Reviewerly/exHarmony huggingface dataset:
Chinese Medical Named Entity Recognition Based on Parameter Transfer Learning

To reduce the dependence on the labeled data in the target domain of Chinese medical named entity recognition task, we studied the application of parameter transfer learning in Chinese medical named entity recognition. The method firstly combines the data of different domains with the target domains data for word embedding training, so as to achieve semantic information sharing at the representation layer. Secondly, the internal encoding layer parameters of the source domain model are transferred to the target domain model by training the source domain model. Finally, the parameter transfer is combined with the constructed weak label dataset to solve the inconsistent distribution of labels in target and source domains, which means the parameter transfer in decoding layer is achieved. The bottom-up parameter transfer of the neural network is achieved through the above steps. Experiment shows that the proposed method can successfully improve the recognition performance of the target domain model on small samples, and reduce the dependence of the target domain labeling data.
Conditional Generative Adversarial Network Based Workload Generation for Cloud Cluster

Improving cluster utilization by scheduling and decision-making is a long-standing research problem for cloud vendors. The workload models can improve decision-making by providing a provision of the future workload for the public cloud scheduler. However, capturing the correlations in real traces was proven to be hard in former research. In this paper, we introduce a conditional generative adversarial network (CGAN) based model, which can provide a long-time provision for the cloud cluster. In the proposed approach, the workload model is generated by three-stage training with conditional GAN, which is not limited by the assumption of Poisson distribution. Besides, the conditional representation is redesigned based on Time2Vec, which makes the inter-job correlations among virtual machines (VMs) can be modeled accurately. According to our validation, the job arrival model accuracy is raised from 52.7% to 80.3% compared with the Poisson regression method, which indicated that the proposed CGAN-based model is a more universal and accurate generative model for large-scale cloud clusters.
Creation of an Improved Software Testing Framework for the Cloud Environment

Software testing is the process of identifying flaws as a programme is being run so that a client can receive software that has no defects. Testing is the methodical, step-by-step detection of various sorts of defects with the least amount of time and effort possible. Software testing is a crucial tool for evaluating the quality of software. Testing is a method used to evaluate the quality, accuracy, and completeness of generated computer software. Testing is used to check performance, safety, fault tolerance, and security in addition to discovering mistakes. The most crucial method of software quality assurance is testing, which accounts for up to 40% of the budgets of some software companies.The heterogeneous and virtual networks are the main focus of this research. Therefore, we have found that explaining cloud computing is the best strategy when conducting research. There are various tools of cloud like cloudsim (To simulate cloud & its software architecture), live migration on cloudsim (To test service migration), Green cloudsim (to test service on cloud for application on green cloud & testing as per reliability as per new green computing norms) are available for software testing and analyze their performance by collecting simulation results.
Research on Robot Intelligent Control Method Based on Deep Reinforcement Learning

In order to improve the local obstacle avoidance ability of intelligent robots, an obstacle avoidance strategy based on deep reinforcement learning is proposed. The double-stream Q network structure is utilized to train and process the laser ranging data, and the motion data of moving obstacles is used as the observation input, which solves the problem of robot local obstacle avoidance effect in complex, dynamic and unknown environment. Simulation results show that after 1000 simulated obstacle avoidance scenarios, the average reward value of double-stream Q network structure is 780.5, and its average number of moving steps is 809.4. Compared with the other two basic deep reinforcement networks, its learning speed is faster, reward value is higher, and effect difference is more obvious, which indicates that it is more superior in solving the robot local obstacle avoidance problem.
Overview of Incorporating Nonlinear Functions into Recurrent Neural Network Models

This paper describes training recurrent neural networks (RNNs) which are able to learn features and long-range dependencies from sequential data. Although training RNNs is mostly plagued by the vanishing and exploding gradient problems, but there is a RNN architecture so called long short-term memory (LSTM) to address these issues. To demonstrate the impact of nonlinearity of activation functions on training recurrent neural networks, the limitations of the LSTM algorithm on speech recognition technology are presented in this study.
Automatic Venue Allocation for Varying Class Sizes Using Scoring and Heuristic Hill-Climbing

The following paper addresses an approach to the problem of venue allocation. Currently, classes are assigned to venues that are inadequate in terms of size and are inconveniently situated from the respective faculty building of the class. This results in classes from different schools being allocated venues across a university. Therefore, the aim of this research is to design an algorithm to aid appropriate and effective class allocation. In doing so, a Bayesian network was implemented to generate synthetic data, and Bayesian estimation was used to learn the parameters of the network. Thereafter, the network was scored using a greedy hill-climbing approach with random restart to ensure a global optimal solution. The program was tested with various datasets of different sizes, displaying the scalability of the program. This approach was more efficient than a brute force solution to the problem as the system produced the best allocations of venues to classes in time complexity of $$O(kn^2)$$ . It also favors closer venues over venues with slightly better accommodation. Ultimately, this solution will allow universities and other academic institutions to allocate the best venue, in terms of size and distance, to each class, improving the overall functioning of a university or academic institution.
FROM THE DESK OF EDITOR-IN-CHIEF

FROM THE DESK OF EDITOR-IN-CHIEF It is a New Dawn, a New Era, heralding the reviving of the “The Nursing Scope”, a journal of destiny that went comatose in 1991 after the publication of volume three (3), Issue 1. From that time, it seemed as if the excellent work done by the Pioneer Editorial Board and the past National Executives of University Graduate Nurses Association of Nigeria (UGONSA), also known as Graduate Nurses Association of Nigeria (GNAN) was wasted efforts. However, Destiny said “No”, by prompting young visionary leaders, who are members of UGONSA, including some past National Secretaries in Diaspora to mobilize and see that the seeming wasted efforts of the pioneer team are not wasted efforts. This is the rebirth of our Nursing journal. The doggedness, commitment, and resilience of this group in making this feat a reality is worthy of the praise and support of all Nurses both in Nigeria and in Diaspora. The road to establishing a channel to give the Nursing Profession in Nigeria, a voice to reckon with in academic and clinical matters and in other areas of Nurses' contributions to the health system has been very tortuous and rigorous. This is not surprising, taking cognizance of the difficult and tortuous terrain Nursing Education itself in Nigeria has passed through to this point in time. Nevertheless, substantive progress has been made in Education, Research, and Practice. I, therefore, urge us all to come together in the spirit of teamwork to increase the tempo and speed to make, “The Nursing Scope” one of the best global journals through our collective efforts. Together we can make this happen. YES, WE CAN! Prof. (Mrs.) Ijeoma O. Ehiemere, Editor-in-Chief, The Nursing Scope
Inflation’s Impact on Sudan Exports 1990-2020: An ARDL Approach

Purpose: This study investigates the inflation-export nexus in Sudan over the period 1990- 2020. Design/methodology: The study is based on quantitative and qualitative methods, estimating the export function and measuring the impact of its determinants over a prolonged period of time; the Autoregressive Distributed Lag (ARDL) methodology was used to analyse the co-integration. Findings: Findings have undoubtedly shown inflation’s negative impact on exports over the period 1990-2020. Broadly speaking, the long-term results indicated that the most important variable affecting exports is the gross domestic product (GDP), followed by inflation. Interestingly, the results indicated that the exchange rate was not significant, neither in the short nor in the long term. Implications: The study recommends inflation's control policy as a perquisite for an export development strategy; this overcomes barriers and paves the road for shifting Sudan's economy to productive agendas. Therefore, targeting inflation will contribute to export diversification and strengthening the product's value chain. The research findings reconsider the weight of export’s determinants and will reposition focusing to inflation control rather than exchange rate policy. Originality/value: The paper introduces a new approach in modelling the inflation-export nexus: (1) elaborate on export's determinants and their weights; and (2) recommending guidelines to adapt inflation policy with export development strategy. Keywords: Export, Inflation, Sudan economy, ARDL.
Multi-scale Convolutional Feature Fusion For 6D Pose Estimation

In order to obtain accurate pose estimate and satisfy real-time needs, 6D pose estimation of objects is an important task to handle challenging under certain circumstances, such as noisy background, and lighting fluctuations. We propose a 6D pose estimation method with the multi-scale convolutional feature fusion based on the correspondence point method. First, the multi-scale convolutional feature fusion module is added to the encoder-decoder structure corresponding to the extraction of critical points to obtain multi-scale information, and enrich the features extracted from each layer of the network. At the same time, the chain of convolutional layers with residual learning is added in the skip connections of the network to effectively solve the problem of too large a gap between the semantic information of the higher and lower layers. The experimental results demonstrate that the ADD(-S) metric and 2D projection metric on LINEMOD dataset reaches 96.22% and 92.5%, respectively, which are improved compared with the DPOD network based on the correspondence point method. The accuracy is improved by 9.3 and 9.8 percentage points, respectively, for smaller size objects in the dataset. The effectiveness of this method is demonstrated in the 6D pose estimation task based on the corresponding point.
Synthetic aperture Radar image denoising algorithm based on improved anisotropic diffusion

In order to solve the problem of speckle noise in synthetic aperture radar (SAR) images, an image denoising algorithm based on improved anisotropic diffusion is proposed in this paper. In this algorithm, the diffusion coefficient c(x) based on the P-M equation diffusion filtering algorithm is improved to obtain the SAR image filtering algorithm, and the iteration termination condition is added.This method can not only solve the problem of isolated noise points existing in traditional P-M model filtering, but also has a good effect on image edge preservation. The simulation results show that the improved P-M model can eliminate the noise and keep the edge information of the image well.
Evilpi: Exploiting Public Wi-Fi Using Raspberry Pi and Prevention Techniques

A technological advancement is made every day in the world, which aims toward making everyone's life a little easier. The internet, smartphones, cars, planes, smart homes and so many more, all help us in our day-to-day life. With advancing technology there is a price to pay, there are hackers who want to exploit or use the technology for their own personal agendas. One such advancement is the Raspberry Pi Computer; a credit card-sized computer, capable of processing data/information just like a normal computer. It has infinite applications in robotics, automobile, aeronautics, and smart technologies like smart homes and offices, but at the same time, it can be used by "Black or Red Hat Hackers" who want to use it for stealing information or making a political statement. Most of the security-related papers focus on how to mitigate attacks without even telling 'What is the attack', 'How can it be harmful?' or 'How the attackers actually do it?' This paper focuses on different ways to attack Public Wi-Fi and Networks using different network attacks like Evil Twin Attacks, DNS Spoofing, and De-Authentication Attacks. The paper tells about how can an attacker, performs the attack using Raspberry Pi without even inputting any commands and also elaborates upon how can a person protect his or her private systems like smartphones or laptops from these attacks.
PARTIAL IMAGE ENCRYPTION USING DCT IMAGE-BASED DES ALGORITHM

Recently, there has been a lot of interest in the DES algorithm, due to its importance in data encryption. DES and PIN code are always synonymous, DES has also been adopted in non-digital media such as phone lines, audio audiences and the banking industry. One of the advantages of the DES algorithm is that it works on binary numbers (1s, 0s) and bits. In our proposal, we present a new method for partial encryption-images by a specific approach from the frequencies of DCT coefficients. The best solution to such risks is encryption of all data of these applications, this procedure is inconvenient when dealing with images because if users want to obtain a vision of such data, they require a complete data processing for decryption, which requires a large processing capacity. In this paper, we show how partial encoding can meet application requirements without full encoding load, in the introduction paragraph, reference is made to diagrams explaining the combination of image coding and coding. Then, accreditation is an approach that implements partial image encoding based on JPEG. This procedure is based on a technique that aims to perform two basic conditions: The first is to keep the value of the public bit, while the second is to keep agreeing with JPEG format. We present a new proposal that includes multiple coding, enforceability, self-sufficiency, spatial selectivity, and coordination compliance. We also show how the application needs are met in real-time. When comparing this style with other technology as quad tree AES. Keywords: Partial Encryption, DCT, Selective Encryption, Pluralism DOI： https://doi.org/10.35741/issn.0258-2724.58.1.49
Electronic Health Record Management through Blockchain and Cloud Computing

The research paper describes a solution aimed at improving the existing standards for patient data management through Electronic Health Records (EHRs). The doctrinal solution for sharing and storing patient records is the relatively insecure digital Electronic Health Records (EHRs), which are neither present on a network nor cryptographically secure enough to be considered safe from breaches. This conventional method of storage deprives doctors and researchers of capitalizing on modern technologies and healthcare engines to perform clinical tests, improve patient care, and contribute to future clinical research. The existing solution also has the downside of being exposed to data breaches and cyber-attacks, as well as the lack of a trustworthy and consistent data-sharing platform, which leaves the healthcare industry with no alternative other than sharing this sensitive data over email, which comes with the added drawback of restricting and delaying patient access to their healthcare data. The main idea behind this research project is to simply tackle and overcome these two major obstacles—data availability and data security—by creating a platform with an economical design. Patient data management using blockchain and cloud computing has the potential to significantly improve the security, privacy, and efficiency of healthcare systems. Blockchain technology, which was originally developed for use in cryptocurrencies such as Bitcoin, is a decentralized, distributed ledger that allows multiple parties to securely record transactions without the need for a central authority. In the context of healthcare, blockchain could be used to store and manage patient data, including medical records, test results, and prescription information. On the other hand, cloud computing refers to the use of remote servers hosted on the internet to store, process, and manage data. In healthcare, cloud computing can be used to store and manage large amounts of data, including patient records, medical images, and research data. Combining blockchain and cloud computing can offer several benefits for patient data management, such as improved security, enhanced privacy, and greater availability. Our main objective, therefore, in this project is to put patients at the center of the healthcare system and provide services backed by secure standards.
Innovative Security Technology for Kyc Documents Record Maintenance

Data security is a practice of physical, administrative and data security. It also includes organizational policy and procedures. Data security is extremely important. Hashing plays a very important role in data security. Hashing is the process of translating a given key into a code. A hash function can be used to generate a new digital fingerprint. Hash algorithms are typically used to show that the file being was not changed by an intruder. Data Hash ensures that you are verified every time you interact with a business. It offers a detailed audit trail, so there’s no uncertainty when it comes to your information. In this paper, we proposed the concept for the secure concept of the KYC verification.
Taylor Neural Network for Real-World Image Super-Resolution

Due to the difficulty of collecting paired Low-Resolution (LR) and High-Resolution (HR) images, the recent research on single image Super-Resolution (SR) has often been criticized for the data bottleneck of the synthetic image degradation between LRs and HRs. Recently, the emergence of real-world SR datasets, e.g., RealSR and DRealSR, promotes the exploration of Real-World image Super-Resolution (RWSR). RWSR exposes a more practical image degradation, which greatly challenges the learning capacity of deep neural networks to reconstruct high-quality images from low-quality images collected in realistic scenarios. In this paper, we explore Taylor series approximation in prevalent deep neural networks for image reconstruction, and propose a very general Taylor architecture to derive Taylor Neural Networks (TNNs) in a principled manner. Our TNN builds Taylor Modules with Taylor Skip Connections (TSCs) to approximate the feature projection functions, following the spirit of Taylor Series. TSCs introduce the input connected directly with each layer at different layers, to sequentially produces different high-order Taylor maps to attend more image details, and then aggregate the different high-order information from different layers. Only via simple skip connections, TNN is compatible with various existing neural networks to effectively learn high-order components of the input image with little increase of parameters. Furthermore, we have conducted extensive experiments to evaluate our TNNs in different backbones on two RWSR benchmarks, which achieve a superior performance in comparison with existing baseline methods.
Color Multiscale Block-ZigZag LBP (CMB-ZZLBP): An Efficient and Discriminant Face Descriptor

Literature reports numerous local descriptors based on extracting rich information from color space formats. The color scale format provides more robustness as compared to grayscale counterparts. This work introduces such descriptors called Color Multiscale Block-ZigZag LBP (CMB-ZZLBP) for Face Recognition (FR). CMB-ZZLBP is the advanced method of MB-ZZLBP. In MB-ZZLBP, first mean patch is generated (from 9 regions of the 6 × 6 patch) and then zigzag pixels are compared to develop MB-ZZLBP code. MB-ZZLBP forms the histogram representation of 256, by computing MB-ZZLBP code in each position. The major issue with MB-ZZLBP is that it restricts its robustness due to grayscale feature extraction. By introducing CMB-ZZLBP, this issue is resolved effectively. In CMB-ZZLBP, the MB-ZZLBP feature extraction is done from each component of the RGB color space format. Further, all three channel features are integrated to build the CMB-ZZLBP feature size. FLDA is used to achieve compressed feature representation, and classification is performed from SVM and NN. Experiments justify the effectiveness of CMB-ZZLBP against MB-ZZLBP on Georgia Technology Face Dataset (GTFD). CMB-ZZLBP proves its dominance against various literature techniques also. CMB-ZZLBP secures the best ACC of 96.66% on a training size of 9.
Live Action And Sign Language Recognition Using Neural Network

Nonverbal communication, such as sign language, uses exterior body movements to convey key messages either simultaneously or concurrently with spoken words. Body movements are a part of sign language. Different types of physical nonverbal communication, such as proxemics, solely expressive displays, and displays of shared attention from the specific messages are conveyed by gestures. Culture-specific gestures can transmit quite diverse meanings in various social or cultural context. In this project we are going to track the live human actions and recognize the pattern of the languages. To identify each movement of the joint , elbows and face. The Deep Learning algorithm is given in this project to allocate live video action of the human signs languages in words and sentences. The proposed and current algorithms will convey the live action body language classification based on LSTM which is greater than the other algorithms. The success of the outcome gained is anticipated to rise if the NN method is supported by adding sequential input of continuous frame.
Organ Donation and Transplantation Framework based on Ethereum Blockchain

The organ donation and transplantation methods in use today present a variety of requirements and barriers as well as technical, clinical, ethical, and legal limitations regards registration, matching of donors and recipients, organ procurement, delivery, and transplantation. In order to provide a fair and effective procedure, a complete framework for organ donation and transplantation is necessary as well as to enhance patient satisfaction and trust. In this paper, we describe a completely decentralised, secure, trackable, auditable, private, and trustworthy private Ethereum blockchain-based system for coordinating organ donation and transplantation. We create smart contracts and outline the development, testing, and verification of six algorithms. By performing research on privacy, security, and confidentiality and contrasting our solution with the available alternatives, we assess the effectiveness of the suggested solution.