Reviewerly/exHarmony huggingface dataset:
A New Approach to Detect Power Quality Disturbances in Smart Cities Using Scaling-Based Chirplet Transform with Strategically Placed Smart Meters

The growth of Internet of Things (IoT)-enabled devices has increased the amount of data created by the distribution network’s periphery nodes, requiring more data transfer capacity. Recent applications’ real-time requirements have strained standard computing paradigms, and data processing has struggled to keep up. Edge computing is employed in this research to detect distribution network faults, allowing for instant sensing and real-time reaction to the control room for faster investigation of distribution problems and power outages, making the system more reliable. Moreover, to overcome the challenges of fault detection, advanced signal processing methods need to be integrated with the Adaboost classifier. An Adaboost-based edge device, suitable for installation on top of a power pole, is proposed in this research as a means of real-time fault detection. To increase throughput, decrease latency and offload network traffic, data collecting, feature extraction and Adaboost-based problem identification are all performed in an integrated edge node. Enhanced detection accuracy (98.67%) and decreased latency (115.2 ms) verify the effectiveness of the suggested approach. In this research, we enhance the classical chirplets transform to create the scaling-basis chirplet transform (SBCT) for time–frequency (TF) analysis. This approach modulates the TF basis around the relevant time function to modify the chirp rate with frequency and time. By carefully selecting the sampling frequency, it is possible to discriminate between short circuit fault and high-impedance fault (HIF) by calculating spectral entropy. The TF representation obtained with the SBCT provides considerably higher energy concentrations, even for signals with numerous components, closely spaced frequencies and heavy background noise.
Research advanced in image style transfer based on deep learning

Image style transfer (IST) aims to combine the style of one image with the content of another to make the resulting image has both the attributes of the style image and the characteristics of the content image. Benefited from rapid developments of deep learning, the methods based on Generative Adversarial Networks (GAN) have greatly improved the accuracy of IST. In this paper, to promote the research of IST based on Convolutional Neural Networks (CNN), some main methods of it are presented. The developments of CNN and GAN are reviewed, and basic principles and techniques of IST based on CNN and GAN are introduced in detail. Moreover, some representative datasets for this task are summarized, then the evaluation metrics and results are presented. Moreover, some problems of current methods are discussed. Finally, application prospects in related fields and future research directions of IST are presented.
Adaptive Pruning for Multi-Head Self-Attention

This paper provides an adaptive pruning approach to compress Multi-Head Self Attention (MHSA) models. The main aim is to suppress redundant attention heads, requiring high computational complexity, without substantially affecting performance. Through head pruning, we propose more flexible and efficient models for the object detection tasks. Specifically, we propose to enhance the architectures of the two state-of-the-art MHSA-based models: Bottleneck Transformers (BoTNet) and Attention Augmented Convolutional Networks (AACN). Our approach relies on the alternation between the escalation and the ablation of heads. We selected the less productive heads. We suggest to exploit two and four heads, rather than four and eight heads for BotNet and AACN models, respectively. Our experiments on ImageNet and Pascal VOC datasets prove that our light-weighted architectures are more efficient compared with the original heavy-weighted ones. We reach close performances and achieve faster convergence during training, which allows easier transfer and deployment.
ABDUCTIVE REASONING AS A MODEL FOR MUSICAL CREATION IN JULIO ESTRADA’S MUSICAL WORKS

The paper deals with two philosophical postulates that although at first sight both do not have a direct link, it is my hypothesis that there may be a connection between them since in each one imagination plays an important role in the process of scientific discovery. On the one hand, it deals with the abductive reasoning by Charles Sanders Pierce (1839–1914), focusing on the problem related with abduction and scientific imagination, graphicacy or the ability to represent ideas through images, beelddenken or “pictorial thinking” as a form of communicating ideas without the use of any verbal form, and synthetic topology responsible for studying the qualitative relations of any 3-D geometrical structure. On the other hand, the author presents the philosophical postulates of the Theory of Composition: discontinuum-continuum by Julio Estrada (*1941), where reality and imagination play an important role in the process of musical creation, presenting a two-fold methodology of creation, (1) the macro timbre where the homogenization of the components of the rhythmsound takes place, and (2) the chronography, a graphic representation of the rhythmsound in movement. The latter is exemplified in this paper with the three examples of Estrada’s musical compositions, (1) the graphical score of eua’on UPIC (1980), (2) graphical recording for the series yuunohui’ (1983–2020), and (3) the topological variations included in the ishini’ioni String Quartet (1984–1990).
Outlier Detection Model Based on Autoencoder and Data Augmentation for High-Dimensional Sparse Data

This paper aims to address the problems of data imbalance, parameter adjustment complexity, and low accuracy in high-dimensional data anomaly detection. To address these issues, an autoencoder and data augmentation-based anomaly detection model for high-dimensional sparse data is proposed (SEAOD). First, the model solves the problem of imbalanced data by using the weighted SMOTE algorithm and ENN algorithm to fill in the minority class samples and generate a new dataset. Then, an attention mechanism is employed to calculate the feature similarity and determine the structure of the neural network so that the model can learn the data features. Finally, the data are dimensionally reduced based on the autoencoder, and the sparse high-dimensional data are mapped to a low-dimensional space for anomaly detection, overcoming the impact of the curse of dimensionality on detection algorithms. The experimental results show that on 15 public datasets, this model outperforms other comparison algorithms. Furthermore, it was validated on industrial air quality datasets and achieved the expected results with practicality.
Chaos-Based Construction of LWEs in Lattice-Based Cryptosystems

In postquantum cryptography, the problem of learning with errors (LWE) has been widely used to create secure encryption algorithms. Nevertheless, the transmission of a large-dimensional public key matrix brings heavy overhead to communication systems. Addressing this problem, we propose a simpler scheme to generate the public key matrix with elements admitting uniform distributions. From the perspective of chaos, we employ logistic mapping and modulo lattice operations to generate uniform random numbers that feature good randomness. The public key with a large number of elements can be described by only a few parameters, which significantly reduces the transmission cost. On the basis of uniformly distributed random numbers, one can also construct random numbers admitting discrete Gaussian distributions.
An Improved War Strategy Optimization Algorithm for Big Data Analytics

Big data analysis is confronted with the obstacle of high dimensionality in data samples. To address this issue, researchers have devised a multitude of intelligent optimization algorithms aimed at enhancing big data analysis techniques. Among these algorithms is the War Strategy Optimization (WSO) proposed in 2022, which distinguishes itself from other intelligence algorithms through its potent optimization capabilities. Nevertheless, the WSO exhibits limitations in its global search capacity and is susceptible to becoming trapped in local optima when dealing with high-dimensional problems. To surmount these shortcomings and improve the performance of WSO in handling the challenges posed by high dimensionality in big data, this paper introduces an enhanced version of the WSO based on the carnivorous plant algorithm (CPA) and shared niche. The grouping concept and update strategy of CPA are incorporated into WSO, and its update strategy is modified through the introduction of a shared small habitat approach combined with an elite strategy to create a novel improved algorithm. Simulation experiments were conducted to compare this new War Strategy Optimization (CSWSO) with WSO, RKWSO, I-GWO, NCHHO and FDB-SDO using 16 test functions. Experimental results demonstrate that the proposed enhanced algorithm exhibits superior optimization accuracy and stability, providing a novel approach to addressing the challenges posed by high dimensionality in big data.
CONSEQUENCES — The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

Recommender systems make algorithmic decisions about what will be shown to whom, billions of times every day across the web. These decisions have consequences that can often be far-reaching. Indeed, users that are exposed to certain items, might be convinced to explore interests that are novel to them. At the same time, exposure is often linked to economic incentives for the item producer, which platform-level metrics will be impacted by as well. Feedback loops in existing systems also imply that algorithmic decisions made by the model itself, have an impact on the data future model iterations will be trained and evaluated on.
Dynamics of one two-level-atom interacting with a multiple cavity modes

We discuss how to implement quantum logic gates by considering a two-level-atom driven by a strong microwave field and successively interacting with m+1 cavity modes. The scheme is insensitive to the initial state of the atom, and the operation time is independent of the number of cavity modes involved in the system operations. This scheme is used to realize two quantum logic gates (m-target-qubit controlled-global-phase gate and Multi-qubit phase shift gate) in a time much shorter than the photonic lifetime. We also studied the influence of decoherence on the fidelity. In general, our system is reasonably less sensitive to the photonic and atomic decay rates and therefore it can be experimentally realized.
Healthcare Applications Using Blockchain with a Cloud-Assisted Decentralized Privacy-Preserving Framework

In recent times, cloud-enabled healthcare services have gained much attention in fulfilling the analysis of privacy risks associated with effective decision management systems including trustworthiness and secure data sharing. This system has revolutionized the medical architecture to evolve unprecedented opportunities in using the technologies of next-generation networks, including services, control, and signaling, to effectively improve content delivery to individuals and organizations. However, it is a highly complex matter to distribute confidential data over a public network because data privacy and device security are at risk. As a result, a cloud-based healthcare application is introduced that integrates the computation capabilities of ubiquitous computing to provide extensive communications over dedicated Internet access in order to store health records. To manage access control mechanisms and process sensitive data without extensive computation, the existing cloud-centric systems access remote servers via dedicated networks. Based on a centralized architecture, a dedicated network uses different application domains to deliver information to the healthcare industry. Unfortunately, computation complexity greatly deteriorates the performance of peer-to-peer (P2P) communications. Thus, this paper presents a cloud-assisted decentralized privacy preserving framework (CA-DPPF) using blockchain and key agreement (KA) mechanisms to achieve secure data storage and privacy. The detailed security analysis proves that the proposed scheme fulfills the desired security properties of healthcare supply chain management (H-SCM) such as conditional traceability, data immutability, and data integrity. Overall, exploratory analysis shows that CA-DPPF guarantees better transaction efficiencies such as less latency and more throughput in order to improve the service utilization factor.
Nathan Selikoff: Explorations in Higher Dimensionality and Complexity

While data scientists pursue new extremes of high-dimensional data, and data visualization professionals attempt to visualize within that space, Nathan Selikoff's work suggested a unique perspective on relationship among complexity, higher dimensions, and sensing what otherwise cannot be sensed. His long-tailed pursuit of visualizing complexity piqued our interest as we interviewed him for this article.
Construction and Application of a Complex Network-Based Case Knowledge Base in an Assisted Instruction System

With the development of the business administration major, case teaching has become an effective teaching method. However, a lot of case resources exist in the business administration field, and the correlation between these cases is complex, which poses challenges to teachers and students in terms of information overload and knowledge organization. Existing case knowledge bases in assisted instruction systems often have shortcomings in terms of knowledge relationship strength calculation, text semantic similarity calculation, hierarchical knowledge clustering, and propagation evolution. To solve these problems, this study proposed a method of constructing and applying a case knowledge base in an assisted instruction system based on a complex network. This method mainly includes three aspects: calculation and reasoning of ontology relationship strength, calculation of ontology text semantic similarity, and hierarchical knowledge clustering and propagation evolution in complex networks. Through a comprehensive study of these three aspects, a more efficient and intelligent case knowledge base in an assisted instruction system was constructed, which not only improved the teaching efficiency and quality of the business administration major but also had the potential to promote teaching in other disciplines and fields. In addition, this study also provided new perspectives and methods in related fields, which is of great significance for efficiently organizing and utilizing knowledge.
A Distributed Conflict-Free Task Allocation Method for Multi-AGV Systems

In the era of Industry 4.0, as the main force of intelligent logistics systems, multi-Automated Guided Vehicle (AGV) systems have developed rapidly. At present, multi-AGV systems are a research hotspot, where task allocation as a key technology is being paid much attention. In this study, a new task allocation scheme for multi-AGV systems is proposed based on a distributed framework. The AGVs can autonomously select tasks, plan paths, and communicate with its neighbors to ensure that all tasks are completed at a low cost and conflicts are avoided. While ensuring total connectivity, the proposed method can avoid the calculation pressure of task center surges when the number of AGVs increases sharply, and has the advantages of good flexibility and good real-time performance. In addition, some examples are provided to demonstrate the effectiveness of the connectivity maintainer and task allocation method.
Nonlinear phase error correction method based on multi-grayscale coding

Fringe projection profilometry is a non-contact and highly efficient 3D measurement technique widely used in various applications. However, the nonlinear intensity response of digital projectors affects measurement accuracy. While increasing the number of fringe projections can reduce the errors caused by nonlinear problems, it significantly prolongs the measurement time. In order to improve both accuracy and speed simultaneously, a nonlinear phase error correction method based on multi-grayscale coding is proposed. The intensity response curve of the system is fitted by the grayscale images, and then the grayscale values of the phase-shifting fringe images are corrected to reduce the nonlinear error. In order to reduce the number of fringe projections and speed up the measurement, the multi-grayscale coding method is used to divide the phase interval by the order of the gray values of the same pixel in multiple grayscale images. The experimental results validate the efficacy of the proposed multi-grayscale coding method. An accurate phase calculation is achieved, and a single reconstruction can be achieved with only seven photos. After the nonlinear correction, the phase accuracy of the three-step phase-shifting algorithm is increased by 50.77%, and the reconstruction accuracy of the standard ball is increased by 46.38%.
On Irish bacteriometallogenesis and its wider connotations

Rapid and widespread access of surface waters to jostling segments of the upper crust marks the first step to metallogenesis in the Irish early Carboniferous. Overall, siting of these ore-forming systems involves two types of fluid-charged autocatalytic cracking engines. The more obvious is a province-wide diffuse type, excavating downward from the generally submarine surface to where waters are heated, dissolve metals and become buoyant enough to exploit channel ways back to the surface. Another, rather more obscure engine, starts near the mantle-crust boundary and either produces a through-going crustal structure to vectorially guide pressurized mantle volatiles and perhaps accompanying magma toward the surface, or it autonomously drills its way through the entire crust by penetrative convection, as in a diatreme. Either way ore-forming solutions are perhaps best incubated where both engines interact within the crust. But precipitation of minable metal requires structural down warp basins and associated saline, sulphate-reducing microbiome traps. So, whilst there is no unique ex-planation for the distribution of the orebodies, the Navan orebody, its southwest extension (SWEX) and its neigh-bour to the south, Tara Deep, do fall on a putative N-S Geofracture (Gf 3) first proposed in 1968/1969. But the N-S Geofracture hypothesis runs strongly against the “academic” grain! Moreover, no further discoveries can be unequivocally assigned to the hypothesis. So why persist with the notion at all? Well, there have been some other indications of such structures in adjacent countries and, most notably and farthest afield, on Mars which acts as a time machine for our planet! There the seismically active Cerberus Fossae structures have similar crustal joint aspect ratios of 40 to 55km, as with the putative Irish (and Scottish) examples. And five cold springs, sequentially younger to the east, happen to lie well-spaced along a 250km stretch of the northernmost Fossa. Yet there are no signs of mineral sulphide accumulations on Mars. Perhaps lacking there were sulphate-reducing bacteria required for the deposition of economic ores as in Ireland. Indeed, it was the discovery of light sulphate sulphur in the Irish ores, along with lithochemical and geological evidence for an exhalative aspect to them, that inspired the submarine alkaline vent theory (AVT) for the emergence of life. In this theory exothermic serpentinization drives hydrothermal convection cells to produce the exhalations into the Hadean Ocean. Here the free-energy convert-ing iron minerals (oxyhydroxides and sulphides) act as nano-engines in spontaneously precipitated membranes to generate the appropriate organic molecules required for life’s onset from volatile and hydrothermal delivery of CO2, H2, CH4 and the trace elements. These prebiotic nanoengines are powered by the electrical and pH disequilibria focused across the mineral membranes amounting to ~1 volt.
An Overview of Content-Based Image Retrieval Methods and Techniques

With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large-scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR, and many effective CBIR algorithms have been established. Content-based image retrieval helps to discover identical images in a big dataset that match a query image. The query image's representative feature similarities to the dataset images typically assist in ranking the images for retrieval. There are various past studies on different handicraft feature descriptors according to the visual features that describe the images: color, texture, and shape. However, deep learning has been the dominant alternative to manually planned feature engineering; it automatically takes the features from the data. The current work reviews recent advancements in content-based image retrieval. For a deeper understanding of the advancement, the explanation of current state-of-the-art approaches from various vantage points is also conducted. This review employs a taxonomy encompassing various retrieval networks, classification types, and descriptors and this study will help researchers make more progress in image retrieval
Student Attentiveness Classification Using Geometric Moments Aided Posture Estimation

Body posture provides enough information regarding the current state of mind of a person. This idea is used to implement a system that provides feedback to lecturers on how engaging the class has been by identifying the attentive levels of students. This is carried out using the posture information extracted with the help of Mediapipe. A novel method of extracting features from the key points returned by Mediapipe using geometric moments is proposed. Geometric moments aided features classification performs better than the general distances and angles features classification. In order to extend the single person pose classification to multi-person pose classification, object detection is implemented. Feedback is generated regarding the entire lecture and provided as the output of the system.
Penerapan Permodelan Nielsen Pada Website Aplikasi Inventaris Barang di Pondok Pesantren Miftahul Jannah

The development of technology is increasingly fast and developing, so the need for a technology is one of the websites-based software technology, with this website being used to make difficult work easy, the purpose of this research is to design and build an inventory application for Islamic boarding schools Miftahul Jannad. in the survey village by applying the Nielsen modeling. The research method used is the method of observation, interviews and literature studies, the approach method is the Nielsen modeling method, where Nielsen's modeling consists of learnability, efficiency, memorability, few errors, satisfaction, the results of Nielsen's modeling are obtained to test the validity of learnability with counted r counts. 0.868, 0.845, 0.736, 0.619, Efficiency with r count 0.813, 0.461.0.636, 0.557, for Memorability is 0.773, 0.830, 0.814, 0.578 for memorability with the calculated r value of 0.773, 0.830, 0.814, 0.578, then for few errors obtained a value of 0.621, 0.765, 0.704, 0.653, then for satisfaction with a value of 0.732, 0.559, 0.662, 0.652, which means that with the calculated r value is more r table, where the r table is obtained 0.320, the data in the Nielsen modeling is valid, while for the reliability test it is stated that the results of the Nielsen modeling test are reliable.