Reviewerly/exHarmony huggingface dataset:
Cyberbullying Detection Based on Emotion

Due to the detrimental consequences caused by cyberbullying, a great deal of research has been undertaken to propose effective techniques to resolve this reoccurring problem.The research presented in this paper is motivated by the fact that negative emotions can be caused by cyberbullying.This paper proposes cyberbullying detection models that are trained based on contextual, emotions and sentiment features.An Emotion Detection Model (EDM) was constructed using Twitter datasets that have been improved in terms of its annotations.Emotions and sentiment were extracted from cyberbullying datasets using EDM and lexicons based.Two cyberbullying datasets from Wikipedia and Twitter respectively were further improved by comprehensive annotation of emotion and sentiment features.The results show that anger, fear and guilt were the major emotions associated with cyberbullying.Subsequently, the extracted emotions were used as features in addition to contextual and sentiment features to train models for cyberbullying detection.The results demonstrate that using emotion features and sentiment has improved the performance of detecting cyberbullying by 0.5 to 0.6 recall.The proposed models also outperformed the state-of-the-art models by a 0.7 f1-score.The main contribution of this work is two-fold, which includes a comprehensive emotionannotated dataset for cyberbullying detection, and an empirical proof of emotions as effective features for cyberbullying detection.
VLSD—An Efficient Subgroup Discovery Algorithm Based on Equivalence Classes and Optimistic Estimate

Subgroup Discovery (SD) is a supervised data mining technique for identifying a set of relations (subgroups) among attributes from a dataset with respect to a target attribute. Two key components of this technique are (i) the metric used to quantify a subgroup extracted, called quality measure, and (ii) the search strategy used, which determines how the search space is explored and how the subgroups are obtained. The proposal made in this work consists of two parts, (1) a new and efficient SD algorithm which is based on the equivalence class exploration strategy, and which uses a pruning based on optimistic estimate, and (2) a data structure used when implementing the algorithm in order to compute subgroup refinements easily and efficiently. One of the most important advantages of this algorithm is its easy parallelization. We have tested the performance of our SD algorithm with respect to some other well-known state-of-the-art SD algorithms in terms of runtime, max memory usage, subgroups selected, and nodes visited. This was completed using a collection of standard, well-known, and popular datasets obtained from the relevant literature. The results confirmed that our algorithm is more efficient than the other algorithms considered.
CollisionGP: Gaussian Process-Based Collision Checking for Robot Motion Planning

Collision checking is the primitive operation of motion planning that consumes most time. Machine learning algorithms have proven to accelerate collision checking. We propose CollisionGP, a Gaussian process-based algorithm for modeling a robot's configuration space and query collision checks. CollisionGP introduces a Pòlya-Gamma auxiliary variable for each data point in the training set to allow classification inference to be done exactly with a closed-form expression. Gaussian processes provide a distribution as the output, obtaining a mean and variance for the collision check. The obtained variance is processed to reduce false negatives (FN). We demonstrate that CollisionGP can use GPU acceleration to process collision checks for thousands of configurations much faster than traditional collision detection libraries. Furthermore, we obtain better accuracy, TPR and TNR results than state-of-the-art learning-based algorithms using less support points, thus making our proposed method more sparse.
A Certification Framework for E-Commerce Digital Competencies

In this paper, we introduce a comprehensive certification framework for digital competencies.This framework delineates the regulations, processes, and administration necessary for executing certification of a particular subset of competencies within the DigComp framework.It stipulates distinct requisites for the methodology employed, assessment instruments, evaluation criteria, and procedures for awarding certificates.The framework encompasses the enhancement of fundamental skills, particularly digital competencies, as well as involving digitally disenfranchised demographics with technology.It can be used to evaluate and certify varying degrees of digital proficiency and to instruct individuals in utilizing essential business-to-consumer e-commerce functionalities.
A Jigsaw Puzzle Solver-Based Attack on Image Encryption Using Vision Transformer for Privacy-Preserving DNNs

In this paper, we propose a novel attack on image encryption for privacy-preserving deep neural networks (DNNs). Although several encryption schemes have been proposed for privacy-preserving DNNs, existing cipher-text-only attacks (COAs) have succeeded in restoring visual information from encrypted images. Image encryption using the Vision Transformer (ViT) is known to be robust against existing COAs due to the operations of block scrambling and pixel shuffling, which permute divided blocks and pixels in an encrypted image. However, the correlation between blocks in the encrypted image can still be exploited for reconstruction. Therefore, in this paper, a novel jigsaw puzzle solver-based attack that utilizes block correlation is proposed to restore visual information from encrypted images. In the experiments, we evaluated the security of image encryption for privacy-preserving deep neural networks using both conventional and proposed COAs. The experimental results demonstrate that the proposed attack is able to restore almost all visual information from images encrypted for being applied to ViTs.
Comprehensive review for healthcare data quality challenges in blockchain technology

There are several features inherent in blockchain, including decentralized storage, distributed ledger, immutability, security and authentication, and it has shifted away from the hype to be used practically in different industries, such as in the healthcare sector. The use of blockchain technology has allowed the provision of improved services to industries. The objective of this paper is to demonstrate how the use of blockchain is influenced by data quality issues in the healthcare industry. The article is structured as a systematic literature review study that uses several articles issued in various databases from 2016 onwards. In this review study, 65 articles were chosen and grouped into a single key aspect of the challenge in the healthcare sector. The findings obtained were analyzed based on factors in three domains, classified as issues pertinent to the adoption, operational and technological domains. This review study aims to use the findings to provide support to the practitioners, stakeholders and professionals, whose purpose is to carry out and manage transformation projects pertinent to blockchain in the field of healthcare. In addition, the organizations would be facilitated in their decision-making processes when the potential blockchain users are made to comprehend the implicit factors related to blockchain.
Hopf physical reservoir computer for reconfigurable sound recognition

The Hopf oscillator is a nonlinear oscillator that exhibits limit cycle motion. This reservoir computer utilizes the vibratory nature of the oscillator, which makes it an ideal candidate for reconfigurable sound recognition tasks. In this paper, the capabilities of the Hopf reservoir computer performing sound recognition are systematically demonstrated. This work shows that the Hopf reservoir computer can offer superior sound recognition accuracy compared to legacy approaches (e.g., a Mel spectrum + machine learning approach). More importantly, the Hopf reservoir computer operating as a sound recognition system does not require audio preprocessing and has a very simple setup while still offering a high degree of reconfigurability. These features pave the way of applying physical reservoir computing for sound recognition in low power edge devices.
How to use <scp>AI</scp> in pathology

Abstract AI plays an important role in pathology, both in clinical practice supporting pathologists in their daily work, and in research discovering novel biomarkers for improved patient care. Still, AI is in its starting phase, and many pathology labs still need to transition to a digital workflow to be able to enjoy the benefits of AI. In this perspective, we explain the major benefits of AI in pathology, highlight key requirements that need to be met and example how to use it in a typical workflow.
On Feasibility of Decision Trees for Edge Intelligence in Highly Constrained Internet-of-Things (IoT)

Internet-of-Things (IoT) edge devices have limited resources in terms of area and power. Machine Learning based intelligent filtering can be effective in reducing the data footprint. In this work, we report a feasibility study of using decision trees (DTs) on the edge. The main contribution of this work is to demonstrate that decision trees are equally effective compared to popular neural networks (multi-layer perceptrons). We trained four datasets (Iris, Heart Disease, Breast Cancer, and Credit Card) with supervised decision tree-based learning with accuracy comparable to that of MLPs. We synthesized the DTs to gate-level implementation with the Synopsys Design Compiler in 32 nm CMOS technology node. Compared to MLP implementations, DTs can save approximately 97-98% in both area and power.
Exploring Architecture, Dataflow, and Sparsity for GCN Accelerators: A Holistic Framework

Recent years have seen an increasing number of Graph Convolutional Network (GCN) models employed in various real-world applications. However, designing efficient architectures for GCN acceleration remains challenging due to the varied sparsity across graph datasets. Despite significant efforts, very few of the existing works have considered a holistic view of the entire GCN accelerator design, and therefore, the dynamic interactions between architecture, dataflow (i.e., data reuse and parallelization strategies), and compression format are not well studied
TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics

Transformer neural networks are rapidly being integrated into state-of-the-art solutions for natural language processing (NLP) and computer vision. However, the complex structure of these models creates challenges for accelerating their execution on conventional electronic platforms. We propose the first silicon photonic hardware neural network accelerator called TRON for transformer-based models such as BERT, and Vision Transformers. Our analysis demonstrates that TRON exhibits at least 14× better throughput and 8× better energy efficiency, in comparison to state-of-the-art transformer accelerators.
A non-coplanar high-precision calibration method for cameras based on an affine coordinate correction model

Abstract Traditional non-coplanar calibration methods such as Tsai’s method have many problems, such as insufficient calibration accuracy, inconvenient operation, inaccurate models, etc. This paper proposes a new high-precision non-coplanar calibration method that aims to solve these problems. Like Tsai’s method, the proposed calibration method utilizes a one-dimensional displacement stage and a two-dimensional plane target to generate a virtual 3D feature point sequence. As an improvement, an affine coordinate correction model is applied to ensure the accuracy and orthogonality of the obtained virtual 3D coordinates. A novel and accurate camera calibration model is further established. Compared with Tsai’s model, which uses a radial alignment constraint and ignores the orthonormal constraint of the rotation matrix, the proposed calibration model fully considers the degrees of freedom of the camera’s parameters to be calibrated, as well as the lens’s nonlinear distortion parameters. More accurate analytical solutions of intrinsic and extrinsic parameters can be obtained with the proposed calibration model. Finally, a novel high-precision non-coplanar calibration method is proposed based on the proposed calibration model. The reprojection experiment proves that the calibration accuracy of this calibration method is better than that of Tsai’s and Zhang’s calibration methods under the same calibration conditions. As a supplement, a novel binocular camera system extrinsic parameter calibration method with known intrinsic parameters is proposed. With accurate intrinsic and extrinsic parameters, the binocular camera system’s relative measurement accuracy could be within 1/10 000. Overall, this method can be used in experimental and industrial applications that require high-precision calibration parameters.
Perception-Aware Image-Based Visual Servoing of Aggressive Quadrotor UAVs

The maintenance of visual features within the sensor field of view poses a significant challenge for underactuated aerial vehicles such as quadrotors, especially during aggressive maneuvers. However, the existing image-based visual servo (IBVS) control methods rely on strict target visibility assumptions or impose excessive constraints on the quadrotor's agility to meet this requirement. Furthermore, the effectiveness of the visibility constraint defined in prior works remains unverified in aggressive flight tests. To address these issues, we present a robust IBVS scheme for quadrotors to perform aggressive maneuvers while ensuring target visibility. Based on the nonlinear model-predictive control framework, we propose a novel underactuation compensation scheme to eliminate the need for a virtual camera frame, which enables us to formulate the true target visibility constraint. We then introduce a quaternion-based representation of spherical visual features to handle the nonsmooth vector field problem on the 2-sphere and derive its corresponding image kinematics. We validate our method through three challenging visual servo tasks where agile maneuvers are desired: fast landing, aggressive long-distance flight, and dynamic object tracking. Extensive simulation and experiment show that our method consistently achieves a target-visible rate of 100% in all the image frames, even under a maximum pitch of 21.04 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> . The results validate the effectiveness of our visibility constraint under large robot rotations and underscore its importance in enabling robust and aggressive flights.
Efficient Phishing Detection and Prevention Using Support Vector Machine (SVM) Algorithm

Phishing issues influence the electronic trade in light of the fact that web-based clients trust the Internet climate less. Phishers use procedures that advance to bait online clients, making new phishing sites and spreading messages that attempt to persuade Internet clients to follow deceitful connections to get to their sites. Phishing sites utilize refined procedures that direct internet-based clients to open another page, which has not yet been added to the boycott. A phishing assault that utilizes these new sorts of strategies is known as a zero-day assault. Against phishing techniques can be isolated into specialized or non-specialized arrangements. Nontechnical arrangements used to safeguard the client from phishing assault rely upon utilizing mindfulness and preparing projects to show online customers how to perceive phishing messages and sites. Specialized arrangements, in any case, rely upon building recognition and security models in view of preparing datasets.
Application of Python Scientific computing library and Simulation in Circuit Analysis

Complicated manual calculation and drawing often appear in circuit analysis, which brings difficulty to circuit analysis. Manual ways to analysis of the circuit is timeconsuming and laborious, and prone to errors. Python can solve the problem better in circuit analysis instead of manual ways. Python is a high-level, general-purpose interpreted programming language that is widely used in scientific computing and engineering. It also includes a vast collection of Python packages for scientific computing. This ecosystem of scientifically oriented libraries ranges from generic core libraries– such as NumPy, SciPy, and Matplotlib –to more specific libraries for particular problem domains [1]. 1So Python has many applications in circuit analysis, such as equation solving, curve drawing, logic calculation and so on. In this paper, we apply Sympy library of Python to solve the equations of branch current method, use Matplotlib library to draw the sinusoidal AC signal curve, and use programming language of Python to realize the design of logic which is also the realization of logic functions. With the rapid development of computer technology, the application of new technology and software tools in circuit analysis will greatly improve the efficiency of circuit analysis.
Blockchain-based NFT for Healthcare System

In this paper, the utilization of non-fungible tokens (NFT) for medical services is presented. The use of technology in the healthcare and medical sectors has increased exponentially due to the epidemic. However, security, transparency, handling of medical reports, and accessibility are the main problems in the medical sector. To address the aforementioned problems, we have proposed the Hyperledger blockchain with NFTs to ensure ownership of patient records, track all related activities, and provide transparency, security, and fast accessibility. Medical data, including x-rays, test reports, prescriptions, sonography reports, etc., is represented with NFTs, which are digital assets that are stored using distributed ledger technology (DLT) with unique addresses that cannot be copied. In addition, we have also used the Inter Planetary File System (IPFS) and Pinata for secure storage and fast access with security and transparency. The results show that NFT with a private blockchain like Hyperledger is a viable solution for the healthcare system.
Happiness through Metaverse: Health and Innovation Relationship

Happiness is the state of emotion characterized by various aspects including satisfaction, day-to-day activities, and balance of emotions. Feeling of accomplishment and living the life you want are the common signs of happy people whereas Metaverse is the concept of the world that exists virtually, it helps in creating a virtual environment for users. Consequently, research related to the contribution of the Metaverse in the field of mental health is increasing. In this paper the relationship between innovation (Metaverse) and mental health (happiness) according to research work is analysed, the result of analysis is presented through several graphs. According to the reviews of papers, it has been observed that there is a great future and improvement in the field of psychology with the help of emerging technologies i.e. Metaverse, AR, and VR.
Graphical Local Genetic Algorithm for High-Dimensional Log-Linear Models

Graphical log-linear models are effective for representing complex structures that emerge from high-dimensional data. It is challenging to fit an appropriate model in the high-dimensional setting and many existing methods rely on a convenient class of models, called decomposable models, which lend well to a stepwise approach. However, these methods restrict the pool of candidate models from which they can search, and these methods are difficult to scale. It can be shown that a non-decomposable model can be approximated by the decomposable model which is its minimal triangulation, thus extending the convenient computational properties of decomposable models to any model. In this paper, we propose a local genetic algorithm with a crossover-hill-climbing operator, adapted for log-linear graphical models. We show that the graphical local genetic algorithm can be used successfully to fit non-decomposable models for both a low number of variables and a high number of variables. We use the posterior probability as a measure of fitness and parallel computing to decrease the computation time.
A Comparative Study of Various Versions of YOLO Algorithm to Detect Drones

Object detection algorithms with various versions of YOLO are compared with parameters like methodology, dataset used, image size, precision, recall, technology used etc. to get a conclusion as which algorithm would be the best and effective for the detection of objects. Nowadays, due to the low price and ease of use, drones can pose a malicious threat. In the field of public security and personal privacy, it is important to deploy drone detection system in restricted areas. This comparative analysis model gives a wide picture of how various object detection algorithms work, and helps in understanding the best algorithm to be used for the detection of drones with highest accuracy and precision.
Seeing history come to life with augmented reality: the museum experience of generation Z in Göbeklitepe

This study investigates whether AR innovations will positively contribute to the visiting intentions of young people. We collected data from 402 Gen Z participants in the museum at Göbeklitepe, one of the first human settlements in Turkey. Study results suggest that visual appeal and enjoyment affect AR technology and that enjoyment and escapism affect revisit intention. In addition, AR had a mediating effect between the enjoyment experience and RVI. There was no moderating effect of emotions between AR and revisit intention. Our findings expand the literature on offering museum experiences to Gen Z.
Adaptively secure multi-authority attribute-based broadcast encryption in fog computing

Fog computing is considered an important development in cloud computing, with shorter response times, less data transfer load and a more secure distributed service architecture that can be accessed by a wide range of end devices. As with cloud computing, it also faces the challenge of how to securely distribute sensitive data. Ciphertext-Policy Attribute-based Broadcast Encryption (CP-ABBE) can protect sensitive data and provide fine-grained access control with user revocation. However, existing CP-ABBE schemes suffer many limitations when implemented in a fog computing environment: a single authority performs poorly in handling requests or data from many IoT devices, leading to system delays or limited functionality; expensive decryption costs make the computation and storage overheads unfriendly to devices with limited resources; these schemes are only proven selectively secure, meaning that security was proven in a weaker model. In this paper, using threshold secret sharing and decryption delegation, we propose an adaptively secure attribute-based multi-authority broadcast encryption (MA-ABBE) scheme that breaks through these limitations. The comparison and analysis results show that our scheme is practical and achieves adaptive security, enables user-side fast decryption and low storage overhead, and provides a secure and efficient option for protecting sensitive data in fog computing environments.