Reviewerly/exHarmony huggingface dataset:
Accuracy Improvement of Short-Term Photovoltaic Power Forecasting Based on PCA and PSO-BP

The prediction of photovoltaic power generation is of great significance to safe and reliable operation of power grid. To solve the problem of low accuracy of photovoltaic power output prediction, this paper proposes a short-term power prediction method of photovoltaic power generation based on principal component analysis (PCA) and particle swarm optimization (PSO) neural network. Firstly, PCA is used to screen the original data to reduce the dimension and complexity of the data. Then, PSO is used to optimize the weights and thresholds of neural network, which makes up for the shortcomings of traditional BP neural network, such as long training time and easy to fall into local extreme points. The number of hidden layer nodes of neural network is determined by trichotomy, and the PSO-optimized neural network photovoltaic power generation output prediction model based on PCA is constructed. Finally, the actual photovoltaic power generation data and meteorological data are used for example analysis. The prediction error of the proposed model is reduced by 23.82%. The results show that compared with the previous model, the proposed model has more accurate photovoltaic output prediction under different weather types. It is reduced by 19.01%, 23.28% and 29.18% under sunny, cloudy and overcast weather conditions respectively, which verifies the effectiveness of the proposed method.
Design of Acoustic System in Smart Classroom

Aiming at the problems of substandard acoustic design, inability to automatically collect data, and inability to support normalized interactive teaching in the current construction and use of smart classrooms, the article proposes a set of voice processing systems, personalized teaching data analysis systems, and mobile terminals. And the acoustic system architecture of the audio system management platform. With the goal of creating a comfortable sound environment, it analyzes acoustic design methods and measures from the perspective of architectural acoustics and electroacoustics, introduces voice processing technology into smart classrooms, and achieves zero-intervention sound collection during classroom teaching through suspended microphone arrays. Due to the complex background noise environment in the classroom and the small number of training samples, the article proposes a voiceprint recognition algorithm based on feature compensation and eigentone adaptation, and verifies the effectiveness of the algorithm. The smart classroom acoustic system architecture based on speech processing technology proposed in this paper can effectively improve the experience of students and teachers of smart classroom users in teaching practice, facilitate the interaction between students and teachers, and improve the learning efficiency of students. It can provide a certain theoretical reference for future smart classroom research.
Collaborative Human Machine Attention Module for Character Recognition

The deep learning models, which include attention mechanisms, are shown to enhance the performance and efficiency of the various computer vision tasks such as pattern recognition, object detection, face recognition, etc. Although the visual attention mechanism is the source of inspiration for these models, recent attention models consider 'attention' as a pure machine vision optimization problem, and visual attention remains the most neglected aspect. Therefore, this paper presents a collaborative human and machine attention module which considers both visual and network's attention. The proposed module is inspired by the dorsal (`where') pathways of visual processing and can be integrated with any convolutional neural network (CNN) model. First, the module computes the spatial attention map from the input feature maps, which is then combined with the visual attention maps. The visual attention maps are created using eye-fixations obtained by performing an eye-tracking experiment with human participants. The visual attention map covers the highly salient and discriminating image regions as humans tend to focus on such regions, whereas the other relevant image regions are processed by spatial attention map. The combination of these two maps results in the finer refinement in feature maps, resulting in improved performance. The comparative analysis reveals that our model not only shows significant improvement over the baseline model but also outperforms the other models. We hope that our findings using a collaborative human-machine attention module will be helpful in other computer vision tasks as well.
New Approach of Sarcasm Detection In Indonesian Marketplace Product Review

Product reviews on e-commerce become very important for prospective buyers to determine the items to be bought. Unfortunately, the results of the product reviews are not accurate because of the meaningful reviews of sarcasm. Sarcasm is a form of intelligent speech where the speaker or writer says or writes the opposite of the meaning. Reviewers sometimes give 5 stars review but are accompanied by negative comments with the intention of being sarcastic. The number of reviews continues to grow so it is very difficult to make limits to classify whether the number of product reviews is positive or negative. Thus, the use of the Fuzzy Mamdani method is very important to determine the value of the majority of sentiments. This study presents a new approach to the field of sarcasm detection by combining the methods of sentiment analysis and Fuzzy Mamdani by comparing the allegedly sarcastic reviews of reviews from other buyers of similar products. The results of this study indicate that the greater the difference between positive and negative reviews, the greater the review is negative.
Adaptive K-valued K-means clustering algorithm

Based on particle swarm optimization (PSO), the algorithm for selecting appropriate K values is improved by combining k-mean algorithm. When the algorithm converges, the expansion and reduction of K value can be determined by comparing the relationship between different K value selection and global optimal fitness. Experiments show that the improved algorithm can assist K value selection effectively and obtain a better clustering effect.
DICOM compression and decompression method using double cone

The increase of information in the medical environment caused by digital imaging settings is notable. The search and use of these technological tools aimed at medicine require a greater availability of storage, generating increasing costs. In medicine, together with information technology, there is a format of images used in exams, diagnostics, tomography, among others. This format, entitled DICOM, was created in order to standardize uses in medical devices for exam answers. An open question is the compression of DICOM data, in order to maintain quality, maintaining high rates of compression. This presents a new method for compressing and decompressing DICOM data using a dual cone bijector function and a video codec, called DC (Double Cone). This work offers 3 changes to the DC method (DC1, DC2 and DC3). The results obtained with a new technique show that the compression, although with loss, has a similarity index very close to the original image (SSIM = 0.99), and an accuracy ratio equal to 69.51, in the better case. The better performing version was the DC2.
Tree-Capsule: Tree-Structured Capsule Network for Improving Relation Extraction

Relation extraction benefits a variety of applications requiring relational understanding of unstructured texts, such as question answering. Recently, capsule network-based models have been proposed for improving relation extraction with better capability of modeling complex entity relations. However, they fail to capture the syntactic structure information of a sentence which has proven to be useful for relation extraction. In this paper, we propose a Tree-structured Capsule network based model for improving sentence-level Relation Extraction (TCRE), which seamlessly incorporates the syntax tree (Generally, syntax trees include constituent trees and dependency trees.) information (constituent tree is used in this work). Particularly, we design a novel tree-structured capsule network (Tree-Capsule network) to encode the constituent tree. Additionally, an entity-aware routing algorithm for Tree-Capsule network is proposed to pay attention to the critical relevant information, further improving the relation extraction of the target entities. Experimental results on standard datasets demonstrate that our TCRE significantly improves the performance of relation extraction by incorporating the syntactic structure information.
Teaching exploration of piano playing and singing based on big data analysis

Under the background of “Internet”, cloud computing and big data technology have been applied in various fields, including piano teaching, which can show better teaching effect. With the help of big data piano “playing” teaching software and previous practical work experience, this paper expands the piano playing time and space for students to develop a greater platform. It is precisely because of the characteristics of big data that the application of modern technology becomes a reality and promotes the students' learning motivation of piano singing.
PNFM

We propose a filter level pruning method, named PNFM, to compress the storage space and computational complexity of CNN(Convolution Neural Network). We judge the importance of the pruned filter by the change rate of the feature image output from the latter layer. In order to improve the efficiency of pruning, we also propose a method to make tiny data sets for pruning based on cluster algorithm. We verify the performance of our method on ILSVRC-12 benchmark. It achieves 3.21× FLOPs reduction and 16.92× storage space compression on VGG-16, with only 0.55% top-5 accuracy drop. At the same time, we also rigorously verify the feasibility of the method of making tiny data sets. It can achieve the same accuracy for pruning with at least 10 times less data.
Recent Trends and Opportunities of Remote Multi- Parameter PMS using IoT

Healthcare is an aspect of a person's life which precedes all else. Only when a person is of sound health, can they strive to achieve his/her goals in life. Not all people have equal access to the best healthcare due to financial issues, no accessibility, or other reasons. The aim of this paper is to provide a review of the recent advances of a new low-cost and effective remote Multi-Parameter Patient Monitoring System (PMS) and Remote Patient Monitoring (RPM) systems. These systems are equipped at the patients' home and can monitor the patients' condition continuously and alert the relevant authority when the patient needs attention. These systems are necessary to evaluate the response of the disease to treatment and to identify adverse drug reactions. These systems allow their users to leave the hospital early and be monitored and checked on from their home. With the review, the authors discuss some issues available in most systems. The paper also includes some directions for future research. Alternatively, with emerging field of IoT, remote patient monitoring to fundamentally change healthcare services.
Visual Data Science

Organizations are collecting an increasing amount of data every day. To make use of this rich source of information, more and more employees have to deal with data analysis and data science. Exploring data, understanding its structure, and finding new insights, can be greatly supported by data visualization. Therefore, the increasing interest in data science and data analytics also leads to a growing interest in data visualization and exploratory data analysis. We will outline how existing data visualization techniques are already successfully employed in different data science workflow stages. In some cases, visualization is beneficial, while still future research will be needed for other categories. The vast amount of libraries and applications available for data visualization has fostered its usage in data science. We will highlight the differences among the libraries and applications currently available. Unfortunately, there is still a clear gap between visualization research developments over the past decades and the features provided by commonly used tools and data science applications. Although basic charting options are commonly available, more advanced visualization techniques have hardly been integrated as new features yet.
Classifications of Breast Cancer Diagnosis using Machine Learning

Breast Cancer (BC) is amongst the most common and leading causes of deaths in women throughout the world. Recently, classification and data analysis tools are being widely used in the medical field for diagnosis, prognosis and decision making to help lower down the risks of people dying or suffering from diseases. Advanced machine learning methods have proven to give hope for patients as this has helped the doctors in early detection of diseases like Breast Cancer that can be fatal, in support with providing accurate outcomes. However, the results highly depend on the techniques used for feature selection and classification which will produce a strong machine learning model. In this paper, a performance comparison is conducted using four classifiers which are Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest on the Wisconsin Breast Cancer dataset to spot the most effective predictors. The main goal is to apply best machine learning classification methods to predict the Breast Cancer as benign or malignant using terms such as accuracy, f-measure, precision and recall. Experimental results show that Random forest is proven to achieve the highest accuracy of 99.26% on this dataset and features, while SVM and KNN show 97.78% and 97.04% accuracy respectively. MLP shows the least accuracy of 94.07%. All the experiments are conducted using RStudio as the data mining tool platform.
Separating Sessions Smoothly

This paper introduces Hypersequent GV (HGV), a modular and extensible core calculus for functional programming with session types that enjoys deadlock freedom, confluence, and strong normalisation. HGV exploits hyper-environments, which are collections of type environments, to ensure that structural congruence is type preserving. As a consequence we obtain an operational correspondence between HGV and HCP -- a process calculus based on hypersequents and in a propositions-as-types correspondence with classical linear logic (CLL). Our translations from HGV to HCP and vice-versa both preserve and reflect reduction. HGV scales smoothly to support Girard's Mix rule, a crucial ingredient for channel forwarding and exceptions.
Implementation of a System That Helps Novice Users Work with Linked Data

On the Semantic Web, resources are connected to each other by the IRI. As the basic unit is comprised of linked data, machines can use semantic data and reason their relations without additional intervention on the Semantic Web. However, it is necessary for users who first encounter the Semantic Web to understand its underlying structure and some grammatical rules. This study suggests linking data sets of the Semantic Web through the Euler diagram, which does not require any prior knowledge. We performed a user study with our relationship-building system and verified that users could better understand linked data through the usage of the system. Users can indirectly be guided by using our Euler diagram-based data relationship-building system to understand the Semantic Web and its data linkage system. We also expect that the data sets defined through our system can be used in various applications.
Computer-Assisted Teaching and Cultivate Students’ Innovative Thinking Ability

With the rapid development of computer-assisted teaching technology, it can not only improve the efficiency of classroom teaching and enhance the interaction between teachers and students, but also effectively activate the classroom atmosphere and increase students’ interest in learning. As an important development direction of computer technology in the future, the application prospect in classroom teaching is very broad. By enabling knowledge to be presented in front of students more three-dimensionally, creating a more realistic classroom teaching atmosphere for students, achieving visual and auditory dual stimulation, so that students’ learning initiative and learning effects are comprehensively enhanced. This article analyzes the application in computer-assisted teaching, and then discusses computer-assisted teaching and cultivating students’ innovative thinking ability.
AI Model Compression for Edge Devices Using Optimization Techniques

In recent years Artificial Intelligence (AI) models have gained applications in various fields. A wave of Machine Learning (ML) and AI is happening as the domination of edge devices is becoming set in stone. Edge devices are becoming more powerful and all over, so a lot of ML tasks can happen right on our edge devices rather than using the high-end system. The computational cost, accuracy and limited power are considered very important while deploying an AI model on edge devices. It is crucial to reduce the size of dense neural networks such that they can be deployed on edge devices without losing much accuracy. There is growth in the number of researches to reduce the computation cost by maintaining the accuracy of Convolutional Neural Network (CNN) model. The pure inference efficiency is also becoming one of the most important issue, here is where pruning and quantization play a significant role. In this paper, we are proposing a methodology that compresses large AI model and improves the inference time such that it can be deployed on edge devices. The accuracy loss of the proposed algorithm drops by only 0.44% after pruning and quantization on CNN seven layer architecture.
It Was Never About the Language: Paradigm Impact on Software Design Decisions

Programming languages development has intensified in recent years. New ones are created; new features, often cross-paradigm, are featured in old ones. This new programming landscape makes language selection a more complex decision, both from the companies points of view (technical, recruiting) and from the developers point of view (career development). In this paper, however, we argue that programming languages have a secondary role in software development design decisions. We illustrate, based on a practical example, how the main influencer are higher-level traits: those traditionally assigned with programming paradigms. Following this renovated perspective, concerns about language choice are shifted for all parties. Beyond particular syntax, grammar, execution model or code organization, the main consequence of the predominance of one paradigm or another in the mind of the developer is the way solutions are designed.
Bilingual model of future specialists’ training by means of artistic design

The article presents the authors’ bilingual model of future specialists’ training by means of artistic design. It outlines the content, forms, methods, criteria, and indicators of future specialists’ training in the conditions of bilingual educational process. The authors demonstrate that the development of the bilingual model contributed to the variety of students’ artistic and creative activities forms (team, group, individual) and to a wide range of methods used. The formation of the analyzed skills manifests itself in the level scale (primary, sufficient, high). The primary level of creative skills formation is realized on the basis of the developed criteria (motivation-oriented, content-procedural, evaluative-reflexive). The experimental study was carried out in several stages using theoretical, empirical methods of research. The authors also used the Pearson’s consistency criterion x2 to analyze statistical hypotheses based on observational data. The research outlines the levels of future specialists’ abilities development (association, visual (figurative) creativity, transformations, variability of thinking). The authors used the N. Vyshniakova and R. Tkach’s "Creativity" test, the S. Zievert’s "Freedom of associations" modified test.
Deep Metric Learning for Multi-Label and Multi-Object Image Retrieval

Content-based image retrieval has been a hot topic among computer vision researchers for a long time. There have been many advances over the years, one of the recent ones being deep metric learning, inspired by the success of deep neural networks in many machine learning tasks. The goal of metric learning is to extract good high-level features from image pixel data using neural networks. These features provide useful abstractions, which can enable algorithms to perform visual comparison between images with human-like accuracy. To learn these features, supervised information of image similarity or relative similarity is often used. One important issue in deep metric learning is how to define similarity for multi-label or multi-object scenes in images. Traditionally, pairwise similarity is defined based on the presence of a single common label between two images. However, this definition is very coarse and not suitable for multi-label or multi-object data. Another common mistake is to completely ignore the multiplicity of objects in images, hence ignoring the multi-object facet of certain types of datasets. In our work, we propose an approach for learning deep image representations based on the relative similarity of both multi-label and multi-object image data. We introduce an intuitive and effective similarity metric based on the Jaccard similarity coefficient, which is equivalent to the intersection over union of two label sets. Hence we treat similarity as a continuous, as opposed to discrete quantity. We incorporate this similarity metric into a triplet loss with an adaptive margin, and achieve good mean average precision on image retrieval tasks. We further show, using a recently proposed quantization method, that the resulting deep feature can be quantized whilst preserving similarity. We also show that our proposed similarity metric performs better for multi-object images than a previously proposed cosine similarity-based metric. Our proposed method outperforms several state-of-the-art methods on two benchmark datasets.
qprof: A gprof-Inspired Quantum Profiler

We introduce qprof, a new and extensible quantum program profiler able to generate profiling reports of quantum circuits written using various quantum computing frameworks. We describe the internal structure and working of qprof and provide practical examples on quantum circuits with increasing complexity along with benchmarks of the tool execution time on large circuits. This tool will allow researchers to visualise their quantum algorithm implementation in a different and complementary way and reliably localise the bottlenecks for efficient code optimisation.