Reviewerly/exHarmony huggingface dataset:
Deep Successive Convex Approximation for Image Super-Resolution

Image super-resolution (SR), as one of the classic image processing issues, has attracted increasing attention from researchers. As a highly ill-conditioned, non-convex optimization issue, it is difficult for image SR to restore a high-resolution (HR) image from a given low-resolution (LR) instance. Recent researchers have tended to regard image SR as a regression task and to design an end-to-end convolutional neural network (CNN) to predict the pixels directly, which lacks inherent theoretical analysis and limits the effectiveness of the restoration. In this paper, we analyze image SR from an optimization perspective and develop a deep successive convex approximation network (SCANet) for generating HR images. Specifically, we divide non-convex optimization into several convex LASSO sub-problems and use CNN to adaptively learn the parameters. To boost network representation, we use residual feature aggregation (RFA) blocks and devise a spatial and channel attention (SACA) mechanism to improve the restoration capacity. The experimental results show that the proposed SCANet can restore HR images more effectively than other works. Specifically, SCANet achieves higher PSNR/SSIM results and generates more satisfying textures.
Computer-Aided Formalization of Internal Consistent Product Family Models

The product line approach enables the rapid building of software products by reusing core assets. How to manage the commonality and variability of product family is the core problem of product line engineering. The feature modeling approach is one of the main approaches to solve this problem. However, this approach does not define the internal functions of features. Some researchers use formal descriptions to define the function of features, but they do not consider the connection between feature specification and feature model. These product family modeling approaches result in product family models that suffer from inconsistent descriptions, which are difficult to resolve manually. To this end, this paper proposes a computer-aided approach to construct an internally consistent product family model. The method performs an inconsistency check during the formalization of each feature in the given initial feature model, pushing the evolution of the feature model and feature specification. The creation and evolution of feature models are achieved through a template-based feature modeling approach. The method is implemented by means of templates, each of which contains two parts of knowledge: the elements required for a class of feature model, and a way to convert the given elements into a formal description. An inconsistency check is achieved through a rule-based detection approach. Each rule contains two parts: a class of conditions that the feature model and the feature specification satisfy, and the actions to resolve inconsistencies once the conditions are satisfied. We have developed a tool. The tool guides the user to build an internally consistent product family model that contains the feature model and feature specifications. Experiments have shown that our approach can provide effective automation support for the process of formal product family modeling.
Blockchain-based distributed database management technology

currently, the development of a new class of software called “decentralized applications” is gaining popularity. Its foundations were laid by the technologies of Bitcoin and BitTorrent, aroused the interest of developers in the methods of their implementation. Over the past few years, the whole world has been excited by the explosion in popularity of cryptocurrencies, which has brought blockchain technology into the spotlight. its application is not limited to the creation of electronic money and platforms for their exchange. One of the promising areas for the introduction of blockchain technology is its use in the banking sector to improve the security of operations and reduce costs. This paper focuses on Blockchain technology and investigates its applications, implementations, and constituents. and how blockchain applications enhancing database management.
Geology of the Chiltern Chalk aquifer, southern England

The unique geological history which resulted in the evolution of the Chiltern Hills to the north of London, The United Kingdom, created the underlying foundations for everything that we see there on the surface today. The roots of the Chiltern Hills lie in their Chalk foundations. To understand the details of the way the chalk acts as an aquifer it is important to understand first the origins of the chalk sediment and how the subsequent geological history of the region has impacted on the rocks preserved today.
RSMDA: Random Slices Mixing Data Augmentation

Advanced data augmentation techniques have demonstrated great success in deep learning algorithms. Among these techniques, single-image-based data augmentation (SIBDA), in which a single image’s regions are randomly erased in different ways, has shown promising results. However, randomly erasing image regions in SIBDA can cause a loss of the key discriminating features, consequently misleading neural networks and lowering their performance. To alleviate this issue, in this paper, we propose the random slices mixing data augmentation (RSMDA) technique, in which slices of one image are placed onto another image to create a third image that enriches the diversity of the data. RSMDA also mixes the labels of the original images to create an augmented label for the new image to exploit label smoothing. Furthermore, we propose and investigate three strategies for RSMDA: (i) the vertical slices mixing strategy, (ii) the horizontal slices mixing strategy, and (iii) a random mix of both strategies. Of these strategies, the horizontal slice mixing strategy shows the best performance. To validate the proposed technique, we perform several experiments using different neural networks across four datasets: fashion-MNIST, CIFAR10, CIFAR100, and STL10. The experimental results of the image classification with RSMDA showed better accuracy and robustness than the state-of-the-art (SOTA) single-image-based and multi-image-based methods. Finally, class activation maps are employed to visualize the focus of the neural network and compare maps using the SOTA data augmentation methods.
Ethnic Culture in the System of Spiritual and Moral Education

The article introduces the history and prospects of spiritual and moral education. The authors identified the pedagogical possibilities of ethnic culture and its inclusion in the system of spiritual and moral education. The analysis was based on the scientific potential proven by historical practice. It provided an objective assessment of the genesis of spiritual and moral education in its dynamics. As a pedagogical tool, ethnic culture is a treasure trove of moral and ethical value orientations and life meanings. The article clarifies the concepts of spiritual and moral education and ethnic culture, as well as identifies the ways of developing the spiritual and moral potential of university students. The research relied on the fundamental standards developed by the leading educationalists. It identified the place of ethnic culture in the system of spiritual and moral education developed by the specific cultural and historical conditions and based on the invaluable experience of the life of the people. Ethnic culture finds its expression in the requirements for the content of spiritual and moral education. The research makes it possible to use the academic and educational potential of extracurricular activities in the spiritual and moral education of university students.
A Novel Procedural Content Generation Algorithm for Tower Defense Games

The purpose of this article is to present a procedural content generation algorithm for tower defense games. The algorithm was developed for an isometric tower-defense game with roguelite elements. This algorithm can generate content that improves replayability by generating surprising new levels according to the expected gameplay in this game genre. Each time that the player starts a new game he is faced with new generated road maps, enemies, and spawn points. Finally, we made a playtest session to evaluate several aspects of the algorithm and of the game, such as the levels and enemies’ generation, gameplay, performance, and others.
Path Planning based on Astar Algorithm in Automatic Driving

With the rise of automatic driving technology, it needs to be studied more and more problems, path planning is this paper needs to study, at present, the existing algorithms are mainly some traditional algorithms and some improved algorithms on the traditional algorithms, for example, we are familiar with the automatic driving in the path planning algorithm mainly used Astar algorithm, Dijkstra Algorithm and in the Astar Algorithm derived on the basis of the DSTARLITE Algorithm, and weight Astar Algorithm, these traditional algorithms have different shortcomings, some calculations are complex, some convergence speed is slow, so comprehensive reading of the literature, this paper puts forward the study of path planning based on Astar Algorithm, the Path planning based on Astar algorithm can exactly plan the shortest path for the self-driving vehicle in the shortest time.
A SigFox-Based Blockchain Network for Electronic Health Records

In the healthcare sector, accessibility. integrity, and privacy of patients' data is a primary concern. Traditionally, patients' medical history and records were saved on servers that are susceptible to cyber attacks and connectivity issues. In this study, a system that promotes interoperability and data interchange between healthcare providers whilst ensuring data confidentiality and privacy of patients' medical information is presented. This paper introduces an Electronic Healthcare Record (EHR) system using Blockchain structure that is connected using SigFox long-range wireless technology eliminating the need for third-party systems. Healthcare personnel can obtain, share, and edit patients' medical history across long distances while ensuring data integrity. The proposed system can be accessed via personal computers and mobile devices, hence, improving the system's reliability and performance.
Application of an Optimized Logic Track Initiation Algorithm on Vehicular Radar

Track initiation is the primary problem in multi-target tracking. The logical method of track initiation is a sequential processing technology, with the advantages of small calculation and high efficiency, and a modified logic method can be used to further improve the efficiency of track initiation. This paper optimizes the logic method from three aspects: the selection of logical rules, the limitation of signal-to-noise ratio (SNR) and the screening of point in wave gate, based on a vehicle radar, combined with the application scenarios of radar. The actual test results show that the false track can be reduced by about half, when the influence on the target track is small.
Deep Learning Approach for Gesture Recognition on Millimeter- Wave Radar

Gesture recognition has gained world-wide attention in the field of human-computer interaction. Built on millimeter-wave radar, the gesture recognition system has specific advantages of high integration, all-day operation, and robustness in poor lighting. This paper presents a 77 GHz millimeter-wave (MMW) radar to build a gesture recognition algorithm, which uses frequency-domain notch and sliding window search methods to mitigate interference and automatically extract useful signals from gesture echoes. We establish the gesture radar data collection platform on an MMW radar, design common gestures, and collect massive gesture radar data in the time-frequency domain to form a dataset. A convolutional neural network is built for gesture recognition, and the accuracy can reach up to 94.72 % based on the collected gesture radar dataset.
A Bayesian Network Structure Hybrid Learning Algorithm Based on Improved Butterfly Optimization Algorithm

Aiming at the problems of poor learning effect and long learning time in Bayesian network structure learning applied in complex electromagnetic environment, a Bayesian network structure hybrid learning algorithm based on improved butterfly optimization algorithm is proposed. First, the mutual information is used to constrain the search space to obtain the initial population, and then the improved butterfly optimization algorithm is used to search for the optimal structure. Using the ASIA network as the simulation model, the experimental results show that the improved algorithm not only has better accuracy in the optimization process, but also saves learning time.
Image Visualization of Machine Electromagnetic Vision System based on HSV Model

In order to solve difficult technical problems of accurate electromagnetic (EM) spectrum sensing, EM field control and application, machine EM vision system - EM eye system is designed. EM eye system can form a vivid image of the world as like human's eyes, as well as a way of long-range target detection of radar. The architecture of EM eye system and HSV model image visualization representation method of EM field information are proposed. Finally, the rationality and feasibility of the design scheme are analysed through the experimental verification.
An Adaptive Maneuvering Target Tracking Algorithm with Unknown Prior Information in Beamformer

Due to the unknown prior information of the target, it is difficult to initialize the frequency of beamformer adjust-ment, and the instantaneously considerable errors occur when a target suddenly maneuvers. An adaptive beamformer adjustment frequency method for The Extended Kalman Filter (EKF) based on Current Statistical (CS) dynamics model is proposed in this paper, by updating the beamformer adjustment frequency parameter in CS model adaptively according to EKF's output result of each step. Simulation results are presented comparing original CS model to adaptive CS model in different situations and illustrating the adaptive algorithm proposed in this paper has a better performance.
Hidden Attacks of SAR Images Recognition Based on Generative Adversarial Network

With the development of modern electronic countermeasures technology, intelligent devices represented by cognitive radar can accurately perceive complex electromagnetic environments, and can dynamically adjust their working methods according to the environment, with high countermeasure performance. However, cognitive methods applying deep learning exhibit potential vulnerabilities when confronted with adversarial examples. Therefore, looking for confrontation samples of radar images and effectively confusing the enemy's cognitive radar equipment can effectively improve our confrontation ability in complex electromagnetic environments. Taking synthetic aperture radar (SAR) as an example, this paper proposes a more stealthy adversarial sample generation method under the premise of only considering digital attacks. The experimental results show that the adversarial samples generated by this method have a higher attack success rate, and the disturbance is more subtle and difficult to detect.
Enabling Catalyst Adoption in SPARC

This paper reports on Catalyst usability and initial adoption by SPARC analysts. The use case approach highlights the analysts’ perspective. Impediments to adoption can be due to deficiencies in software capabilities, or analysts may identify mundane inconveniences and barriers that prevent them from fully leveraging Catalyst. With that said, for many analyst tasks Catalyst provides enough relative advantage that they have begun applying it in their production work, and they recognize the potential for it to solve problems they currently struggle with. The findings in this report include specific issues and minor bugs in ParaView Python scripting, which are viewed as having straightforward solutions, as well as a broader adoption analysis.
Planning for millions of NPCs in Real-Time

We address the problem of scaling the generation of plans in real-time to control the behaviors of several millions of Non-Player Characters (NPCs) in video-games and virtual worlds. Search-based action planning, introduced in the game F.E.A.R. in 2005, has an exponential time complexity managing at most several tens of NPCs per frame. A close study of the plans generated in first-person shooters shows that: (1) states are vectors of enumerated values, (2) both initial and final states can be totally defined, (3) actions are both post-unique and unary, (4) plans are totally ordered, and (5) actions occur only once in plans. (1) to (5) satisfy the Simplified Action Structure (SAS) linear time planning framework SAS- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{T}_{1}$</tex> . We strengthen previous claims on this framework saying that the associated linear time algorithm <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{P}$</tex> is capable of managing several millions of NPCs per frame by testing it on three new realistic benchmarks that are based on commercial video games.
Multiuser, multimodal sensemaking cognitive immersive environment with a task-oriented dialog system

This paper is a conceptual paper that explores how the sensemaking process by intelligence analysts completed within a cognitive immersive environment might be impacted by the inclusion of a progressive dialog system. The tools enabled in the sensemaking room (a specific instance within the cognitive immersive environment) were informed by tools from the intelligence analysis domain. We explore how a progressive dialog system would impact the use of tools such as the collaborative brainstorming exercise [1]. These structured analytic techniques are well established in intelligence analysis training literature, and act as ways to access the intended users' cognitive schema as they use the cognitive immersive room and move through the sensemaking process. A prior user study determined that the sensemaking room encouraged users to be more concise and representative with information while using the digital brainstorming tool. We anticipate that addition of the progressive dialog function will enable a more cohesive link between information foraging and sensemaking behaviors for analysts.
Investigating the Effect of Hyperparameter Values and Size on Swarm Optimization Effectiveness

The present manuscript investigates the correlations between the control hyperparameters of a particle swarm optimization algorithm, the size of the swarm, and the algorithm's effectiveness in locating optimal solutions. This is achieved by analyzing a large volume of experiments that cover a range of swarm sizes using a selection of twenty-one standard benchmark functions. The findings are further analyzed via a ranking process to determine the swarm behavior. It is found that swarms below a specific minimum size do not achieve a good optimization solution. In addition, specific hyperparameter combinations consistently achieve very good solutions in optimization tasks, while other combinations consistently demonstrate a poor optimization behavior. Statistical tests are used to analyze the results comprehensively.
Consensus Learning with Complete Graph Regularization for Incomplete Multi-view Clustering

Incomplete multi-view clustering (IMC) is a long-standing and challenging problem due to the fact that multi-view dataset are often incomplete in practical applications. In this paper, we propose a new IMC method by learning the consensus representation of incomplete multi-view with embedding a complete graph regularization. Specifically, we first exploit the common representation of multiple views by embedding missing-view indicator matrix into the matrix factorization operation, such that the learned new representation is well shared among all views. Then, we learn the combination relationship between similarity matrices of each view to obtain the complete similarity graph. By combining the consensus representation with the complete graph regularization, our proposed method can simultaneously learn the consensus representation and preserve the local geometric information of both the available and missing instances of multiple views. Experimental results on several widely used missing-view datasets clearly demonstrate the promising clustering performance of the proposed method in comparison with most state-of-the-art IMC methods.
On Neural Consolidation for Transfer in Reinforcement Learning

Although transfer learning is considered to be a milestone in deep reinforcement learning, the mechanisms behind it are still poorly understood. In particular, predicting if knowledge can be transferred between two given tasks is still an unresolved problem. In this work, we explore the use of network distillation as a feature extraction method to better understand the context in which transfer can occur. Notably, we show that distillation does not prevent knowledge transfer, including when transferring from multiple tasks to a new one, and we compare these results with transfer without prior distillation. We focus our work on the Atari benchmark due to the variability between different games, but also to their similarities in terms of visual features.
Analytics for Cybersecurity Policy of Cyber-Physical Systems

Guidelines, directives, and policy statements are usually presented in “linear” text form - word after word, page after page. However necessary, this practice impedes full understanding, obscures feedback dynamics, hides mutual dependencies and cascading effects and the like-even when augmented with tables and diagrams. The net result is often a checklist response as an end in itself. All this creates barriers to intended realization of guidelines and undermines potential effectiveness. We present a solution strategy using text as “data”, transforming text into a structured model, and generate network views of the text(s), that we then can use for vulnerability mapping, risk assessments and note control point analysis. For proof of concept we draw on NIST conceptual model and analysis of guidelines for smart grid cybersecurity, more than 600 pages of text.
An Adaptive Version of Differential Evolution for Solving CEC2014, CEC 2017 and CEC 2022 Test Suites

In this paper, an extended version of LSHADE, an enhanced variant of Differential evolution (DE), is proposed by adding equation modifications and self-adaptive characteristics to the basic LSHADE algorithm. The proposed algorithm is named as adaptive LSHADE (ALSHADE) and incorporates some major changes in the crossover, mutation and population adaptation operations of LSHADE algorithm. The modifications proposed include the introduction of linearly decreasing distribution for crossover rate and scaling factor using chaotic mutation operator. Apart from these modifications, the algorithm also employs linear population size reduction to reduce the computational burden of the algorithm. The proposed algorithm is tested on CEC 2014, CEC 2017 & CEC 2022 benchmark suits and is compared with respect to some major algorithms in literature. WIlcoxon's rank-sum tests have been performed to test the significance of the algorithm statistically. Experimental and statistical results prove that the proposed ALSHADE performs significantly better with respect to other algorithms in literature and can be considered as a potential candidate to become a state-of-the-art algorithm.