Reviewerly/exHarmony huggingface dataset:
Highly efficient and indistinguishable single-photon sources via phonon-decoupled two-color excitation

Single-photon sources with near-unity efficiency and indistinguishability play a major role in the development of quantum technologies. However, on-demand excitation of the emitter imposes substantial limitations to the source performance. Here, we show that coherent two-color pumping allows for population inversion arbitrarily close to unity in bulk quantum dots thanks to a decoupling effect between the emitter and its phonon bath. Driving a micropillar single-photon source with this scheme, we calculate very high photon emission into the cavity mode (0.95 photons per pulse) together with excellent indistinguishability (0.975) in a realistic configuration, thereby removing the limitations imposed by the excitation scheme on single-photon source engineering.
Leaks and lawfare: adding a Legal Filter to Herman and Chomsky’s propaganda model

Motivated by the conundrum “when does the law make it illegal to reveal illegal activity?”, this article explores the relationship between legal structures and the free flow of information by adding a Legal Filter to the Propaganda Model. The Legal Filter represents how elite powers use legal constructions to block information from mainstream media through three layers: Undisclosed Information, Lawfare, and Legal Standing. The addition of a Legal Filter compliments and strengthens current discussions about informational and surveillance capitalism, mis/dis/malinformation proliferation and understanding the growing number of internet information leaks and “hack and dumps.” (Herman, E. S., & Chomsky, N. 1988 Herman, E. S., & Chomsky, N. (1988). Manufacturing consent: The political economy of the mass media. Pantheon. [Google Scholar]. Manufacturing consent: The political economy of the mass media. Pantheon).
Image hiding by using spatial domain steganography

This article provides an overview of steganography and its use for hiding images in other images. Steganography is a technique that allows users to hide information in plain sight, making it difficult for unauthorized parties to detect or access the information. Spatial domain steganography is a popular technique for hiding images within other images, where the least significant bits of the cover image are modified to embed the secret image. The article discusses the advantages of steganography and its use in various applications such as digital watermarking and secure communication. The article also provides an overview of the various techniques used for spatial domain steganography, and how these techniques can be implemented using programming languages such as Python. Finally, the article concludes by emphasizing the importance of using steganography responsibly and ethically.
Better Qualitative Searching for Effecting the Performance of Machine Translation

By improving a better comprehension of the human language for linguistically based human-computer communication, natural language processing (NLP) contributes to the empowerment of intelligent machines. Instead of hand-coding massive sets of rules, NLP can use machine learning to learn these rules automatically by examining a set of rules. With a view to better and clearer results in the endeavor to achieve success in the field of language translation, we have gone through some of the techniques like Machine Translation, Punctuation Restoration, Name Entity Recognition, and Sentiment Analysis. Here we mentioned different state-of-the-art methods for different techniques of NLP. This paper offers an abstract analysis of the current state of language translation techniques.
Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework

We present an analysis and evaluation of breast cancer detection and diagnosis using segmentation models. We used an advanced semantic segmentation method and a deep convolutional neural network to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images. To improve the segmentation results, we used six models to analyse 309 patients, including 151 benign and 158 malignant tumour images. We compared the Unet3+ architecture with several other models, such as FCN, Unet, SegNet, DeeplabV3+ and pspNet. The Unet3+ model is a state-of-the-art, semantic segmentation architecture that showed optimal performance with an average accuracy of 82.53% and an average intersection over union (IU) of 52.57%. The weighted IU was found to be 89.14% with a global accuracy of 90.99%. The application of these types of segmentation models to the detection and diagnosis of breast cancer provides remarkable results. Our proposed method has the potential to provide a more accurate and objective diagnosis of breast cancer, leading to improved patient outcomes.
Bayesian Analysis Methods for Two-Level Diagnosis Classification Models

Understanding whether or not different types of students master various attributes can aid future learning remediation. In this study, two-level diagnostic classification models (DCMs) were developed to represent the probabilistic relationship between external latent classes and attribute mastery patterns. Furthermore, variational Bayesian (VB) inference and Gibbs sampling Markov chain Monte Carlo methods were developed for parameter estimation of the two-level DCMs. The results of a parameter recovery simulation study show that both techniques appropriately recovered the true parameters; Gibbs sampling in particular was slightly more accurate than VB, whereas VB performed estimation much faster than Gibbs sampling. The two-level DCMs with the proposed Bayesian estimation methods were further applied to fourth-grade data obtained from the Trends in International Mathematics and Science Study 2007 and indicated that mathematical activities in the classroom could be organized into four latent classes, with each latent class connected to different attribute mastery patterns. This information can be employed in educational intervention to focus on specific latent classes and elucidate attribute patterns.
State-of-the-Art Research in Blockchain of Things for HealthCare

Existing blockchain approaches exhibit a diverse set of dimensions, and on the other hand, IoT-based health care applications manifest a wide variety of requirements. The state-of-the-art analysis of blockchain concerning existing IoT-based approaches for the healthcare domain has been investigated to a limited extend. The purpose of this survey paper is to analyze current state-of-the-art blockchain work in several IoT disciplines, with a focus on the health sector. This study also attempts to demonstrate the prospective use of blockchain in healthcare, as well as the obstacles and future paths of blockchain development. Furthermore, the fundamentals of blockchain have been thoroughly explained to appeal to a diverse audience. On the contrary, we analyzed state-of-the-art studies from several IoT disciplines for eHealth, and also the study deficit but also the obstacles when considering blockchain to IoT, which are highlighted and explored in the paper with suggested alternatives.
Condensed Nearest Neighbour Rules for Multi-Label Datasets

Reducing the size of the training set, that is, replacing it with a condensing set, while maintaining the classification accuracy as much as possible is a very common practice to speed up instance-based classifiers. Data reduction techniques, also known as prototype selection or generation algorithms, can be used to accomplish this. There are numerous such algorithms that can be found in the literature that are effective for single-label classification problems, but the majority of them cannot be used for multi-label data where an instance may belong to multiple classes. Due to the numerous binary condensing sets it creates, the well-known Binary Relevance transformation method cannot be combined with a Data Reduction algorithm. Condensed Nearest Neighbor is a well-known parameter-free single-label prototype selection algorithm. This study proposes three variations of that algorithm for training datasets with multiple labels. An experimental study that we conducted over nine distinct datasets shows that our three proposed approaches provide good reduction rates while not tampering with the classification rates.
A cooperative particle swarm optimization with difference learning

Particle swarm optimization (PSO) is applied in complex model solving problems due to its fast convergence rate and concise algorithm structure. However, the balancing ability of PSO between exploration and exploitation still faces significant challenges. Therefore, a cooperative-based difference learning particle swarm optimization (CDLPSO) is proposed. In CDLPSO, dual swarm time-varying adjustment (DST) based on the subswarm division method is introduced on different periods, while using an appropriate subswarm division method to promote the cooperative learning of particles. Furthermore, a pros-cons coevolution mechanism (PCC) is designed in cooperative learning based on swarms. The elite subswarm can adaptively select a group of proper particles as the swarm updates, thereby obtaining a better exploration performance. Meanwhile, a stage-based cross-learning strategy (SBC) which is adopted to utilize common subswarm rationally adjusts different learning strategies on different stages. To further enhance the convergence accuracy, the common subswarm is added in more superior particles for learning. Ultimately, CDLPSO is used to solve the complex function problems on the CEC2013, CEC2022 test suite and a practical application. The experimental results demonstrate that CDLPSO can significantly balance exploitation and exploration abilities.
Negative elements of cryptocurrencies: Exploring the drivers of Bitcoin carbon footprints

This paper adopts an interactive network approach to investigate the factors driving the carbon footprint of Bitcoin, a negative aspect of cryptocurrencies. Our findings demonstrate that the dynamics of Bitcoin prices, including both returns and volatility, have a significant impact on the system comprising carbon emissions, energy prices, carbon prices, and financial indicators. During the first two years of the COVID-19 pandemic period, the spillover effects are observed to be particularly strong. Furthermore, we find that the dynamics of Bitcoin prices play a crucial role in driving its associated carbon emissions.
Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models

This study extends the examination of the Efficient-Market Hypothesis in Bitcoin market during a five-year fluctuation period, from September 1 2017 to September 1 2022, by analyzing 28,739,514 qualified tweets containing the targeted topic “Bitcoin”. Unlike previous studies, we extracted fundamental keywords as an informative proxy for carrying out the study of the EMH in the Bitcoin market rather than focusing on sentiment analysis, information volume, or price data. We tested market efficiency in hourly, 4-hourly, and daily time periods to understand the speed and accuracy of market reactions towards the information within different thresholds. A sequence of machine learning methods and textual analyses were used, including measurements of distances of semantic vector spaces of information, keywords extraction and encoding model, and Light Gradient Boosting Machine (LGBM) classifiers. Our results suggest that 78.06% (83.08%), 84.63% (87.77%), and 94.03% (94.60%) of hourly, 4-hourly, and daily bullish (bearish) market movements can be attributed to public information within organic tweets.
Distributed Repair of Deep Neural Networks

Deep Neural Networks (DNNs) are applied in several safety-critical domains and their trustworthiness is of paramount importance. For example, DNNs used in autonomous driving as classifiers should not misclassify detected objects; however, since obtaining perfect accuracy is not possible, special attention should be given to the most critical cases, e.g., pedestrians. This has been confirmed by the consortium of our partners from the automotive domain that provided us with specific risk levels for different misclassifications. A recent approach to improve DNN performance is to localise DNN weights responsible for the misclassifications and then adjust (repair) them to improve the misclassifications. However, they under-perform when they need to consider multiple misclassifications, and they do not consider the risk levels of the different misclassifications. To tackle this, we propose DISTRREP, a distributed repair approach that first finds the best fixes for each critical misclassification, and then integrates them in a single repaired DNN model, by considering the risk levels. We assess DISTRREP over three DNN models and a dataset of autonomous driving images, by considering requirements specified by our industrial partners. Experiments show that DISTRREP is more effective than baseline approaches based on retraining, and other risk-unaware repair approaches.
Exploring Innovative Methods for Enhancing Data Security in Computing

Thousands of gigabytes of data are produced each minute by everyone in today's society. This data is being stored in clouds worldwide. The study addressed these security issues in a variety of ways. This research suggests a cloud computing security architecture based on cryptography and steganography. In addition to using symmetric and steganographic techniques, the model is said to meet all security standards, such as security resilience, secrecy, authentication, integrity, and non-repudiation. This method employs the Advanced Encryption Standard (AES) 256 and steganography to provide multilayer encryption and decryption at both the transmitter and receiver sides, boosting cloud storage security. This security paradigm delivers transparency to cloud users and service providers to alleviate security worries. The suggested model is written in Python and operates on the Amazon Web Services cloud. While compared to the old method, this approach enhances data security and saves time when uploading and downloading text files.
Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data

The credit card has become the most popular payment method for both online and offline transactions. The necessity to create a fraud detection algorithm to precisely identify and stop fraudulent activity arises as a result of both the development of technology and the rise in fraud cases. This paper implements the random forest (RF) algorithm to solve the issue in the hand. A dataset of credit card transactions was used in this study. The main problem when dealing with credit card fraud detection is the imbalanced dataset in which most of the transaction are non-fraud ones. To overcome the problem of the imbalanced dataset, the synthetic minority over-sampling technique (SMOTE) was used. Implementing the hyperparameters technique to enhance the performance of the random forest classifier. The results showed that the RF classifier gained an accuracy of 98% and about 98% of F1-score value, which is promising. We also believe that our model is relatively easy to apply and can overcome the issue of imbalanced data for fraud detection applications.
Constructions of Spectrally Null Constrained Complete Complementary Codes via the Graph of Extended Boolean Functions

Complete complementary codes (CCCs) have important applications in communication, radar, and information security. In modern communication and radar systems, certain spectrum is reserved or prohibited from transmission, which leads to the so-called spectrally null constrained (SNC) problem. Compared with vast works on conventional CCCs, relatively little is known about SNC-CCCs. One objective of this paper is to derive several constructions of CCCs with more flexible settings from the graph of extended Boolean functions. This generalizes earlier achievements on CCCs from recent literature. Another objective of this paper is to employ the graphs of extended Boolean functions and polynomial representation of sequences to construct SNC-CCCs.
FTW-GAT: An FPGA-Based Accelerator for Graph Attention Networks With Ternary Weights

Graph attention networks (GATs) are a mainstream graph neural network (GNN) model. They have better performance in some tasks compared to other GNN models. The challenge is that graph data structures are irregular and data dependency in GATs is complex. General-purpose hardware cannot provide enough performance or energy efficiency. Therefore, a specialized accelerator for GATs is needed. In this brief, we propose FTW-GAT to accelerate GAT inference. The key idea of our approach is to quantize the weights of GATs to ternary values, which can greatly simplify processing elements (PEs), eliminate the dependence on digital signal processors (DSPs) and reduce power consumption. Then, we use operation fusion, multi-level pipelining and graph partitioning to improve the parallelism. Finally, we implement the accelerator on a Xilinx VCU128 FPGA platform. The results show that FTW-GAT achieves performance speedup by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$390\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$17\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.4\times $ </tex-math></inline-formula> , and energy efficiency by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4007\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$261\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.1\times $ </tex-math></inline-formula> compared to CPUs, GPUs and a prior GAT accelerator.
Decoupled variational autoencoder with interactive attention for affective text generation

Artificial emotional intelligence (AEI) is an important ability for intelligent systems. Representing emotional states as a continuous sentiment intensity could achieve a more fine-grained sentiment application than traditional categorical approaches. By using sentiment intensity, the main challenge of the existing variational autoencoder methods lies in distinguishing emotional information from semantic information. Practically, it is difficult to ensure that the input training texts do not contain any sentiment. If the disentangled latent variable for text generation contains sentiment features that conflict with the assigned sentiment, the generated texts will be messy. Therefore, we propose a decoupled variational autoencoder (VAE) with interactive attention to solve this problem. The proposed method applies a sentiment decoupler to extract the sentiment from the text. Then, sentiment embeddings are applied to map the intensities into the latent space. To enhance the representation ability of sentiment embeddings and the performance of the decoder, we consider affective text generation as a process of denoising, design a noisy sampling strategy in training, and then continuously update the emotional information with variational attention through a dynamic update mechanism. Extensive experiments are conducted on the Yelp dataset and the Amazon dataset, and the experimental results show that our method outperforms other VAE-based methods in fine-grained affective text generation.
Multi-objective Boolean grey wolf optimization based decomposition algorithm for high-frequency and high-utility itemset mining

&lt;abstract&gt; &lt;p&gt;In itemset mining, the two vital goals that must be resolved from a multi-objective perspective are frequency and utility. To effectively address the issue, researchers have placed a great deal of emphasis on achieving both objectives without sacrificing the quality of the solution. In this work, an effective itemset mining method was formulated for high-frequency and high-utility itemset mining (HFUI) in a transaction database. The problem of HFUI is modeled mathematically as a multi-objective issue to handle it with the aid of a modified bio-inspired multi-objective algorithm, namely, the multi-objective Boolean grey wolf optimization based decomposition algorithm. This algorithm is an enhanced version of the Boolean grey wolf optimization algorithm (BGWO) for handling multi-objective itemset mining problem using decomposition factor. In the further part of this paper decomposition factor will be mentioned as decomposition. Different population initialization strategies were used to test the impact of the proposed algorithm. The system was evaluated with 12 different real-time datasets, and the results were compared with seven different recent existing multi-objective models. Statistical analysis, namely, the Wilcoxon signed rank test, was also utilized to prove the impact of the proposed algorithm. The outcome shows the impact of the formulated technique model over other standard techniques.&lt;/p&gt; &lt;/abstract&gt;
A Detection of Deep Fake in Face Images Using Deep Learning

Fake images are one of the most widespread phenomena that have a significant influence on our social life, particularly in the world of politics and celeb. Nowadays, generating fake images has become very easy due to the powerful yet simple applications in mobile devices that navigate in the social media world and with the emergence of the Generative Adversarial Network (GAN) that produces images which are indistinguishable to the human eye. Which makes fake images and fake videos easy to perform, difficult to detect, and fast to spread. As a result, image processing and artificial intelligence play an important role in solving such issues. Thus, detecting fake images is a critical problem that must be controlled and to prevent these numerous harmful effects. This research proposed utilizing the most popular algorithm in deep learning is (Convolution Neural Network) to detect the fake images.&#x0D; The first steps includes a preprocessing which start with converting images from RGB to YCbCr color space, after that entering the Gamma correction. finally extract edge detection by entering the Canny filter on them. After that, utilizing two different method of detection by applying (Convolution Neural Network with Principal Component Analysis) and (Convolution Neural Network without Principal Component Analysis) as a classifiers.&#x0D; The results reveal that the use of CNN with PCA in this research results in acceptable accuracy. In contrast, using CNN only gave the highest level of accuracy in detecting manipulated images.