Reviewerly/exHarmony huggingface dataset:
Sparrow Search Optimizer for Constrained Engineering Optimal Designs

Recent augmented swarm intelligence Sparrow Search optimizer (SSO) was prompted by anti-predation, gathering, and group premised activities of sparrow birds. Its effectiveness also tested and proved on several benchmarks. And the outcomes of speed reducer design challenge are not analyzed with other optimizers. Due to regular changes in Artificial Intelligence era, it still needs to test for sophisticating uses at global market. In this proposed research paper, simulation experiments are conducted on four optimal engineering structural designs to prove complex challenges solving nature with efficacy of novel SSO algorithm. Optimal engineering structural design of I beam optimal design mean fitness value is 0.006626, gear train optimal design fitness value is 1.0939E-2l, compression spring optimal fitness value is0.012674, and multi plate clutch optimal structure fitness value is 0.389650 respectively. And outcomes are validated with AOA and RIFO optimizers, analysis reveals that SSO algorithm outperforms other in terms mean fitness value in precision, consistency, and resilience, according to simulation findings.
A Key Generation Algorithm for Cryptographic Algorithms to Improve Key Complexity and Efficiency

In the recent days people use a variety of online software applications to move data from one location to another. To secure personal data from hackers, software or applications use cryptographicbased algorithms. The encryption and decryption processes are the foundation of the cryptographic algorithm, which will be carried out with the help of a key. Cryptographic algorithms employ automatic key generation that starts with the user’s password, whereas another generating key directly interprets the password. A symmetric algorithm uses the same key for both encryption and decryption. For key generation, symmetric algorithms use a second or subsidiary method. The password is protected using this key generation process from various key attacks. The key generation algorithm functions as an interpreter, transforming a password from a human-readable form to a machine-readable form. An increase in permutations and combinations helps to protect the password; additional characteristics are being added to the algorithm. To get around the restricted key bit size, this paper proposed the KEY SECURE-KEY (KSKEY) algorithm. However, increasing the key size is not a suitable way to encrypt data because multi-core processors in modern microprocessors are significantly increasing processingspeed.
Intelligent Analysis on Frameworks for Mobile App Development

Establishing new mobile applications that are adaptable across multiple cross platforms is a strenuous task. There are several frameworks for mobile application development existing in the market which has got its own tremendous features and benefits, but choosing the right framework for app development is very crucial for any developer. This study compares the benefits and efficiency of the three most popular and preferred frameworks by the end-users; React-Native, Flutter, and Ionic Frameworks and choose the most optimized and suitable framework for mobile app development. These designed frameworks are platform independent, and the yield was evaluated and appeared to be satisfactory by the users in the respective platform. These methodologies have been implemented using Javascript, Dart, HTML and CSS.
Age and Gender Detection to Detect the Manipulated Images using CNN

There has been a proliferation of photographs during the last two decades. The number of photographs of human faces accessible has exploded in recent years, thanks in large part to the proliferation of smartphones and the rise in popularity of selfies. This has led to a surge in research into methods for accurately determining a person age and gender solely from a photograph of the face. This research work intends to tackle this intricate challenge. Specifically, this study examines methods for determining a person's age, gender, and other characteristics based only on a static portrait of their face. Distinct models are trained to perform each task and compare the results of utilizing pre-trained CNN (Convolutional Neural Network) designs like VGG16 and ResNet50 and SE-ResNet50 on the VGGFace2 dataset to train the features collected by these networks. In addition to sharing the best practices to perform feature extraction using machine learning, this study also provides a benchmark performance analysis on several techniques. Even the most basic linear regression can be trained so that the extracted data performs better, however, CNNs were trained from scratch for age estimation.
An Enhanced Deep Learning Model for Handwritten Tamil Character Identification

In the data processing industry, data entry is regarded as a great bottleneck. An eminent Character Recognition (OCR) system acts as a key solution for this concern. The OCR is being familiaized in universal level for different languages. Till now, only a limited explores were made for the Tamil Handwritten character recognition (HCR). Existing research works provides best performances for many languages, but for the work towards Tamil language is not satisfactory in the recognition area. To resolve this demerit this paper proposed an enhanced CNN for Tamil HCR utilizing Adam optimizer. Initially, encode the target variable using “one-hot4ncode” method. Then, the encoded image is preprocessed using RGB to grey conversion, image resizing, slant removal and array conversion. Subsequently, word and characters are segmented as of the preprocessed image. Next the segmented images are provided as input to the CNN classifier. The CNN is enhanoed by utilizing Adam optimizer algorithm (AOA) which optimizes the weight value for minimizing its loss function. finally, the experiential outcomes of the proposed CNN are contrasted to the prevailing SVM and some other machine learning algorithms. The proposed recognition system shows preeminent performance when comparing with the prevailing methods.
Comprehensive Assessment of Reverse Social Engineering to Understand Social Engineering Attacks

This paper gives a systematic analysis of social engineering attacks, including their categorizations, methods of detection, and strategies for mitigating them. Digital technology has recently advanced, making human-to-human contact easier and faster than before. However, without the proper safeguards, social media platforms and internet services could reveal confidential and sensitive information. Malicious actors can easily access communication networks by employing social engineering techniques. Hackers use social engineering to obtain sensitive data from victims, such as bank account details, passwords, and medical histories, for their own gain. Social engineering poses a serious threat to network security because it preys on people’s innate tendency toward trust. The paper carefully assesses reverse social engineering (RSE) and its impact on social engineering attacks.
Multifactor Mutual Authentication of IoT Devices and Server

The process of authenticating a user or device before granting access to a system or resources is known as authentication. To put it another way, authentication entails verifying a user’s identity. A quintessential part of security is authentication. The security mechanism is incomplete without authentication. Because it enables enterprises to keep their networks safe by allowing only authenticated users to access its protected resources, authentication is crucial. With IoT’s many applications and widespread adoption, protecting IOT devices from outside threats is of paramount importance. To guarantee that linked IoT devices can be trusted to be who they say they are, strong device authentication is needed. As a result, a novel authentication algorithm based on device serialization and digital signature authentication is suggested in this paper. According to the suggested approach, a device will only be permitted access to the network i f it has successfully completed multi-factor authentication; otherwise, the authentication procedure will fail and must be repeated from scratch. Through the use of multifactor authentication, the technique additionally uses digital signatures to validate the server in addi tion to the device.
Gaze-Augmented Drone Navigation

The use of unmanned aerial vehicles (UAVs) or drones, has significantly increased over the past few years. There is a growing demand in the drone industry, creating new workforce opportunities such as package delivery, search and rescue, real estate, transportation, agriculture, infrastructure inspection, and many others, signifying the importance of effective and efficient control techniques. We propose a scheme for controlling a drone through gaze extracted from eye-trackers, enabling an operator to navigate through a series of way points. Then we demonstrate and test the utility of our approach through a pilot study against traditional controls. Our results indicate gaze as a promising control technique for navigating drones revealing novel research avenues.
Learning Distributed Representations and Deep Embedded Clustering of Texts

Instructors face significant time and effort constraints when grading students’ assessments on a large scale. Clustering similar assessments is a unique and effective technique that has the potential to significantly reduce the workload of instructors in online and large-scale learning environments. By grouping together similar assessments, marking one assessment in a cluster can be scaled to other similar assessments, allowing for a more efficient and streamlined grading process. To address this issue, this paper focuses on text assessments and proposes a method for reducing the workload of instructors by clustering similar assessments. The proposed method involves the use of distributed representation to transform texts into vectors, and contrastive learning to improve the representation that distinguishes the differences among similar texts. The paper presents a general framework for clustering similar texts that includes label representation, K-means, and self-organization map algorithms, with the objective of improving clustering performance using Accuracy (ACC) and Normalized Mutual Information (NMI) metrics. The proposed framework is evaluated experimentally using two real datasets. The results show that self-organization maps and K-means algorithms with Pre-trained language models outperform label representation algorithms for different datasets.
Stochastic surprisal: An inferential measurement of free energy in neural networks

This paper conjectures and validates a framework that allows for action during inference in supervised neural networks. Supervised neural networks are constructed with the objective to maximize their performance metric in any given task. This is done by reducing free energy and its associated surprisal during training. However, the bottom-up inference nature of supervised networks is a passive process that renders them fallible to noise. In this paper, we provide a thorough background of supervised neural networks, both generative and discriminative, and discuss their functionality from the perspective of free energy principle. We then provide a framework for introducing action during inference. We introduce a new measurement called stochastic surprisal that is a function of the network, the input, and any possible action. This action can be any one of the outputs that the neural network has learnt, thereby lending stochasticity to the measurement. Stochastic surprisal is validated on two applications: Image Quality Assessment and Recognition under noisy conditions. We show that, while noise characteristics are ignored to make robust recognition, they are analyzed to estimate image quality scores. We apply stochastic surprisal on two applications, three datasets, and as a plug-in on 12 networks. In all, it provides a statistically significant increase among all measures. We conclude by discussing the implications of the proposed stochastic surprisal in other areas of cognitive psychology including expectancy-mismatch and abductive reasoning.
A new insight into linguistic pattern analysis based on multilayer hypergraphs for the automatic extraction of text summaries

Forensic linguistics and stylometry have in the exploration of linguistic patterns one of their fundamental tools. Mathematical structures such as complex multilayer networks and hypergraphs provide remarkable resources to represent and analyze texts. In this paper, we present a model that includes some specific mesoscopic relations between the different types of words in a corpus (lexical words, verbs, linking words, other words) according to the sentences or paragraphs in which they appear. This model is supported by various mathematical structures such as partial multiline graphs, multilayer hypergraphs, and their derivative graphs. The methodology proposed from this new point of view is of singular help to find meaningful sentences from any text to set up an automatic summary of the text and, eventually, to determine its linguistic level.
A Cross Stage Partial Network with Strengthen Matching Detector for Remote Sensing Object Detection

Remote sensing object detection is a difficult task because it often requires real-time feedback through numerous objects in complex environments. In object detection, Feature Pyramids Networks (FPN) have been widely used for better representations based on a multi-scale problem. However, the multiple level features cause detectors’ structures to be complex and makes redundant calculations that slow down the detector. This paper uses a single-layer feature to make the detection lightweight and accurate without relying on Feature Pyramid Structures. We proposed a method called the Cross Stage Partial Strengthen Matching Detector (StrMCsDet). The StrMCsDet generates a single-level feature map architecture in the backbone with a cross stage partial network. To provide an alternative way of replacing the traditional feature pyramid, a multi-scale encoder was designed to compensate the receptive field limitation. Additionally, a stronger matching strategy was proposed to make sure that various scale anchors may be equally matched. The StrMCsDet is different from the conventional full pyramid structure and fully exploits the feature map which deals with a multi-scale encoder. Methods achieved both comparable precision and speed for practical applications. Experiments conducted on the DIOR dataset and the NWPU-VHR-10 dataset achieved 65.6 and 73.5 mAP on 1080 Ti, respectively, which can match the performance of state-of-the-art works. Moreover, StrMCsDet requires less computation and achieved 38.5 FPS on the DIOR dataset.
The Effectiveness of Augmented Reality in Physical Sustainable Education on Learning Behaviour and Motivation

This research explores the integration of information technology into physical education and uses augmented reality (AR) as an auxiliary tool to explore the impact of this teaching mode on the learning motivation, knowledge, and learning behaviour of beginner runners. A pre- and post-test control group design is adopted in this study. The study participants were 56 students implemented into a 16-session football teaching course. Before the teaching was implemented, the Learning Motivation Scale, independent variables were different learning modes, while the dependent ones were the participants’ learning performances, including their learning outcomes and motives, as well as their skill learning behaviours. The findings of this research are as follows: first, the experimental group (using AR) achieves better learning outcomes for motor skills than the control group. The experimental group also experiences stronger learning motives and better motor skill performances than the control one. Finally, the experimental group has a more positive attitude towards using the teaching materials, and the materials are more acceptable to them in comparison to the control one. This research contribution uses the advantages of AR to adopt an innovative teaching model, which can be highly appealing and motivating for learner behaviour. In practice, it also solves the deficiencies in the integration of information technology into physical education at this stage.
A Quantitative and Qualitative Review of Blockchain Research from 2015 to 2021

Blockchain has the potential to reconfigure the contemporary economic, legal, political and cultural landscape, causing a flood of research on this topic. However, limited efforts have been made to conduct retrospective research to appraise the blockchain studies in the recent period, easily leading to a neglect of new technological trends. Consequently, the present research designs a quantitative- and qualitative-analysis procedure to review the latest research status. Adopting a four-step workflow, six research hotspots (i.e., the specific application areas of blockchain technology, the integration of blockchain and other technologies, the driving factors of blockchain, the values of blockchain technology, the types of blockchain and the core technologies of blockchain) and five research frontiers (i.e., entrepreneurship, contract, industrial internet, data management and distributed ledger technology) were detected using quantitative analysis. Furthermore, three other topics (i.e., the Internet of things, access control and trust) and two research gaps (i.e., the true effect of blockchain technology on firms’ operational efficiency and the regulation of the “dark sides” of blockchain technology) were also identified, using qualitative analysis. Finally, the evolutionary paths were qualitatively analyzed, and then three phases of blockchain research were summarized. The conclusions are able to provide a more comprehensive enlightenment regarding blockchain’s research hotspots, research frontiers, evolutionary paths and research gaps in the recent period, from 2015 to 2021, and to provide a reference for future research.
On the security of ring learning with error‐based key exchange protocol against signal leakage attack

Abstract Due to the rapid development of mobile communication and hardware technologies, several mobile‐based web applications have gained popularity among mobile users. Mobile users can utilize these devices to access numerous services over the Internet. To ensure secure communication, different key exchange and authentication (KEA) protocols are proposed and frequently used. However, due to the advent of quantum computers, numerous quantum‐safe KEA protocols are also developed using various complex mathematical problems in ideal lattices. As it is an emerging and developing area, we analyze the security of recently suggested ring learning with error based KEA protocols. The goal of this study is to gain a comprehensive understanding of quantum‐safe KEA mechanisms. For our study, we have considered Dharminder's LWE‐based KEA mechanism and Dharminder and Chandran's LWE‐based KEA mechanism. These protocols enable effective communication and provide a better means for safely transmitting messages between user and server. However, we have discovered that a fundamental security weakness in these methods makes them vulnerable to signal leakage attacks (SLA). Based on our analysis, we demonstrated security weakness against SLA and provide the road‐map for secure construction.
Deep convolutional neural networks accurately predict breast cancer using mammograms

Breast cancer in women is the most frequently diagnosed and major leading cause of cancer deaths. Due to the complex nature of microcalcification and masses, radiologists fail to diagnose breast cancer properly. In this research paper, we have employed a novel Deep Convolutional Neural Network (DCNN) model using a transfer learning strategy and compared the results with Machine Learning (ML) techniques such as Support vector machine (SVM) kernels and Decision Trees based on different features extracting strategies to distinguish cancer mammograms from normal subjects. In this study, we first extracted the hand-crafted features such as as texture, morphological, entropy-based, scale-invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) and fed into machine learning algorithm for classification. We then utilized the deep learning algorithms with transfer learning approach. The deep learning models yielded the highest detection performance with default and optimized parameters i.e. GoogleNet yielded accuracy (99.26%), AUC (0.9998) with default parameters and AlexNet yielded accuracy (99.26%), AUC (0.9996) with optimized parameters. The results reveal that proposed approach is more robust for early detection of breast mammograms which can be best utilized for improved diagnosis and prognosis.
Hyperspectral image mixed noise removal via tensor robust principal component analysis with tensor-ring decomposition

ABSTRACTDue to the instability of sensors and other factors, hyperspectral images (HSIs) are inevitably polluted by various types of mixed noise. To explore a better denoising method based on the existing research, combining the denoising advantages of tensor-ring (TR) decomposition and tensor robust principal component analysis (TRPCA), a mixed-noise removal method for HSIs is proposed in this paper. First, TRPCA maintains the tensor structure of the image itself, accurately recovers the low-rank part and the sparse part from their sum and separates the sparse noise in the form of sparse tensors. Then, TR decomposition is introduced to denoise the low-rank tensors. To verify the effectiveness and superiority of this method, experiments are carried out on two simulated data sets and two real data sets. Compared with the traditional denoising methods and several existing improved denoising methods from both visual and quantitative aspects, the proposed TRPCA-TR method provides better denoising results.KEYWORDS: Hyperspectral image (HSI)mixed noise removaltensor ring (TR) decompositiontensor robust principal component analysis (TRPCA) AcknowledgementsThe authors would like to thank the anonymous reviewers for their constructive comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe simulated Indian Pines data set used in this study were obtained from the MultiSpec, Purdue University, West Lafayette, IN (https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html). The Washington DC Mall data set (WDC Mall), the Salinas-A data set and the real Indian Pines data set used in this study were obtained from the Remote Sensing Laboratory, School of Surveying and Geospatial Engineering, Tehran, Iran (https://rslab.ut.ac.ir/en/data).Notes1. https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html. (He et al. Citation2016).2. https://rslab.ut.ac.ir/data.3. https://rslab.ut.ac.ir/data.4. https://rslab.ut.ac.ir/data.Additional informationFundingThis work was supported in part by the Sichuan Science and Technology Program under Grant Nos. 2021YJ0351 and 2021YFG0319, in part by the Opening Fund of Geomathematics Key Laboratory of Sichuan Province under Grant Nos. scsxdz2021yb02 and scsxdz2019zd03.
DUMask: A Discrete and Unobtrusive Mask-Based Interface for Facial Gestures

Interactions using the face, not only enable multi-tasking but also enable us to create hands-free applications. Previous works in HCI used sensors attached directly to the person’s face or inside their mouth. However, a mask, which has now become a norm in our everyday life and is socially acceptable, has rarely been used to explore facial interactions. We designed, “DUMask”, an interface that uses face parts covered by a mask to discretely enable 14 (+1 default) interactions. DUMask uses an infrared camera embedded inside an off-the-shelf face mask to recognize the gestures, and we demonstrate the effectiveness of our interface through in-lab studies. We conducted two user studies evaluating the experience of both the wearer and the onlooker, which validated that the interface is indeed inconspicuous and unobtrusive.