Reviewerly/exHarmony huggingface dataset:
A Model Driven Approach for Unifying user Interfaces Development

In this paper, we dealt with the rapid development of client web applications (frontend) in a context where development frameworks are legion.In effect with the digital transformation due to the COVID-19 pandemic we are witnessing an ever-increasing demand of the application development in a relatively short time.To this is added the lack of skilled developers on constantly evolving technologies.We therefore offer a low-code platform for the automatic generation of client web applications, regardless of the platform or framework chosen.First, we defined an interface design methodology based on a portal.We then implemented our model driven architecture which consisted of defining a modeling and templating language, centered on user data, flexible enough to not only be used in various fields but also be easily used by a citizen developer.
Multi-Task Reinforcement Meta-Learning in Neural Networks

Artificial Neural Networks (ANN) is one of the main and widespread tools for creating intelligent systems.And, they are actively used for data analysis in many areas such as robotics, computer vision, natural language processing, etc.The learning process of ANN is one of the most labor-intensive stages in ANN.There are many different modifications of ANNs and methods for their training.Currently, deep neural networks are becoming one of the most popular methods of machine learning due to their effectiveness in areas such as speech recognition, medical informatics, computer vision, etc.It is known that ANN training depends on the type of input data.In this paper, reinforcement learning is considered, as popular method used in cases where information is reinforced by signals from the external environment with which the model interacts.The purpose of this paper is to develop a reinforcement meta-learning algorithm that would be efficient in terms of quality and speed of learning.However, despite the significant scientific progress in deep learning, existing algorithms are not efficient enough to solve problems in the real world.In addition, such algorithms require a significant amount of learning time, which complicates the development process.To solve these problems, the use of meta-learning or "learning to learn" algorithms has recently been especially relevant.The paper proposes an approach to reinforcement meta-learning using a multitasking weight optimizer.experimentally shown that the proposed approach is more efficient than the known MAML (Model-Agnostic Meta-Learning) algorithm.The proposed MAML SPSA-Track method shows an improvement in efficiency by an average of 4%, and MAML SPSA-Delta by 8%, respectively.Moreover, the last algorithm spends on average 2 times less time on push-v2 and pick-place-v2 tasks.
An Evaluation Method for Service-Oriented Architecture Maturity Model

SOA maturity model was used to clarify and provide a common definition of SOA inside an organization.The model provides an abstract overview of SOA adoption by characterizing evolutionary levels.However, this study found that there is a lacking on how the previous models were evaluated to show that the model is conforming to the specification and can be implemented in the real-world environment.Therefore, this study aims to provide the evaluation method for the SOA maturity model through the verification and validation process.The Integrated Adoption Maturity for Service-Oriented Architecture (IAMSOA) model was chosen and the verification process is being performed through expert review where the study identifies the experts, determines the verification criteria, and collects and analyzes the feedback; while the validation was performed through case study by identifying the organization, determining the validation criteria, brainstorming, and collecting and analyzing the feedback.The verification results show that the evaluated model is comprehensive, understandable, accurate, and well-organized.Moreover, the validation results reveal that it is feasible and practical to be executed in the real environment.Conclusively, this study has successfully evaluated one of the SOA maturity models and shows the verification and validation process in detail which can be re-enacted in different projects and settings.
Rider Driven African Vulture Optimization with Multi Kernel Structured Text Convolutional Neural Network for Classifying e-Commerce Reviews

Opinion mining is a natural language processing based on sentiment classification technique to determine the sentiment of the reviews.The major existing text Convolutional Neural Network (CNN) algorithms are derived based on × size kernels which extract ineffective review text-features and lead to less classification accuracy.Moreover, most of the traditional CNN versions output three classes such as positive, negative, and neutral as their classification results.Hence, a novel algorithm namely 'RAVO driven Multi-Size Kernel structured Text CNN for classifying ecommerce reviews (MSK-TCNN-RAVO)' is proposed in this work.This proposed approach utilizes five multi-size kernels ( × , × , × , × , × ), multi-dimensional kernels (1D & 2D), and integrates varying size kernels to extract text-features effectively.In addition, the performance of multi-kernel CNN is highly enhanced by RAVO algorithm based on rider optimization.Moreover, the proposed approach is highly effective to process 'review-stop-words removal' that decrease the complexity and time consumption of the opinion mining process.Most existing systems use single pooling operations which reduce feature map processing performance, hence, dual pooling operations (both Max and Average pooling) are employed in this research.Furthermore, it is configured to generate five classification outputs such as bad, fair, neutral, good, and excellent to support better decisionmaking with 95.5% accuracy.This method is evaluated using different quality metrics and five review-databases to measure the performance, and the results reveal that the proposed method outperforms the other existing review classification algorithms.
Energy-based Collaborative Filtering Recommendation

The core value of the recommendation model is the using of the measures to measure the difference between the jumps (e.g.pearson), some other studies based on the magnitude of the angle in space (e.g.cosine), or some other studies study the level of confusion (e.g.entropy) between users and users, between items and items.Recommendation model provides an important feature of suggesting the suitable items to user in common operations.However, the classical recommendation models are only concerned with linear problems, currently there is no research about nonlinear problems on the basis of potential/energy approach to apply for the recommendation model.In this work, we mainly focus on applying the energy distance measure according to the potential difference with the recommendation model to create a separate path for the recommendation problem.The theoretical properties of the energy distance and the incompatibility matrix are presented in this article.Two experiment scenarios are conducted on Jester5k, and Movielens datasets.The experiment result shows the feasibility of the energy distance measures/ the potential in the recommendation systems.
The Science of Emotion: Malaysian Airlines Sentiment Analysis using BERT Approach

Sentiment analysis has grown to be one of the most active research areas in Natural Language Processing (NLP). Sentiment analysis, also known as opinion mining, uses a series of methods, techniques and tools to study people’s opinions, views and sentiment towards a wide range of topics such as products, services, events and issues. In the airline industry, millions of people today use social networking sites such Twitter, Skytrax, TripAdvisor to express their emotions, opinions, and share information about the aircraft service. It is a hidden gem to the airline company to gain valuable insight from this data and have the broadest possible view into what people are saying about the airline’ brand online. Hence, this paper explores six different sentiment analysis models: Random Forest, Multinomial Naive Bayes, Linear Support Vector Classifier, Ensemble Method, Bidirectional Long Term Short Memory (Bi-LSTM) and BERT model, in order to determine and develop the best model to be used. The best model was then used to determine the social status, company reputation, and brand image of Malaysian airline companies. In conclusion, the BERT model was found to perform the best out of the six models tested, scoring an accuracy of 86%. Keywords: Supervised Learning, Ensemble Learning, Deep Learning, Transfer Learning, Airline Sentiment
How 'digital' is the new Serbian Law on Digital Assets?

The paper focuses on the legislative changes from 2021 that the Law on Digital Assets introduced into the legal system of the Republic of Serbia and contextualizes the amendments from the perspective of existing practice. Introductory part evaluates the issue of whether blockchain technology can secure trust and safety in the transactions that are executed via Internet by parties from all over the world, as well as reasons that created the regulatory framework for values that are transferred over distributed ledger technologies infrastructures. Main part of the paper examines in detail the legislative solutions within the Law on Digital Assets as well as key exceptions and terms and their impact on the local economy. The section is followed by an overview of the draft legislation in EU in the field as well as how it may impact Serbian economy as a third country. Aside from concluding remarks on legislative changes domestically, the paper outlines potential upcoming challenges related to blockchain and instruments that may require a different approach in regulation.
Distributed Soccer Training Smart Sensors for Multitarget Localization and Tracking

This paper presents an in-depth study and analysis of the localization and tracking of multiple targets in soccer training using a distributed intelligent sensor approach. An event-triggered mechanism is used to drive the acoustic array sensors in the distributed acoustic array sensor network, which solves the problem of increased communication load caused by frequent communication of microphones and effectively reduces the communication load between microphones as well as the energy consumption of the acoustic array sensor network. By designing a suitable state estimation equation for the acoustic source target and fully utilizing the measurement and state estimation information of its nodes as well as the state estimation information of neighboring nodes, the next moment state of the acoustic source target can be accurately predicted. A correlation filtering tracking algorithm based on multiscale spatial co-localization is proposed. In the proposed algorithm, the tracker contains a total of several subfilters with different sampling ranges. Then, this paper also proposes a collaborative discrimination method to judge the spatial response of the target image samples of each filter and jointly localize the target online. Based on this, this paper further explores the potential of correlation filter tracking algorithms in complex environments and proposes a robust correlation filter tracking algorithm that fuses multiscale spatial views. The cross-view geometric similarity measure based on multiframe pose information is proposed, and the matching effect is better than that based on single-frame cross-view geometric similarity; to solve the problem of player appearance similarity interference, a graph model-based cross-view appearance similarity measure learning method is further proposed, with players in each view as nodes, player appearance depth features as node attributes, and connections between cross-view players as edges to construct a cross-view player graph. The similarity obtained by the graph convolutional neural network training is better than the appearance similarity calculated based on simple cosine distance.
Methodological Reflections on Capturing Augmented Space: Insights From an Augmented Reality Field Study

The growing popularity of augmented reality has led to an increased overlaying of physical, offline space with digital, augmented space. This is particularly evident in the public space of big cities, which already feature a multitude of holographic content that can be experienced via augmented reality devices. But how can we methodically capture the interrelation between physical and augmented space? In this augmented reality field study, a historical building was holographically reconstructed in its original size on a public city square. The test people were then able to move around and view the hologram from different angles via high-tech augmented reality glasses. Due to its explorative character and constantly changing field conditions, including, among other things, the Covid-19 pandemic, we had to critically reflect and adapt our methods to take into account technical, environmental, social, operationalisation, and recruitment issues. After evaluating our solutions to these issues, this article aims to illustrate the methodological challenges and opportunities of augmented reality field studies and to provide an overview of best practices for capturing the interrelationship of physical and augmented space.
On the Problems and Countermeasures of College Students’ Mental Health and Safe Work under Network Environment

Under the network environment, mental health and safety work on college is related to national security and campus security. Moreover, it affects the healthy development of college students. In the era of ever-changing mobile Internet, colleges and universities must take practical countermeasures against the problems in mental health and safety education on college and improve the relevance and effectiveness of college students' network safety education. This paper first finds the problems of college students' mental health and safety through questionnaire survey and analyzes the current situation of college students' mental health and safety under the current networked environment. In response to the problems found, we propose complete and systematic educational countermeasures. At the same time, we need to firmly establish the strategic awareness of network security education and strengthen the construction of network security education in colleges and universities. We should strengthen the construction of network security education in colleges and universities, form a joint effort of schools, families, government, and society, and improve the quality and effectiveness of network security education.
A cognitive inquiry into similarities and differences between translation and paraphrase: Evidence from eye movement data

Intralingual translation has long been peripheral to empirical studies of translation. Considering its many similarities with interlingual translation, also described as translation proper, we adopted eye-tracking technology to investigate the cognitive process during translation and paraphrase, an exemplification of intralingual translation. Twenty-four postgraduate students were required to perform four types of tasks (Chinese paraphrase, English-Chinese translation, English paraphrase, Chinese-English translation) for source texts (ST) of different genres. Their eye movements were recorded for analysis of the cognitive effort and attention distribution pattern. The result demonstrated that: (1) Translation elicited significantly greater cognitive efforts than paraphrase; (2) Differences between translation and paraphrase on cognitive effort were modulated by text genre and target language; (3) Translation and paraphrase did not differ strikingly in terms of attention distribution. This process-oriented study confirmed higher cognitive efforts in inter-lingual translation, which was likely due to the additional complexity of bilingual transfer. Moreover, it revealed significant modulating effects of text genre and target language.
Deep Learning based Speech and Gesture Recognition System for the Disabled

Speech and Gesture recognition systems constitute an ideal aid for the disabled with speech and hearing impairments. Approximately, there are 466 million people in the world with hearing impairment and around 16 million with speech impairment. They require an external aid to recognize their speech and gestures, to express their thoughts and ideas to the world. The proposed Speech and Gesture Recognition System (SGRS) takes forward to solve the communication barriers faced by the disabled subjects, by recognizing both the speech and gestures of the subjects with promising accuracy using the convolutional neural network. The proposed SGRS model is competent to convert the sign-language into pictures and speech to text as well with high accuracy. Thus, SGRS can be a suitable aid for the subjects with speech and hearing impairment. SGRS has been evaluated with standard evaluation scores such as validation accuracy, validation loss, recall, precision and F1-score and has been proved to be proficient.
Development of Digital Pocket Book for Disaster Mitigation Materials Coronavirus Disease 2019 (COVID-19) to Increase Student Resilience to Disasters

Entering the beginning of 2020 there was an extraordinary phenomenon, namely the outbreak of the Coronavirus Disease (COVID-19) which attacked almost all parts of the country in the world. The COVID-19 pandemic disaster causes mild respiratory infections to severe respiratory infections such as pneumonia and the greatest risk can cause death. Other negative impacts include increasing stress and anxiety in individuals. The individual's capacity to deal with and reduce stress and anxiety can arise from having resilience. Resilience is the resilience possessed by individuals, groups, or communities that allow them to face, prevent, minimize and even eliminate the adverse effects of unpleasant conditions, or even change the conditions that include them into a natural thing to overcome, to overcome. Therefore, students need to have resilience abilities. The importance of increasing student resilience in efforts to mitigate the COVID-19 disaster through education is very necessary because schools are an effective means of conveying information. Submission of learning about the dangers of COVID-19 mitigation can be delivered through a learning process with innovative learning media. Based on the results of observations in one high school in Bogor City, it was found that the level of student resilience was still relatively low, namely 56%. The purpose of this study was to determine the effect of digital pocket books on COVID-19 disaster mitigation materials in increasing student resilience to disasters and to determine the effectiveness of digital pocket books on COVID-19 disaster mitigation materials. The research was conducted using themethod Research and Development using the ADDIE model (analyze, design, development, implementation, evaluation). The population in this study were students of class XI MIPA 2 with a sample of 29 students. The results showed that digital pocket books were able to increase students' resilience to disasters to 69% which was included in the medium category. The effectiveness of learning media has a very effective category which is obtained from the average percentage of student responses of 90% and teacher responses of 84%. Keywords: Digital Pocket Book; COVID-19 Disaster Mitigation, Resilience.
La inspiración en la inteligencia artificial y el caso de los datos cuyo contenido son obras protegidas por el derecho de autor

Artificial Intelligence (AI) technologies use data as part of their processes for training and defining solutions. In the case of data whose content is made up of works protected by Copyright, it is not appropriate to understand that works are “used”. AI does not make use of works as in the physical or digital world. The process of analyzing and processing data in AI is analogous to the mental process of inspiration. Just as inspiration in other people’s works is not deemed use of a work and is the starting point for works protected by Copyright, the training of data representative of protected works must also be understood as inspiration.
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Real-world data often follows a long-tailed distribution, which makes the performance of existing classification algorithms degrade heavily. A key issue is that samples in tail categories fail to depict their intra-class diversity. Humans can imagine a sample in new poses, scenes, and view angles with their prior knowledge even if it is the first time to see this category. Inspired by this, we propose a novel reasoning-based implicit semantic data augmentation method to borrow transformation directions from other classes. Since the covariance matrix of each category represents the feature transformation directions, we can sample new directions from similar categories to generate definitely different instances. Specifically, the long-tailed distributed data is first adopted to train a backbone and a classifier. Then, a covariance matrix for each category is estimated, and a knowledge graph is constructed to store the relations of any two categories. Finally, tail samples are adaptively enhanced via propagating information from all the similar categories in the knowledge graph. Experimental results on CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018 have demonstrated the effectiveness of our proposed method compared with the state-of-the-art methods.
Enhancing AES with Key Dependent S-Box and Transpose MDS Matrix

Privacy is an important feature in transmission of data. Due to continuous flow of data over network, there may be a chance of attacks on sensitive data, either passive or active. To provide security, Cryptography plays a vital role by scrambling data into unreadable form. Several techniques have been developed to provide security for digital data transmission. AES is one of the efficient encryption engaged for the past decade. There is every need to improve its security mechanism periodically. In this paper we implement Transpose mix column and S-BOX Rotation to enhance efficiency. This proposed model provides more Avalanche Effect than traditional AES. Moreover, Diffusion and Confusion are crucial in Cryptography. The more the diffusion rate the more secure the data is and is shown in this proposed model. However, S-Box rotation provides more confusion. AES is most efficiently used in Wi-Fi, particularly in WPA2-PSK(AES), so enhancing security mechanism is needed.
Analyzing the Influence of Smart-device Visual Features, Viewing Distance, and Content Factors on Video Streaming QoE

Quality of experience (QoE) over wireless networks has attracted attention from industry and academia due to an increase in video streaming applications. Several researchers have attempted to understand the factors affecting QoE and design appropriate quality control strategies. Normally, video streaming is initiated by a user who accesses video contents over wireless networks using a smart device held at various viewing distances. Each aforementioned factor has the potential to affect QoE of the viewed session. However, several studies explore the behavior of wireless networks on video streaming QoE. To understand the effects of other factors on QoE, this paper investigates the influence of the device's visual features, viewing distance, and content factors on video streaming. The study adopted an emulation technique to conduct multi-factor experiments designed using the Taguchi method. The 5-ways ANOVA analysis revealed that the effects of smart-device visual features, viewing distance, and content types are significant on video streaming QoE at p&lt;0.05. Moreover, smart devices with a pixel density index of more than 200 ppi produce high QoE, with the viewing distance limited to 45 cm. Lastly, the video bitrate greater than 1024 kbps produced a good QoE regardless of the frame rates.