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This paper compares Machine Learning and LSTM-based Deep Learning methods for sentiment analysis of Mobile Legends app reviews. Using a dataset of 10,000 reviews labeled as positive, negative, and neutral, the study evaluates traditional models with TF-IDF and PyCaret AutoML and compares them against an LSTM model designed to capture sequential text dependencies. The results show that the LSTM model outperforms the classical Machine Learning baselines, achieving 92% accuracy and a weighted F1-score of 91%. The findings indicate that deep learning is more effective for handling informal and context-dependent user review text.
This paper describes a rapid feasibility study of using GPT-4, a large language model (LLM), to (semi)automate data extraction in systematic reviews. Despite the recent surge of interest in LLMs there is still a lack of understanding of how to design LLM-based automation tools and how to robustly evaluate their performance. During the 2023 Evidence Synthesis Hackathon we conducted two feasibility studies. Firstly, to automatically extract study characteristics from human clinical, animal, and social science domain studies. We used two studies from each category for prompt-development; and ten for evaluation. Secondly, we used the LLM to predict Participants, Interventions, Controls and Outcomes (PICOs) labelled within 100 abstracts in the EBM-NLP dataset. Overall, results indicated an accuracy of around 80%, with some variability between domains (82% for human clinical, 80% for animal, and 72% for studies of human social sciences). Causal inference methods and study design were the data extraction items with the most errors. In the PICO study, participants and intervention/control showed high accuracy (>80%), outcomes were more challenging. Evaluation was done manually; scoring
In this study, we investigate how supporting serendipitous discovery and analysis of online product reviews can encourage readers to explore reviews more comprehensively prior to making purchase decisions. We propose two interventions -- Exploration Metrics that can help readers understand and track their exploration patterns through visual indicators and a Bias Mitigation Model that intends to maximize knowledge discovery by suggesting sentiment and semantically diverse reviews. We designed, developed, and evaluated a text analytics system called Serendyze, where we integrated these interventions. We asked 100 crowd workers to use Serendyze to make purchase decisions based on product reviews. Our evaluation suggests that exploration metrics enabled readers to efficiently cover more reviews in a balanced way, and suggestions from the bias mitigation model influenced readers to make confident data-driven decisions. We discuss the role of user agency and trust in text-level analysis systems and their applicability in domains beyond review exploration.
Book reviews play important roles in scholarly communication especially in arts and humanities disciplines. By using Web of Science's Science Citation Index Expanded, Social Sciences Citation Index, and Arts & Humanities Citation Index, this study probed the patterns and dynamics of book reviews within these three indexes empirically during the past decade (2006-2015). We found that the absolute numbers of book reviews among all the three indexes were relatively stable but the relative shares were decreasing. Book reviews were very common in arts and humanities, common in social sciences, but rare in natural sciences. Book reviews are mainly contributed by authors from developed economies such as the USA and the UK. Oppositely, scholars from China and Japan are unlikely to contribute to book reviews.
As first discovered by Choptuik, the black hole threshold in the space of initial data for general relativity shows both surprising structure and surprising simplicity. Universality, power-law scaling of the black hole mass, and scale echoing have given rise to the term ``critical phenomena''. They are explained by the existence of exact solutions which are attractors within the black hole threshold, that is, attractors of codimension one in phase space, and which are typically self-similar. This review gives an introduction to the phenomena, tries to summarize the essential features of what is happening, and then presents extensions and applications of this basic scenario. Critical phenomena are of interest particularly for creating surprising structure from simple equations, and for the light they throw on cosmic censorship and the generic dynamics of general relativity.
Ferromagnets are key materials for sensing and memory applications. In contrast, antiferromagnets that represent the more common form of magnetically ordered materials, have so far found less practical application beyond their use for establishing reference magnetic orientations via exchange bias. This might change in the future due to the recent progress in materials research and discoveries of antiferromagnetic spintronic phenomena suitable for device applications. Experimental demonstrations of the electrical switching and electrical detection of the Néel order open a route towards memory devices based on antiferromagnets. Apart from the radiation and magnetic-field hardness, memory cells fabricated in antiferromagnets are inherently multilevel which could be used for neuromorphic computing. Switching speeds attainable in antiferromagnets far exceed those of the ferromagnetic and semiconductor memory technologies. Here we review the recent progress in electronic spin-transport and spin-torque phenomena in antiferromagnets that are dominantly of the relativistic quantum mechanics origin. We discuss their utility in pure antiferromagnetic or hybrid ferromagnetic/antiferromagnetic
This focused issue attempts to provide a comprehensive introduction into the field of antiferromagnetic spintronics. Apart from the brief overview below, it features five review articles. The intention is to cover in a coherent and complementary way key physical aspects of the antiferromagnetic spintronics research. These range from microelectronic memory devices and optical manipulation and detection of antiferromagnetic spins, to the fundamentals of antiferromagnetic dynamics in uniform or spin-textured systems, and to the interplay of antiferromagnetic spintronics with topological phenomena. The antiferromagnetic ordering can take a number of forms including fully compensated collinear, non-collinear, and non-coplanar magnetic lattices, compensated and uncompensated ferrimagnets, or metamagnetic materials hosting an antiferromagnetic to ferromagnetic phase transition. Apart from the variety of distinct magnetic crystal structures, the focused issue also encompasses spintronic phenomena and devices studied in antiferromagnet/ferromagnet heterostructures and in synthetic antiferromagnets.
The recent demonstrations of electrical manipulation and detection of antiferromagnetic spins have opened up a chapter in the spintronics story. In this article, we review the emerging research field that is exploring synergies between antiferromagnetic spintronics and topological structures in real and momentum space. Active topics include proposals to realize Majorana fermions in an antiferromagnetic topological superconductors, to control topological protection of Dirac points by manipulating antiferromagnetic order parameters, and to exploit the anomalous and topological Hall effects of zero net moment antiferromagnets. We explain the basic physics concepts behind these proposals, and discuss potential applications of topological antiferromagnetic spintronics.
Antiferromagnets as active elements of spintronics can be faster than their ferromagnetic counterparts and more robust to magnetic noise. Owing to the strongly exchange-coupled magnetic sublattice structure, antiferromagnetic order parameter dynamics are qualitatively different and thus capable of engendering novel device functionalities. In this review, we discuss antiferromagnetic textures -- nanoparticles, domain walls, and skyrmions, -- under the action of different spin torques. We contrast the antiferromagnetic and ferromagnetic dynamics, with a focus on the features that can be relevant for applications.
This paper investigates sentiment classification of Steam game reviews using an attention-based Bidirectional Long Short-Term Memory (BiLSTM) model. Using a dataset of 50,000 reviews sampled from a larger Steam review corpus, the authors compare a traditional machine learning baseline based on TF-IDF and PyCaret AutoML with a deep learning approach implemented in PyTorch. The proposed BiLSTM+Attention model is trained with class-weighted cross-entropy to address class imbalance and achieves 83% accuracy and 85% weighted F1-score on the test set, with 90% recall for negative reviews. The paper also presents attention visualizations to show interpretability by highlighting sentiment-bearing words. The study concludes that the BiLSTM+Attention model is effective for analyzing user sentiment in Steam reviews and useful for helping developers understand player feedback.
Ongoing research in the area of advanced cathode materials for sodium-ion batteries (SIBs) is expected to reduce reliance on lithium-ion batteries (LIBs), providing more affordable and sustainable energy storage solutions. Polyanionic compounds have emerged as promising options due to their stable structure and ability to withstand high-voltage conditions as well as fast charging capabilities. This review offers a thorough discussion of phosphate-based polyanionic cathodes for SIBs, exploring their structure, electrochemical performance with various transition metals, and existing challenges. We discuss different polyanionic frameworks, such as ortho-phosphates, fluoro-phosphates, pyro-phosphates, mix pyro-phosphates, and NASICON-based phosphates, highlighting their unique structural characteristics and ability to perform well across a wide potential range. Further, we delve into the mechanisms governing sodium storage and tunability of redox potentials in polyanionic materials, providing insights into the factors that affect their electrochemical performance. Finally, we outline future research directions and potential avenues for the practical applications of polyanionic high-vol
Ultralight dark matter refers to the lightest potential dark matter candidates. We will focus on the mass range that has been studied using astrophysical and cosmological observations, corresponding to a mass $10^{-24} \, \mathrm{eV} \lesssim m \lesssim 10^{-18} \, \mathrm{eV}$. We will discuss the motivations for this mass range. The most studied model in this range corresponds to a minimally coupled, single, classical, spin-0 field comprising all dark matter. However, the work exploring extensions of this model (for example, higher spin, self-coupled, multiple field, and mixed models) will be one of the focuses of this review. The phenomenology associated with ultralight dark matter is rich and includes linear effects on the primordial power spectrum, core structures forming at the center of halos, nonlinear effects resulting in heating of stellar distributions, and non-relativistic effects relating to pulsar signals and black hole superradiance, to name a few. This set of effects has been studied using an equally extensive set of numerical tools. We will summarize the most common ones and discuss their applications and limitations. Ultralight dark matter also has a wide variety
Increasing demands on medical imaging departments are taking a toll on the radiologist's ability to deliver timely and accurate reports. Recent technological advances in artificial intelligence have demonstrated great potential for automatic radiology report generation (ARRG), sparking an explosion of research. This survey paper conducts a methodological review of contemporary ARRG approaches by way of (i) assessing datasets based on characteristics, such as availability, size, and adoption rate, (ii) examining deep learning training methods, such as contrastive learning and reinforcement learning, (iii) exploring state-of-the-art model architectures, including variations of CNN and transformer models, (iv) outlining techniques integrating clinical knowledge through multimodal inputs and knowledge graphs, and (v) scrutinising current model evaluation techniques, including commonly applied NLP metrics and qualitative clinical reviews. Furthermore, the quantitative results of the reviewed models are analysed, where the top performing models are examined to seek further insights. Finally, potential new directions are highlighted, with the adoption of additional datasets from other rad
This paper reviews the work done on black hole interior volume, entropy, and evaporation. An insight into the basics for understanding the interior volume is presented. A general analogy to investigate the interior volume of a black hole, the associated quantum mode's entropy, and the evolution relation between the interior and exterior entropy is explained. Using this analogy, we predicted the future of information stored in a BH, its radiation, and evaporation. The results are noted in tables (\ref{tab:1}) and (\ref{tab:2}). To apply this analogy in BH space-time, we investigated the interior volume, entropy, and evaluation relation for different types of BHs. Finally, we also investigated the nature of BH radiation and the probability of particle emission during the evaporation process.
The rapid development of science and technology has been accompanied by an exponential growth in peer-reviewed scientific publications. At the same time, the review of each paper is a laborious process that must be carried out by subject matter experts. Thus, providing high-quality reviews of this growing number of papers is a significant challenge. In this work, we ask the question "can we automate scientific reviewing?", discussing the possibility of using state-of-the-art natural language processing (NLP) models to generate first-pass peer reviews for scientific papers. Arguably the most difficult part of this is defining what a "good" review is in the first place, so we first discuss possible evaluation measures for such reviews. We then collect a dataset of papers in the machine learning domain, annotate them with different aspects of content covered in each review, and train targeted summarization models that take in papers to generate reviews. Comprehensive experimental results show that system-generated reviews tend to touch upon more aspects of the paper than human-written reviews, but the generated text can suffer from lower constructiveness for all aspects except the exp
Better representation of the uncertainty in a data visualisation is a focus of recent research activity. A problem with the current literature is that there is a lack of clarity about the definition of uncertainty and what it means to represent it in a plot. This confusion results in a significant amount of conflicting results in the literature, especially in experiments that assess the effectiveness of different uncertainty representations. In this review, we summarise the current literature, provide workable definitions, and illustrate these definitions with examples. In doing so, we ask what it really takes to achieve transparency in statistical graphics. It is hoped that it will be useful for guiding new graphics methodology and experimental research.
The detection of out-of-distribution data points is a common task in particle physics. It is used for monitoring complex particle detectors or for identifying rare and unexpected events that may be indicative of new phenomena or physics beyond the Standard Model. Recent advances in Machine Learning for anomaly detection have encouraged the utilization of such techniques on particle physics problems. This review article provides an overview of the state-of-the-art techniques for anomaly detection in particle physics using machine learning. We discuss the challenges associated with anomaly detection in large and complex data sets, such as those produced by high-energy particle colliders, and highlight some of the successful applications of anomaly detection in particle physics experiments.
In this paper, I review the book: Gary S. Berger, Michael DiRuggiero. "Einstein: The Man and His Mind." Bologna: Damiani, 2022. Illustrations. 209 pp. $70.00, cloth, ISBN 978-88-6208-784-1.
In modern collider experiments, the quest to explore fundamental interactions between elementary particles has reached unparalleled levels of precision. Signatures from particle physics detectors are low-level objects (such as energy depositions or tracks) encoding the physics of collisions (the final state particles of hard scattering interactions). The complete simulation of them in a detector is a computational and storage-intensive task. To address this computational bottleneck in particle physics, alternative approaches have been developed, introducing additional assumptions and trade off accuracy for speed.The field has seen a surge in interest in surrogate modeling the detector simulation, fueled by the advancements in deep generative models. These models aim to generate responses that are statistically identical to the observed data. In this paper, we conduct a comprehensive and exhaustive taxonomic review of the existing literature on the simulation of detector signatures from both methodological and application-wise perspectives. Initially, we formulate the problem of detector signature simulation and discuss its different variations that can be unified. Next, we classify
Governments' net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing of energy supply and demand. These will be achieved by the advanced information and control infrastructures of smart grids which allow the interoperability among various stakeholders. Under this circumstance, increasing number of consumers produce, store, and consume energy, giving them a new role of prosumers. The integration of prosumers and accommodation of incurred bidirectional flows of energy and information rely on two key factors: flexible structures of energy markets and intelligent operations of power systems. The blockchain and artificial intelligence (AI) are innovative technologies to fulfil these two factors, by which the blockchain provides decentralised trading platforms for energy markets and the AI supports the optimal operational control of power systems. This paper attempts to address how to incorporate the blockchain and AI in the smart grids for facilitating prosumers to participate in energy markets. To achieve this objective, first, this paper reviews how policy designs price