Reviewerly/exHarmony huggingface dataset:
A Method for Improving the Data Availability of Distributed Photovoltaic Power Plants

Abstract The growth of distributed photovoltaics is very fast, and there have been situations such as short-term low-voltage distributed photovoltaics sending power to high voltage levels, a voltage exceeding limits, and inadequate operation and maintenance. If data analysis is not done well, it will affect the operation of the power grid and the income of the power station. This article analyses the reasons for the limited data quality of existing distributed photovoltaic monitoring platforms. It proposes a method that can improve the data availability of distributed photovoltaic power plants. By reviewing and repairing abnormal data, efficient application of data can be achieved. It can identify 6 types of abnormal data from 25, and 535 and complete the correction of 6 types of abnormal data. After correction, by comparing the data from nearby power stations, it can be seen that the root mean square deviation is 8.25%.
220 kV grid optimization in Shengli Oilfield

Abstract As an important oil industry base in China, Shengli oilfield has infrastructure supporting facilities for power generation, transmission, substation, and distribution. Based on the grid data, a BPA simulation model is built for the oilfield area, and a grid optimization scheme is proposed for the problems of the existing 220 kV grid in the Shengli oilfield. The simulation results show that the safety and stability of the oilfield grid are improved under the optimized scheme, which verifies the feasibility and effectiveness of the scheme.
Towards Blur Mixed Instance Search Combined with Image-wise and Object-wise Feature

Abstract With development of intelligent robots and autonomous driving, images/videos with motion blurs occur frequently. However, these images/videos with blurs might be retrieved. Instance search has been studied for many years in the computer vision field. However, few works are focused on instance search on blurred images. In this paper, we propose a framework of instance search on blurred image datasets which is built on three components including query blur mixing, image-wise feature based ranking, and instance-wise feature based re-ranking. Due to lack of the benchmark, we also collect and build a blurred image dataset on which we conduct performance experiments. Experimental results show that our solution is significantly promising in instance search task.
Correlation of Nursing Students’ Digital Literacy Levels and Counseling Skills

Objective: This research was conducted as a descriptive, and correlational study to analyze the association between nursing students’ digital literacy levels and counseling skills. &#x0D; Material and Method: The study sample comprised 383 nursing students. The Personal Information Form, the Digital Literacy Scale, and the Counseling in Nurses Scale were used in the data collection process. Descriptive statistics (number, percentage), the independent samples t-test, an F-test, Pearson’s correlation test, and multiple regression analysis test were utilized in the analysis of research data. &#x0D; Results: It was found that nursing students had high digital literacy levels (66.05±12.30 points) and counseling skill scores (45.17±4.11 points). In addition, a moderate positive correlation (r=.367) was obtained between the students' digital literacy and counseling skill scores. A significant difference was determined between nursing students' educational status, daily internet use time, internet use time according to students' selfevaluations and their digital literacy scores. In addition, it was found that there was a significant difference between age, education level, daily internet usage time, choosing a profession willingly and counseling skills (p
Word Embedding for Text Classification: Efficient CNN and Bi-GRU Fusion Multi Attention Mechanism

The proposed methodology for the task of text classification involves the utilization of a deep learning algorithm that integrates the characteristics of a fusion model. The present model is comprised of several attention-based Convolutional Neural Networks (CNNs) and Gate Recurrent Units (GRUs) that are organized in a cyclic neural network. The Efficient CNN and Bi-GRU Fusion Multi Attention Mechanism is a method that integrates convolutional neural networks (CNNs) and bidirectional Gated Recurrent Units (Bi-GRUs) with multi-attention mechanisms in order to enhance the efficacy of word embedding for the purpose of text classification. The proposed design facilitates the extraction of both local and global features of textual feature words and employs an attention mechanism to compute the significance of words in text classification. The fusion model endeavors to enhance the performance of text classification tasks by effectively representing text documents through the combination of CNNs, Bi-GRUs, and multi-attention mechanisms. This approach aims to capture both local and global contextual information, thereby improving the model’s ability to process and analyze textual data. Moreover, the amalgamation of diverse models can potentially augment the precision of text categorization. The study involved conducting experiments on various data sets, including the IMDB film review data set and the THUCNews data set. The results of the study demonstrate that the proposed model exhibits superior performance compared to previous models that relied solely on CNN, LSTM, or fusion models that integrated these architectures. This superiority is evident in terms of accuracy, recall rate, and F1 score.
Brain Storm Optimization Algorithm with an Adaptive Parameter Control Strategy for Finding Multiple Optimal Solutions

Abstract Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet challenging. Brain storm optimization (BSO) is a relatively new paradigm of swarm intelligence algorithm that has been shown to be effective in solving global optimization problems, but it has not been fully exploited for multimodal optimization problems. A simple control strategy for the step size parameter in BSO cannot meet the need of optima finding task in multimodal landscapes and can possibly be refined and optimized. In this paper, we propose an adaptive BSO (ABSO) algorithm that adaptively adjusts the step size parameter according to the quality of newly created solutions. Extensive experiments are conducted on a set of multimodal optimization problems to evaluate the performance of ABSO and the experimental results show that ABSO outperforms existing BSO algorithms and some recently developed algorithms. BSO has great potential in multimodal optimization and is expected to be useful for solving real-world optimization problems that have multiple optimal solutions.
Friction Firestarter: A Toolkit for Designing Meaningful Friction in Game User Interfaces

Despite the prevailing paradigm of user-friendliness and enjoyment in mainstream game design and user interface design, intentional friction in game user interfaces that can be used to create meaningful experiences and to encourage reflection in players. This work aims to explore such use of intentional friction, providing designers with a valuable resource to generate unconventional game interfaces. As a starting point, we previously identified six strategies for intentional friction: (1) exploit memory shortcomings; (2) faulty feedback; (3) mismatched mental models; (4) impairment of ability; (5) deliberate inefficiency; and (6) oppressive constraints. Afterwards, to help operationalise these strategies and identify others, we ran co-creation workshops with game and UI designers, which lead to the development of a tool composed of three decks of cards, combining additional friction strategies, intentions, emotions, and ideation triggers, and enabling designers to create expressive game interfaces that intentionally incorporate friction as a design strategy. The Friction Firestarter toolkit is intended to inspire designers to explore various options and think creatively about friction in UI design.
In[the Hate Booth]: A Case Study on How to Deal with Online Hate Speech

In today's hyper-connected world, digital games and online gaming communities occupy a prominent place in the communication system, in social media, forums or Internet communities, where online hate speech (OHS) takes place, frequently and publicly, triggering toxic environments. In this chapter we present a case study based on interviews, distributed in two sessions, to ten participants with 12 and 13 years old, and an experience over the SG In[The Hate Booth], as a counterproposal to address OHS. The qualitative data approaches three aspects: the experiences with OHS, the perspectives about OHS and the possible solutions to counteract OHS. We conclude that OHS is a common complaint from players and a characteristic behavior in game communities. Data shows that even users who don’t identify themselves with this behavior accept it as part of online environments and agree that this toxicity continues outside the in-game screen with effects in everyday life. The pedagogical approach, namely through SG, is perceived as a possible measure to counteract the OHS.
Designing Playful Artefacts for People Living with Dementia: A Methodological Approach with Undergraduate Students

This paper reports on a set of methodologies and strategies applied in an academic environment with undergraduate students, in the context of a semester-long project aimed at creating playful artefacts for people living with dementia. This project was carried out under the REMIND—Design for People with Dementia research and resulted from a partnership between Lusofona University and Memória de Mim Daycare Center, an Alzheimer Portugal service. Its main goal was to design artefacts based on biographical and cultural characteristics of the Center's users. In addition, it aimed to introduce concepts of social design and inclusive design in academic learning through practice in real contexts. The students had regular direct contact with health professionals from the Center, developed extensive research on dementia, main symptoms, and forms of manifestation, and gathered a set of biographical and cultural information that allowed the design of artefacts potentially more oriented to the interests and characteristics of the Center’s users. These artefacts were created according to a set of premises, namely, accessibility, flexibility, and inclusiveness in the forms of use; focus on the users’ experience, knowledge, and interests; cognitive stimulation; encouragement of communication/conversation among users. In this paper, the methodologies used, positive aspects and constraints of the work process, and results obtained are reported, aiming at the replicability and continuity of learning practices integrated into social and community contexts.
Identifying Themes of Right-Wing Extremism in Hindutva Discourse on Twitter

Hindutva, the core political ideology of India’s current ruling party, the Bharatiya Janata Party (BJP), seeks to transform constitutionally secular India into a Hindu Rashtra (“Hindu nation”). Although Hindutva has all of the features of right-wing extremism (RWE), it is nevertheless viewed as an insular sociopolitical phenomenon, due to the Eurocentric nature of RWE discourse. Recent theoretical and analytical research has sought to showcase the similarity between RWE and Hindutva, but empirical research on their relationship has been largely absent. To fill this gap, in our study, we collected 15 million tweets and used network analysis to identify prominent themes of RWE—including exclusionary nationalism, conspiracy theories, and anti-minority violence and hate speech—among the supporters of Hindutva and the BJP. Furthermore, in our toxicity analysis (performed to understand which themes produced higher levels of toxicity), we found that Hindi-language tweets related to conspiracy theories and anti-minority violence or hate speech were more toxic than English-language tweets. Given that the growing global presence and normalization of RWE-based ideas and movements are sources of concern for everyone invested in the idea of liberal democratic society, our research broadens the discussion on RWE to include the Indian context and invites researchers to further investigate Hindutva from the perspective of RWE.
Data Representation with No-Code Augmented Reality Authoring Tools

This study is framed within information design and focuses on data representation using augmented reality (AR) and its appropriation by designers. The need to investigate no-code AR authoring tools was motivated by the conclusions drawn during the development of “Floating Companies” AR prototype, which highlight the limited access of designers without programming skills to this technology. We intend to identify the limitations of no-code tools regarding development platforms, such as Unity, and represent data with no-code tools. The methodology used entails three phases—the collection and characterization of no-code AR tools; the review of its limitations regarding the development milestones in FLOC; and the proposal of data representation based on no-code tools. The AR tools landscape offers several free platforms which do not require programming skills. It was found that the analyzed tools do not support algorithmic data representation, which forces any representation to be designed manually in a customized way, presenting limitations regarding the amount of data, but also opportunities. The type of project that no-code platforms support falls within the concept of communicative visualization—a type of visualization that does not intend to deeply analyze the data, but rather to communicate and engage public.
Gaps, Goals, and Actions for the Development of the AfterLab: A Laboratory for Design Applied to the Dance Floor Experience

Clubs—like those devoted to disco, techno and house music—have worked as spaces for design, artistic and technological experimentation, creating a beneficial relationship between the discipline of design and the activity of clubbing. However, this relationship has caught little attention from design researchers, overlooking the possibilities that the club has to offer as a space for design experimentation and research. As an effort to foster these advantages, this chapter proposes the creation of the AfterLab as a laboratory for design—understood as the application of art and technology to our lives—applied to the dance floor experience. This proposal is divided in two parts: the first analyzes the economic, social, and aesthetic dimensions of clubbing; and the second exposes the research questions, goals, formats and initial actions proposed for the AfterLab, as well as some ethical guidelines. Once the AfterLab becomes a reality, it can benefit designers, artists, and professionals related to the area. Furthermore, the AfterLab can advance and deepen the relationship between design and clubbing, while preserving and improving local club cultures. The present text is shared with the scientific community in the hope of generating feedback and igniting synergies of collaboration.
Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules

Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0–1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: (1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. (2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations of two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7% and 4.3% in mean reciprocal rank (MRR).
Innovative Mode of English Education Based on Internet

INTRODUCTION: With the deepening application of cloud computing, mobile Internet, and big data technology in education, China's education informatization presents a new trend of integration and innovation, especially in the areas of resource sharing and interconnection, online and offline integration of teaching and learning, and good management and education governance based on big data, "Internet+Education" will have a systematic change impact on education. It will systematically impact education and open new paths for developing and utilizing English curriculum resources in colleges and universities in the Internet era.OBJECTIVES: To enhance the development level of online education in China and promote the sustainable development of online English education in China; to solve the problems of rigidity and unremarkable innovation in English education mode in colleges and universities and to promote the development of the integration of Internet technology and English teaching, and to improve the level of combining the theory and practice of English online education in China.METHODS: Firstly on the use of literature theoretical research to find the necessity of the Internet's English education innovation model research and adapt to the theoretical model; secondly, through the method of questionnaires and interviews from the teachers and students of two perspectives to study the effect of the Internet-based English education innovation model; finally, the use of empirical methods to validate the level of efficiency of the Internet's English education innovation model.RESULTS: The research on the innovative model of English education based on the Internet is necessary; the result that "Internet+" education can improve the teaching effect can be obtained from both the teachers' and students' perspectives; the empirical method verifies that the efficiency of the innovative model of English education based on the Internet is higher than that of the traditional one.CONCLUSION: Most teachers and students know or have come into contact with online college English course resources, but their understanding could be better. Moreover, they have used them but have yet to have the opportunity to participate in their development, which is related to the level of teachers' and students' professional knowledge and ability and also predicts the growth and utilization of course resources by college English teachers. The comparison of teachers' and students' expectations leads to the conclusion that teachers and students in the studied universities have positive attitudes and high expectations and expect to develop and use more systematic and scientific online English course resources.
Hourglass 3D CNN for Stereo Disparity Estimation for Mobile Robots

Stereo cameras allow mobile robots to perceive depth in their surroundings by capturing two separate images from slightly different perspectives. This is necessary for tasks such as obstacle avoidance, navigation, and spatial mapping. By utilizing a convolutional neural network (CNN), existing works in stereo cameras based on depth estimation have achieved superior results. However, the critical requirement for depth estimation for mobile robots is to have an optimal tradeoff between computational cost and accuracy. To achieve such a tradeoff, attention-aware feature aggregation (AAFS) has been proposed for real-time stereo matching on edge devices. AAFS includes multistage feature extraction, an attention module, and a 3D CNN architecture. However, its 3D CNN architecture learns contextual information ineffectively. In this paper, a deep encoder–decoder architecture is applied to an AAFS 3D CNN to improve depth estimation accuracy. Through evaluation, it is proven that the proposed 3D CNN architecture provides significantly better accuracy while keeping the inference time comparable to that of AAFS.
Homomorphic Encryption

This chapter presents the beginnings of homomorphic encryption, definitions, and types. Also, an entire section is focused on fully homomorphic encryption, the most interesting and promising type of homomorphic encryption. Further, the chapter presents recent advancements in homomorphic encryption discussing the advantages, disadvantages, performance, security requirements and presents case studies and practical applications in different domains (for example, Internet-of-Things, Machine Learning, Artificial Intelligence, etc.) Lastly, the chapter presents the challenges and research directions for homomorphic encryption.
Convolution Neural Network Based Model for Classification and Identification of Fake Profile on Social Network

The use of social media for business and politics has led to more bad behaviour. Like personal, corporate, and political propaganda, social media engagement has encouraged people to do things that aren't good for them. The intruder gets a full platform to spread criticism after they hide behind a hacked profile. This study takes important parts from social media data that show a complicated connection in order to find out if there are any problems. The system is good at figuring out what's wrong with people's behaviour by looking for malicious or fake social network accounts. Influence, Homophily, and Balance Theory are used in a theory-based social framework to improve user space accuracy in classifying dangerous users. In this case, it uses the Jaccard coefficient to figure out how similar two things are. Use graphical and linguistic clues to classify end-users in User-space. The built-in framework is tested with standard parameters, like a confusion matrix, to see how well it works. It's better for social atom anomaly detection to use the friend connection identification framework that was suggested.
Visual Cryptography: An Emerging Technology

In the present era, the data has become the most important asset for an individual, community and organization. Data handling and protection from attacks has become a key concern for people. To protect the information from uninvited invasions and attacks, researchers are developing new security techniques. Numerous cryptography methods have been discovered, and many more are still hidden. The visual cryptography is one of them. In this paper, visual cryptography, a new technique, has been highlighted. Visual cryptography is a method of data hiding in which the data is encrypted and concealed using visuals. A secret image is split up into numerous share images in visual cryptography. Although these share images are noisy or damaged, they are nonetheless relevant. Combining these share photos can make the original secret photo visible.