Reviewerly/exHarmony huggingface dataset:
Security interoperability in heterogeneous IoT platforms

The Internet of Things (IoT) has strong potential to improve the accessibility and usability of our daily lives and industries, and various related technologies are being developed. In particular, IoT platforms are the role of middleware in IoT environments and are software that connects multiple devices such as various sensors, access points, and data networks. In addition, the open authorization (OAuth) 2.0 is an authorization framework standard for delegating authority to a third party and is widely used to protect APIs in IoT platforms and the existing web environments. However, interoperability and security are barriers that hinder the potential. Technologies have often been developed without adequate considerations for interoperability and security, such as the OAuth 2.0 framework widely used in IoT domains. Furthermore, most studies did not consider interoperability, even though interoperability among heterogeneous domains is critical for the IoT to share resources. No study addressed the limitation of the conventional OAuth 2.0 framework to support interoperability, and the studies did not comprehensively discuss the security of the OAuth 2.0 framework. Therefore, based on the well-known threats of the conventional OAuth 2.0 framework, we identify possible threats and proper countermeasures for the interoperable OAuth 2.0 framework (IOAF) to achieve security interoperability. In addition, we propose a new authorization code for multiple domains (ACMD) grant flow using a proof key for code exchange (PKCE), which is more secure than the current ACMD flow.
A Chinese Intelligent Teaching Platform for Colleges Based on Cloud Computing

In China, the Chinese subject is called “the mother of the encyclopedia.” Learning Chinese well is the basic condition for learning other subjects well. Therefore, Chinese education has always undertaken the important task of teaching the mother tongue and cultural inheritance and has a great influence on the development process of China’s education. Society is facing new opportunities and challenges since the 21st century, and all the mobile Internet, cloud computing, and Internet of Things achieve great development. Using the increasingly mature technology of data information collection, analysis, and processing can realize a more comprehensive evaluation of students in teaching activities. In the current upsurge of big data, it is more and more widely used in the field of education, such as learning analysis, network education platform, education information management platform, education AP, and smart campus. In this context, Chinese teaching needs more open courses, which requires us to organically integrate the existing information technology and Chinese teaching, and on this basis, integrate modern educational data sets and learning analysis technology into Chinese teaching and learning, to promote the improvement of the quality of language teaching and learning. The development of information technology in the intelligent era provides an opportunity for the establishment and application of an intelligent classroom. This paper constructs an intelligent and efficient new classroom equipped with smart devices teaching mode based on defining the concept and connotation of a classroom equipped with smart devices. The proposed model is combined with the current situation and the development of classrooms equipped with smart devices. It also analyzes the difficulties and pain points of traditional College Chinese teaching. The research results can provide a reference for the implementation of intelligent classroom teaching mode in College Chinese and other general courses in higher vocational colleges.
Fighting evil is not enough when refactoring metamodels

In model-driven engineering, metamodels are central artifacts that allow to capture domain concepts and build domain-specific languages. However, bad design decisions, continuous changes, and the evolution of requirements may introduce bad smells and deteriorate the quality of metamodels. Refactoring metamodels is a complex task as it should be performed according to many conflicting quality attributes while maximizing the removal of smells. In this paper, we propose a generic automated approach based on a multi-objective heuristic search to refactor metamodels. The process aims at generating a set of refactoring recommendations with various quality trade-offs from which the modeler can choose the most appropriate for her context. We evaluate the efficiency of our approach with a user-based experiment, on time to perform understandability and extendibility tasks, as well as the correctness of the task output. Our results show that, globally, considering trade-offs between quality and smell removal is significantly better than focusing on smell removal alone. The observed difference is statistically significant for the extendibility but only partially for the understandability.
Exploiting Reservoir Computing for Optical Secure Communication

Abstract A reservoir computing (RC) based on vertical cavity surface emitting laser (VCSEL) subjected to optical feedback is adopted to realize chaos synchronization in optical chaotic communication. Combined with the application of chaos masking (CMS) technology in chaotic secure communication, chaotic signal generated by distribution feedback (DFB) laser after optical feedback and optical injection, that is used as chaotic carrier of hiding original information, and act as target signal of the RC. Then masked information is used for RC training and transmission. Next, utilizing the weights and error offsets trained out by RC at transmitter, carrier can be reconstructed by RC at receiver. Finally, message is acquired by subtracting the reconstructed carrier from received masked signal. Taking image data transmission in 10 km fiber as an example, the chaos synchronization between the carrier signal and the constructed carrier signal is 0.9935, Q-factor is 10 and bit error rate (BER) is 5×10 -28 with simulation.
Size-efficient sparse population for strictly structured quantum genetic algorithm

Quantum genetic algorithm is a field of research to discover a potential structure to realize an effective heuristic, evolutionary optimization technique powered by quantum computation. Apart from contemporary efforts to look for a novel quantum evolutionary design, some studies suggest the idea of strictly structured quantum genetic algorithm, which rigorously imitates the classical practice via a sparse population to sample a few individuals from the given problem's domain, as opposed to the conventional quantum practice that transforms the entire domain into a population itself. Albeit having its own advantages, the algorithm requires to periodically measure and reinitialize the quantum system over generations. It therefore leads to several computational inefficiency issues including the wasteful population initialization, during which individuals that rather marginally contribute to the overall optimization are iteratively generated. This paper proposes that the algorithmic inefficiency from the mentioned problem can be alleviated by preemptively eliminating a large portion of undesirable individuals and still maintain the tasked optimization result to an acceptable degree. The main idea is to deliberately reduce the amount of randomness among the quantum population, thereby discarding individuals that do not contain any fitted genes. A number of tests were conducted on continuous optimization problems to validate the theory of the proposed method, resulting that it can effectively reduce the size of the involved population while minimizing the loss in the original algorithm's performance.
Super-Resolution for Brain MR Images from a Significantly Small Amount of Training Data

We propose two essential techniques to effectively train generative adversarial network-based super-resolution networks for brain magnetic resonance images, even when only a small number of training samples are available. First, stochastic patch sampling is proposed, which increases training samples by sampling many small patches from the input image. However, sampling patches and combining them causes unpleasant artifacts around patch boundaries. The second proposed method, an artifact-suppressing discriminator, suppresses the artifacts by taking two-channel input containing an original high-resolution image and a generated image. With the introduction of the proposed techniques, the network achieved generation of natural-looking MR images from only ~40 training images, and improved the area-under-curve score on Alzheimer&rsquo;s disease from 76.17% to 81.57%.
Does attention mechanism possess the feature of human reading? A perspective of sentiment classification task

Purpose To understand the meaning of a sentence, humans can focus on important words in the sentence, which reflects our eyes staying on each word in different gaze time or times. Thus, some studies utilize eye-tracking values to optimize the attention mechanism in deep learning models. But these studies lack to explain the rationality of this approach. Whether the attention mechanism possesses this feature of human reading needs to be explored. Design/methodology/approach The authors conducted experiments on a sentiment classification task. Firstly, they obtained eye-tracking values from two open-source eye-tracking corpora to describe the feature of human reading. Then, the machine attention values of each sentence were learned from a sentiment classification model. Finally, a comparison was conducted to analyze machine attention values and eye-tracking values. Findings Through experiments, the authors found the attention mechanism can focus on important words, such as adjectives, adverbs and sentiment words, which are valuable for judging the sentiment of sentences on the sentiment classification task. It possesses the feature of human reading, focusing on important words in sentences when reading. Due to the insufficient learning of the attention mechanism, some words are wrongly focused. The eye-tracking values can help the attention mechanism correct this error and improve the model performance. Originality/value Our research not only provides a reasonable explanation for the study of using eye-tracking values to optimize the attention mechanism but also provides new inspiration for the interpretability of attention mechanism.
A user-centric threat model and repository for cyber attacks

There are several threat-modeling schemes and most notably the one introduced by Microsoft, called STRIDE. These threat modeling schemes offer useful taxonomies that can be further utilized to capture key features and security aspects of software applications. However, to the best of our knowledge, the literature lacks a "user-centric" threat-modeling scheme through which the security features are perceived from a user's perspective. This paper introduces UC-STRIDE, a user-centric threat model, which enables identification of threats and communicates their associated risks from the user's point of view. UC-STRIDE extends the Microsoft STRIDE threat model to incorporate important aspects of threat modeling interested for the end users such as valuable assets, attack targets, and how the attack is launched. We introduce a repository of over 100 cyber attacks and their descriptions annotated with their immediate technical and non-technical consequences called "CogSec". The repository can be useful in communicating risks with stakeholders who may have little to no cybersecurity expertise. To illustrate the applicability of UC-STRIDE, we present a use case through which the application of the introduced user-centric threat modeling is demonstrated.
Reliability awareness algorithm of IT service management center considering load balancing

In order to effectively improve the IT service quality of IT service management center, on the basis of considering load balancing, an IT service quality perception algorithm of IT service management center based on principal component analysis is designed. Based on the analysis of IT service process of IT service management center, the system software module is designed, including information management module, external perception evaluation module, internal perception evaluation module, comprehensive perception evaluation module and perception evaluation result analysis module; In the comprehensive perception evaluation module, the IT service quality perception evaluation system is constructed, the IT service quality perception evaluation indicators are obtained, the IT service quality perception value and the IT user expectation value of the IT system are determined, the principal component analysis method is used to calculate the difference between the IT user expectation value and the actual perception value, and the IT service quality perception evaluation model is constructed, Complete the design of reliability awareness algorithm of IT service management center considering load balancing. The simulation results show that the difference between the evaluation results obtained by the system and the actual survey results is small, and the perceived evaluation effect of IT service quality is good.
Analytical Method 1

Chapter 3 provides a brief overview of the particular approach to corpus linguistics adopted in this book: namely, corpus linguistics as a method (as opposed to corpus linguistics as a theory) (McEnery & Hardy, 2012; Tognini-Bonelli, 2001). The specific corpus linguistic analytical methods used, such as collocation and keyness analysis, and the statistical tests and cut-offs associated with each analysis type are detailed. Using data from the MI 1984–2014 Corpus (specifically the data collected during a pilot study and an illness-specific sample of the data), each analytical method used is exemplified. The utility of each analysis type for analysing ideology in texts is also discussed.
Research on Style Design of Suit Based on Computer Interactive Genetic Algorithm

In order to study the suit style design based on computer interactive genetic algorithm. Firstly, this paper summarizes the development status of garment customization and interactive evolutionary computing and studies the steps and system flow of customized garment design based on interactive evolutionary computing and the automatic generation method of garment style. Then, taking the suit as an example, the principal component analysis method is used to extract the elements of clothing design and code it. Combined with the interactive genetic algorithm, the user-satisfied clothing style binary string is generated, and the style binary string is decoded into a visual style map through the decoding algorithm. Finally, based on MATLAB platform, a customized clothing system prototype based on interactive evolutionary computing is realized. Users can carry out innovative clothing design through interactive graphical interface, automatically draw the clothing style designed by users, and provide method and technical support for clothing customization. The experimental results show that in the process of interaction design, try to solve the initial population required by the design, evolve and derive offspring on a large scale through genetic operation, retrieve the whole design feasible region, and obtain the optimal solution in line with user preferences.
IA-BERT

Sarcasm as a form of figurative language has been widely used to implicitly convey an offensive opinion. While preliminary researches have constantly tried to identify the sarcasm lying in a text directly from tokens within the text, it is insufficient because sarcasm does not have specific vocabularies as in polarized sentences. Especially in threads or discussions, sarcasm can be identified after getting the context information from previous replies or discussions. To this end, we propose IA-BERT, a model architecture that considers contextual information to identify incongruity features that lie in sarcastic texts. IA-BERT is embedded with a feature attention layer that combines features extracted from the response alone and interactive features obtained from the context and the response. The model leverages BERT pretrained embedding and yields a performance improvement from the standard fine-tuned BERT classifier. IA-BERT also outperforms the sophisticated architecture of LCF-BERT in the accuracy and F1-score.
ARTiCo - AR in Tissue Converting

For fully up-taking the advances achieved in pervasive monitoring systems within Industry 4.0, novel intelligent interfaces are needed to ease interactions with human personnel and enable them to access the available data and services ergonomically. To this end, we present a mobile app based on augmented reality that allows operators to receive location-aware notifications and access and visualize real-time information and guidance for troubleshooting and plant maintenance. The app has been tailored to respond to the needs of a specific scenario, i.e. tissue converting, where it has provided encouraging results.
Knowledge graph entity alignment networks with multi-information aggregation

Embedding-based methods have currently become the hit of the entity alignment, among which Graph Convolutional Networks model(GCNs) is receiving widespread attention because of its powerful capability of identifying isomorphic structures. Note that the aligned entities in real-world knowledge graphs always have non-isomorphic neighborhood structures, which may cause the GCN-based method to generate different representations for them. In addition, most embedding-based entity alignment methods ignore the relation information and semantic information of entities, which can cause inferior performance because of the lack of necessary information. This paper proposes a novel KG alignment framework named MGN(Multi-information aggregation Graph neural Networks), which aims at capturing the multiaspects features to learn the representations of entities at the same time. Our method can make up for the lacking of feature information and ease the non-isomorphism of structure effectively. The experiments on three real-world crosslingual datasets demonstrate that the MGN can get better results over the state-of-the-art alignment methods.
Low power and high throughput authenticated encryption chip for radio frequency applications

A novel authenticated encryption chip for radio frequency communication application was developed. A range of low power techniques were applied to reduce the power consumption. The proposed authenticated encryption chip exhibits ultra low power dissipation of 139.2 μW/MHz @ 1.8V V<sub>DD</sub> and ultra high throughput of 1.07 Gbps with a reasonable core area of 0.461 mm2 (46.8 k gates) under SMIC 180 nm technology.
Design of aircraft formation clustering communication routing protocol based on evolutionary game theory

With the development of passive detection system, aircraft communication is being seriously threatened. In order to improve the stability of aircraft network cooperative mission communication, a routing protocol design method of aircraft formation clustering communication based on evolutionary game theory was proposed. This method constructs a dynamic networking protocol based on ad-hoc, which realizes the selection Pareto equilibrium in the network cluster by using the evolutionary game theory. This paper puts forward the concept of life factor, which solves the problem of network instability caused by frequent disconnection of communication links, and finally realizes the combination of evolutionary game theory and the communication design method of routing protocol in aircraft formation cluster. Compared with the traditional routing protocol, the aircraft formation clustering communication routing protocol based on evolutionary game has the best survival rate in the mission cycle. In the same mission scenario and under the same conditions, the normalized life factor decline rate of the evolutionary game clustering communication routing protocol is significantly lower, and has better RF stealth performance.
Generative image steganography based on VAE-GAN

Aiming at the problems of distortion, low quality, and multiple transmissions of generative stego-image based on generative adversarial networks, a new generative image steganography method combing variational autoencoders (VAE) and generative adversarial networks (GAN) is proposed. This method combines the advantages of VAE and GAN. First, the binary secret information is grouped, and flag bits are added, which is converted into noise according to the mapping relationship. The noise is input into the VAE-GAN generator to obtain a high-quality stego-image group and randomly combine a new image. When extracting information, the stego-image is input to the extractor to recover the noise and converted into a binary message according to the mapping relationship. Finally, the secret information is recovered according to the flag bit. Experimental results show that the method not only improves the quality of stego-image but also has a high steganography capacity.
An improved generative adversarial network for Chinese font generation

Since drawing a complete set of high-quality Chinese calligraphy fonts by hand is time-consuming and labor-intensive, in order to realize the automatic drawing of Chinese calligraphy fonts, this paper proposes an encoder-decoder network model based on residual structure. The model realizes the automatic generation of Chinese calligraphy fonts through the adversarial training of the generator and the discriminator. Experiments show that our method can not only generate realistic calligraphy fonts, but also has the ability to generate a complete font library.
ACANet: across-scale context attention network for real-time semantic segmentation

With the recent advance of various context aggregation approaches, remarkable progress has been achieved in semantic segmentation. However, it is still challenging to fully exploit the discriminative across-scale context information in an efficient manner. In this paper, we introduce an <i>across-scale context attention network</i> (<i>ACANet</i>) for real-time semantic segmentation. Instead of compute complex query-dependent attention map, we calculate query-independent attention map to aggregate contexts. Experimental results on Cityscape and Camvid datasets demonstrate the effectiveness of our method. In particular, our network achieves 77.4% on the Cityscape test set with a 32 FPS for 1024×2048 images on a single RTX 2080Ti GPU.
Comparing Baselines for Corpus Analysis

The authors review different baselines for the study of alternant choices, emphasizing that normalization to a standard number of words – while straightforward in its application – will in many cases not provide a meaningful measure of frequency. Instead, it is argued, we need a baseline indicating opportunities of use, such as phrase or sentence counts. Exemplifying their proposal with reference to get- and be-passives and the presence or absence of agentive by-phrases, the authors demonstrate a sequence of measures taken to make the quantities that are compared more meaningful and defensible, based on linguistically informed selections of baseline quantities (number of main verbs, passives or potentially alternating passives). Crucially, this process must involve a categorization of observations by the researcher to ensure that mutual substitution is plausible in each case. To calibrate this manual data verification exercise to a manageable level, the authors apply a method of uneven category sub-sampling to the data, and use it to adjust variance estimates and confidence intervals in their analysis.