Reviewerly/exHarmony huggingface dataset:
Web-Based Patient Recommender Systems for Preventive Care: Protocol for Empirical Research Propositions

Preventive care helps patients identify and address medical issues early when they are easy to treat. The internet offers vast information about preventive measures, but the sheer volume of data can be overwhelming for individuals to process. To help individuals navigate this information, recommender systems filter and recommend relevant information to specific users. Despite their popularity in other fields, such as e-commerce, recommender systems have yet to be extensively studied as tools to support the implementation of prevention strategies in health care. This underexplored area presents an opportunity for recommender systems to serve as a complementary tool for medical professionals to enhance patient-centered decision-making and for patients to access health information. Thus, these systems can potentially improve the delivery of preventive care.This study proposes practical, evidence-based propositions. It aims to identify the key factors influencing patients' use of recommender systems and outlines a study design, methods for creating a survey, and techniques for conducting an analysis.This study proposes a 6-stage approach to examine user perceptions of the factors that may influence the use of recommender systems for preventive care. First, we formulate 6 research propositions that can be developed later into hypotheses for empirical testing. Second, we will create a survey instrument by collecting items from extant literature and then verify their relevance using expert analysis. This stage will continue with content and face validity testing to ensure the robustness of the selected items. Using Qualtrics (Qualtrics), the survey can be customized and prepared for deployment on Amazon Mechanical Turk. Third, we will obtain institutional review board approval because this is a human subject study. In the fourth stage, we propose using the survey to collect data from approximately 600 participants on Amazon Mechanical Turk and then using R to analyze the research model. This platform will serve as a recruitment tool and the method of obtaining informed consent. In our fifth stage, we will perform principal component analysis, Harman Single Factor test, exploratory factor analysis, and correlational analysis; examine the reliability and convergent validity of individual items; test if multicollinearity exists; and complete a confirmatory factor analysis.Data collection and analysis will begin after institutional review board approval is obtained.In pursuit of better health outcomes, low costs, and improved patient and provider experiences, the integration of recommender systems with health care services can extend the reach and scale of preventive care. Examining recommender systems for preventive care can be vital in achieving the quadruple aims by advancing the steps toward precision medicine and applying best practices.PRR1-10.2196/43316.
Towards a Rule Modeling Framework for Context-aware Smart Service Systems

Since business rules aim at enforcing regulations in an organization, they are critical in governing business activities from a managerial standpoint. On the other hand, another type of rules has emerged in context-aware service systems: context rules. Context rules are employed for context reasoning to recommend and operate the right services in an appropriate manner. In this sense, context rules ensure the smartness of services in smart service systems. For decades, researchers and practitioners have addressed rule modelling and rule management in information systems and business services. However, in relation to context-aware services in smart service systems, there is a lack of exploring the rule aspect, especially considering how business rules and context rules are involved in such a system. The purpose of this paper is to propose a rule modelling framework (called RuCBS framework) for expressing rules in context-aware smart service systems over the three aspects of service science (Management, Science, and Engineering). The framework presents concepts, a meta-model that connects these concepts, and rule patterns. The framework is validated with a case study on banking services. Future research directions on rules in context-aware smart service systems are also discussed.
Controlling Digital Systems via Intelligent Networks

: Control is important to improve hardware performance. Most electronic systems are designed according to&#x0D; the device and then manufactured as an attached electronic device. However, if conditions change or the factory is&#x0D; modernized then the control device must be replaced. This is due to the complexity of the control unit represented by&#x0D; the program implementation algorithms, in addition to the time delay caused by digital and analog signal converters&#x0D; (ADC - DAC), and in this research it is replaced by deep neural networks It is a thriving field with practical and&#x0D; medical applications and is characterized by its ability to learn and train as it is a branch of machine learning and&#x0D; artificial intelligence. The results proved that the functioning of the neural networks and their performance are better&#x0D; than the control system where the value of the difference between the two is equal to zero.
Implementation of moving object tracker system

The field of computer vision is increasingly becoming an active area of research with tremendous efforts being put towards giving computers the capability of sight. As human beings we are able to see, distinguish between different objects based on their unique features and even trace their movements if they are within our view. For computers to really see they also need to have the capability of identifying different objects and equally track them. This paper focuses on that aspect of identifying objects which the user chooses; the object chosen is differentiated from other objects by comparison of pixel characteristics. The chosen object is then to be tracked with a bounding box for ease of identification of the object's location. A real time video feed captured by a web camera is to be utilized and it’s from this environment visible within the camera view that an object is to be selected and tracked. The scope of this paper mainly focuses on the development of a software application that will achieve real time object tracking. The software module will allow the user to identify the object of interest someone wishes to track, while the algorithm employed will enable noise and size filtering for ease of tracking of the object.
Spatial Path Smoothing for Car-like Robots Using Corridor-Based Quadratic Optimization

The global planning module of car-like robots usually plans a coarse spatial path, which may be non-smooth and kinematically infeasible for car-like robots. This study proposes an efficient spatial path smoothing approach, which is capable of optimizing a rough spatial path to be a high quality one. Two novel designs contribute to the proposed approach. One is a direct corridor construction method that provides an optimization region for path optimization. Based on a redefined path representation in the generated corridor, the second one is a core spatial path optimization method, where the optimization problem is formulated as a multi-objective quadratic programming (QP) with corridor and maximum-curvature constraints. Meanwhile, integrating a two-steps strategy into an optimization process yields a good trade-off between efficiency and quality. Experiment results validate that the proposed approach has the ability to online generate a high quality path.
Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s (Cloud Computing) networks are distributed and dynamic as signals appear/disappear or lose significance. MLTs (Machine learning Techniques) train datasets which sometime are inadequate in terms of sample for inferring information. A dynamic strategy, DevMLOps (Development Machine Learning Operations) used in automatic selections and tunings of MLTs result in significant performance differences. But, the scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. RFEs (Recursive Feature Eliminations) are computationally very expensive in its operations as it traverses through each feature without considering correlations between them. This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets. The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers. The proposed AKFA (Adaptive Kernel Firefly Algorithm) is for selecting features for CNM (Cloud Network Monitoring) operations. AKFA methodology is demonstrated using CNSD (Cloud Network Security Dataset) with satisfactory results in the performance metrics like precision, recall, F-measure and accuracy used.
Proposal of a Disrupted Road Detection Method in a Tsunami Event Using Deep Learning and Spatial Data

Tsunamis generated by undersea earthquakes can cause severe damage. It is essential to quickly assess tsunami-damaged areas to take emergency measures. In this study, I employ deep learning and develop a model using aerial photographs and road segment data. I obtained data from the aerial photographs taken after the Great East Japan Earthquake; the deep learning model used was YOLOv5. The proposed method based on YOLOv5 can determine damaged roads from aerial pictures taken after a disaster. The feature of the proposed method is to use training data from images separated by a specific range and to distinguish the presence or absence of damage related to the tsunami. The results show that the proposed method is more accurate than a comparable traditional method, which is constructed by labeling and learning the damaged areas. The highest F1 score of the traditional method was 60~78%, while the highest F1 score of the proposed method was 72~83%. The traditional method could not detect locations where it is difficult to determine the damage status from aerial photographs, such as where houses are not completely damaged. However, the proposed method was able to detect them.
Gas path diagnosis method for gas turbine fusing performance analysis models and extreme learning machine

The gas path analysis, which can quantify the performance degradation of gas turbine components, has been extensively applied to the gas path diagnosis. However, the precondition of this method is that the number of measurable parameters for the gas turbine to be diagnosed should not be less than the number of its health factors. In the existing research, this precondition can be guaranteed through common approaches such as screening the degraded components by a model-based prediagnosis process or recognizing the degraded components by using tools such as an ANN or a support vector machine. However, the diagnosis speed, recognition accuracy, and robustness of these approaches need to be improved. Therefore, a diagnosis method fusing the gas path performance analysis model and the extreme learning machine was proposed in this paper and applied to a GE LM2500+SAC gas turbine. The working mechanism of similarity ranking-gas path diagnosis-rationality check was introduced in the fusion method, endowing it with a higher recognition accuracy rate, stronger robustness, and higher diagnostic accuracy.
Universally Composable Oblivious Transfer with Low Communication

In this paper, a universally composable 1-out-of-N oblivious transfer protocol with low communication is built. This protocol obtained full simulation security based on the modulo learning with rounding (Mod-LWR) assumption. It can achieve universally composable security in the random oracle machine (ROM) model by combining random OT based on the key exchange protocol with the authentication encryption algorithm. It can be proven to resist static adversary attacks by simulating all corruption cases. Based on computer simulation and detailed mathematical derivation, this protocol was practicable and had better efficiency and lower communication.
Three-stream network and improved attention mechanism-based blind image quality assessment

This paper proposes a network model based on a three-stream network and improved attention mechanism for blind image quality assessment (TSAIQA). The inputs of the three streams are the distorted image, the pseudoreference image obtained by the improved generative adversarial network (GAN), and the gradient map of the distorted image. The distorted image stream focuses on the holistic quality-related features, the pseudoreference image stream is used to supplement the lost features due to distortion, and the gradient stream explicitly extracts the quality-related structural features. In addition, spatial and channel attention mechanisms combining first- and second-order information are proposed, and the improved attention mechanisms are applied to the three-stream network to optimize spatial and channel-level features effectively. Finally, the three-stream fusion features are input to the quality regression network to predict the image quality. To demonstrate the effectiveness of the proposed model, experiments are conducted on four classical IQA databases and two new large-scale databases. The experimental results show that the results of our TSAIQA model outperform the most advanced IQA methods and confirm the effectiveness of the proposed network structure and attention mechanisms.
On covering problems in hierarchical poset spaces

Given a ground set U and a collection C of subsets of U , we investigate the covering number, which is the cardinality of the smallest subcollection S whose union covers the universe U .This paper embraces three covering-related problems in hierarchical poset spaces: the classic, the short covering and normal codes.In the first two problems, the classical and the short-covering ones, the covering numbers are studied for collections of R-balls and R-strips, respectively.We prove that determining the covering number in hierarchical spaces depends only on their correspondents in the Hamming space.It allows us to express the covering number in hierarchical spaces.Later on, we characterize all H-normal codes in binary hierarchical spaces, that are a distinctive family of R-coverings.
A modified eye‐in‐hand stereo visual control for grasping unknown objects via Scara robot

Abstract Here, the switch proportional integral derivative controller (PID) stereo image‐based visual servoing controller is designed for controlling the manipulator robots. The proposed control algorithm separates the rotational and translational motion of the camera, so the stereo image‐based visual servoing control could be decomposed into three separate stages with different gains. Despite the increasing computation and complexity of relationships, instead of kinematics, robot dynamics are used to guide the robot to make the simulation more consistent with the real condition. To reduce response time and improve the robot's trajectory, the object positions in the image space are predicted by the Extended Kalman filter. The results showed that the proposed method produces a more accurate response than the classical methods that use robot kinematics. Adding the trajectory estimation algorithm improves the time response of the system and its industrial application capabilities.
Using dynamic voltage frequency scaling and service‐level agreement to reduce energy consumption in cloud datacenters based on distance

Abstract Energy consumption in cloud is an important topic. Previous works have reduced energy consumption, but have not consider and optimal selection of servers in a datacenter. In our paper, we try to elicit optimal selection of servers in a datacenter. We will modify the architecture of the system and the proposed algorithm using service‐level agreement (SLA) for datacenter servers in this selection, we consider the distance between the servers and the job and if there is a violation of SLA, the migration of virtual machines (VMs) occurs. On the other hand, unnecessary migration of VMs increases energy consumption. So we provide an algorithm for migrating VMs, and because we need task scheduling to perform tasks in the cloud, we will also provide algorithms for tasks migrating. Energy consumption is important for us, we use dynamic voltage frequency scaling to reduce energy consumption. On the other hand, the selection of service providers is done in the SLA management, and these service providers are located on different datacenters, we consider the parameter of the distance to the service consumer in selecting the appropriate datacenter.
Encoding with Combination Orientation Technique for RDH in Dual Stego Images

Abstract In this paper, initially, message intensities are used to create two encoding tables: an index and a code sequence. The table is then updated with $n$ secret data encoded into it. If the code sequence of the second encoding table matches the code sequence of the preceding or succeeding half of the two encoding tables, the second encoding table is folded. The folding is done when maximum intensity occurs in the utmost succeeding part of the two encoding tables. Finally, using the combination orientation approach, the encoded indices are embedded in the stego images. In the extraction phase, using the two stego images and the encoding with the combination orientation method, the encoded indices are obtained. The decoding is performed to extract the secret message as well as the cover image and there is no occurrence of overflow and underflow problems. It has a high visual quality and a high embedding capacity. The proposed technique achieved a maximum $\mathrm{PSNR}$ of $49.49$dB and a $648\ 242$-bit embedding capacity. When compared to traditional techniques, the experimental results show that the proposed method outperforms them.
Protection of noisy multipartite entangled states of superconducting qubits via universally robust dynamical decoupling schemes

In this paper, we demonstrate the efficacy of the universally robust dynamical decoupling (URDD) sequence to preserve multipartite maximally entangled quantum states on a cloud-based quantum computer via the IBM platform. URDD is a technique that can compensate for experimental errors and simultaneously protect the state against environmental noise. To further improve the performance of the URDD sequence, phase randomization (PR) as well as correlated PR (CPR) techniques are added to the basic URDD sequence. The performance of the URDD sequence is quantified by measuring the entanglement in several noisy entangled states (two-qubit triplet state, three-qubit Greenberger–Horne–Zeilinger (GHZ) state, four-qubit GHZ state and four-qubit cluster state) at several time points. Our experimental results demonstrate that the URDD sequence is successfully able to protect noisy multipartite entangled states and its performance is modestly improved by adding the PR and CPR sequences.
فعالية وسائل بطاقة الصورة الأبجدية في ترقية إتقان مفرادت اللغة العربية بمدرسة نهضة الوطن كوتارج المتوسطة الإسلامية

This research entitled The effectiveness of flashcard media Alphabet in improving mastery of Arabic vocabulary Class VII MTs NW Kotaraja. This research is classified as quantitative research with experimental research. In this research to find out about how the use of flashcard media Alphabet in improving the mastery of Arabic vocabulary students and the effectiveness of the use of flashcard media Alphabet in improving the mastery of Arabic vocabulary students. As for the results obtained after applying the method in accordance with the steps that have been made in the lesson plan, it is known that the alphabet flashcard media is effective in improving the mastery of Arabic vocabulary of Class VII Mts NW Kotaraja students. It can be seen from the calculation of T-test statistics show that there is an effectiveness of the use of flashcard media Alphabet to the mastery of Arabic vocabulary learners.
Quantum multi-party private set union protocol based on least common multiple and Shor’s algorithm

Private set union (PSU) allows several parties to obtain the union of their private sets without disclosing each party’s private information. Existing PSU protocols often have polynomial complexity for the complete set size or complicated process. In this paper, a quantum multi-party PSU protocol based on least common multiple (LCM) and Shor’s algorithm is proposed, which enables the union of multiple sets to be computed all at once. In order to increase the one-time success probability of the protocol, we first improved Shor’s period-finding algorithm, which is used in LCM computation and integer factoring. Each party’s private set is encoded into an integer obtained by multiplying several prime numbers, thus the PSU problem is transformed into an LCM problem. The LCM of these integers is computed by using the improved Shor’s period-finding algorithm, and then factored to derived the union set. We prove the correctness of the proposed protocol, and its unconditional security against semi-honest attacks. Complexity analysis shows that our protocol has logarithmic complexity for the complete set size.
DeepDLP: Deep Reinforcement Learning based Framework for Dynamic Liner Trade Pricing

Liner trade differs from other traffic flow in that it is characterized by substantially higher traffic volumes due to its low cost and high capacity. In the liner trade, pricing models should be distinguished by their ability to adapt to changing market conditions and provide a realistic picture of market trends. However, the prevalent manual pricing model used today in the industry can hardly adapt to the changing environment. We design a Deep Reinforcement Learning (RL) Framework for Liner Trade Problem (DeepDLP) to identify price strategy based on the characteristics of liner trade environment. We address four key problems with DeepDLP that significantly affect pricing. First, to address the issue of RL's challenging convergence due to current market variances, we use the Long-Short Time Memory (LSTM) to recognize environmental changes. Secondly, to avoid accumulated bias over time in LSTM, DeepDLP uses the price adjustment result of RL as the input of the Dynamic Price Prediction Module. Thirdly, in order to reduce noise and take past external information into account, we employ a historical information sensitive network in the Dynamic Price Prediction Module. Last but not least, we designed a balanced reward that allows the model to take into account both revenue maximization per unit of time and the sale of extra capacity. We first perform data wrangling and analysis on the actual data of a top liner trade company to identify the major routes and affecting elements in order to demonstrate the effectiveness and efficiency of DeepDLP. Next, we simulate real liner trade scenarios and conduct several experiments. The outcome shows that DeepDLP outperforms baselines and validates the significance of the designs.