Reviewerly/exHarmony huggingface dataset:
Physics-Assisted Long-Short-Term-Memory Network for Forecasting of Fouling in a Regenerative Heat Exchanger

Abstract Air Preheater (APH) is a regenerative heat exchanger employed in power plants for improving the boiler thermal efficiency. Fouling of APH is a serious problem as it deteriorates heat transfer efficiency and causes unplanned shutdowns. This complex physico-chemical phenomenon is governed by APH operating conditions, flue gas composition and ambient conditions. We propose a physics-assisted Long-Short-Term-Memory (LSTM) network model to forecast the fouling of APH. A physics-based soft sensor, indicative of chemical deposition within the APH, is used as an additional feature. The physics-assisted basic and autoregressive LSTM models are found to be more accurate than the basic and autoregressive LSTM models, owing to additional insights coming from the physics-based soft sensor. They can help in effective predictive maintenance of APH by preempting forced outages of the plant due to fouling, up to three months in advance. The proposed framework can be easily adapted for forecasting of fouling in heat exchangers used in diverse industries.
Knowledge-Enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering

Multi-hop Knowledge Base Question Answering (KBQA) aims to find the answer entity in a knowledge base which is several hops from the topic entity mentioned in the question. Existing Retrieval-based approaches first generate instructions from the question and then use them to guide the multi-hop reasoning on the knowledge graph. As the instructions are fixed during the whole reasoning procedure and the knowledge graph is not considered in instruction generation, the model cannot revise its mistake once it predicts an intermediate entity incorrectly. To handle this, we propose KBIGER (Knowledge Base Iterative Instruction GEnerating and Reasoning), a novel and efficient approach to generate the instructions dynamically with the help of reasoning graph. Instead of generating all the instructions before reasoning, we take the $$(k-1)$$ -th reasoning graph into consideration to build the k-th instruction. In this way, the model could check the prediction from the graph and generate new instructions to revise the incorrect prediction of intermediate entities. We do experiments on two multi-hop KBQA benchmarks and outperform the existing approaches, becoming the new-state-of-the-art. Further experiments show our method does detect the incorrect prediction of intermediate entities and has the ability to revise such errors.
Doge Tickets: Uncovering Domain-General Language Models by Playing Lottery Tickets

Over-parameterized pre-trained language models (LMs), have shown an appealing expressive power due to their small learning bias. However, the huge learning capacity of LMs can also lead to large learning variance. In a pilot study, we find that, when faced with multiple domains, a critical portion of parameters behave unexpectedly in a domain-specific manner while others behave in a domain-general one. Motivated by this phenomenon, we for the first time posit that domain-general parameters can underpin a domain-general LM that can be derived from the original LM. To uncover the domain-general LM, we propose to identify domain-general parameters by playing lottery tickets (dubbed doge tickets). In order to intervene the lottery, we propose a domain-general score, which depicts how domain-invariant a parameter is by associating it with the variance. Comprehensive experiments are conducted on the Amazon, Mnli, and OntoNotes datasets. The results show that the doge tickets obtains an improved out-of-domain generalization in comparison with a range of competitive baselines. Analysis results further hint the existence of domain-general parameters and the performance consistency of doge tickets.
A Survey of Image Captioning Algorithms Based on Deep Learning

As a cross task of natural language processing and computer vision, image captioning is a key technology to explore the transformation of artificial intelligence visual perception to high-level semantic understanding. It is widely used in image retrieval, blind navigation, early childhood education, human-computer interaction, safe assisted driving and other fields. Firstly, the research progress of image captioning is briefly described, and then the image captioning algorithms based on deep learning methods are classified and summarized according to their development history, and the main ideas and core issues of various algorithms are elaborated. Finally, the implementation results of various algorithms are compared, the problems existing in the existing algorithms are summarized and analyzed, and the future development trend of image captioning is prospected.
Estimation of Data Parameters Using Cluster Optimization

Machine learning is the kind of process to turns out to be an undividable fraction of any methodical work is mostly because of the simplicity of use and the simplicity of generation of data. The data generation task is pricey or tedious, the data is usually generated in parallel amid various research groups and they are communal by scientists. For this activity is the initial task, is frequently to cluster the data into several categories to set up which data from what source are related to each other. In this paper, optimization is usually is implemented in use for such a clustering activity is proposed. When the data is accomplished then they are entire jumbled jointly. One way to prepare an optimization difficulty is to primary decide on a number of clusters that the data may be separated to. After that, for each cluster in not many parameters are used variables for the optimization task. The parameters have to explain a similarity role for a cluster. The activity can be achieved through an optimization solve the prediction activity can be achieved using an optimization process. This process is used a Semi-Supervised learning approach and Data Mining like the K-Nearest Neighboring process and form the path route cluster. The prediction parameter of SRGM is approximated based upon these data clusters using Least Square Estimation.
Deep Learning for Spectral Filling in Radio Frequency Applications

Due to the Internet of Things (IoT) proliferation, Radio Frequency (RF) channels are increasingly congested with new kinds of devices, which carry unique and diverse communication needs. This poses complex challenges in modern digital communications, and calls for the development of technological innovations that (i) optimize capacity (bitrate) in limited bandwidth environments, (ii) integrate cooperatively with alreadydeployed RF protocols, and (iii) are adaptive to the ever-changing demands in modern digital communications. In this paper we present methods for applying deep neural networks for spectral filling. Given an RF channel transmitting digital messages with a pre-established modulation scheme, we automatically learn novel modulation schemes for sending extra information, in the form of additional messages, “around” the fixed-modulation signals (i.e., without interfering with them). In so doing, we effectively increase channel capacity without increasing bandwidth. We further demonstrate the ability to generate signals that closely resemble the original modulations, such that the presence of extra messages is undetectable to third-party listeners. We present three computational experiments demonstrating the efficacy of our methods, and conclude by discussing the implications of our results for modern RF applications.
Exploring the effects of segmentation when learning with Virtual Reality and 2D displays: a study with airport security officers

With novel 3D imaging technology based on computed tomography (CT) set to replace the current 2D X-ray systems, airports face the challenge of adequately preparing airport security officers (screeners) through knowledge building. Virtual reality (VR) bears the potential to greatly facilitate this process by allowing learners to experience and engage in immersive virtual scenarios as if they were real. However, while general aspects of immersion have been explored frequently, less is known about the benefits of immersive technology for instructional purposes in practical settings such as airport security.In the present study, we evaluated how different display technologies (2D vs VR) and segmentation (system-paced vs learner-paced) affected screeners' objective and subjective knowledge gain, cognitive load, as well as aspects of motivation and technology acceptance. By employing a 2 x 2 between-subjects design, four experimental groups experienced uniform learning material featuring information about 3D CT technology and its application in airport security: 2D system-paced, 2D learner-paced, VR system-paced, and VR learner-paced. The instructional material was presented as an 11 min multimedia lesson featuring words (i.e., narration, onscreen text) and pictures in dynamic form (i.e., video, animation). Participants of the learner-paced groups were prompted to initialize the next section of the multimedia lesson by pressing a virtual button after short segments of information. Additionally, a control group experiencing no instructional content was included to evaluate the effectiveness of the instructional material. The data was collected at an international airport with screeners having no prior 3D CT experience (n=162).The results show main effects on segmentation for objective learning outcomes (favoring system-paced), germane cognitive load on display technology (supporting 2D). These results contradict the expected benefits of VR and segmentation, respectively. Overall, the present study offers valuable insight on how to implement instructional material for a practical setting.
Acute Leukemia Classification and Prediction in Blood Cells Using Convolution Neural Network

Nowadays, human health is paramount to any other thing in the world. But health is affected due to many reasons. Doctors and scientists are constantly working to find solutions to health issues. The main issue is blood-related problems because blood is the foundation of our body. Cancers related to blood are very critical and cause human death. Leukemia is a kind of most cancers that arises inside the bone marrow and outcomes in an excessive wide variety of peculiar white blood cells. If acute leukemia cannot be treated in a short time, there is less time for humans to survive. It is important to detect cancer at an early stage and be able to treat it. It takes more time to cure so early detection is vital for treating cancers. In this research, machine learning is used to predict cancer cells in the blood. So, we used convolutional neural networks (CNN) to train the model and find cancer cells in the blood at an early stage. This research shows the prediction of blood cancer cells and displays the differences between the normal and cancer cells image using CNN classification process.
3D Content Viewer Environment for Distant Learning

The paper deals with the development of a web-based virtual environment that can effectively display 3D models in the form of teaching assistance. AR and 3D virtual environments working directly inside the web browser were used to display virtual objects. Since augmented reality is not implemented within the modern curricula for teaching purposes, our proposed approach to virtual environments complements this missing component and helps educate students in a more interactive way. Within the paper, the process by which the individual virtual environments were created and how they have the ability to display 3D models with animations is described. Based on this, three interconnected viewer environments have been created: firstly the 3D virtual environment, secondly an AR that displays models and objects using a marker and thirdly a markerless AR.
Common Ground in Linguistic Theory and Internet Pragmatics: Forms of Dynamic Multicultural Interaction

The notion of common ground entails that prior to a conversation, mutually shared knowledge is available to interlocutors by virtue of the situational context or a shared cultural background. Within linguistic pragmatic theories, recipient design is a determining factor for cooperation in interaction. The socio-cognitive approach to communicative interaction acknowledges the importance of cooperation and common ground but maintains that interlocutors tend to adhere to their individual background knowledge and experience for production and comprehension. The shared knowledge base may therefore not be fully available prior to the exchange but, rather, established dynamically and interactively in the course of the conversation. Discussing internet memes, it will be shown that stable core common ground and dynamic emergent common ground are fundamental assets for the description of contemporary and future phenomena in digital communication. I will argue that internet memes represent a kind of communication where emergent common ground is aspired to rather than resorted to as an emergency solution when core common ground is lacking.
An efficient algorithm to extract Skyline itemsets

Mining skyline frequent-utility patterns (SFUPs) is the discovery of itemsets that surpasses all other itemsets in both frequency and utility in transactional database. The discovery of these itemsets is important for managers in finding items that customers buy many times and bring high profits for businesses. In recent years, there have been many algorithms proposed to exploit skyline frequent-utility patterns, of which SKYFUP-D is the most efficient algorithm. However, this algorithm still has limitations in both execution time and storage space. In this paper, we propose an effective method to exploit SFUPs faster by applying pruning strategies to reduce the number of candidates. Experimental results show that the execution time and storage space are significantly improved.
Content-based recommender system by user's visual attention

Nowadays, some of the world's biggest entertainment among the population is the consumption of streaming services such as movies or series through digital platforms such as Netflix, Disney+, Prime Video, HBO Max, Hulu, and Apple TV+. Traditional recommender algorithms process explicit information where the user indicates which movies he/she liked on the platform. However, implicit information like the user's visual attention adapted to personal interests may lead to better personalized recommendations. This has motivated the development and innovation of movie recommendation systems. Hence, we propose a novel recommendation algorithm based on the user's visual attention. The algorithm was implemented on a digital platform named Poppukōn where the movies are presented on the platform according to different recommendations like genre, ratings by likes and stars and two based on eye tracking features. The Poppukōn platform was implemented using the Django framework for the Back-End and the Vue framework for the Front-End. Experiments were carried out to find out the user satisfaction about the personalized recommendations presented on the platform. Our results provide evidence that using eye tracking features in a movie recommender system improve the user satisfaction using implicit (gaze) and explicit (rating) information.
Roboviz: A Game-Centered Project for Information Visualization Education

Due to their pedagogical advantages, large final projects in information visualization courses have become standard practice. Students take on a client–real or simulated–a dataset, and a vague set of goals to create a complete visualization or visual analytics product. Unfortunately, many projects suffer from ambiguous goals, over or under-constrained client expectations, and data constraints that have students spending their time on non-visualization problems (e.g., data cleaning). These are important skills, but are often secondary course objectives, and unforeseen problems can majorly hinder students. We created an alternative for our information visualization course: Roboviz, a real-time game for students to play by building a visualization-focused interface. By designing the game mechanics around four different data types, the project allows students to create a wide array of interactive visualizations. Student teams play against their classmates with the objective to collect the most (good) robots. The flexibility of the strategies encourages variability, a range of approaches, and solving wicked design constraints. We describe the construction of this game and report on student projects over two years. We further show how the game mechanics can be extended or adapted to other game-based projects.
The Relationship between Learning Motivation, Achievement, and Anxiety of English Language among Pre-service Kindergarten Teachers

In this study, the effects of English Learning Motivation on English Achievement of preservice kindergarten teachers between English anxiety groups were examined in the EFL environment.For this research purpose, 86 participants with experience in taking English lectures majoring in early childhood education and childcare at a four-year university located in D City were selected.The research data were extracted based on the questionnaire for about a month from March to April 2022 and the credits of major-related English lectures during the semester.The data were analyzed by frequency analysis, correlation analysis, t-test, and regression analysis through the SPSS 20.0 program.As a result, there were significant correlations among English Anxiety, English learning motivation, and English achievement of pre-service early childhood teachers, and there were significant differences in the level of English learning motivation and English achievement between English anxiety groups.In addition, in the group with high English anxiety, English learning motivation had a significant positive effect on English achievement.This is thought to contribute to laying the foundation for enhancing the Englishrelated capabilities of pre-service kindergarten teachers at the present time when kindergarten teachers need to participate in the development and implementation of appropriate English education programs for young children.
Using machine translation in EFL writing

Despite its increased popularity, the use of machine translation (MT) in L2 writing has mostly attracted critical views from L2 teachers and learners. A quick look at the literature reveals that a systematic overview of using MT with a specific focus on L2 English writing is lacking. This chapter, therefore, aims to explore and review the extent and nature of relevant empirical studies with a qualitative, quantitative, and mixed-methods design. Through a scoping review of 16 journal articles, the purpose of this chapter is to identify and present the major bibliographic features, research methods, analysis techniques, and the primary research foci in these studies. An analysis of available research may help provide insight into the scope, perceptions, and use of MT in English as a Foreign Language (EFL) writing. As per the nature of a scoping review, the chapter will conclude with suggestions for areas of future research.
Digitalization impact on higher education – potential and risks

Abstract4The topis of education is often superseded by business, mainly because business is associated with rapid technological advancement and often brings lucrative profits.Perhaps this is why significant paradigm changes in the education field take place only during crises or exceptional situations that force a change of the current approach.So it was this time, that the pandemic of covid-19 revealed how much education systems are not enough adapted to the technologically changing world.The main goal of the article is to present a general overview of the process of digitalization in the higher education field.The introduction part highlights the importance of today9s education.The second part of the article describes the characteristic of digitalization in the context of education.The third section indicates the potential and risks of digitalization.The next part of the article points out the use of digitalization in the didactic process.
Physical interpretation of nonlocal quantum correlation through local description of subsystems

Characterization and categorization of quantum correlations are both fundamentally and practically important in quantum information science. Although quantum correlations such as non-separability, steerability, and non-locality can be characterized by different theoretical models in different scenarios with either known (trusted) or unknown (untrusted) knowledge of the associated systems, such characterization sometimes lacks unambiguous to experimentalist. In this work, we propose the physical interpretation of nonlocal quantum correlation between two systems. In the absence of complete local description of one of the subsystems quantified by the local uncertainty relation, the correlation between subsystems becomes nonlocal. Remarkably, different nonlocal quantum correlations can be discriminated from a single uncertainty relation derived under local hidden state (LHS)-LHS model only. We experimentally characterize the two-qubit Werner state in different scenarios.
1D Sine-Map-Coupling-Logistic-Map for 3D model encryption

With the rise of technologies of VR technology, AR technology, and 3D printing, the application of 3D models has become more and more extensive. The data of the 3D model is the floating point and has a unique storage format, and the traditional 2D image encryption algorithms are unsuitable for 3D models. Therefore, based on 1D Sine-Map-Coupling-Logistic-Map (1D-SMCLM), a 3D model encryption algorithm is designed in this paper. The 1D-SMCLM is a new chaotic system with large parameter space and good chaotic characteristics. The keystream generated by the 1D-SMCLM has good randomness and is very suitable for cryptographic systems. In the new encryption algorithm (SMCLM-3ME), the vertices of the 3D models are divided into integer and decimal vertices. The integer part is encrypted by the strategy of simultaneous scrambling and diffusion. The 3D ciphertext model is obtained by combining the integer and fractional parts. Experimental results show that the SMCLM-IE exhibits excellent performance.
The Object Detection of Underwater Garbage with an Improved YOLOv5 Algorithm

Litter deposition in aquatic environments has devastating effects on marine ecological environment and poses a threat to a sustainable economy. Autonomous Underwater Vehicles (AUV) could solve the issue nicely by detecting and clearing litter. A good object detection algorithm is very important in the process of AUV detection and garbage collection. In this research, YOLOv5 was applied as the detection algorithm of the detector and the prediction side of the algorithm was improved. The anchor boxes of the model are re-clustered by using the improved KMeans++ algorithm, the loss function was optimized and the box loss function of the original model was replaced by CIoU. When detecting the trash_ICRA19 dataset, the results demonstrated that the improved model achieved a detection accuracy of 88.7%, a mean average precision (mAP) of 90.6%. The mean average precision of the research work was 9.6% higher than previous studies. The results showed that the improved model could realize the detection and identification of plastic waste in water.