Reviewerly/exHarmony huggingface dataset:
Methods of medical data management based on blockchain technologies

The healthcare sector in Ukraine has long been in need of change, and many opportunities of blockchain technology can help it lead the transformation of this sector and ensure compliance with the requirements of the efficiency, safety, novelty, and economic benefits. State-of-the-art on known decisions on the use of blockchain technologies in the information technology for medical data management showed that, despite a large number of different solutions, effective methods and solutions for medical data management based on blockchain technologies are currently lacking. Therefore, improving the efficiency of medical data management by developing methods of medical data management based on blockchain technologies is currently an urgent problem. The paper developed a method for assessing the sufficiency of medical data, which provides: the ability to analyze a variety of medical data for their sufficiency; the ability to quantify the sufficiency of medical data; in case of insufficiency of the medical data, formation of recommendations on supplementing the medical data to be able to upload them to the "Block Data" section of the blockchain block. The paper developed a method of performing transactions on medical data, which consists of the stages of entering information into the blockchain and obtaining information from the blockchain. The developed method of performing transactions on medical data provides the ability to decide on the addition of transactions and medical data from the patient to the blockchain; the ability to decide on the issuance of data from the blockchain on request from a doctor.
YouTube Ad View Sentiment Analysis using Deep Learning and Machine Learning

Sentiment Analysis is currently a vital area of research.With the advancement in the use of the internet, the creation of social media, websites, blogs, opinions, ratings, etc. has increased rapidly.People express their feedback and emotions on social media posts in the form of likes, dislikes, comments, etc.The rapid growth in the volume of viewer-generated or user-generated data or content on YouTube has led to an increase in YouTube sentiment analysis.Due to this, analyzing the public reactions has become an essential need for information extraction and data visualization in the technical domain.This research predicts YouTube Ad view sentiments using Deep Learning and Machine Learning algorithms like Linear Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN).Finally, a comparative analysis is done based on experimental results acquired from different models.
Few-Shot Learning on Graphs

Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representation learning models for specific tasks often suffer from label sparsity issue as data labeling is always time and resource consuming. In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge. There have been many studies working on FSLG recently. In this paper, we comprehensively survey these work in the form of a series of methods and applications. Specifically, we first introduce FSLG challenges and bases, then categorize and summarize existing work of FSLG in terms of three major graph mining tasks at different granularity levels, i.e., node, edge, and graph. Finally, we share our thoughts on some future research directions of FSLG. The authors of this survey have contributed significantly to the AI literature on FSLG over the last few years.
Sustainable semantic similarity assessment

The automatic semantic similarity assessment field has attracted much attention due to its impact on multiple areas of study. In addition, it is also relevant that recent advances in neural computation have taken the solutions to a higher stage. However, some inherent problems persist. For example, large amounts of data are still needed to train solutions, the interpretability of the trained models is not the most suitable one, and the energy consumption required to create the models seems out of control. Therefore, we propose a novel method to achieve significant results for a sustainable semantic similarity assessment, where accuracy, interpretability, and energy efficiency are equally important. We rely on a method based on multi-objective symbolic regression to generate a Pareto front of compromise solutions. After analyzing the output generated and comparing other relevant works published, our approach’s results seem to be promising.
Theoretical Principles and Perspectives of Hyperspectral Imaging Applied to Sediment Core Analysis

Hyperspectral imaging is a recent technology that has been gaining popularity in the geosciences since the 1990s, both in remote sensing and in the field or laboratory. Indeed, it allows the rapid acquisition of a large amount of data that are spatialized on the studied object with a low-cost, compact, and automatable sensor. This practical article aims to present the current state of knowledge on the use of hyperspectral imaging for sediment core analysis (core logging). To use the full potential of this type of sensor, many points must be considered and will be discussed to obtain reliable and quality data to extract many environmental properties of sediment cores. Hyperspectral imaging is used in many fields (e.g., remote sensing, geosciences and artificial intelligence) and offers many possibilities. The applications of the literature will be reviewed under five themes: lake and water body trophic status, source-to-sink approaches, organic matter and mineralogy studies, and sedimentary deposit characterization. Afterward, discussions will be focused on a multisensor core logger, data management, integrated use of these data for the selection of sample areas, and other opportunities. Through this practical article, we emphasize that hyperspectral imaging applied to sediment cores is still an emerging tool and shows many possibilities for refining the understanding of environmental processes.
Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization

Text summarization aims to generate a short summary for an input text.In this work, we propose a Non-Autoregressive Unsupervised Summarization (NAUS) approach, which does not require parallel data for training.Our NAUS first performs edit-based search towards a heuristically defined score, and generates a summary as pseudo-groundtruth.Then, we train an encoder-only non-autoregressive Transformer based on the search result.We also propose a dynamic programming approach for length-control decoding, which is important for the summarization task.Experiments on two datasets show that NAUS achieves state-of-the-art performance for unsupervised summarization, yet largely improving inference efficiency.Further, our algorithm is able to perform explicit length-transfer summary generation. 1
E-Voting using Ethereum Blockchain

The revolutionary concept of blockchain has given a rise to multiple facets of secure, accountable, online services. We witness the rise of E-voting as a critical topic related to online services. Blockchain with smart contracts emerges as a good candidate to use in the development of safer, cheaper, more secure, more transparent, and easier to use e-voting systems. Ethereum and its network are one of the most viable candidates for the implementation of an e-voting system using blockchain, due to their consistency, widespread use, and provision of smart contracts logic. An e-voting system must be secure, as it should prevent duplication of votes while being fully transparent and protecting the privacy of the voters involved. In this work, we have implemented and tested a sample voting web - application as a smart contract for the Ethereum network using the Ethereum wallets and the Solidity language. Eventually, when elections are held, the Ethereum blockchain will store votes and voting results. Users can connect their Ethereum wallet and vote for the candidate they want to, and these transaction requests are processed by the consensus of each Ethereum node. These agreements create a transparent voting environment.
A simple information fusion method provides the obstacle with saliency labeling as a landmark in robotic mapping

Mapping is the key technology of the robot slam algorithm, mainly used to obtain environmental geometric information. How to effectively improve the ability to obtain semantic information on the map becomes essential. Salient object segmentation with depth can be used as a kind of salient semantic information that helps the robot better understand the environment. This paper put forward a consistent salient determination method based on visual and lidar images for complementary advantages. Our method is based on the simple information fusion of the two sensors' salient object key features, including approximate centroids and contour widths. Theoretical analysis and experimental results demonstrate that the proposed method has the advantage of strong illumination robustness and can work under weak light conditions, which can solve the task in the visual image when the front salient object blocks the back salient object. It can be used to detect salient objects with approximate size in indoor and outdoor sparse scenes. The detected salient objects can be used as semantic labeling labeled in the environment and as the relative position reference for helping the robot improve the accuracy of mapping, positioning, and planning the motion more intelligently.
Facing the FACS—Using AI to Evaluate and Control Facial Action Units in Humanoid Robot Face Development

This paper presents a new approach for evaluating and controlling expressive humanoid robotic faces using open-source computer vision and machine learning methods. Existing research in Human-Robot Interaction lacks flexible and simple tools that are scalable for evaluating and controlling various robotic faces; thus, our goal is to demonstrate the use of readily available AI-based solutions to support the process. We use a newly developed humanoid robot prototype intended for medical training applications as a case example. The approach automatically captures the robot's facial action units through a webcam during random motion, which are components traditionally used to describe facial muscle movements in humans. Instead of manipulating the actuators individually or training the robot to express specific emotions, we propose using action units as a means for controlling the robotic face, which enables a multitude of ways to generate dynamic motion, expressions, and behavior. The range of action units achieved by the robot is thus analyzed to discover its expressive capabilities and limitations and to develop a control model by correlating action units to actuation parameters. Because the approach is not dependent on specific facial attributes or actuation capabilities, it can be used for different designs and continuously inform the development process. In healthcare training applications, our goal is to establish a prerequisite of expressive capabilities of humanoid robots bounded by industrial and medical design constraints. Furthermore, to mediate human interpretation and thus enable decision-making based on observed cognitive, emotional, and expressive cues, our approach aims to find the minimum viable expressive capabilities of the robot without having to optimize for realism. The results from our case example demonstrate the flexibility and efficiency of the presented AI-based solutions to support the development of humanoid facial robots.
Speckle Noise Removal Model Based on Diffusion Equation and Convolutional Neural Network

The image denoising model based on convolutional neural network (CNN) can achieve a good denoising effect. However, its robustness is poor, and it is not suitable for direct noise removal tasks. Differently, the image denoising method based on the diffusion equation is more stable and has theoretical guarantees. In order to give full play to the advantages of CNN and diffusion equation in image denoising, this paper proposes a speckle noise denoising model via a combination of the two tools. Firstly, based on the mathematical model of speckle noise, a class of neural network speckle noise removal model which mixes residual learning and structure learning is proposed using image decomposition theory. Then, in order to solve the hyperparameter problem that the model depends on noise variance, a noise variance estimation algorithm based on a nonlinear diffusion equation is proposed. Finally, a speckle noise denoising model based on diffusion equation and CNN is obtained. Numerical simulation experiments verify the accuracy of the variance estimation algorithm and also the denoising effect and practical application value of the proposed method.
YOU SHALL NOT COMPUTE on my Data: Access Policies for Privacy-Preserving Data Marketplaces and an Implementation for a Distributed Market using MPC

Personal data is an attractive source of insights for a diverse field of research and business. While our data is highly valuable, it is often privacy-sensitive. Thus, regulations like the GDPR restrict what data can be legally published, and what a buyer may do with this sensitive data. While personal data must be protected, we can still sell some insights gathered from our data that do not hurt our privacy. A data marketplace is a platform that helps users to sell their data while assisting buyers in discovering relevant datasets. The major challenge such a marketplace faces is balancing between offering valuable insights into data while preserving privacy requirements. Private data marketplaces try to solve this challenge by offering privacy-preserving computations on personal data. Such computations allow for calculating statistics or training machine learning models on personal data without accessing the data in plain. However, the user selling the data cannot restrict who can buy or what type of computation the data is allowed.
Between Awareness and Acceptance: a more mature School Teachers’ Perspective on Integrated Learning one year after the pandemic outbreak

One year after the outbreak of the pandemic that provoked a forced and massive adoption of technology enhanced learning practices, followed by a continuous evolution of their delivery modalities (on-line learning, blended learning, parallel blended learning, hybrid learning) and, finally, by a strong commitment to come back to a “new normal”, we have investigated, by mean of a survey, the evolution of: a) perceptions and perspectives of Italian school’s teachers about integrated learning; b) the undergoing innovation process characterized by unprecedented features and vastness. The exploration has been conducted by integrating perspectives and factors introduced in the past by several models - TAM, UTAUT, DOI, TOE, KAM - to describe technology innovation and adoption processes. We observed a higher perceived teachers' technological and pedagogical preparation, together with a higher readiness of the schools to react to unexpected events or sudden prescriptions. A readiness that should be ascribed mainly to the quality of the management and to an increase in the level of collaboration and cohesion among the actors of the learning process, rather than to an enhancement of the technological infrastructures. Collaboration, indeed, emerges as the main peculiarity of this last year, an engine capable to foster the spread of competences and a beacon capable to guide the choice of teaching practices. The influence of contextual factors appears not relevant and that of the “perceived values” somewhat marginal. As far as the innovation process: the awareness phase developed satisfactory and, in parallel, the acceptance phase also started. The possible transition to the adoption phase appears uncertain and not easy to characterize. At the time of the survey, however, the teachers perceived, the integrated learning as a modality that could be used in the future to realize 36% of the school activities.
Multiple attention-based encoder–decoder networks for gas meter character recognition

Abstract Factories swiftly and precisely grasp the real-time data of the production instrumentation, which is the foundation for the development and progress of industrial intelligence in industrial production. Weather, light, angle, and other unknown circumstances, on the other hand, impair the image quality of meter dials in natural environments, resulting in poor dial image quality. The remote meter reading system has trouble recognizing dial pictures in extreme settings, challenging it to meet industrial production demands. This paper provides multiple attention and encoder–decoder-based gas meter recognition networks (MAEDR) for this problem. First, from the acquired dial photos, the dial images with extreme conditions such as overexposure, artifacts, blurring, incomplete display of characters, and occlusion are chosen to generate the gas meter dataset. Then, a new character recognition network is proposed utilizing multiple attention and an encoder–decoder structure. Convolutional neural networks (CNN) extract visual features from dial images, encode visual features employing multi-head self-attention and position information, and facilitate feature alignment using the connectionist temporal classification (CTC) method. A novel two-step attention decoder is presented to improve the accuracy of recognition results. convolutional block attention module (CBAM) reweights the visual features from the CNN and the semantic features computed by the encoder to improve model performance; long short-term memory attention (LSTM attention) focuses on the relationship between feature sequences. According to experimental data, our system can effectively and efficiently identify industrial gas meter picture digits with 91.1% identification accuracy, faster inference speed, and higher accuracy than standard algorithms. The accuracy and practicality of the recognition can fulfill the needs of instrument data detection and recognition in industrial production, and it has a wide range of applications.
Super Resolution for Augmented Reality Applications

Latest developments in machine learning (ML), adversarial networks, combined with increasingly powerful IoT devices via the introduction of efficient processors, are bringing about the implementation of near real-time object detection and classification for augmented reality (AR) and virtual reality (VR) applications. This paper intends to explore new object detection and classification technologies leveraging super-resolution (SR), that have the potential to be integrated into small, mobile and low-power ARNR devices. SR in conjunction with novel object detection and classification algorithms are examined in the presented paper, with the ultimate goal of proposing a low-footprint Generative Adversarial Network (GAN)-based framework capable of receiving an LR input and outputting an SR-supported recognition model based on FRRCNN, YOLOv3 or Retina.
Few-shot Text Steganalysis Based on Attentional Meta-learner

Text steganalysis is a technique to distinguish between steganographic text and normal text via statistical features. Current state-of-the-art text steganalysis models have two limitations. First, they need sufficient amounts of labeled data for training. Second, they lack the generalization ability on different detection tasks. In this paper, we propose a meta-learning framework for text steganalysis in the few-shot scenario to ensure model fast-adaptation between tasks. A general feature extractor based on BERT is applied to extract universal features among tasks, and a meta-learner based on attentional Bi-LSTM is employed to learn task-specific representations. A classifier trained on the support set calculates the prediction loss on the query set with a few samples to update the meta-learner. Extensive experiments show that our model can adapt fast among different steganalysis tasks through extremely few-shot samples, significantly improving detection performance compared with the state-of-the-art steganalysis models and other meta-learning methods.
Differential Private Deep Learning Models for Analyzing Breast Cancer Omics Data

Proper analysis of high-dimensional human genomic data is necessary to increase human knowledge about fundamental biological questions such as disease associations and drug sensitivity. However, such data contain sensitive private information about individuals and can be used to identify an individual (i.e., privacy violation) uniquely. Therefore, raw genomic datasets cannot be publicly published or shared with researchers. The recent success of deep learning (DL) in diverse problems proved its suitability for analyzing the high volume of high-dimensional genomic data. Still, DL-based models leak information about the training samples. To overcome this challenge, we can incorporate differential privacy mechanisms into the DL analysis framework as differential privacy can protect individuals' privacy. We proposed a differential privacy based DL framework to solve two biological problems: breast cancer status (BCS) and cancer type (CT) classification, and drug sensitivity prediction. To predict BCS and CT using genomic data, we built a differential private (DP) deep autoencoder (dpAE) using private gene expression datasets that performs low-dimensional data representation learning. We used dpAE features to build multiple DP binary classifiers to predict BCS and CT in any individual. To predict drug sensitivity, we used the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. We extracted GDSC's dpAE features to build our DP drug sensitivity prediction model for 265 drugs. Evaluation of our proposed DP framework shows that it achieves improved prediction performance in predicting BCS, CT, and drug sensitivity than the previously published DP work.
Near-Infrared Spectroscopic Study of OH Stretching Modes in Kaolinite and Dickite

Kaolinite and dickite are differently ordered polytypes of kaolinite-group minerals, whose differences are in the stacking mode of layers and ion occupation. Fourier transform infrared spectroscopy was used to collect information about the differences between the two minerals. The common characteristics of kaolinite and dickite are bands near 4530 and 7068 cm−1, which are attributed to the combination of the inner Al-OH stretching vibration and outer Al-OH bending vibration and the overtone of the inner Al-OH stretching vibration, respectively. The difference is that kaolinite has secondary peaks at 4610 and 7177 cm−1, and the secondary peak of dickite is near 4588 cm−1. The OH stretching vibration has the first fundamental overtone of the stretching vibration in the range of 7000–7250 cm−1. In addition to the overtones generated by single OH stretching vibrations, overtones combining different OH stretching vibrations are also found, which are formed by adjacent peaks of OH stretching vibrations. The average factor of the first fundamental overtone with an OH-group stretching vibration is approximately 1.95. The near-infrared spectrum (NIR) of phyllosilicates is closely related to their structure and isomorphism. Therefore, the near-infrared region can distinguish between kaolinite and dickite and provide a basis for deposit research and geological remote sensing.