Reviewerly/exHarmony huggingface dataset:
Self-supervised deep subspace clustering with entropy-norm

Auto-Encoder based Deep Subspace Clustering (DSC) has been widely applied in computer vision, motion segmentation and image processing. However, existing DSC methods suffer from two limitations: (1) they ignore the rich useful relational information and the connectivity within each subspace due to the reconstruction loss; (2) they design convolutional networks individually according to specific datasets. To address the above problems and improve the performance of DSC, we propose a novel algorithm called Self-Supervised deep Subspace Clustering with Entropy-norm(S $$^{3}$$ CE) in this paper. Firstly, S $$^{3}$$ CE introduces self-supervised contrastive learning to pre-train the encoder instead of requiring a decoder. Besides, the trained encoder is used as a feature extractor to segment subspace by combining self-expression layer and entropy-norm constraint. This not only preserves the local structure of data, but also improves the connectivity between data points. Extensive experimental results demonstrate the superior performance of S $$^{3}$$ CE in comparison to the state-of-the-art approaches.
Navigating the development challenges in creating complex data systems

Data science systems (DSSs) are a fundamental tool in many areas of research and are now being developed by people with a myriad of backgrounds. This is coupled with a crisis in the reproducibility of such DSSs, despite the wide availability of powerful tools for data science and machine learning over the past decade. We believe that perverse incentives and a lack of widespread software engineering skills are among the many causes of this crisis and analyse why software engineering and building large complex systems is, in general, hard. Based on these insights, we identify how software engineering addresses those difficulties and how one might apply and generalize software engineering methods to make DSSs more fit for purpose. We advocate two key development philosophies: one should incrementally grow—not plan then build—DSSs, and one should use two types of feedback loop during development—one that tests the code’s correctness and another that evaluates the code’s efficacy. With the explosion of machine learning models of increasing complexity for research applications, more attention is needed for the development of good quality codebases. Sören Dittmer, Michael Roberts and colleagues discuss how to embrace guiding principles from traditional software engineering, including the approach to incrementally grow software, and to use two types of feedback loop, testing correctness and efficacy.
Role of artificial intelligence based image-processing techniques for port development activities in physical tidal models

The Cochin Port physical tidal model available at Central Water and Power Research Station (CWPRS), Ministry of Jal Shakti, Government of India, is used to simulate the tidal conditions of real-time Cochin Port. Analysis of physical tidal model takes time and requires specialised knowledge in order to analyse and identify the velocity profile through manual instrumentation. The use of artificial intelligence (AI) based image processing techniques may reduce the analysis time and improve the accuracy of physical tidal model. An attempt was made to determine the velocity profile of Cochin Port model (scaled down model) at CWPRS through AI-based video image-processing analysis using MATLAB. Based on the analysis, it was observed that magnitude of horizontal velocity is varying for 1579 times in about 30.36 sec duration from 0.01156 m/s to 0.32986 m/s with a mean velocity magnitude of about 0.20357 m/s. The results indicated that digital videography-based image processing approach with the assistance of trained AI algorithms gives the précised velocity profile having 578 unique magnitude variations out of 1579 readings in 30.36 sec, which is far better than the manual instrumentation, and therefore, manual instrumentation may be substituted with the developed artificial intelligence (AI) based image processing technique.
A Novel Classification Algorithm of English Accent Signal based on Multi-Parameter Neural Network

To solve the challenge of efficient signal processing consider the complex scenarios, this paper proposes the novel classification algorithm of English accent signal based on multi-parameter neural network. In the existing traditional research, most of the processing methods regard the time-varying signal as short-term stationary and linear, and the focus is on the information contained in the time domain and frequency domain, so as to perform signal classification without reference. In this paper, the advantages of neural network in pattern recognition are used, and the relevant parameters are optimized through the theoretical analysis of the “optimal” classification rules. In the designed neural model, the graph features are considered to better extract the features, and then the bagging algorithm is applied to classify the models. Through the experiment compared with the C-4.5, CNNs, PSO and BP, it is evident that the proposed algorithm performs better in classification accuracy aspect.
Blockchain and its Methodologies: Future Digital Transformation

The transformation the world with digitalization is today's scenario for the exchange of many activities of stakeholders for ease and portability. Converting this digitalization to another level in terms of currency or value and transforming securely will be a big challenge in the future days. The terms of information sharing from one end to another end through multiple scenarios in confidential and authentication mode are completely addressed. In this connection, Blockchain addresses all the questions arising along the way. Blockchain means a block that contains the information, which creates multiple repositories, in turn, technology used for high-security purposes, which is believed to be the next revolution of the internet. Blockchain stores information across the network, not just decentralized but distributed, which cannot be easily modified and is difficult to access without prior permission to update or corrupt. This research study mainly focuses on how the blockchain will be used in many fields, and play a crucial role for future purposes but not limited to including records maintenance, banking transactions, balloting, e-commerce, etc.
A Cloud Cluster Test Framework for English Machine Translation based on Prior Information

Machine translation technology can convert source language speech into target language text, and is one of the important branches in the field of natural language processing. In this study, the cloud cluster test framework for the English machine translation based on prior information is proposed. The shortcomings of traditional rule-based machine translation are also obvious, because the rule base and dictionary base based on manual customization are highly dependent on the human experience and knowledge, which is time-consuming and labor-intensive. Its core advantage is the prior information that will help to handle the complex scenarios. In the designed framework, the novel semantic algorithm is proposed to assist the extraction of the text features. Then, the proposed information translation algorithm is proposed with the Transformer model. Lastly, to meet the real-time requirement, the cloud cluster is combined. In the test, the BP neural network and Ada boost based algorithms are compared and the proposed model has the higher accuracy
PHP Architecture Optimization of Electric Power Service Government Scientific Decision-Making Platform

This study focuses on PHP architecture optimization of electric power service government scientific decision-making platform. The novelty of the study contains the following aspects. (1) For the service interface test automation tool, the model-driven testing is an important strategy to realize automated testing. Testers use the model to abstractly describe the system under test, and generate the effective test cases based on the model to realize test automation. A novel test method is designed to improve the testing performance. (2) The PHP architecture is optimized to serve as the efficient tool. (3) In terms of simulation decision-making, some studies have learned from the interaction process between the agent and the environment through reinforcement learning models, these ideas are proposed for the electric power service government scientific decision-making. Then, through the testing under different scenarios, the performance of the designed model is validated and the future study plan is also marked in the final section.
Real-Time Data Cloud Transmission and Early Warning Algorithm for Outdoor Sports with Smart Glasses

Through the review analysis, it is evident that most of the current big data mining methods are based on known abnormal characteristics for big data mining. Existing algorithms ignore relevant information and prior information, which reduces the reliability and efficiency of big data mining and increases the overhead of processing big data, resulting in a decrease in the overall availability and performance of big data. Hence, real-time data cloud transmission and the early warning algorithm for outdoor sports with smart glasses is studied. This research study presents 2 aspects of novelty: (1) For the data cloud transmission, the UDT is selected, the application program also uses the UDT socket interface to transmit data, and the UDT calls UDP through the Socket interface provided by the operating system. Then, the OBEX is combined to improve the efficiency. (2) For the early warning algorithm, we consider using the grid space as the clustering area to then reduce the time spent on retrieving the clustering area, ensuring high-precision picking of the clustering center and greatly improving the efficiency. Through the comparison simulation under different data sets, the missing and false alarm tests are both satisfactory.
Gender and Age Detection Techniques for Blind People using Principal Component Analysis

Predicting someone's gender and age-based solely on appearance is challenging for an AI model, as it requires recognizing and understanding complex patterns in images and videos. This kind of prediction is particularly used for blind people. Different kinds of techniques are applied to analyze and predict gender and age classification with the help of images. In the previous methods, accuracy and live prediction have different challenges such as accuracy and prediction rates. This work proposes a hybrid model to predict and analyze gender and age classification. This work consists of Haar Cascade, Histogram of Oriented Gradients, Hessian Filter and Principal Component Analysis. The Haar Cascade algorithm finds the face from the video images with constant speed and time. The Histogram-orientation extracts the features from the images, and the Hessian Filter finds the wrinkles and, based on that, finds the age. Principal Component Analysis is used to visualize and find the patterns from the images. This proposed hybrid work is implemented using 10137 images, and based on that, training and testing are performed. The proposed work is evaluated using recall, f1-score and precision. The proposed work achieved 87.30%. and 86.9% precision for gender classification and age classification respectively. This, compared to the previous work, produced effective results for gender and age detection.
Fall Detection &amp; Daily Living Activity Recognition using CNN

Understanding human behaviour and incorporating users' social circumstances into computer systems are the two main objectives of the emerging academic field known as “human-centered computing”. One of the more cutting-edge, challenging, and alluring applications of the framework is the sensing of human body mobility utilizing mobile devices to gather context information on people's actions. This study suggested a deep learning-based automated fall detection solution. A collection of carefully chosen characteristics acquired by tracking human body silhouettes are input into a CNN -based classifier.
A Productive Model for Secured Data Sharing in Blockchain Technology based IoT

Authentication and access control are critical issues in IoT networks, as these networks comprise a large number of connected devices that exchange sensitive data. One of the biggest challenges in IoT authentication is the lack of uniformity among devices, which makes it difficult to implement a standard mechanism. Additionally, many IoT devices makes implementing robust authentication protocols challenging. Another challenge is the issue of access control, which is critical to ensure that only authorized users and devices can access sensitive data. Access control mechanisms should be implemented at various levels, including the device level, network level, and application level. However, in many cases, IoT devices lack the necessary features to implement advanced access control mechanisms. To address these challenges, IoT manufacturers and developers must prioritize security in their design and development processes. This includes implementing robust authentication and access control mechanisms, ensuring software and firmware updates are timely, and conducting regular security audits to identify and address vulnerabilities. Additionally, IoT devices should be designed with security in mind, and security features should be a core component of the device's design, rather than an afterthought. This work aims to solve the problem of authentication and access control in IoT networks, using Blockchain technology, proposing a consensus algorithm and a trust model between nodes.
Smart PPE using LoRaWAN Technology

In all sectors, employee safety is one of the crucial steps to achieve. Workplace safety may be effectively enhanced through the proposed design of smart enabled Personal Protective Equipment (PPE). The LoRaWAN low-power wireless communication technology, which can monitor and notify different harmful conditions in real-time, is enabled even in long-range communication between sensors and gateways. Sensors, such as DHT11, MQ-2 gas sensor and Pulse Rate sensor that monitor the surroundings and the user's vital signs, that are embedded into the Smart PPE. Nevertheless, putting IOT coal mine monitoring systems into practice has several challenges. For instance, sensors need to be dependable, precise, and long-lasting in critical industrial conditions. To enable effective data interchange and processing, various sensors must be appropriately integrated. To safeguard sensitive employee information, privacy and security concerns must also be considered Recent developments like Machine learning and Artificial intelligence algorithms can assist in effectively evaluating enormous volumes of data, seeing patterns and abnormalities, and anticipating future security risks. As a result, safety managers can take rapid action to stop accidents. The main objective of this research study is to build a Smart PPE with intelligence that tracks and detects potentially harmful circumstances using LoRaWAn technology and a variety of sensors.
A Novel Redundant Data Retrieval Model based on Parallel Batch Algorithm

For the effective information retrieval models, traditional unsupervised cross-modal hashing methods try to learn hash functions from the underlying structure, distribution, and topological information of the data in order to preserve the original data spatial structure. Hence, to conduct more efficient analysis, this research study proposes a novel redundant data retrieval model based on parallel batch algorithm. Batch parallel proofreading is oriented to large-scale text offline proofreading scenarios, and has powerful computing power provided by the cluster computing. With this operation, the data will be pre-processed to better clean the row data. Then, the hash model is integrated to consider the key as a divisor, the dividend as a maximum prime number p not greater than the length of the hash table m, and uses the remainder of the calculation result as the final hash function. Furthermore, the model is revised to construct the efficient data retrieval task. Through testing, the performance of the proposed algorithm is proven to be efficient.
Novel COVID-19 Prediction Model in Python Using FB Prophet

Since end of 2019, the coronavirus disease has spread quickly to nations all over the world. This has had a huge impact on many nations' economies and also on the global health system. As there is no effective treatment for cure, it's indeed vital to foresee COVID situations ahead to make the appropriate plans. Despite the fact that there are many models for predicting COVID-19, none of them have predicted for a specific number of days i.e., 30 days or 90 days or 1 week. To address this issue, the proposed study employs the Facebook prophet model for the job of COVID-19 case predictions using Python over the following thirty days. The prophet is a data-driven time series projecting technique using an incremental approach that matches non-linear tendencies with annual, monthly, and everyday periodicity and vacation impacts.
Africa between Financialization and its Commodities: Post-pandemic Economic Development Path

The objective of this article is to delve into the development of the region’s financial fragility and economic instability process in an environment where, in the absence of the United States, other agents such as Turkey, the United Arab Emirates, Saudi Arabia, Russia, and the growing presence of China, have emerged. An inflationary environment, high indebtedness, and rising interest rates in the short term are placing countries of the region to the limit due to the lack of a full employment policy given the burden of servicing external debt and the need to import food, fertilizers, and fuel. This situation prioritizes institutional investors who play a determining role in the economic development of the African region.
Is Detecting Discontinuity Difficult? Evidence from the Visual Trend Classification of Scatterplots

Abstract Visualization of data comes with the promise that even unexpected aspects of the data can be detected. For instance, viewers might discover that patterns in a scatterplot follow a non-linear trend. Using two experiments, we examined whether and when scatterplots depicting different types of mathematical functions are classified correctly and confidently. In Experiment 1, 237 participants categorized scatterplots that depicted more or less noisy linear, negative exponential, or discontinuous step functions. Results indicated a classification advantage for the continuous linear and negative exponential functions over the discontinuous step function. Generalizing from these findings, in Experiment 2, 231 participants categorized scatterplots that depicted more or less noisy versions of a broader range of functions, including rising continuous linear, quadratic (half parabola), and discontinuous linear (kinked) functions and rising and falling continuous quadratic (full parabola), sine wave, and discontinuous linear (inverted-v) functions. Generally, results suggested that scatterplots based on discontinuous functions are difficult to classify. Overall, the results indicated a classification advantage for the continuous functions over the discontinuous functions and underscore the need for scientists and practitioners to be extremely careful when developing theory and making decisions based on exploration of raw data visualizations.
Robust feature selection for high-dimensional datasets using a GPU-accelerated ensemble of cooperative coevolutionary optimizers

Feature selection is an increasingly important step in the application of machine learning and knowledge discovery techniques to high-dimensional datasets. However, the growing complexity and size of datasets have made feature selection increasingly challenging, as selecting an optimal subset of features can be computationally very expensive, especially when a robust solution is required. To address this issue, we present an approach based on ensembles of cooperative coevolutionary optimisers and its parallelisation for hybrid multi-core CPU and GPU computation. The application of the developed algorithm to some typical high-dimensional datasets is discussed in the paper. According to the preliminary results, the proposed framework represents a valuable tool for addressing the computational challenges faced in feature selection, and it can be potentially applied to a wide range of machine learning and knowledge discovery tasks.
Thea - a QoS, Privacy, and Power-aware Algorithm for Placing Applications on Federated Edges

Federations between Edge Computing infrastructure providers represent a promising approach for improving the applications' Quality of Service (QoS) and the infrastructure's resource usage. At the same time, federated edges impose particular provisioning challenges, as data protection policies implemented by certain providers within a federation may conflict with the privacy requirements of services carrying out sensitive information (e.g., databases). In addition, the popularization of complex software architectures (e.g., composite applications) sets strict latency requirements that narrow the provisioning possibilities even further. Previous research efforts targeting federated edges have focused either on coupling with end-user performance requirements (e.g., latency and privacy) or on satisfying infrastructure providers' objectives (e.g., power consumption reduction), but none on balancing both. This paper presents Thea, a novel approach for provisioning composite applications on federated edges which optimizes applications' latency and privacy while reducing the infrastructure's power consumption. Simulated experiments show that Thea can achieve near-optimal results, reducing application latency and privacy issues by 50% and 42.11% and the infrastructure's power consumption by 18.95% compared to state-of-the-art approaches.
Federated Learning Meets Blockchain: a Power Consumption Case Study

Federated learning (FL) is emerging as the most promising approach to collaboratively train a machine learning (ML) model on a common task without centralizing data. During each FL round, participants locally train a partial model with its on-premises data. Such models are subsequently aggregated to derive a global one. How these partial models are combined is a primary concern. Traditional approaches usually rely on a parameter server that introduces many weaknesses such as single point of failure, lack of trustworthiness among unknown participants, and incapacity to handle the traffic generated from millions of devices. Thus, to overcome such concerns, blockchain has recently been proposed as a valuable solution to improve the robustness of FL approaches. The full-blown benefits of using blockchain enable tackling the limits of centralized servers. However, energy consumption is still one of the significant factors inhibiting its widespread due to the current discussions on climate change and sustainability. Recently, a growing number of research works have been focusing on integrating FL and blockchain, nevertheless, adequate analysis and estimate of their energy and power consumption are often lacking. This paper presents an estimate of the power consumption of FlowChain, an architecture that integrates FL with blockchain to simplify the use of FL. Experimental results demonstrate that the overall power consumption significantly depends on the ML model adopted.