Reviewerly/exHarmony huggingface dataset:
Efficient algorithms for discovering high-utility patterns with strong frequency affinities

In recent years, high-utility pattern mining has been studied extensively. However, most of these studies have addressed mining high-utility patterns (HUPs) without consideration for their frequencies, leading to the mining of meaningless HUPs. One of the approaches to solving this problem is to use HUP mining with strong affinity frequencies. In this paper, we propose two algorithms to discover HUPs with strong affinity frequencies: DHUP-Miner (Discriminative High-Utility pattern - Miner) and its parallel version, DHUP-Miner*. Several novel pruning strategies are applied to reduce the search space for potential DHUPs. Experimental results show that the proposed algorithms are faster than the state-of-the-art algorithm (FDHUP) for both sparse and dense benchmark datasets. Moreover, the parallel algorithm (DHUP-Miner*) was found to handle large datasets well.
Reversible Data Hiding in JPEG Images Under Multi-Distortion Metric

Reversible Data Hiding (RDH) in JPEG images is valuable for many applications, such as archive management and image authentication. Recently, there emerged a lot of related works for JPEG RDH, however, the current methods utilize the histogram-shifting-based framework in a constant distortion metric, which does not consider the property of DCT coefficients whose modification distortion is miscellaneous with respect to DCT frequency. In this paper, we propose a novel JPEG RDH scheme under multi-distortion metric. At first, the modification distortion is defined as the impact in the spatial domain caused by modifying DCT coefficients. Different from previous works, here we select coefficients with values ±1, ±2 as cover, which will not cause extra bitrate-expansion and decrease the shift distortion. By minimizing both the modification distortion and shift distortion, a heuristic block selecting strategy is proposed. With the selected coefficients and the corresponding distortion, we use recursive histogram modification under the inconsistent-distortion metric for message embedding. The experimental results show that the proposed method can effectively improve visual quality as well as bring small bitstream expansion. Additionally, the undetectability of the proposed method outperforms those of the existing methods.
Predicting User Quitting Ratio in Adaptive Bitrate Video Streaming

To improve user engagement such as viewing time, this paper addresses the understanding and prediction of the user quitting ratio for users watching videos using adaptive bit rate video streaming.The user quitting ratio is defined as the percentage of users still watching videos at a given time.To perform this study, five subjective experiments involving up to 264 participants were conducted in a laboratory setting.Results indicated the effects of coding quality, initial buffering, and midway stalling on user quitting ratio.Then, a framework was defined to predict the user quitting ratio as a function of time.This framework achieves good prediction accuracy and can be used in multiple scenarios including when quality adaptation and stalling occur.Finally, it is suitable for monitoring applications where bitstream are encrypted and low processing cost is required.
Provenance and tectonic setting of the Early Permian sedimentary succession in the southern edge of the Sydney Basin, eastern Australia

Early Permian basins formed during a major phase of crustal extension are common throughout eastern Gondwana. One of these basins, the Sydney Basin, has not been subjected to geochemical studies aimed at determining the sedimentary provenance and the tectonic setting. This study provides new geochemical data of the Lower Permian siliciclastic successions in the southern edge of the Sydney Basin. Published petrographic analysis indicates a source area containing sedimentary, felsic volcanic, plutonic, and low‐grade metamorphic rocks. Major, trace and rare earth element data suggest that the clastic rocks are geochemically mature and originated from a continental source that has been subjected to moderate to strong weathering. Further, they indicate that most source rocks were of felsic composition and that only the oldest sediments possess a more mafic signature. The provenance data presented in this study, along with published palaeocurrent data, point to the Lachlan orogen being the main source of sediments for the basin. Multi‐dimensional discrimination diagrams suggest a continental rift setting and agree with the published sequence stratigraphic framework that suggests a deepening‐upward depositional trend associated with tectonic subsidence. This study documents geochemically the provenance of the sedimentary successions within the southern edge of the Sydney Basin and the continental rift origin of the basin.
DGTL-Net: A Deep Generative Transfer Learning Network for Fault Diagnostics on New Hard Disks

Intelligent fault diagnosis of hard disks becomes significantly important to guarantee reliability of current cloud-based industrial systems. Most intelligent diagnostic methods are commonly based on assumptions that data from different disks are subject to the same distribution and there are sufficient faulty samples for training the models. However, in reality, there are types of hard disks from different manufacturers and their SMART encoding varies widely across manufacturers. It results in distribution discrepancy among disks and influences the generalization of machine learning methods. Moreover, hard disks usually work in healthy state that faulty events rarely happen on most of them, or especially never occur on new ones. Thus, this paper proposes a deep generative transfer learning network (DGTL-Net) for intelligent fault diagnostics on new hard disks. The DGTL-Net combines the deep generative network that generates fake faulty samples and the deep transfer network that solves the problem of distribution discrepancy between hard disks. An iterative end-end training strategy is also proposed for DGTL-Net to get the most optimal parameters of generative and transfer network simultaneously. Experiments have been conducted to prove that our method achieves better performance.
Zoom-to-Inpaint: Image Inpainting with High-Frequency Details

Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details. In this paper, we propose applying super-resolution to coarsely reconstructed outputs, refining them at high resolution, and then downscaling the output to the original resolution. By introducing high-resolution images to the refinement network, our framework is able to reconstruct finer details that are usually smoothed out due to spectral bias – the tendency of neural networks to reconstruct low frequencies better than high frequencies. To assist training the refinement network on large upscaled holes, we propose a progressive learning technique in which the size of the missing regions increases as training progresses. Our zoom-in, refine and zoom-out strategy, combined with high-resolution supervision and progressive learning, constitutes a framework-agnostic approach for enhancing high-frequency details that can be applied to any CNN-based inpainting method. We provide qualitative and quantitative evaluations along with an ablation analysis to show the effectiveness of our approach. This seemingly simple, yet powerful approach, outperforms existing inpainting methods.
Detection and mitigation of fraudulent resource consumption attacks in cloud using deep learning approach

Fraudulent Resource Consumption (FRC) attacks can be synthesized by subtly consuming metered resources of the cloud servers over a sustained period of time. The objective of the attacker in such attacks is to exploit the utility pricing model by fraudulently consuming cloud resources. This skillful over-consumption of resources results in a considerable financial burden to the client. These attacks are characterized by low-intensity HTTP requests per hour, akin to requests by legitimate users. Hence, the attack requests differ in intent but not in content, which makes FRC attacks hard to detect. In this paper, we propose P-estimation detection scheme to effectively detect these attacks. This is accomplished by training several deep learning LSTM models based on the web server logs. An estimate of attack percentage is calculated and then used to deploy the appropriate detection model. This technique takes into account the dynamic nature of websites where the popularity of web pages can change with time, by retraining and updating the detection models periodically. To the best of the authors' knowledge, this technique outperforms all the existing FRC detection techniques with a False Negative Rate (FNR) and False Positive Rate (FPR) of 0.0059% and 0.0% respectively. The proposed technique is able to detect attacks as low as 2% intensity. In addition to the detection scheme, this paper also delivers a mitigation and attribution technique to identify such attackers and block them.
Real Estate Management via a Decentralized Blockchain Platform

Blockchain technology is one of the key technological breakthroughs of the last decade. It has the ability to revolutionize numerous aspects of society, including financial systems, healthcare, e-government and many others. One such area that is able to reap the benefits of blockchain technology is the real estate industry. Like many other industries, real estate faces major administrative problems such as high transaction fees, a lack of transparency, fraud and the effects of a middleman including undue influence and commissions. Blockchain enables supporting technologies to overcome the obstacles inherent within the real estate investment market. These technologies include smart contracts, immutable record management and time-stamped storage. We utilize these key properties of blockchain technology in our work by proposing a system that has the ability to record real estate transactions in a private blockchain, using smart contracts. The immutability of the blockchain ledger and transactions can provide a safe space for the real estate business. Blockchain technology can also assist the authentication process by hastening background checks. Personal digital keys are provided to parties that are involved in a contract, thus minimizing the risk of fraud. We also discuss the rationale behind the advantages of using a blockchain in this manner, and how we selected the consensus mechanism for our proposed system.
The Impact of Artificial Intelligence on the Chess World

This paper focuses on key areas in which artificial intelligence has affected the chess world, including cheat detection methods, which are especially necessary recently, as there has been an unexpected rise in the popularity of online chess. Many major chess events that were to take place in 2020 have been canceled, but the global popularity of chess has in fact grown in recent months due to easier conversion of the game from offline to online formats compared with other games. Still, though a game of chess can be easily played online, there are some concerns about the increased chances of cheating. Artificial intelligence can address these concerns.
A Day In the Life of a Quantum Error

When a fault occurs in a computational system, it is of interest what effects it has. If it is known what can go wrong, it may also be known how to mitigate or correct for it. While complex, it is possible to obtain this information with rigorous fault injection in classical systems. This is also desirable for quantum systems, unfortunately it is much more difficult. The exponential information content of quantum systems makes this likely impossible for larger systems. However, analyses on smaller systems may provide valuable insight into the behavior of larger systems. This letter analyzes the effect of error on different parts of quantum programs.
Assessing the Role of Opportunity and Low Self-Control in Juvenile Hacking

Gottfredson and Hirschi’s general theory of crime argues individuals with low self-control are more likely to engage in crime on and off-line. There is less research considering the role of opportunity, as low self-control should increase individuals’ willingness to act on opportunities to offend. The importance of opportunity is distinct for cybercrime, as technology access may be differentially impacted by various demographic factors. This study surveyed 1,411 South Australian adolescents enrolled at secondary schools across a large metropolitan region to examine the relationships between opportunity and self-control for four forms of computer hacking. A series of binary logistic regression models illustrated that distinct opportunity factors and low self-control are associated with each form of hacking.
Low-Rank Constrained Super-Resolution for Mixed-Resolution Multiview Video

Multiview video allows for simultaneously presenting dynamic imaging from multiple viewpoints, enabling a broad range of immersive applications. This paper proposes a novel super-resolution (SR) approach to mixed-resolution (MR) multiview video, whereby the low-resolution (LR) videos produced by MR camera setups are up-sampled based on the neighboring HR videos. Our solution analyzes the statistical correlation of different resolutions between multiple views, and introduces a low-rank prior based SR optimization framework using local linear embedding and weighted nuclear norm minimization. The target HR patch is reconstructed by learning texture details from the neighboring HR camera views using local linear embedding. A low-rank constrained patch optimization solution is introduced to effectively restrain visual artifacts and the ADMM framework is used to solve the resulting optimization problem. Comprehensive experiments including objective and subjective test metrics demonstrate that the proposed method outperforms the state-of-the-art SR methods for MR multiview video.
Blind image quality prediction with hierarchical feature aggregation

Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of image with no pristine image for comparison, which attracts extensive attention and is of wide applications. Research on human visual system (HVS) indicates visual perception is classically modeled as a hierarchical process. Meanwhile, empirical evidence shows that different levels of distortion generate individual degradation on hierarchical features. Although previous BIQA methods exploited different levels of feature, the degradations and correlations on hierarchical features are not detailedly analyzed. In this paper, hierarchical degradation is systematically analyzed by quantitative and qualitative experiments using interpretable hierarchical features. Additionally, as there exist strong relationships, these features cannot be simply concatenated for quality prediction. Inspired by the Bayesian brain theory, correlations among hierarchical features are thoroughly deduced with joint probability. Guided by the mathematical derivation, a new feature aggregation network (FANet) is designed to eliminate correlations for hierarchical features fusion. Finally, a novel end-to-end BIQA framework based on hierarchical feature aggregation is proposed, which eminently analyzes the degradations and correlations on hierarchical features. Experimental results on five benchmark databases show that the proposed method achieves state-of-the-art performance. Furthermore, cross-database evaluations demonstrate the generalization capability and robustness of the proposed method.
Automated White Blood Cell Counting in Nailfold Capillary Using Deep Learning Segmentation and Video Stabilization

White blood cells (WBCs) are essential components of the immune system in the human body. Various invasive and noninvasive methods to monitor the condition of the WBCs have been developed. Among them, a noninvasive method exploits an optical characteristic of WBCs in a nailfold capillary image, as they appear as visual gaps. This method is inexpensive and could possibly be implemented on a portable device. However, recent studies on this method use a manual or semimanual image segmentation, which depends on recognizable features and the intervention of experts, hindering its scalability and applicability. We address and solve this problem with proposing an automated method for detecting and counting WBCs that appear as visual gaps on nailfold capillary images. The proposed method consists of an automatic capillary segmentation method using deep learning, video stabilization, and WBC event detection algorithms. Performances of the three segmentation algorithms (manual, conventional, and deep learning) with/without video stabilization were benchmarks. Experimental results demonstrate that the proposed method improves the performance of the WBC event counting and outperforms conventional approaches.
Preprocessing of unstructured medical data: the impact of each preprocessing stage on classification

Nowadays, it is still important to develop methods for processing data, in particular medical texts, in Russian. In this paper, we checked how each stage of text pre-processing affects the result of the classifier. The paper analyzed 269923 records of allergic anamnesis of patients, 11670 of which were placed for further processing. We consider the main stages of pre-processing: tokenization, deletion of stop words, error correction, document cropping, normalization, class harmonization, and vectorization. To vectorize the data, we have selected the Bag-of-Words. The method of logistic regression was chosen for classification, since it has easy reproducibility and interpretation. Precision, recall and F-measure were selected as evaluation metrics. The results (F = 88.12%) showed that the most effective was the stage of normalization and error correction.
&lt;p&gt;Nomogram Based on Risk Factors for Type 2 Diabetes Mellitus Patients with Coronary Heart Disease&lt;/p&gt;

This study aimed to study risk factors for coronary heart disease (CHD) in type 2 diabetes mellitus (T2DM) patients and establish a clinical prediction model.A total of 3402 T2DM patients were diagnosed by clinical doctors and recorded in the electronic medical record system (EMRS) of six Community Health Center Hospitals from 2015 to 2017, including the communities of Huamu, Jinyang, Yinhang, Siping, Sanlin and Daqiao. From September 2018 to September 2019, 3361 patients (41 patients were missing) were investigated using a questionnaire, physical examination, and biochemical index test. After excluding the uncompleted data, 3214 participants were included in the study and randomly divided into a training set (n = 2252) and a validation set (n = 962) at a ratio of 3:1. Through lead absolute shrinkage and selection operator (LASSO) regression analysis and logistic regression analysis of the training set, risk factors were determined and included in a nomogram. The C-index, receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were used to validate the distinction, calibration and clinical practicality of the model.Age, T2DM duration, hypertension (HTN), hyperuricaemia (HUA), body mass index (BMI), glycosylated haemoglobin A1c (HbA1c), high-density lipoprotein (HDL-C) and low-density lipoprotein (LDL-C) were significant factors in this study. The C-index was 0.750 (0.724-0.776) based on the training set and 0.767 (0.726-0.808) based on the validation set. Through ROC analysis, the set area was 0.750 for the training set and 0.755 for the validation set. The calibration test indicated that the S:P of the prediction model was 0.982 in the training set and 0.499 in the validation set. The decision curve analysis showed that the threshold probability of the model was 16-69% in the training set and 16-73% in the validation set.Based on community surveys and data analysis, a prediction model of CHD in T2DM patients was established.
Differentially private synthetic mixed-type data generation for unsupervised learning

We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data and privately train a model for generating synthetic data that will satisfy similar statistical properties as the original data. This learned model can generate an arbitrary amount of synthetic data, which can then be freely shared due to the post-processing guarantee of differential privacy. Our framework is applicable to unlabeled mixed-type data, that may include binary, categorical, and real-valued data. We implement this framework on both binary data (MIMIC-III) and mixed-type data (ADULT), and compare its performance with existing private algorithms on metrics in unsupervised settings. We also introduce a new quantitative metric able to detect diversity, or lack thereof, of synthetic data.
Application of remote sensing and reflectance spectroscopy to explore iron-enriched domains in the north region of the intracontinental sector of the Araçuaí West Congo Orogen

The large iron ore reserves of Brazil are already known, but there are still few studies regarding mineral prospection in smaller deposits and/or areas with lower contents. Exploration techniques that cover large areas at low costs, such as remote sensing, are important for setting targets for mineral exploration. This paper proposes to use of Sentinel-2 images and reflectance spectroscopy to evaluate the use of these techniques for iron prospecting, using two deposits in the state of Bahia, Brazil, as case studies. To validate image processing results, samples were collected in these areas for analysis of optical petrography, x-ray fluorescence and reflectance spectroscopy. Images were processed at a spatial resolution of 20 and 60 m with Mixture Tuned Matched Filtering and Spectral Angle Mapper. Corresponding spectral types were determined based on the analyses of mineralogy variation and composition of the spectral mixture. It was also possible to establish a correlation of spectral types with iron content. The results achieved showed that Sentinel-2 data can be used for spectral mapping of different lithotypes composed of iron oxides, with Reflectance Spectroscopy as an important tool for mapping, and that the use of images in spatial resolution 20 m are sufficient to discriminate different types of ore. Image processing has shown that Sentinel-2 sensor data can support iron ore mineral research and the discovery of new mineralized areas.
Spreading nonlocality in a quantum network

Starting from several copies of bipartite noisy entangled states, we design a global and optimal local measurement-based protocol in one- and two-dimensional lattices by which any two or more prefix sites can be connected via entanglement. Production of bipartite as well as multipartite entangled states in a network is verified in a device independent way through the violation of Bell inequalities with two settings per site and with continuous range of settings. We also note that if the parties refuse to perform local measurements, the entanglement distribution scheme fails. We obtain critical values of noise allowed in the initial state so that the resulting output state show nonlocal correlation in different networks with arbitrary number of connections. We report that by employing our method, it is possible to create a Bell-violating multipartite entangled state from non-Bell violating bipartite states in an one-dimensional lattice with minimal coordination number being six. Such a feature of superadditivity in violation can also be observed in a triangular two dimensional lattice but not in a square lattice.