Reviewerly/exHarmony huggingface dataset:
Annotative Software Product Line Analysis Using Variability-Aware Datalog

Applying program analyses to Software Product Lines (SPLs) has been a fundamental research problem at the intersection of Product Line Engineering and software analysis. Different attempts have been made to “lift” particular product-level analyses to run on the entire product line. In this paper, we tackle the class of Datalog-based analyses (e.g., pointer and taint analyses), study the theoretical aspects of lifting Datalog inference, and implement a lifted inference algorithm inside the Soufflé Datalog engine. We evaluate our implementation on a set of Java and C-language benchmark annotative software product lines. We show significant savings in processing time and fact database size (billions of times faster on one of the benchmarks) compared to brute-force analysis of each product individually.
Formation of the reading comprehension skill in primary school students by visualization

This article describe the problem of shaping scrupulous reading comprehension in primary school students through visualisation tools and identifies its key factors. The scientific psychological and pedagogical and methodological literature, the current legal framework of primary education in Ukraine, educational experience of the research problems of foreign and domestic teachers, the results of the PISA research were analyzed. Foreign theories of interpretation of the term “reading comprehension” are reviewed and our own understanding of the essence of the concept is suggested on their basis. Presents its own classification of modern visualisation tools. It describes the author’s organizational and methodological model of shaping reading comprehension by visualization means, as well as the work done by the author on shaping of scrupulous reading comprehension by visualization means among primary school students. The research involved a set of theoretical (analysis of scientific literature, legal framework, synthesis, generalisation, systematization) and empirical methods (expert assessment, modelling; questionnaires, interviews, monitoring of the educational process, method of mathematical statistics).
A Game for Task Offloading in Reputation-Based Consortium Blockchain Networks

This letter studies the task offloading in reputation-based consortium blockchain networks, where the tasks are transmitted to Edge Computing Servers (ECSs) due to limited resources. We propose a novel Delegated Proof of Stake (DPoS) consensus mechanism in which validation nodes (including active nodes and backup nodes) are voted based on their reputation. The incentive identifies the reputation and the consensus delay as two major factors determining the reward. A three-stage Stackelberg game is developed to jointly minimize cost of the users and maximize utilities of the master node and the validation nodes. We analyze the unique Stackelberg equilibrium exists in the proposed game by the backward induction. The simulation results demonstrate that the designed incentive is feasible for trust management, and the proposed consensus has lower delay and higher decentralization compared with the traditional DPoS.
Belief-Based Preference Structure and Elicitation in the Graph Model for Conflict Resolution

A belief-based preference structure along with its associated elicitation approach is incorporated into the graph model for conflict resolution (GMCR) to model and analyze the multistakeholder strategic conflicts involving ambiguous evidences, incomplete information, and nonlinear causal relationships. More specifically, the relative preference is first redefined using a belief structure capable of capturing uncertainties of various types such as vagueness and/or ignorance in subjective judgments regarding the complex real-world conflicts. Next, a flexible and realistic methodology based on evidential reasoning is put forward to elicit the belief preference information over feasible states within the GMCR model. Then, 16 stability definitions (solution concepts) are extended to accommodate diverse uncertainties in preferences and facilitate the informed conflict analysis. The application and interpretation of the foregoing preference structure and associated stability definitions are demonstrated with an illustrative example.
Blockchain-based transaction validation for patient interoperability in Healthcare 4.0

With the progressive increase in the use of disruptive technology in healthcare services, diversified solutions are being worked upon to make medical services patient-friendly and secure. In general, patient personal details and medical history are rigorously maintained in electronic health records. The data are sensitive and must be secured. Often data must be shared to obtain a second expert opinion and alternative solutions. This potentially increases data exposure, thereby risking breaches and unauthorized access. The blockchain architecture has immense potential to securely communicate and transform health, clinical, medical, and life science service delivery as required by healthcare applications in an immutable and decentralized manner. Blockchain innovation has caught the interest of health experts and biomedical researchers in different areas of medical care such as longitudinal clinical records, drug advancement, automated complaints, interoperability in people's health, clinical exploration, patient portals, information security, and supply chain management system. It is being considered whether blockchain-integrated healthcare will disturb the flow of medical services. Still, medical associations are keenly observing its potential for ideas such as secure patient identification. Blockchain with features such as decentralized networks and tamper-proof platforms makes it an ideal technology to be investigated for its prospects in healthcare. This chapter provides an in-depth examination of the different intricacies of the blockchain in the healthcare sector, particularly with the 4.0 edition. The chapter also presents various techniques for secured transaction and validation methods of healthcare data that are evolving rapidly. Factors enhancing patient interoperability with the blockchain structure focusing on state-of-the-art security and privacy aspects have been addressed in the work. In addition, the work also presents a case study with already-existing electronic healthcare support application with online assistance and data accumulation at a cloud-based service to reduce the physical work done by patients, work also enumerates open issues, and challenges with the technology suggested.
Performance Analysis of Deep Neural Network Models for Weather Forecasting in Bangladesh

Climatology and Weather forecasting play an important role to determine future climate expectations and help the farmer to make a plan for crop irrigation, fertilization, and suitable days for working in the field. Forecasting weather is a challenging task due to the uncontrolled nature of the surrounding atmosphere. Nowadays, deep learning models are widely used for weather forecasting that explores the hidden hierarchical patterns in big weather datasets to extract high-level features. In this paper, we investigate the performance of Multilayer Perceptron (MLP), ARIMA, and Bi-directional Long Short-Term Memory (BiLSTM) models for forecasting weather in agricultural applications of Bangladesh. The performance of the models is evaluated using Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The results depict that BiLSTM provides better performance compared to other state-of-the-art models to predict accurate weather in Bangladesh.
A Fog-Assisted Framework for Intelligent Video Preprocessing in Cloud-Based Video Surveillance as a Service

Video preprocessing is one of the vital steps before extracting useful information from surveillance videos. This article identifies the key issue in cloud-based video surveillance as a service (VSaaS). If the recording environment is almost static and storing 24/7 video recording leads to additional storage, computational and network complexity to the cloud user. To address this problem, the existing architecture of the VSaaS is thoroughly studied and proposed a fog-assisted framework for preprocessing the incoming video. The two important functionalities of this framework are intelligent frame-selection and frame-reconstruction. During frame-selection, the lightweight background subtraction algorithm based on random projection with structural similarity index manages to effectively select the necessary frames to store in the cloud. This framework also provides an option for the user to successfully reconstruct the full-length video using frame inbetweening method. The proposed algorithm is tested with CDNET-2014 and LASIESTA data. The results are evaluated in-terms of running time, selected frames per video, percentage of reduction and quality of video experiencing. The result shows that the proposed frame-selection manages to reduce nearly 60% of storage requirement for the CDNET-2014 data. The frame reconstruction algorithm reconstructs the full-length video without affecting the quality of video experiencing.
Multi-magnification Networks for Deformable Image Registration on Histopathology Images

We present an end-to-end unsupervised deformable registration approach for high-resolution histopathology images with different stains. Our method comprises two sequential registration networks, where the local affine network can handle small deformations, and the non-rigid network is able to align texture details further. Both networks adopt the multi-magnification structure to improve registration accuracy. We train the proposed networks separately and evaluate them on the dataset provided by the University Hospital Frankfurt, which contains 41 multi-stained histopathology whole-slide images. By comparing with methods using the single-magnification structure, we confirm that the proposed multi-view architecture can significantly improve the performance of the local affine registration algorithm. Moreover, the proposed method achieves high registration accuracy of contents at the cell level and is potentially applicable to other medical image alignment tasks.
Ownership Verification of DNN Architectures via Hardware Cache Side Channels

Deep Neural Networks (DNN) are gaining higher commercial values in computer vision applications, e.g., image classification, video analytics, etc. This calls for urgent demands of the intellectual property (IP) protection of DNN models. In this paper, we present a novel watermarking scheme to achieve the ownership verification of DNN architectures. Existing works all embedded watermarks into the model parameters while treating the architecture as public property. These solutions were proven to be vulnerable by an adversary to detect or remove the watermarks. In contrast, we claim the model architectures as an important IP for model owners, and propose to implant watermarks into the architectures. We design new algorithms based on Neural Architecture Search (NAS) to generate watermarked architectures, which are unique enough to represent the ownership, while maintaining high model usability. Such watermarks can be extracted via side-channel-based model extraction techniques with high fidelity. We conduct comprehensive experiments on watermarked CNN models for image classification tasks and the experimental results show our scheme has negligible impact on the model performance, and exhibits strong robustness against various model transformations and adaptive attacks.
Selective Intra-Image Similarity for Personalized Fixation-Based Object Segmentation

Personalized Fixation-based Object Segmentation (PFOS) aims at segmenting the gazed objects in images conditioned on personalized fixations. However, the performances of existing PFOS methods are degraded when facing anomalous fixation maps (some fixations fall in the background) or enormous objects because of their poor localization ability. In this paper, we propose a novel Selective Intra-image Similarity Network (SISNet) that achieves significant performance by precisely localizing the gazed objects. First, we propose a Response Purifying Module (RPM) to eliminate the false response regions caused by anomalous fixations in the background. By suppressing these false responses, we can significantly reduce the negative impacts caused by anomalous fixations. Second, we propose an intra-image similarity module (ISM) to better localize large objects by integrating more long-range information. In addition, we propose a new Discriminative Intersection-over-Union metric that evaluates whether PFOS methods can produce distinctive predictions for varying fixations. Experiments on the PFOS and our proposed OSIE-CFPS-UN datasets prove that our network achieves remarkable improvements and outperforms existing state-of-the-art methods. Code has been published at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.github.com/moothes/SISNet</uri> .
Metaheuristic Optimization for Mobile Robot Navigation Based爋n燩ath燩lanning

Recently, the path planning problem may be considered one of the most interesting researched topics in autonomous robotics. That is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite. A promising route planning for mobile robots on one side saves time and, on the other side, reduces the wear and tear on the robot, saving the capital investment. Numerous route planning methods for the mobile robot have been developed and applied. According to our best knowledge, no method offers an optimum solution among the existing methods. Particle Swarm Optimization (PSO), a numerical optimization method based on the mobility of virtual particles in a multidimensional space, is considered one of the best algorithms for route planning under constantly changing environmental circumstances. Among the researchers, reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile robots. This paper proposes a PSO Weighted Grey Wolf Optimization (PSOWGWO) algorithm. PSOWGWO is a hybrid algorithm based on enhanced Grey Wolf Optimization (GWO) with weights. In order to measure the statistical efficiency of the proposed algorithm, Wilcoxon rank-sum and ANOVA statistical tests are applied. The experimental results demonstrate a 25% to 45% enhancement in terms of Area Under Curve (AUC). Moreover, superior performance in terms of data size, path planning time, and accuracy is demonstrated over other state-of-the-art techniques.
An Adaptive Push-Pull for Disseminating Dynamic Workload and Virtual Machine Live Migration in Cloud Computing

Adapting a dynamic load dissemination system for distributed computing pasture has become a hot spot problem of current research. In the instance of overloaded VM’s or node failure, the associated resources face difficult to determine which VM should be selected for load exchanging and/or how many VM’s should migrate to manage load imbalance. This work, introduces a Hierarchical Adaptive Push-Pull system for disseminating dynamic workload and live migration of VM’s among resources in the Cloud. Adhering to the Adaptive Push-Pull, Cloud Resources frequently pull’s the workload or through VM Managers based on load dynamics. In contrast, status information pertaining to Cloud Resources maintained by the Cloud Resource Managers that possess push capability to push the workload only to those VM’s which are capable enough to receive additional load. These two practices contain balancing possessions through efficient load management complications and simulation result addresses reduced load deviation and scalable resources utilization.
Method to Create a Metaverse Using Smartphone Data

With the development of internet technology, several IT companies and users have become interested in virtual worlds, called metaverses. However, one of the main problems for a metaverse is the number of resources required to develop it. To reduce the burden of high computing power and other related resources, we propose a method that uses mobile phone functions and data to generate a personal virtual space as there is still a research gap in this area. In this study, we propose a method to intuitively generate a personal virtual space using smartphone data. We propose the development of a new type of metaverse application using the photo data saved on a smartphone. We hypothesized that using the new metaverse application induces more happiness and excitement than using the smartphone gallery application to view memorable photos. To evaluate the new metaverse application, we measured the emotional responses of users and compared the two applications. The results indicate that using the new metaverse application results in higher happiness and excitement.
DHNet: Salient Object Detection With Dynamic Scale-Aware Learning and Hard-Sample Refinement

During the annotation procedure of salient object detection, researchers usually locate the approximate location of the salient objects first and then process the pixels that need to be finely annotated. Following this idea, we find that the existing methods have limited exploration for solving the problem of positioning salient objects. Furthermore, no effective solution has been proposed for the hard-sample problem related to this task. Therefore, we propose dynamic scale-aware learning to learn dynamic scale weights that vary with different images to solve the first problem. Second, we design a dense sampling strategy for hard samples to construct a graph representation with samples from different classes and different confidence levels. Then, we achieve targeted feature aggregation based on the constructed graph with the help of the graph attention mechanism. We conduct extensive experiments on five benchmark datasets using comprehensive evaluation metrics. The results show that our method outperforms the current state-of-the-art approaches.
Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives

Smart healthcare systems are gaining popularity with the rapid development of intelligent sensors, the Internet of Things (IoT) applications and services, and wireless communications. However, at the same time, several vulnerabilities and adversarial attacks make it challenging for a safe and secure smart healthcare system from a security point of view. Machine learning has been used widely to develop suitable models to predict and mitigate attacks. Still, the attacks could trick the machine learning models and misclassify outputs generated by the model. As a result, it leads to incorrect decisions, for example, false disease detection and wrong treatment plans for patients. In this paper, we address the type of adversarial attacks and their impact on smart healthcare systems. We propose a model to examine how adversarial attacks impact machine learning classifiers. To test the model, we use a medical image dataset. Our model can classify medical images with high accuracy. We then attacked the model with a Fast Gradient Method Sign attack (FGSM) to cause the model to predict the images and misclassify them inaccurately. Using transfer learning, we train a VGG-19 model with the medical dataset and later implement the FGSM to the Convolutional Neural Network (CNN) to examine the significant impact it causes on the performance and accuracy of the machine learning model. Our results demonstrate that the adversarial attack misclassifies the images, causing the model’s accuracy rate to drop from 88 to 11%.
Analyzing Enterprise Architecture Models by Means of the Meta Attack Language

The digital transformation exposes organizations to new threats endangering their business. A way to uncover these threats is threat modeling and attack simulations. However, modeling an entire organization by hand is time consuming and error prone. Therefore, we propose to reuse Enterprise Architecture (EA) models. In this work, we propose a mapping from ArchiMate, a common EA modeling language, to coreLang, a threat modeling language, and use the resulting models to perform attack simulations to foresee possible attack paths. Then, we play back the results of the attack simulations to the EA model and complete the round-trip. To demonstrate our approach, we developed a prototype performing the transformation from ArchiMate to coreLang and applied our approach to the well-known ArchiSurance example.
Adaptive Cost Volume Fusion Network for Multi-Modal Depth Estimation in Changing Environments

In this letter, we propose an adaptive cost volume fusion algorithm for multi-modal depth estimation in changing environments. Our method takes measurements from multi-modal sensors to exploit their complementary characteristics and generates depth cues from each modality in the form of adaptive cost volumes using deep neural networks. The proposed adaptive cost volume considers sensor configurations and computational costs to resolve an imbalanced and redundant depth bases problem of conventional cost volumes. We further extend its role to a generalized depth representation and propose a geometry-aware cost fusion algorithm. Our unified and geometrically consistent depth representation leads to an accurate and efficient multi-modal sensor fusion, which is crucial for robustness to changing environments. To validate the proposed framework, we introduce a new multi-modal depth in changing environments (MMDCE) dataset. The dataset was collected by our own vehicular system with RGB, NIR, and LiDAR sensors in changing environments. Experimental results demonstrate that our method is robust, accurate, and reliable in changing environments. Our codes and dataset are available at our project page.
Forecasting time series using neural networks on the example of primary sales of a pharmaceutical company

This article is devoted to neural network forecasting of primary sales of a pharmaceutical company. The research includes an analysis of the main forecasting methods, various types of neural network architectures used to predict time series, as well as an analysis of neural network training methods. The primary sales of the pharmaceutical company AKRIKHIN JSC were considered as the applied basis of the study. As part of the study, an analysis of the revenue of the pharmaceutical company AKRIKHIN JSC from 2016 to 2021 was carried out, the selection of the main hyperparameters of the neural network was carried out and a forecast of primary sales for 4 weeks ahead was obtained. The practical significance of the research results is due to the fact that forecasting primary sales gives management an understanding of which direction the company is moving in and whether additional management decisions are needed to meet the planned values. This study can be used as an alternative method of sales forecasting, so the results will be in demand among analysts of pharmaceutical companies in sales planning departments.