Reviewerly/exHarmony huggingface dataset:
Cute Helper: A Study on the Effect of Virtual Character Expressions on Players’ Engagement in a Game for Collecting Artwork Descriptions

This study implements a virtual character as a moderator in JUSTIN, a game designed and developed for collecting descriptions of ukiyo-e artworks on a live-streaming platform. The game has shown to be effective. However, the repetitive nature and the necessity to play JUSTIN many rounds make its players less interested in continuing the game. Hence, we examine if a virtual character can improve the player’s enjoyment and engagement experience. To conduct a control experiment, we develop a prototype that simulates JUSTIN, but with only one player playing the game at a time, and run an experiment with it. Our preliminary results show that a virtual character that changes its expression according to the game situation is promising in promoting the enjoyment and engagement of JUSTIN players.
MiaoSuan Wargame: A Multi-Mode Integrated Platform for Imperfect Information Game

Just like data plays a fundamental role in perceptual intelligence, simulated environments become the cornerstone of artificial agents in cognitive intelligence. Meanwhile, the evaluation of decision agents has put forward an urgent need for human-machine accessible game platforms, on which humans and agents can interact and confront, even the “Turing test” can be conducted. In this work, MiaoSuan wargame platform, a human-computer gaming platform for multi-agent imperfect information game is proposed. It provides multiple scenes and tasks, together with various confrontation forms (computer vs. computer, human vs. computer, and human/computer hybrid) and game modes (online confrontation, offline training, and Turing test) for agents’ training and evaluation. The MiaoSuan platform is constructed as a modular architecture with separable engine, AI interface, human interface and other functional modules, in which different game environments and tasks can be conveniently integrated. Two kinds of workflows, online confrontation and offline training, are designed to schedule different modules to achieve their corresponding game modes. Besides, based on this platform, a “Turing test” method for agent evaluation can be implemented automatically. It introduces a comprehensive evaluation of agents’ performance through subjective human assessment or objective data analysis, rather than only depending on their game scores. The MiaoSuan wargame platform is widely used in individual practice and organized events. It is available at http://wargame.ia.ac.cn/.
Task Relabelling for Multi-task Transfer using Successor Features

Deep Reinforcement Learning has been very successful recently with various works on complex domains. Most works are concerned with learning a single policy that solves the target task, but is fixed in the sense that if the environment changes the agent is unable to adapt to it. Successor Features (SFs) proposes a mechanism that allows learning policies that are not tied to any particular reward function. In this work we investigate how SFs may be pre-trained without observing any reward in a custom environment that features resource collection, traps and crafting. After pre-training we expose the SF agents to various target tasks and see how well they can transfer to new tasks. Transferring is done without any further training on the SF agents, instead just by providing a task vector. For training the SFs we propose a task relabelling method which greatly improves the agent’s performance.
Improving Bidding and Playing Strategies in the Trick-Taking game Wizard using Deep Q-Networks

In this work, the trick-taking game Wizard with a separate bidding and playing phase is modeled by two interleaved partially observable Markov decision processes (POMDP). Deep Q-Networks (DQN) are used to empower self-improving agents, which are capable of tackling the challenges of a highly non-stationary environment. To compare algorithms between each other, the accuracy between bid and trick count is monitored, which strongly correlates with the actual rewards and provides a well-defined upper and lower performance bound. The trained DQN agents achieve accuracies between 66% and 87% in self-play, leaving behind both a random baseline and a rule-based heuristic. The conducted analysis also reveals a strong information asymmetry concerning player positions during bidding. To overcome the missing Markov property of imperfect-information games, a long short-term memory (LSTM) network is implemented to integrate historic information into the decision-making process. Additionally, a forward-directed tree search is conducted by sampling a state of the environment and thereby turning the game into a perfect information setting. To our surprise, both approaches do not surpass the performance of the basic DQN agent.
Mjx: A framework for Mahjong AI research

Numerous games have served as testbeds for artificial intelligence (AI) research to measure its progress. Mahjong is a highly challenging multi-agent imperfect information game with a vast player population. However, a challenge with using Mahjong as a testbed for AI is the lack of a publicly available framework that is fast, easy to use and implements popular rules for human players. We propose and describe Mjx, an open-source Mahjong framework, which implements one of the most popular Mahjong rules, riichi Mahjong (Japanese Mahjong). We compared the execution speed of Mjx with existing popular open-source software and demonstrated that it achieves 100x faster performance. Mjx is available at https://github.conmjx-project/mjx.
Mitigating Cowardice for Reinforcement Learning Agents in Combat Scenarios

A common approach in reinforcement learning (RL) is to give the agent a static reward for successfully completing the task or punishing it for failing. However, this approach leads to a behaviour similar to fear in combat scenarios. It learns a sub-optimal policy improving over time while retaining elements of cowardice in updating the policy. Cowardice can be avoided by removing static rewards given to the agent at the terminal state, but this lack of reward can negatively affect performance. This paper presents a novel approach to solve these issues by decaying this reward or punishment based on the agent’s performance at the terminal state and evaluates the proposed method across three separate games of varying levels of complexity—The Legend of Zelda, Megaman X, and M.U.G.E.N. All three games are based on combat scenarios where the goal is to defeat the opponent by reducing its health to zero. In all environments, the agents receiving decayed reward and punishment are more stable when training, achieve higher win rates, and require fewer actions per game than their statically rewarded counterparts.
MultiTree MCTS in Tabletop Games

We introduce MultiTree Monte Carlo Tree Search (MT-MCTS), in which a tree is constructed independently for each player. This permits deeper search for the acting agent’s own move, at the cost of a poorer opponent model and the loss of conditioning a move on the specific action of another player.We test MT-MCTS in eleven different tabletop board and card games, with varying numbers of players. The main benefit occurs in simultaneous-move games, where independent trees better model the information structure. We find that in other games MT-MCTS can outperform vanilla MCTS, which incorporates all players in a single tree, but that this advantage usually decreases as the computational budget increases, and the cost of poor opponent modelling outweighs the gain from deeper search.
Combining Monte-Carlo Tree Search with Proof-Number Search

Proof-Number Search (PNS) and Monte-Carlo Tree Search (MCTS) have been successfully applied for decision making in a range of games. This paper proposes a new approach called PN-MCTS that combines these two tree-search methods by incorporating the concept of proof and disproof numbers into the UCT formula of MCTS. Experimental results demonstrate that PN-MCTS outperforms basic MCTS in several games including Lines of Action, MiniShogi, Knightthrough, and Awari, achieving win rates up to 94.0%.
Ordering Levels in Human Computation Games using Playtraces and Level Structure

Prior work using skill chains for matchmaking-based dynamic difficulty adjustment in human computation games required skill chains to be manually defined for a game, and each level to be manually annotated with the individual skills needed to complete that level. In this work, we present two approaches for defining level orderings for DDA in the platformer HCG Iowa James without using such manually-defined skill chains and annotations. The first involves sequences of action-context pairs found in gameplay traces. The second consists of applying K-means clustering on segments of levels. Our results show that both new approaches outperform baseline random level ordering and perform similarly to the skill chain approach.
Online Game Level Generation from Music

Game consists of multiple types of content, while the harmony of different content types play an essential role in game design. However, most works on procedural content generation consider only one type of content at a time. In this paper, we propose and formulate online level generation from music, in a way of matching a level feature to a music feature in real-time, while adapting to players’ play speed. A generic framework named online player-adaptive procedural content generation via reinforcement learning, OPARL for short, is built upon the experience-driven reinforcement learning and controllable reinforcement learning, to enable online level generation from music. Furthermore, a novel control policy based on local search and k-nearest neighbours is proposed and integrated into OPARL to control the level generator considering the play data collected online. Results of simulation-based experiments show that our implementation of OPARL is competent to generate playable levels with difficulty degree matched to the “energy” dynamic of music for different artificial players in an online fashion.
Clustering emotions in Portuguese

In this paper we present some exploratory studies of emotion words basedon large annotated corpora of Portuguese. Those corpora were automaticallyannotated with emotionality, and each emotion word was assigned one or moregroups out of 26 emotion groups. Our goal is to evaluate those groups byapplying different statistical approaches to our material, namely based on (a)co-occurrence in a sentence as a sign of closeness of meaning, and (b) wordembeddings. After looking at the full material, we turn our attention to twospecific emotion groups: Amor (‘love’) and Desespero (‘despair’), investigating whether clustering with those underlying techniquescan help improve the shape, or redesign, particular emotion groups. In thepaper we suggest some novel forms of measuring semantic coherence on wordembedding models. Since computational research on emotion words in Portuguese is stillrare, our methods and resources will lay the ground for future investigations.@font-face{font-family:"Cambria Math";panose-1:2 4 5 3 5 4 6 3 2 4;mso-font-charset:0;mso-generic-font-family:roman;mso-font-pitch:variable;mso-font-signature:-536870145 1107305727 0 0 415 0;}@font-face{font-family:Times;panose-1:0 0 5 0 0 0 0 2 0 0;mso-font-charset:0;mso-generic-font-family:auto;mso-font-pitch:variable;mso-font-signature:-536870145 1342185562 0 0 415 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal{mso-style-unhide:no;mso-style-qformat:yes;mso-style-parent:"";margin:0in;mso-pagination:none;mso-hyphenate:none;font-size:11.0pt;font-family:"Times New Roman",serif;mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-US;mso-fareast-language:EN-US;}.MsoChpDefault{mso-style-type:export-only;mso-default-props:yes;font-size:11.0pt;mso-ansi-font-size:11.0pt;mso-bidi-font-size:11.0pt;font-family:"Calibri",sans-serif;mso-ascii-font-family:Calibri;mso-ascii-theme-font:minor-latin;mso-fareast-font-family:Calibri;mso-fareast-theme-font:minor-latin;mso-hansi-font-family:Calibri;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-bidi;mso-ansi-language:EN-US;mso-fareast-language:EN-US;}.MsoPapDefault{mso-style-type:export-only;mso-hyphenate:none;}div.WordSection1{page:WordSection1;}
The human experience of comprehending source code in virtual reality

Virtual reality (VR) is an emerging technology used in various domains such as medicine, psychotherapy, architecture, and gaming. Recently, software engineering researchers have started to explore virtual reality as a tool for programmers. However, few studies examine source code comprehension in VR. This paper explores the human experience of comprehending source code in VR and compares it to source code comprehension in a desktop environment. We conducted a study with 26 graduate student programmers. We measured actual productivity, perceived productivity and used the NASA Task Load Index (TLX) survey to measure various factors such as mental demand, physical demand, temporal demand, performance, effort, and frustration. We found that the programmers experienced more physical demand, effort, and overall task load when reading and comprehending code in VR. However, we did not observe any statistically significant differences in the programmers’ measured productivity or perceived productivity between VR and desktop comprehension.
Contextual Equivalence in a Probabilistic Call-by-Need Lambda-Calculus

To support the understanding of declarative probabilistic programming languages, we introduce a lambda-calculus with a fair binary probabilistic choice that chooses between its arguments with equal probability. The reduction strategy of the calculus is a call-by-need strategy that performs lazy evaluation and implements sharing by recursive let-expressions. Expected convergence of expressions is the limit of the sum of all successful reduction outputs weighted by their probability. We use contextual equivalence as program semantics: two expressions are contextually equivalent if and only if the expected convergence of the expressions plugged into any program context is always the same. We develop and illustrate techniques to prove equivalences including a context lemma, two derived criteria to show equivalences and a syntactic diagram-based method. This finally enables us to show correctness of a large set of program transformations with respect to the contextual equivalence.
A Robust and Effective Anomaly Detection Model for Identifying Unknown Network Traffic

Background: Network security is getting more serious and has attracted much attention in recent years. Anomaly detection is an important technology to identify bad network flows and protect the network, which has been a hot topic in the network security field. However, in an anomaly detection system, the unknown network flows are always identified as some known flows in the existing solutions, which results in poorer identification performance. Objective: Aiming at detecting unknown flows and improving the detection performance, based on the KDD’99 dataset from a simulated real network environment, we analyzed the dataset and the main factors which affect the accuracy, and proposed a more robust and effective anomaly detection model (READM) to improve the accuracy of the detection. Methods: Based on unknown flows determination, the extra unknown type class is trained by neural network and identified by deep inspection method. Then, the identification result for unknown class will be updated to the detection system. Finally, the newly proposed robust and effective anomaly detection model (READM) is constructed and validated. Results: Through experiments comparison and analysis, the results indicate that READM achieves higher detection accuracy and less prediction time, which proves more efficient and shows better performance. Conclusion: Our study found that the existence of unknown flows always results in error detection and becomes the main factor influencing the detection performance. So, we propose a robust and effective anomaly detection model based on the construction and training of the extra unknown traffic class. Through the comparison of three experiments with different ways of thinking, it is proved that READM improves detection accuracy and reduces prediction time. Besides, after comparing with other solutions, it also shows better performance and has great application value in this field.
Reusable, FAIR Humanities Data

While stakeholders including funding agencies and academic publishers implement more stringent data sharing policies, challenges remain for researchers in the humanities who are increasingly prompted to share their research data. This paper outlines some key challenges of research data sharing in the humanities, and identifies existing work which has been undertaken to explore these challenges. It describes the current landscape regarding publishersâ€™ research data sharing policies, and the impact which strong data policies can have, regardless of discipline.&#x0D; Using Routledge Open Research as a case study, the development of a set of humanities-inclusive Open Data publisher data guidelines is then described. These include practical guidance in relation to data sharing for humanities authors, and a close alignment with the FAIR Data Principles.
A Logic for Formalizing Properties of LF Specifications

A logic is presented for formalizing properties of specifications in the Edinburgh Logical Framework or LF. In this logic, typing judgments in LF serve as atomic formulas and quantification is permitted over LF terms and contexts. Quantifiers of the first variety are qualified by simple types that describe the functional structure associated with the variables they bind. Quantifiers over contexts are typed by context schemas that constrain their instantiations to adhere to a regular structure. The semantics of the logic is based ultimately on an understanding of derivability in LF. As such, valid formulas in the logic represent meta-theoretic properties of object systems that are encoded via LF signatures. The logic is complemented by a proof system that is briefly discussed. There are two categories to the rules in this proof system. One collection of rules captures the meanings of logical connectives and quantifiers. Another collection provides a means for analyzing atomic formulas based on an understanding of derivability in LF; these rules build in the capability for a case-analysis style reasoning about LF judgements and for induction over the heights of LF derivations.
Analysis and Identification of Forensic Events Using Non-parametric Density Estimation

The goal of this article is to show how the non-parametric probability density estimation method can be used to visualize the occurrence of criminal events. For this purpose, an algorithm of kernel estimators using effective Epanechnikov function is applied. The purpose of this procedure is to reveal where criminal incidents occur most frequently. Data representing the location of crimes in the city of Baltimore, Maryland between 1963–2014 are selected for the investigation. After information processing, only the most relevant incidents are regarded, i.e.: aggravated assault, auto theft, burglary, common assault, larceny, larceny from auto and street robbery. It is shown, that based upon the algorithm used in this research, it is possible to visualize areas on the city map where the highest probability of a particular crime is determined.
Advances in Forgery Detection of Driving Licences Using Truthfulness Degrees

We develop a methodology which allows to detect forgeries in driving licences based on an analysis of two serial codes usually included in licences issued in European countries. Results from the initial analysis show the usefulness of the methodology, and that the best code for detection depends on the particularities of the licence in each issuing country.
Mixup Augmentation Improves Age Prediction from T1-Weighted Brain MRI Scans

Age predictions from T1-weighted (T1w) MR brain images using deep learning models have become increasingly more accurate, mainly with the construction of larger and more complex model architectures, such as cascade networks, but also with the use of larger training datasets. We adopted and evaluated a data augmentation strategy called Mixup that combines input T1w brain scans and associated output ages for the brain age regression task. On a multi-site dataset of 2504 T1w brain scans we evaluated and tested multiple mixing factor distributions, applied mixing of similar/different sample pairs based on low/high age difference, and combined mixing in auxiliary variables. We found consistent improvements in prediction accuracy with the use of Mixup augmentation, with minimal computational overhead, and, despite using a simple VGG-based deep learning model architecture, achieved a highly competitive mean absolute error as low as 2.96 years.
Proficiency Level Classification of Foreign Language Learners Using Machine Learning Algorithms and Multilingual Models

This paper addresses the problem of classifying the proficiency of second language learners using multilingual models. Such models can be extremely useful in applications supporting the learning of multiple, even rare languages. Experiments based on Czech, German and Italian languages have been reported in the literature. This dataset was extended with texts in English. SVM, random forest, and logistic regression methods were used to train the model with different sets of language features. For the monolingual models – which served as benchmarks – the best results were observed for the random forest and SVM methods. For multilingual models, in contrast to other studies, the best results were obtained using the SVM algorithm. Models trained on a feature set containing n-grams of POS, n-grams of dependencies, and POS distribution performed better than models trained only on n-grams of POS, used in other works on multilingual models. The experiments confirmed the feasibility of using multilingual models in place of monolingual ones. Multilingual models were also able to classify texts in a language that was not involved in model learning.
Updating the Result Ontology Integration at the Concept Level in the Event of the Evolution of Their Components

Ontology integration is the task of combining a set of ontologies into a single ontology. Such ontology should contain all the knowledge expressed in the partial ontologies, without all the potential conflicts between them being resolved. In the literature, one may find several approaches to this problem, however, to the best of our knowledge most of them assume that the input ontologies remain static in time, therefore there is no risk of the outcome of integration becoming stale. If one of the partial ontologies changes over time (evolve) then the outcome of its integration may become obsolete. Therefore, a necessity of performing ontology integration once again appears. However, such a procedure may be very time and resource consuming. In this paper, we propose a solution for this issue. We claim that it is possible to update the integrated ontology based solely on a description of changes applied to the input ontologies, acquiring a similar quality as if the ontology integration be conducted from scratch.