backbone network into multiple low-dimensional spaces via a novely proposed Variational Feature Decoupling Module. The decoupled feature vectors are then re-combined with a shuffle operation and a Feature Augmentation Layer to enrich the minor co-occurrence relations, mitigating the label imbalance. Different from most other methods, VFA does not modify the network architecture or introduce extra computation cost in inference phase. We conduct comprehensive experiments on four benchmarks of two visual multi-label classification tasks, pedestrian attribute recognition and multi-label image recognition, and the results demonstrate the effectiveness and generality of the proposed VFA.
SatGAN: Satellite Image Generation using Conditional Adversarial Networks

Mapping accurate satellite images from street view is a challenging image-to-image translation task. Recent advances in Generative Adversarial Networks (GANs) have shown promising results in image-to-image translation. Pix2Pix is a generalized framework for image-to-image translation which uses Conditional Adversarial Networks. It has performed well on a large number of datasets. However, it uses a simple pixel-level reconstruction loss due to which the output suffers distortion in some cases. In this paper, we propose SatGAN, which is based on Pix2Pix but adds the perceptual reconstruction loss with the pixel-level reconstruction loss to produce colourful and blur free images. The perceptual reconstruction loss forces the Generator to generate samples having a similar feature representation with that of the ground truth. We experimentally prove that SatGAN produces both qualitatively and quantitatively better results than Pix2Pix for street-view to satellite dataset.
LSM and Digital Education Transformation

Abstract The article describes the research carried by the authors in the global improvement framework of electronic tools in distance education. The text discusses digital transformation problems associated with using pedagogical concepts by universities in distance learning utilising learning management systems. In researching the problems, investigated the leading pedagogical technologies of behaviourism, cognitivism and constructivism, and aspects of personality-oriented and project-based learning. It was revealed a main regular concepts methods that can be applied in distance learning. The authors analysed a sample of the most popular LMSs for the sufficiency of functions to implement these pedagogical methods. As a result of research, the studied LMS has a fairly wide range of functions. However, these functions are not sufficient enough to allow all indicated pedagogical methods using. To compensate for the lack of LMS functionality, the authors recommend practising external electronic resources could be integrated into the learning management systems used by universities.
Learning Refined Features for Open-World Text Classification

Open-world classification requires a classifier not only to classify samples of the observed classes but also to detect samples which are not suitable to be classified as the known classes. State-of-the-art methods train a network to extract features for separating known classes firstly. Then some strategies, such as outlier detector, are used to reject samples from unknown classes based on the feature space. However, this network as a feature extractor cannot model comprehensive features of known classes in an open world scenario due to limited training data. This causes a problem that the strategies are unable to separate unknown classes from known classes accurately in this feature space. Motivated by the theory of psychology and cognitive science, we utilize class descriptions summarized by human to refine discriminant features and propose a regularization with class descriptions. The regularization is incorporated into DOC (one of state-of-the-art models) to improve the performance of open-world classification. The experiments on two text classification datasets demonstrate the effectiveness of the proposed method.
Reinforcedet: Object Detection By Integrating Reinforcement Learning With Decoupled Pipeline

Recent object detection methods largely rely on numerous pre-defined anchors that suffer from huge computational cost and resource consumption. To solve this issue, we propose a low-memory deep reinforcement learning based anchor-free object detection approach, namely ReinforceDet, which computes few but accurate region proposals for detection. Specifically, the extracted feature maps are fed into a reinforcement learning network to localize objects as initial region proposals with our re-designed reward function and then adopt another neural network to refine them. To speed up this process in test phase, we decouple the two-branch CNN networks as light-head cascaded subnetworks, named IoU-net and bounding box net. Experimental results show that ReinforceDet could obtain the state-of-the-art performance with much lower compitational and memory cost.
APNET: Attribute Parsing Network for Person Re-Identification

Most person re-identification methods rely solely on the pedestrian identity for learning. Person attributes, such as gender, clothing colors, carried bags, etc, are however seldom used. These attributes are highly identity-related and should be capitalized fully. Thus, we propose Attribute Parsing Network (APNet), an architecture designed for both image and person attribute learning and retrievals. To further enhance the re-id performance, we propose to leverage saliency maps and human parsing to boost the foreground features, which when trained together with the global and local networks, resulted in more generic and robust encoded representations. This proposed method achieved state-of-the-art accuracy performance on both Market1501 (87.3% mAP and 95.2% Rank1) and DukeMTMC-reID (78.8% mAP and 89.2% Rank 1) datasets.
A wavelet-based low frequency prior for single-image dehazing

The ever-growing sector of computer vision and image processing demands real-time enhancement techniques to satisfactorily restore hazy images. Although dark channel prior is most notable for single image haze removal, its major drawback is its large processing time. In this chapter, we propose a time-efficient wavelet-based prior, namely low-frequency prior. Low-frequency prior assumes that the majority of haze is contained in the low-frequency components of a hazy image. Here, we have transformed the hazy image into the wavelet domain using discrete wavelet transform to segregate the low- and high-frequency components, and treat them accordingly. Only the spatial low-frequency components are subjected to dark channel prior dehazing. The obtained dehazed image with low contrast can then be subjected to the novel fuzzy contrast enhancement framework presented in this chapter. Qualitative and quantitative comparisons with other state-of-the-art methods prove the primacy of the proposed framework.
A Comparative Study on Symmetric and Asymmetric Key Encryption Techniques

With the rise in use of internet in various fields like education, military, government sector, banking, the security and privacy of the info has been the foremost concern. As in today's era, most of the handling of data and transactions are done online. When the data is transferred from the one end of sender to the other end of receiver online, it's eavesdropped by an intruder and thus could be a threat to the secrecy or confidentiality of the info. The hottest technique that protects the confidentiality of the data is cryptography which converts the plain text into scrambled form which is unreadable. Then the receiver applies a reverse mechanism to decrypt the unreadable data to readable form. This mechanism is known as encryption-decryption process or cryptography. Cryptography can be both symmetric and asymmetric. Here the authors discuss symmetric and asymmetric algorithms.
HSEGAN: Hair Synthesis and Editing Using Structure-Adaptive Normalization on Generative Adversarial Network

Human hair is a kind of special material with complex and varied high-frequency details. It is a challenging task to synthesize and edit realistic and fine-grained hair using deep learning methods. In this paper, we propose HSEGAN, a novel framework consisting of two condition modules encoding foreground hair and background respectively, followed by a hair synthesis generator that synthesizes the final result based on the encoded input. For the purpose of efficient and effective hair generation, we propose hair structure-adaptive normalization (HSAN) and use several HSAN residual blocks to build the hair synthesis generator. HSEGAN allows for explicit manipulation of hair at three different levels, including color, structure and shape. Extensive experiments on FFHQ dataset demonstrate our method can generate higher-quality hair images than state-of-the-art methods, yet consume less time in the inference stage.
Two Level Prediction Error and Three Direction Shifting for Hiding Data in Digital Video

Along with the development of information technology, information exchange has occured more frequently. Information security is one of the aspects that must be resolved to protect the data being transferred from a sender to a recipient without any third-party interference. Data hiding is a possible method to prevent third parties from detecting confidential information. A reliable data hiding method is required to make secret information hard to break. In this research, we improve the performance of the previous studies by designing two prediction errors to make the values in the second prediction error more similar to their closest values. It increases the payload capacity that can be embedded and reduces the possibility of its changes during the shifting process. Based on the experiments, we find that there is an increase in the value of the Peak Signal to Noise Ratio (PSNR) from that of other methods for various video covers and payload capacities. Furthermore, this method is also reversible, so the extracted video is the same as the cover.
New binary and ternary quasi-cyclic codes with good properties

One of the most important and challenging problems in coding theory is to construct codes with best possible parameters and properties. The class of quasi-cyclic (QC) codes is known to be fertile to produce such codes. Focusing on QC codes over the binary field, we have found 113 binary QC codes that are new among the class of QC codes using an implementation of a fast cyclic partitioning algorithm and the highly effective ASR algorithm. Moreover, these codes have the following additional properties: a) they have the same parameters as best known linear codes, and b) many of the have additional desired properties such as being reversible, LCD, self-orthogonal or dual-containing. Additionally, we present an algorithm for the generation of new codes from QC codes using ConstructionX, and introduce 33 new record breaking linear codes over GF(2), GF(3) and GF(5) produced from this method.
Design of high order QAM modulation and demodulation system

research-article Design of high order QAM modulation and demodulation system Share on Authors: Liu Changlong The 54th Research Institute of CETC, China The 54th Research Institute of CETC, ChinaView Profile , Chang Yinghui The 54th Research Institute of CETC, China The 54th Research Institute of CETC, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 223Pages 1–3https://doi.org/10.1145/3469213.3470430Online:18 August 2021Publication History 0citation21DownloadsMetricsTotal Citations0Total Downloads21Last 12 Months21Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Application of Decision Tree Integrated Hybrid Classifier in Feature-Fused Robot Big Data

LightGBM is a fast distributed, high-performance gradient boosting framework based on decision tree algorithm, which can be used for sorting, classification, regression and many other machine learning tasks. In order to realize the classification of the terrain environment where the robot is located, this paper uses the gradient boosting tree LightGBM to identify and classify the IMU signals of the robot feet. First of all, the quaternion Euler transform and segmentation are performed on the signal. Secondly, the different period segments of the signal are analyzed and feature fused from the time domain and frequency domain respectively. Finally, the decision tree integrated hybrid classifier with the best parameters, namely LightGBM, is employed to classify the robot on the ground. The experimental results show that the accuracy of this method for the recognition of robot IMU signals on different surfaces reaches 90.7%.
Domain Adaptation using NDWI index for Water-land Semantic Segmentation

Recently, deep-learning based method has achieved great success in remote sensing image segmentation. Deep neural networks for semantic segmentation always require a large number of samples, which becomes a tough problem especially in remote sensing area. Unsupervised domain adaptation utilizes the labeled source dataset to train the unlabeled target dataset, which can reduce the labeling cost greatly. The key challenge of this task is how to alleviate the data distribution discrepancy of feature between the two datasets meanwhile keep the detail information in target dataset. This paper proposes a novel framework based on AdaptSegNet model that combined with remote sensing derived indices. We concatenate the NDWI index to the output layer of segmentation module, which can reflect waterbody at pixel-level with value from -1 to 1 and improve the segmentation accuracy greatly. we adopt 3 × 3 convolution to learn a weight that update the class probability after concatenation. Experiments are conducted from the GID dataset to the Landsat8 datasets. Our method achieves 90.0% mean intersection-over-union (IoU) and outperforms all of the baselines
Soft-Information Improvement using Multilayer Perceptron (MLP) in Bit-Patterned Magnetic Recording (BPMR) Systems

The effects of both inter-track interference and inter-symbol interference are the main hindrances in alternated magnetic recording technologies such as bit-patterned magnetic recording. To increase an areal density of hard disk drives over 1 terabit per square inch; therefore, these two effects must be coped with some powered signal processing techniques. In this paper, we propose to use the multilayer perceptron, which is one of the artificial neural networks, to improve the soft information or log-likelihood ratios that are obtained from parallel soft-output Viterbi algorithm (SOVA) detectors. The computer-simulated results reveal that our proposed recording system can efficiently yield better bit error rate performance when it was compared with the previously proposed magnetic recording systems.
Derivational Complexity and Context-Sensitive Rewriting

Abstract Context-sensitive rewriting is a restriction of rewriting where reduction steps are allowed on specific arguments $$\mu (f)\subseteq \{1,\ldots ,k\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>f</mml:mi> <mml:mo>)</mml:mo> <mml:mo>⊆</mml:mo> <mml:mo>{</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> of k -ary function symbols f only. Terms which cannot be further rewritten in this way are called $$\mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> -normal forms. For left-linear term rewriting systems (TRSs), the so-called normalization via $$\mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> - normalization procedure provides a systematic way to obtain normal forms by the stepwise computation and combination of intermediate $$\mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> -normal forms. In this paper, we show how to obtain bounds on the derivational complexity of computations using this procedure by using bounds on the derivational complexity of context-sensitive rewriting. Two main applications are envisaged: Normalization via $$\mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> -normalization can be used with non-terminating TRSs where the procedure still terminates; on the other hand, it can be used to improve on bounds of derivational complexity of terminating TRSs as it discards many rewritings.
Multiple Security Protection Algorithm for GF-2 Images Based on Commutative Encryption and Watermarking

In the process of storage, transmission and use of GF-2 image, both data security transmission and data copyright protection should be considered. The combination of cryptography and digital watermarking is an effective way to solve this problem. However, there are some problems in the existing algorithms that combine cryptography with digital watermarking, such as high operation sequence requirements, low security, and easy leakage of keys. To solve these problems, a new multiple security protection algorithm for GF-2 image based on commutative encryption and watermarking is proposed in this paper. The integer wavelet transform is used to decompose the original GF-2 image at first, and the low frequency data is extracted and is divided into sub-block with the size of m × n. Then, each sub-block is encrypted and watermarked based on the additive homomorphism of Paillier algorithm. Finally, all sub-blocks that completed the CEW operation are merged, and the merged data is performed inverse IWT to generate the encrypted-watermarked data. The proposed algorithm can achieve both security transmission by encryption and copyright protection by watermark, and the encryption algorithm and the watermarking algorithm are commutative. In addition, the watermark can be extracted from both ciphertext data and plaintext data.
AdaCon: Adaptive Context-Aware Object Detection for Resource-Constrained Embedded Devices

Convolutional Neural Networks achieve state-of-the-art accuracy in object detection tasks. However, they have large computational and energy requirements that challenge their deployment on resource-constrained edge devices. Object detection takes an image as an input, and identifies the existing object classes as well as their locations in the image. In this paper, we leverage the prior knowledge about the probabilities that different object categories can occur jointly to increase the efficiency of object detection models. In particular, our technique clusters the object categories based on their spatial co-occurrence probability. We use those clusters to design an adaptive network. During runtime, a branch controller decides which part(s) of the network to execute based on the spatial context of the input frame. Our experiments using COCO dataset show that our adaptive object detection model achieves up to 45% reduction in the energy consumption, and up to 27% reduction in the latency, with a small loss in the average precision (AP) of object detection.
A New Challenge: Behavioural Analysis Of 6-DOF User When Consuming Immersive Media

Thanks to recent advances in computer graphics, wearable technology, and connectivity, Virtual Reality (VR) has landed in our daily life. A key novelty in VR is the role of the user, which has turned from merely passive to entirely active. Thus, improving any aspect of the coding-delivery-rendering chain starts with the need for understanding user behaviour. To do so, we investigate the navigation trajectories of users within a 6-Degrees-of-Freedom (DoF) VR environment. Specifically, we investigate the main differences and similarities between 3 and 6-DoF navigation through existing methodologies adopted to study user behaviour in 3-DoF settings. Our simulation results, based on real navigation paths of users while displaying dynamic volumetric media in 6-DoF conditions, show the limitations of clustering algorithms for 3-DoF in assessing user similarity in 6-DoF. Given these observations, we state the need for developing new solutions for the analysis of 6-DoF trajectories.
Gesture Recognition Based on sEMG and Support Vector Machine

With the continuous development and progress of computer vision, the application scene of human gesture feature recognition is more and more widely, gesture feature recognition in human-computer interaction is a hot topic. The paper proposes a signal-vision model that combines support vector machine and gesture EMG signal to recognize different gesture features and sEMG signals. The model collects different gesture signals through the signal acquisition devices and removes noise through the design of sliding window filter which combines with the machine vision gesture detection framework. The paper also reduces the dimension through SVM models to predict and discriminate two different gesture features and visualizes them, we do the gesture detection and recognition so we can match the different gesture model and sEMG signal acquisition model. We demonstrate the potential of the approach proposed through extensive experiments and results show that the proposed model has significantly distinguish the different gesture features combined with support vector machine which can perform well in recognize and classify different gesture features.