Reviewerly/exHarmony huggingface dataset:
PERCEPTION OF NURSES ABOUT NURSING TELECONSULTATION IN PRIMARY CARE

ABSTRACT Objective analyze the nurse’s perception about nursing teleconsultation in Primary Health Care through the workflows characterization, potential, challenges and teleconsultation in nursing feasibility. Method exploratory-descriptive study with a qualitative approach. Data were collected, between the months of May and June 2021, with 19 nurses from four health districts of a capital in south of Brazil, using digital questionnaire. Data were systematized in Microsoft ExcelⓇ and analyzed following the criteria of the Discourse of the Collective Subject. This process was carried out with the support of the DSCsoftⓇ Program. Results the results were organized into five thematic categories: Nursing teleconsultation flow in Primary Health Care; Teleconsultation process; Instruments used during the teleconsultation; Potentialities and challenges identified during the teleconsultation in the Primary Health Care; and, Applicability: services that can be resolved through teleconsultation. Among the challenges are the inherent problems of communication and internet connectivity and the distrust of users in relation to this new consultation modality. Conclusion telecare in nursing is a recent technological innovation, with several challenges as: the possible failure in communication between the professional and the user; internet connection problems; lack of ability to travel from new tools; lack of privacy and excessive noise and distrust of users about the service provided. With the teleconsultation, there was an extension of the users' access to the health services, there was greater agility and efficiency in the services provided to the population.
Fintech: Exploring the Digital Transformation of Banking &amp; Financial Services

Fintech has exploded since the Internet revolution. Today, fintech has given rise to numerous segments, and many financial technologies have gained wider application. Among which the most active fintech areas are: online payments via digital currency without the need for cash; P2P lending, which enables direct person-to-person lending without the use of intermediaries; robo-advice, which use algorithms to provide investment advice; and distributed ledger technology (DLT) blockchain. They are all widely used in some countries around the world, but have not yet undergone a complete replacement. These emerging financial technologies have undoubtedly revolutionary implications for traditional finance, providing new ideas for the development of the financial sector. The problems they generate deserve attention as well. This paper introduces the development history of these four financial technologies, compares them with the traditional way of operation in the financial field in which they applied, and analyzes the advantages and limitations of these financial technologies. Finally, reflections on the future of fintech development are presented.
Crossover for Cardinality Constrained Optimization

To understand better how and why crossover can benefit constrained optimization, we consider pseudo-Boolean functions with an upper bound B on the number of 1-bits allowed in the length- n bit string (i.e., a cardinality constraint). We investigate the natural translation of the OneMax test function to this setting, a linear function where B bits have a weight of 1+ 1/ n and the remaining bits have a weight of 1. Friedrich et al. [TCS 2020] gave a bound of Θ ( n 2 ) for the expected running time of the (1+1) EA on this function. Part of the difficulty when optimizing this problem lies in having to improve individuals meeting the cardinality constraint by flipping a 1 and a 0 simultaneously. The experimental literature proposes balanced operators, preserving the number of 1-bits, as a remedy. We show that a balanced mutation operator optimizes the problem in O(n log n ) if n-B = O (1). However, if n-B = Θ ( n ), we show a bound of Ω ( n 2 ), just as for classic bit mutation. Crossover together with a simple island model gives running times of O ( n 2 / log n ) (uniform crossover) and \(O(n\sqrt {n})\) (3-ary majority vote crossover). For balanced uniform crossover with Hamming-distance maximization for diversity, we show a bound of O ( n log n ). As an additional contribution, we present an extensive analysis of different balanced crossover operators from the literature.
Bug severity classification in software using ant colony optimization based feature weighting technique

At the present, the delivery of the software should be time-bound without affecting the quality of the software. However, bug severity can affect the timely delivery of software. It is a crucial component of the software engineering, including maintenance and testing. Both phases are essential for bug severity classification but require much time. Generally, bug triage is responsible for classifying the bugs based on criticality/severeness. The manual execution of this process is error-prone. Consequently, a model for automatic bug classification is required to help the bug triage. In this work, the ant colony optimization (ACO) based feature extraction technique is proposed to extract more relevant features for bug severity classification. Furthermore, the ACO technique is integrated with NB, SVM, DeepFM and F-SVM techniques for predicting bug severity and classifying bugs into multi-severity classes. Several benchmark projects such as Eclipse, Mozilla, OpenFOAM, JBoss, and Firefox, are considered to evaluate the efficacy of the techniques above. The simulation outcomes are expressed in terms of Accuracy, Precision, Recall, and F1-measure. It is noted that the outcomes of the SVM, NB, DeepFM and F-SVM approaches are improved by the ACO-based feature weighting technique. The accuracy rate of ACO-F-SVM, ACO-NB, ACO-SVM, ACO-DeepFM, NB, SVM, F-SVM, DeepFM techniques are ranging in between 85.73 and 89.38%, 78% to 80%, 73% to 76%, 92.67% to 97.27 %, 71% to 77%,65% to 74%, 78.21% to 81.28% and 90.02% to 95.24% respectively for five benchmark projects. Further, proposed techniques are also produced better simulation results as compared with state-of –the-art techniques. Friedman and post hoc statistical tests are also conducted on proposed techniques.
Guided deterministic policy optimization with gradient-free policy parameters information

Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) are two classical deterministic policy gradient algorithms. It is worth noting that the policies of both DDPG and TD3 are completely dependent on the gradient of critics. This will cause the policy to be unstable and easy to converge to the local optimum in the learning process. Although the idea of maximum entropy learning can provide more effective exploration, it can only be applied to the algorithm using stochastic policy, not to DDPG and TD3. In this paper, we propose a deterministic policy optimization method combining gradient-free policy parameters information (GFPPI). Specifically, we obtain a new set of policies by injecting Gaussian noise into the policy parameters, and then weight these policy parameters based on critics to obtain GFPPI. Finally, GFPPI is used as the regularization term of the policy optimization function to guide the policy update. GFPPI can mitigate premature policy convergence and facilitate exploration with optimistic principles. We provide the theoretical guarantee for monotonic improvement of expected cumulative return using augmented loss function with GFPPI, experimentally analyze the role of GFPPI in policy optimization and combine it with deterministic policy gradient information for policy optimization. The experiments on OpenAI gym demonstrate that GFPPI can improve sample efficiency and enable the algorithm to get higher performance.
Landmark selection and path planning for unmanned vehicles with position error corrections

Unmanned vehicles have been widely used for sensing, surveillance, surveying, communication, transportation, and many other purposes. Navigation is always an essential operational function in the course of their movements. In the case of the absence of Global Positioning System (GPS) signals, the positioning need of an unmanned vehicle is generally empowered by its onboard Inertial Navigation System (INS). An inherent deficiency of INS, however, is that position errors inevitably occur and will gradually accumulate over time. A feasible approach to correcting errors is to use the fixed reference points with known coordinates in the cruising environment, which we call landmarks, to calibrate the vehicle’s position when it passes by. The vehicle must choose a path along which a sufficient number of landmarks can serve the position calibration requirement. In such a context, this paper introduces a joint landmark selection and path planning problem for unmanned vehicles navigating in two-dimensional (2D) and three-dimensional (3D) environments, in which the vehicles’ position drifting errors need to be rectified through communicating with preset landmarks. To characterize and tackle the problem, a set of new types of subpaths are defined and a bi-objective integer programming model using this building block is formulated for simultaneously minimizing the landmark deployment cost and the vehicle cruise distance. An efficient dynamic programming algorithm, which features a multi-criterion label-correcting form, is developed for searching for the Pareto-optimal solution set of landmark and path selections. Numerical and computational analyses are conducted in both 2D and 3D scenarios, in which a pair of origin and destination places are typically hundreds of kilometers apart and 4 datasets of different scales including 500, 1,000, 1,500, and 2,000 candidate landmarks are randomly generated. A sensitivity analysis reveals the impact of different position drifting increments caused by external environment interferences, varying correction abilities, and heterogeneous landmark deployment costs on the solution behavior. The influence of the number of candidate landmarks, position error limit, and error increment rate on computing time is also evaluated. All these test results conclude that the algorithm is capable of finding in seconds the entire set of Pareto-optimal landmarks and paths in real-world applications.
Boosted Binary Quantum Classifier via Graphical Kernel

In terms of the logical structure of data in machine learning (ML), we apply a novel graphical encoding method in quantum computing to build the mapping between feature space of sample data and two-level nested graph state that presents a kind of multi-partite entanglement state. By implementing swap-test circuit on the graphical training states, a binary quantum classifier to large-scale test states is effectively realized in this paper. In addition, for the error classification caused by noise, we further explored the subsequent processing scheme by adjusting the weights so that a strong classifier is formed and its accuracy is greatly boosted. In this paper, the proposed boosting algorithm demonstrates superiority in certain aspects as demonstrated via experimental investigation. This work further enriches the theoretical foundation of quantum graph theory and quantum machine learning, which may be exploited to assist the classification of massive-data networks by entangling subgraphs.
CNN-based deep learning method for predicting the disease response to the Neoadjuvant Chemotherapy (NAC) treatment in breast cancer

The objective of the study is to evaluate the performance of CNN-based proposed models for predicting patients' response to NAC treatment and the disease development process in the pathological area. The study aims to determine the main criteria that affect the model's success during training, such as the number of convolutional layers, dataset quality and depended variable.The study uses pathological data frequently used in the healthcare industry to evaluate the proposed CNN-based models. The researchers analyze the classification performances of the models and evaluate their success during training.The study shows that using deep learning methods, particularly CNN models, can offer strong feature representation and lead to accurate predictions of patients' response to NAC treatment and the disease development process in the pathological area. A model that predicts 'miller coefficient', 'tumor lymph node value', 'complete response in both tumor and axilla' values with high accuracy, which is considered to be effective in achieving complete response to treatment, has been created. Estimation performance metrics have been obtained as 87%, 77% and 91%, respectively.The study concludes that interpreting pathological test results with deep learning methods is an effective way of determining the correct diagnosis and treatment method, as well as the prognosis follow-up of the patient. It provides clinicians with a solution to a large extent, particularly in the case of large, heterogeneous datasets that can be challenging to manage with traditional methods. The study suggests that using machine learning and deep learning methods can significantly improve the performance of interpreting and managing healthcare data.
Quantum Lernmatrix

We introduce a quantum Lernmatrix based on the Monte Carlo Lernmatrix in which n units are stored in the quantum superposition of log2(n) units representing On2log(n)2 binary sparse coded patterns. During the retrieval phase, quantum counting of ones based on Euler's formula is used for the pattern recovery as proposed by Trugenberger. We demonstrate the quantum Lernmatrix by experiments using qiskit. We indicate why the assumption proposed by Trugenberger, the lower the parameter temperature t; the better the identification of the correct answers; is not correct. Instead, we introduce a tree-like structure that increases the measured value of correct answers. We show that the cost of loading L sparse patterns into quantum states of a quantum Lernmatrix are much lower than storing individually the patterns in superposition. During the active phase, the quantum Lernmatrices are queried and the results are estimated efficiently. The required time is much lower compared with the conventional approach or the of Grover's algorithm.
A Variation of the Algorithm to Achieve the Maximum Entropy for Belief Functions

Evidence theory (TE), based on imprecise probabilities, is often more appropriate than the classical theory of probability (PT) to apply in situations with inaccurate or incomplete information. The quantification of the information that a piece of evidence involves is a key issue in TE. Shannon's entropy is an excellent measure in the PT for such purposes, being easy to calculate and fulfilling a wide set of properties that make it axiomatically the best one in PT. In TE, a similar role is played by the maximum of entropy (ME), verifying a similar set of properties. The ME is the unique measure in TE that has such axiomatic behavior. The problem of the ME in TE is its complex computational calculus, which makes its use problematic in some situations. There exists only one algorithm for the calculus of the ME in TE with a high computational cost, and this problem has been the principal drawback found with this measure. In this work, a variation of the original algorithm is presented. It is shown that with this modification, a reduction in the necessary steps to attain the ME can be obtained because, in each step, the power set of possibilities is reduced with respect to the original algorithm, which is the key point of the complexity found. This solution can provide greater applicability of this measure.
SIVED: A SAR Image Dataset for Vehicle Detection Based on Rotatable Bounding Box

The research and development of deep learning methods are heavily reliant on large datasets, and there is currently a lack of scene-rich datasets for synthetic aperture radar (SAR) image vehicle detection. To address this issue and promote the development of SAR vehicle detection algorithms, we constructed the SAR Image dataset for VEhicle Detection (SIVED) using Ka, Ku, and X bands of data. Rotatable bounding box annotations were employed to improve positioning accuracy, and an algorithm for automatic annotation was proposed to improve efficiency. The dataset exhibits three crucial properties: richness, stability, and challenge. It comprises 1044 chips and 12,013 vehicle instances, most of which are situated in complex backgrounds. To construct a baseline, eight detection algorithms are evaluated on SIVED. The experimental results show that all detectors achieved high mean average precision (mAP) on the test set, highlighting the dataset’s stability. However, there is still room for improvement in the accuracy with respect to the complexity of the background. In summary, SIVED fills the gap in SAR image vehicle detection datasets and demonstrates good adaptability for the development of deep learning algorithms.
Work schedule optimization for electric agricultural robots in orchards

We developed an optimal work-scheduling algorithm for autonomous spraying electric robots in orchards. Orchards are often located in mountainous areas, where the slopes are greater than those in plain areas. The work efficiency of agricultural operations in orchards thus depends on the quality of the work schedule, which in turn requires that the work schedule be optimized. We developed an algorithm that considers the returns of an agricultural robot electric vehicle to a replenishment base for pesticide refills and battery recharging. Our proposed method consists of three steps: the reconstruction of individual robots' work schedules, optimization of the overall work schedule, and optimization of the direction of entry for each driving lane in a schedule. We used a multi-layered genetic algorithm (LGA) to solve these problems. A comparison of the work schedules created by the LGA with those generated by other algorithms confirmed that the LGA can create an optimum or a suboptimum work schedule. We also created work schedules for multiple robots, which demonstrated that the proposed method can be applied to robot-operation problems such as the determination of the required number of robots within the constraints of total work time and robot specifications. Our results confirmed that the quality of a work schedule for robot EVs improves when the parameters related to robot modeling and field modeling are optimized, suggesting the possibility of applying the proposed method to robot and field design problems.
CredibleExpertRank: Leveraging Social Network Analysis and Opinion Mining to Facilitate Reliable Information Retrieval on Knowledge-Sharing Sites

Asking and answering questions are common activities in both the workplace and everyday life. Knowledge-sharing websites have become a popular resource for obtaining instant and searchable answers. However, users of these sites may encounter challenges in acquiring timely and appropriate content from user-provided answers owing to factors such as limited expertise, spam, and time constraints. Identifying trustworthy experts who can provide relevant and reliable answers in knowledge-sharing communities is crucial to overcome this issue. In this study, we propose a solution to the problem of identifying credible experts on knowledge-sharing sites by introducing the CredibleExpertRank algorithm. Our algorithm calculates a CredibleExpert score based on two main factors: activity and credibility. The credibility score is determined by analyzing users’ interactions related to questioning, answering, recommending, and mining users’ opinions, while the activity score reflects the user’s level of participation on the platform. We conducted experiments to evaluate the performance of the CredibleExpertRank algorithm, using user satisfaction measures for answers to given questions. Our findings confirmed that the credible experts identified by our algorithm provided more relevant and timely answers compared to other ordinary users. The timely nature of the credible experts’ answers was due to the reflection of their activity factor, while the superior performance in relevance was attributed to the high recommendation rate of their answers and positive evaluations received from opinion mining results. Our study undertakes an extensive investigation focused on the identification and prioritization of credible experts, revealing their profound advantages in significantly enhancing the overall quality of knowledge-sharing platforms. We proposed the CredibleExpertRank algorithm as a powerful method for effectively identifying trustworthy experts and giving priority to their answers. Through a meticulous process of experimental evaluation, we provide compelling evidence that this approach leads to substantial improvements in both search efficiency and reliability on knowledge-sharing sites. By highlighting the potential benefits derived from the identification of credible experts, our study underscores their pivotal role in elevating the overall performance of knowledge-sharing platforms.
On Fuzzy and Case-Based Dynamic Software Development Process Modeling and Simulation Approach

The software development process (SDP) is complex because it uses specific knowledge, skills, tools, and techniques to deliver value to people, and the user’s needs are always growing and becoming more complex. Practitioners and academics have proposed different approaches for improving the successful implementation of the SDP. This remains a non-trivial task, and the number of successful software development projects increases only slightly each year. This paper proposes a new approach for adapting the classical SDP to a dynamic, knowledge-intensive, and context-dependent model by applying the case-handling and fuzzy inference approaches for SDP modeling and simulation. The advantages and novelties of this approach consist in: SDP dynamicity modeling using the case-handling approach; the inclusion of human factor uncertainty into the execution of SDP activities through fuzzification; and agent-based simulation, which allows for the inclusion of human actors in SDP simulations. Modeling and simulation of Agile SDPs were carried out using this prototype. Experiments show the correspondence of the proposed approach to the needs of dynamically changing and human-factor-dependent SDPs, as well as the possibilities for modeling and simulating the SDP, predicting SDP execution, and observing possible risks.
Review of Key Derivation Functions in Cryptographic Systems

Because of its significance in the formation of the core of encryption/decryption algorithms, the key derivation function (KDF) or pseudorandom number generator (PRNG) plays a major part in the cryptographic system.Random numbers are required for the usage of encryption and decryption techniques in a variety of network security applications.In this research we Focus on key generation functions, which are an important aspect of symmetric and asymmetric algorithms.Disgusting the background of PRNG basics and attempting to characterize and evaluate several publications that offer proposals for key generators.