Reviewerly/exHarmony huggingface dataset:
Multiple objectives dynamic VM placement for application service availability in cloud networks

Abstract Ensuring application service availability is a critical aspect of delivering quality cloud computing services. However, placing virtual machines (VMs) on computing servers to provision these services can present significant challenges, particularly in terms of meeting the requirements of application service providers. In this paper, we present a framework that addresses the NP-hard dynamic VM placement problem in order to optimize application availability in cloud computing paradigm. The problem is modeled as an integer nonlinear programming (INLP) optimization with multiple objectives and constraints. The framework comprises three major modules that use optimization methods and algorithms to determine the most effective VM placement strategy in cases of application deployment, failure, and scaling. Our primary goals are to minimize power consumption, resource waste, and server failures while also ensuring that application availability requirements are met. We compare our proposed heuristic VM placement solution with three related algorithms from the literature and find that it outperforms them in several key areas. Our solution is able to admit more applications, reduce power consumption, and increase CPU and RAM utilization of the servers. Moreover, we use a deep learning method that has high accuracy and low error loss to predict application task failures, allowing for proactive protection actions to reduce service outage. Overall, our framework provides a comprehensive solution by optimizing dynamic VM placement. Therefore, the framework can improve the quality of cloud computing services and enhance the experience for users.
A Noval Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

This work proposes a novel approach to object recognition, particularly for human faces, based on the principle of human cognition. The suggested approach can handle a dataset or problem with a large number of classes for classification more effectively. The model for the facial recognition-based object detection system was constructed using a combination of decision tree clustering based multi-level Backpropagation neural network classifier-TFMLBPNN-DTC and hybrid texture feature (ILMFD+GLCM) and applied on NS and ORL databases. This model produced the classification accuracy (±standard deviation) of 95.37 ±0.951877% and 90.83 ± 1.374369% for single input and 96.58 ±0.5604582% and 91.50 ± 2.850439% for group-based decision for NS and ORL database respectively. The better classification results encourage its application to other object recognition and classification issues. This work's basic idea also makes it easier to improve classification management for a wide range of classes.
Reliable Breast Cancer Diagnosis with Deep Learning: DCGAN-Driven Mammogram Synthesis and Validity Assessment

Breast cancer imaging is paramount to quickly detecting and accurately evaluating the disease. The scarcity of annotated mammogram data presents a significant obstacle when building deep learning models that can produce reliable outcomes. This paper proposes a novel approach that utilizes deep convolutional generative adversarial networks (DCGANs) to effectively tackle the issue of limited data availability. The main goal is to produce synthetic mammograms that accurately reproduce the intrinsic patterns observed in real data, enhancing the current dataset. The proposed synthesis method is supported by thorough experimentation, demonstrating its ability to reproduce diverse viewpoints of the breast accurately. A mean similarity assessment with a standard deviation was performed to evaluate the credibility of the synthesized images and establish the clinical significance of the data obtained. A thorough evaluation of the uniformity within each class was conducted, and any deviations from each class’s mean values were measured. Including outlier removal using a specified threshold is a crucial process element. This procedure improves the accuracy level of each image cluster and strengthens the synthetic dataset’s general dependability. The visualization of the class clustering results highlights the alignment between the produced images and the inherent distribution of the data. After removing outliers, distinct and consistent clusters of homogeneous data points were observed. The proposed similarity assessment demonstrates noteworthy effectiveness, eliminating redundant and dissimilar images from all classes. Specifically, there are 505 instances in the normal class, 495 instances in the benign class, and 490 instances in the malignant class out of 600 synthetic mammograms for each class. To check the further validity of the proposed model, human experts visually inspected and validated synthetic images. This highlights the effectiveness of our methodology in identifying substantial outliers.
Report for ISSMGE TC309/TC304/TC222 fourth ML dialogue on “Machine learning supremacy projects”

The ISSMGE TC309/TC304/TC222 and ASCE Geo-Institute Risk Assessment Management Committee Fourth Machine Learning (ML) in Geotechnics Dialogue was held at the Okayama Convention Center on 5 December 2023. The dialogue focused on “machine learning supremacy projects”, which involve the use of novel ML techniques to analyse emerging data, potentially bringing disruptive value to transform research and practice. The discussions centred around four real-world projects covering tunnelling, pile foundations, and geophysical investigation, to showcase the latest developments and implementations of ML, exchange views on the associated technical and non-technical challenges, and explore further developments to transform practice on a broader scale. These projects highlight the need for efficient ML techniques to analyse large volumes of real-time data for 3D heterogeneous soil/rock domains, and the benefits of standardising data formats among practitioners and researchers. Meanwhile, as data become valuable assets, there is a need to establish guidelines or protocols to protect data privacy and ensure data trustworthiness while creating incentives for owners to share data. Several recommendations include (1) creating benchmark examples using real data and Class A prediction (i.e. prediction made before the event) exercises from real projects to accelerate research in ML methods and (2) involving regulators in future dialogue and exploring the incorporation of data-driven approaches in design codes to complement traditional methods.
Which pedagogical approaches are more effective in mobile learning? A meta‐analysis and research synthesis

Abstract Background Study Several meta‐analysis studies have investigated the effects of mobile learning on learning performance. However, limited attention has been paid to pedagogy in mobile learning, making quantitative evidence of the effects of pedagogical approaches on learning performance in mobile learning scarce. Filling this gap can therefore help stakeholders understand which mobile pedagogical approaches might work or not under which learning conditions, hence achieving better learning experience and performance. Objectives To address this gap, this study conducted a meta‐analysis and research synthesis of the effects of integrated pedagogical approaches on students' learning performance in mobile learning. Additionally, this study analysed the field of education, level of education, learning setting, sample size, and mobile device as moderating variables of the effect of pedagogical approaches. Methods The software Comprehensive Meta‐Analysis V.3 was used for this meta‐analysis, where Hedges' g was calculated for the effect sizes. Specifically, 70 quantitative studies ( N = 5575 participants) were coded and analysed. Results The results indicate that pedagogical approaches in mobile learning have a large effect on students' learning performance ( g = 0.93, p &lt; 0.001). The most effective pedagogical approach was project‐based learning (huge effect), while collaborative learning, situated learning and game‐based learning had large effects. Finally, cognitive theory of multimedia learning and inquiry‐based learning had medium effects. The results also indicate that the effect is moderated by the field of education, the level of education, the learning setting, the sample size, and the mobile device. Finally, it is found that study quality might influence the overall effect size. Conclusions The findings of this study can be beneficial to both researchers and practitioners as they highlight and discuss which pedagogical approaches could be more effective in mobile learning under specific conditions. Further research, on the other hand, should focus on covering additional moderators of learning performance like mobile device type and screen size which is one of the limitations of this study.
Blockchain Empowered Solar Energy Trading: A Decentralized Grid Integration Revolution

The growing prominence of solar energy in decentralized renewable energy landscapes underscores the need for efficient solar energy trading mechanisms and seamless grid integration. This decentralized renewable energy sources, especially solar energy, present a significant prospect for the production of sustainable energy. This investigation explores the use of blockchain technology as a basic framework to tackle the problems associated with grid integration and solar energy trading in a decentralized setting. It uses the built-in advantages of blockchain technology, such as trust, transparency, and smart contract automation, to address the design and execution of a platform tailored for solar energy. As a result, the system efficiency increased to 93% and the transaction confirmation time was cut from 4.5 to 2.5 s with the lowest possible transaction cost. Furthermore, this study intends to offer insightful information about the technological viability, security, and scalability of blockchain solutions in the field of solar energy, while highlighting the significance of indicators of performance for an in-depth evaluation. This novel strategy is a crucial development for the field of green energy as it promises to revolutionize the way solar energy is traded, used, and integrated into modern grids.
Semantic and Geometric-Aware Day-to-Night Image Translation Network

Autonomous driving systems heavily depend on perception tasks for optimal performance. However, the prevailing datasets are primarily focused on scenarios with clear visibility (i.e., sunny and daytime). This concentration poses challenges in training deep-learning-based perception models for environments with adverse conditions (e.g., rainy and nighttime). In this paper, we propose an unsupervised network designed for the translation of images from day-to-night to solve the ill-posed problem of learning the mapping between domains with unpaired data. The proposed method involves extracting both semantic and geometric information from input images in the form of attention maps. We assume that the multi-task network can extract semantic and geometric information during the estimation of semantic segmentation and depth maps, respectively. The image-to-image translation network integrates the two distinct types of extracted information, employing them as spatial attention maps. We compare our method with related works both qualitatively and quantitatively. The proposed method shows both qualitative and qualitative improvements in visual presentation over related work.
The efficacy of augmented reality exposure therapy in the treatment of spider phobia—a randomized controlled trial

The evidence for the use of Augmented Reality (AR) in treating specific phobias has been growing. However, issues of accessibility persist, especially in developing countries. The current study examined a novel, but relatively simple therapist guided smartphone-based AR Exposure Treatment (ARET) of spider phobia. Participants who reported symptoms of Arachnophobia were randomized into one of three comparison groups: ARET (n = 20), traditional in vivo exposure therapy (IVET; n = 18) and a waitlist control group (n = 17). Behavioral approach, subjective symptom measures, and galvanic skin response were assessed pre- and post-treatment. The study was concluded with a one-month follow up assessment. Results indicated that both treatment groups showed statistically significant and clinically meaningful improvements in behavioral approach at post-test that were maintained at 1 month follow- up, compared to the wait-listed group. Moreover, the treatment groups demonstrated significant improvements in subjective symptom report at 1-month follow up. Given its utility and potential accessibility, our findings suggest that future AR evaluation research could be conducted in therapy settings with minimal resources.
A Path Planning System for Orchard Mower Based on Improved A* Algorithm

The application of intelligent mobile robots in agriculture has emerged as a new research frontier, with the integration of autonomous navigation technology and intelligent agricultural robots being the key to the widespread adoption of smart agricultural machinery. This paper investigates comprehensive coverage path planning for tracked lawnmowers within orchard environments and addresses challenges related to task allocation and battery life. Firstly, in this study, the motion model of the tracked lawnmower was initially simplified based on assumptions about the orchard environment. Force analyses were conducted on each of its motion mechanisms. For the known orchard environment, a grid-based mapping technique was employed to model the orchard environment. Then, in order to improve the algorithm speed and reduce the number of turns during the lawnmower’s traversal, the A* search algorithm was enhanced by combining the method of robot cluster traversal in the orchard environment. Finally, the improved method was simulated and verified in the MATLAB platform to investigate the influence of the number of lawnmower clusters on the path planning in the connected and non-connected orchards. Furthermore, two sets of on-site field trials were meticulously designed to validate the reliability, practicality, and efficacy of the simulation experiments.
Investigating translanguaging strategies and online self-presentation through internet slang on Douyin (Chinese TikTok)

Abstract This study explores the translanguaging strategies used in internet slang on Douyin, a popular social media platform in China. By analyzing 331 examples (encompassing 313 translanguaging strategies), we investigate how individuals utilize various translanguaging resources, including multimodal, multilingual, multisemiotic, and multisensory resources, to manage their online self-presentation. Our findings suggest that multilingual resources, such as new Chinglish, Pinyin initialism, and hybrid words (combining Chinese, English, and Arabic numerals), were the most frequently used strategies. Additionally, we found that multisensory resources, including homophonic puns and modal particles, were also commonly utilized. In contrast, multimodal resources, such as emojis, and multisemiotic resources, such as keyboard-generated emoticon, hashtag, and punctuation mark, were less frequently employed. These findings reveal that Chinese internet users display a high degree of creativity and adaptability in their online communication, drawing on a wide range of linguistic and semiotic resources to express their identities and project their desired image. Overall, this study highlights the importance of translanguaging in online communication and its role in shaping self-presentation in the digital age.
Ensuring Consistency between Noise and Detection in Hierarchical Bayesian Inference

Abstract Hierarchical Bayesian inference can simultaneously account for both measurement uncertainty and selection effects within astronomical catalogs. In particular, the imposed hierarchy encodes beliefs about the interdependence of the physical processes that generate the observed data. We show that several proposed approximations within the literature actually correspond to inferences that are incompatible with any physical detection process. This generically leads to biases and is associated with the assumption that detectability is independent of the observed data given the true source parameters. We show several examples of how this error can affect astrophysical inferences based on catalogs of coalescing binaries observed through gravitational waves, including misestimating the redshift evolution of the merger rate as well as incorrectly inferring that general relativity is the correct theory of gravity when it is not. In general, one cannot directly fit for the “detected distribution” and “divide out” the selection effects in post-processing. Similarly, when comparing theoretical predictions to observations, it is better to simulate detected data (including both measurement noise and selection effects) rather than comparing estimates of the detected distributions of event parameters (which include only selection effects). While the biases introduced by model misspecification from incorrect assumptions may be smaller than statistical uncertainty for moderate catalog sizes ( O (100) events), they will nevertheless pose a significant barrier to precision measurements of astrophysical populations.
Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

Abstract This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.
A Comprehensive Examination of Mobile Augmented Reality in Tourism (MART) Adoption

This research investigates the adoption of mobile augmented reality in tourism (MART) utilizing the unified theory of acceptance and use of technology two (UTAUT2) framework. Centered on Pondicherry, India, the research specifically investigates the integration of AI-powered services through the MART platform in the tourism sector. Analyzing data from 137 participants who engaged with MART for service bookings, the study employs descriptive and inferential statistics. It scrutinizes the influence of perceived effectiveness, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit on users' behavioral intention. The findings unveil significant positive impacts, underscoring the pivotal role these factors play in shaping users' intentions to embrace MART. This research contributes valuable insights to the comprehension of technology adoption within the tourism domain, offering practical implications for designers and policymakers aiming to enhance user experiences and foster widespread MART adoption.
Learning likelihood ratios with neural network classifiers

A bstract The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions, and more. Many modern scientific applications, however, make use of data- or simulation-driven models for which computing the likelihood ratio can be very difficult or even impossible. By applying the so-called “likelihood ratio trick,” approximations of the likelihood ratio may be computed using clever parametrizations of neural network-based classifiers. A number of different neural network setups can be defined to satisfy this procedure, each with varying performance in approximating the likelihood ratio when using finite training data. We present a series of empirical studies detailing the performance of several common loss functionals and parametrizations of the classifier output in approximating the likelihood ratio of two univariate and multivariate Gaussian distributions as well as simulated high-energy particle physics datasets.
A hybrid quantum algorithm to detect conical intersections

Conical intersections are topologically protected crossings between the potential energy surfaces of a molecular Hamiltonian, known to play an important role in chemical processes such as photoisomerization and non-radiative relaxation. They are characterized by a non-zero Berry phase, which is a topological invariant defined on a closed path in atomic coordinate space, taking the value <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&amp;#x03C0;</mml:mi></mml:math> when the path encircles the intersection manifold. In this work, we show that for real molecular Hamiltonians, the Berry phase can be obtained by tracing a local optimum of a variational ansatz along the chosen path and estimating the overlap between the initial and final state with a control-free Hadamard test. Moreover, by discretizing the path into <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> points, we can use <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> single Newton-Raphson steps to update our state non-variationally. Finally, since the Berry phase can only take two discrete values (0 or <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&amp;#x03C0;</mml:mi></mml:math>), our procedure succeeds even for a cumulative error bounded by a constant; this allows us to bound the total sampling cost and to readily verify the success of the procedure. We demonstrate numerically the application of our algorithm on small toy models of the formaldimine molecule (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:msub><mml:mi>H</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi>C</mml:mi><mml:mo>=</mml:mo><mml:mi>N</mml:mi><mml:mi>H</mml:mi></mml:mrow></mml:math>).
Self-Supervised High Dynamic Range Imaging: What Can Be Learned from a Single 8-bit Video?

Recently, Deep Learning-based methods for inverse tone mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular. These methods manage to fill over-exposed areas convincingly both in terms of details and dynamic range. To be effective, deep learning-based methods need to learn from large datasets and transfer this knowledge to the network weights. In this work, we tackle this problem from a completely different perspective. What can we learn from a single SDR 8-bit video? With the presented self-supervised approach, we show that, in many cases, a single SDR video is sufficient to generate an HDR video of the same quality or better than other state-of-the-art methods.