Reviewerly/exHarmony huggingface dataset:
NSE-PSO: Toward an Effective Model using Optimization Algorithm and Sampling Methods for Text Classification

Background and Objectives: With the extensive web applications, review sentiment classification has attracted increasing interest among text mining works. Traditional approaches did not indicate multiple relationships connecting words while emphasizing the preprocessing phase and data reduction techniques, making a huge performance difference in classification. Methods: This study suggests a model as an efficient model for sentiment classification combining preprocessing techniques, sampling methods, feature selection methods, and ensemble supervised classification to increase the classification performance. In the feature selection phase of the proposed model, we applied n-grams, which is a computational method, to optimize the feature selection procedure by extracting features based on the relationships of the words. Then, the best-selected feature through the particle swarm optimization algorithm to optimize the feature selection procedure by iteratively trying to improve feature selection. Results: In the experimental study, a comprehensive range of comparative experiments conducted to assess the effectiveness of the proposed model using the best in the literature on Twitter datasets. The highest performance of the proposed model obtains 97.33, 92.61, 97.16, and 96.23% in terms of precision, accuracy, recall, and f-measure, respectively. Conclusion: The proposed model classifies the sentiment of tweets and online reviews through ensemble methods. Besides, two sampling techniques had applied in the preprocessing phase. The results confirmed the superiority of the proposed model over state-of-the-art systems.
Genetic Based Face Recognition for Healthcare Applications

Face recognition is a significant biometric credential in the field of security authentication. It additionally assumes a noteworthy job in image processing and it is applicable in various systems like verifying the identity of the person and in security purpose. Recognizing the face with varying background, poses and illumination are the complexity involved in this face recognition. Many algorithms exist for face recognition, of which, Discrete Wavelet Transform (DWT) with Principal Component Analysis (PCA) works better for recognition of faces. An algorithm using 3 Level-DWT and modified PCA is proposed for feature extraction. The PCA and reconstruction of images using Inverse PCA, help not only for dimensionality reduction, but also to find the least principal components (PC) of an image from which the significant features of a face image can be extracted. The significant features thus extracted are used for classifying genetic and non-genetic faces. Using extracted features from 3 level DWT and PCA, Support vector machine (SVM) is utilized to classify the faces genetically. The proposed extracted features does not intend to certain features like ears, nose and eyes of the face, but corresponds to identify the faces which are genetically similar. Based on the statistical measure analysis, the proposed algorithm 3 Level dwt with modified PCA works well in extracting the features for identifying the faces which are genetically closer. This face recognition application system can be effectively used to treat a patient in other location with complete security. There is no chance for data stealing, since the concerned doctors and patient only will take part in the system. The identification of genetic faces will turn out to be an achievement in the field of health care monitoring systems.
A New Forecasting Algorithm Based on Neighbors for Streaming Electricity Time Series

This work presents a new forecasting algorithm for streaming electricity time series. This algorithm is based on a combination of the K-means clustering algorithm along with both the Naive Bayes classifier and the K nearest neighbors algorithm for regression. In its offline phase it firstly divide data into clusters. Then, the nearest neighbors algorithm is applied for each cluster producing a list of trained regression models, one per each cluster. Finally, a Naive Bayes classifier is trained for predicting the cluster label of an instance using as training the cluster assignments previously generated by K-means. The algorithm is able to be updated incrementally for online learning from data streams. The proposed algorithm has been tested using electricity consumption with a granularity of 10 min for 4-h-ahead predicting. Our algorithm widely overcame other four well-known effective online learners used as benchmark algorithms, achieving the smallest error.
Application of Computer Internet of Things Technology in Building Intelligent System

Abstract With the continuous development, computer technology promotes the rapid development of science, and the Internet of things (hereinafter referred to as IOT) technology is the product of the rapid development of science and technology. With the increasing popularity of IOT technology, modern technology has been gradually applied to various fields. Building intelligent system has also begun to apply IOT technology, which has become an important area of building intelligent system. A complete intelligent building system is composed of many different networks, which requires IOT technology to play an important role. Building intelligent system construction needs to rely on a variety of technologies, such as network system, information system, lighting system and security defense system, which will achieve various functional systems. Therefore, IOT technology can not only achieve the goal of unattended, but also reduce the cost of building management. Through IOT technology, we promote the communication between things and people. Firstly, this paper analyzes IOT technology. Finally, this paper lists the application of IOT technology in intelligent building system
CNN-based Rain Reduction in Street View Images

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.
Comparing representations that support object, scene, and face recognition using representational trajectory analysis.

Deep convolutional neural networks have become an increasingly powerful tool to probe the nature of visual representation in the primate visual system. With extensive training, these networks learn to transform pictoral inputs through several intermediate stages into a format that is optimized for a given task. To what degree do networks trained specifically on faces, places, or objects have similar or different representational stages at each layer? Here we introduce a tool to visualize the “representational trajectories” that models take from input stages to output stages. Specifically, we trained models with a common base architecture (Alexnets) on either object categorization, scene categorization, or face identification. Next, we measured the responses in all units of each layer to a mixed probe set consisting of objects, scenes, and faces. Then, we computed the representational dissimilarity between all layers of all models. Finally, we used multidimensional scaling to plot layers with similar geometries close to each other, which provides a simple visualization of where along the processing stages models have similar and divergent representational formats. This trajectory analysis revealed that all three networks learned similar geometries in early layers, despite having different visual experience. The models diverged in mid and late layers, with object- and scene-trained networks learning more similar geometries than face trained networks. A randomly-weighted, untrained Alexnet showed no similarity to the other three network trajectories, indicating that the early representational similarity is not solely induced through a hierarchical architecture itself. Taken together, these computational results indicate that face, place, and object stimulus domains naturally share early and intermediate level image features, before diverging towards more specialized feature spaces. Further, this work introduces representational trajectory analysis as a comparative approach for understanding what is learned by deep neural network models across variations in architecture, training sets, and tasks.
Literary Reading Lesson in Third Grade

The article considers a variant of a literary reading lesson, in which third grade students study a story with elements of a fairy tale by A.P. Gaidar - "Hot Stone". The teacher uses active teaching methods, offering younger schoolchildren paired and group forms of work, as well as educational research on the concept of “the meaning of life” with its different content. Work on the theme and ideological meaning of a literary work contributes to the formation of an active life position in younger students. A feature of the lesson is the inclusion of local history material in its content.
To the question of the digital platform “bioinformatics” creating and its system-forming solutions

Abstract The article reports the main provisions of the concept and solutions for creating the digital platform in the field of bioinformatics and the formation of the thematically oriented and industrial digital ecosystems on its basis. The composition and structure of the digital platform are discussed: information repositories, data and knowledge bases, thematically oriented software repository, task-oriented services for various target groups of users. Within the framework of the platform, it is also planned to organize a system of high-quality access to specialized data centres and high-performance computing infrastructure. Particular attention is devoted to one of the components of such platform - the project office for bioresource collections management. The project office has registered such types of collections as animal collections: wild and laboratory animals, live breeding, museum zoological animal collections, farm animals; plant collections: herbarium funds of plants biological diversity, living collections of natural flora, agricultural plants. Collection types such as collections of human biomaterials, cell culture collections, microorganism collections are important for medical research.
Honey, I’m Home: An Adventure Game with Procedurally Generated Narrative Puzzles

We present Honey, I’m Home, a short 2D adventure game which makes use of the SPHINX framework for procedurally generating narrative puzzles. The player guides the protagonist, a journalist for a local newspaper, through four game areas, interacting with numerous characters and objects along the way. The player must solve puzzles to complete each area, combining objects and interacting with characters in the game. The procedural generation of puzzles ensures that while the gameworld remains largely identical between replays, the puzzles encountered are different. The aim of Honey, I’m Home was to serve as a tool for our two-fold evaluation of the SPHINX algorithm, from its functionality in game development, as well as its effect on player experience. To this end a small user study was also conducted on Honey, I’m Home.
Predicting Scores of Medical Imaging Segmentation Methods with Meta-learning

Deep learning has led to state-of-the-art results for many medical imaging tasks, such as segmentation of different anatomical structures. With the increased numbers of deep learning publications and openly available code, the approach to choosing a model for a new task becomes more complicated, while time and (computational) resources are limited. A possible solution to choosing a model efficiently is meta-learning, a learning method in which prior performance of a model is used to predict the performance for new tasks. We investigate meta-learning for segmentation across ten datasets of different organs and modalities. We propose four ways to represent each dataset by meta-features: one based on statistical features of the images and three are based on deep learning features. We use support vector regression and deep neural networks to learn the relationship between the meta-features and prior model performance. On three external test datasets these methods give Dice scores within 0.10 of the true performance. These results demonstrate the potential of meta-learning in medical imaging.
A Computational Account of Serial and Parallel Processing in Visual Search

We present a new computational model of visual search that follows on prior theoretical work by Buetti and Lleras emphasizing the contributions of parallel peripheral processing to visual search performance. The model uses concurrent parallel (distributed attention) and serial (focused attention) evaluative processes for inspecting items in a visual display. Search items are assigned random priorities for attentional selection. These priorities immediately begin to decay, and are refreshed based on feature similarity to the search template. Items are stochastically selected for focused attention based on Luce's choice axiom defined over their priorities. Selected items are matched to a search template and either accepted as the target or rejected as a distractor. During this serial process, the priorities of the remaining search items are updated in parallel, in proportion to their proximity to fixation. The resulting model successfully simulates the typical logarithmic slopes found in human data when the target-distractor similarity is medium to low (e.g., Buetti et al., 2016; Wang et al., 2017). It also produces linear search slopes when target-distractor similarity is elevated. We present simulations of these and other classic visual search phenomena, like the difference between feature and conjunction search, as well as search asymmetries.
Guessing the Age of Acquisition of Italian Lemmas through Linear Regression

The age of acquisition of a word is a psycholinguistic variable concerning the age at which a word is typically learned. It correlates with other psycholinguistic variables such as familiarity, concreteness, and imageability. Existing datasets for multiple languages also include linguistic variables such as the length and the frequency of lemmas in different corpora. There are substantial sets of normative values for English, but for other languages, such as Italian, the coverage is scarce. In this paper,a set of regression experiments investigates whether it is possible to guess the age of acquisition of Italian lemmas that have not been previously rated by humans. An intrinsic evaluation is proposed, correlating estimated Italian lemmas’ AoA with English lemmas’ AoA. An extrinsic evaluation - using AoA values as features for the classification of literary excerpts labeled by age appropriateness - shows how es-sential is lexical coverage for this task.
Personalised Recommendation Systems and the Impact of COVID-19: Perspectives, Opportunities and Challenges

Personalised Recommendation Systems that utilize machine learning algorithms have had much success in recent years, leading to accurate predictions in many e-business domains. However, this environment experienced abrupt changes with the onset of the COVID-19 pandemic centred on an exponential increase in the volume of customers and swift alterations in customer behaviours and profiles. This position paper discusses the impact of the COVID-19 pandemic on the Recommendation Systems landscape and focuses on new and atypical users. We detail how online machine learning algorithms that are able to detect and subsequently adapt to changes in consumer behaviours and profiles can be used to provide accurate and timely predictions regarding this evolving consumer sector. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
Exploiting Sentence Order in Document Alignment

We present a simple document alignment method that incorporates sentence order information in both candidate generation and candidate re-scoring. Our method results in 61% relative reduction in error compared to the best previously published result on the WMT16 document alignment shared task. Our method improves downstream MT performance on web-scraped Sinhala–English documents from ParaCrawl, outperforming the document alignment method used in the most recent ParaCrawl release. It also outperforms a comparable corpora method which uses the same multilingual embeddings, demonstrating that exploiting sentence order is beneficial even if the end goal is sentence-level bitext.
RealitySketch: Embedding Responsive Graphics and Visualizations in AR through Dynamic Sketching

We present RealitySketch, an augmented reality interface for sketching interactive graphics and visualizations. In recent years, an increasing number of AR sketching tools enable users to draw and embed sketches in the real world. However, with the current tools, sketched contents are inherently static, floating in mid air without responding to the real world. This paper introduces a new way to embed dynamic and responsive graphics in the real world. In RealitySketch, the user draws graphical elements on a mobile AR screen and binds them with physical objects in real-time and improvisational ways, so that the sketched elements dynamically move with the corresponding physical motion. The user can also quickly visualize and analyze real-world phenomena through responsive graph plots or interactive visualizations. This paper contributes to a set of interaction techniques that enable capturing, parameterizing, and visualizing real-world motion without pre-defined programs and configurations. Finally, we demonstrate our tool with several application scenarios, including physics education, sports training, and in-situ tangible interfaces.
Reform and implementation of technical English course for brewing engineering undergraduates based on STEAM theory

Technical English is a compulsory course for brewing engineering undergraduates. To improve the quality and effectiveness of teaching, we introduced science, technology, engineering, arts and mathematics (abbreviated to STEAM) education theory in technical English teaching of brewing engineering students, and selected, designed, integrated and optimized the teaching contents. With the help of the Rain Classroom, Massive Open Online Courses of Tsinghua University (XuetangX), Open Language, Wine Folly, training courses of Wine and Spirit Education Trust (WSET) and other platform resources, teaching methods, assessment ways and multi-element teaching evaluation system have been innovatively reformed. By using advanced teaching methods such as mind mapping drawing, micro-video production, situational dialogues, topic speech, creative program design and design competition of wine label and bottle packaging, it could effectively improve students' learning interest and engagement, and enhance students' knowledge of professional English and comprehensive application ability.
A Dictionary Approach to Domain-Invariant Learning in Deep Networks

In this paper, we consider domain-invariant deep learning by explicitly modeling domain shifts with only a small amount of domain-specific parameters in a Convolutional Neural Network (CNN). By exploiting the observation that a convolutional filter can be well approximated as a linear combination of a small set of dictionary atoms, we show for the first time, both empirically and theoretically, that domain shifts can be effectively handled by decomposing a convolutional layer into a domain-specific atom layer and a domain-shared coefficient layer, while both remain convolutional. An input channel will now first convolve spatially only with each respective domain-specific dictionary atom to absorb domain variations, and then output channels are linearly combined using common decomposition coefficients trained to promote shared semantics across domains. We use toy examples, rigorous analysis, and real-world examples with diverse datasets and architectures, to show the proposed plug-in framework's effectiveness in cross and joint domain performance and domain adaptation. With the proposed architecture, we need only a small set of dictionary atoms to model each additional domain, which brings a negligible amount of additional parameters, typically a few hundred.
Research on Image Inpainting Based on Generative Adversarial Network

In recent years, the rapid development of deep learning has achieved remarkable results in many scientific research fields. Especially in the field of computer vision, deep learning has almost reached the highest level of image processing. Related deep learning methods have also been applied to the field of image inpainting, making researchers begin to use deep learning models to solve the problem of digital image inpainting. The generation of the adversarial network model has greatly improved the inpainting technology of digital images. This paper builds an image inpainting framework based on the generative adversarial network. The inpainting process is divided into two parallel stages, namely reconstruction inpainting and generation inpainting. The network structure of this paper is composed of two parts: a generating network and a discriminating network. The generated network generates an image, and the discriminating network judges whether the image generated by the generating network is consistent with the real image. The loss function of the network uses the loss function of WGAN-GP to calculate the loss of parameters, in order to update the network alternately, making the generated image more natural and realistic. This paper uses the Places2 data set to verify the algorithm, and combines the subjective evaluation method with the objective evaluation method to evaluate the quality of the inpainting image. Experiments show that the algorithm inpainting effect is better.
Exploring Semantic Capacity of Terms

We introduce and study semantic capacity of terms.For example, the semantic capacity of artificial intelligence is higher than that of linear regression since artificial intelligence possesses a broader meaning scope.Understanding semantic capacity of terms will help many downstream tasks in natural language processing.For this purpose, we propose a two-step model to investigate semantic capacity of terms, which takes a large text corpus as input and can evaluate semantic capacity of terms if the text corpus can provide enough cooccurrence information of terms.Extensive experiments in three fields demonstrate the effectiveness and rationality of our model compared with well-designed baselines and human-level evaluations.
Quantum Random Number Generation on Alibaba Cloud Servers

Four different quantum random number generators have been integrated into the Alibaba cloud server with post-processing modules to generate true random numbers. With certain post-processing, the generated random numbers are fed into different applications on the server, such as Ant Financial and Smart Access Gateway.