Reviewerly/exHarmony huggingface dataset:
Shapley regressions: A framework for statistical inference on machine learning models.

Machine learning models often excel in the accuracy of their predictions but are opaque due to their non-linear and non-parametric structure. This makes statistical inference challenging and disqualifies them from many applications where model interpretability is crucial. This paper proposes the Shapley regression framework as an approach for statistical inference on non-linear or non-parametric models. Inference is performed based on the Shapley value decomposition of a model, a pay-off concept from cooperative game theory. I show that universal approximators from machine learning are estimation consistent and introduce hypothesis tests for individual variable contributions, model bias and parametric functional forms. The inference properties of state-of-the-art machine learning models - like artificial neural networks, support vector machines and random forests - are investigated using numerical simulations and real-world data. The proposed framework is unique in the sense that it is identical to the conventional case of statistical inference on a linear model if the model is linear in parameters. This makes it a well-motivated extension to more general models and strengthens the case for the use of machine learning to inform decisions.
A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling

Segmentation of the prostate in magnetic resonance (MR) images has many applications in image-guided treatment planning and procedures such as biopsy and focal therapy. However, manual delineation of the prostate boundary is a time-consuming task with high inter-observer variation. In this study, we proposed a semiautomated, three-dimensional (3D) prostate segmentation technique for T2-weighted MR images based on shape and texture analysis. The prostate gland shape is usually globular with a smoothly curved surface that could be accurately modeled and reconstructed if the locations of a limited number of well-distributed surface points are known. For a training image set, we used an inter-subject correspondence between the prostate surface points to model the prostate shape variation based on a statistical point distribution modeling. We also studied the local texture difference between prostate and non-prostate tissues close to the prostate surface. To segment a new image, we used the learned prostate shape and texture characteristics to search for the prostate border close to an initially estimated prostate surface. We used 23 MR images for training, and 14 images for testing the algorithm performance. We compared the results to two sets of experts’ manual reference segmentations. The measured mean &plusmn; standard deviation of error values for the whole gland were 1.4 &plusmn; 0.4 mm, 8.5 &plusmn; 2.0 mm, and 86 &plusmn; 3% in terms of mean absolute distance (MAD), Hausdorff distance (HDist), and Dice similarity coefficient (DSC). The average measured differences between the two experts on the same datasets were 1.5 mm (MAD), 9.0 mm (HDist), and 83% (DSC). The proposed algorithm illustrated a fast, accurate, and robust performance for 3D prostate segmentation. The accuracy of the algorithm is within the inter-expert variability observed in manual segmentation and comparable to the best performance results reported in the literature.
Life Nurturing in the Illustrated Daoyin of the 24 Solar Terms (二十四节气)

China's “24 Solar Terms”, known as the fifth invention after China's Four Major Inventions, is a knowledge system and social practice formed through observations of the sun's annual motion, and cognition of the year's changes in season, climate and phenology. More and more people in the world have been eager to know more about it since UNESCO inscribed China's “24 Solar Terms” on the Representative List of the Intangible Cultural Heritage of Humanity in 2016. As a way of nurturing life, the Illustrated Daoyin of the 24 Solar Terms includes twenty-four exercise forms, corresponding to the 24 solar terms respectively, which reflects the significant idea of complying with the law of yin-yang changes in the universe. It presents a series of sitting and standing exercises designed to prevent diseases that occur during seasonal changes throughout the year. The ancient Chinese wisdom of time and life nurturing has been witnessed in China's “24 Solar Terms” and “Illustrated Daoyin of the 24 Solar Terms”. This article aims to arouse readers’ concern and interest in life nurturing by introducing how to do all the exercises presented by the Illustrated Daoyin of the 24 Solar Terms, which is helpful to the prevention and cure of diseases as well as good for health.
Multiseg pipeline: automatic tissue segmentation of brain MR images with subject-specific atlases

Automated segmentation and labeling of individual brain anatomical regions is challenging due to individual structural variability. Although, atlas-based segmentation has shown its potential for both tissue and structure segmentation, the inherent natural variability as well as disease-related changes in MR appearance is often inappropriately represented by a single atlas image. In order to have a more accurate representation, several atlases may be used for the segmentation task in a given neuroimaging study. In this paper, we present the MultisegPipeline, it uses multiple atlases that have been visually inspected and capture the expected variability in a neonatal population. The MultisegPipeline transfers the labeled regions from each atlas to the target image using deformable registration (ANTs or QuickSilver is available for this task). Additionally, the set of labels are merged using a label fusion technique that reduces the errors produced by the registration. The final output is a single label map that combines the results produced by all atlases into a consensus solution. In our study, the MultisegPipeline is used to segment brain MR images from 31 infants, a leave-one-out strategy was used to test our framework. The average dice score coefficient was 0.89.
Virtual Identity Performance Evaluations of Anonymous Authentication in IDaaS Framework

Identity-as-a-Service (IDaaS) is one of the most famous fruitful authentication services for cloud deployment to the Software-as-a-Service (SaaS) model. It is a third party approach for identity management, including creation, authentication, and privacy assurance. In this paper, the Virtual Identity ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{ID}$ </tex-math></inline-formula> ), as a new realization for IDaaS terminology which can be used in virtual environments, is proposed to improve the user privacy and to provide the anonymous Single sign-on (SSO) in such types of distributed environments. Actually, two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{ID}$ </tex-math></inline-formula> frameworks based on the Identity-Based Encryption (IBE) and Pseudonym-Based Encryption (PBE) approaches are proposed and then implemented using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MIRACL$ </tex-math></inline-formula> library. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{ID}$ </tex-math></inline-formula> approaches performance is evaluated analytically by implementing a mathematical model based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$BCMP$ </tex-math></inline-formula> (Baskett Chandy Muntz Palacios) queuing model. In addition, a simulation-based evaluation using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$OPNET$ </tex-math></inline-formula> Modeler is introduced to compare the analytical-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$BCMP$ </tex-math></inline-formula> queuing model against the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$OPNET$ </tex-math></inline-formula> simulation results. Moreover, the proposed approaches are compared against state-of-the-art work.
Karlheinz Stockhausen - Karlheinz Stockhausen: <i>Klavierstücke I–XI</i>. Sabine Leibner. Wergo 73412.

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Leveraging bitmap indexing for subgraph searching

Deciding whether a query graph is a subgraph of some other in a very large database of small graphs is a problem of major interest in many application domains. As an example, it arises in the searching for specific molecular substructures in currently available molecular databases, whose sizes may reach levels close to one hundred million. State of the art methods to solve this problem follow a filter-then-verify (FTV) paradigm, where an indexing technique is first used in a filtering stage to obtain result candidates and a subgraph isomorphism algorithm is next applied to the candidates in a verification stage to obtain the final result. Among all the available techniques of the state of the art, two of them have demonstrated better performance when applied to large datasets, namely, the GraphGrepSX (GGSX) and CT-Index (CTI). In this paper, three new indexing techniques, one based on GGSX and two based on CTI, are proposed. In particular, Bitmap GGSX (BM-GGSX) leverages the use of bitmaps in the trie structure used by GGSX to achieve performance gains of around 90% in the filtering stage. Column-Wise CT-Index (CW-CTI) exploits a column-wise representation of the fingerprints (bitmaps) used by CT-Index to reduce the filtering times around 80% for small queries (8 edges). Finally, K-Means CT-Index (KM-CTI), constructs a binary tree of bitmaps from the CT-Index fingerprints to reach filtering time reductions of around 70% for medium queries (20 edges) and 75% for large queries (40 edges).
Grover-based Ashenhurst-Curtis decomposition using quantum language quipper

Functional decomposition plays a key role in several areas such as system design, digital circuits, database systems, and Machine Learning. This paper presents a novel quantum computing approach based on Grover’s search algorithm for a generalized Ashenhurst-Curtis decomposition. The method models the decomposition problem as a search problem and constructs the oracle circuit based on the set-theoretic partition algebra. A hybrid quantum-based algorithm takes advantage of the quadratic speedup achieved by Grover’s search algorithm with quantum oracles for finding the minimum-cost decomposition. The method is implemented and simulated in the quantum programming language Quipper. This work constitutes the first attempt to apply quantum computing to functional decomposition.
Camera Adversarial Transfer for Unsupervised Person Re-Identification.

Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training. In this paper, we propose to unify two trends in unsupervised person re-ID: clustering & fine-tuning and adversarial learning. On one side, clustering groups training images into pseudo-ID labels, and uses them to fine-tune the feature extractor. On the other side, adversarial learning is used, inspired by domain adaptation, to match distributions from different domains. Since target data is distributed across different camera viewpoints, we propose to model each camera as an independent domain, and aim to learn domain-independent features. Straightforward adversarial learning yields negative transfer, we thus introduce a conditioning vector to mitigate this undesirable effect. In our framework, the centroid of the cluster to which the visual sample belongs is used as conditioning vector of our conditional adversarial network, where the vector is permutation invariant (clusters ordering does not matter) and its size is independent of the number of clusters. To our knowledge, we are the first to propose the use of conditional adversarial networks for unsupervised person re-ID. We evaluate the proposed architecture on top of two state-of-the-art clustering-based unsupervised person re-identification (re-ID) methods on four different experimental settings with three different data sets and set the new state-of-the-art performance on all four of them. Our code and model will be made publicly available at this https URL.
3D Organ Shape Reconstruction from Topogram Images

Automatic delineation and measurement of main organs such as liver is one of the critical steps for assessment of hepatic diseases, planning and postoperative or treatment follow-up. However, addressing this problem typically requires performing computed tomography (CT) scanning and complicated post-processing of the resulting scans using slice-by-slice techniques. In this paper, we show that 3D organ shape can be automatically predicted directly from topogram images, which are easier to acquire and have limited exposure to radiation during acquisition, compared to CT scans. We evaluate our approach on the challenging task of predicting liver shape using a generative model. We also demonstrate that our method can be combined with user annotations, such as a 2D mask, for improved prediction accuracy. We show compelling results on 3D liver shape reconstruction and volume estimation on 2129 CT scans (This feature is based on research, and is not commercially available. Due to regulatory reasons its future availability cannot be guaranteed).
A comparison of unimodal and multimodal models for implicit detection of relevance in interactive IR

Implicit detection of relevance has been approached by many during the last decade. From the use of individual measures to the use of multiple features from different sources (multimodality), studies have shown the feasibility to automatically detect whether a document is relevant. Despite promising results, it is not clear yet to what extent multimodality constitutes an effective approach compared to unimodality. In this article, we hypothesize that it is possible to build unimodal models capable of outperforming multimodal models in the detection of perceived relevance. To test this hypothesis, we conducted three experiments to compare unimodal and multimodal classification models built using a combination of 24 features. Our classification experiments showed that a univariate unimodal model based on the left‐click feature supports our hypothesis. On the other hand, our prediction experiment suggests that multimodality slightly improves early classification compared to the best unimodal models. Based on our results, we argue that the feasibility for practical applications of state‐of‐the‐art multimodal approaches may be strongly constrained by technology, cultural, ethical, and legal aspects, in which case unimodality may offer a better alternative today for supporting relevance detection in interactive information retrieval systems.
Real Time Fuzzy Based Traffic Flow Estimation and Analysis

Real-time traffic flow analysis using road mounted surveillance cameras presents multitude of benefits. In this paper, we used surveillance videos to design optical flow based technique for robust motion analysis and estimation. Region growing method is employed for detection of objects of interest. Autonomous density estimation of vehicles is crucial for traffic congestion analysis so that countermeasures can be taken at the earliest possible opportunity. A video based data extraction scheme for traffic data is proposed to determine the right traffic conditions which alleviates the false alarms and detrimental noise effects. Evaluation of proposed system is done by applying approach on several surveillance videos obtained from different sources and scenarios. An experimental study illustrates estimation and analysis results accuracy as compared to state-of-the-art approaches.
Distance Weighted K-Means Algorithm for Center Selection in Training Radial Basis Function Networks

The accuracies rates of the neural networks mainly depend on the selection of the correct data centers. The K-means algorithm is a widely used clustering algorithm in various disciplines for centers selection. However, the method is known for its sensitivity to initial centers selection. It suffers not only from a high dependency on the algorithm's initial centers selection but, also from data points. The performance of K-means has been enhanced from different perspectives, including centroid initialization problem over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the centers produces by the algorithm. To solve this problem, a new method to find the initial centers and improve the sensitivity to the initial centers of K-means algorithm is proposed. This paper presented a training algorithm for the radial basis function network (RBFN) using improved K-means (KM) algorithm, which is the modified version of KM algorithm based on distance-weighted adjustment for each centers, known as distance-weighted K-means (DWKM) algorithm. The proposed training algorithm, which uses DWKM algorithm select centers for training RBFN obtained better accuracy in predictions and reduced network architecture compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment; hence, the new network was undergoing a hybrid learning process. The network called DWKM-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to proposed method for root mean square error (RMSE) in radial basis function network (RBFN). The proposed method yielded a promising result with an average improvement percentage more than 50 percent in RMSE.
Intelligent Systems and Human Being

Problems discussed are, on the one hand, the participation of a human being in the functioning of the intelligent system as a person who perceives information, forms the notion of the essence of a phenomenon, and makes a control decision, on the other hand, the psychophysical studies aimed at elucidating the connection between mental phenomena and physical processes (i.e. physicochemical processes specific for a living organism). There is an intermediate functional unit, which is usually hidden from strict scientific knowledge, and it significantly complicates the creation of artificial intelligence systems. The essence of problems is considered and some ways of their solution are outlined.
Application of System Calls in Abnormal User Behavioral Detection in Social Networks

Abnormal user detection is one of the key issues in online social network security research. Attackers spread advertising and other malicious messages through stolen accounts, and malicious actions seriously threaten the information security of normal users with the credit system of social networks. For this reason, in the literature, there are a considerable amount of research work which detect abnormal accounts in social networks, however, these efforts ignore the problem of the seamless integration of machine learning with human behaviour-based analysis. This paper reviews the main achievements of abnormal account detection in online social networks in recent years from three aspects: behavioral characteristics, content-based, graph-based, and proposes a new social network abnormal user detection method based on system calls in computer’s kernel. Using enumeration sequence and hidden semi-Markov method, a hierarchical model of anomaly user detection in social networks is established.
Comment: Causal Inference Competitions: Where Should We Aim?

Data competitions proved to be highly beneficial to the field of machine learning, and thus expected to provide similar advantages in the field of causal inference. As participants in the 2016 and 2017 Atlantic Causal Inference Conference (ACIC) data competitions and co-organizers of the 2018 competition, we discuss the strengths of simulation-based competitions and suggest potential extensions to address their limitations. These suggested augmentations aim at making the data generating processes more realistic and gradually increase in complexity, allowing thorough investigations of algorithms’ performance. We further outline a community-wide competition framework to evaluate an end-to-end causal inference pipeline, beginning with a causal question and a database, and ending with causal estimates.
A Method for Identifying and Recommending Reconstructed Clones

Reconstruction of existing clone code is limited to a single version of static analysis and ignores the evolution of cloned code, resulting in a lack of effective methods for cloning code refactoring decisions. Therefore, this paper proposes a clone code that needs to be reconstructed and tracked from the perspective of software evolution, and recommends cloned code that needs to be reconstructed. Firstly, the evolution history information closely related to the cloned code is extracted from the clone detection, clone mapping, clone family and software maintenance log management system. Secondly, the clone code that needs to be reconstructed is identified, and the cloned code of the trace is identified, and then extracted and weighted. Construct related static and evolution features and build a feature sample database. Finally, a variety of machine learning methods are used to compare and select the best classifiers to recommend refactoring clones. This article conducts experiments on nearly 170 versions of 7 software, and recommends that the accuracy of refactoring cloned code reaches over 90%. Provide more accurate and reasonable code refactoring advice for software development and maintenance personnel.
Dynamic Priority based Weighted Scheduling Algorithm in Microservice System

With the development of cloud computing and container virtualization, the microservice architecture has received more and more attention. While microservices bring many benefits, they also bring some challenges. A user's request may require multiple microservices to collaborate, and chained invocations between microservices result in higher latency than a monolithic architecture. In this paper, we propose a dynamic priority based weighted scheduling algorithm for the problem that the latency of chained-service requests is too long in the case of high concurrency. Experiments show that the algorithm can reduce the latency of chained-service requests effectively in the case of little impact on the latency of leaf-service requests.
High capacity reversible data hiding in MP3 based on Huffman table transformation

In practice, most audio files such as MP3 and AAC are stored and transmitted in the form of compressed files, which can serve as the cover in audio steganography. Currently, the prevailing audio steganography methods are not ideal because of the drawbacks. Some are characterized by low capacity while others are irreversible. In this paper, we propose a method to embed secret messages in MP3 encoding. Our strategy is to hide the information by Huffman table transformation. We extract secret information by analyzing side information. Experimental results show that our method can greatly improve the steganographic capacity with low distortion and high security. Meanwhile, it featured with higher decoding rate and reversible over some state-of-the-art methods.