Reviewerly/exHarmony huggingface dataset:
Coordinated flight path planning for a fleet of missiles in high-risk areas

Abstract This paper addresses the flight path planning problem for multiple missiles engaging stationary targets in high-risk areas. Targets protected by air defence are preferably engaged by a fleet or swarm of missiles, not individual missiles. The concept of a swarm attack is that a large number of approaching missiles overwhelm air defence. The deployment of missiles is often part of a broader mission including further participants. Flight path planning is then an integral element of mission planning, requiring strict timing coordination of all members involved. The flight times of the missiles are dictated by the master planning. We present algorithms for offline planning and online re-planning of flight paths for a fleet of missiles with flight time constraints. The algorithms are based on an advanced bidirectional RRT * algorithm that generates risk-minimizing flight paths with predefined flight times. Online planning generates the flight paths of the fleet sequentially, maintaining a safety distance between the missiles to prevent mutual collision. Offline planning uses a global optimization approach to determine an optimal selection of flight paths from a large set of potential paths. The selection is performed by a branch and bound algorithm that determines optimal cliques in the path compatibility graph. The optimization is embedded in an iterative algorithm that allows to successively improve the mission success.
A Novel Zeroing Neural Network for Solving Time-Varying Quadratic Matrix Equations against Linear Noises

The solving of quadratic matrix equations is a fundamental issue which essentially exists in the optimal control domain. However, noises exerted on the coefficients of quadratic matrix equations may affect the accuracy of the solutions. In order to solve the time-varying quadratic matrix equation problem under linear noise, a new error-processing design formula is proposed, and a resultant novel zeroing neural network model is developed. The new design formula incorporates a second-order error-processing manner, and the double-integration-enhanced zeroing neural network (DIEZNN) model is further proposed for solving time-varying quadratic matrix equations subject to linear noises. Compared with the original zeroing neural network (OZNN) model, finite-time zeroing neural network (FTZNN) model and integration-enhanced zeroing neural network (IEZNN) model, the DIEZNN model shows the superiority of its solution under linear noise; that is, when solving the problem of a time-varying quadratic matrix equation in the environment of linear noise, the residual error of the existing model will maintain a large level due to the influence of linear noise, which will eventually lead to the solution’s failure. The newly proposed DIEZNN model can guarantee a normal solution to the time-varying quadratic matrix equation task no matter how much linear noise there is. In addition, the theoretical analysis proves that the neural state of the DIEZNN model can converge to the theoretical solution even under linear noise. The computer simulation results further substantiate the superiority of the DIEZNN model in solving time-varying quadratic matrix equations under linear noise.
Deep Forest and Pruned Syntax Tree-Based Classification Method for Java Code Vulnerability

The rapid development of J2EE (Java 2 Platform Enterprise Edition) has brought unprecedented severe challenges to vulnerability mining. The current abstract syntax tree-based source code vulnerability classification method does not eliminate irrelevant nodes when processing the abstract syntax tree, resulting in a long training time and overfitting problems. Another problem is that different code structures will be translated to the same sequence of tree nodes when processing abstract syntax trees using depth-first traversal, so in this process, the depth-first algorithm will lead to the loss of semantic structure information which will reduce the accuracy of the model. Aiming at these two problems, we propose a deep forest and pruned syntax tree-based classification method (PSTDF) for Java code vulnerability. First, the breadth-first traversal of the abstract syntax tree obtains the sequence of statement trees, next, pruning statement trees removes irrelevant nodes, then we use a depth-first based encoder to obtain the vector, and finally, we use deep forest as the classifier to get classification results. Experiments on publicly accessible vulnerability datasets show that PSTDF can reduce the loss of semantic structure information and effectively remove the impact of redundant information.
Neural Teleportation

In this paper, we explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks. Neural teleportation teleports a network to a new position in the weight space and preserves its function. This phenomenon comes directly from the definitions of representation theory applied to neural networks and it turns out to be a very simple operation that has remarkable properties. We shed light on the surprising and counter-intuitive consequences neural teleportation has on the loss landscape. In particular, we show that teleportation can be used to explore loss level curves, that it changes the local loss landscape, sharpens global minima and boosts back-propagated gradients at any moment during the learning process.
The Machine Learning Model for Identifying Bogus Profiles in Social Networking Sites

In recent years, social networking sites have gained popularity on the internet, attracting hundreds of thousands of users who spend billions of minutes utilising them. Social networking sites have now taken over many people’s life. Every day, a large number of people create profiles on social media sites and contact others regardless of their location or time. Users’ personal information is at risk on social networking sites posing security risks. It is important to identify the bogus user’s social media identities so that the person who is pushing threats can be uncovered. In this proposed work, the Ensemble learner approach helps to increase the accuracy of detecting fake profiles through automated user profile classification. The study investigates the different text, image, audio, and video attributes that can be used to discern the difference between a fake and a legitimate account. The Ensemble learner approach utilises these attributes and assesses their performance on real-world social media datasets using performance metrics such as accuracy, precision, recall, and F-1 score. The experimental evaluation indicates that the average accuracy achieved by the proposed ensemble learner approach is 97.67%, precision of 92.56% and a recall of 82%, compared to learning models (Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic regression).
Efficient Image Segmentation of Cardiac Conditions after Basketball Using a Deep Neural Network

The evaluation of heart health status is the reference standard for measuring the intensity of exercise performed by different individuals. Thus, the effective analysis of heart conditions is an important research topic. In this study, we propose a system designed to segment images of the right ventricle. In this system, the right ventricle of the heart is segmented using an improved model called RAU-Net. The sensitivity and specificity of the network are enhanced by improving the loss function. We adopted an extended convolution rather than ordinary convolution to increase the receptive field of the network. In the network-sampling phase, we introduce an attention module to improve the accuracy of network segmentation. In the encoding and decoding stages, we also introduce three residual modules to solve the gradient explosion problem. The results of experiments are provided to show that the proposed algorithm exhibited better segmentation accuracy than an existing algorithm. Moreover, the algorithm can also be trained more rapidly and efficiently.
Tool Support for Improving Software Quality in Machine Learning Programs

Machine learning (ML) techniques discover knowledge from large amounts of data. Modeling in ML is becoming essential to software systems in practice. The accuracy and efficiency of ML models have been focused on ML research communities, while there is less attention on validating the qualities of ML models. Validating ML applications is a challenging and time-consuming process for developers since prediction accuracy heavily relies on generated models. ML applications are written by relatively more data-driven programming based on the black box of ML frameworks. All of the datasets and the ML application need to be individually investigated. Thus, the ML validation tasks take a lot of time and effort. To address this limitation, we present a novel quality validation technique that increases the reliability for ML models and applications, called MLVal. Our approach helps developers inspect the training data and the generated features for the ML model. A data validation technique is important and beneficial to software quality since the quality of the input data affects speed and accuracy for training and inference. Inspired by software debugging/validation for reproducing the potential reported bugs, MLVal takes as input an ML application and its training datasets to build the ML models, helping ML application developers easily reproduce and understand anomalies in the ML application. We have implemented an Eclipse plugin for MLVal that allows developers to validate the prediction behavior of their ML applications, the ML model, and the training data on the Eclipse IDE. In our evaluation, we used 23,500 documents in the bioengineering research domain. We assessed the ability of the MLVal validation technique to effectively help ML application developers: (1) investigate the connection between the produced features and the labels in the training model, and (2) detect errors early to secure the quality of models from better data. Our approach reduces the cost of engineering efforts to validate problems, improving data-centric workflows of the ML application development.
Managing the classroom fund of an educational instituton

The article describes the experience of organizing work with classroom funds in educational institutions. We carried out a comparative analysis of foreign and national approaches to determining the criteria for the effective use of classroom funds as well as to modeling and automating processes related to classroom fund management. The research presents an author-proposed interpretation of setting classroom fund management objective. Moreover, it identifies a system of control actions, describes author-developed approaches to creating models, methods, and technologies for managing classroom funds. We have proposed several basic principles for adapting and implementing classroom fund management systems in educational institutions on the basis of the generalized experience of developing, implementing, and using classroom fund management technologies at various levels and with different models of education. Using the example of the Siberian State University of Geosystems and Technologies, we describe the experience of implementing a system that comprehensively solves the problems related to classroom fund management. They are as follows: keeping count, profiling, planning, occupancy forecasting, distribution, monitoring, and assessing the efficiency of using. The technologies and models of classroom fund management described in the article can be adapted and used to improve classroom fund management systems in organizations of various levels and models of education.
Interactive technologies of practical literacy in the digital educational mainstream structure

The article is made in line with the new educational paradigm, replacing the installation of “ready knowledge” with the installation of independent “production of knowledge”, i. e., to “learn to learn”. Digitalization of education has qualitatively changed the content of education, making it informational, and the organization of educational and cognitive activities more effective. This process manifests itself as creativity. The results of the work of teachers, methodologists and scientists are a universal means of education and the subject of training. Continuing education involves the development and expansion of subject learning and the components of its methodological system through interaction with other components of the information and educational environment. This, in turn, requires the search for new organizational forms and means of implementing such educational activities in a modern information and communication environment, which will make it possible to use the didactic capabilities of this environment to a much greater extent. It is noted that the communicative aspect and communication processes, tools and technologies should contribute to the organization of effective dialogue (communication), the perception and assimilation of communicative competencies by students, overcoming illiterate use of the Russian language in cyberspace. As practice shows, the latter has become a real philological disaster. The language of virtual communication is not equal to the language of real communication. The article notes that one of the promising directions in the conditions of digital transformation of education is interactive learning as a special form of organization of cognitive activity and a way of cognition carried out in the process of joint educational and cognitive activity of students, in which all participants interact with each other and exchange information. Digital technologies can be used in teaching Russian at all levels of education. At the same time, the teacher can create educational materials with their own digital interactive content based on the proposed HTML environment programs. On the website “The Power of Knowledge” (EOR “Alphabet”) and on the portal of KubGU (section “Innovative computer didactics”), you can see several examples of such training. The authors believe that the process of digital technologies’ dominance in all spheres of public activity (digital mainstream) should lead to an increase in the general level of practical literacy in the Russian language among the entire population of the country.
LCIF-Net: Local criss-cross attention based optical flow method using multi-scale image features and feature pyramid

Although CNN-based optical flow methods have achieved remarkable performance in terms of computational accuracy and efficiency, the issue of edge-blurring caused by large displacements remains an open challenge. To address this problem, we propose a local criss-cross attention based optical flow estimation method using multi-scale image features and feature pyramid. First, we design an image pyramid-based feature extraction sub-network and then incorporate it into the feature pyramid network to construct a hybrid feature extraction module, which is able to extract multi-scale structural and semantic information from the input images. Second, we concatenate a local criss-cross attention module with the hybrid feature extraction module to build a global feature encoder. The global feature encoder further captures the long-range dependencies within the feature map to improve the large displacement estimation performance. Finally, we combine the global feature encoder with an iterative optical flow decoder, and thus propose a novel network named LCIF-Net. We demonstrate its significant performance benefits on MPI-Sintel and KITTI datasets. Compared with the existing optical flow estimation methods, our LCIF-Net remarkably improves the accuracy and robustness for the optical flow estimation, especially in the regions with large displacements and motion edges.
MATTE: Multi-task multi-scale attention

In this work, we propose a general method for learning task and scale based attention representations in Multi-Task Learning (MTL) for vision. It relies on learning and maintaining cross-task and cross-scale representations of visual information, whose interaction contributes to a symmetrical improvement across the entire task pool. Apart from learning data representations, we additionally optimize for the most beneficial interaction between tasks and their representations at different scales. Our method adds an attention modulated feature as residual information to the processing of each scale stage within the model, including the final layer of task outputs. We empirically show the effectiveness of our method through experiments with current multi-modal and multi-scale architectures on diverse MTL datasets. We evaluate MATTE on high and low level vision MTL problems, against MTL and single task learning (STL) counterparts. For all experiments we report solid performance improvements in both qualitative and quantitative performance.
Cross-view temporal graph contrastive learning for session-based recommendation

Session-based recommendation (SBR) aims at recommending items given the behavior sequences of anonymous users in a short-term session. Many recent SBR methods construct all sessions as a global graph that captures cross-session item transition patterns (i.e., users' global preference) to alleviate the problem of session data sparsity. However, these methods neglect that users' interests will drift as the time between the sessions constantly increases, limiting the performance improvement of SBR. To fill this gap, we divide all sessions into a group of time-slices and model the cross-session item transitions for every time-slice. We further construct two augmentation views (i.e., temporal graph and temporal hypergraph views) to model pairwise and high-order item transitions on SBR. In addition, we construct contrastive learning between two views to improve the recommendation performance by maximizing the mutual information between the item representations learned from the two views. Experiments on three public real-world datasets (i.e., Diginetica, Retailrocket, and Yoochoose) show that our model is consistently superior to the other state-of-the-art baselines, especially in time-sensitive datasets. For instance, our model achieves 14.42% and 11.72% improvements in terms of [email protected] and [email protected] on the Diginetica dataset, respectively.
Single image dehazing via color balancing and quad-decomposition atmospheric light estimation

In recent years, single image dehazing methods have achieved good performance. But there still exists some problems, such as color distortion, excessive smoothing of image texture details and image over-saturation. For solving these problems, this paper proposes an improved single image dehazing algorithm based on Color Balancing and Quad-Decomposition (CBQD). In the pre-processing stage, we define an indicator for detecting image unbalance color channel, and correct color of images that are prone to color distortion in some special environments (e.g., sandstorms). In the dehazing phase of the algorithm, we apply the improved quad-decomposition technique for eliminating the interference of non-atmospheric light source and certain bright objects, thus can estimate atmospheric light accurately. Meanwhile, we obtain the image depth information by the linear model and estimate the initial image transmission map by fused depth map, and then refine the transmission map using a gradient domain-guided filter with edge-preserving characteristics. Finally, we can restore the scene radiance, and the dehazed images have clear edges, rich texture, no halo artifacts, and natural color. Extensive experiments verifies the superiority of CBQD over state-of-the-art image dehazing techniques in terms of both the recovery quality and efficiency.
Metaverse Applications as a Tool in Urban Policy Design

The purpose of this chapter is to make assessments of the metaverse's utility in urban policies. Within the scope of the study, the role of the metaverse in increasing people's quality of life, increasing efficiency in business processes, and creating policies that will meet expectations by changing the understanding of service delivery in public administration will be discussed. A literature review was conducted in this context in order to develop a theoretical framework for the concept of the metaverse. In the literature review, articles, book chapters, proceedings papers, websites, and reports were used. In the application part, the projects carried out by the municipalities on the metaverse were examined in detail within the scope of city management.
The Development of a Metaverse App for a Blended Learning in Higher Education Institutions

COVID-19 has led to the rise of blended learning, and many innovations have been introduced worldwide. Metaverse in education is one of the innovations. The chapter aims to introduce students, lecturers, and people coming to a higher education institution in Brunei Darussalam to a metaverse app. The app's objectives are to solve the problems encountered in online learning and simultaneously allow physical learning in a safer environment. The chapter also includes the CATWOE framework that aims to provide an overview of how the various transformations taking place in the field of education affect how students learn. The road map in knowledge management implementation is also shared in this chapter to show the stages of implementation in developing the metaverse app. This chapter will also discuss the challenges and limitations of a metaverse app in education.
Using MBTI Agents to Simulate Human Behavior in a Work Context

The use of agent-based simulations to study human behavior has provided a significant advance in a better understanding of the impact of the human factor in different contexts, including work. In this sense, instruments such as the Myers–Briggs Type Indicator (MBTI), which allows categorizing different individuals’ personality types following their characteristics and behavioral preferences, can provide a significant advance in these studies. In this work, we intend to explore MBTI to simulate different psychological types described in the theory, in order to evolve the comprehension of human factors in organizations.
Population diversity and inheritance in genetic programming for symbolic regression

Abstract In this work we aim to empirically characterize two important dynamical aspects of GP search: the evolution of diversity and the propagation of inheritance patterns. Diversity is calculated at the genotypic and phenotypic levels using efficient similarity metrics. Inheritance information is obtained via a full genealogical record of evolution as a directed acyclic graph and a set of methods for extracting relevant patterns. Advances in processing power enable our approach to handle previously infeasible graph sizes of millions of arcs and vertices. To enable a more comprehensive analysis we employ three closely-related but different evolutionary models: canonical GP, offspring selection and age-layered population structure. Our analysis reveals that a relatively small number of ancestors are responsible for producing the majority of descendants in later generations, leading to diversity loss. We show empirically across a selection of five benchmark problems that each configuration is characterized by different rates of diversity loss and different inheritance patterns, in support of the idea that each new problem may require a unique approach to solve optimally.