Reviewerly/exHarmony huggingface dataset:
BAC: A block alliance consensus mechanism for the mine consortium blockchain

Abstract Safety is an important issue in the mining industry and the Internet of Things (IoT) plays an important role to enhance the safety of the underground working environment. The IoT is used to transfer data generated by underground sensors to cloud storage for further processing. However, third‐party platforms are often a target for cyber attacks. Serious mining accidents might occur if the data were tampered with. In the overground scenario, the security of trading data is also an important issue. The Mine Consortium Blockchain (MCB) is proposed to solve the above problems. The MCB avoids the risk of centralized storage and enables data security, provenance and transparency by taking advantage of blockchain technology. The MCB platform ensures that only designated participants can process mineral data. Any violation is immutably recorded in the MCB and is easily traced back by other participants. Classical consensus mechanisms as the core technology of the blockchain cannot be directly and appropriately applied to the mining industry. A Block Alliance Consensus (BAC) mechanism, which is suitable for all consortium blockchain scenarios, is proposed to improve the performance of the MCB. In addition, the block structure of the underground sensor data is optimized: the blocks only contain a hash of the sensor data and the data being stored in the cloud. The efficiency of the BAC is demonstrated by simulation experiments where the performance of the BAC consensus mechanism is compared with with the performance of classical consensus mechanisms. The MCB and the BAC consensus mechanism were also implemented on Hyperledger Fabric. Finally the Hyperledger Caliper evaluation tool was used to evaluate the performance of the system.
Deformable medical image registration based on CNN

This study aims to develop and test a new image registration method in which full-scale skip connections in the encoding process is added into the registration network, which can predict the deformation field more accurately by retaining more features and information in the decoding process.Two improved registration networks are connected in series in the registration framework. Each registration network uses the unsupervised learning registration method to predict a small deformation field, and the last two small deformation fields are superimposed to obtain the final deformation field. The model is evaluated by the oasis datasets (brain T1-weighted MRI images), one image is selected as the fixed image, while 383 images are used as training images and 30 images are used as test images. Wavelet decomposition and reconstruction are also used to enhance the image.Compared with the affine method, the voxelmorph-1 method and the voxelmorph-2 method, applying the new registration network was proposed by this study improves the registration accuracy by 28.6%, 1.2% and 0.2%, respectively.The experimental results demonstrate that the method proposed in this study can improve the accuracy of image registration effectively.
Peer-to-peer deep learning with non-IID data

Collaborative training of deep neural networks using edge devices has attracted substantial research interest recently. The two main architecture approaches for the training process are centrally orchestrated Federated Learning and fully decentralized peer-to-peer learning. In decentralized systems, edge devices, known as agents, collaborate in a peer-to-peer architecture, avoiding the need for a central system to orchestrate the process. Decentralized peer-to-peer (P2P) learning techniques are well researched under the assumption of independent and identically distributed (IID) data across the agents. IID data is seldom observed in real-world distributed systems, and the training performance varies significantly with non-IID data. This paper proposes a decentralized learning variant of the P2P gossip averaging method with Batch Normalization (BN) adaptation for P2P architectures. It is well-known that BN layers accelerate the convergence of the non-distributed deep learning models. Recent research confirms that Federated Learning methods benefit from using the BN method with some aggregation alterations. Our work demonstrated BN effectiveness in P2P architectures by mitigating the non-IID data characteristics across decentralized agents. We also introduce a variant of the early stopping technique that, combined with BN layers, acts as a fine-tuning technique for agent models. We validated our approach by conducting numerous simulations of different model-topology-communication combinations and comparing them to other decentralized baseline approaches. The evaluations were conducted on the next word prediction task using user comments from the Reddit and StackOverflow datasets representing comments from two different domains. Simulations showed that our approach, on average, achieves a mean relative top accuracy increase of 16.9% in ring (19.9% for Reddit, 13.9% for StackOverflow) and 29.8% in sparse (32.9% for Reddit, 26.6% for StackOverflow) communication topologies compared to the best baseline approach. Our code is available at https://github.com/fipu-lab/p2p_bn.
Blockchain Security when Messages are Lost

Security analyses for consensus protocols in blockchain research have primarily focused on the synchronous model, where point-to-point communication delays are upper bounded by a known finite constant. These models are unrealistic in noisy settings, where messages may be lost (i.e. incur infinite delay). In this work, we study the impact of message losses on the security of the proof-of-work longest-chain protocol. We introduce a new communication model to capture the impact of message loss called the 0-∞ model, and derive a region of tolerable adversarial power under which the consensus protocol is secure. The guarantees are derived as a simple bound for the probability that a transaction violates desired security properties. Specifically, we show that this violation probability decays almost exponentially in the security parameter. Our approach involves constructing combinatorial objects from blocktrees, and identifying random variables associated with them that are amenable to analysis. This approach improves existing bounds and extends the known regime for tolerable adversarial threshold in settings where messages may be lost.
Human Collective Intelligence Inspired Multi-View Representation Learning — Enabling View Communication by Simulating Human Communication Mechanism

In real-world applications, we often encounter multi-view learning tasks where we need to learn from multiple sources of data or use multiple sources of data to make decisions. Multi-view representation learning, which can learn a unified representation from multiple data sources, is a key pre-task of multi-view learning and plays a significant role in real-world applications. Accordingly, how to improve the performance of multi-view representation learning is an important issue. In this work, inspired by human collective intelligence shown in group decision making, we introduce the concept of view communication into multi-view representation learning. Furthermore, by simulating human communication mechanism, we propose a novel multi-view representation learning approach that can fulfill multi-round view communication. Thus, each view of our approach can exploit the complementary information from other views to help with modeling its own representation, and mutual help between views is achieved. Extensive experiment results on six datasets from three significant fields indicate that our approach substantially improves the average classification accuracy by 4.536% in medicine and bioinformatics fields as well as 4.115% in machine learning field.
Analysis of Time Factor with Resource Provisioning Frameworks in Cloud Environment for Improving Scheduling Performance

Time is critical in the resource provisioning process in the Cloud Computing paradigm when serving cloud resources to cloud users. It's difficult for a cloud provider to serve a large number of users while also reducing long wait times after they've submitted a request. It is possible to improve the time factor by using a systematic resource provisioning process. This paper examines several time-based resource provisioning frameworks in greater detail. Many researchers focused on various time parameters that assist cloud service providers in providing the best resource-serving services to their customers. The primary goal of this paper is to assist future researchers, as well as cloud providers in observing and selecting the best time-based resource provisioning technique also they can emphasize building a new dynamic resource provisioning paradigm in the future with this work’s observations. To validate these observations, a novel Particle Swarm Optimization (PSO) based model is designed in this text, which uses the selected time-based resource provisioning technique, and applies it to real-time cloud scenarios. It was observed that the proposed model was able to showcase better efficiency of scheduling, and optimum cloud utilization when compared with other time-based resource provisioning models for different cloud deployments
Experiences and Challenges of Indigenous Students in Higher Education During the Pandemic

This chapter describes and analyzes the experiences of indigenous students of higher education during the COVID-19 pandemic. They are undergraduates of the bachelor's degree of elementary education who attend a public university located in the north of Mexico, as part of an affirmative action policy that began in 2017. Through the phenomenological method, with an interpretative framework and qualitative approach, this research examines the testimonies of 15 key informants using in-depth interviews, in order to convey the academic challenges they went through during the school lockdown and isolation period of the health emergency caused by the SARS-COV-2 virus, including the development of their dissertation to obtain their degree. The chapter reveals the extreme difficulties they experienced due to the lack of suitable computer equipment, internet connection, and absence of an adequate digital competence. For the ones that had to do their thesis report in order to graduate, having to write their theses in a second language meant an additional obstacle as well.
Performance Comparison of Feature Detectors on Various Layers of Underwater Acoustic Imagery

Image feature matching is essential in many computer vision applications, and the foundation of matching is feature detection, which is a crucial feature quantification process. This manuscript focused on detecting more features from underwater acoustic imageries for further ocean engineering applications of autonomous underwater vehicles (AUVs). Currently, the mainstream feature detection operators are developed for optical images, and there is not yet a feature detector oriented to underwater acoustic imagery. To better analyze the suitability of existing feature detectors for acoustic imagery and develop an operator that can robustly detect feature points in underwater imageries in the future, this manuscript compared the performance of well-established handcrafted feature detectors and that of the increasingly popular deep-learning-based detectors to fill the gap in the literature. The datasets tested are from the most commonly used side-scan sonars (SSSs) and forward-looking sonars (FLSs). Additionally, the detection idea of these detectors on the acoustic imagery phase congruency (PC) layer was innovatively proposed with the aim of finding a solution that balances detection accuracy and speed. The experimental results show that the ORB (Oriented FAST and Rotated BRIEF) and BRISK (Binary Robust Invariant Scalable Keypoints) detectors achieve the best overall performance, the FAST detector is the fastest, and the PC and Sobel layers are the most favorable for implementing feature detection.
Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2 n possible subsets, making it challenging to select the optimum collection of features using typical methods.As a result, a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization (DTO-GW) algorithms has been developed in this research.Instability can result when the selection of features is subject to metaheuristics, which can lead to a wide range of results.Thus, we adopted hybrid optimization in our method of optimizing, which allowed us to better balance exploration and harvesting chores more equitably.We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes.In the proposed method, the number of features selected is minimized, while classification accuracy is increased.To test the proposed method's performance against eleven other state-of-theart approaches, eight datasets from the UCI repository were used, such as binary grey wolf search (bGWO), binary hybrid grey wolf, and particle swarm optimization (bGWO-PSO), bPSO, binary stochastic fractal search (bSFS), binary whale optimization algorithm (bWOA), binary modified grey wolf optimization (bMGWO), binary multiverse optimization (bMVO), binary bowerbird optimization (bSBO), binary hysteresis optimization (bHy), and binary hysteresis optimization (bHWO).The suggested method is superior 4532 CMC, 2023, vol.74, no.2 and successful in handling the problem of feature selection, according to the results of the experiments.
Deep Learning Based Face Detection and Identification of Criminal Suspects

Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade.One of the most tedious tasks is to track a suspect once a crime is committed.As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person's face who has committed the crime, but also their identity.Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network (DNN) model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed.After detection and extraction of the face in the image by face cropping, the captured face is then compared with the images in the Criminal Database.The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric.After plotting the values in a graph to find the threshold value, we conclude that the confidence rate of the encoder model is 0.75 and above.
Age and Gender Classification Using Backpropagation and燘agging燗lgorithms

Voice classification is important in creating more intelligent systems that help with student exams, identifying criminals, and security systems.The main aim of the research is to develop a system able to predicate and classify gender, age, and accent.So, a new system called Classifying Voice Gender, Age, and Accent (CVGAA) is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem, as the adaptive backpropagation algorithm had an accuracy of 98% and the Bagging algorithm had an accuracy of 98.10% in the gender identification data.Bagging has the best accuracy among all algorithms, with 55.39% accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.
Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation燨perator

The effectiveness of the logic mining approach is strongly correlated to the quality of the induced logical representation that represent the behaviour of the data.Specifically, the optimum induced logical representation indicates the capability of the logic mining approach in generalizing the real datasets of different variants and dimensions.The main issues with the logic extracted by the standard logic mining techniques are lack of interpretability and the weakness in terms of the structural and arrangement of the 2 Satisfiability logic causing lower accuracy.To address the issues, the logical permutation serves as an alternative mechanism that can enhance the probability of the 2 Satisfiability logical rule becoming true by utilizing the definitive finite arrangement of attributes.This work aims to examine and analyze the significant effect of logical permutation on the performance of data extraction ability of the logic mining approach incorporated with the recurrent discrete Hopfield Neural Network.Based on the theory, the effect of permutation and associate memories in recurrent Hopfield Neural Network will potentially improve the accuracy of the existing logic mining approach.To validate the impact of the logical permutation on the retrieval phase of the logic mining model, the proposed work is experimentally tested on a different class of the benchmark real datasets ranging from the multivariate and timeseries datasets.The experimental results show the significant improvement in the proposed logical permutation-based logic mining according to the domains such as compatibility, accuracy, and competitiveness as opposed to the plethora of standard 2 Satisfiability Reverse Analysis methods.
An Efficient Hybrid Model for Arabic Text Recognition

In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing.The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works.In this work, we presented an efficient and new system for offline Arabic handwritten text recognition.Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a Connectionist Temporal Classification layer (CTC).Moreover, during the training phase of the model, we introduce an algorithm of data augmentation to increase the quality of data.Our proposed approach can recognize Arabic handwritten texts without the need to segment the characters, thus overcoming several problems related to this point.To train and test (evaluate) our approach, we used two Arabic handwritten text recognition databases, which are IFN/ENIT and KHATT.The Experimental results show that our new approach, compared to other methods in the literature, gives better results.
Aspect Level Songs Rating Based Upon Reviews in English

With the advancements in internet facilities, people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks, reviews, ratings, etc. after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis (SA) is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects.As a case study, Songs are opted to design and test the decision model.Different aspects of songs namely music, lyrics, song, voice and video are picked.For the reason, reviews of 20 songs are scraped from YouTube, pre-processed and formed a dataset.Different machine learning algorithms-Naïve Bayes (NB), Gradient Boost Tree, Logistic Regression LR, K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) are applied.ANN performed the best with 74.99% accuracy.Results are validated using K-Fold.
Blockchain Merkle-Tree Ethereum Approach in Enterprise Multitenant Cloud燛nvironment

This research paper puts emphasis on using cloud computing with Blockchain (BC) to improve the security and privacy in a cloud.The security of data is not guaranteed as there is always a risk of leakage of users' data.Blockchain can be used in a multi-tenant cloud environment (MTCE) to improve the security of data, as it is a decentralized approach.Data is saved in unaltered form.Also, Blockchain is not owned by a single organization.The encryption process can be done using a Homomorphic encryption (HE) algorithm along with hashing technique, hereby allowing computations on encrypted data without the need for decryption.This research paper is composed of four objectives: Analysis of cloud security using Blockchain technology; Exceptional scenario of Blockchain architecture in an enterprise-level MTCE; Implementation of cipher-text policy attribute-based encryption (CP-ABE) algorithm; Implementation of Merkle tree using Ethereum (MTuE) in a Multi-tenant system.Out of these four objectives, the main focus is on the implementation of CP-ABE algorithm.CP-ABE parameters are proposed for different levels of tenants.The levels include inner tenant, outer tenant, Inner-Outer-Tenant, Inner-Outer-External-Tenant, Outer-Inner-Tenant, External-Outer-Inner-Tenant and the parameters such as token, private key, public key, access tree, message, attribute set, node-level, cipher-text, salting which will help in providing better security using CP-ABE algorithm in a multitenant environment (MTE) where tenants can be provided with different levels of security and achieved 92 percentage of authenticity and access-control of the data.
A Dual Attention Encoder-Decoder Text Summarization Model

A worthy text summarization should represent the fundamental content of the document.Recent studies on computerized text summarization tried to present solutions to this challenging problem.Attention models are employed extensively in text summarization process.Classical attention techniques are utilized to acquire the context data in the decoding phase.Nevertheless, without real and efficient feature extraction, the produced summary may diverge from the core topic.In this article, we present an encoder-decoder attention system employing dual attention mechanism.In the dual attention mechanism, the attention algorithm gathers main data from the encoder side.In the dual attention model, the system can capture and produce more rational main content.The merging of the two attention phases produces precise and rational text summaries.The enhanced attention mechanism gives high score to text repetition to increase phrase score.It also captures the relationship between phrases and the title giving them higher score.We assessed our proposed model with or without significance optimization using ablation procedure.Our model with significance optimization achieved the highest performance of 96.7% precision and the least CPU time among other models in both training and sentence extraction.