Reviewerly/exHarmony huggingface dataset:
Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing

The COVID-19 pandemic has changed the way we practice medicine. Cancer patient and obstetric care landscapes have been distorted. Delaying cancer diagnosis or maternal-fetal monitoring increased the number of preventable deaths or pregnancy complications. One solution is using Artificial Intelligence to help the medical personnel establish the diagnosis in a faster and more accurate manner. Deep learning is the state-of-the-art solution for image classification. Researchers manually design the structure of fix deep learning neural networks structures and afterwards verify their performance. The goal of this paper is to propose a potential method for learning deep network architectures automatically. As the number of networks architectures increases exponentially with the number of convolutional layers in the network, we propose a differential evolution algorithm to traverse the search space. At first, we propose a way to encode the network structure as a candidate solution of fixed-length integer array, followed by the initialization of differential evolution method. A set of random individuals is generated, followed by mutation, recombination, and selection. At each generation the individuals with the poorest loss values are eliminated and replaced with more competitive individuals. The model has been tested on three cancer datasets containing MRI scans and histopathological images and two maternal-fetal screening ultrasound images. The novel proposed method has been compared and statistically benchmarked to four state-of-the-art deep learning networks: VGG16, ResNet50, Inception V3, and DenseNet169. The experimental results showed that the model is competitive to other state-of-the-art models, obtaining accuracies between 78.73% and 99.50% depending on the dataset it had been applied on.
Console-Based Mapping of Mongolia Using GMT Cartographic Scripting Toolset for Processing TerraClimate Data

This paper explores spatial variability of the ten climatic variables of Mongolia in 2019: average minimal and maximal temperatures, wind speed, soil moisture, downward surface shortwave radiation (DSRAD), snow water equivalent (SWE), vapor pressure deficit (VPD), vapor pressure anomaly (VAP), monthly precipitation and Palmer Drought Severity Index (PDSI). The PDSI demonstrates the simplified soil water balance estimating relative soil moisture conditions in Mongolia. The research presents mapping of the climate datasets derived from TerraClimate open source repository of the meteorological and climate measurements in NetCDF format. The methodology presented the compiled observations of Mongolia visualised by GMT coding approach using Generic Mapping Tools (GMT) cartographic scripting toolset. The results present 10 new maps of climate data over Mongolia made using automated cartographic techniques of GMT. Spatial environmental and climate analysis were conducted which determine relative distribution of PDSI and temperature extremes, precipitation and soil moisture, wind speed and DSRAD. The DSRAD showed minimum at 40 Wm−2, maximum at 113 Wm−2 in the Gobi Desert region, SWE (up to 491 mm), VAP and VPD compared with landmass parameters represent powerful cartographic tools to address complex regional climate and environmental issues in Mongolia, a country with contrasting topography, extreme climate conditions and unique environmental setting.
Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method

The support vector classifier (SVC) is one of the most powerful machine learning algorithms. This algorithm has been accepted as an effective method in three-dimensional geological modeling. Although the model selection has a great impact on the performance of SVC algorithm, most of mining studies have neglected it and used the grid search method. Therefore, in this study, a new approach is proposed for improving the selection of SVC models. This approach uses particle swarm optimization (PSO) to determine the important parameters of SCV such as penalty and kernel parameters. The proposed approach was applied in the modeling process of the Iju porphyry copper deposit to delineate alteration and mineralization zones. The optimal penalty and kernel parameters were found to be 27.2 and 2−4.75 for alteration zone, and 22.72 and 2−6.23 for mineralization zone, respectively. With 97.4% and 97.01% rates of accuracy for mineralization and alteration zones, the PSO results showed reasonable performance in classification. The proposed approach had better accuracy than grid search method. Therefore, because of its better performance, the geological models were developed using the PSO method to be used as a basis for future resource evaluation.
Highly sensitive real-time detection of phase change process based on photonic spin Hall effect

Phase change materials, such as vanadium dioxide (VO2) and Titanium dioxide (Ti2O3) have received extensive attention because of the dramatic changes in their intrinsic properties during phase transitions. However, due to the rapid transition rate and wide dynamics, monitoring of processes is challenging. Previous detection methods are lack of speed and simplicity and require multiple interventions, which largely introduce human factors influencing the results and make it difficult to guarantee the accuracy and visualization. In this paper, the photonic spin Hall effect is used for real-time detection and highly sensitive analysis of the phase transition process of VO2 films. By incorporating with quantum weak measurement, the photonic spin-Hall shift acts as the pointer, and the phase transition process of VO2 is characterized effectively. The high measurement resolution with 63 S/(m μm) is achieved due to weak-value amplification. In our scheme, it does not involve any mechanical adjustment of optical components, thus enabling real-time, visual, non-contact detection of dynamic phase transition processes.
Public-key Authenticated Encryption with Keyword Search

With the rapid development of cloud computing, an increasing number of companies are adopting cloud storage technology to reduce overhead. However, to ensure the privacy of sensitive data, the uploaded data need to be encrypted before being outsourced to the cloud. The concept of public-key encryption with keyword search (PEKS) was introduced by Boneh et al. to provide flexible usage of the encrypted data. Unfortunately, most of the PEKS schemes are not secure against inside keyword guessing attacks (IKGA), so the keyword information of the trapdoor may be leaked to the adversary. To solve this issue, Huang and Li presented public key authenticated encryption with keyword search (PAEKS) in which the trapdoor generated by the receiver is only valid for authenticated ciphertexts. With their seminal work, many PAEKS schemes have been introduced for the enhanced security of PAEKS. Some of them further consider the upcoming quantum attacks. However, our cryptanalysis indicated that in fact, these schemes could not withstand IKGA. To fight against the attacks from quantum adversaries and support the privacy-preserving search functionality, we first introduce a novel generic PAEKS construction in this work. Then, we further present the first quantum-resistant PAEKS instantiation based on lattices. The security proofs show that our instantiation not only satisfies the basic requirements but also achieves enhanced security models, namely the multi-ciphertext indistinguishability and multi-trapdoor privacy. Furthermore, the comparative results indicate that with only some additional expenditure, the proposed instantiation provides more secure properties, making it suitable for more diverse application environments.
A Wind Turbine Fault Classification Model Using Broad Learning System Optimized by Improved Pelican Optimization Algorithm

As a classification model, a broad learning system is widely used in wind turbine fault diagnosis. However, the setting of hyperparameters for the models directly affects the classification accuracy of the models and it generally relies on practical experience and prior knowledge. In order to effectively solve the problem, the parameters of the broad learning system such as the number of feature nodes, the number of enhancement nodes, and the number of mapped features layer were optimized by the improved pelican optimization algorithm, and a classification model was built based on the broad learning system optimized by the improved pelican optimization algorithm. The classification accuracy of the proposed model was the highest and reached 98.75%. It is further shown that compared with the support vector machine, deep belief networks, and broad learning system models optimized by particle swarm optimization algorithm, the proposed model effectively improves the accuracy of wind turbine fault diagnosing.
Research on information sharing incentive mechanism of China's port cold chain logistics enterprises based on blockchain

Blockchain technology resolves the bottleneck of traditional centralized data storage technology. It can better facilitate the seamless operation and supervision of port cold chain logistics and can meet the massive business demand for cold chain imports and the surging demand for pandemic prevention and control. However, it is a challenging task to incentivize cold chain enterprises in the maritime industry to adopt blockchain technology and actively participate in information sharing platform. Using evolutionary game theory, through an analysis of evolutionary stability strategies, this paper explores the evolutionary path of port cold chain logistics enterprises that participate in information sharing. An incentive mechanism based on blockchain smart contract is designed to encourage cold chain logistics enterprises to actively participate in information sharing within chains. Smart contracts can continuously encourage enterprises to participate in information sharing by timely adjusting the incentive participation costs of participating enterprises.
The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support

Smart contracts are self-executing programs that run on the blockchain and make it possible for peers to enforce agreements without a third-party guarantee. The smart contract on Ethereum is the fundamental element of decentralized finance with billions of US dollars in value. Smart contracts cannot be changed after deployment and hence the code needs to be verified for potential vulnerabilities. However, smart contracts are far from being secure and attacks exploiting vulnerabilities that have led to losses valued in the millions. In this work, we explore the current state of smart contracts security, prevalent vulnerabilities, and security-analysis tool support, through reviewing the latest advancement and research published in the past five years. We study 13 vulnerabilities in Ethereum smart contracts and their countermeasures, and investigate nine security-analysis tools. Our findings indicate that a uniform set of smart contract vulnerability definitions does not exist in research work and bugs pertaining to the same mechanisms sometimes appear with different names. This inconsistency makes it difficult to identify, categorize, and analyze vulnerabilities. We explain some safeguarding approaches and best practices. However, as technology improves new vulnerabilities may emerge. Regarding tool support, SmartCheck, DefectChecker, contractWard, and sFuzz tools are better choices in terms of more coverage of vulnerabilities; however, tools such as NPChecker, MadMax, Osiris, and Sereum target some specific categories of vulnerabilities if required. While contractWard is relatively fast and more accurate, it can only detect pre-defined vulnerabilities. The NPChecker is slower, however, can find new vulnerability patterns.
Optimization of constraint engineering problems using robust universal learning chimp optimization

Due to the challenging constraint search space of real-world engineering problems, a variation of the Chimp Optimization Algorithm (ChOA) called the Universal Learning Chimp Optimization Algorithm (ULChOA) is proposed in this paper, in which a unique learning method is applied to all previous best knowledge obtained by chimps (candid solutions) to update prey’s positions (best solution). This technique preserves the chimp’s variety, discouraging early convergence in multimodal optimization problems. Furthermore, ULChOA introduces a unique constraint management approach for dealing with the constraints in real-world constrained optimization issues. A total of fifteen commonly recognized multimodal functions, twelve real-world constrained optimization challenges, and ten IEEE CEC06-2019 suit tests are utilized to assess the ULChOA's performance. The results suggest that the ULChOA surpasses sixteen out of eighteen algorithms by an average Friedman rank of better than 78 percent for all 25 numerical functions and 12 engineering problems while outperforming jDE100 and DISHchain1e + 12 by 21% and 39%, respectively. According to Bonferroni-Dunn and Holm's tests, ULChOA is statistically superior to benchmark algorithms regarding test functions and engineering challenges. We believe that the ULChOA proposed here may be utilized to solve challenges requiring multimodal search spaces. Furthermore, ULChOA is more widely applicable to engineering applications than competitor benchmark algorithms.
Transforming healthcare ecosystems through blockchain: Opportunities and capabilities for business process innovation

Blockchain is an unprecedented enabler and booster of business process innovation, especially in the healthcare sector. Nonetheless, its transformational potential has yet to be fully harnessed because of the challenges that digitalization has posed to Business Process Management (BPM) and the inherent complexity of the healthcare processes. Accordingly, through the ambidextrous BPM theoretical lenses, this paper investigates what are the main opportunities for Blockchain-driven healthcare process innovation and which BPM capabilities may unleash them. Two exploratory case studies were conducted to probe the Blockchain-driven process redesign: a case of exploitative BPM, focusing on drug logistics, and a case of explorative BPM, focusing on integrated home care. Our findings suggest that Blockchain may enable Healthcare innovation opportunities, such as the redesign of the information flow and the development of new forms of collaboration in the health ecosystem. In addition, the most relevant BPM capabilities to pursue such innovations were identified. These results integrate the BPM research stream and enhance the understanding of Blockchain-driven healthcare process innovation.
Bao

Recent efforts applying machine learning techniques to query optimization have shown few practical gains due to substantive training overhead, inability to adapt to changes, and poor tail performance. Motivated by these difficulties, we introduce Bao (the Bandit optimizer). Bao takes advantage of the wisdom built into existing query optimizers by providing per-query optimization hints. Bao combines modern tree convolutional neural networks with Thompson sampling, a well-studied reinforcement learning algorithm. As a result, Bao automatically learns from its mistakes and adapts to changes in query workloads, data, and schema. Experimentally, we demonstrate that Bao can quickly learn strategies that improve end-to-end query execution performance, including tail latency, for several workloads containing longrunning queries. In cloud environments, we show that Bao can offer both reduced costs and better performance compared with a commercial system.
The Big Data Paradox in Clinical Practice

The big data paradox is a real-world phenomenon whereby as the number of patients enrolled in a study increases, the probability that the confidence intervals from that study will include the truth decreases. This occurs in both observational and experimental studies, including randomized clinical trials, and should always be considered when clinicians are interpreting research data. Furthermore, as data quantity continues to increase in today's era of big data, the paradox is becoming more pernicious. Herein, I consider three mechanisms that underlie this paradox, as well as three potential strategies to mitigate it: (1) improving data quality; (2) anticipating and modeling patient heterogeneity; (3) including the systematic error, not just the variance, in the estimation of error intervals.
Hand gesture recognition framework using a lie group based spatio-temporal recurrent network with multiple hand-worn motion sensors

The primary goal of hand gesture recognition with wearables is to facilitate the realization of gestural user interfaces in mobile and ubiquitous environments. A key challenge in wearable-based hand gesture recognition is the fact that a hand gesture can be performed in several ways, with each consisting of its own configuration of motions and their spatio-temporal dependencies. However, the existing methods generally focus on the characteristics of a single point on hand, but ignores the diversity of motion information over hand skeleton, and as a result, they suffer from two key challenges to characterize hand gestures over multiple wearable sensors: motion representation and motion modeling. This leads us to define a spatio-temporal framework, named STGauntlet, that explicitly characterizes the hand motion context of spatio-temporal relations among multiple bones and detects hand gestures in real-time. In particular, our framework incorporates Lie group-based representation to capture the inherent structural varieties of hand motions with spatio-temporal dependencies among multiple bones. To evaluate our framework, we developed a hand-worn prototype device with multiple motion sensors. Our in-lab study on a dataset collected from nine subjects suggests our approach significantly outperforms the state-of-the-art methods with the achievement of 98.2% and 95.6% average accuracies for subject dependent and independent gesture recognition, respectively. Specifically, we also show in-wild applications that highlight the interaction capability of our framework.
Part I: A friendly introduction to latent class analysis

Latent class analysis (LCA) offers a powerful analytical approach for categorizing groups (or "classes") within a heterogenous population. LCA identifies these hidden classes by a set of predefined features, known as "indicators". Unlike many other grouping analytical approaches, LCA derives classes using a probabilistic approach. In this first paper, we describe the common applications of LCA, and outline its advantages over other analytical subgrouping methods.
Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap

Recently, many researchers have been interested in the application of chaos in cryptography. Specifically, numerous research works have been focusing on chaotic image encryption. A comprehensive survey can highlight existing trends and shed light on less-studied topics in the area of chaotic image encryption. In addition to such a survey, this paper studies the main challenges in this field, establishes an ecosystem for chaotic image encryption, and develops a future roadmap for further research in this area.
The Role of Personalization in the User Experience, Preferences and Engagement with Virtual Reality Environments for Relaxation

Virtual Reality Environments (VREs) are widely deployed in mental health treatments, often associated with relaxation techniques. The personalization of natural VR-based scenarios is a key element that can further facilitate users' sense of presence and relaxation. This study explored the role of VREs' personalization in the user experience with an environment supporting relaxation, by deploying mixed methods.A non-clinical sample of 20 individuals participated in exposure to a supportive body-scan-guided relaxation VRE. In the personalized conditions, the participants had the option of choosing the context (e.g., sea, mountain, or countryside) and including in the scenario different types of sounds, visual elements, and changing the time of day and weather. In the standard conditions, individuals were exposed to a relaxing VRE, but they could choose neither the context nor the auditory and visual elements. The order of presentation of the personalized vs non-personalized environments was randomized. Measures regarding relaxation, state-anxiety perceived levels, VRE-related symptoms, the usability of the Virtual Reality (VR) setting, sense of presence, pleasure, activation, engagement, and level of immersion experienced were collected before and after exposure to the VR environments.Findings showed that personalized VREs were preferred by users. Participants generally preferred to experience a greater immersivity, pleasure, engagement, and relaxation in the personalized virtual settings.The study further confirms the role of personalization as a component positively contributing to relaxation and engagement. Future research may further assess this effect in the context of large-scale controlled studies involving clinical and non-clinical populations.
Determinants of innovation outcomes: The role of institutional quality

This paper uses unconditional quantile regression analysis to interrogate the effects of institutional quality on innovation outcomes. We assess important determinants of innovation such as property rights (including enforcement of intellectual property rights), human resources within R&D and high-technology exports. Regarding intellectual property rights, while most previous research focuses on patent strength (de jure or book law), we focus on de facto patent enforcement. Using data from the World Bank, the Heritage Foundation and a new patent enforcement index, we construct a panel of fifty countries covering 1998–2017. Our analysis reveals important new insights including the strongly negative impact of patent enforcement and human resources within R&D on less innovative economies, and the varying impacts across quantiles for key variables such as high-technology exports. We find that both stronger institutions and patent enforcement are not necessarily the best route to boosting innovation, especially in economies where existing innovative capacity is weak
Inertial-Measurement-Unit-Based Novel Human Activity Recognition Algorithm Using Conformer

Inertial-measurement-unit (IMU)-based human activity recognition (HAR) studies have improved their performance owing to the latest classification model. In this study, the conformer, which is a state-of-the-art (SOTA) model in the field of speech recognition, is introduced in HAR to improve the performance of the transformer-based HAR model. The transformer model has a multi-head self-attention structure that can extract temporal dependency well, similar to the recurrent neural network (RNN) series while having higher computational efficiency than the RNN series. However, recent HAR studies have shown good performance by combining an RNN-series and convolutional neural network (CNN) model. Therefore, the performance of the transformer-based HAR study can be improved by adding a CNN layer that extracts local features well. The model that improved these points is the conformer-based-model model. To evaluate the proposed model, WISDM, UCI-HAR, and PAMAP2 datasets were used. A synthetic minority oversampling technique was used for the data augmentation algorithm to improve the dataset. From the experiment, the conformer-based HAR model showed better performance than baseline models: the transformer-based-model and the 1D-CNN HAR models. Moreover, the performance of the proposed algorithm was superior to that of algorithms proposed in recent similar studies which do not use RNN-series.