Reviewerly/exHarmony huggingface dataset:
Neural Augmentation Using Meta-Learning for Training of Medical Images in Deep Neural Networks

Image data augmentation techniques are state-of-art method for drive of classification and segmentation of multidimensional medical imaging by method of deep neural network approaches. Neural augmentation is considered as type of data augmentation in medical image processing which is widespread and actively used method in enhancing the measures of performance of neural network. The novel approaches introduced on the grounds of deep network which expanded tremendously over the years as it improves the clinical assistance for the doctors in the treatment of patients. The major concern in healthcare domain relies on identification of tumor in the medical imaging modalities at probable early stage in humans, and deep neural network plays vigorous role in the detection of tumors using neural network with possible less layers. Neural network augmentation with the gradient of meta-learning in deep learning models will assess performance of convolutional neural network (CNN) model based on assessment parameters like accuracy and loss function along with augmented images. Deep convolutional network get to trained using meta-learning approach, and then, augmentation will be accomplished. This proposed work implements the neural augmentation with meta-learning approach in neural network subsequently training the dataset to extemporize accuracy rate in training and validation.
Parallel Agglomerative Hierarchical Clustering Algorithm Implementation with Hadoop MapReduce

Current expectations on popular classification methods in today's data mining software are increasing due to the large volumes of generated data from online interactive systems. Hierarchical agglomerative clustering (HAC) algorithm is a popular algorithm that is adopted for clustering both similarity and dissimilarity datasets. Solutions of data analytics with parallel and distributed architectures are promising models for storing, retrieving and analyzing large datasets. Hadoop MapReduce framework is a distributed and parallel platform to handle large datasets. Classification algorithms redefined to use features of Hadoop MapReduce method to provide an ability to solve the problem of clustering on large volume of data. In this paper, we are proposing an agglomerative hierarchical clustering with parallel implementation, using MapReduce approach for large dataset. It divides dataset into data blocks; each data block is classified to groups on different distributed data node of Hadoop MapReduce with agglomerative hierarchical clustering method. The implementation is fully MapReduce programming model, which will have nodes that pull task assignment and global status for determining their individual node status. The implementation defines an execution environment that facilitates algorithm execution over different datasets. The significance of this approach provide the proposed clustering model over the distributed environment with an increase in efficiency for large data analysis.
A Deep Drive into Knowledge Management for Improving Software Process and Product: Visions and Research Directions

Software engineering organizations strive to develop the high-quality software. To accomplish the goal of the developing a high-quality software, the software process has to be improved. To facilitate the improvement of the software process, knowledge management plays a significant role, and the comprehensions on knowledge management studies explore the different perspectives to enhance the software process. Up to the present time, little research has revealed how knowledge management initiatives are important to enhance the development process to further improve the software product. This research appraises the literature on software development by centering and inspecting knowledge management visions in software development to address the stated research gap. Specifically, the aim of the present work is to explore the knowledge management literature, identify the knowledge gaps, and enhance future research in this domain. Our findings highlight how software process improvement has been supported by knowledge management. It also put emphasis on the prominence of information technology for knowledge management.
Hybrid Model for Deepfake Detection

In recent years, there have been booming improvements in the fields of deep learning and artificial intelligence (AI). However, the growth also has resulted in a new age of realistic digital manipulation of videos known as deepfakes. With this new research, it is difficult to differentiate between real and manipulated media. The growing popularity of deepfakes and easy availability of open source creating tools have become a concern of privacy and a threat to society. This has led to the need to develop an accurate and efficient tool that can identify and stop the harm these media might cause. Recently, there has been a lot of exploration and research for identifying these synthetic media. Meanwhile, there is a lot of focus to find the best feature scales for detection and improving the existing algorithms. Features and attributes such as background comparison, eye blinking patterns, facial artifacts, and pose estimation are currently being used. The proposed Hybrid Model for Deepfake Detection (HMDD) which is aimed to achieve better performance in detecting.
A Genetic Algorithm Framework to Solve Two-Dimensional Maze Problem

Genetic algorithm (GA), which is an instance under the repository of evolutionary algorithm (EA), follows the problem-solving approach inspired from biological activities like mutation, crossover and selection. As an attempt of showcasing the performance of GA on simple real-world optimization problems, this paper proposes to present a genetic algorithm framework to solve the two-dimensional toy maze problem. The proposed framework is named as GAmaze. The novelty of GAmaze lies in the way it updates the population from generation to generation. The GAmaze makes use of fitness information of the candidate solutions to optimize the individuals’ learning capacity and to make them reach the final goal by making them to understand how to overcome obstacles and find a path. The detailed methodology of the proposed GAmaze is presented in this paper. The experimental results revealed that the GAmaze could solve different instances of the maze problem. The detailed inferences along with the limitations are presented in this paper.
A Comparison of Hourly Solar Energy Generation Forecasting Using RNN and LSTM Network

Nowadays, various advanced techniques, like big data and deep learning, have been used in energy management systems for improving energy efficiency. Various research has been done on solar power generation prediction using multiple machine learning approaches. However, it is not easy to make exact predictions due to the alternative nature of solar energy. Therefore, this work aims to accurately implement and compare the neural network models to predict solar power generation. The models in this study are implemented using Python, TensorFlow, and Keras. The performance of the models is evaluated using 500 KWp grid-connected plant data. This work forecasted a solar power generation for 72 h using a time series dataset. Approximately, three years of the dataset are collected from January 2019 to October 2021 with hourly resolution from Airport Depot, Delhi, India. This paper compared the performance of the recurrent neural network (RNN) and long short-term memory (LSTM) in the form of accuracy. The three-year energy generation dataset is split into train and test data. The train data uses three years of energy generation data from January 2019 to September 2021, while the test data are used for October 2021. As a result, the determinant of coefficient (R $$^{2}$$ ) is a statistical error matrix representing the predicted values’ accuracy. The results show that the LSTM model predicts solar power generation with high accuracy (R $$^{2}$$ = 0.92).
Leveraging Social Media for Second Language Spanish Pronunciation Instruction

Broadly, research suggests that second language (L2) learners benefit from explicit pronunciation interventions (e.g., Kissling, 2013; Yoshida &amp; Fukada, 2014); however, COVID-19 has impeded the teaching of various aspects of L2s (Yi &amp; Jang, 2020), such as pronunciation. Twelve L1 English speakers from a U.S. college participated in two interventions via Instagram tutorials incorporating the IPA and articulatory features for instruction on the five vowels (/a,e,i,o,u/) and word-stress in Spanish (Lord, 2005). Tutorial presentation was counterbalanced between two participant groups. Participants recorded themselves reading a controlled, continuous speech task (Offerman &amp; Olson, 2016), and ten tokens were analyzed in a pretest and posttest to assess changes in vowel and stress production. Tokens consisted of Spanish and English cognates (e.g., color), as these cognates are often pronounced with the vowel height, diphthongization, and word-stress of English, leading to differences that can impede intelligibility (Simões, 1996). Results were analyzed via a Likert scale rating of 1-5 (1=Non-native; 5=Native-like) by three native or native-like Spanish speakers, concluding that learners significantly improved their production of Spanish vowels with a slight trend for improvement in stress.
Bicriteria Algorithms for Maximizing the Difference Between Submodular Function and Linear Function Under Noise

Submodular optimization is an essential problem in many fields due to its diminishing marginal benefit. This property of submodular function plays an important role in many applications. In recent years, the problem of maximizing a non-negative monotone submodular function minus a linear function under various constraints has gradually emerged and is widely used in many practical scenarios such as team formation and recommendation. In this paper, We focus on maximizing a non-negative monotone normalized submodular function minus a linear function under $$\epsilon $$ –multiplicative noise and the result is similar in the case of $$\epsilon $$ -additive noise. Many previous studies were conducted in a noiseless environment, here we consider optimization of this problem in a noisy environment for the first time. In addition, our study will be conducted under two situations, that is, the cardinality constraint and the matroid constraint. Based on these two situations, we propose two bicriteria approximation algorithms respectively and all these algorithms can obtain good results.
Learning Signed Network Embedding via Muti-attention Mechanism

In consideration of most signed network embeddings only focusing on the low-order neighbors of the target node, they fail to make effective use of the high-order neighbors of the target node, and the link direction and sign of the node neighbors will affect the target node to varying degrees. Therefore, a SNEMA model using structure balance theory a multi head attention mechanism to aggregate high-order neighbors is proposed. The model gathers the information of high-order neighbors based on structural balance theory, captures node neighbors of different structure types through a multi head attention mechanism, obtains the low-dimensional feature vector representation of nodes through processing and learning, and applies the obtained network representation to the downstream task of link prediction. The experimental results on four real social network data sets show that the network representation obtained by the SNEMA model helps to improve the accuracy of link prediction, which shows that the SNEMA model has achieved better results in signed network representation learning.
Security on Ethereum: Ponzi Scheme Detection in Smart Contract

Ethereum has many transaction security issues such as Ponzi schemes, which are hidden in a large number of smart contracts. And they are difficult to be detected. Therefore, we propose a novel multi-granularity multi-scale convolutional neural network model (MM-CNN) to detect Ponzi schemes in smart contracts. A multi-granularity method is used to compress the smart contract opcodes with similar function to obtain multi-granularity frequency data of opcodes in MM-CNN. Then, we use a multi-scale convolution kernel to extract features of frequency data. The experiments show that the frequency features are the best measurements to represent the attributes of the Ponzi scheme. In the multi-granularity method, fine-grained opcode has a stronger ability to express Ponzi attributes. The recall rate of MM-CNN on the verification set is 98.07%, which shows the effectiveness of the scheme.
In Search of a Common Spectrum Vocabulary: Spectrum Sharing and Interference Taxonomies

New technologies and the resulting change to regulatory approaches has led to an explosion of complexity and crowding in the radio spectrum. This rapidly evolving landscape has led to the increased importance of two fields: interference management and dynamic spectrum sharing. However, the progress of both fields is hampered by the lack of shared vocabularies which, if developed, would streamline communication between relevant parties in government, industry, and academia. This paper first presents a taxonomy to aid in the description of key spectrum sharing concepts and architectures to achieve a similar goal. Next, it defines a hierarchical taxonomy of physical-layer interference as an educational and practical tool for detection, identification, location, reporting, mitigation, and remediation of interference. Armed with these new tools, the use of these two taxonomies is demonstrated through the application of the classification systems to spectrum-sharing regimes and prominent case studies.
CS-Net: A Two-Step Epithelium Tissue Segmentation Regression Network with CS-Gate Attention on Histology Images

Background and Objective: The segmentation of oropharyngeal epithelium tissue within Haematoxylin&Eosin(HE)-stained pathological images is of great clinical significance for the pathological analysis and diagnosis. However, due to the imperfection of staining, staining fainting, or variations between staining methods in different labs, the contrast between epithelium and stroma may not be obvious in the HE-stained image.Materials and methods: To address these challenges, we propose a two-step method, including a stain-style transfer step and a threshold segmentation step. In the first step, we propose a stain-style transfer network named CS-Net, together with our proposed attention module named CS-Gate, to convert the HE-stained image into an Immunohistochemistry (IHC)-stained image, aiming to enhance the appearance of epithelial tissue. In the second step, we perform threshold segmentation on the synthetic IHC-stained image to finally obtain a binary mask of oropharyngeal epithelium tissue.Results: We conduct experiments on Oropharyngeal Cancer Tissue-Microarray Image dataset, and the results show that CS-Net can generate a more precise binary mask than UNet-based segmentation networks(achieving an accuracy of 92.48%), while restoring colors and details better than general GAN-based networks (about 10% higher in Structural Similarity). Then we use Breast Cancer Cases (BCSS) public dataset as external validation, and find out that CS-Net has a stronger generalization capability. The code of CS-Net is available at https://github.com/frankie98kung/CS-Net.
Air Finger Pressing Activity-Based Character Input System on a Virtual Keyboard

Non-touch-based keyboard became an urgent need in many situations where humans cannot directly touch the keyboard due to hygienic or security issues in human computer interaction. In this paper, a non-touch keyboard has been created using Leap Motion and Unity. The key layout is based on the qwerty type. There are five types of keyboards that we have created to provide support for both Japanese and English characters. The five types are default type, orthogonal type, round default type, round orthogonal type, and flick keyboard. In addition, a word prediction function has been implemented to assist user input. A word prediction function was added to the keyboard of the round orthogonal type, which has the highest input accuracy. It’s input accuracy was 96.84% and the character per minute (cpm) was 77.67. The flick keyboard recorded an input accuracy of 97.6% and a cpm of 56.485. This is comparable to other studies in terms of input accuracy and much higher in terms of cpm.
Metadata Interoperability and Multilingual Information Retrieval in Multitenant Enterprise Learning Platforms: An Approach

There is an increased focus today on continuous learning of the workforce to drive sustainable growth of an enterprise. To achieve this, an effective means for creating, storing, and retrieving learning resources within an enterprise is acknowledged as most critical. Multitenant learning management platforms of today must address the challenges that arise in large learning organizations due to factors like diversity of content and content providers, a multitude of modes and mediums of learning, cultural legacy, information security and sensitivity, as well as ownership and licensing considerations. In such a context, this work highlights the need for an extended standard for multilingual metadata interoperability among learning contents and processes across content providers. In the absence of such a standard, it also proposes an approach for the multitenant learning management platforms to address some of the challenges through a combination of multilingual search index design and on-the-fly metadata transformation layers.
On the Application and Design of Multimedia Assisted Physical Education —Taking the Making of Web Multimedia Cai Courseware as an Example

Using the methods of literature, induction and logical analysis, under the background of the rapid rise and vigorous development of computer multimedia technology and has become an indispensable element in the field of education and teaching, this paper deeply analyzes the advantages and disadvantages of multimedia to classroom teaching, and points out the problems existing in the actual teaching process by taking the production of Web Multimedia CAI courseware as an example, This paper explores the construction of the framework diagram of sports multimedia teaching curriculum system, puts forward the construction of Sports Multimedia Curriculum interface interaction form, technical system design, steps, database connection and login window, and carries out technical system test and monitoring on the application of multimedia curriculum system, in order to provide innovative reference for the classroom mode of multimedia assisted sports teaching.
Quantitative provenance analysis through deep learning of rare earth element geochemistry: A case from the Liuling Group of the East Qinling Orogen, Central China

With the ever-growing availability of massive geo-data, deep learning has been widely applied to geoscientific questions such as sedimentary provenance analysis. However, randomly selected initial weights (and also biases) and possible loss of population diversity in traditional neural network learning remain problematic. To address this issue, in this study, we proposed a new deep neural network model by incorporating genetic algorithm (GA) and simulated annealing algorithm into the BP neural network, i.e., the GA-SA-BP model. We then applied this new model to rare earth element (REE) geochemical data of the Liuling Group of the East Qinling Orogen to investigate its provenance. Our results showed that among other deep learning algorithms, the new model presents the best performance with good measuring metrics (e.g., over 85% of accuracy, over 0.82 of F1-macro-average, F1-micro-average, and Kappa coefficient, and smallest (&amp;lt;0.15) Hamming distance). Here, we interpreted in accordance with the classification results that the southern margin of the North China Craton and the South Qinling Orogen are likely two major sources of the Liuling Group, suggesting a bidirectional deposition route of sediments from the north and south. Therefore, we proposed a foreland basin environment as the likely tectonic setting for the Liuling Group, which is consistent with current geological understanding. Our observations suggested that the GA-SA-BP model (or improved deep learning models) coupled with REE geochemistry is capable of provenance analysis.
Implementing efficient attribute encryption in IoV under cloud environments

CP-ABE is a flexible cryptographic algorithm that enables efficient implementation of access control techniques in cloud architectures. However, when CP-ABE is applied to the IoV environment, the weak performance of IoV individuals will inevitably reduce the efficiency of the overall cryptosystem. In addition, various contingencies in IoV require CP-ABE to be able to dynamically respond to various access requests, and generating only a single ciphertext cannot satisfy various additional access requests. In this paper, we propose an encryption algorithm VM-CP-ABE for IoV in cloud environment, which has the features of offline encryption and outsourced decryption. The offline encryption process utilizes the large amount of offline time of individuals in the vehicular network to generate the initial ciphertext segments, while executing its secret sharing process for each offline phase of the ciphertext segments, thus completing the initial combination of ciphertext parameters. The outsourced decryption can generate transformation keys, thus stripping the most complex pairing operations in the decryption process from the weak performing IoV individuals and transferring this part of operations to the more powerful outsourced individuals. In addition, VM-CP-ABE can generate special hierarchical ciphertexts that will have a smaller ciphertext space. And it can avoid the problem of space efficiency due to the high volume of access control policy transfer. We show the static security of all mechanisms of VM-CP-ABE and conclude with a detailed efficiency analysis. VM-CP-ABE is more time- and space-efficient than traditional attribute encryption for extensive sets of IoV attributes in cloud environments.
Decision-directed Kalman particle filter with application to the MIMO phase noise channel

Demodulation of data transmitted over time-varying channels with a free running hidden Markov state, like the phase noise channel or the fading channel, requires that the receiver tracks the hidden channel state. The tracking technique adopted in the paper is based on non-data-aided sequential importance sampling, also known as particle filtering. The paper proposes a new particle filtering framework for data communication receivers based on an importance distribution such that each individual particle becomes a decision-directed Kalman filter relying upon its local symbol-by-symbol hard decisions. In this framework, different particles are left free to take different sequences of decisions. This leaves to the receiver the possibility of exploring different sequences of transmitted modulation symbols. The weight of the particle will be high for those particles that took in the past the correct sequence of decisions, while will be low for those particles that took wrong decisions. In the resampling procedure, particles with high weight will survive, while particles with low weight will be terminated, leaving space to the birth of new particles resampled from the surviving ones. The crucial point in importance sampling is the choice of the importance distribution and the main novelty of the paper is the proposal of an importance distribution such that the particles of the particle filter become decision-directed Kalman filters. One important benefit brought by our proposed method is that, being non-data-aided, it does not need pilot symbols, thus allowing to preserve the transmission rate. A significant application example, presented and developed in the paper, is constituted by MIMO systems affected by phase noise, where the channel state vector consists of many parameters.