Reviewerly/exHarmony huggingface dataset:
New Blind Image Moments Watermarking Method Based on Evolutionary Optimization of Embedding Parameters

In image watermarking, the locations where the watermark is embedded in the frequency domain and the embedding strength influence the overall performance of the blind watermarking procedure.The present paper aims to propose a new blind watermarking method based on the dither modulation by developing an automatic selection of the optimum embedding parameters that guarantee high-quality watermarked images and low bit error rates during the extraction process.The proposed automatic search method for the best discrete moments subsets is based on an evolutionary algorithm and adopts a specific coding strategy with a group of genes representing the embedding positions.The second part of the chromosome is reserved for the embedding strength coding, followed by the application of different evolutionary operators on the evolution pool.Our study explores the impact of maximum generation, population size, and cutting-point positions with different crossover and mutation rates.The performances under different attack conditions are evaluated, and a comparative study is established with other conventional selection methods and other discrete transforms.Results show that our proposed optimization algorithm baptized EWIMps achieves the best trade-off between robustness and imperceptibility with a peak signal-to-noise ratio varying from 28.33 dB to 59.87 dB and a normalized cross-correlation value from 0.707 to 1.
Genetic-Based Virtual Machines Consolidation Strategy With Efficient Energy Consumption in Cloud Environment

In cloud computing environments, virtualization is used to share physical machine (PM) resources among multiple users by creating virtual machines (VMs).Running the PM consumes a large amount of energy.Additionally, the PM will be overloaded when the demand for resources exceeds the PM capacity.This overload on the PM leads to violations of Service Level Agreements (SLAs).Dynamic VM consolidation techniques use live migration of VMs to optimize resource utilization and minimize energy consumption.However, excessive migration of VMs impacts negatively the application performance due to the incurred overhead at the runtime.This paper presents a modified genetic-based VM consolidation (MGVMC) strategy that aims to replace VMs in an online manner taking into account energy consumption, SLA violations, and the number of VM migrations.The MGVMC strategy utilizes the genetic algorithm to migrate VMs to the appropriate PM in a way that minimizes the number of over-utilized and under-utilized physical machines (PMs) as low as possible.The performance of the MGVMC strategy was evaluated using the CloudSim Plus framework with a large number of VMs and workload traces from the PlanetLab platform.The experimental results revealed that the MGVMC strategy achieved a significant improvement in energy consumption, SLA violations, and the number of VM migrations compared to other recent approaches.These results demonstrate the effectiveness of the MGVMC strategy in optimizing VM consolidation in the cloud environment.
Exploiting Quantum Power Flow in Smart Grid Co-Simulation

Our electricity infrastructure is getting more complex and heterogeneous. Holistically analyzing grids is therefore increasingly challenging. Co-simulation, i.e. the coordinated execution of independent subsystem simulators, is inherently well suited to handling these challenges. However, the computational needs of calculating power flows within the simulated grid may limit the scalability for large-scale co-simulations. Recent advances in quantum computing offer a potential solution to these concerns: The computing paradigm‘s potential for exponentially speeding up power flow has been shown. To utilize these capabilities for smart grid simulations, we propose quantum–classical co-simulation: integrating simulators running on quantum hardware with an otherwise classical co-simulation. Specifically, we focus on exploiting quantum power flow in smart grid co-simulations. This concept is promising for applications that require comprehensive grid simulation and whose scalability is impeded by the computational properties of power flow. This paper highlights the concept of quantum–classical co-simulation, and advocates for its criticality and applications in supporting smart grid analytics. We encourage and facilitate research by recommending a five-item research roadmap. We also provide a detailed discussion on the potential obstacles in implementing this concept, to help bring its theoretical value to practice.
TIMCPOT: Tcl/Tk Interface for Measuring Crustal Phase-Onset Time

Abstract In recent years, the seismological community has seen an amazing increase in the amount of seismic arrival time picks with the help of artificial intelligence. But, the accuracy of this data remains a challenge to seismologists. In comparison with automatic picks, manual picks have sufficient reliability and accuracy because manual picks are often used not only as a benchmark to evaluate automatic picking algorithms, but also as training samples to train artificial intelligence models. However, measurement through manual labor is a laborious and time-consuming task. To alleviate the manual labor involved in measuring phase arrival time, we developed a graphical interface named Tcl/Tk Interface for Measuring Crustal Phase-Onset Time (TIMCPOT). The TIMCPOT is mainly equipped with two modules: one is designed for measuring seismic arrival time, and the other is for relocating hypocenter. TIMCPOT simplifies user interaction with seismic data, thus reducing most manual labor. TIMCPOT provides several graphical workflows to carry out detection, identification, revision, and comparison operations on measurement work. The measurement procedure can be operated either manually or by autopicker. For the sake of allowing the user to interact easily with seismic data, TIMCPOT is designed to be executed in a semiautomatic model during several necessary procedures such as visually inspecting results and manually modifying picks. By applying a near real-time displaying technique, TIMCPOT can create a travel time curve figure and comparison chart for manual picks in a timely fashion, which provides a rapid way to estimate data quality. The measurement efficiency and travel time data quality have been significantly improved using this graphical interface. An explicit demonstration was shown to validate the powerful performance mentioned earlier. The primary advantage of TIMCPOT is that it reduces manual labor while retaining valuable manual experience on measuring phase picks. Of course, TIMCPOT has several limitations. However, most limitations will be improved in future development.
Progressive detail-content-based similarity retrieval over large lung CT image database based on WSLN model

With the explosive growth of the medical images, the high-resolution CT image(CI) data is of great value for medical research as well as a clinical diagnosis. Finding CIs similar to the input one from the large- scale CI database can effectively assist physicians to diagnose. The state-of-the-art content-based similarity retrieval of CIs often ignores the effect of image details on retrieval accuracy, and the retrieval accuracy and efficiency are usually not satisfactory. To address this challenge, in this paper, we take lung CI as an example and propose a progressive Detail-Content-based Similarity Retrieval (DCSR) of the lung CIs based on a Weakly Supervised deep Learning Network(WSLN) model. Two enabling techniques (i.e., the WSLN model and the DIndex scheme) are proposed to facilitate the DCSR processing of the large lung CIs. The experimental dataset is from three public datasets. Extensive experiments show that the WSLN-based DCSR method is about 45% more effective than the state-of-the-art methods in terms of the mean average precision(MAP). Meanwhile, the retrieval efficiency of the DIndex scheme is about 200% higher than that of sequential retrieval.
Reference synthetic-dataset for novelty detection in oil production data: A perceptive evaluation along with case studies from 51 oilfields

In the last couple of years, several studies were concerned with novelty detection in oil production time-series data to eliminate their deleterious effects. Although many algorithms were investigated over and over, the reported outperforming models are poles apart. Moreover, the reportedly high scores in each study could not be verified individually, which led to uncertainty of algorithms performance in large oilfields. This declared the inevitability of comprising a reference dataset to retain a fair comparison between the existing algorithms and to encourage future development of superior solutions. In this article: we (1) put forward a reference-dataset that consists of 200 synthetic oil wells, derived from UNISIM public datasets, to serve as a benchmark for novelty detection in oilfields, (2) evaluate existing methodologies along with a novel ensemble-learning model on the proposed dataset to serve as a reference point for future research, and (3) validate robustness of the initiated reference-dataset by comparing results on a set of real oilfield data to its results. Apart from that, we investigate the role of statistics in optimizing the selection of outliers cut-off threshold to fulfill a complete automatic approach, which is inaugurated for the first time in oil production data, to the best of our knowledge. Reference dataset and the implemented code are published on GitHub for the intent of re-producibility.
Lookup Table Modular Reduction: A Low-Latency Modular Reduction for Fast ECC Processor

Modular multiplication is used extensively in many cryptosystems, such as in Elliptic Curve Cryptography (ECC). This is why the speed of the modular multiplication has a high impact on the overall speed of the cryptography computation. Recent works utilizing a lookup table for inferring value have shown a promising way for fast computation of modular re-duction, which can be used to construct a much faster modular multiplier than the conventional methods on FPGA. In this work, we explore an alternative way to implement the said technique, which we will call Lookup Table Modular Reduction (LUTMR). We show that in this technique, the modulo value used for generating the modular reduction circuit has a high impact on the generated circuit efficiency. With the LUTMR technique, three modular multipliers for curve Secp256k1, NIST-P384, and BLS12-381 are implemented on FPGA, which has shown to be the fastest compared to recent works. The NIST-P384 ECC processor is also implemented with the designed modular multiplier. It can compute the scalar multiplication in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$75.08 \ \mu \mathrm{s}$</tex> , the fastest and lowest in Time-Area criteria among recent works.
Grateful Chatbots: Public Sensemaking through Individual Gratitude Interventions

This paper outlines a research plan that aims to build resilient and adaptive systems for public sensemaking through conversational agent-enabled, narrative-based, autonomous self-actualization interventions. Our research follows a design science research (DSR) methodological approach that is comprised of iterative design cycles (DC) and provides design principles (DP) and testable propositions (TP) as an outcome. The individual-centered approach serves as a basis to explore ways to harness these narrative-based interventions for large-scale public sensemaking. The proposed DP have yet to be fully evaluated as testable propositions in an experimental setting, but we are considering future directions for improving the developmental intervention or allowing for easier evaluation.
Deep Learning and Industrial Internet of Things to Improve Smart City Safety

the Internet of Things (IoT) has grown rapidly in recent years, enabling the manufacture of a vast array of devices that may improve different operational components in a wide variety of enterprises. This study is being done to lay the groundwork for the establishment of "smart cities," or metropolitan regions where robots will outnumber humans. Data sensitivity has increased, particularly in the commercial and industrial sectors of the economy. We will also go through the myriad security risks that have occurred because of this company's rapid growth. In this study, deep learning, perceptrons, and convolutional neural networks are highlighted as the deep learning (DL) approaches for IoT applications in smart cities. Deep learning, perceptrons, and convolutional neural networks are only a few of the additional methodologies considered. We also discuss the benefits and drawbacks of various types of safety precautions. Furthermore, we evaluate the lessons learned, the obstacles that remain, and the future trends in the use of DL approach to improve Industrial Internet of Things (IIoT) safety.
Prediction of Specific Language Impairment in Children using Cepstral Domain Coefficients

Specific Language Impairment (SLI) is a language disorder which prevents children from achieving language skills. Children that are affected with SLI are late to speak and may not generate any words until they are two years old. Children affected by SLI have difficulty constructing and comprehending coherent sentences. Most of the time, the effects caused by SLI last far beyond adolescence. Furthermore, due to absence of a clear cause of the illness, SLI is likely to go unnoticed by the majority of parents and teachers. This implies that automated identification of children with SLI is required. This paper presents an automated method of SLI detection with speech utterances with a relatively simpler and lighter machine learning model. The fundamental aim of this article is to develop a speaker-independent technique for SLI identification based on spoken utterances that is accurate and efficient using the minimum number of features. Two models are created that combine Mel-frequency cepstral coefficients (MFCC) with Neural network and Linear prediction coefficients (LPC) with Multi-layer Perceptron (MLP), respectively. These utterences are collected from SLI-diagnosed and healthy children’s speech samples. Overall results show that MFCC features when combined with MLP give the best performance. The best results acquired during model training, in comparison to accuracy rates of the aforementioned techniques, demonstrates the proposed method’s efficiency and efficacy.
Metaverse Skills for Executives and Senior Managers: The Pros and Cons

The COVID-19 pandemic has accelerated the shift from traditional office setups to remote working, driven by information and communication technology advancements. As a result, the metaverse concept is gaining popularity in modern organizations, allowing users to create avatars for virtual work, socializing, and other activities. While its corporate adoption is rising, managers must acquire the necessary skills to integrate and utilize the technology successfully. However, technological progress can be disruptive, making it essential to weigh the benefits and drawbacks.Methodology: This proposal aims to investigate metaverse skills that managers require for remote working using virtual realities, assessing the positive and negative risks for employees and management within business organizations. Using secondary data from reliable online databases, a qualitative research approach was used to understand the pros and cons of the metaverse and remote work.Purpose: The study examines the essential skills managers need to adopt metaverse virtual realities for remote working and how employees and organizations can implement it while maintaining a positive work environment. Keywords such as metaverse, remote working, virtual reality, and information and communication technology are critical. As technology evolves, managers and organizations must consider the metaverse's inherent advantages and disadvantages to ensure a successful transition to remote.Research Questions: What are the necessary skills needed by managers towards the adoption of metaverse virtual realities for remote working? How can employees and organizations adapt to implementing metaverse for remote work and sustain a positive work environment?
CNN-RNN Deep Learning Networks for Pattern Recognition Problems

Neural networks have remarkably solved many problems. A giant network with a high processing requirement and long training time is being investigated owing to having a massive amount of data. Therefore, parallel processing is essential for practical applications of network training. This study aims to develop the best neural network to accelerate a face recognition system. The standards used to compare tasks were speed and accuracy used to develop an algorithm providing the best performance in a parallel system. Moreover, this study aimed to combine neural networks and parallel programming to develop a swift person recognition system in a camera stream. Video frames were saved, and the images were fed to a neural network built in a parallel structure. This study provides readers with a better understanding of the new and current trends of artificial neural networks (ANN) models, successfully addressing PR issues to permit research emphasis and themes. The system used libraries, such as MPI, graphical computing compute unified device architecture (CUDA) libraries, and the softmax function to provide the required performance with high accuracy and good delay results. The results of this study showed that the parallel system using a conventional neural network (CNN) was 30% better than the serial system for face recognition and that the CNN networks provided the best accuracy among all the other systems.
Social Media Analytics through Big Data Using Hadoop Framework

In the current decades, data from social networks has increased dramatically. Since outmoded tools and techniques are not appropriate for working with such a large amount of data, a good structure is needed to accumulate these vast volumes of data and provide the opportunity for finale users to analyze them. In this research work, we use data analysis techniques to support businessmen for the development of new strategies. We use Apache Hadoop framework and apache flume platform for obtaining social media analytics. Apache flume works as a local host for the Hadoop data file system (HDFS). Iron man 3 movie reviews on twitter have been executed and analyzed by using apache flume and hive. By observing the table created by apache hive from twitter tweets it is shown that only 2 movie viewers observe that it is negative, some of them observe the movie not of standard while giving a neutral opinion or negative option, but most of them like the movie by giving positive remarks.
Trade Offs between Measurement and Track Fusion at the System Level

A hotly debated topic that has been around for decades-probably as long as the Kalman Filter-is what advantage is there to fusing measurements as opposed to fusing tracks at the system level. Many systems use the measurements and have been designed to do so. On the other hand, some systems only utilize tracks from sensors and have had excellent success in this manner. One line of thinking is that it depends on the target that is being tracked. If the target is expected to maneuver in a significant manner then it may be better to use measurements in order to reduce overall lag in the system. Conversely if the target is not expected to maneuver significantly then tracks from each sensor may be a better alternative to allow for bias estimation and removal. The motivation for this paper is not to provide the definitive answer to this topic, but rather the purpose of this paper is to explore and document many of benefits of each approach. It will begin with a basic three-sensor scenario where both measurement and track fusion are performed with the same update rates.
Decentralized Data Fusion of Dimension-Reduced Estimates Using Local Information Only

This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small.
Glaucus: A Complex-Valued Radio Signal Autoencoder

A complex-valued autoencoder neural network ca-pable of compressing & denoising radio frequency signals with arbitrary model scaling is proposed. Complex-valued time sam-ples received with various impairments are encoded into an embedding vector, then decoded back into complex-valued time samples. The embedding and the related latent space allow search, comparison, and clustering of signals. Traditional signal processing tasks like specific emitter identification, geolocation, or ambiguity estimation can utilize multiple compressed embed-dings simultaneously. This paper demonstrates an autoencoder implementation capable of compression by a factor of 64 that is still resilient against RF channel impairments. The autoencoder allows individual scaling by network depth, width, and resolution or in a compound sense to target both embedded and data center deployments. The common building block is inspired by the fused inverted residual block (Fused-MBConv), popularized by EfficientNetV2 & MobileNetV3, but with kernel sizes more appropriate for time-series signal processing. A complex-valued PyTorch implementation is available along with a pre-trained model, at https://github.com/the-aerospace-corporation/glaucus.