Reviewerly/exHarmony huggingface dataset:
It's your loss

In software product lines, variability models are used to explicitly capture commonalities and variability of a set of software systems. Many variability modeling approaches have been developed over a period of more than 30 years. Most of them are only described in academic papers, which makes it difficult to assess their properties and find the right approach for a specific use case. New approaches are developed regularly, adding to the ever-growing plethora of variability modeling approaches. Transforming variability models, i.e., of one type to another, would help to better understand and compare existing approaches and would also enable users to switch between approaches. Since variability modeling approaches differ especially in terms of scope and expressiveness, it is difficult to implement transformations without information loss. Thus, in this paper, we analyze concrete variability modeling approaches, present a mapping of key concepts between them, and identify and classify the information lost in one-way and roundtrip transformations. We evaluate their applicability by transforming different models of varying size and complexity using an existing implementation of transformations. We argue that our classification of information loss contributes to a better understanding of different variability modeling approaches, simplifies the comparability, and allows users to grasp the impact of transformations.
Improved Henon Chaotic Map-based Progressive Block-based visual cryptography strategy for securing sensitive data in a cloud EHR system

The core objective of secret sharing concentrates on developing a novel technique that prevents the destruction and leakage of original data during the distribution and encoding processes. Progressive Visual Cryptography (VC) is considered for the potential over the traditional VC schemes since the former does not require and does not suffer from the limitations of requiring a minimum number of participants during the process of encryption and sharing. The chaotic map-based Progressive VC is superior in facilitating predominant secrecy under sharing and encryption. In this paper, an Improved Henon Chaotic Map-based Progressive Block-based VC (IHCMPBVC) scheme is proposed to prevent the leakage and destruction of sensitive information during an exchange and encryption. This proposed IHCMPBVC technique uses the merits of Henon and Lorentz maps for effective encryption since it introduces the option of deriving non-linear behavior that results in sequence generation that covers the complete range with proper distribution in order to minimize the degree of leaks in sharing. The simulation results of the proposed IHCMPBVC technique investigated using entropy, PSNR, and Mean Square Error were improved at an average rate of 27%, 23%, and 31%, predominant to the baseline VC approaches considered in the comparison.
An Efficient Image Encryption Technique Using Logistic Map and 2D-TCLM

Nowadays, multimedia data transfer through internet has rapidly increased. This has drawn the interest of the miscreants towards theft and misuse of images for certain malign purpose. Image encryption can provide confidentiality to these images by restricting the miscreants from performing various security attacks. In this paper an image encryption technique using hybrid chaotic maps - Logistic Map and 2D - Tent Cascade Logistic Map (2D-TCLM) has been proposed. The result of the encryption is promising. Various performance analyses have confirmed the security features of the proposed technique. It has been also shown that the proposed image encryption technique is better than the existing techniques with respect to majority of the key parameters, making it suitable for implementation in real-time image communication over insecure public channel.
Label distribution feature selection with feature weights fusion and local label correlations

Label distribution learning, where each sample is associated with a distribution of description degree, suffers from the curse of dimensionality like other traditional learning paradigms. Feature selection as a pre-processing technique is commonly used to reduce the dimension of data. Currently, label distribution feature selection focuses on exploiting label correlations under the assumption that all samples share the same label correlations. However, the corresponding label correlations for different groups of samples may tend to be different in real-world tasks. To tackle the issue, this paper presents a novel approach named label distribution feature selection with feature weights fusion and local label correlations. First, instances are separated into several clusters regarded as local samples. Then, a two-strategy approach is presented based on the local samples. For the first, the feature weights obtained from all local samples are fused into uniform feature significance, which can effectively improve the feature discrimination. For the second, to reflect the influence of label correlations locally, a local correlation matrix is encoded via the label space of local samples as additional features. Subsequently, the feature weights for this strategy are generated from a new feature space composed of additional and original features. Finally, a subset of optimal and relevant features is selected from the top-ranked features based on feature weights obtained from the above two strategies. Extensive experiments on fifteen benchmark datasets verify that the proposed algorithm is superior to five state-of-the-art approaches in terms of six evaluation metrics.
Machine learning framework for automatic image quality evaluation involving a mammographic American College of Radiology phantom

The image quality (IQ) of mammographic images is essential when making a diagnosis, but the quality assurance process for radiological equipment is subjective. We therefore aimed to design an automatic IQ evaluation architecture based on a support vector machine (SVM) dedicated to evaluating images taken of mammography American College of Radiology (ACR) phantom.A total of 461 phantom images were acquired using mammographic equipment from 10 vendors. Two experienced medical physicists scored the images by consensus. The phantom datasets were randomly divided into training (80%) and testing (20%) sets. Each phantom image (with 6 fibers, 5 specks, and 5 masses) was detected by using bounding boxes, then cropped and divided into 16 pattern images. We identified 159 features for each pattern image. Manual scores were used to assign 3 labels (visible, invisible, and semivisible) to each pattern image. Multiclass-SVM models were trained with 3 types of patterns. Sub-datasets were randomly selected at 10% increments of the total dataset to determine a minimal effective training subset size for the automatic framework. A feature combination test and an analysis of variance were performed to identify the most influential features.The accuracy of the model in evaluating fiber, speck, and mass patterns was 90.2%, 98.2%, and 88.9%, respectively. The performance was equivalent when the sample size was at least 138 (30% of 461) phantom images. The most influential feature was the position feature.The proposed SVM-based automatic IQ evaluation framework applied to a mammographic ACR phantom accurately matched manual evaluations.
Correcting biased value estimation in mixing value-based multi-agent reinforcement learning by multiple choice learning

Multi-agent reinforcement learning (MARL) has become more and more popular over the past decades, and many value-based MARL methods are proposed in the past few years. Neural networks play important roles in these methods and are used to predict the value of the state–action pair, i.e. Q-value and actions of agents are chosen based on this. However the inaccurate prediction of the neural network leads to the biased Q-value estimation, which will cause inefficient usage of the experience data and poor performance. Unlike ensemble methods that just reduce the variance of predictions, multiple choice learning (MCL) methods exploit the cooperation among all the candidate models. This paper corrects the biased Q-value by exploiting the collaboration between the ensemble model and MARL to obtain a stabler and preciser Q-value estimator. In this paper, a new MARL method called Multiple Choice QMIX is developed to address the biased Q-value issue, which also extends the application scenarios of MCL methods. Specifically, we propose a voting network to learn the confidence level of each estimator and thus can provide the best prediction by combining their results. And a voting hindsight loss is proposed to encourage the voting network to overcome the overestimation of the Q-value. We also conduct experiments on four challenging tasks of the StarCraft II micromanagement benchmark. Experiment results show that our method obtains a faster convergence rate and stabler performance in multi-agent tasks.
Social dimension of higher education: definition, indicators, models

The article deals with the problem of strengthening the social dimension of higher education. It discusses the definition of social dimension, its indicators, models of student retention and student engagement. The article argues that students should act as active researchers of the topic of social dimension and present the ways to update the content of university courses for Sociology majors, such as "Mathematical and statistical methods of social information analysis", "Social statistics and demography", "Multivariate data analysis", "Structural equation modeling" and other courses for bachelors, master students, and PhDs in Sociology.
Intelligent Matching Method for Heterogeneous Remote Sensing Images Based on Style Transfer

Intelligent matching of heterogeneous remote sensing images is a common basic problem in the field of intelligent remote sensing image processing. Aiming at the difficulty of matching satellite-aerial remote sensing images, this paper proposes an intelligent matching method for heterogeneous remote sensing images based on style transfer. First, based on the idea of image style transfer of a generative adversarial networks, this method improves the conversion effect of the model on heterogeneous images by constructing a new generative network loss function and converts satellite images into aerial images. Then, the advanced deep learning-based matching Algorithms D2-Net and LoFTR are used to achieve matching between the generated aerial image and the original aerial image. Finally, this transformation relationship is mapped to the corresponding satellite-aerial image pair to obtain the final matching result. The image style transfer experiments and the matching experiments we carry out under different test datasets show that the smooth cycle-consistent generative adversarial networks proposed in this paper can effectively reduce the complexity of the algorithm and improve the quality of image generation. In addition, combining it with deep learning-based feature matching methods can effectively improve the accuracy and robustness of the matching algorithm. Our code and data can be found at: <uri>https://gitee.com/AZQZ/intelligent-matching</uri>.
Validation of Physics-Based Image Systems Simulation With 3-D Scenes

Image systems simulation software can accelerate innovation by reducing many of the time-consuming and expensive steps in designing, building, and evaluating image systems. To realize this potential, it is necessary to build trust in physics-based end-to-end image systems simulations. Toward this goal, we describe and experimentally validate an end-to-end physics-based image systems simulation of a digital camera. The simulation models the spectral radiance of 3-D scenes, the formation of the spectral irradiance by multielement optics, and the conversion of the irradiance to digital values (DVs) by the image sensor. We quantify the accuracy of the simulation by comparing real and simulated images of a precisely constructed, 3-D high-dynamic range (HDR) test scene.
Large-Scale Multi-View Clustering via Fast Essential Subspace Representation Learning

Large-scale Multi-View Clustering (LMVC) is a hot research problem in the fields of signal processing and machine learning, and many anchor-based multi-view subspace clustering algorithms are proposed in recent years. However, most existing methods usually concentrate on the issue of reducing the time cost and ignore the exploration of the complementary information during the clustering process. To this end, we propose a Fast Essential Subspace Representation Learning (FESRL) method for large-scale multi-view subspace clustering. Specifically, FESRL introduces the orthogonal transformation to investigate both the complementary and consensus information across multiple views. The essential subspace representation can be learned in a linear time cost. Experiments conducted on several benchmark datasets illustrate the competitiveness of the proposed method.
Automated Machine Learning for COVID-19 Forecasting

When responding to a pandemic situation, policy makers rely on forecasts of the spread. In the context of the current COVID-19 pandemic, various sophisticated epidemic and machine learning models have been used for forecasting. These models, however, rely on carefully selected architectures and detailed data that is often only available for specific regions. Automated machine learning (AutoML) addresses these challenges by allowing to automatically create forecasting pipelines in a data-driven manner, resulting in high-quality predictions. In this paper we study the role of open data along with AutoML systems in acquiring high-performance forecasting models for COVID-19. Here, we adapted the AutoML framework auto-sklearn to the time series forecasting task and introduced two variants for multi-step ahead COVID-19 forecasting which we refer to as (a) multi-output and (b) repeated single output forecasting. We studied the usefulness of anonymized open mobility data sets (place visits, and the use of different transportation modes) in addition to open mortality data. We evaluated three drift adaptation strategies to deal with concept drifts in data by (i) refitting our models on part of the data, (ii) the full data, or (iii) retraining the models completely. We compared the performance of our AutoML methods in terms of RMSE with five baselines on two testing periods (over 2020 and 2021). Our results show that combining mobility features and mortality data improves forecasting accuracy. Furthermore, we show that when faced with concept drifts, our method refitted on recent data using place visits mobility features outperforms all other approaches for 22 of the 26 countries considered in our study.
Hardware and software tools for teaching the basics of quantum informatics to students of specialized (high) schools

The article defines the criteria for choosing a cloud-based platform for mastering the basics of quantum informatics by students of a specialized (high) schools: cross-browser; intuitive interface; the possibility of free access; access without registration and simplified registration; the presence of a systematized reference system with examples; support for the development of the environment by the developer; support for working in a personal educational environment; support for working with quantum algorithms in graphical mode; automatic conversion of quantum algorithms from graphic format to program code text; support for the Ukrainian-language localization; availability of a mobile application; responsive design. The possibilities of platforms for implementing quantum algorithms from the following companies are analyzed: Microsoft, QuTech, Amazon Braket, IBM. The choice of the IBM Quantum cloud-based platform is justified. Work at IBM Quantum Composer and IBM Quantum Lab is described. Information about quantum operations and gates is presented: their designation in IBM Quantum Composer and IBM Quantum Lab, the gate matrix, and the purpose of the gate. An example of implementing quantum teleportation in the form of a circuit and program is given.
A multistate modeling approach for organizational cybersecurity exploration and exploitation

This study examines the dynamic stages of exploration and exploitation efforts by organizations in their cybersecurity responses using multistate modeling. Using textual data from the annual 10-K reports of S&P 100 organizations, this study uses a combination of text analytics and Markov chain approach to quantify exploration and exploitation in organizational cybersecurity responses. The study models two and four states of exploration and exploitation based on their cybersecurity responses over time and uses a continuous-time Markov chain approach to analyze transitions between states as organizations adapt their responses over time to achieve ambidexterity. The two-state Markov model focuses on the firm-level Exploration and Exploitation states whereas the four-state model captures deeper levels of exploration and exploitation by considering Surviving, Investigating, Reinforcing, and Balancing as possible states for exploration and exploitation of cybersecurity responses. We analyze the dynamics of organizational exploration-exploitation behaviors by modeling longitudinal transition probabilities across different states. Implications for research and practice are discussed. • Organizational cybersecurity relies on the simultaneous pursuit of exploration and exploitation • A framework was proposed for studying exploration and exploitation in the organizational learning process • Four states are identified to describe the dynamics of exploration and exploitation in organizational cybersecurity space. • Text Analytics was applied to annual report data to create longitudinal exploration and exploitation variables. • A Markov chain was proposed to model transitions between different states of organizational exploration and exploitation.
The ABS simulator toolchain

ABS is a language for behavioral modeling of distributed, time- and resource-sensitive communicating systems. ABS is based on an executable actor-based semantics with asynchronous method calls, with method call results being delivered via future variables. Data is modeled via a functional, side-effect-free layer of algebraic data types and parametric functions. Actor behavior is expressed in a sequential, imperative way, with explicit suspension points for in-actor cooperative scheduling. A declarative time and resource model allows modeling of time-sensitive actor behavior in a compositional way. A software product line language layer implements model variability via code deltas and feature models. This paper describes the toolchain that makes it possible to simulate ABS models, and lists the most important case studies done with ABS.
Cyber Threats, Attack Strategy, and Ethical Hacking in Telecommunications Systems

There are rising cybersecurity concerns in the telecommunication sector as hackers intensify their sophistication. The techniques employed by hackers are constantly evolving and so are their tools. Data theft is the end goal for numerous attacks, with hackers seeking predominantly personal data, credentials, and credit card information. Telecom companies are a big target for cyber-attacks because they build, control, and operate critical infrastructure that is widely used to communicate and store large amounts of sensitive data. Hackers often compromise telecommunication systems and attempt to steal users' information, defraud people and also attack telecom services and infrastructures. Cyber criminals often achieve their aim using different strategies, including the exploitation of vulnerabilities in both software and hardware and other possible loopholes. Given that telecom companies control critical infrastructure, the impact of an attack can be very high and far-reaching, this chapter examines the attack strategies and ethical hacking in telecommunication with a view to help the industry to understand, prepare and defend themselves against existing and potential cyber threats and attacks.
On Neural Network Equivalence Checking Using SMT Solvers

Two pretrained neural networks are deemed (approximately) equivalent if they yield similar outputs for the same inputs. Equivalence checking of neural networks is of great importance, due to its utility in replacing learning-enabled components with (approximately) equivalent ones, when there is need to fulfill additional requirements or to address security threats, as is the case when using knowledge distillation, adversarial training, etc. In this paper, we present a method to solve various strict and approximate equivalence checking problems for neural networks, by reducing them to SMT satisfiability checking problems. This work explores the utility and limitations of the neural network equivalence checking framework, and proposes avenues for future research and improvements toward more scalable and practically applicable solutions. We present experimental results, for diverse types of neural network models (classifiers and regression networks) and equivalence criteria, towards a general and application-independent equivalence checking approach.
On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model

Service robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necessary to define a certain variability that will be closed at runtime. MAPE-K (Monitor–Analyze–Plan–Execute over a shared Knowledge) loops are a very popular scheme to address this real-time self-adaptation. As stated in their own definition, they include monitoring, analysis, planning, and execution modules, which interact through a knowledge model. As the problems to be solved by the robot can be very complex, it may be necessary for several MAPE loops to coexist simultaneously in the robotic software architecture endowed in the robot. The loops will then need to be coordinated, for which they can use the knowledge model, a representation that will include information about the environment and the robot, but also about the actions being executed. This paper describes the use of a graph-based representation, the Deep State Representation (DSR), as the knowledge component of the MAPE-K scheme applied in robotics. The DSR manages perceptions and actions, and allows for inter- and intra-coordination of MAPE-K loops. The graph is updated at runtime, representing symbolic and geometric information. The scheme has been successfully applied in a retail intralogistics scenario, where a pallet truck robot has to manage roll containers for satisfying requests from human pickers working in the warehouse.
Improvement of Image Stitching Using Binocular Camera Calibration Model

Image stitching is the process of stitching several images that overlap each other into a single, larger image. The traditional image stitching algorithm searches the feature points of the image, performs alignments, and constructs the projection transformation relationship. The traditional algorithm has a strong dependence on feature points; as such, if feature points are sparse or unevenly distributed in the scene, the stitching will be misaligned or even fail completely. In scenes with obvious parallaxes, the global homography projection transformation relationship cannot be used for image alignment. To address these problems, this paper proposes a method of image stitching based on fixed camera positions and a hierarchical projection method based on depth information. The method does not depend on the number and distribution of feature points, so it avoids the complexity of feature point detection. Additionally, the effect of parallax on stitching is eliminated to a certain extent. Our experiments showed that the proposed method based on the camera calibration model can achieve more robust stitching results when a scene has few feature points, uneven feature point distribution, or significant parallax.