Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Social Networks Messages

In the modern era of artificial intelligence and machine learning, data mining is becoming an important tool for determining public opinion and social research. In this regard, sentiment analysis is a new method of studying public opinion, in particular, as a nontrivial approach to the analysis of political texts. This paper examines the nature of sentiment analysis in political texts, identifies the problems which researchers face when analyzing political texts, and identifies the difficulties that affect the accuracy of results. The aim of this study is to determine the relevance of sentiment analysis in the analysis of political texts. It presents an ongoing work that is developing an algorithm combining a lexical-oriented approach with machine learning, that studies stylistic devices (e.g., sarcasm, irony and hyperbole), and provides options for determining the sentiment of texts in sentences containing these stylistic devices. As a result of the experiments, patterns that affect the accuracy of the analysis result are identified and ways to handle them are suggested in order to improve the accuracy of the results. Options for determining the sentiment of texts in sentences containing stylistic devices are provided as a contribution to the scientific field.
Generic conversion method for various spatial domain filters in quantum image processing

Quantum image processing deals with image processing applications in quantum computing. Features of quantum computing like superposition and parallelism give quantum algorithms superiority over classical algorithms. Image filters serve an important task in image processing for applying image transformations. In this paper, we proposed an algorithm for quantum image filters with a generic structure to support different filters. Pixel intensities are represented as rotation in Y-axis of Bloch sphere. The filter is implemented as addition or subtraction of the angles in Y-axis. The proposed algorithm is able to implement different image filters similar to a classical filter. We studied and presented the performance of the filter for edge detection and smoothing operation. The results are then compared to images from a classical filter. The algorithm is further compared with other existing QIP algorithms.
Towards Efficient Annotations for a Human-AI Collaborative, Clinical Decision Support System: A Case Study on Physical Stroke Rehabilitation Assessment

Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being explored to support various decision-making tasks in health (e.g. rehabilitation assessment). However, the development of such AI/ML-based decision support systems is challenging due to the expensive process to collect an annotated dataset. In this paper, we describe the development process of a human-AI collaborative, clinical decision support system that augments an ML model with a rule-based (RB) model from domain experts. We conducted its empirical evaluation in the context of assessing physical stroke rehabilitation with the dataset of three exercises from 15 post-stroke survivors and therapists. Our results bring new insights on the efficient development and annotations of a decision support system: when an annotated dataset is not available initially, the RB model can be used to assess post-stroke survivor's quality of motion and identify samples with low confidence scores to support efficient annotations for training an ML model. Specifically, our system requires only 22 - 33% of annotations from therapists to train an ML model that achieves equally good performance with an ML model with all annotations from a therapist. Our work discusses the values of a human-AI collaborative approach for effectively collecting an annotated dataset and supporting a complex decision-making task.
On 0012-avoiding inversion sequences and a conjecture of Lin and Ma

The study of pattern avoidance in inversion sequences recently attracts extensive research interests. In particular, Zhicong Lin and Jun Ma conjectured a formula that counts the number of inversion sequences avoiding the length-four pattern 0012. Our object of the paper is to confirm this conjecture.
GAMMA: Graph Attention Model for Multiple Agents to Solve Team Orienteering Problem With Multiple Depots

In this work, we present an attention-based encoder-decoder model to approximately solve the team orienteering problem with multiple depots (TOPMD). The TOPMD instance is an NP-hard combinatorial optimization problem that involves multiple agents (or autonomous vehicles) and not purely Euclidean (straight line distance) graph edge weights. In addition, to avoid tedious computations on dataset creation, we provide an approach to generate synthetic data on the fly for effectively training the model. Furthermore, to evaluate our proposed model, we conduct two experimental studies on the multi-agent reconnaissance mission planning problem formulated as TOPMD. First, we characterize the model based on the training configurations to understand the scalability of the proposed approach to unseen configurations. Second, we evaluate the solution quality of the model against several baselines-heuristics, competing machine learning (ML), and exact approaches, on several reconnaissance scenarios. The experimental results indicate that training the model with a maximum number of agents, a moderate number of targets (or nodes to visit), and moderate travel length, performs well across a variety of conditions. Furthermore, the results also reveal that the proposed approach offers a more tractable and higher quality (or competitive) solution in comparison with existing attention-based models, stochastic heuristic approach, and standard mixed-integer programming solver under the given experimental conditions. Finally, the different experimental evaluations reveal that the proposed data generation approach for training the model is highly effective.
New sincerity: Polymodelicity of scientific communication

This article deals with some aspects of the implementation of the open science principles in different countries and the problems of society, in particular the scientific community, in the process of transition to open access depending on the level of economic development of the country. Attention is also paid to how a change in the cultural paradigm may affect scientific communication and the publication of research results. Until now, around the world, they have been subject to the requirements of assessing the effectiveness of scientific work using bibliometric indicators. Nowadays, the shortcomings of this approach are becoming obvious, most of all, ignoring the creative component of scientific work and the existence of other, in addition to the publication of articles, types of scientific work.
Integrated prediction intervals and specific value predictions for regression problems using neural networks

Improving the robustness of neural nets in regression tasks is key to their application in multiple domains. Deep learning-based approaches aim to achieve this goal either by improving their prediction of specific values (i.e., point prediction), or by producing prediction intervals (PIs) that quantify uncertainty. We present IPIV, a deep neural network for producing both a PI and a value prediction. Our loss function expresses the value prediction as a function of the upper and lower bounds, thus ensuring that it falls within the interval without increasing model complexity. Moreover, our approach makes no assumptions regarding data distribution within the PI, making its value prediction more effective for various real-world problems. Experiments and ablation tests on known benchmarks show that our approach produces tighter uncertainty bounds than the current state-of-the-art approaches for producing PIs, while maintaining comparable performance to the state-of-the-art approach for value-prediction. Additionally, we go beyond previous work and include large image datasets in our evaluation, where IPIV is combined with modern neural nets.
DrawnNet: Offline Hand-Drawn Diagram Recognition Based on Keypoint Prediction of Aggregating Geometric Characteristics

Offline hand-drawn diagram recognition is concerned with digitizing diagrams sketched on paper or whiteboard to enable further editing. Some existing models can identify the individual objects like arrows and symbols, but they become involved in the dilemma of being unable to understand a diagram's structure. Such a shortage may be inconvenient to digitalization or reconstruction of a diagram from its hand-drawn version. Other methods can accomplish this goal, but they live on stroke temporary information and time-consuming post-processing, which somehow hinders the practicability of these methods. Recently, Convolutional Neural Networks (CNN) have been proved that they perform the state-of-the-art across many visual tasks. In this paper, we propose DrawnNet, a unified CNN-based keypoint-based detector, for recognizing individual symbols and understanding the structure of offline hand-drawn diagrams. DrawnNet is designed upon CornerNet with extensions of two novel keypoint pooling modules which serve to extract and aggregate geometric characteristics existing in polygonal contours such as rectangle, square, and diamond within hand-drawn diagrams, and an arrow orientation prediction branch which aims to predict which direction an arrow points to through predicting arrow keypoints. We conducted wide experiments on public diagram benchmarks to evaluate our proposed method. Results show that DrawnNet achieves 2.4%, 2.3%, and 1.7% recognition rate improvements compared with the state-of-the-art methods across benchmarks of FC-A, FC-B, and FA, respectively, outperforming existing diagram recognition systems on each metric. Ablation study reveals that our proposed method can effectively enable hand-drawn diagram recognition.
Machine Learning-Enhanced Play Fairway Analysis for Uncertainty Characterization and Decision Support in Geothermal Exploration

Geothermal exploration has traditionally relied on geological, geochemical, or geophysical surveys for evidence of adequate enthalpy, fluids, and permeability in the subsurface prior to drilling. The recent adoption of play fairway analysis (PFA), a method used in oil and gas exploration, has progressed to include machine learning (ML) for predicting geothermal drill site favorability. This study introduces a novel approach that extends ML PFA predictions with uncertainty characterization. Four ML algorithms—logistic regression, a decision tree, a gradient-boosted forest, and a neural network—are used to evaluate the subsurface enthalpy resource potential for conventional or EGS prospecting. Normalized Shannon entropy is calculated to assess three spatially variable sources of uncertainty in the analysis: model representation, model parameterization, and feature interpolation. When applied to southwest New Mexico, this approach reveals consistent enthalpy trends embedded in a high-dimensional feature set and detected by multiple algorithms. The uncertainty analysis highlights spatial regions where ML models disagree, highly parameterized models are poorly constrained, and predictions show sensitivity to errors in important features. Rapid insights from this analysis enable exploration teams to optimize allocation decisions of limited financial and human resources during the early stages of a geothermal exploration campaign.
Characteristics of Pervious Concrete Incorporating Cockleshell as Coarse Aggregate for Pavements

Large amounts of marine by-product waste have emerged as a major environmental issue in many parts of the world. However, considering the limitations of natural materials, the application of this type of marine waste, such as seashells, in concrete can reduce its negative impacts on the global environment. Hence, this research study aimed to investigate the properties of pervious concrete for pavements by incorporating crushed cockleshells, a marine by-product waste, which is used as a natural coarse aggregate. The natural coarse aggregate fraction was partially (25%, 50%, and 75% by mass) replaced by cockleshells. Tests carried out were skid resistance, densities (fresh and hardened state), voids content ratio by volumetric method and image analysis, compressive and tensile strengths, permeability, and their relationships. It was observed that the density and both compressive strength and tensile strength of the lightweight pervious concrete incorporating cockleshell showed lower values compared with the control mixture, whereas voids content, water permeability, and skid resistance revealed higher performance. It is suggested that seashell waste could still be utilized as a partial aggregate at a replacement level of up to 50% for adequate compressive strength of pervious concrete for nonstructural purposes.
Identity-Based Designated-Verifier Proxy Signature Scheme with Information Recovery in Telemedicine System

With the promotion of Remote Medical Treatment, the sharing of big telemedicine data becomes more and more popular. Telemedicine based on wireless sensor networks collects blood pressure, pH value, pulse, and other medical information from the telemedicine healthcare terminal. The medical information is sent to the hospital or medical server for processing. The security protection of patient medical data, such as confidentiality and authenticity, has gradually become a critical problem to be solved in the development of cloud medical service platform. A provably secure identity-based designated-verifier proxy signature scheme with information recovery for cloud medical diagnosis network is proposed. The scheme is on the basis of computational Diffie-Hellman difficult problem and existential unforgeable against adaptive chosen message attacks in the random oracle model. The performance analysis shows that the scheme is appropriate to the remote medical diagnosis system.
Construction and Research of Guqin Sound Synthesis and Plucking Big Data Simulation Model Based on Computer Synthesis Technology

The application and economic efficiency evaluation mode of traditional composition in the field of modern music cannot meet the needs of various types of music in China, especially Guqin music. Based on this, this paper studies the big data simulation model of Guqin sound synthesis and plucking based on computer composition technology. The computer composition technology uses the discrete dynamic modeling technology of complex system to complete the computer simulation of Guqin sound through the analysis of the correlation between Guqin music data and realizes the storage and analysis of the generated Guqin sound data. In addition, the technology can analyze the data information of composition mode in the piano sound plucking simulation model with the classical Guqin music data stored in the cloud system over the years and then feed it back to relevant professionals for verification. The experimental results show that the Guqin sound simulation model can efficiently compare and analyze the melody and other data of classical Guqin sound with the simulated Guqin sound and can realize secondary data mining. This paper studies the application of computer composition method based on discrete dynamic modeling technology in Guqin sound simulation, which has certain reference significance for improving the cloud data in China’s modern music field and the intelligent construction of Guqin sound data cloud storage system.
Exploring Innovative Development Strategies for Safety Education in the Context of Big Data and the Internet of Things

With the continuous development of Internet technology, human beings have entered the era of big data, which has had a significant impact on people’s lives and work, both positively and negatively. With the continuous application of big data in all walks of life, safety education innovation in China’s colleges and universities has also undergone changes. Through the use of big data, the quality of safety education innovation in China’s colleges and universities has been significantly improved, and to a certain extent, the development of safety education innovation in China’s colleges and universities has been promoted. However, influenced by a variety of factors, there are still numerous problems in the safety education innovation of some colleges and universities in China in the era of big data. Based on this, the article focuses on the impact of big data and the Internet of Things on the innovation of safety education in China’s colleges and universities. By analyzing the characteristics and essence of big data, the article proposes corresponding optimization countermeasures to achieve fundamental changes in education methods, transform the traditional teaching classroom, and improve teaching efficiency. The future of education informatization will develop faster and with a higher degree of informatization. School administrators, teachers, and students should keep pace with the development of technology, improve their own literacy, and contribute to the reform and innovation of education informatization.
Dynamic Deep Learning Algorithm (DDLA) for Processing of Complex and Large Datasets

Nowadays, deep learning (DL) is blueprint for the machine learning (ML). Compare with NL, DL is most efficient, time and with low cost There are no limitations for the learning approaches in DL. DL algorithms can extract high-quality of features based on the dataset provided. Due to the fast development in internet technology, huge data is started processing by dividing according to the requirements. DL algorithms are more compatible for processing of huge and complex datasets such as pattern matching, recognition of handwriting, recognition of speeches, analysis of stock markets and many more. DL has more advancement on various applications and this will solve various issues in complicated pattern applications. Previously, datasets such as handwritten is used to find the accurate result with the Ensemble novel classifier (ENC). But this not worked up to the mark. In this paper, dynamic deep learning algorithm (DDLA) is designed and developed to process the complicated and complex datasets. This is the combination of Auto Encoder (AE) and Adaptive Convolutional neural network (A-CNN). Experimental results show the performance of the EDDLA with ENC.
Real-time style transfer with efficient vision transformers

Style Transfer aims at transferring the artistic style from a reference image to a content image. While Deep Learning (DL) has achieved state-of-the-art Style Transfer performance using Convolutional Neural Networks (CNN), its real-time application still requires powerful hardware such as GPU-accelerated systems. This paper leverages transformer-based models to accelerate real-time Style Transfer on mobile and embedded hardware platforms. We designed a Neural Architecture Search (NAS) algorithm dedicated to vision transformers to find the best set of architecture hyperparameters that maximizes the Style Transfer performance, expressed in Frame/seconds (FPS). Our approach has been evaluated and validated on the Xiaomi Redmi 7 mobile phone and the Raspberry Pi 3 platform. Experimental evaluation shows that our approach allows to achieve a 3.5x and 2.1x speedups compared to CNN-based Style Transfer models and Transformer-based models respectively1.
Virtual Reality for Enhancing Safety in Construction

Virtual reality (VR) is the most advanced form of visualization and simulation. Although its benefits on critical thinking and cognitive abilities are proven, VR is not strongly utilized in construction. The research is driven by the fact that “learning by doing” is more effective than traditional methods. Therefore, the goal of this research is to develop and test a prototype for the subsequent large-scale utilization of VR in enhancing safety in construction sites and in education. The objectives are to (1) verify the applicability of VR in construction education, (2) evaluate the prototype developed in terms of efficiency in enhancing safety performance, (3) evaluate the model in terms of sickness and usability, and (4) provide recommendations to be implemented in the model to be used in the large-scale testing of the VR technology within education in the construction field. After the development and testing of the prototype, results indicate that the immersive VR model significantly enhanced hazard identification skills compared to traditional safety training. In addition, the assessment of its efficiency and feasibility have shown the potential of VR in improving construction education; thus, paving the way for having VR as the main complementary tool to traditional education on a larger scale in the future.
Prediction of the 3D Distribution of NOx in a Furnace via CFD Data Based on ELM

A novel method for the prediction of three-dimensional (3D) spatial distribution of NOx in a furnace is proposed and evaluated. Computational fluid dynamics (CFD) simulations are conducted to generate the data sets of 3D NOx spatial distribution. The data sets are partitioned based on NOx generation mechanisms to improve the model accuracy. Combining the Pearson coefficient and mutual information (PMI), the model input variables are optimized by feature selection. The prediction model of 3D NOx spatial distribution in the furnace is established based on extreme learning machine (ELM). The experiments are conducted considering a 350 MW coal-fired boiler with a change in the burner tilt angles under a rated load. The experimental results show that the data-driven method based on PMI-ELM can realize the rapid prediction of the 3D spatial distribution of NOx in the furnace with 12.84% mean absolute percentage error.
HAI-GEN 2022: 3rd Workshop on Human-AI Co-Creation with Generative Models

Recent advances in generative AI have resulted in a rapid and dramatic increase to the fidelity of created artifacts, from realistic-looking images of faces [9] to antimicrobial peptide sequences that treat diseases [5] to faked videos of prominent business leaders [4, 10]. We believe that people skilled within their creative domain can realize great benefits by incorporating generative models into their own work: as a source of inspiration, as a tool for manipulation, or as a creative partner. Our workshop will bring together researchers and practitioners from both the HCI and AI disciplines to explore and better understand the opportunities and challenges in building, using, and evaluating human-AI co-creative systems.
Filter group delays equalization for 2D discrete wavelet transform applications

This paper presents a new approach to perform near-perfect image reconstruction for an online/real-time two-dimensional discrete wavelet transform (2D-DWT) analysis/synthesis applications, such as image enhancement, compression, and watermarking. The proposed new model is based on equalizing the cumulative filter group delays over all calculation paths by inserting appropriate delays. This model has been implemented in MATLAB/SIMULINK and compared to the default model using six wavelet-based filter sets and two dyadic decomposition/reconstructed levels. Peak signal-to-noise ratios (PSNRs) of images reconstructed by the proposed equalized model present an improvement of more than 60 dB, compared to those of images reconstructed by the default model.