Reviewerly/exHarmony huggingface dataset:
Feature selection in high‐dimensional microarray cancer datasets using an improved equilibrium optimization approach

Summary Optimal feature selection of a high‐dimensional micro‐array datasets has gained a significant importance in medical applications for early detection and prevention of disease. Traditional Optimal feature selection percolates through a population‐based meta‐heuristic optimization technique, a Machine Learning classifier and traditional wrapper method for transforming the original feature set into a better feature set. These techniques require a number of iterations for the convergence of random solutions to the global optimum with high‐dimensionality issues such as over‐fitting, memory constraints, computational costs, and low accuracy. In this article, an efficient equilibrium optimization technique is proposed for an optimized feature selection that increases the diversity of the population in the search space through Random Opposition based learning and classify the best features using a 10‐fold cross‐validation‐based wrapper method. The proposed method is tested with six standard micro‐array datasets and compared with the conventional algorithms such as Marine Predators Algorithm, Harris Hawks Optimization, Whale Optimization Algorithm, and conventional Equilibrium Optimization. From the statistical results using the standard metrics, it is interpreted that the proposed method converges to the global minimum in a few iterations through optimized feature selection, fitness value and higher classification accuracy. This proves its efficacy in exploring and finding a better solution as compared to the counterpart algorithms. In addition to complexity analysis, these results indicate a global optimum solution, an effective representation of least amount of data‐high dimensionality reduction and an avoidance of over‐fitting problems. The source code is available at https://github.com/balasv/ROBL‐EOA/blob/main/ROBL_EOA.ipynb
Using Live Spam Beater (LiSB) Framework for Spam Filtering during SMTP Transactions

This study introduces the Live Spam Beater (LiSB) framework for the execution of email filtering techniques during SMTP (Simple Mail Transfer Protocol) transactions. It aims to increase the effectiveness and efficiency of existing proactive filtering mechanisms, mainly based on simple blacklists. Since it implements some proactive filtering schemes (during SMTP transaction), when an email message is classified as spam, the sender can be notified by an SMTP response code as a result of the transaction itself. The presented framework is written in Python programming language, works as an MTA (Mail Transfer Agent) server that implements an SMTP (Simple Mail Transfer Protocol) reverse proxy and allows the use of plugins to easily incorporate new filtering techniques designed to operate proactively. We also include a plugin to perform proactive content-based filtering through the analysis of words included in the body of the email message. Finally, we measured the performance of the plugin and the framework (time required for operation and accuracy) obtaining values suitable for their use during SMTP transactions.
메타버스 플랫폼, 가상공간, 창의융합, 팀기반 학습, 협력적 문제해결

The purpose of this study was to propose implications for the use of the metaverse platform in terms of education through the case of operating the creative convergence project of A university case using the metaverse platform. To this end, the metaverse platform observed participation in the activity process in the project learning activity environment designed in the virtual space, Gathertown, and conducted a survey and interview with learners after the activity. As a result of the study, the survey showed high interest in the metaverse platform, the necessity of pre-education on how to use it, the degree of intimacy formation, interaction, solidarity formation, reality, team activity immersion, satisfaction with the team activity process, and team activity results. In the in-depth interview, the positive effects of using other metabus platforms were confirmed such as self-expression for non-face-to-face identity, achievement of cooperative problem solving, collaboration and creativity through various tools, learning contextual experience through sharing of game and learning contents, in-depth learning and space design. Based on the results of this study, various implications for the use of the metaverse platform for creative convergence education at the university level were derived. The results of this study are expected to be used as basic data for the educational use of metaverse in universities.
Limitations to Realize Quantum Zeno Effect in Beam Splitter Array — a Monte Carlo Wavefunction Analysis

Effects of non-ideal optical components in realizing quantum Zeno effect in an all-optical setup are analyzed. Beam splitters are the important components in this experimental configuration. Non-uniform transmission coefficient, photon absorption and thermal noise are considered. Numerical simulation of the experiment is performed using the Monte Carlo wavefunction method. It is argued that there is an optimal number of beam splitters to be used for maximizing the expected output in the experiment.
Variational Neural Networks implementation in Pytorch and JAX

<h2>Abstract</h2> Bayesian Neural Networks consider a distribution over the network's weights, which provides a tool to estimate the uncertainty of a neural network by sampling different models for each input. Variational Neural Networks (VNNs) consider a probability distribution over each layer's outputs and generate parameters for it with the corresponding sub-layers. We provide two Python implementations of VNNs with PyTorch and JAX machine learning libraries that ensure reproducibility of the experimental results and allow implementing uncertainty estimation methods easily in other projects.
Graph-based discriminative features learning for fine-grained image retrieval

Fine-grained image retrieval has gradually become a hot topic in computer vision , which aims to retrieve images with the same subcategories from general visual categories. Though fine-grained image retrieval has made a breakthrough with the development of convolutional neural networks, its performance is still limited by the low discriminative feature embedding. To solve this problem, most prior works focus on mining more discriminative features with various strategies. In this paper, we propose a novel graph-based discriminative features learning network for fine-grained image retrieval (GDF-Net). We first design a global fine-grained feature aggregation module, which reconstructs the discriminative features through capturing context correlation based on a K-Nearest Neighbor graph. To reduce storage overhead and speed up retrieval, we further design a semantic hash encoding module, which generates a semantically compact hash code under the guidance of Cauchy quantization loss and bit balance loss. Validated by extensive experiments and ablation studies, our method consistently outperforms state-of-the-art generic retrieval methods as well as fine-grained retrieval methods on three datasets, e.g., CUB Birds, Stanford Dogs and Stanford Cars.
Development of a Simple Methodology Using Meteorological Data to Evaluate Concentrating Solar Power Production Capacity

Evaluation of the Concentrating Solar Power capacity factor is critical to support decision making on possible regional energy investments. For such evaluations, the System Advisor Model is used to perform capacity factor assessments. Among the required data, information concerning direct normal irradiance is fundamental. In this context, the Engerer model is used to estimate direct normal irradiance hourly values out of global horizontal irradiance ground measurements and other observed meteorological variables. Model parameters were calibrated for direct normal irradiance measurements in Évora (Southern Portugal), being then applied to a network of 90 stations, part of the Portuguese Meteorological Service. From the modelled direct normal irradiance, and for stations that comprise 20 years of data, typical meteorological years were determined. Finally, to identify locations of interest for possible installations of Concentrating Solar Power systems, annual direct normal irradiance availabilities and the respective capacity factor, for a predefined power plant using the System Advisor Model, were produced. Results show annual direct normal irradiance availabilities and capacity factors of up to ~2310 kWh/m2 and ~36.2% in Castro Marim and in Faro, respectively. Moreover, this study supports energy policies that would promote Concentrating Solar Power investments in Southern Portugal (Alentejo and Algarve regions) and eastern centre Portugal (Beira Interior region), which have capacity factors above 30%.
DKT-LCIRT: A Deep Knowledge Tracking Model Integrating Learning Capability and Item Response Theory

In the realm of intelligent education, knowledge tracking is a critical study topic. Deep learning-based knowledge tracking models have better predictive performance compared to traditional knowledge tracking models, but the models are less interpretable and also often ignore the intrinsic differences among students (e.g., learning capability, guessing capability, etc.), resulting in a lack of personalization of predictive results. To further reflect the personalized differences among students and enhance the interpretability of the model at the same time, a Deep Knowledge Tracking model integrating Learning Capability and Item Response Theory (DKT-LCIRT) is proposed. The model dynamically calculates students’ learning capability by each time interval and allocates each student to groups with similar learning capabilities to increase the predictive performance of the model. Furthermore, the model introduces item response theory to enhance the interpretability of the model. Substantial experiments on four real datasets were carried out, and the experimental results showed that the DKT-LCIRT model improved the AUC by 3% and the ACC by 2% compared to other models. The results confirmed that the DKT-LCIRT model outperformed other classical models in terms of predictive performance, fully reflecting students’ individualization and adding a more meaningful interpretation to the model.
A Study on the Current Literary System of Maeil Shinbo : Focused on the Case of Kim Sung-jin

This study develops discussions on a literary prize competition organized by newspaper ‘Maeilsinbo’ and short stories written by Kim Seongjin who became a professional writer through the competition. ‘Maeilsinbo’ began to host the literary prize competition for readers since 1912, to review and publish novels submitted by readers. Through this competition, readers transformed themselves from a passive recipient to a productive writer, and some went on to become professional writers.BR Kim Seongjin represents an exemplary case of publishing a novel through the competition and becoming a professional writer. Through the competition, he wrote and actively published novels including ‘Parakho’, ‘Heoyeongsim’, ‘Sujeonno’, and ‘Japgijaeui Yangyak.’ Characters in these novels are all bound by outdated ideas and old-fashioned lifestyle. They fall for gambling, rely on superstitions, have vain dreams without making efforts, neglect education, and obsess over money. Eventually they are punished in different ways, and all four novels have a narrative structure whereby the characters fail to change their outdated ideas and lifestyle, act foolishly, and get convicted. In the novels, the writer put materials familiar to the readers to grab their attention, and by doing so, attempted to deliver messages of enlightenment more effectively. Novels by Kim Seongjin contained elements of ‘improving social customs’ and ‘stimulating the society’, which ‘Maeilsinbo’ advocated for its readers at the time.
Research on service quality evaluation of medical caring and nursing combined institutions for the aged based on interval MTS-grey cloud clustering

The lack of a scientific and complete service quality evaluation system for Medical Caring and Nursing Combined Institutions for the Aged is a critical factor that makes it difficult to improve the quality of their services. Based on the SERVQUAL model, the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged is constructed from tangibles, security, reliability, responsiveness, and empathy. Considering the ambiguity, randomness, grey characteristics, and the interaction between indicators in the service evaluation process of Medical Caring and Nursing Combined Institutions for the Aged, the interval Mahalanobis-Taguchi system (MTS) is introduced into the grey cloud clustering model, and a service quality evaluation model of the interval MTS— grey cloud clustering is proposed. The Medical Caring and Nursing Combined Institutions for the Aged in four typical cities of Jiangsu Province are taken as examples in this study. Feasibility of the proposed method is verified, and targeted measures are thus proposed to provide stronger support and reference for improving the service quality of these institutions.
IQ-GAN: Instance-Quantized Image Synthesis

For humans, it is natural to decompose an image into objects and background scene. Still, modern generative models usually analyze image at the scene level. Hence, it is challenging to control the style and quality of individual object instances. We propose an instance-quantized conditional generative model for the synthesis of images with high-fidelity instances of multiple classes. Specifically, we train two generators simultaneously: a scene generator that synthesizes the background environment and an instance generator that synthesizes each object instance individually. We design a differentiable image compositing layer that assembles the resulting image and allows effective error back-propagation. For our generators $$G_S$$ and $$G_I$$ we developed a new architecture leveraging modulated convolutional blocks. We evaluate our model and baselines on ADE20k, MHPv2, and Cityscapes datasets to demonstrate that our instance-quantized framework outperforms baselines in terms of FID and mIoU scores. Moreover, our approach allows us to separately control the style of each object and learn fine texture details. We demonstrate the effectiveness of our framework in a wide range of image manipulation tasks.
Image segmentation with boundary-to-pixel direction and magnitude based on watershed and attention mechanism

An improved image segmentation algorithm with boundary-to-pixel direction and magnitude (IS-BPDM) is proposed to deal with small regions segmentation while keeping the accuracy of edge segmentation. First, we develop a BPDM network embedded with watershed and attention module and use an adaptive loss function to learn each pixel’s robust and accurate BPDM which is defined as a two-dimensional vector, including direction and magnitude, and pointing from its nearest boundary pixel to itself. Then, we use the learned BPDMs to obtain the refined initial segmented regions by considering the pixels near boundary have shorter magnitude and near root pixels have longer magnitude, meanwhile adjacent pixels in different regions or nearby pixels on both sides of root pixel in same region have opposite directions and nearby pixels in same region have similar directions. Last, we utilize a fast grouping method according to direction similarity to combine these initial segmented regions into final segmentation. The experimental results show that compared with the state-of-the-art segmentation methods, the IS-BPDM approach proposed in this paper achieves better segmentation accuracy and high computational efficiency and outperforms in small regions segmentation on public datasets.
Classification of Neuron Type Based on Average Activity

Neuronal activity recorded in experiments is the basis for the processing, storage and transmission of information in the brain, as well as functional states. It can also carry information about the structure and type of neuronal cells involved in these processes. This paper proposes a new approach to the analysis of this type of data using modern methods of data classification. It was found that the average activity representation of spike sequences carries information about the type of neuronal cells and allows to effectively classify the initial data.
ABD HUKUKU UYARINCA OTONOM ARAÇLAR ÇAĞINDA TEKEFFÜLLER VE FEDERAL HUKUKUN ÜSTÜNLÜĞÜ

Autonomous vehicle (AV) technology promises society many social, economic, and environmental benefits. It is crucial to examine the legal environment behind this breakthrough technology. Under US law, where the AV technology is most regulated, this Article delves into two critical legal issues AVs will face: i) law of warranties and ii) preemption of state common law claims. This Article argues that the role of warranty claims would be limited because most AVs will not be sold to the public in the first years, meaning that there will be no express or implied warranties. However, we will likely witness AVs will be sold to the public at some point in the future. When this happens, express or implied warranties will arise in the contract between the buyer and the seller. Preemption of common law claims, including those relying on warranties, would bring complex and uncertain problems as to whether these claims are preempted by federal law on AVs. Since previous preemption cases related to conventional motor vehicles involved specific designs, this Article asserts that performanceoriented standards––assessing whether an AV drives more safely than a conventional vehicle––will work around unpredictable preemption questions. Moreover, if the threat of future litigation becomes too great for AV manufacturers, federal preemption could limit AV manufacturers’ liability for compensation to injured plaintiffs. These proposals will constitute pragmatic and efficient solutions to the preemption-related, complicated and uncertain problems.
Contrariety Re-encountered: Nonstandard Contraries and Internal Negation<b>**</b>

Abstract This discussion explores the possibility of distinguishing a tighter notion of contrariety evident in the Square of Opposition, especially in its modal incarnations, than as that binary relation holding statements that cannot both be true, with or without the added rider ‘though can both be false’. More than one theorist has voiced the intuition that the paradigmatic contraries of the traditional Square are related in some such tighter way—involving the specific role played by negation in contrasting them—that distinguishes them from other pairs of incompatible statements constructed from the same conceptual materials. Prominent among examples, these other nonstandard pairs are the ‘new contraries’ presented by Robert Blanché’s hexagon(s) of opposition. With special, though not exclusive, attention to these cases, we investigate whether contrariety in the distinguished sense can be captured by adding to the incompatibility condition the further demand that the pair of statements concerned can be represented as the results of applying some sentence operator to the content in its scope, for one of the pair, and, for the other, the application of that same operator to the negation of that content. For one of the two cases, a Blanché case, of nonstandard contrariety singled out for attention, the question of whether such a representation is available is settled at the end of Section 4, and then in a more satisfying way in Section 5, though for the other case, noticed by Peter Simons, the question remains open, after some tentative discussion in one subsection, 6.2, of an Appendix (Section 6).
Modelling and simulation of a single-motor bi-axial sun tracking mechanism

Abstract This work deals with the modelling and simulation in a virtual environment of an innovative bi-axial solar tracker, with two degrees of freedom (DOF), at which the both movements (diurnal and elevation/altitudinal) are driven by a single motor (which is a linear actuator). The tracking mechanism is used for increasing the energetic efficiency of a photovoltaic (PV) platform. The proposed solution allows to reduce the cost of the PV system by minimizing the number of motors, but still preserving the tracking accuracy of the platform. The tracking system in study is one of azimuthal type, at which the main revolute axis, that of the diurnal movement, is positioned vertically. The linear actuator used as motor source directly drives the diurnal movement, and also generates the elevation movement by intercalating a mechanical transmission that contains a bevel gear planetary mechanism and a planar rotary cam-follower mechanism. The virtual prototyping package ADAMS of MSC.Software was used for modeling and simulating the proposed tracking mechanism.
Focusing on your subject: Deep subject-aware image composition recommendation networks

Abstract Photo composition is one of the most important factors in the aesthetics of photographs. As a popular application, composition recommendation for a photo focusing on a specific subject has been ignored by recent deep-learning-based composition recommendation approaches. In this paper, we propose a subject-aware image composition recommendation method, SAC-Net, which takes an RGB image and a binary subject window mask as input, and returns good compositions as crops containing the subject. Our model first determines candidate scores for all possible coarse cropping windows. The crops with high candidate scores are selected and further refined by regressing their corner points to generate the output recommended cropping windows. The final scores of the refined crops are predicted by a final score regression module. Unlike existing methods that need to preset several cropping windows, our network is able to automatically regress cropping windows with arbitrary aspect ratios and sizes. We propose novel stability losses for maximizing smoothness when changing cropping windows along with view changes. Experimental results show that our method outperforms state-of-the-art methods not only on the subject-aware image composition recommendation task, but also for general purpose composition recommendation. We also have designed a multistage labeling scheme so that a large amount of ranked pairs can be produced economically. We use this scheme to propose the first subject-aware composition dataset SACD, which contains 2777 images, and more than 5 million composition ranked pairs. The SACD dataset is publicly available at https://cg.cs.tsinghua.edu.cn/SACD/ .
Coordinated target tracking in sensor networks by maximizing mutual information

Abstract This paper addresses the problem of coordinated target tracking in sensor networks. For a typical target tracking scene with nonlinear bearing-only measurements, we first investigate the mutual information between multiple sensors and the target state. To improve the performance of target tracking, we analyzed the relative positions between sensor agents and the target to be tracked and derived the optimal positions for sensors in the network by mutual information maximization. Simulation results are presented and discussed to demonstrate that the performance of estimated target states could be improved by the proposed method.