Reviewerly/exHarmony huggingface dataset:
FlexComb: A Facial Landmark-Based Model for Expression Combination Generation

Facial expressions are a crucial but challenging aspect of animating in-game characters. They provide vital nonverbal communication cues, but given the high complexity and variability of human faces, the task of capturing the natural diversity and affective complexity of human faces can be a labour-intensive process for animators. This motivates the need for more accurate, realistic and lightweight methods for generating emotional expressions for in-game characters. In this work, we introduce FlexComb, a Facial Landmark-based Expression Combination model, designed to generate a real-time space of realistic facial expression combinations. FlexComb leverages the highly varied CelebV-HQ dataset containing emotions in the wild, and a transformer-based architecture. The central component of the FlexComb system is an emotion recognition model that is trained on the facial dataset, and used to generate a larger dataset of tagged faces. The resulting system generates in-game facial expressions by sampling from this tagged dataset, including expressions that combine emotions in specified amounts. This allows in-game characters to take on variety of realistic facial expressions for a single emotion, which addresses this primary challenge of facial emotion modeling. FlexComb shows potential for expressive facial emotion simulation with applications that include animation, video game development, virtual reality, and human-computer interaction.
Probabilistic Logic Programming Semantics For Procedural Content Generation

Research in procedural content generation (PCG) has recently heralded two major methodologies: machine learning (PCGML) and declarative programming. The former shows promise by automating the specification of quality criteria through latent patterns in data, while the latter offers significant advantages for authorial control. In this paper we propose the use of probabilistic logic as a unifying framework that combines the benefits of both methodologies. We propose a Bayesian formalization of content generators as probability distributions and show how common PCG tasks map naturally to operations on the distribution. Further, through a series of experiments with maze generation, we demonstrate how probabilistic logic semantics allows us to leverage the authorial control of declarative programming and the flexibility of learning from data.
Playing Various Strategies in Dominion with Deep Reinforcement Learning

Deck-building games, like Dominion, present an unsolved challenge for game AI research. The complexity arising from card interactions and the relative strength of strategies depending on the game configuration result in computer agents being limited to simple strategies. This paper describes the first application of recent advances in Geometric Deep Learning to deck-building games. We utilize a comprehensive multiset-based game representation and train the policy using a Soft Actor-Critic algorithm adapted to support variable-size sets of actions. The proposed model is the first successful learning-based agent that makes all decisions without relying on heuristics and supports a broader set of game configurations. It exceeds the performance of all previous learning-based approaches and is only outperformed by search-based approaches in certain game configurations. In addition, the paper presents modifications that induce agents to exhibit novel human-like play strategies. Finally, we show that learning strong strategies based on card combinations requires a reinforcement learning algorithm capable of discovering and executing a precise strategy while ignoring simpler suboptimal policies with higher immediate rewards.
Herd’s Eye View: Improving Game AI Agent Learning with Collaborative Perception

We present a novel perception model named Herd's Eye View (HEV) that adopts a global perspective derived from multiple agents to boost the decision-making capabilities of reinforcement learning (RL) agents in multi-agent environments, specifically in the context of game AI. The HEV approach utilizes cooperative perception to empower RL agents with a global reasoning ability, enhancing their decision-making. We demonstrate the effectiveness of the HEV within simulated game environments and highlight its superior performance compared to traditional ego-centric perception models. This work contributes to cooperative perception and multi-agent reinforcement learning by offering a more realistic and efficient perspective for global coordination and decision-making within game environments. Moreover, our approach promotes broader AI applications beyond gaming by addressing constraints faced by AI in other fields such as robotics. The code is available at https://github.com/andrewnash/Herds-Eye-View
Reconstructing Existing Levels through Level Inpainting

Procedural Content Generation (PCG) and Procedural Content Generation via Machine Learning (PCGML) have been used in prior work for generating levels in various games. This paper introduces Content Augmentation and focuses on the subproblem of level inpainting, which involves reconstructing and extending video game levels. Drawing inspiration from image inpainting, we adapt two techniques from this domain to address our specific use case. We present two approaches for level inpainting: an Autoencoder and a U-net. Through a comprehensive case study, we demonstrate their superior performance compared to a baseline method and discuss their relative merits. Furthermore, we provide a practical demonstration of both approaches for the level inpainting task and offer insights into potential directions for future research.
Entropy as a Measure of Puzzle Difficulty

Evaluating and ranking the difficulty and enjoyment of puzzles is important in game design. Typically, such rankings are constructed manually for each specific game, which can be time consuming, subject to designer bias, and requires extensive play testing. An approach to ranking that generalizes across multiple puzzle games is even more challenging because of their variation in factors like rules and goals. This paper introduces two general approaches to compute puzzle entropy, and uses them to evaluate puzzles that players enjoy. The resulting uncertainty score is equivalent to the number of bits of data necessary to communicate the solution of a puzzle to a player of a given skill level. We apply our new approaches to puzzles from the 2016 game, The Witness. The computed entropy scores largely reproduce the order of a set of puzzles that introduce a new mechanic in the game. The scores are also positively correlated with the user ratings of user-created Witness puzzles, providing evidence that our approach captures notions of puzzle difficulty and enjoyment. Our approach is designed to exploit game-specific knowledge in a general way and thus can extended to provide automatic rankings or curricula in a variety of applications.
Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds

This paper investigates the suitability of using Generative Adversarial Networks (GANs) to generate stable structures for the physics-based puzzle game Angry Birds. While previous applications of GANs for level generation have been mostly limited to tile-based representations, this paper explores their suitability for creating stable structures made from multiple smaller blocks. This includes a detailed encoding/decoding process for converting between Angry Birds level descriptions and a suitable grid-based representation, as well as utilizing state-of-the-art GAN architectures and training methods to produce new structure designs. Our results show that GANs can be successfully applied to generate a varied range of complex and stable Angry Birds structures.
Evolving Interactive Narrative Worlds

An interactive narrative is bound by the context of the world where its story takes place. However, most work in interactive narrative generation takes its story world design and mechanics as given, which abdicates a large part of story generation to an external world designer. In this paper, we close the story world design gap with an evolutionary search framework for generating interactive narrative worlds and mechanics. Our framework finds story world designs that accommodate multiple distinct player roles. We evaluate our system with an action agreement ratio analysis that shows worlds generated by our framework provide a greater number of in-role action opportunities compared to story worlds randomly sampled from the generative space.
Synthesizing Priority Planning Formulae for Multi-Agent Pathfinding

Prioritized planning is a popular approach to multi-agent pathfinding. It prioritizes the agents and then repeatedly invokes a single-agent pathfinding algorithm for each agent such that it avoids the paths of higher-priority agents. Performance of prioritized planning depends critically on cleverly ordering the agents. Such an ordering is provided by a priority function. Recent work successfully used machine learning to automatically produce such a priority function given good orderings as the training data. In this paper we explore a different technique for synthesizing priority functions, namely program synthesis in the space of arithmetic formulae. We synthesize priority functions expressed as arithmetic formulae over a set of meaningful problem features via a genetic search in the space induced by a context-free grammar. Furthermore we regularize the fitness function by formula length to synthesize short, human-readable formulae. Such readability is an advantage over previous numeric machine-learning methods and may help explain the importance of features and how to combine them into a good priority function for a given domain. Moreover, our experimental results show that our formula-based priority functions outperform existing machine-learning methods on the standard benchmarks in terms of success rate, run time and solution quality without using more training data.
Dynamic Difficulty Adjustment via Procedural Level Generation Guided by a Markov Decision Process for Platformers and Roguelikes

Procedural level generation can create unseen levels and improve the replayability of games, but there are requirements for a generated level. First, a level must be completable. Second, a level must look and feel like a level that would exist in the game, meaning a random combination of tiles that happens to be completable is not enough. On top of these two requirements, though, is the player experience. If a level is too hard, the player will be frustrated. If too easy, they will be bored. Neither outcome is desirable. A procedural level generation system has to account for the player's skill and generate levels at the correct difficulty. I address this issue by showing how a Markov Decision Process can be used as a director to assemble levels tailored to a player's skill level, but I've only demonstrated that my approach works with surrogate agents. For my thesis, I plan to build on my past work by creating a full roguelike and platformer and running two player studies to validate my approach.
AFNT: A Secure Data Storage Scheme Based on IOTA Tangle for Wireless Sensor Networks

Blockchain is a distributed ledger technology that enables cryptocurrencies, such as Bitcoin and Ethereum. However, it suffers from the scalability problem, which means that not all generated blocks can be added to the chain in a timely manner. For the Internet of Things (IoT), which involves a large number of interconnected devices, blockchain is infeasible due to the scalability issue. Instead, a different distributed ledger technology, IOTA Tangle, is expected to be widely adopted in IoT applications. Nevertheless, it has been found that the tip selection and weight update operation of IOTA Tangle could negatively affect its efficiency. To tackle this problem, Fishing Net Topology (FNT) was proposed to completely eliminate the tip selection and weight update operation. However, in our research, we noticed that the width of the fishing net used in FNT remains unchanged, which has a serious impact on the performance of FNT. In this paper, we focus on Wireless Sensor Networks (WSNs), which are often used to implement IoT applications. Specifically, we present a secure data storage scheme based on FNT for WSN, Adaptive FNT (AFNT). Our experimental results indicate that AFNT outperforms FNT in terms of layer number, average fishing net width, percentage of over-approved nodes, and percentage of over-approving nodes.
The Impact of the Use of Social Media Sites on the Cognitive Empowerment of Postgraduate Students

The current study aims to recognize the impact of the use of social media sites on cognitive empowerment among postgraduate students. To achieve the study's objectives, my measure of social media sites and cognitive empowerment was developed, and their psychometric characteristics were verified. The study sample was selected from graduate students from Saudi universities in the second semester of 2022-2023. (256) Students, relying on the analytical forecasting curriculum in this study, the results of study found that social media sites affect knowledge empowerment by (5%) and that male students are better than female students in cognitive empowerment, While there were no differences between students and students on social media sites and there were no differences in social networking and cognitive empowerment according to the type of kidney and marital status, Based on the study's findings, some recommendations were made, including the need to develop the positive impact of social media sites as they play a role in their knowledge empowerment.
Patients' perception of physical therapy at the UNEC Health Care Center (CASU) – Nanuque Campus/MG

Introduction: Few longitudinal studies have been done on the perception of patients about physical therapy care, through a questionnaire where it was possible to survey the perception of care at the Health Care Center of UNEC (CASU) - Campus Nanuque/MG. Objective: This study was carried out with the purpose of analyzing the perception of patients about the physiotherapeutic care dedicated to them by Physical Therapy trainees at the Health Care Center of UNEC (CASU) Campus of Nanuque/MG. Methodology: Through a descriptive and exploratory quantitative approach composed of 35 patients from CASU, where the instrument used for the study was the questionnaire adapted from the survey "Evaluation of the satisfaction of users of physiotherapy services", containing 15 closed questions. Results: When analyzing the data obtained, the vast majority of patients (77.17%) considered the attributes of physical therapy care very satisfactory. It is correct to consider that the conducts were followed adequately due to the high percentages defined as excellent (62.86%) to the results obtained with the physiotherapeutic treatment. Conclusion: The study was of great importance to have a survey of the perception of patients obtained through physical therapy and showed a population satisfied with the services it has been receiving, in this case, at the Health Care Center of UNEC (CASU).
Functional Verieties of a Dependent Sentence with the Conjunction “რაკი”

Adjective is one of the organizing components of the sentence structure. Adjectives sharing the same sound composition carry various semantics, participate in different constructions and have a certain function. The conjunction რაკი is used in a hypotaxic construction containing circumstantial dependent clauses of time and cause. Circumstantial dependent clauses of time and cause principally differ from each other in their content, but sometimes it is difficult to accurately qualify the conditional dependent clauses because, apart from formal coincidences, they adapt to the conjunction and the grammatical forms of time and mood of verb-predicate, at the same time the categories of time and cause are closely related to each other. Based on the materials analysed below, we can coclude that the grammatical category of verb—predicate principally matters in identifying the sentences of hypotexic constraction containing the conjunction “რაკი’. Since the cause is to be sought in the past, it reflects the previous event and adjusts to the indicative mood; while the circumstantial dependent clause of time, which is linked with the main clause starting with the conjunction “რაკი”, shows the beginning of the action conveyed in the main clause, and the main clause shows what happened, is happening or will happen in the dependent clause after the indicated action. In case of disputed forms, in order to clarify the function of the the conjunction “რაკი” and distinguish hypotaxic constructions, method of replacing another conjunction and transferring the verb-predicate to the future tense are hily recomended, i.e. if the conjunction “because/as long as” is unable to completely replace the conjunction “რაკი” and&nbsp; the future tense form of the replaced verb-predicate is unnatural, in such case it belongs to the construction of the circumstantial dependent clause of time; in case it is easily replacable then the dependent clause is circumstantial of cause. &nbsp
Vogue: Faster Computation of Private Heavy Hitters

Consider the problem of securely identifying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tau$</tex-math></inline-formula> -heavy hitters, where given a set of client inputs, the goal is to identify those inputs which are held by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tau$</tex-math></inline-formula> clients in a privacy-preserving manner. Towards this, we design a novel system <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {Vogue}$</tex-math></inline-formula> , whose key highlight in comparison to prior works, is that it ensures complete privacy and does not leak any information other than the heavy hitters. In doing so, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {Vogue}$</tex-math></inline-formula> aims to achieve as efficient a solution as possible. To showcase these efficiency improvements, we benchmark our solution and observe that it requires around 14 minutes to compute the heavy hitters for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tau$</tex-math></inline-formula> = 100 on 256-bit inputs when considering 400 K clients. This is in contrast to the state of the art solution that requires over an hour for the same. In addition to the static input setting described above, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {Vogue}$</tex-math></inline-formula> also accounts for streaming inputs and provides a protocol that outperforms the state-of-the-art therein. The efficiency improvements witnessed while computing heavy hitters in both, the static and streaming input settings, are attributed to our new secure stable compaction protocol, whose round complexity is independent of the size of the input array to be compacted.
CDTC: Automatically establishing the trace links between class diagrams in design phase and source code

Abstract Context The UML class diagram is commonly used to model functional structures and software code structures in both the preliminary and detailed design stages. And the abstraction level of UML class diagrams is usually higher than that of source code. Usually, there is a lack of trace links between these class diagrams and the source code, which may cause difficulties in understanding the source code, and affect the software evolution and maintenance. Objective The main goal of this article is to establish the trace links between highly abstracted UML class diagrams in the design phase and source code, and eventually help practitioners better understand source code. Method We propose an approach for the automated trace link establishment between UML class diagrams in the design phase and source code. To address the problem of abstraction level gap between them, we extend the UML class diagram by mining the synonymous phrases of class names and deducing the latent missing relationships between classes from multiple design documents. Then we build the trace links with a two‐phase approach including initial construction with fuzzy matching and further optimization by class relationship inference. Results Experiments on five open‐source projects show that the recalls of our approach are over 94%, and the F2‐scores are over 88%, with the gains of 30% to 60% than the four baselines. Conclusion Our work can be a reference for establishing the initial trace links between highly‐abstracted UML class diagrams and source code. Towards the higher abstraction of design diagrams, we extend UML class diagrams with the statistical analysis on multiple design documents. To guarantee the quality of trace links, we design a two‐phase approach by obtaining the “full but not good enough” trace links and filtering the “probably wrong” links. Experiments show that the main techniques of our approach behave as important role for tracing between high‐level class diagrams and source code.
Identification of Minimum Software Process Development Activities for Development of Categories of Software: An Efficient and Effective Approach

Software Engineering mainly deals with processes to ensure delivery of software product through systematic control of software development process activities. It enforces an engineering discipline to achieve the basic purpose of software through a development methodology. Process model usually consists of activities which are the part of software development process, software product and the role and responsibilities of people involved in software engineering. Not all the activities are necessary for all the type of application software. Since all the activities are not required, we can study the actual requirement in various types of application software. Various types of application software such study would be very useful for faster software development thereby eliminating unnecessary activities in each phase of software development process. The results of studies will be a set of minimum activities for each type of application software that are necessary for the development of good quality software. The application software may be soft computing software, financial software, artificial intelligence software, business software and so on. Our work does not aim to focus on agile engineering which eliminates documentation but we want to scientifically study towards minimization of process activities. We shall be taking number of software development tasks and perform estimation and for full and minimized process model and see the actual results against estimations. Some tasks for simulation of process model shall also be performed.
APPLICATION OF GRAPH THEORY FOR ANALYSIS OF THE BEHAVIOR OF A PLANE ELECTROMAGNETIC WAVE IN A PLANE-LAYERED MEDIUM

An approach is considered that uses directed graphs of the equivalent circuit of microwave circuits to solve the problems of the passage of electromagnetic waves through a plane-layered dielectric medium. The application of graph theory to estimate the transfer coefficient of a multilayer dielectric medium is considered. Using the theory of directed graphs, a transmission coefficient formula is obtained that corresponds to the well-known Airy formulas for a dielectric layer.
MULTIPLEXER MODELING IN MATLAB+SIMULINK

The article deals with the issue of modeling a digital device multiplexer based on a decoder, widely used in digital technology and telecommunication devices