Reviewerly/exHarmony huggingface dataset:
Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm

One of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa. As a result, some previous association rules may become invalid and some new association rules may emerge. We designed a new, more efficient approach for incremental association rule mining using a Fast Incremental Updating Frequent Pattern growth algorithm (FIUFP-Growth), a new Incremental Conditional Pattern tree (ICP-tree), and a compact sub-tree suitable for incremental mining of frequent itemsets. This algorithm retrieves previous frequent itemsets that have already been mined from the original database and their support counts then use them to efficiently mine frequent itemsets from the updated database and ICP-tree, reducing the number of rescans of the original database. Our algorithm reduced usages of resource and time for unnecessary sub-tree construction compared to individual FP- Growth, FUFP-tree maintenance, Pre-FUFP, and FCFPIM algorithms. From the results, at 3% minimum support threshold, the average execution time for pattern growth mining of our algorithm performs 46% faster than FP- Growth, FUFP-tree, Pre-FUFP, and FCFPIM. This approach to incremental association rule mining and our experimental findings may directly benefit designers and developers of computer business intelligence methods.
Physics and Applications of High‐β Micro‐ and Nanolasers

Abstract Micro‐ and nanolasers are emerging optoelectronic components with many properties still to be explored and understood. On the one hand, they make it possible to address fundamental physical questions in the border area between classical physics and quantum physics, on the other hand, they open up new application perspectives in many areas of photonics. This progress report provides an overview of the exciting developments from conventional semiconductor lasers toward nanoscale lasers, whose function relies on increased light–matter interaction in low‐mode‐volume resonators and unconventional gain concepts. The latest advances in the physical understanding of light emission from high‐β lasers, in which a large part of the spontaneous emission is coupled into the laser mode, are highlighted. In the limit of β = 1, this leads to thresholdless lasing and it is shown that quantum optical characterization is required to fully explore the underlying emission processes. In addition, emerging nanolaser concepts based on Fano resonators, topological photonics, and 2D materials are presented. Open questions, future prospects, and application scenarios of high‐β lasers in integrated photonics, quantum nanophotonics, and neuromorphic computing are discussed.
General Non-Markovian Quantum Dynamics

A general approach to the construction of non-Markovian quantum theory is proposed. Non-Markovian equations for quantum observables and states are suggested by using general fractional calculus. In the proposed approach, the non-locality in time is represented by operator kernels of the Sonin type. A wide class of the exactly solvable models of non-Markovian quantum dynamics is suggested. These models describe open (non-Hamiltonian) quantum systems with general form of nonlocality in time. To describe these systems, the Lindblad equations for quantum observable and states are generalized by taking into account a general form of nonlocality. The non-Markovian quantum dynamics is described by using integro-differential equations with general fractional derivatives and integrals with respect to time. The exact solutions of these equations are derived by using the operational calculus that is proposed by Yu. Luchko for general fractional differential equations. Properties of bi-positivity, complete positivity, dissipativity, and generalized dissipativity in general non-Markovian quantum dynamics are discussed. Examples of a quantum oscillator and two-level quantum system with a general form of nonlocality in time are suggested.
Challenges and Open Problems of Legal Document Anonymization

Data sharing is a central aspect of judicial systems. The openly accessible documents can make the judiciary system more transparent. On the other hand, the published legal documents can contain much sensitive information about the involved persons or companies. For this reason, the anonymization of these documents is obligatory to prevent privacy breaches. General Data Protection Regulation (GDPR) and other modern privacy-protecting regulations have strict definitions of private data containing direct and indirect identifiers. In legal documents, there is a wide range of attributes regarding the involved parties. Moreover, legal documents can contain additional information about the relations between the involved parties and rare events. Hence, the personal data can be represented by a sparse matrix of these attributes. The application of Named Entity Recognition methods is essential for a fair anonymization process but is not enough. Machine learning-based methods should be used together with anonymization models, such as differential privacy, to reduce re-identification risk. On the other hand, the information content (utility) of the text should be preserved. This paper aims to summarize and highlight the open and symmetrical problems from the fields of structured and unstructured text anonymization. The possible methods for anonymizing legal documents discussed and illustrated by case studies from the Hungarian legal practice.
Membership inference attacks against synthetic health data

Synthetic data generation has emerged as a promising method to protect patient privacy while sharing individual-level health data. Intuitively, sharing synthetic data should reduce disclosure risks because no explicit linkage is retained between the synthetic records and the real data upon which it is based. However, the risks associated with synthetic data are still evolving, and what seems protected today may not be tomorrow. In this paper, we show that membership inference attacks, whereby an adversary infers if the data from certain target individuals (known to the adversary a priori) were relied upon by the synthetic data generation process, can be substantially enhanced through state-of-the-art machine learning frameworks, which calls into question the protective nature of existing synthetic data generators. Specifically, we formulate the membership inference problem from the perspective of the data holder, who aims to perform a disclosure risk assessment prior to sharing any health data. To support such an assessment, we introduce a framework for effective membership inference against synthetic health data without specific assumptions about the generative model or a well-defined data structure, leveraging the principles of contrastive representation learning. To illustrate the potential for such an attack, we conducted experiments against synthesis approaches using two datasets derived from several health data resources (Vanderbilt University Medical Center, the All of Us Research Program) to determine the upper bound of risk brought by an adversary who invokes an optimal strategy. The results indicate that partially synthetic data are vulnerable to membership inference at a very high rate. By contrast, fully synthetic data are only marginally susceptible and, in most cases, could be deemed sufficiently protected from membership inference.
Underwater image enhancement via integrated RGB and LAB color models

Images taken underwater suffers from color shift and poor visibility because the light is absorbed and scattered when it travels through water. To handle the issues mentioned above, we propose an underwater image enhancement method via integrated RGB and LAB color models (RLCM). In the RGB color model, we first fully consider the leading causes of underwater image color shift, and then the poor color channels are corrected by dedicated fractions, which are designed via calculating the differences between the well and poor color channels. In the LAB color model, wherein the local contrast of the L channel is enhanced by a histogram with local enhancement and exposure cut-off strategy, whereas the difference between the A and B channels is traded-off by a gain equalization strategy. Besides, a normalized guided filtering strategy is incorporated into the histogram enhancement process to mitigate the effects of noise. Ultimately, the image is inverted from the LAB color model to the RGB color model, and a detail sharpening strategy is implemented in each channel to obtain a high-quality underwater image. Experiments on various real-world underwater images demonstrate that our method outputs better results with natural color and high visibility.
The role of national cybersecurity strategies on the improvement of cybersecurity education

Digital information and telecommunication technologies have not only become essential to individuals' daily lives but also to a nation's sustained economic growth, societal well-being, critical infrastructure resilience, and national security. Consequently, the protection of a nation's cyber sovereignty from malicious acts is a major concern. This signifies the importance of cybersecurity education in facilitating the creation of a resilient cybersecurity ecosystem and in supporting cyber sovereignty. This study reviews a sample from world-leading countries National Cybersecurity Strategic Plans (NCSPs) and analyzes the associated existing cybersecurity education and training improvement initiatives. Furthermore, a proposal to adopt the Goal-Question-Outcomes(GQO)+Strategies paradigm into cybersecurity education and training programs curricula improvement to national cybersecurity strategic goals is presented. The proposal maps cybersecurity strategic goals to cybersecurity skills and competencies using the National Initiative for Cybersecurity Education (NICE) framework. The newly proposed cybersecurity education and training programs' curricula learning outcomes were generated from the GQO+Strategies paradigm based on the three major cybersecurity strategic goals: Development of secure digital and information technology infrastructure and services, defending from sophisticated cyber threats, and enrichment of individuals' cybersecurity maturity and awareness. It is highly recommended that cybersecurity university program administrators utilize the proposed GQO+Strategies to align their program's curriculum to NCSP. Hence, closing the gap that exists with the relevant skills and sustain national cybersecurity workforces.
A robust adaptive blind color image watermarking for resisting geometric attacks

Due to the differences of observation equipment and technical means, the size of the color digital image is not the same. In addition, most of digital watermarking methods used to protect the copyright of color digital images have weak robustness against geometric attacks. Therefore, this paper proposes a robust adaptive blind watermarking method to protect the copyright of color digital images. In the proposed method, the frequency domain characteristics of Slant transform are used to directly calculate the maximum energy coefficient of the pixel block, the maximum element of the coefficient matrix obtained after Slant transform in the spatial domain, and the color digital watermark is embedded by quantifying the maximum energy coefficient. At the same time, the adaptive strategies, which including the adaptive image size, the adaptive quantization step, the adaptive watermark encoding and the adaptive watermark embedding position, are proposed in this paper. Moreover, the proposed method can use geometric features of the image to correct many attacked images. Compared with the state-of-the-art watermarking methods, the proposed method can achieve acceptable imperceptibility and has larger watermark capacity, stronger robustness, higher security and better real-time performance.
Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning

Segmentation of a liver in computed tomography (CT) images is an important step toward quantitative biomarkers for a computer-aided decision support system and precise medical diagnosis. To overcome the difficulties that come across the liver segmentation that are affected by fuzzy boundaries, stacked autoencoder (SAE) is applied to learn the most discriminative features of the liver among other tissues in abdominal images. In this paper, we propose a patch-based deep learning method for the segmentation of a liver from CT images using SAE. Unlike the traditional machine learning methods, instead of anticipating pixel by pixel learning, our algorithm utilizes the patches to learn the representations and identify the liver area. We preprocessed the whole dataset to get the enhanced images and converted each image into many overlapping patches. These patches are given as input to SAE for unsupervised feature learning. Finally, the learned features with labels of the images are fine tuned, and the classification is performed to develop the probability map in a supervised way. Experimental results demonstrate that our proposed algorithm shows satisfactory results on test images. Our method achieved a 96.47% dice similarity coefficient (DSC), which is better than other methods in the same domain.
Examining incel subculture on Reddit

The online presence of incels, or involuntary celibates, has been an increasing security concern for researchers, practitioners, and policymakers in recent years, given that self-identified incels – including Alek Minassian and Elliot Rodger – used the Internet to disseminate incel ideology and manifestos prior to committing acts of violence. However, little is empirically known about the incel movement in general or their online communities in particular. The present study draws from a set of comments from r/Incels, a now defunct but once popular subreddit dedicated to the incel community, and compares the most highly-upvoted comments (n = 500) to a random set of other comments (n = 500) in the subreddit. This qualitative analysis focuses on identifying subcultural discourse that is widely supported and engaged with by members of the online community and the extent to which incels utilize this online space to reaffirm deviant behavior. Our study underscores the importance, as well as the difficulties, of drawing from online sources like web-forums to generate new knowledge on deviant communities and behaviors. We conclude with a discussion of the implications of this analysis, its limitations, and avenues for future research.
Blockchain Technology in Operations &amp; Supply Chain Management: A Content Analysis

Scholars are increasingly examining how the distributed blockchain technology can counter specific supply chain and operations management challenges. Various research approaches emerge from different scholarly backgrounds, but the interrelation of research areas and current trends has not been adequately considered in a systematic review. We employ a data-driven content analysis approach to examine previous research on blockchain technology in operations management and supply chain management. We investigate the extent to which blockchain technology was considered in scholarly works, structure the research efforts, and identify trends, interrelated themes, and promising research opportunities. Quantitative and qualitative content analysis is conducted on an extensive literature sample of 410 articles. Results indicate an optimistic attitude due to potentials such as tracking and tracing abilities, efficiency increases, and trust-building. Conceptual studies dominate the literature set, with increasing qualitative research efforts. Grand theories are seldomly addressed in the studies. Blockchain technology is outlined as particularly useful when combined with other technologies like IoT. We also identified sustainability implications of the technology, such as enabling transparency for SC stakeholders. Cryptocurrencies can facilitate further efficiency gains if legal uncertainties are reduced. The study is concluded with managerial and theoretical implications and future research opportunities.
Measurement-device-independent quantum secret sharing with hyper-encoding

Quantum secret sharing (QSS) is a typical multi-party quantum communication mode, in which the key sender splits a key into several parts and the participants can obtain the key by cooperation. Measurement-device-independent quantum secret sharing (MDI-QSS) is immune to all possible attacks from measurement devices and can greatly enhance QSS’s security in practical applications. However, previous MDI-QSS’s key generation rate is relatively low. Here, we adopt the polarization-spatial-mode hyper-encoding technology in the MDI-QSS, which can increase single photon’s channel capacity. Meanwhile, we use the cross-Kerr nonlinearity to realize the complete hyper-entangled Greenberger–Horne–Zeilinger state analysis. Both above factors can increase MDI-QSS’s key generation rate by about 10 3 . The proposed hyper-encoded MDI-QSS protocol may be useful for future multiparity quantum communication applications.
MBiLSTMGloVe: Embedding GloVe knowledge into the corpus using multi-layer BiLSTM deep learning model for social media sentiment analysis

The fast improvement and transformation of online media and unique sites with critical reviews of items, movies, goods, etc. have created a tremendous assortment of assets for clients everywhere around the globe. This information might contain a great deal of data, including product reviews, anticipating market changes, and the extremity of film assessments. Sentiment Analysis (SA) innovation produces phonetic comprehension according to the viewpoint of machines through the handling and investigation of immense amounts of information, which is a hot expedition passageway heading into the field of man-made reasoning, a.k.a. Artificial Intelligence (AI). To address the substance appendage from short texts, we want to investigate the further semantics of words by exploiting thoughtful Machine Learning (ML) and Deep Learning (DL) strategies. In this way, AI, ML, and DL procedures can control and distribute intuition introspection in these difficulties. Our recommended model, based on the DL method and the GloVe word embedding approach, learns the features using a CNN layer and then coordinates those parts into a Multi-Layered Bi-DirectionalLong-Short-Term Memory (MBiLSTM) to capture long-range embedded circumstances. The main aim of this experiment is to give an adequate answer to examine feelings and user reviews in positive and negative classifications. Our runs show that a test accuracy of 92.05% and a validation accuracy of 93.55% can be attained with the given model. The framework is assessed using IMDB datasets. The proposed model outflanks existing pattern models, which show that going past the substance of a tweet is valuable in opinion classification orders since it gives the classifier a deep understanding of the chore.
Terrain feature-aware deep learning network for digital elevation model superresolution

Neural networks (NNs) have demonstrated the potential to recover finer textural details from lower-resolution images by superresolution (SR). Given similar grid-based data structures, some researchers have transferred image SR methods to digital elevation models (DEMs). These efforts have yielded better results than traditional spatial interpolation methods. However, terrain data present inherently different characteristics and practical meanings compared with natural images. This makes it unsuitable for existing SR methods on perceptually visual features of images to be directly adopted for extracting terrain features. In this paper, we argue that the problem lies in the lack of explicit terrain feature modeling and thus propose a terrain feature-aware superresolution model (TfaSR) to guide DEM SR towards the extraction and optimization of terrain features. Specifically, a deep residual module and a deformable convolution module are integrated to extract deep and adaptive terrain features, respectively. In addition, explicit terrain feature-aware optimization is proposed to focus on local terrain feature refinement during training. Extensive experiments show that TfaSR achieves state-of-the-art performance in terrain feature preservation during DEM SR. Specifically, compared with the traditional bicubic interpolation method and existing neural network methods (SRGAN, SRResNet, and SRCNN), the RMSE of our results is improved by 1.1% to 23.8% when recovering the DEM from 120 m to 30 m, by 4.9% to 22.7% when recovering the DEM from 60 m to 30 m, and by 7.8% to 53.7% when recovering the DEM from 30 m to 10 m. The source code that has been developed is shared on Figshare (https://doi.org/10.6084/m9.figshare.19597201).
A multivariate adaptive gradient algorithm with reduced tuning efforts

Large neural networks usually perform well for executing machine learning tasks. However, models that achieve state-of-the-art performance involve arbitrarily large number of parameters and therefore their training is very expensive. It is thus desired to implement methods with small per-iteration costs, fast convergence rates, and reduced tuning. This paper proposes a multivariate adaptive gradient descent method that meets the above attributes. The proposed method updates every element of the model parameters separately in a computationally efficient manner using an adaptive vector-form learning rate, resulting in low per-iteration cost. The adaptive learning rate computes the absolute difference of current and previous model parameters over the difference in subgradients of current and previous state estimates. In the deterministic setting, we show that the cost function value converges at a linear rate for smooth and strongly convex cost functions. Whereas in both the deterministic and stochastic setting, we show that the gradient converges in expectation at the order of O(1/k) for a non-convex cost function with Lipschitz continuous gradient. In addition, we show that after T iterates, the cost function of the last iterate scales as O(log(T)/T) for non-smooth strongly convex cost functions. Effectiveness of the proposed method is validated on convex functions, smooth non-convex function, non-smooth convex function, and four image classification data sets, whilst showing that its execution requires hardly any tuning unlike existing popular optimizers that entail relatively large tuning efforts. Our empirical results show that our proposed algorithm provides the best overall performance when comparing it to tuned state-of-the-art optimizers.