Reviewerly/exHarmony huggingface dataset:
Clinical Machine Learning Modeling Studies: Methodology and Data Reporting

Departments of Neurology (OMD) and Ophthalmology (OMD), Mayo Clinic, Scottsdale, Arizona; Departments of Neurology (JC) and Ophthalmology (JC), Mayo Clinic, Rochester, Minnesota; and School of Computing and Augmented Intelligence (YW), Arizona State University, Phoenix, Arizona. Address correspondence to Oana M. Dumitrascu, MD, MSc, Mayo Clinic Scottsdale: Mayo Clinic Arizona, 13400 E. Shea Boulevard, Scottsdale, AZ 85259; E-mail: [email protected] The authors report no conflicts of interest.
Improved Hypercube Optimisation Search Algorithm for Optimisation of High Dimensional Functions

This paper proposes a stochastic search algorithm called improved hypercube optimisation search (HOS+) to find a better solution for optimisation problems. This algorithm is an improvement of the hypercube optimisation algorithm that includes initialization, displacement-shrink and searching area modules. The proposed algorithm has a new random parameters (RP) module that uses two control parameters in order to prevent premature convergence and slow finishing and improve the search accuracy considerable. Many optimisation problems can sometimes cause getting stuck into an interior local optimal solution. HOS+ algorithm that uses a random module can solve this problem and find the global optimal solution. A set of experiments were done in order to test the performance of the algorithm. At first, the performance of the proposed algorithm is tested using low and high dimensional benchmark functions. The simulation results indicated good convergence and much better performance at the lowest of iterations. The HOS+ algorithm is compared with other meta heuristic algorithms using the same benchmark functions on different dimensions. The comparative results indicated the superiority of the HOS+ algorithm in terms of obtaining the best optimal value and accelerating convergence solutions.
Art and sustainability: can digital technologies achieve sustainability?

Purpose This paper discusses art and sustainability in the 21st century; therefore, we can't ignore the huge technological revolution that currently exists. Art throughout history has been associated to religion, politics and economics, but now it is also connected to technology. Hence, the question arises: Can digital technologies achieve sustainability? In this regard, the research will address the effective role of digital technology in achieving sustainability in art field and its practices. Design/methodology/approach The main research material is derived from my interviews with artists, professors, curators, gallerists and art historians. In addition to the references of sustainability in various fields that are related to art, as Sustainability has become a goal that all sectors are striving to achieve to preserve our planet, as much as we can, especially in the field of art which is closely related to the environment. Findings This paper explores the possibility of achieving sustainability in art field and its practices through digital technologies. In addition, it reveals that countries that have developed digital technologies are able to apply the digital technologies in art and its practices to achieve sustainability, in contrast, developing countries they could not achieve sustainability through digital technologies. Originality/value As part of a joint research project between a developing and developed country, the paper clarifies the different opinions from 14 countries about the extent to which sustainability in art is achieved through digital technology. It also outlines some successful and unsuccessful experiences in achieving sustainability in art.
A simple and effective way to evaluate the accuracy of digital image correlation combined with scanning electron microscopy (SEM-DIC)

Digital image correlation (DIC) is a powerful technique enabling mapping of local strain fields with the spatial resolution down to a few nanometers when combined with scanning electron microscopy. However, the accuracy of strain determination is frequently unknown due to a large number of experimental parameters influencing it. Here we present a simple and time-effective method for quantitative evaluation of uncertainties in strain values obtained by DIC. By imposing a fictive hydrostatic strain a high absolute accuracy of mean strain determination is demonstrated with deviations from the target value below 0.03%. At the same time, the precision decreases strongly with the reduction of the subset and step sizes reflecting a fundamental tradeoff between the spatial resolution and trueness of local strain values.
Web XR User Interface Study in Designing 3D Layout Framework in Static Websites

With computer technology rapidly expanding out in recent years, there is a significant trend that Virtual Reality, Augmented Reality, and Mixed Reality technologies turn into the public. The paper describes Web Extended Reality (XR) and its current circumstance. The research discussed the advantages of web componentization and Page Builder System, which is the famous framework for web componentization. Furthermore, the research analyzes the characteristic of XR. The research designs the Web XR User Interface principles with XR characteristics and componentization design. The principles cover three aspects include main content, scrollbar, and navigation. The research develops the prototype to examine the concept. The prototype shows that the UI principles can provide an immersive user experience. The paper indicates the possible future view based on the design study.
Construction of large-scale equipment sharing cloud service platform based on Wechat Mini Program

With the continuous progress of "Internet +", the development of mobile apps has facilitated people's travel and life. The Wechat Mini Program is simple, fast, and lightweight, allowing users to open the desired application by scanning or searching. Therefore, based on the Wechat Mini Program vantWeapp framework and so on, this paper adopts WeChat developer tools to implement a Wechat Mini Program of large-scale instrument sharing cloud service platform. Rapid appointment of large-scale equipment and scientific and technological services is realized through mobile internet technology, and a full coverage pattern from PC to mobile terminal of scientific instrument resource sharing service is formed to realize "technical services at your fingertips".
Multisource Deep Transfer Learning Based on Balanced Distribution Adaptation

The current traditional unsupervised transfer learning assumes that the sample is collected from a single domain. From the aspect of practical application, the sample from a single-source domain is often not enough. In most cases, we usually collect labeled data from multiple domains. In recent years, multisource unsupervised transfer learning with deep learning has focused on aligning in the common feature space and then seeking to minimize the distribution difference between the source and target domains, such as marginal distribution, conditional distribution, or both. Moreover, conditional distribution and marginal distribution are often treated equally, which will lead to poor performance in practical applications. The existing algorithms that consider balanced distribution are often based on a single-source domain. To solve the above-mentioned problems, we propose a multisource transfer learning algorithm based on distribution adaptation. This algorithm considers adjusting the weights of two distributions to solve the problem of distribution adaptation in multisource transfer learning. A large number of experiments have shown that our method MTLBDA has achieved significant results in popular image classification datasets such as Office-31.
Polygon formation of multiple nonholonomic mobile robots with double-level-control collision avoidance scheme

This paper considers a polygon formation control of multiple robots with nonholonomic constraints enclosing a goal target and double-level-control collision avoidance scheme. Double-level-control scheme consisted of upper-level and lower-level controls are proposed for trajectory generation and tracking control of multi-robot systems. Both upper-level and lower-level controls operate collision avoidance mechanisms based on potential functions. The proposed control scheme guarantees that the group of robots are kept in the polygon formation and driven to a goal, while avoiding collisions during the travel. Moreover, the designed interaction between the upper- and lower-level controls guarantees that the mobile robots are not trapped in local minima or deadlock case. Experiments of the formation of three-robots are conducted to show the performance of the mobile robots in accomplishing a polygon formation while achieving the goal without any collision and no local minima.
Local Path Planning Method for Unmanned Vehicle Based on Model Predictive Control in Hospital Environment

Abstract In order to address the obstacle avoidance problem of driverless vehicles in hospital environment, a local path planning method based on model predictive control is proposed. Firstly, the potential field model of driving environment factors including obstacles, environmental vehicles, roads, and target points is established by using artificial potential field theory. Then, based on model predictive control algorithm combined with driving environment potential field, trajectory planning and tracking are transformed into a unified constrained optimization problem. The objective function and constraint conditions of local path planning for unmanned vehicles are designed, and roll is introduced to the dynamic optimization mechanism. The simulation results show that the error between the path planning and the expected path is less than 0.1 m, the time consumption is at least 3.3 s, and it has strong robustness, which can effectively solve the obstacle avoidance problem of local path of unmanned vehicles.
Two flexible translation-based models for knowledge graph embedding

Knowledge Graph Embedding (KGE), which aims to embed the entities and relations of a knowledge gxraph into a low-dimensional continuous space, has been proven to be an effective method for completing a knowledge graph and improving the quality of the knowledge graph. The translation-based models represented by TransE, TransH, TransR and TransD have achieved great success in this regard. There is still potential for improvement in dealing with complex relations. In this paper, we find that the lack of flexibility in entity embedding limits the model’s ability to model complex relations. Therefore, we propose single-directional-flexible (sdf) models and multi-directional-flexible (mdf) models to increase the flexibility and expressiveness of entity embeddings. These two methods can be applied to the TransD model and its variant models without increasing any time cost and space cost. We conduct experiments on benchmarks such as WN18 and FB15k. The experimental results show that the models significantly surpasses the classical translation models in both tasks of triplet classification and link prediction. In particular, for Hits@1 of link prediction of WN18, we get 71.7% after applying our method to TransD, which is much better than 24.1% of TransD.
From alchemy to modern mineralogy: dating mineral collections via chemical notation

Mineral specimens found in historical collections often include specimen labels, which may provide vital information on the nature, chemistry and origin of the material. However, the evolution of chemical notation, particularly during the eighteenth and nineteenth centuries, led to a wide range of ways in which a given sample could be adequately documented, many of which may still be found in collections in the present day. Prior to the advent of modern mineralogy, samples were labelled using a complex and sometimes baffling language of alchemical symbols with little meaning to modern scientists. The efforts of notable chemists and mineralogists, such as Torbern Bergman, Antoine Lavoisier, John Dalton and Jöns Jacob Berzelius, began to reform and unify chemical nomenclature, providing a number of new terms, symbols and approaches to the description of materials. With the advent of atomic theory in the nineteenth century, new classification schemes based on quantitatively describing the atomic arrangement and composition of minerals were proposed, representing a significant step towards modern mineralogy. Understanding the development of chemical notation over time not only facilitates the identification of mineral specimens but may also provide clues as to the date that the sample was documented, and potentially even the location.
From 2D to 3D: Facilitating Single-Finger Mid-Air Typing on Virtual Keyboards with Probabilistic Touch Modeling

Mid-air text entry on virtual keyboards suffers from the lack of tactile feedback, bringing challenges to both tap detection and input prediction. In this poster, we demonstrated the feasibility of efficient single-finger typing in mid-air through probabilistic touch modeling. We first collected users&#x0027; typing data on different sizes of virtual keyboards. Based on analyzing the data, we derived an input prediction algorithm that incorporated probabilistic touch detection and elastic probabilistic decoding. In the evaluation study where the participants performed real text entry tasks with this technique, they reached a pick-up single-finger typing speed of 24.0 WPM with 2.8&#x0025; word-level error rate.
Recovery from Power Sums

We study the problem of recovering a collection of n numbers from the evaluation of m power sums. This yields a system of polynomial equations, which can be underconstrained (m < n), square (m = n), or overconstrained (m > n). Fibers and images of power sum maps are explored in all three regimes, and in settings that range from complex and projective to real and positive. This involves surprising deviations from the Bézout bound, and the recovery of vectors from length measurements by p-norms.
Machine Learning-based End-to-End QoE Monitoring Using Active Network Probing

Video on Demand (VoD) is responsible for a significant amount of traffic on IP networks. To meet users&#x0027; expectations, network operators need means to monitor and to identify when service quality is degraded in order to take actions to avoid customer churn. Many proposals in the literature correlate network Quality of Service (QoS) metrics with indicators of user Quality of Experience (QoE). However, most solutions cannot monitor end-to-end conditions without modification on video player applications or require deep packet inspection techniques, which may raise privacy issues. In previous work, we proposed a method to estimate QoE using active ICMP probing, which is widely supported by network devices and can be used for end-to-end network measurements. In this work, we improve our previous method by adding a secondary model that operates over the first step of QoE inferences. We also extend the evaluation of our approach by using two wireless and wired testbeds, reporting our results for different end-to-end setups subject to distinct connectivity conditions. Finally, we identify and discuss the advantages and limitations of our methods and assess their suitability in real-world production deployments.
An End-to-end Entity and Relation Joint Extraction Model for Cyber Threat Intelligence

Entity and relation extraction of Cyber Threat Intelligence (CTI) is the basis of building the threat intelligence knowledge graph. Considering the transmission errors in the traditional pipeline model and low accuracy in the extraction of overlapping relational entities, this paper proposes an end-to-end joint extraction model of CTI. First, entity and relation extraction are modeled as a sequential tagging task through a joint tagging strategy. Then, an end-to-end sequence tagging model based on BERT-att-BiLSTM-CRF is constructed to realize the joint extraction of entities and relations. Finally, we extract knowledge triples according to entity and relation matching rules. The experimental results show that compared with other methods, in the large-scale CTI dataset, the joint extraction model proposed in this paper outperforms other models in knowledge triple extraction. F1 score increased by 5.4%, which verifies the effectiveness of this model in information extraction for CTI.
A Detection Algorithm for Audio Adversarial Examples in EI-Enhanced Automatic Speech Recognition

Benefiting from the development of big data, edge computing, and deep learning, splendid breakthroughs have been made in automatic speech recognition (ASR) in recent years. Since then, more and more smart products have chosen speech as the interface for human-computer interaction, which causes popularity of edge intelligence (EI) enhanced automatic speech recognition. While people are enjoying the social changes brought by speech recognition technology, a factor of instability quietly emerged called audio adversarial example which is a type of audio deliberately generated by attackers via adding subtle perturbations to the original audio signal. The added perturbations which sound like certain noise that cannot be precepted by human but will cause ASR system make wrong transcription. Three detection algorithms for audio adversarial examples are proposed in this thesis, namely, the robust detection algorithm based on WER (word error rate), the feature detection algorithm based on ADR (adversarial ratio), and the collaborative detection algorithm based on neural network. The experiment results show that three detection algorithms proposed in this thesis have a great discrimination on audio adversarial examples and achieve high AUC scores. Among them, the cooperative detection is the best and the feature detection is the worst. In addition, we found that robust detection algorithm tends to have a higher accuracy score but a lower recall score, while feature detection algorithm tends to have the converse performance. Moreover, since the proposed collaborative detection method combines the advantages of the robust detection and feature detection methods, it presents a better performance with respect to accuracy, recall, and F1 score.
Incremental robust PCA for vessel segmentation in DSA sequences

In intervention surgery, DSA images provide a new way to observe the vessels and catheters inside the patient. Extracting coronary artery from the dynamic complex background fast improves the effectiveness directly in clinical interventional surgery. This article proposes an incremental robust principal component analysis (IRPCA) method to extract contrast-filled vessels from x-ray coronary angiograms. RPCA is a matrix decomposition method that decomposes a video matrix into foreground and background, commonly used to model complex backgrounds and extract target objects. IRPCA pre-optimizes an x-ray image sequence. When a new x-ray sequence is received, IRPCA optimizes it based on the pre-optimized matrix according to the strategy of minimizing the energy function to obtain the foreground matrix of the new sequence. Besides, based on the idea that the new x-ray sequence introduces new information to the pre-optimized matrix, we propose UIRPCA to improve the performence of IRPCA. Compared with the traditional RPCA method, IRPCA and UIRPCA save much time while ensuring that other indicators remain basically unchanged. The experiment results based on real data show the superiority of the proposed method over other RPCA algorithms.
Product label identification with OCR for model‐specific reuse, repair, and recycling: A case study for washing machines

Abstract In this study, a procedure to automatically identify the product model by evaluating an image of the label is presented. First, object character recognition (OCR) extracts text from the product label. Second, to identify the product model, the extracted text is compared with unique model references in a database, giving access to other model‐specific information. For this comparison, a novel variation of the partial ratio matching algorithm was developed. The product‐model identification procedure is integrated into an interactive web application, which allows for model identification to be performed during preparation for reuse, repair, and recycling: The SmartRe application. Three datasets consisting of 466, 422, and 771 images of washing machine product labels were gathered in collaboration with a (1) professional repair company for consumer devices, (2) a nonprofit repair and reuse center that resells devices in second‐hand stores, and (3) a large recycling company. Results demonstrate that 96%, 91%, and 40% of the product models were correctly read from the product label with OCR, respectively. Of these recognized models, 51%, 88%, and 76% were successfully identified with the SmartRe application by comparing the extracted text with the model database. Further analysis also demonstrated that 72% of the washing machine models identified at the nonprofit repair and reuse center were also found at the recycling facility and that 12% of these models are predicted to be less than 10‐years‐old. This highlights the potential of the SmartRe application to assist in product triage for reuse at recycling centers.
A Decentralized Digital Voting System Based on Block chain Architecture

Voting is the backbone of democracy and the fundamental right of every citizen. BlockChain based Election is like a boon for every nation through which the election can be conducted digitally, Unlike those old (paper based) and traditional (EVM) voting systems it makes the whole process of election safe, smooth and easy. In this era of Covid-19 this Block chain enabled election is the need of the hour, People can cast votes from their own space just with the help of a mobile phone or a computer, Security would get enhanced and threats like EVM hacking, Chaos at election booth would reduce drastically just by the implementation of this advanced voting system. Personal ID&#x0027;s and unique keys would be provided to each and every eligible voter which can&#x0027;t be tampered at any cost. It has two modules to make the entire project look consolidated and unified. First module is the Election Commission who could be liable for undertaking it, appending concerned everyone competing for the voting attached under blockchain. User end will be the resident&#x0027;s component where every eligible voter can choose a leader according to their separate Constituent sitting and the votes would get registered on the blockchain to make it tamper protected.
Survey of Blockchain Methodologies in The Healthcare Industry

Blockchain has been the focus of the research area in the past few years. It is considered the foundation of cryptocurrencies such as Bitcoin and Ethereum. Decentralization is the main feature of Blockchain. Blockchain provides a higher level of security and scalability. Major industries have started to consider blockchain on their platforms such as the healthcare industry which uses the blockchain to secure the patients&#x0027; data, make them anti-tampering and keep track of their Electronic Health Records (EHRs). The work in this paper provides a state of art of different blockchain types and consensus mechanisms. Also, the paper recommends some guidelines to select the appropriate blockchain type, consensus mechanism and blockchain interoperability method that fulfills the healthcare industry requirements.