Reviewerly/exHarmony huggingface dataset:
Research on Design Pattern Detection Method Based on UML Model with Extended Image Information and Deep Learning

Detecting relevant design patterns from system design or source code helps software developers and maintainers understand the ideas behind the design of large-scale, highly complicated software systems, thereby improving the quality of software systems. Currently, design pattern detection based on machine learning has become a hot research direction. Scholars have proposed many design pattern detection methods based on machine learning. However, most of the existing literature only reports the utilization of traditional machine learning algorithms such as KNN, decision trees, ANN, SVM, etc., which require manual feature extraction and feature selection. It is very difficult to find suitable and effective features for the detection of design patterns. In the previous research, we have initially explored a design pattern detection method based on graph theory and ANN. Based on the research work done, we speculate that if we can realize the end-to-end design pattern detection from system design or source code to design pattern with the help of the powerful automatic feature extraction and other advantages of deep learning, the detection effect can be further improved. This paper intends to first explore a UML model that extends image information, called colored UML, so as to transform the design pattern detection problem into an image classification problem; on this basis, the positive and negative sample sets and the system to be recognized are all expressed in the form of colored UML models, the convolutional neural network VGGNet is used to train the data set to extract features, and the extracted features are trained by the SVM for binary classification to judge the pattern instances. Experiments were carried out on three open-source projects. We used three non-machine learning design pattern detection methods and five design pattern detection methods based on traditional machine learning algorithms, as well as the method in this paper. In general, the method proposed in this paper achieved higher precision and recall, and for different programs and their patterns, the precision and recall were stable at more than 85% in most cases. The experimental results demonstrate that this paper can achieve a better effect in recognizing design patterns. The research is, therefore, of both theoretical significance and application value.
How to learn with intentional mistakes: NoisyEnsembles to overcome poor tissue quality for deep learning in computational pathology

There is a lot of recent interest in the field of computational pathology, as many algorithms are introduced to detect, for example, cancer lesions or molecular features. However, there is a large gap between artificial intelligence (AI) technology and practice, since only a small fraction of the applications is used in routine diagnostics. The main problems are the transferability of convolutional neural network (CNN) models to data from other sources and the identification of uncertain predictions. The role of tissue quality itself is also largely unknown. Here, we demonstrated that samples of the TCGA ovarian cancer (TCGA-OV) dataset from different tissue sources have different quality characteristics and that CNN performance is linked to this property. CNNs performed best on high-quality data. Quality control tools were partially able to identify low-quality tiles, but their use did not increase the performance of the trained CNNs. Furthermore, we trained NoisyEnsembles by introducing label noise during training. These NoisyEnsembles could improve CNN performance for low-quality, unknown datasets. Moreover, the performance increases as the ensemble become more consistent, suggesting that incorrect predictions could be discarded efficiently to avoid wrong diagnostic decisions.
Multi types and reasonable space

Accattoli, Dal Lago, and Vanoni have recently proved that the space used by the Space KAM, a variant of the Krivine abstract machine, is a reasonable space cost model for the λ-calculus accounting for logarithmic space, solving a longstanding open problem. In this paper, we provide a new system of multi types (a variant of intersection types) and extract from multi type derivations the space used by the Space KAM, capturing into a type system the space complexity of the abstract machine. Additionally, we show how to capture also the time of the Space KAM, which is a reasonable time cost model, via minor changes to the type system.
Safe couplings: coupled refinement types

We enhance refinement types with mechanisms to reason about relational properties of probabilistic computations. Our mechanisms, which are inspired from probabilistic couplings, are applicable to a rich set of probabilistic properties, including expected sensitivity, which ensures that the distance between outputs of two probabilistic computations can be controlled from the distance between their inputs. We implement our mechanisms in the type system of Liquid Haskell and we use them to formally verify Haskell implementations of two classic machine learning algorithms: Temporal Difference (TD) reinforcement learning and stochastic gradient descent (SGD). We formalize a fragment of our system for discrete distributions and we prove soundness with respect to a set-theoretical semantics.
Assessing contamination sources and environmental hazards for potentially toxic elements and organic compounds in the soils of a heavily anthropized area: the case study of the Acerra plain (Southern Italy)

&lt;abstract&gt; &lt;p&gt;Epidemiological and environmental studies demonstrated that the rate of cancer mortality in the Acerra area, better known as "Triangle of Death", and, more in general, in the Neapolitan metropolitan territory are higher than the regional average values. In the "Triangle of Death" the higher rate of mortality has been mostly related to the presence of toxic wastes illegally buried in agricultural areas which have been contaminating soils and groundwater for decades. Thus, collecting a total of 154 samples over an area of about 100 km&lt;sup&gt;2&lt;/sup&gt;, a detailed study was carried out to assess the geochemical-environmental conditions of soils aiming at defining the environmental hazard proceeding from 15 potentially toxic elements (PTEs), 9 polycyclic aromatic hydrocarbons (PAHs) and 14 organochlorine pesticides (OCPs) related with soil contamination. The study was also targeted at discriminating the contamination sources of these pollutants. Results showed that 9 PTEs, 5 PAHs and 6 OCPs are featured by concentrations higher than the guideline values established by the Italian Environmental laws, especially in the proximities of inhabited centers and industrial areas. The contamination source analysis revealed that, as regards the concentrations of chemical elements, they have a dual origin due to both the natural composition of the soils (Co-Fe-V-Tl-Be) and the pressure exerted on the environment by anthropic activities such as vehicular traffic (Pb-Zn-Sb-Sn) and agricultural practices (Cu-P). As far as organic compounds are concerned, the source of hydrocarbons can be mainly attributed to the combustion of biomass (i.e., grass, wood and coal), while for pesticides, although the use of some of them has been prohibited in Italy since the 1980s, it has been found that they are still widely used by local farmers.&lt;/p&gt; &lt;/abstract&gt;
Advances in digital pottery analysis

Abstract Rapid progress in digitisation and computer techniques have enabled noteworthy new pottery analysis applications in recent decades. We focus on analytical techniques directed specifically at archaeological pottery research in this survey and review the specific benefits these have brought in the field. We consider techniques based on heterogeneous sources such as drawings, photographs, 3D scans and CT volume data. The various approaches and methods are structured according to the main steps in pottery processing in archaeology: documentation, classification and retrieval. Within these categories we review the most relevant papers and identify their advantages and limitations. We evaluate both freely and commercially available analysis tools and databases. Finally, we discuss open problems and future challenges in the field of pottery analysis.
Preserving Privacy when Querying OMOP CDM Databases

Anonymisation is currently one of the biggest challenges when sharing sensitive personal information. Its importance depends largely on the application domain, but when dealing with health information, this becomes a more serious issue. A simpler approach to avoid inadequate disclosure is to ensure that all data that can be associated directly with an individual is removed from the original dataset. However, some studies have shown that simple anonymisation procedures can sometimes be reverted using specific patients' characteristics. In this work, we propose a secure architecture to share information from distributed databases without compromising the subjects' privacy. The anonymiser system was validated using the OMOP CDM data schema, which is widely adopted in observational research studies.
Human-centered integrated care pathways for co-creating a digital, user-centric health information solution

Purpose A broader challenge of co-creating digital solutions with patients addresses the question how to apply an open-access digital platform with trusted digital health information as a measure to transform the way patients access and understand health information. It further addresses use this for adherence to treatment, risk minimization and quality of life throughout the integrated patient journey. The aim of this paper is to demonstrate the early steps in towards progress to co-creating the digital solution. Design/methodology/approach To coordinate the co-creation process, the authors established a multiphased plan to deep-dive into user needs and behaviors across patient journeys, to identify nuances and highlight important patterns in stakeholder and end-user segment at various stages in the patient's journey. Findings A set of tools was designed to serve as a human-centered compass throughout the lifecycle of the project. Those tools include shared objects; personas, user journeys, a set of performance indicators with related requirements – all those tools being consistently refined in ongoing co-creation workshops with members of the cross-functional stakeholder groups. Originality/value In this study, a multidisciplinary, public-private partnership looked at integrated digital tool to improve access, understanding and adherence to treatment for diverse groups of patients across all stages of their health journeys in a number of countries including European Union (EU) and United States of America (USA). As a result of this work, the authors attempt to increase the possibility that the improved availability and understanding of health information from trusted sources translates to higher levels of adherence to treatment, safer use of medication (pharmacovigilance), better health outcomes and quality of life integrated in the patient's journey.
Optimal estimation of broiler movement for commercial tracking

Nowadays, video tracking has taken a considerable part in monitoring systems. It allows identifying and follow every object in the camera field over time. While most of these algorithms are rather well suited to regular movements (following cars, pedestrians), they are often limited in more complex situations (high variations in speed, low detection rate, frequent shape variation). This paper proposes three methods adapted to broilers tracking in commercial environment. Past movements analysis of known broilers enable to estimate their motions and therefore to predict their new position. New unidentified broilers positions are then compared to these predicted positions. Distances between these two sets of positions are then used in the Hungarian Algorithm to assign an ID to new detected broilers regarding their past positions. Our methods differentiate by the way they predict the future positions. Contrary to most methods, they do not seek perfect regularity of movements and can deal with low rate detection. The proposed methods showed better performances than existing one. Tests have been made at 21, 26, and 37 days of age. At 21 days, our best method produces up to 35% fewer errors than a method with no estimation of movement. At 26 days of age, displacement distances can be set to only 68% of the maximum recorded displacement while improving an average of 21% of tracking errors across all methods.
Medical Image Enhancement Based on Laplace Transform, Sobel Operator and Histogram Equalization

Medical image enhancement is one of the most widely used medical image processing techniques in medical domain.Its purpose is to improve the visual effect of the image and facilitate the analysis and understanding of the image by human or machine.The Laplace transform and the Sobel gradient operator are two common ways of performing edge detection, image sharpening and enabling the image to be enhanced.However, each has limitations when used in isolation.The Laplace operator has a good edge detection effect, but it will make the image noise expand; the Sobel operator has a certain ability to smooth the noise, but the edges of the image obtained after processing are rougher.This paper therefore proposes a method based on both Laplace transform and Sobel operator, and histogram equalization of the transformed image is processed to enhance the image.Using a combination of both filtering methods avoids the disadvantage.This method was found to be effective in improving the quality of lung images and skeletal images through several experiments.
Improvement and Application of Dijkstra Algorithms

Taking urban express traffic as an example, this paper lists two optimization improvements of Dijkstra algorithm.One is to improve the calculation method of the algorithm to make the calculation more directional, so as to reduce the total amount of calculation.One is in the form of a graphical model, reducing the number of nodes to optimize the calculation process.By comparing the calculation results with the complexity of the traditional algorithm, the effectiveness of the improved method is proved, the complexity of the algorithm is reduced, and the actual work efficiency is improved.It improves the application ability and realizability of Dijkstra algorithm in practical problems.
Multi-Output Regression with Generative Adversarial Networks (MOR-GANs)

Regression modelling has always been a key process in unlocking the relationships between independent and dependent variables that are held within data. In recent years, machine learning has uncovered new insights in many fields, providing predictions to previously unsolved problems. Generative Adversarial Networks (GANs) have been widely applied to image processing producing good results, however, these methods have not often been applied to non-image data. Seeing the powerful generative capabilities of the GANs, we explore their use, here, as a regression method. In particular, we explore the use of the Wasserstein GAN (WGAN) as a multi-output regression method. The resulting method we call Multi-Output Regression GANs (MOR-GANs) and its performance is compared to a Gaussian Process Regression method (GPR)—a commonly used non-parametric regression method that has been well tested on small datasets with noisy responses. The WGAN regression model performs well for all types of datasets and exhibits substantial improvements over the performance of the GPR for certain types of datasets, demonstrating the flexibility of the GAN as a model for regression.
Comparison of development and characteristics of several educational tools in augmented reality for visualization of 3D models difficult to understand. Chemistry application case

This paper shows the development of different scenarios in augmented reality (AR) using several programming tools, these applications were focused on visualization of 3D Bohr atomic models of the chemical elements from periodic table, organic and inorganic chemical molecules. The development tools were a) Unity3D in conjunction with the EasyAR SDK, b) HTML 5, CSS and the Google ARCore software development kit, c) Spark AR Studio and d) Adobe Aero with Glitch and HTML 5. The different scenarios created and described in this work were designed and developed to improve the learning of topics in an area of chemistry that requires three-dimensional models. The developments allowed 3D visualization and the interaction with the models to achieve several views of them, since the representations of these models are traditionally projected in books in two dimensions, making their understanding and interpretation in a not easy form.
Adapting the Mean Teacher for Keypoint-Based Lung Registration Under Geometric Domain Shifts

Recent deep learning-based methods for medical image registration achieve results that are competitive with conventional optimization algorithms at reduced run times. However, deep neural networks generally require plenty of labeled training data and are vulnerable to domain shifts between training and test data. While typical intensity shifts can be mitigated by keypoint-based registration, these methods still suffer from geometric domain shifts, for instance, due to different fields of view. As a remedy, in this work, we present a novel approach to geometric domain adaptation for image registration, adapting a model from a labeled source to an unlabeled target domain. We build on a keypoint-based registration model, combining graph convolutions for geometric feature learning with loopy belief optimization, and propose to reduce the domain shift through self-ensembling. To this end, we embed the model into the Mean Teacher paradigm. We extend the Mean Teacher to this context by 1) adapting the stochastic augmentation scheme and 2) combining learned feature extraction with differentiable optimization. This enables us to guide the learning process in the unlabeled target domain by enforcing consistent predictions of the learning student and the temporally averaged teacher model. We evaluate the method for exhale-to-inhale lung CT registration under two challenging adaptation scenarios (DIR-Lab 4D CT to COPD, COPD to Learn2Reg). Our method consistently improves on the baseline model by 50%/47% while even matching the accuracy of models trained on target data. Source code is available at https://github.com/multimodallearning/registration-da-mean-teacher .
Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture

Foreground detection is a classic video processing task, widely used in video surveillance and other fields, and is the basic step of many computer vision tasks. The scene in the real world is complex and changeable, and it is difficult for traditional unsupervised methods to accurately extract foreground targets. Based on deep learning theory, this paper proposes a foreground detection method based on the multiscale U-Net architecture with a fusion attention mechanism. The attention mechanism is introduced into the U-Net multiscale architecture through skip connections, causing the network model to pay more attention to the foreground objects, suppressing irrelevant background regions, and improving the learning ability of the model. We conducted experiments and evaluations on the CDnet-2014 dataset. The proposed model inputs a single RGB image and only utilizes spatial information, with an overall F-measure of 0.9785. The input of multiple images is fused, and the overall F-measure can reach 0.9830 by using spatiotemporal information. Especially in the Low Framerate category, the F-measure exceeds the current state-of-the-art methods. The experimental results demonstrate the effectiveness and superiority of our proposed method.
Cache Refinement Type for Side-Channel Detection of Cryptographic Software

Cache side-channel attacks exhibit severe threats to software security and privacy, especially for cryptosystems. In this paper, we propose CaType, a novel refinement type-based tool for detecting cache side channels in crypto software. Compared to previous works, CaType provides the following advantages: (1) For the first time CaType analyzes cache side channels using refinement type over x86 assembly code. It reveals several significant and effective enhancements with refined types, including bit-level granularity tracking, distinguishing different effects of variables, precise type inferences, and high scalability. (2) CaType is the first static analyzer for crypto libraries in consideration of blinding-based defenses. (3) From the perspective of implementation, CaType uses cache layouts of potential vulnerable control-flow branches rather than cache states to suppress false positives. We evaluate CaType in identifying side channel vulnerabilities in real-world crypto software, including RSA, ElGamal, and (EC)DSA from OpenSSL and Libgcrypt. CaType captures all known defects, detects previously-unknown vulnerabilities, and reveals several false positives of previous tools. In terms of performance, CaType is 16X faster than CacheD and 131X faster than CacheS when analyzing the same libraries. These evaluation results confirm the capability of CaType in identifying side channel defects with great precision, efficiency, and scalability.
Blockchain and energy commodity markets: legal issues and impact on sustainability

Abstract In this article, we explore the application of blockchain, a type of distributed ledger technology (DLT), in the field of energy trading. Specifically, we focus on crude oil trade. We argue that the application of blockchain technology and supplementary smart contracts supports responsible sourcing in complex supply chains and helps reduce information asymmetry in both the physical trade and paper trade of energy commodities markets. In order to apply blockchain technology to the energy market, we begin with a discussion on the architecture of blockchain and the different types of blockchain that might be applied in this sector. Further to this, we examine the current oil trading markets, particularly their relevant components, as part of the discussion on blockchain application. Subsequently, we look at how the various types of blockchain may be applied to the markets and examine their advantages and disadvantages. In conclusion, we look at the legal issues that may arise from such application, the potential solutions, and the potential impact of blockchain technology on the United Nations Sustainable Developmental Goals in the future and how a green fintech application can be developed.
Prioritization of god class design smell: A multi-criteria based approach

Context: Design smell Prioritization is a significant activity that tunes the process of software quality enhancement and raises its life cycle. Objective: A multi-criteria merge strategy for Design Smell prioritization is described. The strategy is exemplified with the case of God Class Design Smell. Method: An empirical adjustment of the strategy is performed using a dataset of 24 open source projects. Empirical evaluation was conducted in order to check how is the top ranked God Classes obtained by the proposed technique compared against the top ranked God class according to the opinion of developers involved in each of the projects in the dataset. Results: Results of the evaluation show the strategy should be improved. Analysis of the differences between projects where respondents answer correlates with the strategy and those projects where there is no correlation should be done.