Reviewerly/exHarmony huggingface dataset:
Notes on a Path to AI Assistance in Mathematical Reasoning

These informal notes are based on the author's lecture at the National Academies of Science, Engineering, and Mathematics workshop on "AI to Assist Mathematical Reasoning" in June 2023. The goal is to think through a path by which we might arrive at AI that is useful for the research mathematician.
Scaling Laws for Associative Memories

Learning arguably involves the discovery and memorization of abstract rules. The aim of this paper is to study associative memory mechanisms. Our model is based on high-dimensional matrices consisting of outer products of embeddings, which relates to the inner layers of transformer language models. We derive precise scaling laws with respect to sample size and parameter size, and discuss the statistical efficiency of different estimators, including optimization-based algorithms. We provide extensive numerical experiments to validate and interpret theoretical results, including fine-grained visualizations of the stored memory associations.
High-dimensional SGD aligns with emerging outlier eigenspaces

We rigorously study the joint evolution of training dynamics via stochastic gradient descent (SGD) and the spectra of empirical Hessian and gradient matrices. We prove that in two canonical classification tasks for multi-class high-dimensional mixtures and either 1 or 2-layer neural networks, the SGD trajectory rapidly aligns with emerging low-rank outlier eigenspaces of the Hessian and gradient matrices. Moreover, in multi-layer settings this alignment occurs per layer, with the final layer's outlier eigenspace evolving over the course of training, and exhibiting rank deficiency when the SGD converges to sub-optimal classifiers. This establishes some of the rich predictions that have arisen from extensive numerical studies in the last decade about the spectra of Hessian and information matrices over the course of training in overparametrized networks.
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making

The recent success of Transformer in natural language processing has sparked its use in various domains. In offline reinforcement learning (RL), Decision Transformer (DT) is emerging as a promising model based on Transformer. However, we discovered that the attention module of DT is not appropriate to capture the inherent local dependence pattern in trajectories of RL modeled as a Markov decision process. To overcome the limitations of DT, we propose a novel action sequence predictor, named Decision ConvFormer (DC), based on the architecture of MetaFormer, which is a general structure to process multiple entities in parallel and understand the interrelationship among the multiple entities. DC employs local convolution filtering as the token mixer and can effectively capture the inherent local associations of the RL dataset. In extensive experiments, DC achieved state-of-the-art performance across various standard RL benchmarks while requiring fewer resources. Furthermore, we show that DC better understands the underlying meaning in data and exhibits enhanced generalization capability.
Self-supervised Learning of Contextualized Local Visual Embeddings

We present Contextualized Local Visual Embeddings (CLoVE), a self-supervised convolutional-based method that learns representations suited for dense prediction tasks. CLoVE deviates from current methods and optimizes a single loss function that operates at the level of contextualized local embeddings learned from output feature maps of convolution neural network (CNN) encoders. To learn contextualized embeddings, CLoVE proposes a normalized mult-head self-attention layer that combines local features from different parts of an image based on similarity. We extensively benchmark CLoVE's pre-trained representations on multiple datasets. CLoVE reaches state-of-the-art performance for CNN-based architectures in 4 dense prediction downstream tasks, including object detection, instance segmentation, keypoint detection, and dense pose estimation.
CODE MIXING USED IN TASYA FARASYA BEAUTY VLOGS

This study entitled “Code Mixing Used In Tasya Farasya Beauty Vlogs”. Beauty vloggers often share information about the cosmetic products they use or review beauty and make-up products. Sometimes they mix several languages in their videos, such as mixing English with Indonesian. Language has a very important role in our life. We use language for communicating and people nowadays speak in multiple languages. According to the phenomenon above, this research focuses on code mixing in one of Tasya Farasya's beauty vlogs on her YouTube channel, level of code mixing and to find the reasons why Tasya mixed her language. There are two theories applied in this study, namely the theory of the types of code mixing and reason of code mixing proposed by Hoffman (1991) and the theory of the level of code mixing proposed by Suwito (1996). This research is the qualitative research. The methods applied in collecting the data are the documentation and observing method and by using the note-taking technique. The data is taken mainly from the utterances found in the Tasya Farasya beauty vlogs, as the main data source for this study. The result of this study showed that there are three types of code mixing found used by Tasya videos. There are data collected for the type of code mixing. For the level of code mixing, and for the reason of code mixing. Type of code mixing there are intra-sentential, intra-lexical, involvement in a change of pronunciation. And for level of code mixing there are word level, phrase level, baster level, repetition level, idiom level, clause level. And for the reason of code mixing there are talking about particular topic, being emphatic about something, interjection, repetition used for clarification, expressing group identity, clarifying the speech content for the interlocutor.
Social Deixis in Students’ Writing: A Case Study of Indonesian Secondary Students

The social position or role of participants in communicative events is referred to as social deixis. The parts of language structure that encode individuals' social identities are referred to as social deixis. The current study examined the use of social deixis in students’ writing. It focused on secondary students’ writing. The students were allowed to choose the topic of writing. Using qualitative research, this study collected the data through documentation. The data were analyzed through data condensation, data display, drawing, and conclusion. The findings revealed that students' writing only contained relational social discourse. The students' writing frequently used addressee honorific relational social discourse. For the purpose of showing respect, students frequently used relational social deduction. Readers are persuaded to do a particular action by the students' goal as well.
Development of Leadership Indicators and Approaches for the District Directors of Non-formal and Informal Education in the Digital Era

The development of leadership indicators and approaches for district directors of non-formal and informal education in the digital era is crucial for adapting to technological advancements, enhancing leadership effectiveness, fostering creativity and innovation, promoting digital citizenship, and supporting ongoing professional development. These efforts ultimately contribute to the improvement of educational practices and outcomes in the digital age. The objectives of this research were as follows: To study the components and indicators of leadership among district directors of non-formal and informal education in the digital era. To examine the consistency of the developed leadership indicator structure model for district directors of non-formal and informal education in the digital era using empirical data. To develop a leadership development approach for district directors of non-formal and informal education in the digital era. To study the results of implementing the leadership development approach for district directors of non-formal and informal education in the digital era. The research was conducted in four phases, and statistical analysis included percentage, mean, standard deviation, and Confirmatory Factor Analysis. The results revealed the following: Leadership among district directors of non-formal and informal education in the digital era consisted of five components and fifteen indicators. The leadership development approach for district directors of non-formal and informal education in the digital era included the following components: creativity, participation, digital citizenship, digital vision, and digital professionalism. These components were prioritized based on the findings. The assessment of satisfaction with the leadership development approach for directors of district centers for non-formal and informal education in the digital era indicated a high level of overall satisfaction. These innovative approaches function as catalysts, not only propelling district directors to excel in their roles but also empowering them to champion innovation within the education domain. Most importantly, they play a pivotal role in driving substantial progress in the field of education as our world becomes increasingly digital.
The Effectiveness of Role Play in Improving Speaking Skill of EFL Students

The purpose of this study is to see whether utilizing role play techniques may help Junior High School students improve their speaking abilities. A total of 68 students from the Surabaya’s Junior High School participated in this study. A quasi experimental research has been used in this study. The experimental group and control group, each of which had 34 students, were the two classes that were analysed. Role play techniques were used in the experiment group, and conventional techniques were used in the control group. Independent samples test was used to analyse the research data, which was then provided as quantitative data. The average score of students after the oral test was given using the conventional technique was only 71.25 in the control group and 76.97 in the experimental group. The average score of students after the oral test was given using the role play technique was 81.08 in the control group and 87.64 in the experimental group. As a result, students who are taught using Role play techniques receive higher scores than those who are not.
Level Building Sidekick: An AI-Assisted Level Editor Package for Unity

Developing an original video game requires high investment levels, market research, cost-effective solutions, and a quick development process. Game developers usually reach for commercial off-the-shelf components often available in the engine's marketplace to reduce costs. Mixed-initiative authoring tools allow us to combine the thoughtful work of human designers with the productivity gains of automated techniques. However, most commercial AI-assisted Procedural Content Generation tools focus on generating small independent components, and standalone research tools available for generating full game levels with state-of-the-art algorithms usually lack integration with commercial game engines. This article aims to fill this gap between industry and academia. The Level Building Sidekick (LBS) is a mixed-initiative procedural content generation tool built by our research lab in association with four small independent game studios. It has a modular software architecture that enables developers to extend it for their particular projects. The current version has two working modules for building game maps, an early version of a module for populating the level with NPCs or items, and the first stages of a quest editor module. An automated testing module is planned. LBS is distributed as an AI-Assisted videogame-level editor Unity package. Usability testing performed using the ``Think-Aloud'' methodology indicates LBS has the potential to improve game development processes convincingly. However, at this stage, the user interface and the AI recommendations could improve their intuitiveness. As a general comment, the tool is perceived as a substantial contribution to facilitating and shortening development times, compared to only using the base game engine. There is an untapped market for mixed-initiative tools that assist the game designer in creating complete game levels. We expect to fill that market for our partner development studios and provide the community with an open research and development platform in a standard game engine.
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments

Understanding the interactions of agents trained with deep reinforcement learning is crucial for deploying agents in games or the real world. In the former, unreasonable actions confuse players. In the latter, that effect is even more significant, as unexpected behavior cause accidents with potentially grave and long-lasting consequences for the involved individuals. In this work, we propose using program synthesis to imitate reinforcement learning policies after seeing a trajectory of the action sequence. Programs have the advantage that they are inherently interpretable and verifiable for correctness. We adapt the state-of-the-art program synthesis system DreamCoder for learning concepts in grid-based environments, specifically, a navigation task and two miniature versions of Atari games, Space Invaders and Asterix. By inspecting the generated libraries, we can make inferences about the concepts the black-box agent has learned and better understand the agent's behavior. We achieve the same by visualizing the agent's decision-making process for the imitated sequences. We evaluate our approach with different types of program synthesizers based on a search-only method, a neural-guided search, and a language model fine-tuned on code.
Enhancing Character Depth through Personality Exceptions for Narrative Planners

In the field of narrative planning, implementing character personality is a challenge that’s been tackled many different ways. Most of these methods do not incorporate any method for personality to shift when characters are put in situations that would, through stress or satisfaction, naturally cause the character to behave differently than usual. Through use of situationally-triggered Personality Exceptions, we can support the generation of a story that prominently features such personality shifts as a narrative tool. This feature is made as generic as possible so that it can be attached onto a wide range of personality models in narrative generators. Through adapting Indexter’s indexes of narrative salience towards tracking internal narrative salience in the characters’ memories, we can accurately pinpoint triggers which are used to activate these personality exceptions in thematically relevant situations.
Risk Management: Anticipating and Reacting in StarCraft

Managing risk with imperfect information is something humans do every day, but we have little insight into the abilities of AI agents to do so. We define two risk management strategies and perform an ability-based evaluation using StarCraft agents. Our evaluation shows that nearly all agents mitigate risks after observing them (react), and many prepare for such risks before their appearance (anticipate). For this evaluation, we apply traditional causal effect inference and causal random forest methods to explain agent behavior. The results highlight different risk management strategies among agents, others strategies that are common to agents, and overall encourage evaluating agent risk management abilities in other AI domains.
Expressive Response Curves: Testing Expressive Game Feel with A*

Designing AI models for expressive character behavior is a considerable challenge. Such models represent a massive possibility space of individual behaviors and sequences of different character expressions. Iterating on designs of such models is complex because the possibility spaces they afford are challenging to understand in their entirety and map intuitively onto a meaningful experience for a user. Automated playtesting has primarily been focused on the physical spaces of game levels and the ability of AI players to enact personas and complete tasks within those levels. However, core principles of automated playtesting can be applied to expressive models to expose information about their expressive possibility space. We propose a new approach to automated playtesting for AI character behaviors: Expressive Response Curves (ERC). ERC allows us to map specific actions taken by a player to perform a particular expression to understand the affordances of an expressive possibility space. We present a case study applying ERC to Puppitor rulesets. We show that using this method we can compile paths through Puppitor rulesets to map them and further understand the nature of the expressive spaces afforded by the system. We argue that by using ERC, it is possible to give designers more nuanced information and guidance to create better and more expressive AI characters.
Investigating the Influence of Behaviors and Dialogs on Player Enjoyment in Stealth Games

The player's perception of AI behavior significantly influences their overall game experience. This perception is shaped by both interactive encounters and careful observations, particularly in genres like stealth, where gameplay revolves around planning strategies based on AI enemy movement. This paper aims to derive general insights into the player experience concerning two crucial gameplay elements that impact the perception of NPC intelligence. The first element pertains to the actual behavior of opponent NPCs, while the second focuses on the dialogues employed to highlight NPC decision-making. We conducted a user study to assess whether players can discern between complex and simple NPC behavior during gameplay in a specific scenario of a top-down stealth game prototype. We introduced variations in spoken dialogs to determine their effect on player perception. In the end, our findings revealed that when simple dialogs were used, players derived greater enjoyment from a more complex AI behavior. However, using contextual dialog allowed a simple behavior to match a complex behavior in player enjoyment.
Navigation in Adversarial Environments Guided by PRA* and a Local RL Planner

Real-time strategy games require players to respond to short-term challenges (micromanagement) and long-term objectives (macromanagement) simultaneously to win. However, many players excel at one of these skills but not both. This research is motivated by the question of whether the burden of micromanagement can be reduced on human players through delegation of responsibility to autonomous agents. In particular, this research proposes an adversarial navigation architecture that enables units to autonomously navigate through places densely populated with enemies by learning to micromanage itself. Our approach models the adversarial pathfinding problem as a Markov Decision Process (MDP) and trains an agent with reinforcement learning on this MDP. We observed that our approach resulted in the agent taking less damage from adversaries while travelling shorter paths, compared to previous approaches for adversarial navigation. Our approach is also efficient in memory use and computation time. Interestingly, the agent using the proposed approach also outperformed baseline approaches while navigating through environments that are significantly different from the training environments. Furthermore, when the game design is modified, the agent discovers effective alternate strategies considering the updated design without any changes in the learning framework. This property is particularly useful because in game development the design of a game is often updated iteratively.
Curating, Community, Collaboration

What began in a library science course as a collection development project serendipitously transformed into varied learning experiences for students across disciplines and program levels. This article shares the journey of how a singular lesson idea blossomed into an unintentional, multidisciplinary project that led to unexpected learning outcomes for all involved.
The effect of virtual reality learning on students' motivation: A scoping review

Abstract Background The COVID‐19 pandemic has greatly impacted education, necessitating alternative methods to traditional face‐to‐face teaching. This dramatic change, in tandem with increasing awareness of the metaverse—a virtual reality (VR) world wherein humanity might learn, work and socialise–has made a clear need for a better understanding of the potential contribution of VR‐based simulation environments for students' learning outcomes. Objectives Three research questions regarding the effect of VR experience on students' learning motivation were addressed: RQ1. What methods have been employed to rule out the novelty effect in VR learning? RQ2. What motivation theories have been applied to guide the development of VR learning content? RQ3. What motivation theories have been applied to guide the utilisation of VR in learning activities? Methods Applying Arksey and O'Malley's (International Journal of Social Research Methodology, 8, 19–32, 2005) protocol, 14 articles were included in this review. Results Most included studies indicated that students' motivation had increased after the VR intervention or was higher than other pedagogical conditions. However, the lack of longitudinal research designs means novelty effects in VR learning cannot be ruled out. Knowledge gaps in study designs and theoretical frameworks are discussed. Conclusion Technology and the COVID‐19 pandemic are radically changing teaching and learning methods. More research on the impact of VR simulation learning on student outcomes is needed, with careful theoretical framing to ensure reliable findings. Future studies could explore VR technology to satisfy students' psychological needs and create deeper personal connections.
CREATION OF AN EDUCATIONAL AND PRODUCTION CLUSTER IN THE ULYANOVSK REGION IN THE RADIO ELECTRONICS INDUSTRY

The creation of an educational and production cluster in the Radioelectronics industry on the basis of the Ulyanovsk State Technical University is described.
Effect of multiphoton processes on the asymmetric fluorescence spectrum and spectral correlations of a two-level atom driven bichromatically by two strong laser fields

We focus on the influence of multiphoton effect on the fluorescence spectrum and photon statistics of the bichromatically driven two-level atomic system. The influence of the multiphoton effect of the weaker driving field on the system and spontaneous emission are revealed by Schrieffer-Wolff perturbation theory, respectively. The physical origin of the multiphoton process affecting the fluorescence spectral asymmetry characteristics is fully investigated by studying the population distribution and the dressed atomic transition weight. Further, the suppression condition of the central peak of the central band is investigated. The photon statistical properties of the system are revealed by studying the frequency-resolved correlations between different sidepeaks and the central peak and sidepeak, respectively. In particular, the physical picture of the asymmetry of the two-photon correlation signal that is affected by the multiphoton process of the weaker driving field affecting the timing detection is fully revealed by the two methods of correlation moment and conditional quantum state, respectively. These results of ours provide a theoretical approach for studying multiphoton processes in multichromatically driven quantum systems, as well as for developing quantum simulation techniques such as spin locking.