Reviewerly/exHarmony huggingface dataset:
From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision Making

In the face of complex decisions, people often engage in a three-stage process that spans from (1) exploring and analyzing pertinent information (intelligence); (2) generating and exploring alternative options (design); and ultimately culminating in (3) selecting the optimal decision by evaluating discerning criteria (choice). We can fairly assume that all good visualizations aid in the “intelligence” stage by enabling data exploration and analysis. Yet, to what degree and how do visualization systems currently support the other decision making stages, namely “design” and “choice”? To further explore this question, we conducted a comprehensive review of decision-focused visualization tools by examining publications in major visualization journals and conferences, including VIS, EuroVis, and CHI, spanning all available years. We employed a deductive coding method and in-depth analysis to assess whether and how visualization tools support design and choice. Specifically, we examined each visualization tool by (i) its degree of visibility for displaying decision alternatives, criteria, and preferences, and (ii) its degree of flexibility for offering means to manipulate the decision alternatives, criteria, and preferences with interactions such as adding, modifying, changing mapping, and filtering. Our review highlights the opportunities and challenges that decision-focused visualization tools face in realizing their full potential to support all stages of the decision making process. It reveals a surprising scarcity of tools that support all stages, and while most tools excel in offering visibility for decision criteria and alternatives, the degree of flexibility to manipulate these elements is often limited, and the lack of tools that accommodate decision preferences and their elicitation is notable. Based on our findings, to better support the choice stage, future research could explore enhancing flexibility levels and variety, exploring novel visualization paradigms, increasing algorithmic support, and ensuring that this automation is user-controlled via the enhanced flexibility levels. Our curated list of the 88 surveyed visualization tools is available in the OSF link <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(https://osf.io/nrasz/?view_only=b92a90a34ae241449b5f2cd33383bfcb)</uri> .
Polarization-Enhanced Underwater Detection Method for Multiple Material Targets Based on Deep-Learning

Underwater target detection is an essential topic in the applications of underwater exploration. However, underwater target detection faces serious challenges, such as complex scattering, low visibility, and target clutter. Here a polarization-enhanced underwater multiple material target detection method is proposed to address these challenges. The similarity principle of locally backscattered polarization features is utilized to suppress the influence of backscattered light. Our target detection model combines polarization gradient and edge detection techniques to optimize the detection process, enabling superior target detection and feature extraction. Experimental results indicate that our method has significantly enhanced the detection performance in multiple (overlapping or nonoverlapping) material targets, especially in high turbid underwater scattering environments. This research provides a promising new approach for polarized target detection in underwater environments and opens up new possibilities for underwater multiple-material target detection.
Text-Image Scene Graph Fusion for Multi-Modal Named Entity Recognition

With the popularity and widespread use of social media platforms such as Twitter and Facebook, massive amounts of text and image information posted by a variety of users have flooded these social media platforms. Thus, multi-modal named entity recognition (MNER) has become a research hotspot for the task of extracting named entities from multi-modal data. Empirically, the visual clues unrelated to text data may introduce uncertain or even negative impacts on the named entity recognition. The considerations of the relevance of multi-modal data have been ignored in the previous studies. In this paper, to effectively measure the relationship between text data and visual cues for improving the accuracy of named entities, we propose a text image scene graph fusion (TISGF) approach with a text-image similarity assessment module (TISA) and a text-image fusion module (TIF) for MNER. Specifically, we first construct two (visual and textual) scene graphs to exploit the joint features of objects and relations in text and image and encode the two scene graphs separately using a specific encoder pair. In this way, we can obtain both object-level and relationship-level cross-modal features. Subsequently, TISA is used to compute the similarity of the image and text data and to determine the proportion of visual information that will be retained for fusion. Finally, we use TIF to achieve a unified multi-modal representation of each word and predict the entity type using CRF. Extensive experiment results on two public datasets demonstrate the effectiveness and competitiveness of our proposed method for the MNER task.
Path Planning of Inspection Robot Based on Improved Intelligent Water Drop Algorithm

In response to the limitations observed in the Intelligent Water Droplet (IWD) algorithm for path planning, including its weak problem-solving capability and susceptibility to local optimization, this paper presents an enhanced Intelligent Water Droplet algorithm.The improved algorithm incorporates the distance factor between nodes and the target point into the original algorithm's path probability, enhancing its problem-solving prowess and expediting algorithmic convergence.Simultaneously, a roulette-based probability selection method is introduced to circumvent local optimization during the solution process.Additionally, the algorithm is coupled with the Floyd algorithm to refine the planned path, reducing the number of inflection points to align with the motion characteristics of the inspection robot.Simulation results underscore the effectiveness of the enhanced Intelligent Water Droplet algorithm in mitigating the impact of local optimization in path planning.In comparison to the original IWD algorithm, the optimized path exhibits a 17.42% reduction in length, a 58.3% decrease in the minimum number of iterations required for path convergence, and a 36.3%reduction in the number of path inflection points.Furthermore, the total path length is decreased by 7.7% following path optimization via the Floyd algorithm.
A Complete Tableau Calculus for the Regular MaxSAT Problem

We define a tableau calculus for solving the Maximum Satisfiability problem of regular propositional logic (Regular MaxSAT). Given a multiset of regular clauses Φ, we prove that the calculus is sound in the sense that if the minimum number of contradictions derived among the branches of a completed tableau for Φ is m, then the minimum number of unsatisfied clauses in Φ is m. We also prove that it is complete in the sense that if the minimum number of unsatisfied clauses in Φ is m, then the minimum number of contradictions among the branches of any completed tableau for Φ is m. Furthermore, we describe how to extend the proposed calculus to solve Regular MaxSAT in the case where we consider weighted formulas.
Brazilian coin counter research report

Though coins are less used in the informational era, a significant amount of the population still uses physical coins for financial transactions. Also, when the countrys government collects the coins for equivalent digital currency transactions, coin identification is still vital yet tedious. Moreover, the dated coins and the difference in illuminations. This novel presents a coin identification and validation model based on the Alex Net convolutional model. It identifies the value of a coin through the numbers, animals, and plants on the two sides. The model employs data enhancement, feature attention layers, max pooling, and residue groups. We collected 2826 images of Brazilian coins with reverse motifs, and the experimental accuracy of our model reached 0.97. The code part has shown here: https://github.com/Erik-Xie/-Brazilian-Coin-counter-Research-Report.git
Attention-Aware Patch-Based CNN for Blind 360-Degree Image Quality Assessment

An attention-aware patch-based deep-learning model for a blind 360-degree image quality assessment (360-IQA) is introduced in this paper. It employs spatial attention mechanisms to focus on spatially significant features, in addition to short skip connections to align them. A long skip connection is adopted to allow features from the earliest layers to be used at the final level. Patches are properly sampled on the sphere to correspond to the viewports displayed to the user using head-mounted displays. The sampling incorporates the relevance of patches by considering (i) the exploration behavior and (ii) a latitude-based selection. An adaptive strategy is applied to improve the pooling of local patch qualities to global image quality. This includes an outlier score rejection step relying on the standard deviation of the obtained scores to consider the agreement, as well as a saliency to weigh them based on their visual significance. Experiments on available 360-IQA databases show that our model outperforms the state of the art in terms of accuracy and generalization ability. This is valid for general deep-learning-based models, multichannel models, and natural scene statistic-based models. Furthermore, when compared to multichannel models, the computational complexity is significantly reduced. Finally, an extensive ablation study gives insights into the efficacy of each component of the proposed model.
Nonlinear Model Identification of a Ball and Beam Mechanism using Experimental Data

A ball and beam mechanism is widely utilized in laboratory experiments to demonstrate the behavior of more complex systems. In this research, the phenomena such as nonlinear frictions, dead-zone and time-delay in the ball and beam mechanism's mathematical model is investigated. The following procedures are taken to construct a credible mathematical model of the system for this purpose. Firstly, the ball and beam mechanism's mathematical model, which includes different probable physically meaningful nonlinearities, is simulated using MATLAB\Simulink. Then, the Particle Swarm Optimization (PSO) algorithm is coded to determine the exact nonlinear model of a ball and beam system using the experimental data. Third, the accuracy of the results obtained from the PSO algorithm is tested using the hypothesis test and the confidence interval test. According to the statistical tests, the PSO algorithm is highly accurate in determining the parameters of the actual model of the system.
Breast cancer segmentation using a hybrid AttendSeg architecture combined with a gravitational clustering optimization algorithm using mathematical modelling

Abstract Breast cancer diagnosis relies on breast ultrasound (BUS) and the early breast cancer screening saves lives. Computer-aided design (CAD) tools diagnose tumours via BUS tumour segmentation. Thus, breast cancer analysis automation may aid radiologists. Early detection of breast cancer might help the patients to survive and in context with this many approaches have been demonstrated by different researches, however, some of the works are weak in the segmentation of breast cancer images. to tackle these issues, this study propose a novel Hybrid Attendseg based gravitational clustering optimization (HA-GC) method which is utilized to segment breast cancer as normal malignant, and benign. For this we have taken the dataset known as breast ultrasound (BUS) images. This method constructively segments the breast cancer images. Prior to the segmentation, pre-processing is carried out which can be used to normalize the images incorporated with the removal of unwanted noises and format the images Optimization selects the best qualities. An experiment is conducted and compared the results with the parameters such as Dice coefficient, Jacquard, Precision, and Recall and attained over 90% and ensures the usage of present work in the segmentation of breast cancer images.
SpVOS: Efficient Video Object Segmentation With Triple Sparse Convolution

Semi-supervised video object segmentation (Semi-VOS), which requires only annotating the first frame of a video to segment future frames, has received increased attention recently. Among existing Semi-VOS pipelines, the memory-matching-based one is becoming the main research stream, as it can fully utilize the temporal sequence information to obtain high-quality segmentation results. Even though this type of method has achieved promising performance, the overall framework still suffers from heavy computation overhead, mainly caused by the per-frame dense convolution operations between high-resolution feature maps and each kernel filter. Therefore, we propose a sparse baseline of VOS named SpVOS in this work, which develops a novel triple sparse convolution to reduce the computation costs of the overall VOS framework. The designed triple gate, taking full consideration of both spatial and temporal redundancy between adjacent video frames, adaptively makes a triple decision to decide how to apply the sparse convolution on each pixel to control the computation overhead of each layer, while maintaining sufficient discrimination capability to distinguish similar objects and avoid error accumulation. A mixed sparse training strategy, coupled with a designed objective considering the sparsity constraint, is also developed to balance the VOS segmentation performance and computation costs. Experiments are conducted on two mainstream VOS datasets, including DAVIS and Youtube-VOS. Results show that, the proposed SpVOS achieves superior performance over other state-of-the-art sparse methods, and even maintains comparable performance, e.g., an 83.04% (79.29%) overall score on the DAVIS-2017 (Youtube-VOS) validation set, with the typical non-sparse VOS baseline (82.88% for DAVIS-2017 and 80.36% for Youtube-VOS) while saving up to 42% FLOPs, showing its application potential for resource-constrained scenarios.
A novel chaotic map-based encryption scheme for surveillance videos

Abstract The widespread use of surveillance cameras has initiated privacy concerns among individuals being captured by these devices. Due to the limited internal storage capacity, these cameras often transmit recorded videos over unreliable networks. This situation creates opportunities for attackers to exploit vulnerabilities, such as launching Man-in-the-Middle attacks, replayed scene attacks, and tampering with the video content. Considering these risks, this paper introduces a new chaotic map-based encryption scheme for surveillance videos. The work proposes a novel chaotic map using cascading and coupling operations on functions and is referred to as a Cascaded Coupled Logistic-Sine-Cosine (CCLSC) map. The chaotic performance of the novel CCLSC map has been evaluated using the Lyapunov exponent and the Bifurcation diagram. The scheme to encrypt surveillance videos is designed using the novel CCLSC map and our earlier designed Sine-Tangent-Sine (STS) chaotic map. The process involves extracting individual frames from the video and subsequently encrypts each frame as a digital image. The encryption of each frame involves three steps. The first step generates a Dynamic Chaotic Matrix (DCM) using CCLSC and STS map. The second phase is pixel scrambling using DCM and the final step changes the pixel values using DCM. Our results validate the security, robustness, and effectiveness of the proposed method.
Quantum computing with trapped ions: a beginner’s guide

Abstract This pedagogical article elucidates the fundamentals of trapped-ion quantum computing, which is one of the potential platforms for constructing a scalable quantum computer. The evaluation of a trapped-ion system’s viability for quantum computing is conducted in accordance with DiVincenzo’s criteria.
Special Issue on Applications of Artificial Intelligence on Social Media

The explosive expansion of social media platforms across the globe, including the likes of TikTok, WeChat, Twitter, and Facebook, has ushered in an era of unparalleled possibilities for individuals to forge connections and chronicle their social engagements [...]
Analisis Strategi Pembelajaran PQ4R Untuk Menentukan Ide Pokok Pada Teks Eksplanasi

The low learning outcomes of grade 6 students at SDN 3 Dawuhan, especially when learning to determine the main idea of ​​an explanatory text, is caused by several factors, namely internal and external. External factors include variations in the learning model carried out by the teacher which are still not optimal.&#x0D; The subjects of this study were 6th grade students at SDN 3 Dawuhan, consisting of 10 students. This study uses a descriptive qualitative research approach with data collection techniques through tests, observations and interviews. The purpose of this study was to test the effectiveness of the PQ4R learning strategy (Preview, Questions, Read, Reflect, Reside and Review) in learning Indonesian to determine the main idea of ​​an explanatory text. The results obtained are, 1) increasing the ability to ask questions, 2) increasing self-confidence, 3) increasing the ability to think critically and 4) the ability to understand reading. Suggestions for future research, so that there are efforts to cultivate reading activities for students so that they can increase vocabulary treasury which will further improve reading comprehension skills as well.&#x0D; Keywords: Main idea, PQ4R, Explanatory text
Evolutionary Reduction of the Laser Noise Impact on Quantum Gates

As the size of quantum hardware progressively increases, the conjectured computational advantages of quantum technologies tend to be threatened by noise, which randomly corrupts the design of quantum logical gates. Several methods already exist to reduce the impacts of noise on that matter. However, a reliable and user-friendly one to reduce the noise impact has not been presented yet. Addressing this issue, this paper proposes a relevant method based on evolutionary optimisation and modulation of the gate design. This method consists of two parts : a model of quantum gate design with time-dependent noise terms, parameterised by a vector of laser phases, and an evolutionary optimisation platform aimed at satisfying a trade-off between the gate fidelity and a pulse duration-related metric of the time consuming simulation model. This feature is the main novelty of this work. Another advantage is the ability to treat any noise spectrum, regardless of its characteristics (e.g., variance, frequency range, etc). A thorough validation of the method is presented, which is based on empirical averaging of random gate trajectories. It is shown that evolutionary based method is successfully applied for noise mitigation. It is expected that the proposed method will help designing more and more noise-resisting quantum gates.
The Impact of Total Quality Management on Patient Satisfaction with Mediating Role of Perceived Service Quality of Healthcare

Transformational leadership, work motivation and procedural justice are getting an increased attention in the area of business and economics because of their impact on business in terms of high productivity, job satisfaction, and organizational commitment, higher performance and many other related outcomes. In this study we examined the direct and indirect impact of transformational leadership on work motivation. Procedural justice was used as a mediator to examine the indirect impact of transformational leadership on work motivation. Data was collected from University of Peshawar with 42 respondents. Results indicate that transformational leadership has a positive influence on work motivation and procedural justice partially mediate the relationship between transformational leadership and work motivation. Limitation of the study and future directions were also given. Study recommends that transformational leadership style should be adopted by the management to enhance work motivation and get economic benefits.
Toward an Optimal and Structured Feature Subset Selection for Multi-Target Regression Using Genetic Algorithm

Multi Target Regression (MTR) is a machine learning method that simultaneously predicts multiple real-valued outputs using a set of input variables. A lot of emerging applications that can be mapped to this class of problem. In MTR method one of the critical aspect is to handle structural information like instance and target correlation. MTR algorithms attempt to exploit these interdependences when building a model. This results in increased model complexities, which in turn, reduce the interpretability of the model through manual analysis of the result. However, data driven real-world applications often require models that can be used to analyze and improve real-world workflows. Leveraging dimensionality reduction techniques can reduce model complexity while retaining the performance and boost interpretability. This research proposes multiple feature subset alternatives for MTR using genetic algorithm, and provides a comparison of the different feature subset selection alternatives in conjunction with MTR algorithms. We proposed a genetic algorithm based feature subset selection with all targets and with individual target keeping the structural information intact in the selection process. Experiments are performed on real world benchmarked MTR data sets and the results indicate that a significant improvement in performance can be obtained with comparatively simple MTR models by utilizing optimal and structured feature selection.
Phy-Taylor: Partially Physics-Knowledge-Enhanced Deep Neural Networks via NN Editing

Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To address this challenge, we propose a physics-knowledge-enhanced DNN framework called Phy-Taylor, accelerating learning-compliant representations with physics knowledge. The Phy-Taylor framework makes two key contributions; it introduces a new architectural physics-compatible neural network (PhN) and features a novel compliance mechanism, which we call physics-guided neural network (NN) editing. The PhN aims to directly capture nonlinear physical quantities, such as kinetic energy, electrical power, and aerodynamic drag force. To do so, the PhN augments NN layers with two key components: 1) monomials of the Taylor series for capturing physical quantities and 2) a suppressor for mitigating the influence of noise. The NN editing mechanism further modifies network links and activation functions consistently with physics knowledge. As an extension, we also propose a self-correcting Phy-Taylor framework for safety-critical control of autonomous systems, which introduces two additional capabilities: 1) safety relationship learning and 2) automatic output correction when safety violations occur. Through experiments, we show that Phy-Taylor features considerably fewer parameters and a remarkably accelerated training process while offering enhanced model robustness and accuracy.