Reviewerly/exHarmony huggingface dataset:
Software requirements modeling from the selected set of requirements using fuzzy based approach

Software requirements (SRs) modeling is one of the sub-processes of requirements engineering whose aim is to model and design the SRs before the development of a project. Different techniques are employed for representing the SRs using goal concepts, unified modeling language, etc. There could be a large number of SRs after the completion of the requirements elicitation process. It is not practical to model the complete set of the identified SRs because of the cost, time, and other limitations of an organization. There should be some systematic methodology to identify and select those SRs for modeling that need to be implemented during the software development process. The selection of SRs from the list of the elicited requirements is a multi-criteria decision-making process. In this process different stakeholders participate in the selection of the SRs. Linguistic variables may be used by the stakeholders to specify the preferences of SRs. To deal with this issue, a method has been proposed using a fuzzy based approach so that the selected set of SRs can be modeled and implemented during the development phase. The proposed method is explained by considering the small and large set of SRs for an institute examination system. The ranking value of the functional requirements of an examination system is computed. Based on the ranking order, top three requirements are modeled using use-case diagrams (UCDs) and class diagrams. It is found that both diagrams represent different information about the requirements of an examination system and there is no overlap in the information captured through UCDs and class diagrams.
Fantastic Perspectives and Where to Find Them: Involving Patients and Citizens in Digital Health Research

Abstract Background: Digital contact tracing and exposure notification apps have quickly emerged as a potential solution to achieve timely and effective contact tracing for the SARS-CoV-2 virus. However, their actual uptake remains limited, and citizens, including patients, are rarely consulted and included in the design and implementation process. However, their contribution could be determinant in the success of such apps. The DIGICIT (DIGITal CITizenship) project relied on patient and citizen partnership in research to assess public perspectives on these apps. Objectives: This article has three objectives: (1) describe the methodological process to co-construct a questionnaire and interpret the survey results with patients and citizens, (2) assess their experiences regarding this methodology, and (3) propose best practices for their involvement in digital health research. Methods: The DIGICIT project was developed in four steps : ( 1) creation of the advisory committee composed of patients and citizens, (2) co-construction of a questionnaire, (3) interpretation of survey results, and (4) assessment of the experience of committee participants. Results: Of the 25 applications received, 12 people met our diversity criteria and were included in the advisory committee. 84 survey questions were generated in the first co-construction meeting and were cut down to 36 in the final version. Participants made more than 20 recommendations when interpreting survey results and suggested carrying out focus groups with marginalized populations to increase representativity. They appreciated their inclusion early in the research process, being listened to and respected, the collective intelligence, and the method used for integrating their suggestions. They suggested that the study objectives and roles be better defined, that more time in the brainstorming sessions be allowed, and that discussion outside of meetings be encouraged. Conclusion: Having patients and citizens actively participating in this research constitutes the main methodological strength. They helped reorient the project by adding focus groups to the research protocol. Clear communication of the project objectives, good organization in meetings, and continuous evaluation from participants allow best practices to be achieved for patients' and citizens' involvement in digital health research. Co-construction in research generates critical study design ideas through collective intelligence. This methodology can be used in various clinical contexts and different healthcare settings.
Promotion of Intelligent Digital Computer-Aided Design to the Improvement of Rural Public Environment Design

In order to optimize the rural public environment, protect and build the characteristics and integrity of the rural public environment. This paper introduces the development and research of a digital design platform based on digital computer, taking parallel robot (cross IV) as the object. The basic functional requirement of the digital integrated design platform is parallel robot. In the design stage, it mainly needs six functional modules, including conceptual design module, detailed design module, simulation design module, electromechanical coupling design module, optimization design module, and database module. Based on the analysis and summary of the digital integrated design process of parallel robot, combined with the actual functional requirements, the key technologies and solutions of the digital design platform are determined, the overall framework of the digital design platform is drawn up, and the implementation process of the platform is planned in detail. The Design personnel involved in the design process and their corresponding responsible contents, and the user authority is managed reasonably. Set the parameters of the driving arm and the driven arm. The outer diameter of the driving arm is 58 mm and the thickness is 2 mm. The outer diameter of the driven arm is 14 mm, and the thickness is 2 mm. Their length can be calculated directly according to the parameters provided in the conceptual design. According to the rules set by the software, the automatic assembly operation is carried out to obtain the whole machine model. After entering the SolidWorks motion module, the dynamic interference inspection is carried out first, and then the simulation analysis is carried out. The results show that the selection is correct when the actual model is consistent with the theoretical model. The correct selection and wide application of CAD technology in the production and construction of building decoration engineering will inevitably bring great changes in construction management and concept and become a new trend and trend for construction enterprises to enhance their competitiveness. The aforementioned computer-aided design is a broad application category. It provides a strong foundation for the design of rural public environment.
A Formal Modeling and Verification Scheme with an RNN-Based Attacker for CAN Communication System Authenticity

To enhance the attack resistance of the Controller Area Network (CAN) system and optimize the communication software design, a comprehensive model that combines a variable attacker with the CAN bus (VACB) is proposed to evaluate the bus communication risk. The VACB model consists of a variable attacker and the CAN bus model. A variable attacker is a visualized generation of the attack traffic based on a recurrent neural network (RNN), which is used to evaluate the anti-attack performance of the CAN bus; the CAN bus model combines the data link layer and the application layer to analyze the anomalies in CAN bus data transmission after the attack message. The simulation results indicate that the transmission accuracy and successful response rate decreased by 1.8% and 4.3% under the constructed variable attacker. The CAN bus’s authenticity was promoted after the developers adopted this model to analyze and optimize the software design. The transmission accuracy and the successful response rate were improved by 2.5% and 5.1%, respectively. Moreover, the model can quantify the risk of potential attacks on the CAN bus, prompting developers to avoid it in early development to reduce the loss caused by system crashes. The comprehensive model can provide theoretical guidance for the timing design of embedded software.
Surprises in adversarially-trained linear regression

State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against such examples. It is formulated as a min-max problem, searching for the best solution when the training data was corrupted by the worst-case attacks. For linear regression problems, adversarial training can be formulated as a convex problem. We use this reformulation to make two technical contributions: First, we formulate the training problem as an instance of robust regression to reveal its connection to parameter-shrinking methods, specifically that $\ell_\infty$-adversarial training produces sparse solutions. Secondly, we study adversarial training in the overparameterized regime, i.e. when there are more parameters than data. We prove that adversarial training with small disturbances gives the solution with the minimum-norm that interpolates the training data. Ridge regression and lasso approximate such interpolating solutions as their regularization parameter vanishes. By contrast, for adversarial training, the transition into the interpolation regime is abrupt and for non-zero values of disturbance. This result is proved and illustrated with numerical examples.
UNTRADITIONAL WAYS OF REVEALING OF CREATIVE POTENTIAL OF FUTURE PAINTER-TEACHER

The perspectives of utilization of untraditional methods and ways of actuating and revealing of a personality’s creative potential, which is grounded on a using of visual means, are considered in the article. On the basis of examining of spread untraditional strategies of stirring up and developing of creative abilities of a personality the way’s of perfection of professional training of art departments students of teachers training institutes are observed.
Managing Bufferbloat in Cloud Storage Systems

Today, companies and data centers are moving towards cloud and serverless storage systems instead of traditional file systems. As a result of such a transition, allocating sufficient resources to users and parties to satisfy their service level demands has become crucial in cloud storage. In cloud storage, the schedulability of system components and requests is of great importance to achieving QoS goals. However, the bufferbloat phenomenon in storage backends impacts the schedulability of the system. In a storage server, bufferbloat happens when the server submits all requests immediately to the storage backend due to a large buffer in the backend. In recent decades, many studies have focused on the bufferbloat as a latency problem. Nevertheless, none of these works investigate the impact of bufferbloat on the schedulability of the system. In this paper, we demonstrate that the bufferbloat impacts scheduling and performance isolation and identify utilizing admission control in the storage backend as an easy-to-adopt solution to mitigate bufferbloat. Moreover, we show that traditional static admission controls are inadequate in the face of dynamic workloads in cloud environments. Finally, we propose SlowFast CoDel, an adaptive admission control, as a starting point for developing adaptive admission control mechanisms to mitigate bufferbloat in cloud storage.
A Hydropower Project Investment Estimation Model Based on Chaos BP Combined Neural Network

Based on the in-depth study and research of construction project cost estimation methods and economic benefit analysis methods at home and abroad, this paper proposes an investment forecasting model based on the combination of fuzzy mathematics and improved neural network. First, the classical fuzzy mathematics method is used to classify and screen historical data samples to improve the quality of the samples and improve the accuracy and practicability of the investment prediction model. The composition and other important contents of the project are extracted, and their characteristics are extracted and connected with the investment estimation and benefit analysis of the project, and a hydropower project investment estimation model and an investment benefit analysis model based on an improved neural network are constructed, and the prediction accuracy is as high as 97.2%
A Study of Blockchain-Based Energy-Aware Intelligent Routing Protocols for Wireless Sensor Networks

For high efficiency of wireless sensor networks (WSNs) and to ensure higher security in routing, a trustworthy routing strategy is very important. There are plenty of plans to improve the reliability of routing nodes like using trust management, cryptographic hash, centralized routing decisions, etc. But most routing schemes are hard to achieve in real life because unreliable behaviors are difficult to detect in dynamically routing nodes. In the meantime, there is still no hard-hitting way to prevent the attack of malevolent nodes. With these issues in mind, in this chapter we will look at trusted routing schemes for blockchains and reinforcement learning to ensure better routing security and efficiency for WSNs. Blockchain routing schemes will be discussed in order to obtain information about the routing of routing nodes in the blockchain, which makes it possible to identify routing information identifiably and in completely. The learning model is utilized to help routing nodes dynamically select more dependable and cost-efficient routing links.
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition

Human activity recognition (HAR) through wearable devices has received much interest due to its numerous applications in fitness tracking, wellness screening, and supported living. As a result, we have seen a great deal of work in this field. Traditional deep learning (DL) has set a state of the art performance for HAR domain. However, it ignores the data's structure and the association between consecutive time stamps. To address this constraint, we offer an approach based on Graph Neural Networks (GNNs) for structuring the input representation and exploiting the relations among the samples. However, even when using a simple graph convolution network to eliminate this shortage, there are still several limiting factors, such as inter-class activities issues, skewed class distribution, and a lack of consideration for sensor data priority, all of which harm the HAR model's performance. To improve the current HAR model's performance, we investigate novel possibilities within the framework of graph structure to achieve highly discriminated and rich activity features. We propose a model for (1) time-series-graph module that converts raw data from HAR dataset into graphs; (2) Graph Convolutional Neural Networks (GCNs) to discover local dependencies and correlations between neighboring nodes; and (3) self-attention GNN encoder to identify sensors interactions and data priorities. To the best of our knowledge, this is the first work for HAR, which introduces a GNN-based approach that incorporates both the GCN and the attention mechanism. By employing a uniform evaluation method, our framework significantly improves the performance on hospital patient's activities dataset comparatively considered other state of the art baseline methods.
Scientific methodological basis of teaching primary school pupils language by developing speaking activities

The purpose of this research is to develop speaking activities for primary school students and to get teachers’ opinions on the scientific methodological foundations of language teaching. The participant group of the research consists of 55 primary schoolteachers who are teaching in various primary schools in the city of Almaty, Kazakhstan, in the 2021–2022 academic year. The research was designed according to the phenomenological method, one of the qualitative research methods. Research data were collected with a semi-structured interview form developed by the researchers. As a result of the research, the majority of primary schoolteachers who participated in the research stated that the language skills and speaking skills of the students were partially sufficient. The majority of teachers categorised the methods they applied for the effectiveness of the language education they gave to the students as using different methods, techniques and materials, if there were a problem, giving the students keywords before the speaking activity, enabling the students to develop ideas and asking the students to tell the parts that were interesting to them after the listening activity. The suggestions of the primary schoolteachers participating in the research on establishing the scientific methodological foundations of language teaching by developing speaking activities were mostly categorised as gaining critical speaking skills, providing speaking skills with games, providing speaking skills with different methods and techniques and creating practices that eliminate speaking anxiety in students.&#x0D; &#x0D; Keywords: Language skills, speaking skills, primary school students, teachers’ opinions;
Blind Digital Watermark Based on Discrete Fourier Transformation

The policy and security of digital images are concerned by all artists. Embedding blind digital watermarks to images is an effective solution to such problem. The existing blind digital watermark techniques for images are usually based on the spatial domain method, such as some build-in tools in some software like Photoshop. Those methods' imperceptibility is good, but the robustness is dissatisfactory. Therefore, blind digital watermark based on the frequency domain is becoming popular nowadays. The use of the frequency domain-based techniques can not only keep the imperceptibility but also ensure good robustness and non-removability. According to the reasons mentioned above, this thesis presents a frequency domain-based blind digital watermark technique. This method uses basic discrete Fourier transformation to transform an image to the frequency domain and then embeds a digital watermark. To increase the security, we also introduce some methods to encode watermarks before embedding them to the images. We conduct some experiments by applying the technique to some images with different watermarks. In the experiments, it can embed blind watermarks with the least effect on the quality of the original image and can also successfully extract watermarks from the watermarked image. The results show this technique is highly useable. We also conduct some experiments to simulate the attacks. This technique also shows great robustness after many kinds of different attacking methods.
Sentiment Analysis of online reviews based on LDA and AP-Bert model

The purpose of this paper is to construct a more accurate behavior matrix by using fine-grained aspect level emotion analysis method. Firstly, LDA topic extraction model is used to extract the topic of online review text, and the concerned attributes are extracted. According to the characteristics of online comments, a BERT emotion analysis model with enhanced pooling was proposed. Activation function layer and max-average pooling layer were designed to solve the over-fitting problem of BERT model in the process of emotion analysis. Finally, by combining LDA extraction results and AP-Bert sentiment analysis results, the proportion matrix is obtained. Experimental results show that the accuracy, recall rate and AUC value of AP-Bert model are better than those of the same type of model and original BERT model.
The Evolution of Artificial Intelligence and its Collaboration with Brain Science

The emergence of artificial intelligence is the product of the development of science and technology. At the same time, the development of science and technology has greatly improved the ability of the human brain and artificial intelligence. For example, scientists choose to train artificial intelligence with video games and improve the flexibility of the human brain. One example of artificial intelligence’s success and its rapid development is AlphaGo. Go has long been recognized as one of the most difficult games in the world, and it seems surprising that artificial intelligence could easily win a match against a human master. But, it wasn't easy, and the reason for AlphaGo's victory has a lot to do with machine learning. By mimicking humans and their own evolution to give themselves an edge in the game and win. At the same time, many people think that artificial intelligence is very dangerous, and they think that artificial intelligence will replace or eliminate human beings, but the fact is the same as people's imagination? The benevolent see benevolence and the wise see wisdom. After decades of development, artificial intelligence can reach the level of cooperation with humans. Basic cooperation can no longer meet people's needs, and more advanced cooperation projects need to be developed. Human beings and artificial intelligence help each other, especially in brain science and the application of CNN in image processing. In these studies, human beings provide inspiration for the research and development of artificial intelligence, and artificial intelligence provides convenience for human life. Therefore, we can know that artificial intelligence plays a very important role in the evolution of human beings.
Online Virtual Tourism Platform Based on VR Technology

The COVID-19 pandemic and the development of emerging technologies have brought new challenges and opportunities to tourism. The tourism sector must rethink its relationship with consumers and leverage emerging technologies for business expansion and transformation. Under this circumstance, providing travelers with an immersive virtual environment to meet their travel needs through virtual reality (VR) technology is considered to be a sustainable way to develop tourism1. In this paper, an online virtual art museum travel platform is built based on VR technology, which allows users to roam in the virtual scene and interact with the objects in the scene from the first-person view. VR tourism that provides a highly realistic environment and user-driven interactive functions is regarded as a potential alternative for real-life tourism2, achieving the immersive roaming in the virtual environment. VR tourism alleviates the contradiction between constrained mobility and increased tourism willingness up against the COVID-19 pandemic, and has positive social significance.
Image Encryption and Decryption through Quantum Cryptography

There has been an influence on the theory of numbers because of quantum computers. The approach may have a significant impact outside the realm of classical physics. Quantum information and quantum cryptography are two diverse branches of quantum information processing using qubits instead of binary bits. Theory of quick computation with multiple complexity levels will shortly transform into actual through current developments in quantum computers. It does away with most classical science areas of computational and cybersecurity sciences. The goal of this project is to introduce quantum concepts and practices into cryptography, which will lead to the evolution of quantum cryptography. One of the most incredible works of quantum physics is quantum information theory. It is not possible to calculate the state of any device without disrupting it. The new theory of quantum mechanics has an essential impact on the security of commonly used cryptosystems. This chapter aims to establish a groundbreaking quantum key distribution scheme for digital data.
An attack graph-based approach to network vulnerability analysis

As the global threat situation intensifies, it is difficult for traditional security strategies to effectively respond to complex attack methods. In this paper, we propose a value-aware and memorized attack graph generation method which successfully compresses the strategy space and effectively reduces the algorithm complexity. Through a virtual network experiment, we validate the feasibility of the proposed methods above.