Reviewerly/exHarmony huggingface dataset:
Feature Fusion And Coordinate Attention For Small Target Detection

CenterNet is an anchor-free and one-stage object detection network based on the keypoint. It has the advantages of simple network structure, fast detection speed and high accuracy. Due to the scarcity of small targets characteristics, insufficient low-level semantics information and lack of high-level position information, CenterNet faces great challenges in the process of small target detection and the performance is not ideal. Aiming at the above issues, we use ResNet as the feature extraction network and insert coordinate attention mechanisms into the residual block to make full use of the captured position information. At the same time, the Feature Pyramid Network is introduced, and asymmetric contextual modulation is used for feature fusion between high-level and low-level features, so as to encode semantic information and spatial details more abundantly. Through experimental comparison and analysis, the improved model improves the detection accuracy of small targets by 9.8% on the dataset compared with the original CenterNet, which proves the effectiveness and robustness of the improved model.
Model-driven Continuous Experimentation on Component-based Software Architectures

To build successful software products, developers continuously have to discover what features the users really need. This discovery can be achieved with continuous experimentation, testing different software variants with distinct user groups, and deploying the superior variant for all users. However, existing approaches do not focus on explicit modeling of variants and experiments, which offers advantages such as traceability of decisions and combinability of experiments. Therefore, our vision is the provision of model-driven continuous experimentation, which provides the developer with a framework for structuring the experimentation process. For that, we introduce the overall concept, apply it to the experimentation on component-based software architectures and point out future research questions. In particular, we show the applicability by combining feature models for modeling the software variants, users, and experiments (i.e., model-driven) with MAPE-K for the adaptation (i.e., continuous experimentation) and implementing the concept based on the component-based Angular framework.
Success Factors for the Validation of Requirements for New Product Generations – A Case Study on Using Field Gathered Data

Abstract This paper investigates which activities and success factors can be identified for the data-driven validation of functional requirements. For this purpose, a case study is conducted at a machine tool manufacturer. To validate functional requirements by analyzing data of reference products, these activities must be performed iteratively: basic work, interdisciplinary work, programming and check results. For the successful execution of data-driven validation, the success factors: data origin, acceptance, data quality, knowledge about data and combination of domain knowledge must be considered.
Research on Spectrum Intelligent Monitoring Application Microservice Architecture Based on Docker

The spectrum intelligent monitoring technology based on deep learning can reduce the artificial interference factors of spectrum monitoring and significantly improve the real-time performance and accuracy of spectrum monitoring. However, due to the complex operating environment of deep learning algorithms and the variety of deep learning frameworks, the deployment and transplantation of spectrum monitoring applications are more difficult. This paper proposes a docker-based spectrum intelligent monitoring application microservice architecture, which is mainly divided into spectrum monitoring resource layer, spectrum monitoring resource service layer and spectrum monitoring resource service layer. Docker is used to encapsulate the spectrum monitoring algorithm based on deep learning, and Kubernetes is used for unified arrangement and deployment, which simplifies the deployment and migration of the spectrum monitoring algorithm and improves the efficiency of spectrum monitoring.
Towards Automatic Classification of Design Decisions from Developer Conversations

Documentation of architecture design decisions can be cumbersome, though it is critical to ongoing software maintenance and integration of new features. In practice, developers often discuss design decisions in online communication tools, such as Slack, Microsoft Teams or Gitter. In this paper, we introduce a design decision bot for Slack to reduce the workload for documenting design decisions. The bot heralds the integration of machine learning and language processing to decision recording. Using available communication tools and interactions (Slack and Slack bots) allows developers to document design decisions mid-stride without diverting attention from other development tasks. The bot utilises a "transformer" for decision classification, avoiding rigid and onerous interactions between the bot and developers. The transformer builds on Bi-directional Encoder Representations from Transformers (BERT), a machine learning technique for Natural Language Processing (NLP). In this paper, we present the theoretical concepts of the bot with early implementation details and the results of preliminary evaluations.
Towards safe and robust weakly-supervised anomaly detection under subpopulation shift

Anomaly detection is an important technique to make the data-exploding society safer and more harmonious. The existing weakly-supervised anomaly detection works ignore the problem of subpopulation shift, where novel abnormal subpopulation is not observed during the training time but emerges in the test set. The setting is challenging and valuable, but has not been studied in the previous literature. To solve the problem, we construct an unbiased risk estimator to analyze this new problem. Then, according to the unbiased risk estimator, we raise a novel and effective method called SPSAD (SubPopulations Shift Anomaly Detection). The method combines four components used in tandem. SPSAD firstly suggests a preliminary screening module, which takes advantage of intersection statistic to excavate the primary instances of the novel abnormal subpopulation from unlabeled data. Then, through updating similarity statistic based on primary instances, SPSAD can extract reliable anomalies and normal examples in unlabeled data, respectively. SPSAD then filtrates the instances acquired in the preliminary screening phase to obtain clean instances of novel abnormal subpopulations. Finally, SPSAD constructs a robust risk estimator on the basis of the excavated examples, performing weakly-supervised anomaly detection under subpopulation shift successfully. In addition, we make rigorous theoretical analyses and prove the feasibility of the new problem, which provides a certain theoretical guarantee for the algorithm we design. A range of empirical studies show that our algorithm is significantly better than the state of the art.
Convolutional neural network-based identification of malicious traffic for TLS encryption

By converting network traffic into images and using convolutional network prediction methods, the accuracy of malicious network traffic can be effectively improved. As TLS traffic encryption is used in more and more network applications, while protecting security and privacy, network attackers can achieve the purpose of evading detection by encrypting malicious traffic. The traditional method of using a firewall and anti-virus software to decrypt the encryption key to access the encrypted data is not only inefficient and destroys the original purpose of encryption to protect privacy, but also when the key cannot be decrypted, the traditional method The decryption capability of the traditional method is insufficient. In this paper, we propose a method to transform network traffic into network fingerprint images without decrypting the encrypted data, and then feed the images into a convolutional neural network for prediction to achieve the classification of malicious traffic. The experimental results show that the method of identifying network fingerprint images by using CNN has high accuracy and prediction capability.
Power Systems Dynamic State Estimation using Central Difference Filter

A nonlinear Sigma point Kalman filter known as Central Difference Filter which considers only first order Taylor series approximation with the help of interpolation formula, have been employed here for the first time during dynamic state estimation of power systems states. This paper also exhibits a comparative performance analysis of two estimation techniques namely Central difference filter (CDF) and Cubature Kalman filter technique (CKF) during power systems dynamic state estimation. The estimation is performed employing measurements from Remote terminal units (RTU) and Phasor measurement units (PMU). The whole simulation process is carried out for IEEE 30 bus test system. Holts two parameter linear exponential technique which often utilized as a state forecasting technique has been used here to forecast the systems state at the prediction step. The superiority of CDF over CKF, has been illustrated here on context of computation time and estimation accuracy.
Dataset - Architectural Attack Propagation Analysis for Identifying Confidentiality Issues

Connecting different systems to exchange data in-creases the number of potential vulnerabilities. These vulnerabilities can be combined with access control issues and enables attacker to propagate through the system. We developed an at-tack propagation analysis for analysing this dependency between vulnerabilities and access control policies. This helps architects to estimate the confidentiality of the system. We provide the developed analysis, metamodels, and the evaluation data as a public dataset.
ACIA: A Methodology for identification of Architectural Design Patterns that support Continuous Integration based on Continuous Assessment

Architectural patterns are solutions to common problems in software design. These usually tackle one or more customer requirements (business, functional, or quality requirements). As continuous deployment becomes more important for satisfying customer’s experience in software projects, there is a greater need in supporting continuous integration as its prerequisite. The nature of architectural design patterns is that some are more suitable for continuous integration then others. However, the existing approaches do not prescribe how to tailor architectural design considering integration challenges.To identify patterns suitable for continuous integration, we present a methodology for identification of architectural design patterns that support continuous integration based on continuous assessment. Our methodology, based on Aglie, enables DevOps engineers to assess their integration experience considering design changes, implementation changes, and creation of new version deliverables. The methodology complements common DevOps activities and introduces templates for communicating feedback from DevOps engineers to architects. Architects handle the communicated feedback as requirements and optimise their design accordingly. In this way, the design decisions take shape of solution patterns that, besides other existing requirements, satisfy requirements necessary to facilitate continuous integration.
A Formalization of the Smith Normal Form in Higher-Order Logic

This work presents formal correctness proofs in Isabelle/HOL of algorithms to transform a matrix into Smith normal form, a canonical matrix form, in a general setting: the algorithms are written in an abstract form and parameterized by very few simple operations. We formally show their soundness provided the operations exist and satisfy some conditions, which always hold on Euclidean domains. We also provide a formal proof on some results about the generality of such algorithms as well as the uniqueness of the Smith normal form. Since Isabelle/HOL does not feature dependent types, the development is carried out by switching conveniently between two different existing libraries by means of the lifting and transfer package and the use of local type definitions, a sound extension to HOL.
Internal Audit Information System Design in the University: (Case Study at Islamic State University Sunan Kalijaga Yogyakarta)

Auditing is an essential job within the organization. The audit is an activity that aims to assess financial accountability in realizing good governance, especially in universities. SPI UIN Sunan Kalijaga Yogyakarta has implemented Digital Document-Based Internal Supervision using Google Drive to the audit process. The implementation of Digital Document-Based Internal Audit is in line with the directions of the Supreme Audit Agency of the Republic of Indonesia, which encourages auditors to utilize IT services in the audit process optimally. However, the use of the Google Drive as a means of internal monitoring based on digital documents is still considered to experience obstacles. It needs to design and develop an audit application to assist the implementation of audits within the UIN Sunan Kalijaga Yogyakarta. In order to carry out this idea, the FAST framework is used as a guide in the stages of thinking. To determine the feasibility of the designed system, the System Usability Scale research instrument was used. According to System Usability Scale score the design that has been made is included in the OK category.
Graphulo: Linear Algebra Graph Kernels

In this chapter, the authors provide a brief overview of different classes of graph algorithms, recast some of them into linear algebra operations compatible with GraphBLAS building blocks. The volume, velocity, and variety of data being collected by today’s systems far outpace the ability to provide meaningful results or analytics. Associative arrays are used as the data type for storing and manipulating a large variety of complex data sets. Of the many centrality metrics, there are a few that are particularly well-suited to the GraphBLAS framework. Detection of interesting and anomalous subgraphs has been a problem of interest for the computer science community for many years. The Graphulo initiative at the Massachusetts Institute of Technology is interested in applying the sparse linear algebra kernels of the GraphBLAS specification to associative arrays, which exactly describe NoSQL database tables such as those found in the open source Apache Accumulo.
Package Theft Detection from Smart Home Security Cameras

Package theft detection has been a challenging task mainly due to lack of training data and a wide variety of package theft cases in reality. In this paper, we propose a new Global and Local Fusion Package Theft Detection Embedding (GLF-PTDE) framework to generate package theft scores for each segment within a video to fulfill the real-world requirements on package theft detection. Moreover, we construct a novel Package Theft Detection dataset to facilitate the research on this task. Our method achieves 80% AUC performance on the newly proposed dataset, showing the effectiveness of the proposed GLF-PTDE framework and its robustness in different real scenes for package theft detection.
Age Should Not Matter: Towards More Accurate Pedestrian Detection via Self-Training

Why is there disparity in the miss rates of pedestrian detection between different age attributes? In this study, we propose to (i) improve the accuracy of pedestrian detection using our pre-trained model; and (ii) explore the causes of this disparity. In order to improve detection accuracy, we extend a pedestrian detection pre-training dataset, the Weakly Supervised Pedestrian Dataset (WSPD), by means of self-training, to construct our Self-Trained Person Dataset (STPD). Moreover, we hypothesize that the cause of the miss rate is due to three biases: (1) the apparent bias towards "adults" versus "children"; (2) the quantity of training data bias against "children"; and (3) the scale bias of the bounding box. In addition, we constructed an evaluation dataset by manually annotating "adult" and "child" bounding boxes to the INRIA Person Dataset. As a result, we confirm that the miss rate was reduced by up to 0.4% for adults and up to 3.9% for children. In addition, we discuss the impact of the size and appearance of the bounding boxes on the disparity in miss rates and provide an outlook for future research.
Artificial Intelligence based Face Recognition using Deep Learning

Face recognition is a current digital era technology that plays a dominant role in computer-based communication. However, face recognition techniques are highly tactful, it becomes a provocation to recognize the original face when the angle of the person varies. In the existing system, it takes a long time to recognize the face of each employee and store the database in the form of attendance. To overcome this, the proposed research work has used artificial intelligence (AI) based face recognition using deep learning. It mainly facilitates an attendance automation system for multiple people that processes continuously without any delay by using OpenCV in Python. Even if the employee enters several times, his presence will be marked only once in the database. Thus, our proposed system is efficient in terms of quick attendance when compared to the existing system.
Classification via Compressed Latent Space

This paper introduces a new classification method, which is aimed to solve image classification task. The method is based on data generated from auto-encoder’s hidden layers. The data were classified with the help of ensemble and classical machine learning algorithms. The algorithm was tested on different image data sets and compared with current state-of-the-art approaches.
Recognition of Hitting Action in Cyclic Anaerobic Volleyball by Acute Cooling Based on Improved Spatiotemporal Graph Convolutional Network

Action recognition is the basis of intelligent sports training and has a unique role in improving athletes’ sports training ability. Traditional motion recognition cannot accurately identify changes in human skeletal motion, and the generalization ability is weak, which cannot meet the needs of modern sports. This paper is aimed at studying a new method of human motion recognition and better realizing the intelligent development of the Institute of Physical Education. This paper proposes an improved recognition mode of spatiotemporal graph convolutional network, builds a human skeleton feature recognition model, learns the changes of human skeleton motion, and masters the laws of human skeleton motion changes. This paper comprehensively compares the differences of action recognition of several different algorithms and proves the superiority of this method. Afterwards, a comprehensive comparison of 2D and 3D information data and a comparison of whole body and local action recognition methods were conducted to investigate the experience of athletes and trainers. The experimental results of this paper show that the spatiotemporal graph convolutional network can better realize the recognition of motion features, improve the sports training ability of athletes by 20%, and promote the development of sports.
Deep convolutional neural networks for bias field correction of brain magnetic resonance images

As a low-frequency and smooth signal, the bias field has a certain destructive effect on magnetic resonance (MR) images and is the main obstacle for doctors' diagnosis and image processing (such as segmentation, texture analysis, and registration). Before analyzing a damaged MR image, a preprocessing step is required to correct the bias field in the image. Unlike traditional bias field removal algorithms based on signal models and a priori assumptions, deep learning methods do not require precisely modeling signals and bias fields and do not need to adjust parameters. An MR image with the bias field is input and the corrected MR image is output after the deep neural network being trained on a large training set. In this paper, we propose taking the original image and the local feature images of the bias field in multiple frequency bands obtained by a Log-Gabor filter bank as input, correcting the bias field of a brain MR image through a deep separable convolutional neural network. Meanwhile, to speed up the training process and improve bias correction performance, we apply residual learning and batch normalization. We conducted the same test on BrainWeb simulation database and Human Connectome Project real data set, the consistency of qualitative and quantitative evaluation shows that our training model demonstrates better performance than the traditional state-of-the-art N4 and non-iterative multi-scale (NIMS) methods. Especially for the images with high-intensity non-uniformity level, the bias field has been well corrected.
Automated staining analysis in digital cytopathology and applications

Abstract The progress of digital pathology in recent years has been an opportunity for the development of automated image analysis algorithms for quantitative measurements and computer aided diagnosis. With those new methods comes the need for high staining quality and reproducibility, as image analysis tools are typically more sensible to slight stain variations than trained pathologists. This article presents a method for the automated analysis of cytology slides stains specifically adapted to the challenges encountered in digital cytopathology. In particular, the variety of cell types in cytology slides, the 3D distribution of the cellular material, the presence of superposed cells and the need for independent analysis of sub‐cellular compartments are addressed. The proposed method is applied to the quantification of staining variations for quality control, resulting from changes in the staining protocol such as reagent immersion time or a reagent change. Another demonstrated application is the selection of staining protocol parameters that maximize the visible details in nucleus. Finally the analysis pipeline is also used to compare different stain normalization algorithms on digital cytology slides. Code available at: https://gitlab.com/vitadx/articles/automated_staining_analysis .
Toward aircraft detection and fine-grained recognition from remote sensing images

The detection and recognition of aircraft on images is an important task in the field of optical remote sensing image interpretation. The challenges include the detection of arbitrary orientation targets and the fine-grained recognition of aircraft type. However, most related studies only pay attention to one of these because of the limitation of datasets, and an end-to-end network often cannot simultaneously solve these challenges well. Therefore, we propose a cascade framework for arbitrary-oriented and multitype aircraft detection in remote sensing images based on the convolutional neural network. A rotation-based detector that uses oriented boxes is designed to determine the position and direction of aircraft on remote sensing images. A fine-grained recognition subnetwork that adopts the ensemble learning and the Fisher discriminant regularization is designed to recognize the type of aircraft on images. The subnetwork is cascaded behind the rotation-based detector to achieve more accurate recognition. The mean average precision of this method reaches 83.7% on a challenging dataset, which is 2.4% to 8.2% higher than other advanced methods. Experiments demonstrated that the proposed method can greatly improve the accuracy of aircraft detection and recognition and that the method has good generalization performance.