Reviewerly/exHarmony huggingface dataset:
An automated prescriptive domain data preprocessing algorithm to support multilabel‐multicriteria classification for Indian coastal dataset, crop dataset, and breast cancer dataset

Summary In many real‐world applications, the problem of machine learning is based on multicriteria. The multicriteria learning process becomes problematic with data classification involving multiple class labels. The multilabel‐multicriteria classification (MLMCC) is an emerging research area among the data science community, and it has applications in many domains like sciences, computer vision, government, business, and engineering domains. Most of the existing classification techniques learn single criteria and fail to focus with the multicriteria‐based internal information. In order to overcome the above research problem, we propose an expert‐based pattern driven multilabel learning algorithms (E‐PDMLAs) to handle MLMCC. The whole framework is supported with expert knowledge observed as multicriteria, and it is summarized into three broad categories of linguistic terms, namely, MoreThan(&gt;=), LessThan(&lt;=), and Between(‐). Also, the top level of data analytics is to unfold the prescriptive analysis on the data. In this work, prescribing data is supported with linguistic terms, namely, Critical, Feasible, and Strong. In support of E‐PDMLA, the data have been preprocessed and prescribed into linguistic terms for the better insight of information. In this paper, we propose a novel automated prescriptive and fuzzy‐based preprocessing algorithm (prescriptive fuzzy‐based domain data preprocessing technique [PF‐DDPT]) for all the above three categories. The experimental study has been carried out with Indian coastal dataset, crop dataset, and breast cancer dataset, and the results have been compared with normal and modeling technique. The analysis manifests the strength of our proposed algorithm (PF‐DDPT) over accuracy on preprocessing and prescription based on expert perception.
Training and Inference of Optical Neural Networks with Noise and Low-Bits Control

Optical neural networks (ONNs) are getting more and more attention due to their advantages such as high-speed and low power consumption. However, in a non-ideal environment, the noise and low-bits control may heavily lead to a decrease in the accuracy of ONNs. Since there is AD/DA conversion in a simulated neural network, it needs to be quantified in the model. In this paper, we propose a quantitative method to adapt ONN to a non-ideal environment with fixed-point transmission, based on the new chip structure we designed previously. An MNIST hand-written data set was used to test and simulate the model we established. The experimental results showed that the quantization-noise model we established has a good performance, for which the accuracy was up to about 96%. Compared with the electrical method, the proposed quantization method can effectively solve the non-ideal ONN problem.
Reversible data hiding in an encrypted image using the homomorphic property of elliptic curve cryptography

Reversible Data Hiding (RDH) schemes have recently gained much interest in protecting the secret information and sensitive cover images. For cloud security applications, the third party’s data embedding can be done (e.g., cloud service). In such a scenario, to protect the cover image from unauthorized access, it is essential to encrypt before embedding it. It can be overcome by combining the RDH scheme with encryption. However, the key challenge in integrating RDH with encryption is that the correlation between adjacent pixels begins to disappear after encryption, so reversibility cannot be accomplished. RDH with elliptic curve cryptography is proposed to overcome this challenge. In this paper (ECC-RDH) by adopting additive homomorphism property; the proposed method, the stego image decryption gives the sum of the original image and confidential data. The significant advantages of this method are, the cover image is transferred with high security, the embedding capacity is 0.5 bpp with a smaller location map size of 0.05 bpp. The recovered image and secrets are the same as in the original, and thus 100% reversibility is proved.
QALO-MOR: Improved antlion optimizer based on quantum information theory for model order reduction

The field of optimization science is proliferating that has made complex real-world problems easy to solve. Metaheuristics based algorithms inspired by nature or physical phenomena based methods have made its way in providing near-ideal (optimal) solutions to several complex real-world problems. Ant lion Optimization (ALO) has inspired by the hunting behavior of antlions for searching for food. Even with a unique idea, it has some limitations like a slower rate of convergence and sometimes confines itself into local solutions (optima). Therefore, to enhance its performance of classical ALO, quantum information theory is hybridized with classical ALO and named as QALO or quantum theory based ALO. It can escape from the limitations of basic ALO and also produces stability between processes of explorations followed by exploitation. CEC2017 benchmark set is adopted to estimate the performance of QALO compared with state-of-the-art algorithms. Experimental and statistical results demonstrate that the proposed method is superior to the original ALO. The proposed QALO extends further to solve the model order reduction (MOR) problem. The QALO based MOR method performs preferably better than other compared techniques. The results from the simulation study illustrate that the proposed method effectively utilized for global optimization and model order reduction.
GAU-Nets: Graph Attention U-Nets for Image Classification

Abstract Graph neural network is a research hotspot in the field of deep learning recently, and its application has become more and more extensive. A new graph neural network model is proposed, Graph Attention U-Nets(GAU-Nets for short), which has strong processing capabilities for graph structures. In GAU-Nets, the graph data structure is preliminarily processed through GCN, and then extracted the features by the graph pooling and Unpooling blocks. We innovatively added the attention mechanism to GAU-Nets to avoid forgetting the important information. We have done a lot of image classification experiments on MS-COCO and other datasets. The experimental results prove that GAU-Nets performs better than other traditional graph neural network models. Without bells and whistles, our GAU-Nets method has an accuracy of 69.1% on the MS-COCO data set and 82.1% on the VOC 2007 data set, which has surpassed all benchmark methods.
Enhancing the Low Adoption Rate of M-commerce in Nigeria Through Yorùbá Voice Technology

There has been claims and reports that 70% of m-commerce in Nigeria failed due to non-interactive, non-responsive and non-interesting platform. Despite the popularity and growth of m-commerce globally, developing countries like Nigeria seems to be lagging behind and at the same time, many m-commerce sites have been reported to close down due to unprofitability. The key factors that contribute to this failure are health, literacy e-literacy language and accessibility barrier. All these key factors are formidable barriers to adoption of m-commerce in Nigeria and have discouraged most people from fully adopting m-commerce. Hence, this work explored a Yorùbá voice-based m-commerce system to enhance smooth m-commerce operations and at the same time, enhance the low adoption rate of m-commerce users. This work was able to discover a foundation for advancing the current growing trends of m-commerce making them sustainable which is also applicable to e-commerce or any e-platforms.
Developing intuitive and explainable algorithms through inspiration from human physiology and computational biology

In this letter, we explain how intuitive and explainable methods inspired from human physiology and computational biology can serve to simplify and ameliorate the way we process and generate knowledge resources.
Next generation stock exchange: Recurrent neural learning model for distributed ledger transactions

A distributed stock exchange system encompasses multiple network hosts that participate in the sharing and exchange of resources. In such exchanges, the mediator or stock exchange must manage and delineate all operations in a cohesive manner. Stock exchange (SE) also acts as the transaction manager to provide consistent, isolated, durable, and atomic transactions for participating entities. However, the work for the stock exchange is not so straightforward as it may sound. With multiple transactions happening per second, the global serializability and concurrency control becomes an issue resulting in multiple threats and vulnerabilities. We propose a novel stock exchange that integrates time series prediction to distributed transactions and understanding the rapid global transactions and limitations of resources at the stock exchange. We use distributed acyclic graph (DAG) based distributed ledger technology IOTA to provide security and consensus for independent users. The paper proposes a time-variant model that adjusts its predictions based on transactions, moments of observations, participating entities, and history. We show that our model outcasts other state-of-art schemes in terms of prediction accuracy. Also, the model is fair, fast, and scalable to handle millions of transactions per second.
Interacting with Information in Immersive Virtual Environments

In this paper, we demonstrate the Information Interactions in Virtual Reality (IIVR) system designed and implemented to study how users interact with abstract information objects in immersive virtual environments in the context of information retrieval. Virtual reality displays are quickly growing as social and personal computing media, and understanding user interactions in these immersive environments is imperative. As a step towards effective information retrieval in such emerging platforms, our system is central to upcoming studies to observe how users engage in information triaging tasks in Virtual Reality (VR). In these studies, we will observe the effects of (1) information layouts and (2) types of interactions in VR. We believe this early system motivates researchers in understanding and designing meaningful interactions for future VR information retrieval applications.
Sinkhorn Collaborative Filtering

Recommender systems play a vital role in modern web services. In a typical recommender system, we are given a set of observed user-item interaction records and seek to uncover the hidden behavioral patterns of users from these historical interactions. By exploiting these hidden patterns, we aim to discover users' personalized tastes and recommend them new items. Among various types of recommendation methods, the latent factor collaborative filtering models have dominated the field. In this paper, we develop a unified view for the existing latent factor models from a probabilistic perspective. The unified framework enables us to discern the underlying connections of different latent factor models and deepen our understandings of their advantages and limitations. In particular, we observe that the loss functions adopted by the existing models are oblivious to the geometry induced by the item-similarity. To address this, we propose a novel model—SinkhornCF—based on Sinkhorn divergence. To address the challenge of the expensive computational cost of Sinkhorn divergence, we also propose new techniques to enable the resulting model to be able to scale to large datasets. Its effectiveness is verified on two real-world recommendation datasets.
Design and Simulation of Loan Approval Prediction Model using AWS Platform

To determine and to understand the working of loan systems for the cause of Loan Prediction using the demographic information of various factors that combine to form the nature of the approval using algorithms and concepts of Machine Learning and ultimately deploying this model on Cloud Based Platforms. Machine learning being aided by Cloud services are progressively seeing immense growth in the industry as they have benefits of Scalability, Affordability and easy use of models on systems as and when required. Therefore datasets are designed, automated and put under testing and training. The major aim of this project is to predict which of the customers will have their loan paid or not using prominent algorithms like Decision Tree, Logistic Regression and Random Forest. Logistic Regression Confusion matrix analysis is relatively in accordance to Decision Tree and Random Forest algorithm helping us attain an accuracy of 86% with minimum error.
Zero Trust User Access and Identity Security in Smart Grid Based SCADA Systems

Smart grid based systems are part of modernized critical infrastructures that countries use to operate in various critical sectors ranging from electricity services, nuclear stations, transportation systems, hospital services, waste management, water services, etc. The modern smart grid based systems comprises of operational technology (OT) components and the information technology (IT) components and the convergence of both has introduced new security challenges for the operations of the systems in terms of its safety, reliability, efficiency and stability. The security perimeter is completely redrawn with the OT and IT convergence. The need of the hour is the new approaches towards the security that are innovative and will ensure the security of critical infrastructures. The traditional approach of perimeter-based security defense is obsolete with the convergence of OT and IT. The Zero Trust model for user access and identity is based on concept of “Never Trust but Always Verify” that enable organizations to secure the critical infrastructures by ensuring only trustworthy and validated components are allowed into the network. The perimeter-centric security architecture is replaced by Zero Trust model to ensure and enable security and access decisions for devices, identity and user context to be enforced dynamically. It also ensures that only authorized and authenticated users and devices can have access to the network, systems, applications and data. In this paper we are proposing Zero Trust User Access & Identity Security model that can be implemented in smart grid based SCADA systems.
On the Role of Blockchain in Evolving the Online Business Landscape

This contribution explores the business ecosystems that blockchain technology has enabled, both businesses linked to cryptocurrencies and ones that use blockchain as a distributed immutable database, though not crypto related. Moreover, a marketplace, democratizing access to General AI, is proposed.
Method of Operating Display Screen Based on Absolute Hand Position Information Obtained through QZSS

This paper describes a new method of operating a display screen based on absolute hand position information obtained through the QZSS (Quasi-Zenith Satellite System named “Michibiki”). We, its authors propose a method which makes a rover a manipulator and obtain the absolute position information of the manipulator in a hand to perform positioning according to its position. In our experiment, we obtained the absolute position information of a QZSS rover grasped by a user's hand. Next, we were able to perform positioning operations on a display screen according to the QZSS rover's movement. Our experiment's results show that the method is effective.
UAV Collision Avoidance Considering No-Fly-Zones

This paper proposes a collision avoidance algorithm that ensures minimum separation between the vehicles considering multiple no-fly-zones. The proposed algorithm aims to provide a practical and efficient tactical de-confliction solution for Unmanned Aerial Vehicles (UAVs). The main idea is to utilise the differential geometry concept that computes the minimum heading angle change to avoid the obstacles, and to expand its applicability to polygonal obstacles. This paper validates the minimum separation and efficiency of the proposed algorithm both analytically and numerically.
Dynamic Doppler prediction in high‐speed rail using long short‐term memory neural network

Abstract In the high‐speed rail (HSR), wireless communication systems are suffering considerably from the huge Doppler shift caused by the high speed moving of the train. Moreover, the Doppler shift in HSR changes rapidly, and results in the poor Doppler prediction performance on pay‐load symbols. In this paper, a novel Doppler prediction algorithm based on long short‐term memory (LSTM) neural network is proposed. The proposed algorithm is enable to learn the regularity of the Doppler shift by the pretraining and tracking training, and can achieve better prediction performance for high speed moving than traditional algorithms without modification of the communication protocol.
Integration of Academic Mobile Applications at University

Abstract The development of android smartphones also plays a role in the world of education, such as the presence of android-based learning media, android-based educational information systems, and much more. If a few years ago, the academic information system was limited in the form of a website, now the academic information system has also begun to be developed on Android-based mobile devices. This is quite reasonable considering there is a myriad of advantages possessed by Android-based applications. By using an Android-based academic information system, application users can dig up information anywhere and anytime such as: Independent Lecture Schedule, Online SPP Payment (no need to queue) to fill KRS (Study Plan Card), see KHS (Study Result Card), Trusteeship, Discussions, value announcements and much more, all that can be done via a smartphone. It’s effortless, like “University Information and Communication Center in the Grip. The main advantage of developing the University Mobile Application is in terms of mobility. The application on an Android smartphone can be used anywhere and anytime. Thanks to these characteristics, Mobile Application University is the right answer to overcome obstacles in web-based or desktop-based academic information systems. With the Android-based educational information system application, information system users can access information directly from an Android smartphone. Aside from the mobility side, the development of an Android-based academic information system is also becoming increasingly optimal thanks to the smartphone’s distinctive features such as the notification feature. With the notification feature, the information will be more quickly distributed to application users. Of course, the use of an Android-based academic information system application is more effective when compared to using a web-based educational information system that must wait for users to access the web.
Speaker Recognition Using Noise Robust Features and LSTM-RNN

A tremendous growth has been observed in terms of active research in the field of speaker recognition. This has been mainly due to the increasing need of zero-touch interfaces in devices and mobile biometric authentication systems. This paper discusses implementation of text-independent speaker verification system using long short-term memory (LSTM)-based neural network for speaker modeling by using various approaches for the front-end feature extraction including Mel Frequency Spectral Coefficients (MFSC), Mel Frequency Cepstral Coefficients (MFCC), Gammatone Filter Spectra (GTF), and Gammatone Filter Cepstral Coefficients (GFCC). Additionally, to determine the best-suited speaker verification system for given noisy conditions of environment, all the combinational systems are tested under induced noisy conditions with white noise at −20 and −40 dB, as well as under clean environmental condition. The results show that the MFSC-based LSTM-RNN combination tends to perform better than all the other combinations regardless of the noise added in the dataset.
Differentially Private Publication For Correlated Non-Numerical Data

Abstract Differential privacy has made a significant progress in numerical data preserving. Compared with numerical data, non-numerical data (e.g. entity object) are also widely applied in intelligent processing tasks. But non-numerical data may reveal more user’s privacy. Recently, researchers attempt to take advantage of the exponential mechanism of differential privacy to solve this challenge. Nonetheless, exponential mechanism has a drawback in correlated data protection, which can not achieve expected privacy degree. To remedy this issue, in this paper, an effective correlated non-numerical data release mechanism is proposed by defining the notion of Correlation-Indistinguishability and designing a correlated exponential mechanism to realize Correlation-Indistinguishability in practice. Inspired by the concept of indistinguishability, Correlation-Indistinguishability can guarantee the correlations of the probability distributions between the output distribution and original data the same to an adversary. In addition, we would rather let two Gaussian white samples pass through a designed filter, to realize the definition of Correlation-Indistinguishability, than using independent exponential variables. Experimental evaluation demonstrates that our mechanism outperforms current schemes in terms of security and utility for frequent items mining.
Growth Enhancement Support Scheme (GESS) and Farmers’ Fertilizer Use in Rural Nigeria

Fertilizer use in Nigeria is estimated at 13 kg/ha, which is far below the 200 kg/ha recommended by the Food and Agricultural Organization (FAO). The objective of this investigation was to identify the determinant factors of farmers’ participation in the Nigeria’s growth enhancement support scheme (GESS). In addition, we determined the impact of the GESS on fertilizer use in rural areas. One thousand, two hundred rural farmers were sampled across the six geopolitical zones of Nigeria. Results from the use of recursive bivariate probit model indicated that GESS significantly impacted on the access and usage of fertilizer among the rural farmers; and that contact with extension agents, ownership of mobile phones, power for charging phone batteries, value output, mobile network coverage, ability to read and write were positive determinants of rural farmers participation in the GESS; whereas increased distance to registration and collection centers, and cultural constraints to married women reduced farmers’ tendency to participate in the GESS. The findings suggest that farmers’ participation in the GESS is a critical factor for raising fertilizer use in Nigeria. This implies that food security in sub-Saharan Africa can be achieved by increasing the participation of rural farmers in the growth enhancement support scheme.
NeuroRAN Rethinking Virtualization for AI‐native Radio Access Networks in 6G

ABSTRACT Network softwarization has revolutionized the architecture of cellular wireless networks. State‐of‐the‐art container based virtual radio access networks (vRAN) provide enormous flexibility and reduced life‐cycle management costs, but they also come with prohibitive energy consumption. We argue that for future AI‐native wireless networks to be flexible and energy efficient, there is a need for a new abstraction in network softwarization that caters for neural network type of workloads and allows a large degree of service composability. In this paper we present the NeuroRAN architecture, which leverages stateful function as a user facing execution model, and is complemented with virtualized resources and decentralized resource management. We show that neural network based implementations of common transceiver functional blocks fit the proposed architecture, and we discuss key research challenges related to compilation and code generation, resource management, reliability and security.