Reviewerly/exHarmony huggingface dataset:
Adversarial Confidence Learning for Medical Image Segmentation and Synthesis

Generative adversarial networks (GAN) are widely used in medical image analysis tasks, such as medical image segmentation and synthesis. In these works, adversarial learning is directly applied to the original supervised segmentation (synthesis) networks. The usage of adversarial learning is effective in improving visual perception performance since adversarial learning works as realistic regularization for supervised generators. However, the quantitative performance often cannot improve as much as the qualitative performance, and it can even become worse in some cases. In this paper, we explore how we can take better advantage of adversarial learning in supervised segmentation (synthesis) models and propose an adversarial confidence learning framework to better model these problems. We analyze the roles of discriminator in the classic GANs and compare them with those in supervised adversarial systems. Based on this analysis, we propose adversarial confidence learning, i.e., besides the adversarial learning for emphasizing visual perception, we use the confidence information provided by the adversarial network to enhance the design of supervised segmentation (synthesis) network. In particular, we propose using a fully convolutional adversarial network for confidence learning to provide voxel-wise and region-wise confidence information for the segmentation (synthesis) network. With these settings, we propose a difficulty-aware attention mechanism to properly handle hard samples or regions by taking structural information into consideration so that we can better deal with the irregular distribution of medical data. Furthermore, we investigate the loss functions of various GANs and propose using the binary cross entropy loss to train the proposed adversarial system so that we can retain the unlimited modeling capacity of the discriminator. Experimental results on clinical and challenge datasets show that our proposed network can achieve state-of-the-art segmentation (synthesis) accuracy. Further analysis also indicates that adversarial confidence learning can both improve the visual perception performance and the quantitative performance.
Integrating cost–benefit analysis into the NIST Cybersecurity Framework via the Gordon–Loeb Model

Abstract The National Institute for Standards and Technology (NIST) Cybersecurity Framework has rapidly become a widely accepted approach to facilitating cybersecurity risk management within organizations. An insightful aspect of the NIST Cybersecurity Framework is its explicit recognition that the activities associated with managing cybersecurity risk are organization specific. The NIST Framework also recognizes that organizations should evaluate their cybersecurity risk management on a cost–benefit basis. The NIST Framework, however, does not provide guidance on how to carry out such a cost–benefit analysis. This article provides an approach for integrating cost–benefit analysis into the NIST Cybersecurity Framework. The Gordon–Loeb (GL) Model for cybersecurity investments is proposed as a basis for deriving a cost-effective level of spending on cybersecurity activities and for selecting the appropriate NIST Implementation Tier level. The analysis shows that the GL Model provides a logical approach to use when considering the cost–benefit aspects of cybersecurity investments during an organization’s process of selecting the most appropriate NIST Implementation Tier level. In addition, the cost–benefit approach provided in this article helps to identify conditions under which there is an incentive to move to a higher NIST Implementation Tier.
Focus on Semantic Consistency for Cross-Domain Crowd Understanding

For pixel-level crowd understanding, it is time-consuming and laborious in data collection and annotation. Some domain adaptation algorithms try to liberate it by training models with synthetic data, and the results in some recent works have proved the feasibility. However, we found that a mass of estimation errors in the background areas impede the performance of the existing methods. In this paper, we propose a domain adaptation method to eliminate it. According to the semantic consistency, a similar distribution in deep layer's features of the synthetic and real-world crowd area, we first introduce a semantic extractor to effectively distinguish crowd and background in high-level semantic information. Besides, to further enhance the adapted model, we adopt adversarial learning to align features in the semantic space. Experiments on three representative real datasets show that the proposed domain adaptation scheme achieves the state-of-the-art for cross-domain counting problems.
Efficient approach of recent high utility stream pattern mining with indexed list structure and pruning strategy considering arrival times of transactions

One of various pattern mining techniques, the High Utility Pattern Mining (HUPM) is a method for finding meaningful patterns from non-binary databases by considering the characteristics of the items. Recently, new data continues to flow over time in diverse fields such as sales data of market, heartbeat sensor data, and social network service. Since these data have a feature that recently generated data have higher influence than the old data, research has been focused on how to efficiently extract hidden knowledge from time-sensitive databases. In this paper, we propose indexed list-based algorithm that mines recent high utility pattern considering the arrival time of inserted data in an environment where new data is continuously accumulated. In other words, to treat the importance of recent data higher than the that of old data, our algorithms reduces the utility values of old transactions according to the time the data is inserted by applying damped window model concept. Moreover, we carry out various experiments to compare our method with state-of-the-art algorithms using real and synthetic datasets in diverse circumstances. Experimental results show that our algorithm outperforms competitors in terms of execution time, memory usage, and scalability test.
A Real-Time Deep Network for Crowd Counting

Automatic analysis of highly crowded people has attracted extensive attention from computer vision research. Previous approaches for crowd counting have already achieved promising performance across various benchmarks. However, to deal with the real situation, we hope the model run as fast as possible while keeping accuracy. In this paper, we propose a compact convolutional neural network for crowd counting which learns a more efficient model with a small number of parameters. With three parallel filters executing the convolutional operation on the input image simultaneously at the front of the network, our model could achieve nearly real-time speed and save more computing resources. Experiments on two benchmarks show that our proposed method not only takes a balance between performance and efficiency which is more suitable for actual scenes but also is superior to existing light-weight models in speed.
Improving Prosody with Linguistic and Bert Derived Features in Multi-Speaker Based Mandarin Chinese Neural TTS

Recent advances of neural TTS have made "human parity" synthesized speech possible when a large amount of studio-quality training data from a voice talent is available. However, with only limited, casual recordings from an ordinary speaker, human-like TTS is still a big challenge, in addition to other artifacts like incomplete sentences, repetition of words, etc. Chinese, a language, of which the text is different from that of other roman-letter based languages like English, has no blank space between adjacent words, hence word segmentation errors can cause serious semantic confusions and unnatural prosody. In this study, with a multi-speaker TTS to accommodate the insufficient training data of a target speaker, we investigate linguistic features and Bert-derived information to improve the prosody of our Mandarin Chinese TTS. Three factors are studied: phone-related and prosody-related linguistic features; better predicted breaks with a refined Bert-CRF model; augmented phoneme sequence with character embedding derived from a Bert model. Subjective tests on in- and out-domain tasks of News, Chat and Audiobook, have shown that all factors are effective for improving prosody of our Mandarin TTS. The model with additional character embeddings from Bert is the best one, which outperforms the baseline by 0.17 MOS gain.
A comparative review of optimisation techniques in segmentation of brain MR images

MRI image segmentation, a challenging task in medical diagnostics aids in extracting the summarized information of the anatomy of the human brain, thereby offering the potentiality for accurate treatment of the disease. The purpose of this work is to elevate the performances of different optimizati on techniques that are used in automated segmentation procedures. The performance of four algorithms was evaluated quantitatively over the genetic algorithm-based segmentation, which is the prevailing approach in automated segmentation. The upshot exhibits the accuracy and performance of various optimization techniques with a genetic algorithm.
Joint Geographical and Temporal Modeling Based on Matrix Factorization for Point-of-Interest Recommendation

With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem. However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobility patterns where we find out that users' check-ins happen around several centers depending on their current temporal state. Next, we propose a spatio-temporal activity-centers algorithm to model users' behavior more accurately. Finally, we demonstrate the effectiveness of our proposed contextual model by incorporating it into the matrix factorization model under two different settings: (i) static and (ii) temporal. To show the effectiveness of our proposed method, which we refer to as STACP, we conduct experiments on two well-known real-world datasets acquired from Gowalla and Foursquare LBSNs. Experimental results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques. Also, we demonstrate the effectiveness of capturing geographical and temporal information for modeling users' activity centers and the importance of modeling them jointly.
From Feature Engineering and Topics Models to Enhanced Prediction Rates in Phishing Detection

Phishing is a type of fraud attempt in which the attacker, usually by e-mail, pretends to be a trusted person or entity in order to obtain sensitive information from a target. Most recent phishing detection researches have focused on obtaining highly distinctive features from the metadata and text of these e-mails. The obtained attributes are then used to feed classification algorithms in order to determine whether they are phishing or legitimate messages. In this paper, it is proposed an approach based on machine learning to detect phishing e-mail attacks. The methods that compose this approach are performed through a feature engineering process based on natural language processing, lemmatization, topics modeling, improved learning techniques for resampling and cross-validation, and hyperparameters configuration. The first proposed method uses all the features obtained from the Document-Term Matrix (DTM) in the classification algorithms. The second one uses Latent Dirichlet Allocation (LDA) as a operation to deal with the problems of the “curse of dimensionality”, the sparsity, and the text context portion included in the obtained representation. The proposed approach reached marks with an F1-measure of 99.95% success rate using the XGBoost algorithm. It outperforms state-of-the-art phishing detection researches for an accredited data set, in applications based only on the body of the e-mails, without using other e-mail features such as its header, IP information or number of links in the text.
A new short-arc fitting method with high precision using Adam optimization algorithm

The high-precision short-arc measurement is a huge challenge in scientific research and engineering practice. The popular traditional least square fitting (TLSF) however fails to achieve the precise fitting parameters when the arc gets shorter. In this work, an inequation constrained fitting (ICF) method based on four-parameter circle equation is presented. Lagrangian multiplier and Karush-Kuhn-Tucker criteria are used to redefine the objective function. After that, Adam algorithm is utilized to solve the objective function in iterative way. Adam algorithm has a strong ability to resist noise pollution by virtue of modifying continually the first-order momentum and second-order momentum with average of gradients during the course of iteration. Finally, simulation and experimental results show that our ICF method is more robust and high-precision than TLSF and Hyper method, so it is very competent to measure short arcs with noise even their central angles are close to 5°.
Methodical requirements for training materials of on-line courses on the platform “Higher school mathematics teacher”

The article looks into the issue of developing methodical requirements for the structure and content of online courses. The study is dedicated to the analysis of peculiarities of developing the content of online systems and developing methodical recommendations to educational materials for online courses of training higher school mathematics teachers. The research considers the experts’ experience in preparing, structuring and developing the content for online courses and answers to volunteers who have agreed to test the educational materials of the course. The answers given by the participants have allowed estimating the quality of the developed course and detecting its insignificant drawbacks. The article discusses general requirements to the structure and content of the online course, means for the implementation of a testing subsystem, peculiarities of developing educational video content and educational materials in PDF format, issues of implementing forum and survey subsystems, as well as means of estimating learning outcomes. We have grounds to conclude that the quality of the course is determined by the range of factors, among which we point out the course organization based on weekly planning, implementation of a testing subsystem under conditions of extended functionality, creation of abilities to organize feedback.
Tensor-network-based machine learning of non-Markovian quantum processes

We show how a tensor-network-based machine learning algorithm can learn the structures of generic, non-Markovian, quantum stochastic processes. First, a process is represented as a matrix product operator (MPO) and trained with a database of local input states at different times and the corresponding time-nonlocal output state. We then apply the algorithm to predict the output state of a process at different times for a system that is coupled to a spin-chain environment. We then reconstruct the full process, and we quantify the non-Markovian memory by means of the bond dimension of the MPO for various properties of the system, of the environment, and of their interaction. Our study paves the way for a possible experimental investigation into the process tensor and its properties, and an effective characterization of noise in quantum devices.
PREStO: A Systematic Framework for Blockchain Consensus Protocols

The rapid evolution of blockchain technology has brought together stakeholders from fundamentally different backgrounds. The result is a diverse ecosystem, as exemplified by the development of a wide range of different blockchain protocols. This raises questions for decision and policy makers: How do different protocols compare? What are their trade-offs? Existing efforts to survey the area reveal a fragmented terminology and the lack of a unified framework to reason about the properties of blockchain protocols. In this paper, we work towards bridging this gap. We present a five-dimensional design space with a modular structure in which protocols can be compared and understood. Based on these five axes -- Optimality, Stability, Efficiency, Robustness and Persistence -- we organize the properties of existing protocols in subcategories of increasing granularity. The result is a dynamic scheme -- termed the PREStO framework -- which aids the interaction between stakeholders of different backgrounds, including managers and investors, and which enables systematic reasoning about blockchain protocols. We illustrate its value by comparing existing protocols and identifying research challenges, hence making a first step towards understanding the blockchain ecosystem through a more comprehensive lens.
Unsupervised feature selection with adaptive multiple graph learning

Unsupervised feature selection methods try to select features which can well preserve the intrinsic structure of data. To represent such structure, conventional methods construct various graphs from data. In most cases, those different graphs often contain some consensus and complementary information. To make full use of such information, we construct multiple base graphs and learn an adaptive consensus graph from these base graphs for feature selection. In our method, we integrate the multiple graph learning and the feature selection into a unified framework, which can jointly characterize the structure of the data and select the features to preserve such structure. The underlying optimization problem is hard to solve, and we solve it via a block coordinate descent schema, whose convergence is guaranteed. The extensive experiments well demonstrate the effectiveness of our proposed framework.
A robust graph-based semi-supervised sparse feature selection method

Feature selection is used for excluding redundant features and enhancing learning performance. Abundant unlabeled data are existed in many applications which can be used in semi-supervised feature selection. Semi-supervised sparse feature selection methods have been presented to apply labeled and unlabeled data and consider the correlation among features. Most of the sparse methods utilize square-norm based loss function which is sensitive to outliers. In this paper, we propose a robust Graph-based Semi-Supervised Sparse Feature Selection (GS3FS) method based on the mixed convex and non-convex l2,p-norm (0 < p ≤ 1) minimization on regularization and loss function. The l2,p-norm based regularization ensures that the method selects relevant and sparse features, and the l2,p-norm based loss function makes the method robust to outliers. Proposed method takes the advantages of manifold learning and l2,p-norm minimization on regularization and loss function. The method includes the mixed l2,p-norm which is difficult to solve. To solve the proposed method for the convex (p = 1) and non-convex (0 < p <1) cases, we propose an efficient unified algorithm and prove its convergence. Extensive experiments are carried out on various classification and regression datasets for evaluating the proposed method. The results indicate that the proposed method selects the relevant features and improves the performance of classification and regression models.
Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Support vector regression (SVR) is one of the most powerful and widely used machine learning algorithms regarding prediction. The kernel type, penalty factor and other parameters influence the efficiency and performance of SVR deeply. The optimization of these parameters is held a hot issue. In this work, we propose a SVR based prediction approach using henry gas solubility optimization algorithm (HGSO), which is a recent meta-heuristic algorithm inspired by Henry's law. First, SVR parameters are randomly generated in some certain ranges to form parameter population. Second, the prediction accuracies (PAs) are obtained using the population and SVR. Thirdly, the population and optimal SVR parameters are updated via PAs and HGSO. We repeat the second and third steps until the cut-off conditions are met. Ten low- and high-dimensional benchmark data sets are utilized to assess the prediction accuracy, convergence performance and computational complexity of the presented approach and other well-known algorithms. The experimental results reveal that our approach has the optimum comprehensive performance.
A large scale study on how developers discuss code smells and anti-pattern in Stack Exchange sites

Context: In this paper, we investigate how developers discuss code smells and anti-patterns across three technical Stack Exchange sites. Understanding developers perceptions of these issues is important to inform and align future research efforts and direct tools vendors to design tailored tools that best suit developers. Method: we mined three Stack Exchange sites and used quantitative and qualitative methods to analyse more than 4000 posts that discuss code smells and anti-patterns.Results: results showed that developers often asked their peers to smell their code, thus utilising those sites as an informal, crowd-based code smell/anti-pattern detector. The majority of questions (556) asked were focused on smells like Duplicated Code, Spaghetti Code, God and Data Classes. In terms of languages, most of discussions centred around popular languages such as C# (772 posts), JavaScript (720) and Java (699), however greater support is available for Java compared to other languages (especially modern languages such as Swift and Kotlin). We also found that developers often discuss the downsides of implementing specific design patterns and 'flag' them as potential anti-patterns to be avoided. Some well-defined smells and anti-patterns are discussed as potentially being acceptable practice in certain scenarios. In general, developers actively seek to consider trade-offs to decide whether to use a design pattern, an anti-pattern or not.Conclusion: our results suggest that there is a need for: 1) more context and domain sensitive evaluations of code smells and anti-patterns, 2) better guidelines for making trade-offs when applying design patterns or eliminating smells/anti-patterns in industry, and 3) a unified, constantly updated, catalog of smells and anti-patterns. We conjecture that the crowd-based detection approach considers contextual factors and thus tend to be more trusted by developers than automated detection tools.