Reviewerly/exHarmony huggingface dataset:
Management of Django Web Development in Python

Application Development is not only limited to the business or promotional field, but it also helps to provide the necessary services to the common people across the world by creating an interaction between the companies and the customers through the website. It also creates a platform for the common people who have the ability to attract the audience using their talent. We can take the example of various blog writers, online councilors and fashion influencers who have a large audience because of their quality content and knowledge in their field. We have generally seen how the electronic companies have marked their presence on the online platform via the website which enables the users to contact the company easily. Online retail companies interact with the users via website and provide the orders at the doorsteps of the people. There are various news agencies which had created their website which keeps the users updated with the latest national and international articles. The website development has helped to create a link between the people and the companies, but behind all these facilities there is a powerful language and commands that holds these computational activities and data transfers which helps to create a good interaction between the users and the website owners. Web developers are classified into Front-end and Back-end web developers, Front-end developers help to create an interactive interface that can be easily accessed by the users, whereas Back-end development deals with the main work that is done behind the screens. We can say that it helps to power the website by managing data transfer and other computational activities. In this project, the importance of Django and back-end web development is explained. Using the tools and commands of these languages we can make an interactive and user-friendly interface that can be easily accessed by people. Back-end was earlier known for limited purposes but now when it is explored thoroughly proved to be one of the best languages for website development, gaming, Artificial Intelligence, and highly advanced mobile applications.
A new quantum leader?

IBM has built a quantum computer that has twice as many qubits as its nearest rivals, reports Matthew Sparkes
Identifying IoT Devices Based on Spatial and Temporal Features from Network Traffic

With the rapid growth of the Internet of Things (IoT) devices, security risks have also arisen. The preidentification of IoT devices connected to the network can help administrators to set corresponding security policies according to the functionality and heterogeneity of the devices. However, the existing methods are based on manually extracted features and prior knowledge to identify the IoT devices, which increases the difficulty of the device identification task and reduces the timeliness. In this paper, we present CBBI, a novel IoT device identification approach. On the one hand, CBBI uses a hybrid neural network model Conv-BiLSTM to automatically learn the representative spatial and temporal features from the network traffic, such as the position relationship of the internal organization structure in network communication traffic, the time sequence of the data packets, and the duration of the network flow. On the other hand, CBBI contains the data augmentation module FGAN that solves the problem of data imbalance in deep learning and improves the accuracy of the model. Finally, we used the public dataset and laboratory dataset to evaluate CBBI from multiple dimensions. The evaluation results for different datasets show that our approach achieves the accurate identification of IoT devices.
From CCS-Planning to Testautomation: The Digital Testfield of Deutsche Bahn in Scheibenberg - A Case Study

The digitalization of railway systems should increase the efficiency of the train operation to achieve future mobility challenges and climate goals. But this digitalization also comes with several new challenges in providing a secure and reliable train operation. The work resulting in this paper tackles two major challenges. First, there is no single university curriculum combining computer science, railway operation, and certification processes. Second, many railway processes are still manual and without the usage of digital tools and result in static implementations and configurations of the railway infrastructure devices. This case study occurred as part of the Digital Rail Summer School 2021, a university course combining the three mentioned aspects as cooperation of several German universities with partners from the railway industry. It passes through all steps from a digital Control-Command and Signalling (CCS) planning in ProSig 7.3, the transfer, and validation of the planning in the PlanPro data format and toolbox, to the generation of code of an interlocking for the digital CCS planning to contribute to the vision of test automation. This paper contributes the experiences of the case study and a proof-of-concept of the whole lifecycle for the Digital Testfield of Deutsche Bahn in Scheibenberg. This proof-of-concept will be continued in ongoing and following projects to fulfill the vision of test automation and automated launching of new devices.
Rethink, Revisit, Revise: A Spiral Reinforced Self-Revised Network for Zero-Shot Learning

Current approaches to zero-shot learning (ZSL) struggle to learn generalizable semantic knowledge capable of capturing complex correlations. Inspired by Spiral Curriculum, which enhances learning processes by revisiting knowledge, we propose a form of spiral learning that revisits visual representations based on a sequence of attribute groups (e.g., a combined group of color and shape). Spiral learning aims to learn generalized local correlations, enabling models to gradually enhance global learning and, thus, understand complex correlations. Our implementation is based on a two-stage reinforced self-revised (RSR) framework: preview and review. RSR first previews visual information to construct diverse attribute groups in a weakly supervised manner. Then, it spirally learns refined localities based on attribute groups and uses localities to revise global semantic correlations. Our framework outperforms state-of-the-art algorithms on four benchmark datasets in both zero-shot and generalized zero-shot settings, which demonstrates the effectiveness of spiral learning in learning generalizable and complex correlations. We also conduct extensive analysis to show that attribute groups and reinforced decision processes can capture complementary semantic information to improve predictions and aid explainability.
Facial Expression Recognition Using Aggregated Handcrafted Descriptors based Appearance Method

There have been quite a few studies on facial expression recognition over the years, and it is still a challenging subject due to the significant inter-class variability. Facial expression research in this field focuses on the development of techniques to identify, code, and extract facial expressions to improve prediction by computer. With great success of machine learning, the various texture descriptors are exploited to obtain a better performance. This paper proposes a method based on the aggregation between different descriptors Histogram of oriented Gradient (HOG) and Local Binary Pattern (LBP). First stage the input image has pre-processed to detect dace area which helps to extract most significant features. Then, Diagonal-HOG (D-HOG) also has extracted and aggregated all features. Finally, Support Vector Machine (SVM) has been used a classifier to classify each feature as well as aggregated features. We evaluate our method using Japanese Female Facial Expressions database (JAFFE), experimental results showed that the proposed method is accurate and efficient in recognizing facial expressions.
Incomplete Multiview Clustering via Cross-View Relation Transfer

In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multi-view clustering, the view-missing problem increases the difficulty of learning common representations from different views. To address the challenge, we propose a novel incomplete multi-view clustering framework, which incorporates cross-view relation transfer and multi-view fusion learning. Specifically, based on the consistency existing in multi-view data, we devise a cross-view relation transfer-based completion module, which transfers known similar inter-instance relationships to the missing view and infers the missing data via graph networks based on the transferred relationship graph. Then the view-specific encoders are designed to extract the recovered multi-view data, and an attention-based fusion layer is introduced to obtain the common representation. Moreover, to reduce the impact of the error caused by the inconsistency between views and obtain a better clustering structure, a joint clustering layer is introduced to optimize recovery and clustering simultaneously. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method.
LSMQ: A Layer-Wise Sensitivity-Based Mixed-Precision Quantization Method for Bit-Flexible CNN Accelerator

Model quantization is a prevailing way to accelerate convolutional neural network (CNN). Quantization with mixed precision tends to compress the model better and further improves the computation efficiency. However, it is challenging to identify the optimal bit width for each layer. In this paper, we proposed a mixed-precision quantization(LSMQ) method based on layer-wise sensitivity. We calculated the sensitivity of each layer first, then the weight of each layer would be automatically quantized with unique precision determined by the sensitivity ranking and a valid search strategy without retraining. Moreover, we presented a bit-flexible CNN accelerator that can efficiently support data operations with varying bit widths after mixed-precision quantization. Experiment on LSMQ shows that the top-1 accuracy for VGG16 based on the LSMQ method is 7.31% higher, while the model size is 3.4% smaller compared with previous work.
Query relaxation for portable brick-based applications

Semantic metadata standards pave the way for interoperability by providing building operators and application developers with common schemes to describe building resources. Applications can query building metadata models to retrieve the set of entities and relationships they need to operate, instead of hard-coding references to specific points and objects from the underlying data sources.
Progressive Two-Stage Network for Low-Light Image Enhancement

At night, visual quality is reduced due to insufficient illumination so that it is difficult to conduct high-level visual tasks effectively. Existing image enhancement methods only focus on brightness improvement, however, improving image quality in low-light environments still remains a challenging task. In order to overcome the limitations of existing enhancement algorithms with insufficient enhancement, a progressive two-stage image enhancement network is proposed in this paper. The low-light image enhancement problem is innovatively divided into two stages. The first stage of the network extracts the multi-scale features of the image through an encoder and decoder structure. The second stage of the network refines the results after enhancement to further improve output brightness. Experimental results and data analysis show that our method can achieve state-of-the-art performance on synthetic and real data sets, with both subjective and objective capability superior to other approaches.
XAI Feature Detector for Ultrasound Feature Matching

Feature matching is a crucial component of computer vision that has various applications. With the emergence of Computer-Aided Diagnosis (CAD), the need for feature matching has also emerged in the medical imaging field. In this paper, we proposed a novel algorithm using the Explainable Artificial Intelligence (XAI) [1] approach to achieve feature detection for ultrasound images based on the Deep Unfolding Super-resolution Network (USRNET). Based on the experimental results, our method shows higher interpretability and robustness than existing traditional feature extraction and matching algorithms. The proposed method provides a new insight for medical image processing, and may achieve better performance in the future with advancements of deep neural networks.
Generating adversarial images to monitor the training state of a CNN model

Abstract Deep neural networks have shown effectiveness in many applications, however, in regulated applications like automotive or medicine, quality guarantees are required. Thus, it is important to understand the robustness of the solutions to perturbations in the input space. In order to identify the vulnerability of a trained classification model and evaluate the effect of different perturbations in the input on the output class, two different methods to generate adversarial examples were implemented. The adversarial images created were developed into a robustness index to monitor the training state and safety of a convolutional neural network model. In the future work, some generated adversarial images will be included into the training phase to improve the model robustness.
A comparison of conventional and deep learning methods of image classification

The aim of the research is to compare traditional and deep learning methods in image classification tasks. The conducted research experiment covers the analysis of five different models of neural networks: two models of multi–layer perceptron architecture: MLP with two hidden layers, MLP with three hidden layers; and three models of convolutional architecture: the three VGG blocks model, AlexNet and GoogLeNet. The models were tested on two different datasets: CIFAR–10 and MNIST and have been applied to the task of image classification. They were tested for classification performance, training speed, and the effect of the complexity of the dataset on the training outcome.
Towards Parameter Identification of a Behavioral Model from a Virtual Reality Experiment

In this paper, we investigate the calibration of a mathematical model describing different behaviors occurring during a natural, a societal, or a technological catastrophe. This model was developed in collaboration with geographers and psychologists. To collect information on the level of stress, psychologists of the LPPL laboratory of Nantes (France) led virtual reality experiments. These experiments consisted in immersing individuals in a situation of catastrophe and measuring their electrocardiogram. From the physical and biological data collected, we present the methodology to calibrate the behavioral model. First, a theoretical analysis is carried out to determine (i) if the parameters can be uniquely estimated, (ii) the minimal number of discrete measurements required for the estimation. Then, from these analyses, an estimation procedure is performed to calibrate the mathematical model or at least to have an order magnitude of the model parameters. Through this work, we will show from simulations that the proposed system makes it possible to apprehend non observable human processes.
An Empirical Study on the Employment Monitoring and Early Warning Mechanism of Medical Graduates in Universities with Big Data and Complex Computing System

Based on the management of big data, the analysis and forecast of the employment demand cycle business situation studied in this article is based on the employment cycle theory and a complete set of employment monitoring, employment evaluation, employment forecasting, and policy selection theories and strategies developed around the employment cycle fluctuations, a specific employment phenomenon. First, systematically evaluate the current state of the employment demand boom, appropriately reflect the hot and cold degree of the employment demand boom, and provide necessary information for the government’s regulatory measures, content, and timing. Secondly, it reflects the regulatory effects of graduate employment monitoring, judging whether graduate employment monitoring measures are properly applied, whether they have the effect of smoothing out employment fluctuations, and promoting the country’s employment demand; in addition, business decision makers can take advantage of the employment demand boom, by monitoring the information provided by the early warning system and timely foreseeing the upcoming macrocontrol measures, so that enterprises’ labor adjustments can adapt to the government’s regulatory measures. At the same time, the model proposes a prosperity index method for monitoring and early warning of the employment demand cycle. After selecting and dividing three types of prosperity indicators, the DI index reflecting the trend of the prosperity change and the CI index reflecting the strength of the prosperity change are calculated and constructed. The national employment demand boom monitoring and early warning signal system predicts the trend of the employment boom cycle outside the sample period. The experimental results show that the cyclic prosperity forecast results are consistent not only with the national employment demand prosperity in recent months, but also with the use of the structural measurement ARIMA (p, d, q) model. The alertness value is close, indicating that this indicator system has a good effect on the national employment demand boom monitoring and early warning.
Stochastic modeling and analysis of the bitcoin protocol in the presence of block communication delays

Abstract We analyze the protocol of the bitcoin blockchain by using the PRISM probabilistic model checker. In particular, we (i) extend PRISM with the ledger data type , (ii) model the behavior of the key participants in the protocol—the miners —and (iii) describe the whole protocol as a parallel composition of processes. The probabilistic analysis of the model highlights how forks happen and how they depend on specific parameters of the protocol, such as the difficulty of the cryptopuzzle and the network communication delays. Our results confirm that considering transactions in blocks at depth larger than 5 as confirmed is reasonable because the majority of miners have consistent blockchains up‐to that depth with probability of almost 1. We also study the behavior of networks with churn miners, which may leave the network and rejoin afterwards, and with different topologies.
Accuracy Analysis of Feature-Based Automatic Modulation Classification via Deep Neural Network

A feature-based automatic modulation classification (FB-AMC) algorithm has been widely investigated because of its better performance and lower complexity. In this study, a deep learning model was designed to analyze the classification performance of FB-AMC among the most commonly used features, including higher-order cumulants (HOC), features-based fuzzy c-means clustering (FCM), grid-like constellation diagram (GCD), cumulative distribution function (CDF), and raw IQ data. A novel end-to-end modulation classifier based on deep learning, named CCT classifier, which can automatically identify unknown modulation schemes from extracted features using a general architecture, was proposed. Features except GCD are first converted into two-dimensional representations. Then, each feature is fed into the CCT classifier for modulation classification. In addition, Gaussian channel, phase offset, frequency offset, non-Gaussian channel, and flat-fading channel are also introduced to compare the performance of different features. Additionally, transfer learning is introduced to reduce training time. Experimental results showed that the features HOC, raw IQ data, and GCD obtained better classification performance than CDF and FCM under Gaussian channel, while CDF and FCM were less sensitive to the given phase offset and frequency offset. Moreover, CDF was an effective feature for AMC under non-Gaussian and flat-fading channels, and the raw IQ data can be applied to different channels' conditions. Finally, it showed that compared with the existing CNN and K-S classifiers, the proposed CCT classifier significantly improved the classification performance for MQAM at N = 512, reaching about 3.2% and 2.1% under Gaussian channel, respectively.
Applications of monitoring and tracing the evolution of clustering solutions in dynamic datasets

The clustering approach is widely accepted as the most prominent unsupervised learning problem in data mining techniques. This procedure deals with the identification of notable structures in unlabeled datasets. In modern days clustering of dynamic data, streams play a vital role in policy-making, and researchers are paying particular attention to monitoring the evolution of clustering solutions over time. The data streams evolve continually, and different sources generate data items over time. The clustering solution over this stream is not stationary and changes with the influx of new data items. This paper presents a comprehensive study of algorithms related to tracing the evolution of clusters over time in cumulative datasets. To demonstrate the applications and significance of the tracing cluster evolution, we implement the MONIC algorithm in R-software. This article illustrates how the data segmentation of dynamic streams is done and shows the applications of monitoring changes in clustering solutions with the help of real-life published datasets.
A New Online and Offline Blended Teaching System of College English Based on Computer Internet Technology

Through the analysis and research of the multimedia teaching platform system based on computer Internet technology, this paper proposes constructing a network multimedia teaching platform system based on streaming transmission to realize the sharing of excellent teaching resources in colleges and universities and the development of education and teaching. The design and development of each module of the teaching platform are completed, and the realization technology of each module function is explained. We completed the design and implementation of some auxiliary functions of the teaching platform, including the system usage help module, resource search module, and online on-demand playback functions. After the experiment, we compare the changes of students’ English anxiety before and after the experiment and conduct interviews with them. The results show that the application of the Internet-based scaffolding teaching model in college English reading teaching can alleviate students’ English learning anxiety. Through interviews to understand students’ feedback and evaluation of the new teaching model, it was found that most students hold positive evaluations of the Internet-based scaffolding teaching model, believing that the new teaching model can stimulate students’ English learning enthusiasm and increase confidence in English learning.