Reviewerly/exHarmony huggingface dataset:
Medical Insurance Fraud Detection Based on Block Chain and Deep Learning Approach

To control the medical expenses people are decided to do some insurance plans and the Health Insurance Department's duty of controlling medical expenses has become increasingly vital. Traditional medical insurance settlements are paid per-service, which results in a lot of unnecessary costs. Now a day, the single-disease payment mechanism has been frequently employed to address this issue. However, there is a possibility of fraud with single-disease payments. In this work, we have presented a methodology for detecting the health insurance fraud entrenched block chain and deep learning techniques, that can automatically recognize apprehensive medical records to assure sustainable execution of single-disease payment and reduce medical insurance worker's workload. We also proposed a medical record storage and management procedure based on consortium block chain to assure data security, immutability, traceability, and audit ability. The suggested system may effectively identify fraud and considerably increase the efficiency of medical insurance evaluations, as demonstrated by experiments on two real datasets from two hospitals.
Shared Driving Assistance Design Considering Human Error Protection for Intelligent Electric Wheelchairs

To effectively provide the handicapped with mobility aids, studies on the shared autonomy of robotic systems have been widely cultivated. This study proposes an adaptive shared control strategy to realize reliable and safe driving assistance on an intelligent electric wheelchair with protection against human errors. The theoretical framework of the system is analyzed by the linearized reference wheelchair model and stable characteristics of obstacle avoidance behavior can be subsequently derived according to the Lyapunov analysis and Liénard-Chipart criterion. Based on the convex analysis, the relationships between human input and robot control are investigated to determine shared control weights. As such, safety and reliability can be guaranteed. To verify the performances of the proposed approach, human errors including skill-based errors, decision errors, and violations are considered in the experiments. The experimental results based on a comprehensive study show that the proposed method is capable of enhancing driving safety and reducing operation burden in terms of the designed criteria with fluency, smoothness, and time efficiency while protecting the user from human manual errors.
SiamLight: lightweight networks for object tracking via attention mechanisms and pixel-level cross-correlation

Despite Siamese-based trackers have achieved great success in recent years, researchers have focused more on the accuracy of trackers than their complexity, which leads to their inapplicability in some scenarios, and the real-time speed can be greatly limited. In this work, we propose a lightweight network method called SiamLight for object tracking. MobileNet-V3 is selected as the backbone network. The PG-corr module is added as the feature fusion module, a strategy that decomposes the template feature into spatial and channel kernels, reducing the matching regions and suppressing the effect of similar interference. In addition, we also add the CSM module, which carries out attention to the channel and spatial simultaneously. CSM module not only reduces the number of parameters but also ensures that it can be integrated into existing network architectures as a plug-and-play module. Finally, multiple separable convolution blocks are added to the classification and regression branches to meet our lightweight parameters and Flops requirements. The experiments on LaSOT, VOT2018, VOT2019, OTB100, and UAV123 benchmarks show that the method has fewer Flops and parameters than state-of-the-art trackers.
Biosemiotics for postdigital living: the implications of the implications

Abstract The postdigital condition is discussed from the perspective of Paul Cobley’s biosemiotic approach to culture. While semiotics is often concerned with cultural criticism, there has been no explicit biosemiotic approach to culture, until only recently with Cobley unfurling such a research program. The key to this is the biosemiotic notion of modeling , which accounts for co-evolutionary processes encompassing biology and culture. This approach responds to recent calls in the humanities and social sciences to understand culture as constituted through technology, but also as something not strictly human (more-than-human). By undermining both vitalism and reductionism, biosemiotics avoids biologism and culturalism, which is of much importance for theorizing culture and learning in light of evolution. This has consequences for construing cultural pluralism. Mainstream notions of multiculturalism rely on cultural holism and, hence, advocate the separation of communities and languages for the pretense of maintaining diversity. Cobley’s theory avoids this pitfall, offering a view of cultures as intrinsically heterogeneous and open systems. This suggests further implications for how we understand the aims of literacy and state-run education. We present an account of biocultural learning that accommodates contemporary posthumanist and postdigital orientations. Construing learning as ecologically contextual is necessary for addressing ongoing technological transformations.
Sentiment analysis methods for politics and hate speech contents in Spanish language: a systematic review

The political debate in social networks, and its derivatives such as hate speech, has surfaced at the top of the social agenda due to its impact on public opinion and, consequently, in the communication strategies of political parties, public institutions, media corporations, and lobbies. The scientific community has been working to respond to the demand for tools that allow studying the political attitude of citizens in these networks, focusing on sentiment analysis methodologies. However, their work has been hampered by several significant challenges, such as the absence of standardized investigation methodologies, the filtering of content created by bots and spammers, or the interpretation of slang and other conventionalisms that are specific to microblogging platforms. In addition to these challenges and the generic problems related to the interpretation of human language, researchers from the Spanish-speaking community have found themselves with the additional problem of developing strategies and methodologies suitable for Spanish text, in a scenario dominated by research aimed at the English language. In this paper, we present a systematic review that describes the state of the art in sentiment analysis methods for politics and hate speech contents in the Spanish language, by systematically reviewing the relevant papers available.
QPP++ 2023: Query-Performance Prediction and Its Evaluation in New Tasks

Query-Performance Prediction (QPP) is currently primarily applied to ad-hoc retrieval tasks. The Information Retrieval (IR) field is reaching new heights thanks to recent advances in large language models and neural networks, as well as emerging new ways of searching, such as conversational search. Such advancements are quickly spreading to adjacent research areas, including QPP, necessitating a reconsideration of how we perform and evaluate QPP. This workshop sought to elicit discussion on three topics related to the future of QPP: exploiting advances in IR to improve QPP, instantiating QPP on new search paradigms, and evaluating QPP on new tasks.
DIC Measurement of Welding-Induced Deformation on a Train Bogie Moving Bolster Subassembly

The experimental evaluation of welding-induced distortion is a topic of great interest to both the scientific and industrial communities. With the aim of addressing a specific need in an industrial context, this paper investigates the capabilities of a stereo–Digital Image Correlation (stereo–DIC) system to measure the weld-induced distortion of the front-plate of a bogie train bolster subassembly. Currently, the deviation from planarity of this surface is measured at less than five points using a CMM in the post-weld cooled state. An additional machining process is then used to bring the surface within the tolerance required to join the welded assembly to the train body through a threaded flange. The paper shows that DIC can provide accurate full-field distortion and strain maps over the entire 588 mm × 308 mm surface of the front plate. The distinct strength of DIC over the currently used inspection technique is its ability to provide highly spatially dense data that are unaffected by rigid body motion. This can be advantageous in terms of saving time in the post-weld inspection and reducing errors in the finishing process. In addition, DIC capabilities revealed important information that was not available from the CMM, such as the full-surface map of the initial deviation of the plate from its nominal geometry and its asymmetric deformation after welding. Finally, the full-field nature of the experimental data obtained allows for seamless integration with FE thermo-mechanical simulations for numerical model validation, stress calculation, and optimization of geometry and technological processes.
Digitalization, financing constraints and firm performance

The development of low-level digital technology and communication technology such as “huge wisdom moving cloud” has driven the rise of digital economy, and various fields of social economy have gradually realized deep integration with digital technology. From the micro level of enterprises, digitalization transforms business activities such as research and development, production, supply chain and sales, and forms new data resources to help enterprises achieve lean management through data integration and analysis. To investigate whether digitalization ultimately affects firm performance, this study conducted theoretical discussions, selected Chinese listed companies to study, and empirically tested the relationship. Research has found that digitisation does boost corporate performance. After the robustness test, the conclusion remains the same. To deepen the understanding of the impact of digitalization on corporate performance, a mechanism analysis is also performed in this study. We found that digitization improves corporate performance by improving corporate innovation. In addition, we carried out an applicability analysis. We find that digitalization has a greater impact on firm performance in non-state-owned enterprises and those whose executives have an information technology background. Finally, by means of the economic consequences test, we find that the improvement in corporate performance caused by the growth of the digital hierarchy improves the corporate debt structure in the future. The findings of this study enrich theories related to digitalization and improve empirical evidence for the positive externalities of digitalization.
ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis

Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images. The gap between human color perception, linguistic color terminology, and digital representation are the main challenges for developing methods that properly classify pixels based on color. To address these challenges, we propose a novel method combining geometric analysis, color theory, fuzzy color theory, and multi-label systems for the automatic classification of pixels into 12 conventional color categories, and the subsequent accurate description of each of the detected colors. This method presents a robust, unsupervised, and unbiased strategy for color naming, based on statistics and color theory. The proposed model, “ABANICCO” (AB ANgular Illustrative Classification of COlor), was evaluated through different experiments: its color detection, classification, and naming performance were assessed against the standardized ISCC–NBS color system; its usefulness for image segmentation was tested against state-of-the-art methods. This empirical evaluation provided evidence of ABANICCO’s accuracy in color analysis, showing how our proposed model offers a standardized, reliable, and understandable alternative for color naming that is recognizable by both humans and machines. Hence, ABANICCO can serve as a foundation for successfully addressing a myriad of challenges in various areas of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging.
Real-time object detection using a domain-based transfer learning method for resource-constrained edge devices

This paper presents a domain-based transfer learning method for deep learning-based object detection models where the method enables real-time computation in resource-constrained edge devices. Object detection is an essential task for intelligent platforms (e.g., drones, robots, and autonomous vehicles). However, edge devices could not afford to run huge object detection models due to insufficient resources. Although a compressed deep learning model increases inference speed, the accuracy of the model could be significantly deteriorate. In this paper, we propose an accurate object detection method while achieving real-time computation on edge devices. Our method aims to reduce marginal detection outputs of models according to application domains (e.g., city, park, factory, etc). We classify crucial objects (i.e., pedestrian, car, bench, etc) for a specific domain and adopt a transfer learning in which the learning is solely towards the selected objects. Such approach improves detection accuracy even for a compressed deep learning model like tiny versions of a YOLO (you only look once) framework. From the experiments, we validate that the method empowers the YOLOv7-tiny can provide the comparable detection accuracy with a YOLOv7 model despite of 83% less parameters than that of the original model. Besides, we confirm that our method achieves 389% faster inference on resource-constrained edge devices (i.e., NVIDIA Jetsons) than the YOLOv7.
Dynamic Graph Representation Learning via Coupling-Process Model

Representation learning based on dynamic graphs has received a lot of attention in recent years due to its wide range of application scenarios. Although many discrete or continuous dynamic graph representation learning methods have been proposed, many of them ignore the role of edge types. Through the observation of dynamic graphs in the real world, it is found that the types of various edges are very different in nature. They are roughly divided into two categories according to the frequency of occurrence: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evolutive edges</i> that appear infrequently and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interactive edges</i> that appear frequently. For both types of edges, we propose a coupling-process model (DyCPM) to capture the dynamic mechanisms of them. The model not only generates low-dimensional embedding vectors of nodes, but also aggregates the structural information and temporal information of two kinds of edges. In particular, we design a neural network parameterized discrete process to depict the change law of the topology of evolutive edges and a neural network parameterized temporal point process (TPP) to characterize the temporal dynamic rule of interactive edges. More importantly, we propose a coupling mechanism to transfer the information of the two processes through a shared embedding matrix and finally generate an embedding matrix that aggregates the topology information and temporal information of the two kinds of edges for the dynamic link prediction task. We evaluate our model and several baselines on real datasets. The experimental results show that our model can better aggregate the topology information and temporal information of the two kinds of edges according to their properties and outperforms several state-of-the-art baselines in the performance of dynamic link prediction.
Oversizing co-locatedwind and solar parks to increase the capacity factor

This paper presents a study on oversizing existing wind parks with solar photovoltaics (PV) parks. Oversizing is done by installing larger capacity than stipulated in the connection agreement with the system operator. We use two years of observed wind power output from 264 operational wind parks across Sweden and modeled solar power output. The results show that the required curtailment and the increasing PV capacity has a non-linear relationship, where curtailment losses are small at first but increase quickly after installing more than roughly 50 % of the nominal contracted export capacity. The average curtailment losses largely depend on the anti-correlation between the resources and the latitude location, where sites with stronger anti-correlation and higher latitude location in general have less curtailment losses. The curtailed power is largely a function of the overinstalled PV, where storages should be rated at approximately 35 % less than the overinstalled PV in order to capture at least 90 % of the curtailed power. Furthermore, the autocorrelation of the curtailed energy is largely unaffected by the amount of overinstalled PV and reaches a low correlation after approximately four hours.
A new hybrid particle swarm optimizationalgorithm for optimal tasks scheduling in distributed computing system

Tasks allocation problem in heterogeneous and distributed-multiprocessing computing environments is a nonlinear multi-objective NP-hard problem and from a research perspective, it is considereda major issue. The underlying objective of any allocation mechanism to execute the specified tasks set and enumerating processors is to minimize the overall cost. To solve the tasks allocation problems numerous meta-heuristic techniques have been tried and tested but still, plenty of scopes are there for optimal strategies. In the presented article, a comprehensive task assignment model based on particle swarm optimization (PSO) is developed, which optimizes response time, flowtime, and cost of the distributed computing system. In the present technique, the ‘n’ number of cluster centroids of ‘r’ tasks, is updated by the PSO technique to form the ‘n’ task clusters to minimize the communication costs then their allocation is made by the newly proposed heuristic method. Considering the fact, in the given model objective functions conflict with each other and the good thing about the PSO algorithm are its accuracy and speed. And to come up with the most favorable solution, authors have developed PSO based algorithm.The PSO-based technique that is proposed in this article to solve a given assignment problem in a distributed computing system is able to give better results in a manner of convergence rate as thePSO method integrates local and global search methods in an attempt to strike a balance between research and utilization. To examine the functioning of the developed technique, the well-demonstrated scheduling policies based on different techniques have been compared and obtained finer outcomes. The developed mechanism is acceptable for an erratic number of tasks and processors.
Spatial–spectral mapping to prepare frequency entangled qudits

Entangled qudits, the high-dimensional entangled states, play an important role in the study of quantum information. How to prepare entangled qudits in an efficient and easy-to-operate manner is still a challenge in quantum technology. Here, we demonstrate a method to engineer frequency entangled qudits in a spontaneous parametric downconversion process. The proposal employs an angle-dependent phase-matching condition in a nonlinear crystal, which forms a classical-quantum mapping between the spatial (pump) and spectral (biphotons) degrees of freedom. In particular, the pump profile is separated into several bins in the spatial domain, and thus shapes the down-converted biphotons into discrete frequency modes in the joint spectral space. Our approach provides a feasible and efficient method to prepare a high-dimensional frequency entangled state. As an experimental demonstration, we generate a three-dimensional entangled state by using a homemade variable slit mask.
Combining autoencoder with clustering analysis for anomaly detection in radiotherapy plans

To develop an unsupervised anomaly detection method to identify suspicious error-prone treatment plans in radiotherapy.A total of 577 treatment plans of breast cancer patients were used in this study. They were labeled as either normal or abnormal plans by experienced clinicians. Multiple features of each plan were extracted and selected by the learning algorithms. The training set consisted of feature samples from 400 normal plans and the testing set consisted of feature samples from 158 normal plans and 19 abnormal plans. Using the k-means clustering algorithm in the training stage, 4 normal plan clusters were formed. The distance between the samples in the testing set and the cluster centers were then determined. To evaluate the effect of dimensionality reduction (DR) on detection accuracy, principal component analysis (PCA) and autoencoder (AE) methods were compared.The sensitivity of the anomaly detection model based on PCA and AE methods were 84.2% (16/19) and 94.7% (18/19), respectively. The specificity of the anomaly detection model based on PCA and AE methods were 64.6% (102/158) and 69.0% (109/158), respectively. The areas under the receiver operating characteristic (ROC) curve (AUCs) based on PCA and AE methods were 0.81 and 0.90, respectively.The unsupervised learning method was effective for detecting anomalies from the feature samples. Accuracy could be improved with the introduction of AE-based DR technique. The combination of AE and k-means clustering methods provides an automated way to identify abnormal plans among clinical treatment plans in radiotherapy.
Blockchain Principles and Energy Consumption Concerns

The paper provides a chronology of blockchain and cryptoassets development. It describes the principles of Bitcoin, including blocks and transactions. Cryptocurrencies and smart contracts are enabled thanks to blockchain technology, but this technology considerably increases energy consumption and carbon footprint. Hence, the paper indicates how much energy is consumed during the past 2022 year by different blockchain platforms at world scale. The article points as well some prospects of blockchain larger deployment in the future with the aim of enhancing economic inclusion and development.
Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico

Geothermal energy is considered an essential renewable resource to generate flexible electricity. Geothermal resource assessments conducted by the U.S. Geological Survey showed that the southwestern basins in the U.S. have a significant geothermal potential for meeting domestic electricity demand. Within these southwestern basins, play fairway analysis (PFA), funded by the U.S. Department of Energy’s (DOE) Geothermal Technologies Office, identified that the Tularosa Basin in New Mexico has significant geothermal potential. This short communication paper presents a machine learning (ML) methodology for curating and analyzing the PFA data from the DOE’s geothermal data repository. The proposed approach to identify potential geothermal sites in the Tularosa Basin is based on an unsupervised ML method called non-negative matrix factorization with custom k-means clustering. This methodology is available in our open-source ML framework, GeoThermalCloud (GTC). Using this GTC framework, we discover prospective geothermal locations and find key parameters defining these prospects. Our ML analysis found that these prospects are consistent with the existing Tularosa Basin’s PFA studies. This instills confidence in our GTC framework to accelerate geothermal exploration and resource development, which is generally time-consuming.