Reviewerly/exHarmony huggingface dataset:
Designated-Verifier Aggregate Signature Scheme With Sensitive Data Privacy Protection for Permissioned Blockchain-Assisted IIoT

Aggregate signatures enable the sensor nodes of Industrial Internet of Things to send their signatures to the aggregator to realize signature compression. Before being stored in the data center, sensitive data and non-sensitive data should adopt different data processing methods in the process of sensor data fusion. In the high security analysis scenario of Industrial Internet of Things, only the verifier with a specified high security level can verify the resulting aggregate signature. So far, no one has explored how to ensure sensitive data privacy in the designated-verifier aggregate signatures. Motivated by it, this paper proposes a designated-verifier aggregate signature scheme (named DVAS) based on permissioned blockchain to achieve sensitive data privacy. In this scheme, the aggregator can be used not only to aggregate signatures, but also to sanitize data. Through smart contracts, the aggregator can sanitize the sensitive data according to the contract, and convert the original signature of the sensitive data into a valid signature. Therefore, DVAS can achieve elastic sensitive data privacy, not limited to encryption operations. The security attributes of DVAS include conditional anonymity, unforgeability, immutability and protecting data privacy. At the same time, DVAS realizes accountability through signature verification. Finally, the formal security proof, performance evaluation and experiments indicate that DVAS is secure, effective and practical for Industrial Internet of Things.
Artificial Intelligence Technologies for Evaluating Quality and Efficiency of Education

This work discusses the use of artificial intelligence technologies in the field of education. The use of artificial intelligence technology makes it possible to automate a number of processes. With the help of special algorithms, experts taught the computer to independently perform complex tasks. The author studies the history of the development of the idea of artificial intelligence, starting from ancient Greece, modern times and the Middle Ages, when scientists created inventions that could replace the human labor. The main research results are based on the authors’ conclusion that in the near future artificial intelligence technologies will be an important aspect in the field of education. As a result of the application of artificial intelligence technologies in the educational sphere, extensive opportunities will be available to higher education institutions. The features, the main goal of the educational process is the formation of educated and responsible citizens. At the same time, this creates a number of organizational aspects that require further discussion in the scientific field, which only confirms the significance of the research topic for the modern education system.
Learning by Reasoning: An Explainable Hierarchical Association Regularized Deep Learning Method for Disease Prediction

Multimodal healthcare data provides a huge opportunity for big-data-based disease prediction, supporting the diagnosis and treatment decision-making process for doctors. Many deep learning based methods are developed to yield better performance of multimodal data based disease prediction considering their powerful representation abilities. However, the black-box nature of deep learning methods results in many serious concerns: e.g. the reliability of the prediction performance is questionable; the end-users (i.e., doctors) can not understand the reasons behind the prediction. These issues make it difficult for deep learning based disease prediction systems to apply in practice. Therefore, we aim to tackle the aforementioned challenges and propose an explainable hierarchical association regularized deep learning method to produce interpretable prediction results while maintaining prediction accuracy. The method takes the multimodal healthcare data into consideration for the disease prediction task and constructs a hierarchical association path for each sample. An ingenious loss function is designed to learn consistent features among different data modalities and the disease prediction path with a hierarchical structure. The experimental results based on a public dataset show the superiority of our proposed method. The ablation study and sensitivity analysis verify the effectiveness and necessity of the method design.
Optimization Methods in General

As we have mentioned above, there is no one universal method for different types of optimization problems. We have a set of optimization methods (algorithms) suitable for individual types of problems. Many factors affect how an optimization problem can be effectively solved.
User Study on a Multi-view Environment to Identify Differences Between Biological Taxonomies

The classification of living organisms is essential to understand biodiversity and, therefore, to support its conservation. The taxonomic system classifies living organisms into hierarchically organized groups. Classification is not always a simple task, as even today taxonomists face the challenge that data are frequently revised, and species may be reclassified into other groups. Visualization of hierarchies, particularly biological taxonomies, has been a relevant area of research to support this challenge. Previous user studies determined that expert taxonomists preferred the edge-drawing method to visualize changes between two versions of a taxonomy, using the tool called Diaforá, which presents a single view. In this paper we propose a visualization that consists of using the coordinated multiple view (CMV) technique and focuses on displaying only the differences between two alternative versions of a biological taxonomy. The hypothesis is that the proposed visualization would provide a better cognitive approach to users, facilitating the identification of taxonomic changes. The work consisted first in designing other views and, through a user study, selecting one of them to be integrated into Diaforá to form a CMV system. Then, a second user study was carried out to contrast the original version of Diaforá with its extended version. Participants performed a series of tasks and we obtained measures of effectiveness, efficiency, trust rate and overall engagement rate. Results indicate that multiple coordinated views (CMV) improved users' performance in identifying differences.
The Study of User Experience Within Advertising in Virtual Reality

The growing adoption of virtual reality (VR) introduces marketing campaigns that are unlike traditional advertising techniques. The immersion of VR technology allows advertisers to create simulated interactions for products with a more significant impact than two-dimensional (2D) alternatives. Participants in this study underwent a series of games, embedded with advertisements, to better understand user sentiment between static and immersive advertisements. User surveys spatial data and time-based data show that users prefer and engage more with immersive advertisements rather than 2D banners which are more commonly represented in Web 2.0 environments. Gamifying brand interactions and rewarding user engagement yielded positive user feedback. In addition, this paper highlights key concerns around data security and privacy users may have with advertising marketing agencies should consider. This study outlines potential key mechanisms in driving experiential marketing tactics for virtual environments and increasing advertisement conversions.
Study on the Impact of Service Robot Autonomy on Customer Satisfaction

The rapid development of artificial intelligence technology has accelerated the promotion and application of service robot in the market. Although technology has provided service robot with increasingly autonomous functions, more research is needed on how service robot with different levels of autonomy affects customer satisfaction in service scenarios. Guided by the theory of affordance, this study examined whether service robot operational and decisional autonomy would have effects on customer satisfaction and explored explanatory mechanism. Adopting an experimental vignette method (EVM), the study reveals that direct effect of service robot operational autonomy and indirect effect of decisional autonomy on customer satisfaction, and functional affordance played a positive mediating role in the impact of service robot autonomy on customer satisfaction. The results extend and enrich the relevant literature on human-machine interaction and customer satisfaction research. Our results also provide marketing insights for enterprises to improve autonomous robot design and enhance customer relationships.
Distributed Ledger Technologies for the Energy Sector: Facilitating Interoperability Analysis

The use of distributed data storage and management structures, such as Distributed Ledger Technologies (DLT), in the energy sector has gained great interest in recent times. This opens up new possibilities in e.g. microgrid management, aggregation of distributed resources, peer-to-peer trading, integration of electromobility or proof-of-origin strategies. However, in order to benefit from those new possibilities, new challenges have to be overcome. This work focuses on one of these challenges, which is the need to ensure interoperability when integrating DLT-enabled devices in energy use cases. Firstly, the use of DLTs in the energy sector will be analyzed and the main use cases will be presented. Then, a classification of DLT-Energy use cases will be proposed. Secondly, the need for a common reference architecture framework to analyze those use cases with a focus on interoperability will be discussed and the current activities in research and standardization in this field will be presented. Finally, a new common reference architecture framework based on current activities in standardization will be presented.
Cloud Implementation of Extreme Learning Machine for Hyperspectral Image Classification

Classifying remotely sensed hyperspectral images (HSIs) became a computationally demanding task given the extensive information contained throughout the spectral dimension. Furthermore, burgeoning data volumes compound inherent computational and store challenges for data processing and classification purposes. Given their distributed processing capabilities, cloud environments have emerged as feasible solutions to handle these hurdles. This encourages the development of innovative distributed classification algorithms that take full advantage of the processing capabilities of such environments. Recently, computational-efficient methods have been implemented to boost network convergence by reducing the required training calculations. This paper develops a novel cloud-based distributed implementation of the Extreme Learning Machine (CC-ELM) algorithm for efficient HSI classification. The proposal implements a fault-tolerant and scalable computing design, whilst avoiding traditional batch-based back-propagation. CC-ELM has been evaluated over state-of-the-art HSI classification benchmarks, yielding promising results and proving the feasibility of cloud environments for large remote sensing and HSI data volumes processing. Code available on: https://github.com/mhaut/scalable-ELM-HSI.
Stochastic Parrots or Intelligent Systems? A Perspective on True Depth of Understanding in LLMs

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Selected Methods of Multidimensional Optimization

Rapid development of computer technology that has occurred in recent decades is closely linked to the development of computational algorithms capable of processing enormous amounts of data. In the past, many methods have been developed that can elegantly solve optimization problems and find an optimal solution, but these required strict fulfilment of prerequisites for an objective function and constraints very often. In addition, their effectiveness was hampered by the problem of large amounts of data because it was time consuming. Therefore, the question arose as to whether the algorithm was able to find a solution in polynomial time. In practice, moreover, it is sometimes necessary to obtain a solution in a relatively short time, so in some optimization problems the speed of calculation at the expense of the accuracy of the result, which in many cases was sufficient information, has begun to emphasize. This was the impetus for the development of modern optimization methods. Many of them can be classified as heuristic (discovery) algorithms. It is an algorithm that gradually searches (discovers) for better solutions. The advantage over explicit methods is the ease of input assumptions and the ability to cope with local extrema. The disadvantage is that in general, there is no evidence of their convergence to a global optimum. These methods include the Nelder-Mead method and other stochastic algorithms, e.g., differential evolution method and simulated annealing method.
The Accuracy of PV Power Plant Scheduling in Europe: An Overview of ENTSO-E Countries

The dynamic increase of Europe’s installed photovoltaic capacities does not leave any of the stakeholders of the electricity industry unaffected. One of the effects of the inevitable uncertainty of solar power generation, due to solar energy’s variable nature, is the increasing significance of the provision of reliable power generation forecasts to make the integration and operation of grid systems including photovoltaic power plants possible, as well as to facilitate the related processes of decision making and planning. This study presents the accuracy of the daily and the intraday scheduling of energy generation in the PV systems of the member countries of the European Network of Transmission System Operators (ENTSO-E) in the period 2013 – 2022. The examined countries showed great variation regarding the accuracy of the scheduling of their PV power plants. Overall, it could be established that Germany and Spain were capable of producing more precise forecasts than the other nations in the study. In their case, the annual downward and upward regulatory needs, as percentages of the yearly energy generation, ranged between 5.6% and 2.3% for the daily, and from 6.1% to 2.6% for the intraday forecasts. In some countries the forecasts demonstrated less accuracy, e.g. in Estonia the annual need for downward regulation nearly reached 47% of the energy generated in 2020, while in Switzerland the yearly need for upward regulation exceeded 42% of the produced power in 2018. The novel, practical contribution of the research presented in this paper is that it determines and presents information related to the accuracy of the ENTSO-E countries’ day-ahead and intraday PV forecasting activities that are relevant from a practical perspective to the transmission system operators (TSOs), the main actors of the energy market as well as the decision makers. Furthermore, this information is of help for investors investing in PV power plants not only from an economic perspective, but it may also significantly contribute to the market-related development of the management systems of energy storage solutions related to PV technology.
Spectral Indices to Assess Pollution Level in Soils: Case-Adaptive and Universal Detection Models for Multiple Heavy Metal Pollution Under Laboratory Conditions

This study developed case-adaptive spectral indices for detecting heavy metal pollution in skarn soil, hydrothermal soil, and acid mine drainage (AMD) soil, and a universal index for general cases by merging data from all cases. Sequential analyses were conducted including heavy metal concentration, mineral identification, grain size, and spectral characteristics. A factor analysis was used to combine multiple heavy metal elements into representative factors and used for establishing the spectral indices using the stepwise multiple linear regression (SMLR), which is compared with random forest regression (RFR). The spectral indices of skarn soil predict Zn, As, and Pb pollution from moderate to ultrahigh levels using absorption bands of skarn and supergene minerals. The spectral index for hydrothermal soil predicts pollution of Cr and Ni from very low to very high using absorption bands of hydrothermal alteration or supergene minerals. The index for the AMD soil predicts Al, Fe, and As pollution from very low to very high levels by employing the absorption features of iron hydroxide. The universal index predicts Fe, Zn, and Pb pollution levels from very low to ultrahigh by utilizing the clay and iron oxide absorptions, commonly observed in general soils. All models showed statistical significance with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.66-0.92 for SMLR and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.66-0.95 for RFR. Because our models were derived from large number of samples (600s) for detecting the pollution levels of multiple heavy metal elements for the first time, the models could provide a decision-making tool for soil survey in heavy metal pollution.
Ideology, humanness, and value. On Adorno’s Stravinsky

Theodor W. Adorno?s critique of Igor Stravinsky has itself been repeatedly criticised. Following the same line, the present article takes as its point of departure the philosophical anthropology of Helmuth Plessner, which challenges the premises of Marxist anthropology, on which Adorno based his critique of Stravinsky. Far from regressing to the inhuman and primitive, Stravinsky?s music affirms, in historically adequate modern terms, the constitutive reflectivity of the human embodied condition, thus becoming more ?human?, i.e. meaningful and expressive, than Adorno could have even conceived. Additionally, an account is provided of some groundbreaking musical qualities that underpin the artistic value of Stravinsky?s music, which Adorno also contested.
Deep Unrolling of Non-Linear Diffusion with Extended Morphological Laplacian

This paper presents a deep network based on unrolling the diffusion process with the morphological Laplacian. The diffusion process is an iterative algorithm that can solve the diffusion equation and represents time evolution with Laplacian. The diffusion process is applied to smoothing of images and has been extended with non-linear operators for various image processing tasks. In this study, we introduce the morphological Laplacian to the basic diffusion process and unwrap to deep networks. The morphological filters are non-linear operators with parameters that are referred to as structuring elements. The discrete Laplacian can be approximated with the morphological filters without multiplications. Owing to the non-linearity of the morphological filter with trainable structuring elements, the training uses error back propagation and the network of the morphology can be adapted to specific image processing applications. We introduce two extensions of the morphological Laplacian for deep networks. Since the morphological filters are realized with addition, max, and min, the error caused by the limited bit-length is not amplified. Consequently, the morphological parts of the network are implemented in unsigned 8-bit integer with single instruction multiple data set (SIMD) to achieve fast computation on small devices. We applied the proposed network to image completion and Gaussian denoising. The results and computational time are compared with other denoising algorithm and deep networks.
An Automatic Measurement Method of PCB Stub Based on Rotational Computed Laminography Imaging

Back drilling technique is useful for high-speed printed circuit board design. Currently, the stub length is measured by the destructive method that involves cutting the PCB and obtaining the results by optical imaging of the cut surface. X-ray computed laminography is an applicable technique for high-resolution imaging of plate-like object, which greatly contributes to stub nondestructive measurement. However, the inter-slice aliasing artifacts in the reconstructed images resulting from incomplete projection data make it difficult to positioning the trace layer and drilling tip. For better application to engineering, we propose to segment reconstructed slices (cross sections) of the PCB by self-training network and automatically locate key positions to measure stub length. Experiments on both simulated data and real data are carried out to verify the accuracy of the segmentation and measurement results for the proposed method. The proposed method can not only achieve nondestructive automatic rapid measurement, but obtain similar results to the state-of-art destructive method, which brings great value to via stub measurement in practical engineering applications.
A survey of medical image captioning technique: encoding, decoding and latest advance

Abstract： Medical image captioning is a labor-intensive daily task for radiologists nowadays.The emerging deep medical image captioning technique has its potential to generate medical captions automatically.There are some challenges to be resolved as mentioned below： 1) to organize a feasible and clear structure to readers； 2) to strengthen deep medical image caption task itself； 3) to optimize the introduced methods.First， the aims and objectives are identified.Then， literature is reviewed for the growth of deep medical image caption till 2021， including their latest methods， datasets and evaluation metrics， and comparative analysis between medical image caption task and generic image caption task.Deep image caption technique is introduced on the basis of prior network structure.Current deep medical image caption technique is mainly
Automating Data Personas for Designing Health Interventions

In user experience (UX) research, persona development can be particularly beneficial in designing digital health interventions. From our previous work, we identified challenges that diabetic patients face in adhering to guideline-recommended care through persona development. We developed data personas from Electronic Health Records (EHR) using two-step clustering and combined them with proto personas that were generated by a group of medical experts for the same patient population. Not only did our results prove beneficial for intervention design, but also highlighted fairness issues that may result from the underrepresentation of certain populations in EHR datasets. In our current paper, we validate the results of our prior work and we extend our previous work on persona development to build a model that can automatically associate a patient record to its representative data persona. The model was built using the results of the two-step clustering as the inputs. We believe that building such a model will help clinicians quickly trace new patient records to already developed personas, thereby assisting in immediate health interventions based on the health risk characteristics of the patient.
Optimization in Historical Context

Optimization problems and tasks are widespread in virtually all areas of human activity and decision-making processes (examples can be found in economics, logistics, industrial production, medicine, nature itself, etc.). Such was the historical development and formation of optimization as a scientific discipline that is relatively young. Studying the history of this area, we can see that there are many independent lines of research and separately solved issues known as optimal assignment, transportation problem, maximum flow, shortest spanning tree, shortest path, the traveling salesman problem, etc.