Reviewerly/exHarmony huggingface dataset:
Understanding and Comparative Analysis of Consensus Algorithms

Blockchain technology has emerged as a new security technology that offers a variety of benefits to numerous businesses. Blockchain is working on the concept of distributed ledger technology. Multiple industries are adopting blockchain technology due to its different features like distributed networks, consensus mechanisms, and transparency. Blockchain technology gives a great impact on the business environment. Blockchain technology does not involve third-party interference and provides security and transparency to data. The data inside the blockchain is immutable, and no one will be modified it or delete it. Smart contracts and consensus algorithms are the major components of blockchain technology. A smart contract is a sort of computer software that establishes agreements between the nodes of a blockchain network’s participants. Few major consensus algorithms are PoW, PoS, PBFT, DPoS, and tendermint. This paper evaluates the different consensus algorithms and principles behind using it. Paper demonstrates the steps to be followed for the implementation of consensus algorithms.
Classification of Pap Smear Image of Cervix Cell Using Machine Learning Techniques and Transfer Learning-Based Convolutional Neural Network Architecture and Scrutinizing Their Performances

Different cervical pap smear cell categorization schemes have recently been presented, the majority of which were binary classifications of normal and abnormal cells. This paper presents the findings of a comprehensive investigation on machine learning and deep learning algorithms for binary and multi-class classification on pap smear images from the Herlev dataset. There are 917 photos in this collection, divided into seven normal and pathological categories. The Google Colab platform was used to generate models utilizing all of the techniques using scikit learn and the keras library from TensorFlow. To begin, several repetitions of processes such as feature importance selection, data normalization, standardization, PCA, T-SNE, and others have been imposed on models such as SVM and XGBoost in this work for machine learning approaches. Second, it was demonstrated in this work that a transfer learning-based CNN model from deep learning can outperform machine learning models in terms of binary and multi-class classifications. Furthermore, it was discovered in this work how computationally time efficient it is to apply a transfer learning model, which required roughly 25 min for 100 epochs. Finally, with several iterations of processes and outcomes, this work demonstrates that given enough data for a multi-class pap smear image classification system, the transfer learning CNN model has a higher potential to get the best results than the machine learning models used.
Building the blocks for open science

The value and importance of open science is a central component of how we move research and knowledge forward – sharing all elements of research is fundamental to trust, integrity and reproducibility and therefore the advancement of discovery and trusted knowledge. As a community, however, we need to get better at taking concrete steps to drive forward appropriate measures. Encouraging open sharing and research practices, promoting their benefits, and building an evidence based approach to their value – whilst important, are on their own, not enough. Collaboration across all stakeholders needs to take place – policy makers, funders, publishers, librarians, researchers and organisations/institutions. These are all important conversations that need to continue to take place to effect positive change through collaboration, awareness and innovation. Coupling real world examples with clear policy, better credit and reward systems, better tools and services, explicit funding, practical help and answers to common questions are all essential factors in accelerating openness and sharing to an established norm. There are no easy answers, and no “silver bullet”, but there is much we can act on now. This poster will draw on the thoughts of those key stakeholders across our sector to talk through the building blocks to true sustainable open science – from a code and data, platform, librarian, researcher and publisher perspective – whilst we may all be a small part of the cog, by each playing our part we can contribute, more than we have been able to before, in building the blocks for open science.
INCORPORATING EXTENSIVE READING INTO THE CURRICULUM OF ENGLISH TEACHER EDUCATION

It is believed that teachers have a significant role in helping students improve their reading performance. Hence, it is crucial to develop a good reading attitude of student teachers because they will become teachers in the future. The teacher education program should prepare student teachers as role model readers. Therefore, the curriculum needs to ensure that the courses help the student teachers build their reading habits. One recommended way to improve reading performance is through extensive reading. This study employed descriptive qualitative research to know how extensive reading is implemented in the English teacher education curriculum and how student teachers view the extensive reading practice. The data resources were from the syllabus, the reflection journals, and the interviews. The study revealed that an extensive reading program could be introduced by integrating it into an existing EFL reading course. Although the English language proficiency did not improve significantly, the student teachers got their reading interest improved and learned new pedagogical skills from extensive reading sessions. It can be concluded that extensive reading can be incorporated into the existing EFL curriculum. However, for more beneficial implications for EFL student teachers, pure extensive reading is highly recommended
Improvement of Image Compression by Changing the Mathematical Equation Style in Communication Systems

Compression is an essential process to reduce the amount of information by reducing the number of bits; this process is necessary for uploading images, audio, video, storage services, and TV transmission. In this paper, image compressions with losses from this action will be shown for some common patterns. The compression process uses different mathematical equations that have different methods and efficiencies, so some common mathematical methods for each style are presented taking into consideration the pros and cons of each method. In this paper, it is demonstrated that there is a quality improvement by applying anisotropic interpolation to edge enhancement for its ability to satisfy the dispersed data of the propagation process, which leads to faster compression due to concern for optimum quality rather than fast algorithms. The test images for these patterns showed a discrepancy in the image resolution when the compression coefficient was increased, as the results using three types of image compression methods proved a clear superiority when using “partial differential equations (PDE)”.
Live Coding in Music Theater—A Comprehensive Technique? The Case of<i>Dumrul and the Grim Reaper</i>

Abstract The author analyzes the play Dumrul and the Grim Reaper as a case study to explore the possibilities of live coding in the context of music theater. The live coding method, which focuses on a single main application window, allows one to effectively execute several distributed tasks simultaneously, such as music composition, sound design, sound effects, and musical performance in real-time without losing track of the theatrical text’s flow. In terms of the compositional approach, live coding will be proposed as a holistic sound and music creation technique that centers on using textual systems to depict elaborate forms of artistic expression.
High-speed FPGA-based Design and Implementation of Text Search Processor

In the age of computer evolution, the number of data grows swiftly. Moreover, the requirement of extracting the information from the database becomes urgent. Full-text search provides methods to quickly locate multiple keywords inside extensive text data and has gained more consideration in recent years. The proposed tools, such as Lucene, Hyper Estraier, and Namazu, are based on general-purpose processors. They spend more time on index input documents and require more space to store these indexes. In this work, we provide a text search processor design that could perform the full-text search without indexing. The text search processor offers a high-performance, high-level of parallelism and scalability. The design is deployed on Field Programmable Gate Arrays (FPGA) platforms. More than 70K processing units can be integrated on Xilinx Alveo U50. The working frequency achieves 266-MHz after place and route.
Sensor Cloudlet Interconnecting System for water Reservoirs Security

DAM management systems in India are executed manually and not having any emergency control, unexpected and overcast management. Manual DAM maintenance is one of the challenging tasks due to large geological area and depth. This paper has proposed an design strategy using ESP826 node MCU based auto regulation operation cloudlet design with distinct sensors such as Ultrasonic sensors, temperature and humidity sensor, rainfall sensor and servomotors to detect water pressure, water level, and water flow and warning system. An AI and Block chain (BC) based secure system with sensor cloudlet is effective future technologies to avoid any disaster with added security to DAM infrastructure. IoT systems challenges like heterogeneity, poor interoperability, supply limits, confidentiality and safety loopholes can be overcome with BC and IOT collaboration, improves privacy and security with more reliable solution.
NISQ-Friendly Non-Linear Activation Functions for Quantum Neural Networks

The current generation of quantum computers calls for quantum algorithms that require a limited number of quantum gates and are resilient to noises. A suitable design strategy is variational circuits where parameters of circuits are determined through training, an approach that conforms with characteristics of machine learning applications. In this paper, we propose a low-depth and implementation-efficient non-linear activation function for quantum neural networks (QNNs). The building block of a quantum circuit is quantum gate which is a unitary operation. Thus, building a non-linear component out of quantum gates is challenging. While the majority of prior works used measurement as the source of non-linearity, this method has limited ability in classifying datasets. We propose a quantum circuit for the popular Rectified Linear Unit (ReLU) activation function. Our proposed circuit is based on low-cost quantum gates that can be synthesized into primitive gates in contemporary quantum computers. We exploit QNNs that rely on quantum rotation to define decision boundaries for classification problems. In addition, we use controlled quantum gates to detect correlation in data through entanglement of qubits. Our evaluations reveal that QNNs equipped with our proposed quantum ReLU perform well on standard benchmark datasets while requiring dramatically fewer number of epochs for training compared with classical neural networks. In addition, we run QNNs with different number of quantum layers on an IBM quantum computer and show that our proposed circuits are practical and generate meaningful results on real quantum computers.
An self-adaptive cluster centers learning algorithm based on expectation maximization algorithm

It is called unsupervised learning that does not rely on any labeled value, and finds the relationship between samples by mining the intrinsic characteristics of samples. Clustering algorithm is a kind of unsupervised learning algorithm. Although many clustering algorithms have been studied in modern science and applied in many fields, it is their common problem that the quantity of clusters has to be specified. Based on EM algorithm, this paper proposes a cluster centers learning algorithm (CCL) which can self-adaptively calculate the quantity and parameters of clusters according to the characteristics of samples themselves. The algorithm tentatively fills the shortage of existing clustering algorithms. The paper proposes the elementary merger and splitting criteria. The criteria can determine whether a point is the cluster center according to the characteristics of samples. Based on the elementary criteria, the algorithm proposed by the paper can adapt to calculate the correct quantity of clusters and gives the corresponding clustering parameters. Monte Carlo simulation is used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the algorithm proposed by the paper can start from an arbitrary given cluster center and calculates the cluster centers close to the actual cluster centers of the samples themselves, so as to complete the self-adaptive unsupervised clustering.
Efficient Multi-disease Privacy-Preserving Medical Pre-Diagnosis Based on Partial Homomorphic Encryption

With the development of the Internet, there are more and more sensitive information on medical data, and direct use will result in the leakage of relevant information. These privacy issues largely limit the development of the medical industry, and online medical diagnosis services can break the time and region restrictions. In response to the existing privacy requirements, we use the random forest of machine learning to train the classifier. Compared with other classification models, the random forest classifier has higher accuracy and can process large-scale medical data. In the process of interaction between medical service providers and medical users, SHE (symmetric homomorphic encryption) method and Boneh-Lynn-Shacham(BLS) short signature algorithm are used to ensure the privacy and non-tampering of data during the interaction. Since both the random forest and the user query vector is in the state of ciphertext, we design a security comparison algorithm to ensure that the comparison can be completed without revealing privacy. Futhermore, a disease risk list can be obtained, which can achieve multi-disease diagnosis. We also prove that the proposed protocol is secure and efficient by security analysis and efficiency analysis.
Discrete Zhang Neural Dynamics Algorithms for Time-Varying Matrix Generalized Sinkhorn Scaling

In this paper, we first introduce a continuous model for time-varying matrix generalized Sinkhorn scaling (TVMGSS) on the basis of the continuous Zhang neural dynamics (ZND) model. Subsequently, a high-precision 10-instant Zhang time discretization (ZTD) formula with theoretical analysis is presented. Further, we utilize the 10-instant ZTD formula to discretize the continuous ZND model, resulting in a discrete ZND algorithm named 10-instant discrete ZND (10IDZND) algorithm for TVMGSS. For comparison, two other time discretization formulas are also considered, and the corresponding discrete algorithms for TVMGSS are derived. The comparative numerical experiments are performed, and the results substantiate the effectiveness and superior accuracy of the 10IDZND algorithm. In addition, we verify the effectiveness of the 10IDZND algorithm for higher-dimensional TVMGSS through numerical experiments. Finally, we experimentally investigate the effects of the design parameters and the sampling period on the convergence of the 10IDZND algorithm.
Feature Selection via Normalized Dynamic Change of Selected Feature with Class

Feature selection has been widely used in various application areas such as machine learning, bioinformatics, and natural language processing. Common drawbacks of most of the current feature selection methods are the lack of information about the dynamic change of selected features with the class, and the selection of redundant and irrelevant features. In this paper, we develop a novel feature selection method called Normalized Dynamic Change of Selected Feature with Class (NDCSF), which consider the normalized dynamic information changes between the selected features and the classes by using conditional mutual information and entropy. Moreover, a normalized feature redundancy by using mutual information and entropy is introduced into NDCSF. The experimental results on several benchmark datasets verify that the NDCSF can significantly improve the other several feature selection methods.
Decentralized Federated Learning Under Communication Delays

Federated learning (FL) provides a privacy-preserving approach to training algorithms across multiple edge agents or servers without sharing raw data. Based on dynamic network topologies, we study a fully decentralized FL framework utilizing gradient descent with momentum (MGD) to accelerate the convergence rate. Due to bounded time-varying transmission delays, model updates are asynchronous between different agents, which leads to time-varying information update delay. Extensive experiments are carried out to demonstrate the performance of DMFL over some related algorithms and further analyze the influence of information update delay, network size, and the data distribution on the convergence performance of DMFL.
Topic Recognition Using TED-LIUM Release 3

The widespread of mobile digital cameras and phones facilitate creation of multimedia including audio and video records. Categorizing the spoken documents by themes is a key task in processing the huge collection of audio files. Topic Recognition (TR) is a widely used method in categorizing text corpus since the 1980s. Automatic speech recognition (ASR) is a system that converts speech into text. Typically, topic recognition task for spoken documents is consisting of two phases, ASR phase and text topic classification (TTC) phase. In this paper, we will create two models, one is a model based on text and acoustic features by modifying the pretrained LM (Language Model) + triphone HMM (Hidden Markov Model) model using modified example script in Kaldi, another model is a three-layer neural network based on text features, we will combine these two models to complete the topic recognition task. The models will be trained and tested on the open-source TED-LIUM release-3 dataset. The first model is our topic predicting model, it will utilize the official transcripts provided in the dataset. The second model is created to decode text from audios. We then use the first model to predict topics of the decoded text from the second model. Since the second model will propagate error via the decoded text (i.e., transcripts), we would expect the prediction accuracy based on the decoded text to be moderate. The original dataset is lack of topic labels, we will create and publish an open-source topic labels dataset as the golden standard for future use. Our experiment shows that for multiple topics recognition task, random guess prediction accuracy is merely 0.11, test accuracy of the first model using the official transcripts provided by TED-LIUM r3 is 0.40, test accuracy of the first model using the decoded text from the second model is 0.28. As a reference, human prediction accuracy by the author is 0.53. Multiple topic recognition task is a hard problem, this paper provides a starting point based on the TED-LIUM r3 dataset.
Improved Bloom Filter for Efficient Image Retrieval on Mobile Device

A huge volume of images is uploaded to the mobile cloud environment every day with rapid growth of mobile devices. Different from retrieving image using a personal computer, infor-mation retrieval on mobile devices requires higher efficiency due to limitations in data transmission and memory cost. Therefore, an efficient scalable image retrieval model is proposed in this paper which consists of a two-layer bloom filter. The first layer of the proposed bloom filter generated from the asymmetric cyclical hashing (ACH) is used for primary image existence verification. In order to reduce the false positive rate commonly happening in bloom filters, the second layer of the bloom filter based on secure hashing is applied for verification in the second stage. The proposed model realizes approximated nearest neighbor retrieval with limited cost of storage and protects the stored data from illegal access simultaneously. Experimental results show that the proposed model yields significant better retrieval accuracy than other retrieval methods with more than 6 times faster in retrieval time and a smaller space requirement.
Cloud Resource Usage Forecasting using Neural Network and Artificial Lizard Search Optimization

Cloud computing is a rapidly evolving sector when it comes to efficiently providing computing services over the Internet. The benefits of increased performance, speed, productivity, reliability with reduced costs prove to be an accessory. Organizations tend to invest in forecasting services on a large scale to predict the continuum of resources required. This not only helps them in allocating reserves of funds and equipment for the future but also in efficiently handling resources and minimization. This paper proposes a workload forecasting approach using the neural network and a nature inspired Artificial Lizard Search Optimization (ALSO) algorithm. The experiments are conducted on a benchmark dataset of Google cluster trace.
Deep Temporal Sequence Prediction Neural Network for MIMO Detection

Recovering the transmitted signals in a multiple-input multiple-output (MIMO) system is known to be non-deterministic polynomial hard. It is extremely challenging to obtain a high-quality solution with fairly low computational complexity. To solve the MIMO detection problem effectively, this paper proposes to model it as a time series prediction problem, and a bidirectional temporal convolutional network (Bi- TCN) is designed to address it. In Bi- TCN, the encoder extracts the features of the received signal and the channel matrix by applying non-causal dilated convolution, and the decoder outputs the probability distribution of the recovered transmitted signal in parallel. In the experiments, we compare it with traditional and deep learning-based detectors on both i.i.d. and correlated Rayleigh fading channels, respectively. Experimental results empirically demonstrate that Bi- TCN can achieve near-optimal bit-error-rate (BER) performance with considerably low space complexity.
A Novel Fuzzy Rule Based Neuro-system with Sparse Rule Extraction for Classification Problems

The generation of fuzzy rule base and rule extraction are efficient approaches to enhance the performance of fuzzy rule system. Here, we propose a novel fuzzy neural network structure that can be used for rule extraction. First of all, inspired by the compactly combined fuzzy rule base (CoCo-FRB) and fully combined fuzzy rule base (FuCo-FRB), we develop a new fuzzy rule base, compromise fuzzy rule base (CmPm-FRB), which generates rules by cutting off the long rules and compensating the short ones. In addition, Group Lasso penalty is utilized in the objective function with the rule threshold to produce sparsity in rules in a grouped manner for rule extraction. However, as the Group Lasso penalty is not differentiable at the origin, a tiny bias term is added to the gradient formula to achieve the smoothness. In order to verify the effectiveness of the proposed model, extensive experiments are conducted on ten classification data sets. The empirical results explicitly demonstrate the effectiveness of the proposed model for classification problems.