Reviewerly/exHarmony huggingface dataset:
Towards semantic-driven boolean query formalization for biomedical systematic literature reviews

Study identification refers to formalizing an effective search over biomedical databases for retrieving all eligible evidence for a systematic review. Manual construction of queries, where a user submit a search query for which a biomedical search system such as PubMed would identify the most relevant documents, has been recognized as a very costly step in conducting systematic reviews. The objective of this paper is to present an automatic query generation approach to reduce the time and labor cost of manual biomedical study identification.The evaluation benchmark is the widely adopted CLEF 2018 Technology Assisted Reviews (TAR) collection, with 72 systematic reviews on Diagnosis Test Accuracy. We use and fine-tune pre-trained language models for generating high-level key-phrases and their dense embeddings. We constructed and published a dataset consists of almost one million PubMed articles' abstracts and their keywords for fine-tuning pre-trained language models. We also use concepts that are represented in the Unified Medical Language System, UMLS, for query expansion and embedding generation. We exploit and test different clustering methods, namely Agglomerative clustering, Affinity Propagation, and K-Means, over the generated embeddings to form query clauses.Our proposed methods outperform existing state-of-the-art automatic query generation models across Precision (0.0821 compared with 0.005), Recall (0.9676 compared with 0.878), and F-measures (0.2898 compared with 0.0356 in F3 measure). In addition, some of the proposed methods can even outperform the performance of the manually crafted queries in some specific measures.The proposed model in this paper can be utilized to form an effective initial search query that can be further refined and updated by human reviewers for achieving the desired performance. For future work, we would like to explore the application of the presented query formalization methods in existing study identification methodologies and techniques, especially those that iteratively train machine learning models based on the domain experts' feedback on the relevancy of the retrieved studies.
A GtoG Direct Coding Mapping Method for Multi-Type Global Discrete Grids Based on Space Filling Curves

DGGS (Discrete Global Grid System) has many subdivision models and coding methods. Due to the lack of underlying consistency of different DGGS codes, most of them are converted through longitude–latitude, which greatly reduces the interoperability efficiency of different DGGS data and has become one of the bottlenecks in efficient integration of multi-source DGGS data. Therefore, a direct mapping method from one grid code to another (Grid to Grid, GtoG) for multi-type DGGSs is proposed based on three classical DGGSs (triangular, diamond and hexagonal grids) and two commonly used filling curves (Hilbert curve and Z-curve). The mutual conversion rules of different grids expressing spatial point, line and surface data are constructed. Then, the above method is extended to the spherical icosahedral grid framework, and three different region coding mapping rule tables of the basic inside cells, boundary cells and vertex cells are designed. Finally, the experimental results show that, compared with the longitude–latitude conversion method, the average conversion efficiency of spatial point, line and surface data is increased by 2–4 orders of magnitude. This new method greatly improves the interoperability efficiency and provides a feasible solution for the efficient integration of multi-source DGGS data.
Liquid Proof-of-Stake in Tezos: An Economic Analysis

In this paper, we investigate some economic fundamentals related to the Tezos blockchain platform under the Emmy* consensus protocol. The protocol is based on a liquid version of Proof-of-Stake, in the sense that users can temporarily delegate some or all of their Tz units to full nodes. In addition to increasing the stake of the full node, and thus the probability of being selected as a block baker/endorser, such delegation induces the property of the super-additivity of users’ selection probability of baking/endorsing a block. That is, with delegation, the selection probability may be larger than the sum of the selection probabilities without delegation. In this paper, we study how monetary holdings and stakes can evolve with time, also discussing the individual user and the market implications of delegation.
Full time-dependent counting statistics of highly entangled biphoton states

Highly entangled biphoton states, generated by spontaneous parametric processes, find wide applications in many experimental realizations. There is an increasing demand for accurate prediction of their time-dependent detection. Unlike approaches that have emerged so far, this paper presents an approach providing full time-dependent counting statistics in terms of efficiently computable formulas, valid for a wide range of entanglement and arbitrary interaction times. General spatial modes are taken into account to describe free space and fiber propagation. The time intervals that correspond to the statistics are classified according to their widths. Apart from large and small widths compared to the temporal correlation width, intermediate interval widths give access to accidental correlations between separated time intervals. Moreover, the approach is easily applicable to a modular array of arbitrary optical components and external influences. This is demonstrated on phase-time coding, where the detuning of the interferometers affecting Franson interference is investigated. An acceptable range for the detuning is estimated, such that the security of the key is not compromised.
Alternatives to a nonhomogeneous partial differential equation quantum algorithm

Recently, J. M. Arrazola et al. [Phys. Rev. A 100, 032306 (2019)] proposed a quantum algorithm for solving nonhomogeneous linear partial differential equations of the form $A\ensuremath{\psi}(\mathbf{r})=f(\mathbf{r})$. Its nonhomogeneous solution is obtained by inverting the operator $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{A}$ along with the preparation and measurement of special ancillary modes. In this work we suggest modifications in its structure to reduce the costs of preparing the initial ancillary states and improve the precision of the algorithm for semidefinite operators. These achievements enable easier experimental implementation of the quantum algorithm.
Security risk assessment of IEC61850-based substation automation system

Substation automation system (SAS) based on IEC61850 is the direction of SAS, whose security risk assessment is meaningful. The function-based model (FBM) was proposed, by which SAS could be decomposed into functions and described in mathematical language, and the security risk of SAS is divided into system and function risk. A method calculating function failure probability is presented, logical connection, logical node and function value are defined to measure function failure consequence. Taking T1-1 SAS as example, the feasibility and applicability of the proposed method is verified.
Heuristic Evaluation Versus Guideline Reviews: A Tale of Comparing Two Domain Usability Expert's Evaluation Methods

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Background:</i></b> The usability of university websites is important to ascertain that they serve their intended purpose. Their usability can be evaluated either by testing methods that rely on actual users or by inspection methods that rely on experts for evaluation. Heuristic evaluation and guideline reviews are two inspection methods of usability evaluation. A heuristic evaluation consists of a few general heuristics (rules), which are limited to checking general flaws in the design. A guideline review uses a much larger set of guidelines/suggestions that fit a specific business domain. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Literature review:</i></b> Most of the literature has equated usability studies with testing methods and has given less focus to inspection methods. Moreover, those studies have examined usability in a general sense and not in domain- and culture-specific contexts. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Research questions:</i></b> 1. Do domain- and culture-specific heuristic evaluation and guideline reviews work similarly in evaluating the usability of applications? 2. Which of these methods is better in terms of the nature of evaluation, time needed for evaluation, evaluation procedure, templates adopted, and evaluation results? 3. Which method is better in terms of thoroughness and reliability? <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Research methodology</i></b> : This study uses a comparative methodology. The two inspection methods—guideline reviews and heuristic evaluation—are compared in a domain- and the culture-specific context in terms of the nature, time required, approach, templates, and results. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Results:</i></b> The results reflect that both methods identify similar usability issues; however, they differ in terms of the nature, time duration, evaluation procedure, templates, and results of the evaluation. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Conclusion:</i></b> This study contributes by providing insights for practitioners and researchers about the choice of an evaluation method for domain- and culture-specific evaluation of university websites.
A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

Photovoltaic power generating is one of the primary methods of utilizing solar energy resources, with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy.In order to provide reference strategies for pertinent researchers as well as potential implementation, this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches, statistical approaches and optimization techniques for solar power generation and forecasting.Deep learning-related methods, in particular, can theoretically handle arbitrary nonlinear transformations through proper model structural design, such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for forecasting.The research's results indicate that RBFNN-AG performed the best when applying the predetermined number of days, with an NRMSE value of 4.65%.RBFNN-AG performs better than sophisticated models like DenseNet (5.69%), SLFN-ELM (5.95%), and ANN-k-means-linear regression correction (6.11%).Additionally, scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.
Microaggressions in Academic Spaces: What About the Library?

Microaggressions are subtle aggressions, often unintentional, which are usually directed at certain groups of people, generally minorities. Due to their insidious frequency, they cause great discomfort. They can even cause permanent problems with adapting and integrating of various kinds. University libraries, as the social spaces that they are, accommodate a wide variety of people and can be places where microaggressions occur in different directions: librarians towards users, users towards librarians and among these groups. This study analyzes the incidence of various types of microaggressions in the academic library environment from the point of view of one group of users: academics and researchers. An online survey was completed by a large number of academics and researchers from a large geographic area. Data was obtained on both observed and felt or experienced microaggressions in the library, either towards themselves or other users, or towards the librarian. The results indicate that researchers are more aggressive than librarians, making the library space a safer place for diversity than the university. Nevertheless, for certain aspects the library shows some data that invites reflection and an opportunity to further reduce occurrences.
Real Time Driver Alertness System Based on Eye Aspect Ratio and Head Pose Estimation

Drowsy driving is one of the main causes of traffic accidents that leads to the loss of men and material. There are two methods to detect the alertness of the driver: first method focuses on the driver’s performance and second method focuses on the driver’s state. Furthermore, methods focusing on driver’s state are of two types groups: methods using brain signals and methods using image processing. This paper presents a real-time image processing system to detect the alertness of a driver based on the estimation of eye-aspect ratio (EAR) and the head-pose (HP) estimation. A camera is used to obtain the data of the driver and computer vision based methods are used to detect driver’s state. The video segments captured by the camera are analyzed using image processing techniques. The EAR and the HP are continuously estimated to detect the alertness of a vehicle driver. The proposed scheme will have benefits in minimizing the accidents by alerting the driving about his current state.
A precise measurement method for tube endpoints based on spatial geometry and perspective projection model

Abstract Tubes are widely used in the aerospace field for such things as engines, fuel pipelines, cooling systems, etc. To improve manufacturing efficiency and accuracy, researchers develop multi-camera vision methods for measuring tube geometry parameters. However, these methods cannot measure the exact position of the endpoints of the tube, because the projection points of the tube endpoints have no obvious texture features in the image. To solve the above problem, a precise measurement method for tube endpoints based on spatial geometry and perspective projection model is proposed in this paper. First, we establish a Plücker coordinate system, which treats the end of the tube as a cylinder. Then, several line equations parallel to the center line are calculated on the surface of the cylinder and their intersections with the end face are obtained on these lines, which refers to the optical center of the camera, the radius of the tube, and etc. These intersections are projected onto the centerline and optimized to get the endpoint. Finally, we build a multi-camera vision measurement system consisting of ten industrial cameras to verify the effectiveness of the proposed method. Extensive experiments have shown that the average error of this method is within 0.1 mm, and the standard deviation is within 0.08 mm, which proves the superiority of our proposed method.
Analysis of the Effect of Feature Selection and Class Balancing Methods with Supervised Algorithms in Web Robot Detection Problem

Accurately web robot identification is one of the most challenging problems for web robot activity detection systems. In the last few years, to accurately identify web robots, machine learning classification methods were used. However, it was discovered that the multiclass distribution in the web robot detection system is substantially skewed due to the data set's class imbalance, which causes problems with classification accuracy. The research presented in this paper examines not only the role of feature selection in improving classification accuracy but also addresses the issue of class imbalance using under-sampling of major classes and Synthetic Minority Over-sampling (SMOTE) of minor classes. After that, the classification performance is compared across a number of different classifier types. The results of this study show that oversampling (SMOTE) is more effective than under-sampling at discovering minor classes when employed with the class imbalance data set. Additionally, it was discovered that KNN and XGBoost are more accurately classified than other classifiers throughout this research. However, no single method can be utilized to classify multiclass data. This study proposes that a combination of KNN and XGBoost may be used to classify minor classes in a web robot detection system's unbalanced class data set.
ATM Security System Using Gesture and Hand Vein Recognition

An ATM is a 24/7 banking outlet through which users can avail prompt services like cash withdrawal, cash transfer, balance enquiry, pin change, mini statement, etc. Due to the risk of theft in ATMs, it is important to deploy high security measures while carrying out transactions. This paper presents a more secure method to carry out ATM transactions using hand gesture recognition and hand vein verification. In order to avoid pin hacking using techniques like shoulder surfing, pin verification using hand gesture recognition model is implemented. The 4-digit security pin will be entered by posing hand gestures inside a chamber. This method also focuses on easing the use of ATMs for blind people. The voice based instruction module helps the blind people to carry out the transactions without any assistance from other people. The security is enhanced with the help of a hand vein recognition system which is one of the newest biometric techniques. The vein patterns under the skin are difficult to replicate thereby making it very secure. The hand vein images captured using NoIR camera are preprocessed followed by extraction of features for vein recognition. Since the model is deployed on raspberry pi, it provides a cost effective approach as compared to sensor technology. The results of the proposed model of hand gesture recognition showed an accuracy of around 98% for both the train and test dataset.
A Brief Review on the Role of Blockchain in Supply Chain Management

Blockchain is a proficient technology when used in combination with other intelligent technologies which gives an opportunity to an organization to rethink about improvement of their supply chain internal and external processes. It helps in improvement of transparency and provenance by removing shortfalls and building a better organizational control overall. However, blockchain faces numerous challenges, e.g., transaction speed, decentralization, scalability, interoperability, and lack of standardization that could affect its adoption across organizations. However, a greater number of research are required to overcome the governance, standardization, and technological challenges involved within. Concisely, blockchain in supply chain is still in initial phase, many improvements are needed for better adaptation of blockchain using Machine Learning, Neural Network algorithms to make optimized computation decision of blockchain framework. In this paper, we studied and discussed about blockchain and its type, consensus mechanism, blockchain in supply chain, key issues of blockchain and supply chain and intelligence in blockchain.
Weight matrix as a switch between line attractor and plane attractor of ring neural networks

Continuous attractor refers to a kind of low-dimensional manifold, which is embedded in high-dimensional state space. The line attractor is one-dimensional continuous attractor, while the two-dimensional continuous attractor is called plane attractor. The weight matrix and the external input play the key role for the expression of the continuous attractor. In this paper, some conditions for the parameter selection to achieve line attractor and plane attractor are given. we see that even a pretty small perturbation of the parameter may heavily affect the type and shape of the continuous attractor. The parameter selection scheme is crucial for designing the neural network model.
Multi-feature Machine Learning with Quantum Superposition

Quantum computing in general and quantum machine learning in particular provide promising approaches to solve complex problems. Quantum superposition, which is one of the most important characteristics in quantum computing, allows us to embed and process different information sources (e.g. multi-modality or multi-view inputs) simultaneously. In this study, we investigate for the first time multi-feature machine learning based on quantum superposition. We show that when training with superposed quantum data, the quantum models can easily extend their learning space to gain a significant improvement over the single-feature learning.
A non-definitive auto-transfer mechanism for arbitrary style transfers

In recent years, image style transfers have increasingly become a hot research topic in the field of computer vision. The existing CNN-based style transfer methods have been the basis of a substantial amount of research in the content–style fusion area. Artificially controlled style fusions are applied by deterministic computations. Since these methods limit the automatic learning ability of the model, their style transfer effect is unstable. To solve this problem, we propose a non-definitive style auto-transfer module. This module is based on an attention submodule that guides the model in regard to the channels and the space for content–style fusions. Instead of artificially defining the content–style fusion methods, it lets the model learn on its own. We also propose a feature shuffle operation, which reduces the influence of the style image on the content of the result. In addition, to better preserve the high-level and low-level information of the image, our loss function adopts a multi-scale content–style loss and an edge detection loss. All our experiments are conducted on the WikiArt and Microsoft COCO datasets. The experimental results show that our method can achieve more stable and better visual effects than the existing methods.
Comparative Analysis of Machine Learning Methods Application for Financial Fraud Detection

This paper addresses the fraud detection problem in the context of Big Data used in remote banking systems. The paper aims to propose a new algorithm for automatic detection of fraudulent transactions using machine learning with a performance that allows to apply it in big data systems. The article identifies promising directions for optimizing the operation of methods for fraudulent transactions detection in anti-fraud systems. Architectural approaches to the operation of anti-fraud systems have been studied. Based on this, an architecture for illegal actions prediction in a near real-time mode was proposed. The research task of the article is to find the most suitable machine learning algorithm, with the least training and prediction time, demonstrating high classification performance. To achieve this goal, an analysis of the supervised and ensemble machine learning algorithms was made. The dataset was preprocessed for the experiment with SMOTE resampling and robust scaling techniques. The chosen methods were compared using different metrics: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F$</tex> 1 score, AUC and time consumption for training and classification. As a result of a metrics comparison, it was found that multilayer perceptron (MLP) and boosting methods (Adaptive, Gradient, XGBoost) has the highest classification, but MLP outperforms boosting methods in terms of time consumption for classification. Thus, MLP was selected as the most appropriate algorithm for further integration to proposed Big Data architecture. Based on the data obtained during the experiments, the degree of their implementation in fraud detection systems was assessed and architecture for the anti-fraud detection system for big data was proposed.