Reviewerly/exHarmony huggingface dataset:
Novel windowed linear canonical transform: Definition, properties and application

Linear canonical transform (LCT) has emerged as a powerful tool in nonstationary signal processing. However, it fails to reveal the information of local frequency varying with time due to the global kernel function. In this paper, a novel windowed linear canonical transform (NWLCT) is presented to give a clear time-frequency representation for time-varying signals. Firstly, the definition of NWLCT is proposed by a generalized convolution operator not a sliding window. Some basic properties of NWLCT are derived, such as inverse transform, Parseval theorem and reproducing kernel. Moreover, time-frequency resolution of NWLCT is analyzed theoretically, and the optimal window is derived. Finally, the applications of NWLCT in local spectral analysis for linear frequency modulated (LFM) signals are offered, including mono- and multi-component signals with similar local features. Theoretical and simulation results demonstrate that the proposed NWLCT preserves a crucial physical interpretation similar to classical short-time Fourier transform, which extends low-pass filter banks in the LCT domain. Compared with other time-frequency methods, NWLCT outperforms in improving the energy concentration and being robust to noise in terms of LFM signal processing.
Interaction-Oriented Feature Decomposition for Medical Image Lesion Detection

Common lesion detection networks typically use lesion features for classification and localization. However, many lesions are classified only by lesion features without considering the relation with global context features, which raises the misclassification problem. In this paper, we propose an Interaction-Oriented Feature Decomposition (IOFD) network to improve the detection performance on context-dependent lesions. Specifically, we decompose features output from a backbone into global context features and lesion features that are optimized independently. Then, we design two novel modules to improve the lesion classification accuracy. A Global Context Embedding (GCE) module is designed to extract global context features. A Global Context Cross Attention (GCCA) module without additional parameters is designed to model the interaction between global context features and lesion features. Besides, considering the different features required by classification and localization tasks, we further adopt a task decoupling strategy. IOFD is easy to train and end-to-end in terms of training and inference. The experimental results for datasets in two modalities outperform state-of-the-art algorithms, which demonstrates the effectiveness and generality of IOFD. The source code is available at https://github.com/mklz-sjy/IOFD
A User Independent Recommendation System for Web Series

The number of people streaming content over the Internet has boomed in the recent years. However, streaming platforms have to promote the right kind of titles to the right people in order to keep them interested in using their platform and that is why a recommendation engine is important. Different people choose to like Web series in different ways. Some like to watch multiple shows of their favorite actor, while some prefer some genres over others. Therefore, not only do the recommendations have to be accurate in the sense of similarity, but that similarity also has to be localized to within parameters, set by the user. In this paper, we have proposed a recommendation system that can recommend Web series to a user without any extensive data. Our recommender can recommend TV shows, both using all possible and localized parameters, tailoring the recommendations to exactly what the user wants, without imposing the bias of other users on them.
Texture Feature Based on ANN for Security Aspects

Centered on ELM, to achieve high-performance picture steganography, we suggested an innovative OEPF model. An updated ELM algorithm is used in this method to set up the supervised computational framework to evaluate the optimized hiding picture position with a minimum distortion. The ELM is trained on a part of the image and checked in regression methodology to pick the optimum message hiding spot. It has permitted the supreme outcomes of the expected measurement indicators to be obtained. Training is carried out on the basis of a collection of extracted textures, statistical characteristics and their related visible imperceptibility metrics that use a portion of the graphic. For output enhancement, the learned model is further utilized. To surpass the current innovative frameworks, the proposed model is shown.
A survey on DE – Duplication schemes in cloud servers for secured data analysis in various applications

Data deduplication in a system perspective is termed to be a single or multiple copy of an original data which could increase the computational complexity while accessing such a data. In clinical terms the deduplication is considerably the multiple copy of a same sample. The data deduplication is also vulnerable towards security issues which in turn could affect the performance of a cloud server. This type of several copies had to be handled not only to analyze the data but also to identify the underlying patterns which could support in prediction and classification of the data. There are several deduplication handling schemes in multiple domains had been proposed to handle the repetition of the data from which the most effective and recently proposed schemes had been considered for survey. The deduplication schemes are compared in terms of their performance and their pros and cons are discussed in this article which could pave a path for researchers to propose and perform a better analysis with the deduplication issues.
Industrial Production of Post-industrial Products: How Interactive Visualizations Miss Their Potential as a Journalistic Form of Knowledge

Journalistic interactive visualizations (JIVs) – such as scrollytellings, interactive infographics, and clickable data visualizations – have an epistemic potential to efficiently and intricately mediate rich journalistic knowledge. In practice, however, they usually mediate and oversimplify limited knowledge. To understand why and where, across the production line, JIVs lose their epistemic potential, we map the production process of JIVs in three leading Israeli news organizations based on a combination of in-depth and reconstruction interviews with 22 JIV producers. Findings point to nine prominent bifurcations, where the production course can shape JIVs so that they mediate rich and intricate or limited and simplistic knowledge. Overall, these findings reflect a misfit between an “industrial” production process and a “post-industrial” news product.
STRUCTURAL-FUNCTIONAL FORMATION MODEL OF COMMUNICATIVE ABILITIES BY MEANS OF DRAMATIZATION

The model of using the means of dramatization in teaching languages is a didactic interconnection of three stages - preparatory, aimed primarily at the formation of motivational, emotional components; basic, aimed at the formation of cognitive, communicative foreign language competencies and integration, in which competence components develop, and their integrating basis is the reflexive component. The activity-based approach makes it possible to single out the essential components and reveal the model of the learning system as a synthesis of value-target, cognitive-operational and control-reflexive blocks. The value-target block is a regulator in a methodological system that models its content and focus. In essence, this component determines the meaning of the technology of using the means of dramatization. The cognitive-operational block of the methodological system is a set of knowledge, skills that need to be formed, as well as techniques, means, methods, forms and technologies of teaching, also includes teaching principles, methodological categories and technological characteristics. The reflexive-evaluative block characterizes the comprehension, analysis, assessment of the built system, tracking its results, the degree of realization of the desired and set goals.
PROFESSIONAL COMPETENCE OF A TEACHER IS A SET OF KNOWLEDGE, SKILLS AND PERSONAL QUALITIES

Based on the current modern requirements, only a competent teacher can give a highquality education and educate a person who is required by modern society. Reflecting the essence of professional activity in this context, it was proved that professional competence is a set of knowledge, skills, personal qualities of a teacher pursuing the goal of successful activity. Concepts to classifications of competencies of various nature recognized by the community of teachers are also given. At the same time, it is confirmed that the competence approach is focused on the goals of education as self-determination, self-actualization, socialization, and the development of individuality. In addition, the article highlights the key components of professional competence. In all the functions that the teacher performs in his activity, the characteristics of the competencies corresponding to him are given.
HOW SHOULD STUDENTS STUDY AN ADVANCED TECHNOLOGY OF PRIVATE BLOCKCHAIN - THE HYPERLEDGER FABRIC?

This article provides introduction to Hyperledger Fabric - a representative tool for implementing a private blockchain that has been proven as the best technology. Although this article does not cover all the features and details of Hyperledger Fabric, it covers on the overall structure of Hyperledger Fabric, main files (.yaml) and core shell programs. (1) key terminologies of blockchain (2) main files (.yaml) (3) contents of core shell programs (developed in Go language) (4) procedure to set up VM (Virtual Machine) on AWS (Amazon Cloud) and general computer (5) Procedure to connect to AWS or general VM using VS Code. (6) execution procedure and check executed result.
Clustering-based Sequence to Sequence Model for Generative Question Answering in a Low-resource Language

Despite the impressive success of sequence to sequence models for generative question answering, they need a vast amount of question-answer pairs during training, which is hard and expensive to obtain, especially for low-resource languages. In this article, we present a framework that exploits the semantic clusters among the question-answer pairs to compensate for the lack of enough training data. In the training phase, the question-answer pairs are clustered, and a cluster predictor is trained to identify the cluster each question belongs to. Then, a sequence to sequence model is trained, where there is a different generator for each cluster in the decoder component. During the test phase, the cluster of the input question is first identified using the trained cluster predictor, and the appropriate decoder is exploited. Our experiments on a Persian religious dataset show that the proposed method outperforms the standard sequence to sequence model by a large margin in terms of ROUGE and BLEU scores. This is traced back to the lower number of words in each cluster, leading to a reduction in the number of effective parameters each generator needs to learn, which help the model learn from fewer training data with less overfitting.
Decoding the Kodi Ecosystem

Free and open-source media centers are experiencing a boom in popularity for the convenience they offer users seeking to remotely consume digital content. Kodi is today’s most popular home media center, with millions of users worldwide. Kodi’s popularity derives from its ability to centralize the sheer amount of media content available on the Web, both free and copyrighted . Researchers have been hinting at potential security concerns around Kodi, due to add-ons injecting unwanted content as well as user settings linked with security holes. Motivated by these observations, this article conducts the first comprehensive analysis of the Kodi ecosystem: 15,000 Kodi users from 104 countries, 11,000 unique add-ons, and data collected over 9 months. Our work makes three important contributions. Our first contribution is that we build “crawling” software ( de-Kodi ) which can automatically install a Kodi add-on, explore its menu, and locate (video) content. This is challenging for two main reasons. First, Kodi largely relies on visual information and user input which intrinsically complicates automation. Second, the potential sheer size of this ecosystem (i.e., the number of available add-ons) requires a highly scalable crawling solution. Our second contribution is that we develop a solution to discover Kodi add-ons. Our solution combines Web crawling of popular websites where Kodi add-ons are published (LazyKodi and GitHub) and SafeKodi , a Kodi add-on we have developed which leverages the help of Kodi users to learn which add-ons are used in the wild and, in return, offers information about how safe these add-ons are, e.g., do they track user activity or contact sketchy URLs/IP addresses. Our third contribution is a classifier to passively detect Kodi traffic and add-on usage in the wild. Our analysis of the Kodi ecosystem reveals the following findings. We find that most installed add-ons are unofficial but safe to use. Still, 78% of the users have installed at least one unsafe add-on, and even worse, such add-ons are among the most popular. In response to the information offered by SafeKodi, one-third of the users reacted by disabling some of their add-ons. However, the majority of users ignored our warnings for several months attracted by the content such unsafe add-ons have to offer. Last but not least, we show that Kodi’s auto-update, a feature active for 97.6% of SafeKodi users, makes Kodi users easily identifiable by their ISPs. While passively identifying which Kodi add-on is in use is, as expected, much harder, we also find that many unofficial add-ons do not use HTTPS yet, making their passive detection straightforward. 1
Investigating VR Game Player Experience via Remote Experimentation using the Player Experience Inventory

This research explores player experience of virtual reality (VR) games through two stages of study. In both stages, we employed the Player Experience Inventory (PXI), a validated tool designed to evaluate player experience. In Stage 1, player experience of VR games was investigated via an online survey with 100 participants. We found that Audio-Visual Appeal, Immersion, and Ease of Control contributed most to player experience in VR games. We found no relationship between player experience and age, time spent playing, VR experience, or VR headset. Stage 2 used remote experimentation to compare VR and non-VR games with 10 participants. We found that differences in player experience can be explained by the Immersion, Progress Feedback, and Curiosity constructs of the PXI.
FD-CNN: A Frequency-Domain FPGA Acceleration Scheme for CNN-Based Image-Processing Applications

In the emerging edge-computing scenarios, FPGAs have been widely adopted to accelerate convolutional neural network (CNN)–based image-processing applications, such as image classification, object detection, and image segmentation, and so on. A standard image-processing pipeline first decodes the collected compressed images from Internet of Things (IoTs) to RGB data, then feeds them into CNN engines to compute the results. Previous works mainly focus on optimizing the CNN inference parts. However, we notice that on the popular ZYNQ FPGA platforms, image decoding can also become the bottleneck due to the poor performance of embedded ARM CPUs. Even with a hardware accelerator, the decoding operations still incur considerable latency. Moreover, conventional RGB-based CNNs have too few input channels at the first layer, which can hardly utilize the high parallelism of CNN engines and greatly slows down the network inference. To overcome these problems, in this article, we propose FD-CNN, a novel CNN accelerator leveraging the partial-decoding technique to accelerate CNNs directly in the frequency domain. Specifically, we omit the most time-consuming IDCT (Inverse Discrete Cosine Transform) operations of image decoding and directly feed the DCT coefficients (i.e., the frequency data) into CNNs. By this means, the image decoder can be greatly simplified. Moreover, compared to the RGB data, frequency data has a narrower input resolution but has 64× more channels. Such an input shape is more hardware friendly than RGB data and can substantially reduce the CNN inference time. We then systematically discuss the algorithm, architecture, and command set design of FD-CNN. To deal with the irregularity of different CNN applications, we propose an image-decoding-aware design-space exploration (DSE) workflow to optimize the pipeline. We further propose an early stopping strategy to tackle the time-consuming progressive JPEG decoding. Comprehensive experiments demonstrate that FD-CNN achieves, on average, 3.24×, 4.29× throughput improvement, 2.55×, 2.54× energy reduction and 2.38×, 2.58× lower latency on ZC-706 and ZCU-102 platforms, respectively, compared to the baseline image-processing pipelines.
A Low-code Development Framework for Cloud-native Edge Systems

Customizing and deploying an edge system are time-consuming and complex tasks because of hardware heterogeneity, third-party software compatibility, diverse performance requirements, and so on. In this article, we present TinyEdge, a holistic framework for the low-code development of edge systems. The key idea of TinyEdge is to use a top-down approach for designing edge systems. Developers select and configure TinyEdge modules to specify their interaction logic without dealing with the specific hardware or software. Taking the configuration as input, TinyEdge automatically generates the deployment package and estimates the performance with sufficient profiling. TinyEdge provides a unified development toolkit to specify module dependencies, functionalities, interactions, and configurations. We implement TinyEdge and evaluate its performance using real-world edge systems. Results show that: (1) TinyEdge achieves rapid customization of edge systems, reducing 44.15% of development time and 67.79% of lines of code on average compared with the state-of-the-art edge computing platforms; (2) TinyEdge builds compact modules and optimizes the latent circular dependency detection and message routing efficiency; (3) TinyEdge performance estimation has low absolute errors in various settings.
Sign H3re: Symbol and X-Mark Writer Identification Using Audio and Motion Data from a Digital Pen

Although in many cases contracts can be made or ended digitally, laws require handwritten signatures in certain cases. Forgeries are a major challenge with digital contracts, as their validity is not always immediately apparent without forensic methods. Illiteracy or disabilities may result in a person being unable to write their full name. In this case x-mark signatures are used, which require a witness for validity. In cases of suspected fraud, the relationship of the witnesses must be questioned, which involves a great amount of effort. In this paper we use audio and motion data from a digital pen to identify users via handwritten symbols. We evaluated the performance our approach for 19 symbols in a study with 30 participants. We found that x-marks offer fewer individual features than other symbols like arrows or circles. By training on three samples and averaging three predictions we reach a mean F1-score of F1 = 0.87, using statistical and spectral features fed into SVMs.
Internet of Things Based Korean Cross-Cultural Communication Interactive Talent Training Model under Curriculum, Ideology, and Politics

With the development of society, the exchanges between various countries have become increasingly close, and this open communication mode also affects and changes the communication culture of each country. In the context of globalization, cross-cultural communication is increasing day by day, and cross-cultural communication ability has become a necessary quality for modern talents, so it is imperative to cultivate cross-cultural communication talents. However, the development level of informatization is insufficient, especially since the development of Internet of Things technology is lagging behind, the application level is low, and there are fracture characteristics in the application of each link. Information exchange cannot be effectively realized, information is incomplete, and asymmetry is obvious, and there is no effective information exchange platform. Following the trend of internationalization, scholars are paying more and more attention to the combination of cross-cultural communication and IoT technology. The combination of the two is to realize the real-time monitoring of personnel training. The acquisition and transmission of personnel training information is the primary problem to be solved in the realization of Korean cross-cultural communication. The wireless sensor network (WSN) has the characteristics of wide distribution of nodes, strong self-organization ability of routing, and good adaptability to dynamic changes in topology structure. WSN is used to solve this problem and is a good choice. Therefore, this paper used random forest algorithm and GBDT algorithm to apply WSN technology to the process of interactive talent training and established a network information service (NIS) system for interactive talent training. The experimental results have shown that the random forest algorithm and the GBDT algorithm can further simplify the input feature quantity and both can achieve good prediction results, while the GBDT model of the two models has relatively better prediction performance. The models obtained by the two methods meet the needs of detection parameter optimization, which realizes the real-time development of interactive talent training and realizes the intelligence and high efficiency of interactive talent training.
Structures Guided Dynamic Scene Deblurring Method

In this paper, we integrate image gradient priors into a generative adversarial networks (GANs) to deal with the dynamic scene deblurring task. Even though image deblurring has progressed significantly, the deep learning-based methods rarely take advantage of image gradients priors. Image gradient priors regularize the image recovery process and serve as a quantitative evaluation metric for evaluating the quality of deblurred images. In contrast to previous methods, the proposed model utilizes a data-driven way to learn image gradients. Under the guidance of image gradient priors, we permeate it throughout the design of network structures and target loss functions. For the network architecture, we develop a GradientNet to compute image gradients via horizontal and vertical directions in parallel rather than adopt traditional edge detection operators. For the loss functions, we propose target loss functions to constrain the network training. The proposed image deblurring strategy discards the tedious steps of solving optimization equations and taking further advantage of learning massive data features through deep learning. Extensive experiments on synthetic datasets and real-world images demonstrate that our model outperforms state-of-the-art (SOAT) methods.