Reviewerly/exHarmony huggingface dataset:
PGTask: Introducing the Task of Profile Generation from Dialogues

Recent approaches have attempted to personalize dialogue systems by leveraging profile information into models. However, this knowledge is scarce and difficult to obtain, which makes the extraction/generation of profile information from dialogues a fundamental asset. To surpass this limitation, we introduce the Profile Generation Task (PGTask). We contribute with a new dataset for this problem, comprising profile sentences aligned with related utterances, extracted from a corpus of dialogues. Furthermore, using state-of-the-art methods, we provide a benchmark for profile generation on this novel dataset. Our experiments disclose the challenges of profile generation, and we hope that this introduces a new research direction.
Single Image HDR Synthesis with Histogram Learning

High dynamic range imaging aims for a more accurate representation of the scene. It provides a large luminance coverage to yield the human perception range. In this paper, we present a technique to synthesize an HDR image from the LDR input. The proposed two-stage approach expands the dynamic range and predict its histogram with cumulative histogram learning. Histogram matching is then carried out to reallocate the pixel intensity. In the second stage, HDR images are constructed using reinforcement learning with pixel-wise rewards for local consistency adjustment. Experiments are conducted on HDR-Real and HDR-EYE datasets. The quantitative evaluation on HDR-VDP-2, PSNR, and SSIM have demonstrated the effectiveness compared to the state-of-the-art techniques.
Summary

“Explainable Machine Learning in Medicine” is a comprehensive and insightful book that delves into the emerging field of explainable artificial intelligence (XAI) and its applications in the medical domain. This book provides a thorough exploration of the challenges and opportunities that arise when integrating machine-learning models into medical decision-making processes.
Introduction

Medical data refers to any type of information related to a patient’s health status, medical history, treatments, and outcomes. This data can be collected from various sources, such as electronic health records (EHRs), medical imaging, genomic testing, clinical trials, and public health surveillance. Here are some examples of medical data and their applications.
Time Moves Faster When There is Nothing You Anticipate: The Role of Time in MEV Rewards

We present a novel analysis of a competitive dynamic present on Ethereum known as "waiting games", where validators can use their distinct monopoly position in their assigned slots to delay block proposals in order to optimize returns through Maximal Extractable Value (MEV) payments, a type of incentive outside the Proof-of-Stake incentive scheme. However, this strategy risks block exclusion due to missed slots or potential orphaning. Our analysis reveals evidence that, although there are substantial incentives to undertaking the risks, validators are not capitalizing on waiting games, leaving potential profits unrealized. Moreover, we present an agent-based model to test the eventual consensus disruption caused by waiting games under different settings, arguing that such disruption only occurs with significant delay strategies. Ultimately, this research provides in-depth insights into Ethereum's waiting games, illuminating the trade-offs and potential profit opportunities for validators in this evolving blockchain landscape.
Developing students’ code style skills

Developing the skills of writing clean code in novice programmers is an urgent and essential task for today’s university teachers. Despite numerous studies of this problem, the optimal solution has not yet been found. The research examines the motivation of students to adhere to the code style without any particular methodological influence. It is shown that students with an average of 2.2 years of programming experience understand code style rules and say they are ready to follow them. However, their code contains numerous style errors, which indicate the need for additional pedagogical efforts. In the second part of the study, we present the results of the experiment with another group of students. We applied the method of penalizing students for violating code style requirements, and it showed a positive impact: the total number of errors decreased. Optimistic results were obtained for many criteria. However, the same approach should be applied in subsequent courses to consolidate a positive impact. The paper proposes a method for developing students’ code style skills.
Optimizing Deep Neural Network with Brain Floating Half-Precision Format

Deep neural network (DNN) are becoming common in the industry due to their exceptional performance. This growing utilizations requires larger networks to achieve better performance. Increasing the size of a neural network typically improves accuracy but also increases the memory and computation requirements for training the model. The trade-off between the model accuracy and computational requirements is a crucial consideration in the design and optimization of neural networks. The training of deep neural network (DNN) also incurs high hardware computation costs. To address these issues, we propose adopting the Brain Floating Point (BFLOAT16) half-precision format. BFLOAT16 shares FP32's value range while requiring the same memory as FP16, simplifying conversion without hyper parameter tuning. It accelerates convergence while maintaining accuracy, as seen with Yolov2 Tiny model achieving 47.1 mAP, close to FP32's 47.2. The training time is also reduced to 17.6 Hours from 19.02 Hours taken by the FP32 model. The BFLOAT16 format offers a viable solution for cost-effective DNN training of larger neural networks while improving speed and reducing the hardware cost.
Guided filtering based surface recovery algorithm for white light interferometry

White Light Interferometry (WLI) is a widely used technique for surface recovery. However, it is extremely sensitive to various external disturbances, increasing the uncertainty on measurement results. In this paper, a time-domain guided filtering-based surface recovery algorithm is proposed for WLI. The reference signal is firstly simulated according to the spectral map of illuminator employed in the system. The correlation between the actual correlogram and the simulated one is then analyzed through the generalized cubic correlation delay estimation method. The corrected correlogram is obtained as a local linear transformation of the reference one that has been shifted, where the linear coefficients are estimated using least squares analysis. The surface height is then retrieved based on mapping relationship between the phase and frequency. The capability of the proposed method on noise suppression is investigated through simulation under different levels of additive noise. In the experiment, a step height standard (VLSI,181.0nm±2.1nm) is employed, which verifies the performance of the proposed method on measurement accuracy and reliability.
AI Pair Programming Tool

Abstract: In the ever-evolving landscape of software development, the need for tools that augment developer productivity is paramount. This paper introduces a novel integration of IntelliJ IDEA, a widely used integrated development environment (IDE), with OpenAI's advanced deep learning models. The synergy between these two technologies aims to elevate the coding experience by providing intelligent code assistance, allowing developers to write code more efficiently and with greater accuracy. Our approach leverages OpenAI's natural language processing capabilities to enhance code suggestions, auto-completions, and error detection within IntelliJ IDEA. By analyzing vast code repositories and learning from diverse coding patterns, our system empowers developers with context-aware recommendations, significantly reducing the cognitive load associated with code creation. This paper presents the architecture of our solution, detailing the seamless integration of OpenAI's models into the IntelliJ IDEA IDE. We discuss the methodology behind training the models, emphasizing their adaptability to various programming languages and project structures. Furthermore, we present empirical results demonstrating the effectiveness of our approach in real-world coding scenarios.
A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization

Air combat situation assessment is the basis of target assignment and maneuver decisions. The current air combat situation assessment models, whether nonparametric or parametric, ignore the continuity and timing of situation changes, making the situation assessment results lose tactical significance. Aimed at the shortcomings of current air combat situation assessment, a dynamic air combat situation assessment model based on situation knowledge extraction and weight optimization was proposed by combining a multiple regression model of hidden logic process, a weight optimization model based on grey prospect theory, a weight mapping model based on autoencoder and extreme learning machine (AE-ELM) and an air combat situation characteristic parameter prediction model based on dynamic weight online extreme learning machine (DWOSELM). Firstly, considering the timing and continuity of air combat situation change, a hidden logic process multiple regression model was introduced to realize the segmentation of air combat situation time series data and the extraction of air combat situation primitives. Secondly, the weight optimization method based on grey prospect theory was used to obtain the weight of the evaluation index under different air combat situations. On this basis, the dynamic mapping model between air combat situation characteristic parameters and the weight of index was constructed by using AE-ELM. Then, the dynamic weighted extreme learning machine was used to build the target maneuver trajectory prediction model, and the future position information of the target was predicted. On this basis, the future situation information between the enemy and us was obtained. Finally, the time weight calculation model based on normal cumulative distribution was used to determine the importance of the situation at each time. The situation information at multiple times in the air combat process was fused to obtain the comprehensive air combat situation assessment results at the current time. The simulation results show that the model can fully exploit the influence of historical information, effectively integrate the air combat situation information at multiple moments, and generate the air combat situation assessment results with practical tactical significance according to the individual differences of different pilots.
Assessing language impoliteness of primary school teachers in Indonesia

Purpose This study investigated the use of language impoliteness of primary school teachers in communicating with their students during the learning process. Design/methodology/approach The methodology of this study used a mixed-method approach by involving 100 primary school teachers in the fifth and sixth grades. The researcher used the language politeness/impoliteness scale developed by Mantasiah et al. (2019) to measure teachers' language impoliteness. This scale was developed based on Brown and Levinson's theory who divided politeness maxims into six consisting of tact maxim, generosity maxim, approbation maxim, appreciation maxim, agreement maxim and sympathy maxim. The levels of teachers' language impoliteness were divided into five categories (5 = always, 4 = often, 3 = sometimes, 2 = rarely, 1 = never). Findings The finding shows that 28% of teachers were in the sometimes category and 33% of teachers were in the rarely category. The results signify that some teachers tend to speak impolitely with the students during the learning process. They considered that it was acceptable as they did not know the negative effects caused by their impolite language. Moreover, teachers were more likely to violate sympathy, tact and agreement maxims than other maxims. Practical implications The finding of this study can be applied generally at all levels of education, ranging from primary education to higher education, as a base to develop a program to improve the soft skills of teachers especially the way teachers communicate with their students in the learning process. By understanding language politeness theory, the awareness of teachers in communicating in the learning process can be improved, which can affect positively to the quality of learning process. Originality/value Numerous studies conducted on communication skills of teachers focused on using communication sciences approach. This study tried to use another approach to portray the communication skills of teachers based on language politeness uttered using linguistics approach. Moreover, previous studies preferred to conduct language politeness and impoliteness of teachers in senior high schools and lecturers in university, and there is still lack of studies focusing on language politeness of primary school teachers.
Evaluasi Pengembangan Pembelajaran Biologi Berbasis Media E-Learning pada Mahasiswa Tadris Biologi 3 Semester VI Universitas Islam Negeri Sumatera Utara

The word learning is not far from a word, namely evaluation, along with the development of the times, and with the sophistication of existing technology, various kinds of media have emerged in the application of learning. E-Learning is the answer to all the sophistication that exists at this time. Where learning methods using technology are currently needed because of advances in technology in each place of learning, whether it's a school or college. This study uses the Research and Development method. The purpose of this study is to determine the development of E-Learning media-based learning. The results of the data that the researcher will obtain are the responses of Tadris Biology Students Semester 3 VI UINSU. Hopefully what is expected with this article can be an alternative choice or reference in conducting a study.
Estimation of the geometric measure of entanglement with Wehrl moments through artificial neural networks

In recent years, artificial neural networks (ANNs) have become an increasingly popular tool for studying problems in quantum theory, and in particular entanglement theory. In this work, we analyse to what extent ANNs can accurately predict the geometric measure of entanglement of symmetric multiqubit states using only a limited number of Wehrl moments (moments of the Husimi function of the state) as input, which represents partial information about the state. We consider both pure and mixed quantum states. We compare the results we obtain by training ANNs with the informed use of convergence acceleration methods. We find that even some of the most powerful convergence acceleration algorithms do not compete with ANNs when given the same input data, provided that enough data is available to train these ANNs. We also provide an experimental protocol for measuring Wehrl moments, which is state-independent. More generally, this work opens up perspectives for the estimation of entanglement measures and other SU(2)-invariant quantities, such as the Wehrl entropy, in a way that is more accessible in experiments than by means of full state tomography.
Digital Fairytale Book as a Children's Literature-based Learning Media for freedom to Learn in Primary Education Perspective

Empowering digitized children's literature for integrity recovery might be a solution in the era of freedom-to-learn post-pandemic of Covid-19. This study aims to produce a digital fairytale book that meets the validity and practicality criteria. This research used the research and development (R&D)
Optimized Algorithm Design for Text similarity Detection Based on Artificial Intelligence and Natural Language Processing

This paper presents a Text similarity detection method based on artificial intelligence and natural language processing. The method combines statistical machine learning and deep learning techniques and designs six models from three perspectives: character-level, word-level, and semantic-level. These models include the diff model based on machine learning, cosine similarity model, Jaccard model, TF-IDF model, as well as the SimCSE and SBERT models based on deep learning methods. To fully leverage the characteristics of these models, three scenarios are designed to calculate the similarity scores based on experience and multiple experimental results. The results show that calculating the similarity scores using these three scenarios not only achieves high accuracy but also requires fewer computational resources. As deep learning and natural language processing technologies continue to advance, Text similarity detection methods based on artificial intelligence and natural language processing will continue to be improved and play a more significant role in practice. Future research can explore more models and algorithms to enhance the accuracy and robustness of plagiarism detection.
Location, Location, Location

AbstractI’m writing this editorial from the Boundary Waters Canoe Area (BWCA) in Minnesota, where I am a two-day paddle from civilization. Out here, where there’s no shortage of pine tree–studded islands and rocky outcroppings, a map and the direction of the sun alone only gets you so far. Knowing how to use a compass is an essential skill, one that can keep you from getting lost—or at least get you un-lost if you find yourself confused as to where you are. This knowledge and skill can save hours lost to wrong turns and backtracking.Keywords: Informal EducationEnvironmental ScienceSTEM Additional informationNotes on contributorsBeth MurphyBeth Murphy, PhD (bmurphy@nsta.org), is field editor for Connected Science Learning and an independent STEM education consultant with expertise in fostering collaboration between organizations and schools, providing professional learning experiences for educators, and implementing program evaluation that supports practitioners to do their best work.
Multilayer optoelectronic hybrid convolutional neural network with an optical 4f-system recurrent structure

After decades of development, artificial neural network has become one of the most important research directions of artificial intelligence, and has a wide range of applications and important value in computer vision, natural language processing and other fields. Today, most of the applied artificial neural networks are based on von Neumann electronic hardware. As the semiconductor process approaches the physical limit, the performance growth encounters bottlenecks, and the power consumption problem is difficult to solve, limiting the application and further development of deep learning. Optical neural network provides a way to break through the bottleneck due to its high speed, high parallelism and low power consumption. At present, most optical neural networks are difficult to expand the depth of the network and have limited performance. In this paper, a multilayer optoelectronic hybrid convolutional neural network with an optical 4f-system recurrent structure is proposed. The electronic convolutional layer is replaced by an optical convolutional layer based on the 4f system, and the depth of the neural network is extended by the recurrent structure of the 4f system to improve its performance. Experiments show that the recognition accuracy of CIFAR-10 dataset of the proposed hybrid neural network is close to that of a corresponding electronic neural network. This work provides a possible way to build a deeper optoelectronic hybrid convolutional neural network when dealing with complicated problems.
UJI KELAYAKAN MEDIA PERMAINAN TRUTH OR DARE UNTUK MENINGKATKAN PERILAKU ASERTIF SISWA KELAS XI SMK BINA PRESTASI BANGSA

This study aims to determine the feasibility test of the truth or dare card game media to increase students' assertive behavior. This type of research uses Research and Development (R&amp;D) Borg and Gall research methods. The sample in this study were students of class XI SMK Bina Prestasi Bangsa as many as 30 students. Data processing using Microsoft Excel application. The results of the feasibility test that have been carried out on media experts, material experts, practitioners, and students show an average score of 93.12%. The developed game media has an influence on the average percentage of students' assertive behavior by 12.94% from 61.65% initially to 74.59%. Thus, it shows that the truth or dare game media has had a positive influence and has fulfilled the aspect of product effectiveness. The conclusions in this study are: 1) the truth or dare game media in group guidance to improve assertive behavior is very feasible to use, and 2) truth or dare game media in group guidance is effectively used to improve students' assertive behavior.
Impact on quality and diversity from integrating a reconstruction loss into neural audio synthesis

In digital media or games, sound effects are typically recorded or synthesized. While there are a great many digital synthesis tools, the synthesized audio quality is generally not on par with sound recordings. Nonetheless, sound synthesis techniques provide a popular means to generate new sound variations. In this research, we study sound effects synthesis using generative models that are inspired by the models used for high-quality speech and music synthesis. In particular, we explore the trade-off between synthesis quality and variation. With regard to quality, we integrate a reconstruction loss into the original training objective to penalize imperfect audio reconstruction and compare it with neural vocoders and traditional spectrogram inversion methods. We use a Wasserstein GAN (WGAN) as an example model to explore the synthesis quality of generated sound effects, such as footsteps, birds, guns, rain, and engine sounds. In addition to synthesis quality, we also consider the range of sound variation that is possible with our generative model. We report on the trade-off that we obtain with our model regarding the quality and diversity of synthesized sound effects.
The Impact of Virtual Reality in Service Design Research

This study presents the results of a systematic bibliographic review exploring the impact of virtual reality (VR) technology on service design research. The review identifies the potential benefits of using VR, such as simulating real-world scenarios, understanding emotional and experiential aspects of service design, and facilitating collaboration among stakeholders. However, the review also highlights the complexity and cost of implementing VR technology and the ethical considerations that must be addressed. The work discusses potential applications of VR in healthcare, education, and distance learning, and emphasizes the need for innovative research methods and tools to harness VR's capabilities. The review concludes that future research should address ethical concerns and potential biases associated with VR and explore its long-term impact on service design research and practice. Overall, the review demonstrates that VR has significant potential to transform service design research and enhance service experiences for users.