Question: Treating feature selection as a single-player game.

This paper introduces an efficient correlation-based filter algorithm designed for feature selection in both discrete and continuous class machine learning scenarios. Feature selection methods can be categorized into wrappers, which rely on the learning algorithm to assess feature utility, and filters, which employ data characteristics to evaluate feature relevance independently. Filters are particularly advantageous in large-scale data analysis due to their speed, making them more practical than wrappers. The proposed algorithm is a fast, correlation-based filter that extends the capabilities of traditional filter methods by accommodating both continuous and discrete classification problems. When used as a preprocessing step for various machine learning models, including naive Bayes, instance-based learning, decision trees, locally weighted regression, and model trees, this algorithm often surpasses the performance of the widely recognized ReliefF attribute estimator. It achieves more effective feature selection, substantially reducing data dimensionality by up to fifty percent in most cases. Additionally, the application of this preprocessing step results in significantly smaller decision and model trees, highlighting its utility in enhancing model efficiency and interpretability.

When the customer churn prediction model is built, a large number of features bring heavy burdens to the model and even decrease the accuracy. This paper is aimed to review the feature selection, to compare the algorithms from different fields and to design a framework of feature selection for customer churn prediction. Based on the framework, the author experiment on the structured module with some telecom operator's marketing data to verify the efficiency of the feature selection framework.

This paper takes manifold learning and regression simultaneously into account to perform unsupervised spectral feature selection. We first extract the bases of the data, and then represent the data sparsely using the extracted bases by proposing a novel joint graph sparse coding model, JGSC for short. We design a new algorithm TOSC to compute the resulting objective function of JGSC, and then theoretically prove that the proposed objective function converges to its global optimum via the proposed TOSC algorithm. We repeat the extraction and the TOSC calculation until the value of the objective function of JGSC satisfies pre-defined conditions. Eventually the derived new representation of the data may only have a few non-zero rows, and we delete the zero rows (a.k.a. zero-valued features) to conduct feature selection on the new representation of the data. Our empirical studies demonstrate that the proposed method outperforms several state-of-the-art algorithms on real datasets in term of the kNN classification performance.

Often users receive search results which contain a wide range of documents, only some of which are relevant to their information needs. To address this problem, ever more systems not only locate information for users, but also organise that information on their behalf. We look at two main automatic approaches to information organisation: interactive clustering of search results and pre-categorising documents to provide hierarchical browsing structures. To be feasible in real world applications, both of these approaches require accurate yet efficient algorithms. Yet, both suffer from the curse of dimensionality — documents are typically represented by hundreds or thousands of words (features) which must be analysed and processed during clustering or classification. In this paper, we discuss feature reduction techniques and their application to document clustering and classification, showing that feature reduction improves efficiency as well as accuracy. We validate these algorithms using human relevance assignments and categorisation.

A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label. Textual data, such as documents and web pages, are frequently annotated with more than a single label. For example, a news article concerning the reactions of the Christian church to the release of the “Da Vinci Code” film can be labeled as both religion and movies. The categorization of textual data is perhaps the dominant multi-label application. Recently, the issue of learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of new applications, such as semantic annotation of images [1, 2, 3] and video [4, 5], functional genomics [6, 7, 8, 9, 10], music categorization into emotions [11, 12, 13, 14] and directed marketing [15]. Table 1 presents a variety of applications that are discussed in the literature. This chapter reviews past and recent work on the rapidly evolving research area of multi-label data mining. Section 2 defines the two major tasks in learning from multi-label data and presents a significant number of learning methods. Section 3 discusses dimensionality reduction methods for multi-label data. Sections 4 and 5 discuss two important research challenges, which, if successfully met, can significantly expand the real-world applications of multi-label learning methods: a) exploiting label structure and b) scaling up to domains with large number of labels. Section 6 introduces benchmark multi-label datasets and their statistics, while Section 7 presents the most frequently used evaluation measures for multi-label learn-

We study the construction of prediction algorithms in a situation in which a learner faces a sequence of trials with a prediction to be made in each and the goal of the learner is to make few mistakes We are interested in the case that the learner has reason to believe that one of some pool of known algorithms will perform well but the learner does not know which one A simple and e ective method based on weighted voting is introduced for constructing a compound algorithm in such a circumstance We call this method the Weighted Majority Algorithm We show that this algorithm is robust in the presence of errors in the data We discuss various versions of the Weighted Majority Algorithm and prove mistake bounds for them that are closely related to the mistake bounds of the best algorithms of the pool For example given a sequence of trials if there is an algorithm in the pool A that makes at most m mistakes then the Weighted Majority Algorithm will make at most c log jAj m mistakes on that sequence where c is xed constant

ion algorithms take as input a description of the game and output a smaller but strategically similar — or even equivalent — game. The abstraction algorithms discussed here work with any finite number of players and do not assume a zerosum game. Information Abstraction The most popular kind of abstraction is information abstraction. The game is abstracted so that the agents cannot distinguish some of the states that they can distinguish in the actual game. For example in an abstracted poker hand, an agent is not able to observe all the nuances of the cards that she would normally observe. Lossless Information Abstraction. It turns out that it is possible to do lossless information abstraction, which may seem like an oxymoron at first. The method I will describe (Gilpin and Sandholm 2007b) is for a class of games that we call games with ordered signals. It is structured, but still general enough to capture a wide range of strategic situations. A game with ordered signals consists of a finite number of rounds. Within a round, the players play a game on a directed tree (the tree can be different in different rounds). The only uncertainty players face stems from private signals the other players have received and from the unknown future signals. In other words, players observe each Articles 14 AI MAGAZINE Nash equilibrium Nash equilibrium Original game

Classic approaches to game AI require either a high quality of domain knowledge, or a long time to generate effective AI behaviour. These two characteristics hamper the goal of establishing challenging game AI. In this paper, we put forward Monte-Carlo Tree Search as a novel, unified framework to game AI. In the framework, randomized explorations of the search space are used to predict the most promising game actions. We will demonstrate that Monte-Carlo Tree Search can be applied effectively to (1) classic board-games, (2) modern board-games, and (3) video games.

We analyze the multi-view regression problem where we have two views X = (X, X) of the input data and a target variableY of interest. We provide sufficient conditions under which we can reduce the dimensionality of X (via a projection) without loosing predictive power of Y . Crucially, this projection can be computed via a Canonical Correlation Analysis only on the unlabeled data. The algorithmic template is as f ollows: with unlabeled data, perform CCA and construct a certain projection; with the labeled data, do least squa res regression in this lower dimensional space. We show how, under certain natural assumptions, the number o f labeled samples could be significantly reduced (in comparison to the single view setting) — in particular, we show how this dimen sionality reduction does not loose predictive power ofY (thus it only introduces little bias but could drastically reduce the variance). We explore two separate assumptions under which this is possible and show how, under either assumption alone, dimensionality reduction could reduce the labeled sample complexity. The two assumptions we consider are a conditional independenceassumption and a redundancyassumption. The typical conditional independence assumption is that conditioned onY the viewsX andX are independent — we relax this assumption to: conditioned on some hidden state H the viewsX andX are independent. Under the redundancy assumption, we show that the best predictor from each view is roughly as good as the best predictor u sing both views.

Boosting has become very popular in computer vision, showing impressive performance in detection and recognition tasks. Mainly off-line training methods have been used, which implies that all training data has to be a priori given; training and usage of the classifier are separate steps. Training the classifier on-line and incrementally as new data becomes available has several advantages and opens new areas of application for boosting in computer vision. In this paper we propose a novel on-line AdaBoost feature selection method. In conjunction with efficient feature extraction methods the method is real time capable. We demonstrate the multifariousness of the method on such diverse tasks as learning complex background models, visual tracking and object detection. All approaches benefit significantly by the on-line training.

Recently, there has been considerable progress towards algorithms for approximating Nash equilibrium strategies in extensive games. One such algorithm, Counterfactual Regret Minimization (CFR), has proven to be effective in two-player zero-sum poker domains. While the basic algorithm is iterative and performs a full game traversal on each iteration, sampling based approaches are possible. For instance, chance-sampled CFR considers just a single chance outcome per traversal, resulting in faster but less precise iterations. While more iterations are required, chance-sampled CFR requires less time overall to converge. In this work, we present new sampling techniques that consider sets of chance outcomes during each traversal to produce slower, more accurate iterations. By sampling only the public chance outcomes seen by all players, we take advantage of the imperfect information structure of the game to (i) avoid recomputation of strategy probabilities, and (ii) achieve an algorithmic speed improvement, performing O(n) work at terminal nodes in O(n) time. We demonstrate that this new CFR update converges more quickly than chance-sampled CFR in the large domains of poker and Bluff.


Question: Regarding feature selection, viewing it through the lens of a single participant game.

This paper presents a novel approach for parameter estimation in linear models, known as the 'Lasso' (Least Absolute Shrinkage and Selection Operator). The Lasso method is designed to minimize the residual sum of squares under the constraint that the sum of the absolute values of the coefficients is less than a specified constant. This unique constraint facilitates the generation of sparse models, where some coefficients are precisely zero, thus enhancing model interpretability. Through comprehensive simulation studies, we have observed that the Lasso method combines the benefits of subset selection and ridge regression. It offers the interpretability of subset selection and the stability of ridge regression. Interestingly, the Lasso technique shares similarities with recent advancements in adaptive function estimation by Donoho and Johnstone. The versatility of the Lasso concept allows its application across various statistical models, including generalized linear models and tree-based models, although these extensions are briefly touched upon in this paper.

In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.

This paper provides new insight into kernel methods by using data selection. The kernel trick is used to select from the data a relevant subset forming a basis in a feature space F . Thus the selected vectors de,ne a subspace in F . Then, the data is projected onto this subspace where classical algorithms are applied. We show that kernel methods like generalized discriminant analysis (Neural Comput. 12 (2000) 2385) or kernel principal component analysis (Neural Comput. 10 (1998) 1299) can be expressed more easily. Moreover, it will turn out that the size of the basis is related to the complexity of the model. Therefore, the data selection leads to a complexity control and thus to a better generalization. The approach covers a wide range of algorithms. We investigate the function approximation on real classi,cation problems and on a regression problem. c © 2003 Elsevier B.V. All rights reserved.

In previous research we concluded that a personality profile, based on the Five Factor Model, can be constructed from observations of a player’s behavior in a module that we designed for Neverwinter Nights (Lankveld et al. 2011a). In the present research, we investigate whether we can do the same thing in an actual modern commercial video game, in this case the game Fallout 3. We stored automatic observations on 36 participants who played the introductory stages of Fallout 3. We then correlated these observations with the participants’ personality profiles, expressed by values for five personality traits as measured by the standard NEO-FFI questionnaire. Our analysis shows correlations between all five personality traits and the game observations. These results validate and generalize the results from our previous research (Lankveld et al. 2011a). We may conclude that Fallout 3, and by extension other modern video games, allows players to express their personality, and can therefore be used to create person-

This paper describes an investigation into how real-time but low-cost biometric information can be interpreted by computer games to enhance gameplay without fundamentally changing it. We adapted a cheap sensor, (the Lightstone mediation sensor device by Wild Divine), to record and transfer biometric information about the player (via sensors that clip over their fingers) into a commercial game engine, Half-Life 2. During game play, the computer game was dynamically modified by the player’s biometric information to increase the cinematically augmented “horror” affordances. These included dynamic changes in the game shaders, screen shake, and the creation of new spawning points for the game’s non-playing characters (zombies), all these features were driven by the player’s biometric data. To evaluate the usefulness of this biofeedback device, we compared it against a control group of players who also had sensors clipped on their fingers, but for the second group the gameplay was not modified by the biometric information of the players. While the evaluation results indicate biometric data can improve the situated feeling of horror, there are many design issues that will need to be investigated by future research, and the judicious selection of theme and appropriate interaction is vital. Author

Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.

Creating a design theory for Constructivist Gaming Learning Environment necessitates, among other things, the establishment of design principles. These principles have the potential to help designers produce games, where users achieve higher levels of learning. This paper focuses on twelve design principles: Probing, Distributed, Multiple Routes, Practice, Psychosocial Moratorium, Regime of Competence, Self-Knowledge, Collective Knowledge, Engaging, User Interface Ease of Use, On Demand and Just-in-Time Tutorial, and Achievement. We report on two pilot studies of a qualitative nature in which we test our design principles. Game play testing and observations were carried out on five Massively Multiplayer Online Games (MMOGs): RuneScape, GuildWars, Ragnarok, World of WarCraft, and Final Fantasy XI. Two educational games, Carabella Goes to College and Outbreak at WatersEdge were also observed. Our findings indicate that not all of the popular MMOGs and educational games support all of these principles.

The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

Card games are interesting for many reasons besides their connection with gambling. Bridge is being a game of imperfect information, it is a well defined, decision making game. The estimation of the number of tricks to be taken by one pair of bridge players is called Double Dummy Bridge Problem (DDBP). Artificial Neural Networks are Non – Linear mapping structures based on the function of the human brain. Feed Forward Neural Network is used to solve the DDBP in contract bridge. The learning methodology, supervised learning was used in Back – Propagation Network (BPN) for training and testing the bridge sample deal. In our study we compared back – Propagation algorithm and obtained that Resilient Back – Propagation algorithms by using Hyperbolic Tangent function and Resilient Back – Propagation algorithm produced better result than the other. Among various neural network architectures, in this study we used four network architectures viz., 26x4, 52, 104 and 52x4 for solving DDBP in contract bridge.

This paper deals with the problem of tracking football players in a football match using data from a single moving camera. Tracking footballers from a single video source is di cult: not only do the football players occlude each other, but they frequently enter and leave the camera's eld of view, making initialisation and destruction of a player's tracking a di cult task. The system presented here uses particle lters to track players. The multiple state estimates used by a particle lter provide an elegant method for maintaining tracking of players following an occlusion. Automated tracking can be achieved by creating and stopping particle lters depending on the input player data.

With emergence of new techniques, data in many fields are getting larger and larger, especially in dimensionality aspect. The high dimensionality of data may pose great challenges to traditional learning algorithms. In fact, many of features in large volume of data are redundant and noisy. Their presence not only degrades the performance of learning algorithms, but also confuses end-users in the post-analysis process. Thus, it is necessary to eliminate irrelevant features from data before being fed into learning algorithms. Currently, many endeavors have been attempted in this field and many outstanding feature selection methods have been developed. Among different evaluation criteria, mutual information has also been widely used in feature selection because of its good capability of quantifying uncertainty of features in classification tasks. However, the mutual information estimated on the whole dataset can not exactly represent the correlation between features. To cope with this issue, in this paper we firstly re-estimate mutual information on identified instances dynamically, and then introduce a new feature selection method based on 1194 H. Liu, Y. Mo, J. Zhao conditional mutual information. Performance evaluations on sixteen UCI datasets show that our proposed method achieves comparable performance to other wellestablished feature selection algorithms in most cases.


Question: Treating feature selection as a single-player game.

This paper presents a novel framework for addressing multi-armed bandit problems when the number of arms significantly exceeds the feasible number of trials. We introduce a stochastic hypothesis regarding the mean-reward of an arm upon selection, which serves as a probabilistic indicator of its potential to be near-optimal. Notably, our hypothesis is less stringent than those previously reported in the literature. We propose algorithms that employ upper-confidence-bounds, focusing on a subset of randomly chosen arms, and we establish upper-bounds on the expected regret stemming from this approach. Additionally, we offer a lower-bound analysis that, in certain scenarios, aligns closely (apart from a logarithmic factor) with our upper-bound estimations. Our findings contribute to the understanding of how to efficiently manage vast arm spaces in bandit problems.

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning--pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.

We present an algorithm that provides the one-dimensional subspace, where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions. Our main result shows that the set of possible one-dimensional spaces v, for which the order of the projected class means is identical, defines a convex region with associated convex Bayes error function g(v). This allows for the minimization of the error function using standard convex optimization algorithms. Our algorithm is then extended to the minimization of the Bayes error in the more general case of heteroscedastic distributions. This is done by means of an appropriate kernel mapping function. This result is further extended to obtain the d dimensional solution for any given d by iteratively applying our algorithm to the null space of the (d - l)-dimensional solution. We also show how this result can be used to improve upon the outcomes provided by existing algorithms and derive a low-computational cost, linear approximation. Extensive experimental validations are provided to demonstrate the use of these algorithms in classification, data analysis and visualization.

Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.

Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

This paper deals with the problem of tracking football players in a football match using data from a single moving camera. Tracking footballers from a single video source is di cult: not only do the football players occlude each other, but they frequently enter and leave the camera's eld of view, making initialisation and destruction of a player's tracking a di cult task. The system presented here uses particle lters to track players. The multiple state estimates used by a particle lter provide an elegant method for maintaining tracking of players following an occlusion. Automated tracking can be achieved by creating and stopping particle lters depending on the input player data.

We analyze the multi-view regression problem where we have two views X = (X, X) of the input data and a target variableY of interest. We provide sufficient conditions under which we can reduce the dimensionality of X (via a projection) without loosing predictive power of Y . Crucially, this projection can be computed via a Canonical Correlation Analysis only on the unlabeled data. The algorithmic template is as f ollows: with unlabeled data, perform CCA and construct a certain projection; with the labeled data, do least squa res regression in this lower dimensional space. We show how, under certain natural assumptions, the number o f labeled samples could be significantly reduced (in comparison to the single view setting) — in particular, we show how this dimen sionality reduction does not loose predictive power ofY (thus it only introduces little bias but could drastically reduce the variance). We explore two separate assumptions under which this is possible and show how, under either assumption alone, dimensionality reduction could reduce the labeled sample complexity. The two assumptions we consider are a conditional independenceassumption and a redundancyassumption. The typical conditional independence assumption is that conditioned onY the viewsX andX are independent — we relax this assumption to: conditioned on some hidden state H the viewsX andX are independent. Under the redundancy assumption, we show that the best predictor from each view is roughly as good as the best predictor u sing both views.

Despite the recognized value of Machine Learning (ML) techniques and high expectation of applying ML techniques within various applications, significant barriers to the widespread adoption and local implementation of ML approaches still exist in the areas of trust (of ML results), comprehension (of ML processes), as well as confidence (in decision making) by users. This paper investigates the effects of correlation between features and target values on user confidence in data analytics-driven decision making. Our user study found that revealing the correlation between features and target variables affected user confidence in decision making significantly. Moreover, users felt more confident in decision making when correlation shared the same trend with the prediction model performance. These findings would help design intelligent user interfaces and evaluate the effectiveness of machine learning models in applications.

This paper presents a novel learning framework to provide computer game agents the ability to adapt to the player as well as other game agents. Our technique generally involves a personality adaptation module encapsulated in a reinforcement learning framework. Unlike previous work in which adaptation normally involves a decision process on every single action the agent takes, we introduce a two-level process whereby adaptation only takes place on an abstracted actions set which we coin as agent personality. With the personality defined, each agent will then take actions according to the restrictions imposed in its personality. In doing so, adaptation takes place in appropriately defined intervals in the game, without disrupting or slowing down the game constantly with intensive decision-making computations, hence improving enjoyment for the player. Moreover, by decoupling adaptation from action selection, we have a modular adaptive system that can be used with existing action planning methods. With an actual typical game scenario that we have created, it is shown that a team of agents using our framework to adapt towards the player are able to perform better than a team with scripted behavior. Consequently, we also show the team performs even better when adapted to-

Feature selection is widely used in preparing highdimensional data for effective data mining. Increasingly popular social media data presents new challenges to feature selection. Social media data consists of (1) traditional high-dimensional, attribute-value data such as posts, tweets, comments, and images, and (2) linked data that describes the relationships between social media users as well as who post the posts, etc. The nature of social media also determines that its data is massive, noisy, and incomplete, which exacerbates the already challenging problem of feature selection. In this paper, we illustrate the differences between attributevalue data and social media data, investigate if linked data can be exploited in a new feature selection framework by taking advantage of social science theories, extensively evaluate the effects of user-user and user-post relationships manifested in linked data on feature selection, and discuss some research issues for future work.

The problem of formulating general concepts from specific training examples has long been a major focus of machine learning research. While most previous research has focused on empirical methods for generalizing from a large number of training examples using no domain-specific knowledge, in the past few years new methods have been developed for applying domain-specific knowledge to formulate valid generalizations from single training examples. The characteristic common to these methods is that their ability to generalize from a single example follows from their ability to explain why the training example is a member of the concept being learned. This paper proposes a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization. The EBG method is illustrated in the context of several example problems, and used to contrast several existing systems for explanation-based generalization. The perspective on explanation-based generalization afforded by this general method is also used to identify open research problems in this area.


Question: Regarding feature selection, viewing it through the lens of a single participant game.

In the context of feature subset selection, a machine learning algorithm must discern a pertinent subset of features to concentrate on, effectively disregarding the less relevant ones. To maximize performance with a specific learning algorithm on a given dataset, the feature selection method must account for the interaction between the algorithm and the data. This paper delves into the correlation between optimal feature subset selection and feature relevance. We propose a wrapper method that customizes the feature subset to both the learning algorithm and the domain at hand. Through our investigation, we highlight the advantages and disadvantages of the wrapper strategy and present a series of enhanced designs. We juxtapose the wrapper method with learning without feature subset selection and with Relief, a filter-based approach to feature subset selection. Our results demonstrate substantial accuracy gains for several datasets when using decision trees and Naive-Bayes, two prominent families of induction algorithms. © 1997 Elsevier Science B.V.

Many applications of Machine Learning (ML) involve interactions with humans. Humans may provide input to a learning algorithm (in the form of labels, demonstrations, corrections, rankings or evaluations) while observing its outputs (in the form of feedback, predictions or executions). Although humans are an integral part of the learning process, traditional ML systems used in these applications are agnostic to the fact that inputs/outputs are from/for humans.
 However, a growing community of researchers at the intersection of ML and human-computer interaction are making interaction with humans a central part of developing ML systems. These efforts include applying interaction design principles to ML systems, using human-subject testing to evaluate ML systems and inspire new methods, and changing the input and output channels of ML systems to better leverage human capabilities. With this Interactive Machine Learning (IML) workshop at IUI 2013 we aim to bring this community together to share ideas, get up-to-date on recent advances, progress towards a common framework and terminology for the field, and discuss the open questions and challenges of IML.

When intelligent interfaces, such as intelligent desktop assistants, email classif iers, and recommender systems, customize themselves to a particular end user , such customizations can decrease productivity and increase frustration due to inacc urate predictions—especially in early stages when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amoun t of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally weighted logistic regression for feature labeling by end users , enabling them to point out which features are important for a class, rath er than provide new training instances. We first evaluate our algorithms against other feature labeling algorit hms under idealized conditions using feature labels generated by an oracle . In addition, another of our contributions is an evaluation of feature labeling algorithms under real world conditions using feature labels harvested from actual end users in our user study. Our user study is the first statistical user study for feature labeling invo lvi g a large number of end users (43 participants), all of whom have no background in machine learning. Our supervised and semi-supervised algorithms were among the best perfo rmers when compared to other feature labeling algorithms in the idealized setting and they are also robust to poor quality feature labels provided by ordinary end u sers in our study. We also perform an analysis to investigate the relative gains of incorporatin g the different sources of knowledge available in the labeled training set, the feature labels and the unlabeled data. Together, our results strongly suggest that feature labeling by e d users is both viable and effective for allowing end users to improve the learnin g algorithm behind their customized applicati ons. * Corresponding author E-mail address: dassh@eecs.oregonstate.edu (Shubhomoy Das, 1148 Kelley Engineering Center, Corvallis, OR 97331-5501, USA, Ph: 1-541-908-6949). 1 Early versions of portions of this work appeared in [36, 37] S. Das, T. Moore, W-K. Wong et. al./Artificial Intelligence © 2012 Elsevier B.V. All rights reserved.

In imperfect-information games, the optimal strategy in a subgame may depend on the strategy in other, unreached subgames. Thus a subgame cannot be solved in isolation and must instead consider the strategy for the entire game as a whole, unlike perfect-information games. Nevertheless, it is possible to first approximate a solution for the whole game and then improve it in individual subgames. This is referred to as subgame solving. We introduce subgame-solving techniques that outperform prior methods both in theory and practice. We also show how to adapt them, and past subgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the prior state-of-the-art approach, action translation. Finally, we show that subgame solving can be repeated as the game progresses down the game tree, leading to far lower exploitability. These techniques were a key component of Libratus, the first AI to defeat top humans in heads-up no-limit Texas hold’em poker.

BACKGROUND
Many studies are currently researching the effects of video games, particularly in the domain of cognitive training. Great variability exists among video games however, and few studies have attempted to compare different types of video games. Little is known, for instance, about the cognitive processes or brain structures that underlie learning of different genres of video games.

OBJECTIVE
To examine the cognitive and neural underpinnings of two different types of game learning in order to evaluate their common and separate correlates, with the hopes of informing future intervention research.

METHODS
Participants (31 younger adults and 31 older adults) completed an extensive cognitive battery and played two different genres of video games, one action game and one strategy game, for 1.5 hours each. DTI scans were acquired for each participant, and regional fractional anisotropy (FA) values were extracted using the JHU atlas.

RESULTS
Behavioral results indicated that better performance on tasks of working memory and perceptual discrimination was related to enhanced learning in both games, even after controlling for age, whereas better performance on a perceptual speed task was uniquely related with enhanced learning of the strategy game. DTI results indicated that white matter FA in the right fornix/stria terminalis was correlated with action game learning, whereas white matter FA in the left cingulum/hippocampus was correlated with strategy game learning, even after controlling for age.

CONCLUSION
Although cognition, to a large extent, was a common predictor of both types of game learning, regional white matter FA could separately predict action and strategy game learning. Given the neural and cognitive correlates of strategy game learning, strategy games may provide a more beneficial training tool for adults suffering from memory-related disorders or declines in processing speed, particularly older adults.

From a simple entertainment activity to a learning tool, it is undeniable that video games are one of the most active and relevant areas in today's society. Since their inception, video games experienced an evolution unmatched by almost any other area and today's video games are complex pieces of software built by expensive multidisciplinary teams. Procedural Content Generation (PCG) offers an alternative to manual design of video game content, optimizing the process of development and thus reducing its cost. However, the content that is generated by traditional PCG techniques is usually very generic and it struggles to offer meaningful game experiences to a diverse player base. Recent years have brought some new PCG techniques that try to solve this problem by dynamically adjusting the generation of content to suit the needs of each individual player. The work presented in this paper focuses on the development of a new PCG methodology that aims to close the gap between game developers and their players. This is achieved by providing the developers with relevant real time player and playing context information and thus creating an easier way for developers to adjust their content generation process in run time to better suit the needs of each player. This was achieved by first designing a methodology that models the player, their context and the game. It was also developed a simple game to showcase the potential of such methodology. The end result was a game that adapts some of its content to different types of players and contexts.

Are typical computer game genres still valid descriptors and useful for describing game structure and game content? Games have changed from simple to complex and from single function to multi function. By identifying structural differences in game elements we develop a more nuanced model to categorized games and use cluster analysis as a descriptive tool in order to do so. The cluster analysis of 75 functionally different games shows that the two perspectives (omnipresent and vagrant), as well as challenges, mutability and savability are important functional categories to use in order to describe games. Author

Game designs often center on the game mechanics— rules governing the logical evolution of the game. We seek to develop an intelligent system that generates computer games. As first steps towards this goal we present a composable and cross-domain representation for game mechanics that draws from AI planning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics—what they do in the game. A planner takes a set of generated mechanics and tests whether those mechanics meet playability requirements—controlling how mechanics function in a game to affect player behavior. We demonstrate our system by modeling and generating mechanics in a role-playing game, platformer game, and combined role-playing-platformer game.

Reinforcement Learning methods have been succesfully applied to various optimalization problems. Scaling this up to real world sized problems has however been more of a problem. In this research we apply Reinforcement Learning to the game of Tetris which has a very large state space. We not only try to learn policies for Standard Tetris but try to learn parameterized policies for Generalized Tetris, which varies from game to game in field size and the chance of a single block occuring. In comparison to a non-parameterized policy we find that a parameterized policy is able to outperform the non-parameterized policy. The increased complexity of learning such a policy using the Cross-Entropy method however reaches a limit at which the policy is too complex to learn and performance drops below the non-parameterized policy.

Traditionally, the game of Tic-tac-toe is a pencil and paper game played by two people who take turn to place their pieces on a 3times3 grid with the objective of being the first player to fill a horizontal, vertical, or diagonal row with their pieces. What if instead of having one person playing against another, one person plays against a team of nine players, each of whom is responsible for one cell in the 3times3 grid? In this new way of playing the game, the team has to coordinate its players, who are acting independently based on their limited information. In this paper, we present a solution that can be extended to the case where two such teams play against each other, and also to other board games. Essentially, the solution uses a decentralized decision making, which at first seems to complicate the solution. However, surprisingly, we show that in this mode, an equivalent level of decision making ability comes from simple components that reduce system complexity

Feature selection and discretisation have shown their effectiveness for data preprocessing especially for high-dimensional data with many irrelevant features. While feature selection selects only relevant features, feature discretisation finds a discrete representation of data that contains enough information but ignoring some minor fluctuation. These techniques are usually applied in two stages, discretisation and then selection since many feature selection methods work only on discrete features. Most commonly used discretisation methods are univariate in which each feature is discretised independently; therefore, the feature selection stage may not work efficiently since information showing feature interaction is not considered in the discretisation process. In this study, we propose a new method called PSO-DFS using bare-bone particle swarm optimisation (BBPSO) for discretisation and feature selection in a single stage. The results on ten high-dimensional datasets show that PSO-DFS obtains a substantial dimensionality reduction for all datasets. The classification performance is significantly improved or at least maintained on nine out of ten datasets by using the transformed “small” data obtained from PSO-DFS. Compared to applying the two-stage approach which uses PSO for feature selection on the discretised data, PSO-DFS achieves better performance on six datasets, and similar performance on three datasets with a much smaller number of features selected.


Question: Prejudices in social evaluations: Positivity or negativity?

This study explores the phenomenon of unrealistic optimism regarding susceptibility to health risks, utilizing a community-wide sample from central New Jersey. Through a mailed questionnaire, comparative risk judgments for 32 distinct health hazards were gathered from a random selection of 296 participants. The findings reveal that the tendency to underestimate one's risk relative to peers, often referred to as an optimistic bias, transcends boundaries of age, gender, education, and occupation. It becomes evident that this bias frequently arises when individuals extrapolate from their past experiences to predict their future vulnerability. Consequently, the health risks that most commonly elicit unrealistic optimism are those for which people believe, often incorrectly, that if they haven't encountered the issue yet, they are unlikely to face it in the future. The magnitude of optimistic biases is shown to increase with the perceived preventability of a hazard and decrease with higher perceived frequency and personal encounters with the risk. Additional data underscore the inconsistent correlation between personal risk assessments and objective risk factors, highlighting the complex interplay between subjective perceptions and actual risk.

This paper explores a judgmental heuristic in which a person evaluates the frequency of classes or the probability of events by availability, i.e., by the ease with which relevant instances come to mind. In general, availability is correlated with ecological frequency, but it is also affected by other factors. Consequently, the reliance on the availability heuristic leads to systematic biases. Such biases are demonstrated in the judged frequency of classes of words, of combinatorial outcomes, and of repeated events. The phenomenon of illusory correlation is explained as an availability bias. The effects of the availability of incidents and scenarios on subjective probability are discussed.

The authors argued that emotions characterized by certainty appraisals promote heuristic processing, whereas emotions characterized by uncertainty appraisals result in systematic processing. The 1st experiment demonstrated that the certainty associated with an emotion affects the certainty experienced in subsequent situations. The next 3 experiments investigated effects on processing of emotions associated with certainty and uncertainty. Compared with emotions associated with uncertainty, emotions associated with certainty resulted in greater reliance on the expertise of a source of a persuasive message in Experiment 2, more stereotyping in Experiment 3, and less attention to argument quality in Experiment 4. In contrast to previous theories linking valence and processing, these findings suggest that the certainty appraisal content of emotions is also important in determining whether people engage in systematic or heuristic processing.

Over 500 million people interact daily with Facebook. Yet, whether Facebook use influences subjective well-being over time is unknown. We addressed this issue using experience-sampling, the most reliable method for measuring in-vivo behavior and psychological experience. We text-messaged people five times per day for two-weeks to examine how Facebook use influences the two components of subjective well-being: how people feel moment-to-moment and how satisfied they are with their lives. Our results indicate that Facebook use predicts negative shifts on both of these variables over time. The more people used Facebook at one time point, the worse they felt the next time we text-messaged them; the more they used Facebook over two-weeks, the more their life satisfaction levels declined over time. Interacting with other people "directly" did not predict these negative outcomes. They were also not moderated by the size of people's Facebook networks, their perceived supportiveness, motivation for using Facebook, gender, loneliness, self-esteem, or depression. On the surface, Facebook provides an invaluable resource for fulfilling the basic human need for social connection. Rather than enhancing well-being, however, these findings suggest that Facebook may undermine it.

Three experiments explored the role of negotiator focus in disconnecting negotiated outcomes and evaluations. Negotiators who focused on their target prices, the ideal outcome they could obtain, achieved objectively superior outcomes compared with negotiators who focused on their lower bound (e.g., reservation price). Those negotiators who focused on their targets, however, were less satisfied with their objectively superior outcomes. In the final experiment, when negotiators were reminded of their lower bound after the negotiation, the satisfaction of those negotiators who had focused on their target prices was increased, with outcomes and evaluations becoming connected rather than disconnected. The possible negative effects of setting high goals and the temporal dimensions of the disconnection and reconnection between outcomes and evaluations are discussed.


Question: Biases in social comparisons: Optimism or pessimism in self-assessment?

The Cultural Shaping of Group Enhancement: Investigating Group-Serving Biases.
In Western cultures, self-serving biases are commonly observed, whereas they are less prevalent in Asian cultures. However, considering the contrasting values of individualism and collectivism in Western and Asian societies, it becomes crucial to explore whether these cultural differences extend to group evaluations. It is hypothesized that Asians might exhibit a group-serving bias akin to the self-serving bias found in Westerners, but directed towards the group instead of the self. Two studies were conducted to compare group-serving biases among European Canadian, Asian Canadian, and Japanese students. The first study found that Japanese students appraised a family member less favorably compared to both Canadian student groups. The second study replicated this finding, this time with students' assessments of their respective universities. These results suggest that cultural distinctions in enhancement biases are significant and extend to individuals' evaluations of their groups, indicating that group-serving biases may be as culturally entrenched as self-serving biases.

Narcissists are thought to display extreme affective reactions to positive and negative information about the self. Two experiments were conducted in which high- and low-narcissistic individuals, as defined by the Narcissistic Personality Inventory (NPI), completed a series of tasks in which they both succeeded and failed. After each task, participants made attributions for their performance and reported their moods. High-NPI participants responded with greater changes in anxiety, anger, and self-esteem. Low self-complexity was examined, but it neither mediated nor moderated affective responses. High-NPI participants tended to attribute initial success to ability, leading to more extreme anger responses and greater self-esteem reactivity to failure. A temporal sequence model linking self-attribution and emotion to narcissistic rage is discussed.

Metaphors linking spatial location and affect (e.g., feeling up or down) may have subtle, but pervasive, effects on evaluation. In three studies, participants evaluated words presented on a computer. In Study 1, evaluations of positive words were faster when words were in the up rather than the down position, whereas evaluations of negative words were faster when words were in the down rather than the up position. In Study 2, positive evaluations activated higher areas of visual space, whereas negative evaluations activated lower areas of visual space. Study 3 revealed that, although evaluations activate areas of visual space, spatial positions do not activate evaluations. The studies suggest that affect has a surprisingly physical basis.


Question: Biases in social comparisons: Optimism or pessimism in self-assessment?

This research explores the phenomenon of unrealistic optimism in relation to health risks among college students. A sample of 100 participants was asked to compare their likelihood of encountering 45 distinct health and life-threatening issues against that of their peers. The results revealed a pronounced optimistic bias for 34 out of these 45 hazards, with participants consistently perceiving their own risk to be lower than average. The study further delved into factors contributing to the degree of unrealistic optimism, identifying perceived controllability of the health issue, absence of prior experience with the problem, and the belief that the health issue typically manifests during childhood as significant influencers. Additionally, the research examined the role of beliefs and emotions in shaping individuals' inclination to adopt preventive measures against potential risks. Key findings included: (a) the independent contributions of beliefs regarding risk likelihood, perceptions of risk severity, and feelings of worry to the interest in risk mitigation; (b) the indirect negative impact of unrealistic optimism on the motivation to reduce risk, mediated through reduced worry; and (c) the insufficiency of beliefs about risk likelihood and severity in fully accounting for the level of worry associated with various health hazards.

The aim of this study was to translate into Spanish and to validate the Rosenberg Self-Esteem Scale (RSES), completed by 420 university students. Confirmatory factor analysis revealed that the model that best fit the data, both in the total sample and in the male and female subsamples, was the one-factor structure with method effects associated with positively worded items. The results indicated high, positive correlations between self-esteem and the five dimensions of self-concept. The scale showed satisfactory levels of internal consistency and temporal stability over a four-week period. Lastly, gender differences were obtained. These findings support the use of the RSES for the assessment of self-esteem in higher education.

Eight studies (N=2,973) tested the theory that gratitude is related to fewer depressive symptoms through positive reframing and positive emotion. Study 1 found a direct path between gratitude and depressive symptoms. Studies 2-5 demonstrated that positive reframing mediated the relationship between gratitude and depressive symptoms. Studies 6-7 showed that positive emotion mediated the relationship between gratitude and depressive symptoms. Study 8 found that positive reframing and positive emotion simultaneously mediated the relationship between gratitude and depressive symptoms. In sum, these eight studies demonstrate that gratitude is related to fewer depressive symptoms, with positive reframing and positive emotion serving as mechanisms that account for this relationship.


Question: Biases in social comparisons: Optimism or pessimism in self-assessment?

Imperfect Self-Evaluation: Consequences for Health, Education, and Professional Settings.

Psychological research from various fields indicates that self-evaluations of abilities and character traits are often flawed, in systematic and significant ways. This review examines empirical evidence on the imperfect nature of self-assessment and discusses its implications for three crucial domains: health, education, and the workplace. In general, individuals' self-perceptions have a tenuous or modest connection with their actual behavior and performance. The correlation between self-ratings of ability and actual performance in various domains is often moderate to weak, with other people's predictions of an individual's outcomes sometimes proving more accurate than the individual's own predictions. Moreover, people tend to overrate themselves, claiming to be "above average" in skill (an assertion that defies statistical logic), overestimating the likelihood of engaging in positive behaviors and achieving favorable outcomes, furnishing overly optimistic estimates of when they will complete future tasks, and forming judgments with excessive confidence. Several psychological processes contribute to these flawed self-assessments.

In the health domain, research aligns with these findings. People are unrealistically optimistic about their health risks compared to others, overestimate the uniqueness of their opinions and preferences (e.g., discomfort with alcohol) among their peers, which can negatively impact their health. They are unable to predict their emotional responses to emotionally charged situations, leading to inaccurate predictions of patients' preferences when asked to make treatment decisions on their behalf. Misguided by mistaken but seemingly plausible theories of health and disease, people misdiagnose themselves, which can have severe consequences for their health and longevity.

In education, students' self-assessments of their performance often only moderately align with those of their teachers and mentors. They seem incapable of accurately assessing their comprehension of recently read material. They also tend to be overconfident in newly acquired skills, sometimes due to the common educational practice of massed training, which appears to promote quick skill acquisition and self-confidence but not necessarily skill retention. However, several interventions can be introduced to encourage students to evaluate their skills and learning more accurately.

In the professional setting, flawed self-assessments occur at all levels, from employees overestimating their skills, complicating the provision of meaningful feedback, to CEOs displaying overconfidence in their judgments, particularly when entering new markets or embarking on novel projects, such as acquisitions that harm rather than help their company's stock price. Several interventions are proposed to mitigate the consequences of such flawed assessments, including training people to regularly correct for biased self-ass

A meta-analysis of conformity studies using an Asch-type line judgment task (1952b, 1956) was conducted to investigate whether the level of conformity has changed over time and whether it is related crogs-culturally to individualism-collectivism. The fiterature search produced 133 studies drawn from 17 countries. An analysis of U.S. studies found that conformity has declined since the 1950s. Results from 3 surveys were used to assess a country's individualism-collectivism, and for each survey the measures were found to be significantly related to conformity. Collectivist countries tended to show higher levels of conformity than individualist countries. Conformity research must attend more to cultural variables and to their role in the processes involved in social influence.


Question: Biases in social comparisons: Optimism or pessimism in self-assessment?

In the realm of decision-making under uncertainty, three key heuristics are frequently employed: (i) representativeness, which is typically invoked when assessing the likelihood that a given object or event A is part of class or process B; (ii) availability, often used when estimating the frequency of a category or the plausibility of a scenario; and (iii) adjustment from an anchor, a strategy commonly applied in numerical predictions when a pertinent figure is available. These heuristics, while efficient and often effective, can lead to consistent and predictable errors. By gaining a deeper understanding of these heuristics and the biases they engender, decision-making under uncertainty can be significantly enhanced.

This article explores the cognitive shortcuts that individuals use when faced with uncertainty in decision-making: (i) representativeness, where judgments of probability are made based on how closely an object or event resembles a class or process; (ii) availability, which influences perceptions of frequency and plausibility by relying on how easily examples can be recalled; and (iii) adjustment from an anchor, typically employed in numerical predictions when a relevant starting point is known. Although these heuristics can streamline decision-making, they can also introduce systematic and predictable errors. A deeper insight into these heuristics and the biases they create can lead to improved judgments and decisions in uncertain situations.

Four meta-analyses were conducted to examine gender differences in personality in the literature (1958-1992) and in normative data for well-known personality inventories (1940-1992). Males were found to be more assertive and had slightly higher self-esteem than females. Females were higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). There were no noteworthy sex differences in social anxiety, impulsiveness, activity, ideas (e.g., reflectiveness), locus of control, and orderliness. Gender differences in personality traits were generally constant across ages, years of data collection, educational levels, and nations.

What was noted by E. J. hanger (1978) remains true today: that much of contemporary psychological research is based on the assumption that people are consciously and systematically processing incoming information in order to construe and interpret their world and to plan and engage in courses of action. As did E. J. hanger, the authors question this assumption. First, they review evidence that the ability to exercise such conscious, intentional control is actually quite limited, so that most of moment-to-moment psychological life must occur through nonconscious means if it is to occur at all. The authors then describe the different possible mechanisms that produce automatic, environmental control over these various phenomena and review evidence establishing both the existence of these mechanisms as well as their consequences for judgments, emotions, and behavior. Three major forms of automatic self-regulation are identified: an automatic effect of perception on action, automatic goal pursuit, and a continual automatic evaluation of one's experience. From the accumulating evidence, the authors conclude that these various nonconscious mental systems perform the lion's share of the self-regulatory burden, beneficently keeping the individual grounded in his or her current environment.


Question: NTU RGB+D: A substantial dataset for 3D human activity analysis.

Addressing the Challenge of Long-Term Dependencies with Long Short-Term Memory (LSTM)

Recurrent neural networks (RNNs) have historically struggled with learning to store information over long time intervals due to issues with insufficient and decaying error backflow. This paper revisits Hochreiter's (1991) analysis of the problem and presents a solution through the introduction of a novel, efficient, gradient-based method known as Long Short-Term Memory (LSTM). By selectively truncating the gradient where it does not adversely affect learning, LSTM is capable of bridging minimal time lags exceeding 1000 discrete-time steps. This is achieved by maintaining constant error flow through special units called constant error carousels, which ensure the preservation of error signals over extended periods. Access to this constant error flow is controlled by multiplicative gate units that learn when to open and close, effectively managing the flow of information.

LSTM is characterized by its local nature in both space and time, with computational complexity per time step and weight remaining constant (O.1). The method's effectiveness is demonstrated through experiments involving artificial data characterized by local, distributed, real-valued, and noisy pattern representations. In comparison to other algorithms such as real-time recurrent learning, backpropagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM outperforms in terms of the number of successful runs and learning speed. Notably, LSTM is the first algorithm to successfully solve complex, artificial long-time-lag tasks that have eluded previous recurrent network approaches.

This advancement in LSTM's capability to handle long-term dependencies marks a significant milestone in the field of recurrent neural networks, enabling the modeling of temporal sequences with improved accuracy and efficiency.

Traffic safety is a severe problem around the world. Many road accidents are normally related with the driver's unsafe driving behavior, e.g. eating while driving. In this work, we propose a vision-based solution to recognize the driver's behavior based on convolutional neural networks. Specifically, given an image, skin-like regions are extracted by Gaussian Mixture Model, which are passed to a deep convolutional neural networks model, namely R*CNN, to generate action labels. The skin-like regions are able to provide abundant semantic information with sufficient discriminative capability. Also, R*CNN is able to select the most informative regions from candidates to facilitate the final action recognition. We tested the proposed methods on Southeast University Driving-posture Dataset and achieve mean Average Precision(mAP) of 97.76% on the dataset which prove the proposed method is effective in drivers's action recognition.

Action recognition in videos remains a challenging problem in the machine learning community. Particularly challenging is the differing degree of intra-class variation between actions: While background information is enough to distinguish certain classes, many others are abstract and require fine-grained knowledge for discrimination. To approach this problem, in this work we evaluate different modalities on the recently published Moments in Time dataset, a collection of one million videos of short length.

In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used .

Existing optical flow datasets are limited in size and variability due to the difficulty of capturing dense ground truth. In this paper, we tackle this problem by tracking pixels through densely sampled space-time volumes recorded with a high-speed video camera. Our model exploits the linearity of small motions and reasons about occlusions from multiple frames. Using our technique, we are able to establish accurate reference flow fields outside the laboratory in natural environments. Besides, we show how our predictions can be used to augment the input images with realistic motion blur. We demonstrate the quality of the produced flow fields on synthetic and real-world datasets. Finally, we collect a novel challenging optical flow dataset by applying our technique on data from a high-speed camera and analyze the performance of the state-of-the-art in optical flow under various levels of motion blur.

This paper proposes amulti-level feature learning framework for human action recognition using a single body-worn inertial sensor. The framework consists of three phases, respectively designed to analyze signal-based (low-level), components (mid-level) and semantic (high-level) information. Low-level features capture the time and frequency domain property while mid-level representations learn the composition of the action. The Maxmargin Latent Pattern Learning (MLPL) method is proposed to learn high-level semantic descriptions of latent action patterns as the output of our framework. The proposedmethod achieves the state-of-the-art performances, 88.7%, 98.8% and 72.6% (weighted F1 score) respectively, on Skoda, WISDM and OPP datasets. © 2017 Elsevier B.V. All rights reserved.

This thesis investigates the use of wearable sensors to recognize human activity. The activity of the user is one example of context information – others include the user’s location or the state of his environment – which can help computer applications to adapt to the user depending on the situation. In this thesis we use wearable sensors – mainly accelerometers – to record, model and recognize human activities. Using wearable sensors allows continuous recording of activities across different locations and independent from external infrastructure. There are many possible applications for activity recognition with wearable sensors, for instance in the areas of healthcare, elderly care, personal fitness, entertainment, or performing arts. In this thesis we focus on two particular research challenges in activity recognition, namely the need for less supervision, and the recognition of high-level activities. We make several contributions towards addressing these challenges. Our first contribution is an analysis of features for activity recognition. Using a data set of activities such as walking, standing, sitting, or hopping, we analyze the performance of commonly used features and window lengths over which the features are computed. Our results indicate that different features perform well for different activities, and that in order to achieve best recognition performance, features and window lengths should be chosen specific for each activity. In order to reduce the need for labeled training data, we propose an unsupervised algorithm which can discover structure in unlabeled recordings of activities. The approach identifies correlated subsets in feature space, and represents these subsets with low-dimensional models. We show that the discovered subsets often correspond to distinct activities, and that the resulting models can be used for recognition of activities in unknown data. In a separate study, we show that the approach can be effectively deployed in a semi-supervised learning framework. More specifically, we combine the approach with a discriminant classifier, and show that this scheme allows high recognition rates even when using only a small amount of labeled training data. Recognition of higher-level activities such as shopping, doing housework, or commuting is challenging, as these activities are composed of changing sub-activities and vary strongly across individuals. We present one study in which we recorded 10h of three different high-level activities, investigating to which extent methods for low-level activities can be scaled to the recognition of high-level activities. Our results indicate that for settings as ours, traditional supervised approaches in combination with data from wearable accelerometers can achieve recognition rates of more than 90%. While unsupervised techniques are desirable for short-term activities, they become crucial for long-term activities, for which annotation is often impractical or impossible. To this end we propose an unsupervised approach based on topic models that allows to discover high-level structure in human activity data. The discovered activity patterns correlate with daily routines such as commuting, office work, or lunch routine, and they can be used to recognize such routines in unknown data.

Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at highlevels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities.

Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed.

For 15 years now, visual tracking has been a very active research area of the computer vision community. But an increasing amount of works can be observed in the last five years. This has led to the development of numerous algorithms that can deal with more and more complex video sequences. Each of them has its own strengths and weaknesses. That is the reason why it becomes necessary to compare those algorithms. For this purpose, some datasets dedicated to visual tracking as well as, sometimes, their ground truth annotation files are regularly made publicly available by researchers. However, each dataset has its own specificities and is sometimes dedicated to test the ability of some algorithms to tackle only one or a few specific visual tracking subproblems. This article provides an overview of some of the datasets that are most used by the visual tracking community, but also of others that address specific tasks. We also propose a cartography of these datasets from a novel perspective, namely that of the difficulties the datasets present for visual tracking.

Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center. They often create discomfort with marker suits, and their recording volume is severely restricted and often constrained to indoor scenes with controlled backgrounds. Alternative suit-based systems use several inertial measurement units or an exoskeleton to capture motion with an inside-in setup, i.e. without external sensors. This makes capture independent of a confined volume, but requires substantial, often constraining, and hard to set up body instrumentation. Therefore, we propose a new method for real-time, marker-less, and egocentric motion capture: estimating the full-body skeleton pose from a lightweight stereo pair of fisheye cameras attached to a helmet or virtual reality headset - an optical inside-in method, so to speak. This allows full-body motion capture in general indoor and outdoor scenes, including crowded scenes with many people nearby, which enables reconstruction in larger-scale activities. Our approach combines the strength of a new generative pose estimation framework for fisheye views with a ConvNet-based body-part detector trained on a large new dataset. It is particularly useful in virtual reality to freely roam and interact, while seeing the fully motion-captured virtual body.


Question: NTU RGB+D: A substantial dataset for 3D human action analysis.

This paper introduces a novel real-time action recognition algorithm that effectively integrates depth image information and 3D joint positions to achieve superior accuracy in action recognition. To prevent the loss of subtle yet critical features and to manage local occlusions, we calculate a vector composed of numerous independent local features. Each of these features encapsulates the spatiotemporal variations of depth and depth gradients at a distinct space-time location within the action volume. Additionally, we capture the primary movements of the skeleton by constructing a local histogram of 3D joint position differences. For each joint, we define a 3D space-time motion volume that serves as an importance marker, which is then integrated into the feature vector to enhance action discrimination. To ensure that only the most discriminative features are retained, we employ a random decision forest (RDF) for training. The efficacy of the proposed algorithm is assessed on three established datasets and is compared against nine cutting-edge algorithms. The experimental outcomes demonstrate that, on average, our algorithm surpasses all other methods in terms of accuracy while maintaining a high processing speed of over 112 frames per second.

We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments. Existing methods typically fail for hand-object interactions in cluttered scenes imaged from egocentric viewpoints—common for virtual or augmented reality applications. Our approach uses two subsequently applied Convolutional Neural Networks (CNNs) to localize the hand and regress 3D joint locations. Hand localization is achieved by using a CNN to estimate the 2D position of the hand center in the input, even in the presence of clutter and occlusions. The localized hand position, together with the corresponding input depth value, is used to generate a normalized cropped image that is fed into a second CNN to regress relative 3D hand joint locations in real time. For added accuracy, robustness and temporal stability, we refine the pose estimates using a kinematic pose tracking energy. To train the CNNs, we introduce a new photorealistic dataset that uses a merged reality approach to capture and synthesize large amounts of annotated data of natural hand interaction in cluttered scenes. Through quantitative and qualitative evaluation, we show that our method is robust to self-occlusion and occlusions by objects, particularly in moving egocentric perspectives.

We take a new approach to computing dense scene flow between a pair of consecutive RGB-D frames. We exploit the availability of depth data by seeking correspondences with respect to patches specified not as the pixels inside square windows, but as the 3D points that are the inliers of spheres in world space. Our primary contribution is to show that by reasoning in terms of such patches under 6 DoF rigid body motions in 3D, we succeed in obtaining compelling results at displacements large and small without relying on either of two simplifying assumptions that pervade much of the earlier literature: brightness constancy or local surface planarity. As a consequence of our approach, our output is a dense field of 3D rigid body motions, in contrast to the 3D translations that are the norm in scene flow. Reasoning in our manner additionally allows us to carry out occlusion handling using a 6 DoF consistency check for the flow computed in both directions and a patchwise silhouette check to help reason about alignments in occlusion areas, and to promote smoothness of the flow fields using an intuitive local rigidity prior. We carry out our optimization in two steps, obtaining a first correspondence field using an adaptation of PatchMatch, and subsequently using alpha-expansion to jointly handle occlusions and perform regularization. We show attractive flow results on challenging synthetic and real-world scenes that push the practical limits of the aforementioned assumptions.

Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features [31] and deep-learned features [24]. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely spatiotemporal normalization and channel normalization. The advantages of our features come from (i) TDDs are automatically learned and contain high discriminative capacity compared with those hand-crafted features; (ii) TDDs take account of the intrinsic characteristics of temporal dimension and introduce the strategies of trajectory-constrained sampling and pooling for aggregating deep-learned features. We conduct experiments on two challenging datasets: HMD-B51 and UCF101. Experimental results show that TDDs outperform previous hand-crafted features [31] and deep-learned features [24]. Our method also achieves superior performance to the state of the art on these datasets.

The recent introduction of depth cameras like Leap Motion Controller allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper proposes a novel hand gesture recognition system with Leap Motion Controller. A series of features are extracted from Leap Motion tracking data, we feed these features along with HOG feature extracted from sensor images into a multiclass SVM classifier to recognize performed gesture, dimension reduction and feature weighted fusion are also discussed. Our results show that our model is much more accurate than previous work.

We study the sign language recognition problem which is to translate the meaning of signs from visual input such as videos. It is well-known that many problems in the field of computer vision require a huge amount of dataset to train deep neural network models. We introduce the KETI sign language dataset which consists of 10,480 videos of high resolution and quality. Since different sign languages are used in different countries, the KETI sign language dataset can be the starting line for further research on the Korean sign language recognition.
 Using the sign language dataset, we develop a sign language recognition system by utilizing the human keypoints extracted from face, hand, and body parts. The extracted human keypoint vector is standardized by the mean and standard deviation of the keypoints and used as input to recurrent neural network (RNN). We show that our sign recognition system is robust even when the size of training data is not sufficient. Our system shows 89.5% classification accuracy for 100 sentences that can be used in emergency situations.

We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGBD images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learning, offers several important advantages, such as using one modality available at test time to reconstruct the missing modality. In the RGB-D case, this enables the cross-modality scenarios, such as using depth data for semantically segmentation and the RGB images for depth estimation. We demonstrate the effectiveness of the proposed network on two publicly available RGB-D datasets. The experimental results show that the proposed method works well in both semantic segmentation and depth estimation tasks.

In this work1, we present a method to represent a video with a sequence of words, and learn the temporal sequencing of such words as the key information for predicting and recognizing human actions. We leverage core concepts from the Natural Language Processing (NLP) literature used in sentence classification to solve the problems of action prediction and action recognition. Each frame is converted into a word that is represented as a vector using the Bag of Visual Words (BoW) encoding method. The words are then combined into a sentence to represent the video, as a sentence. The sequence of words in different actions are learned with a simple but effective Temporal Convolutional Neural Network (T-CNN) that captures the temporal sequencing of information in a video sentence. We demonstrate that a key characteristic of the proposed method is its low-latency, i.e. its ability to predict an action accurately with a partial sequence (sentence). Experiments on two datasets, UCF101 and HMDB51 show that the method on average reaches 95% of its accuracy within half the video frames. Results, also demonstrate that our method achieves compatible state-of-the-art performance in action recognition (i.e. at the completion of the sentence) in addition to action prediction.

In the general settings of supervised learning, human action recognition has been a widely studied topic. The classifiers learned in this setting assume that the training and test data have been sampled from the same underlying probability distribution. However, in most of the practical scenarios, this assumption is not true, resulting in a suboptimal performance of the classifiers. This problem, referred to as Domain Shift, has been extensively studied, but mostly for image/object classification task. In this paper, we investigate the problem of Domain Shift in action videos, an area that has remained under-explored, and propose two new approaches named Action Modeling on Latent Subspace (AMLS) and Deep Adversarial Action Adaptation (DAAA). In the AMLS approach, the action videos in the target domain are modeled as a sequence of points on a latent subspace and adaptive kernels are successively learned between the source domain point and the sequence of target domain points on the manifold. In the DAAA approach, an end-to-end adversarial learning framework is proposed to align the two domains. The action adaptation experiments were conducted using various combinations of multi-domain action datasets, including six common classes of Olympic Sports and UCF50 datasets and all classes of KTH, MSR and our own SonyCam datasets. In this paper, we have achieved consistent improvements over chosen baselines and obtained some state-of-the-art results for the datasets.

Improved dense trajectory features have been successfully used in video-based action recognition problems, but their application to face processing is more challenging. In this paper, we propose a novel system that deals with the problem of emotion recognition in real-world videos, using improved dense trajectory, LGBP-TOP, and geometric features. In the proposed system, we detect the face and facial landmarks from each frame of a video using a combination of two recent approaches, and register faces by means of Procrustes analysis. The improved dense trajectory and geometric features are encoded using Fisher vectors and classification is achieved by extreme learning machines. We evaluate our method on the extended Cohn-Kanade (CK+) and EmotiW 2015 Challenge databases. We obtain state-of the-art results in both databases.

Traditionally, the indoor scene classification problem has been approached from a 2D image recognition point of view. In most visual scene classification systems, a descriptor for the input image is generated to obtain a suitable representation that includes features related to color, shape or spatial information. Techniques based on the use of a spatial pyramid have proven to be adequate to perform this step. In the past years, on the other hand, 3D sensors have become widely available, which allows to include new information sources to the framework previously described. In this work we rely on RGB-D data to extend the spatial pyramid approach, aimed at building descriptors that can lead to a more robust representation against changing lighting conditions. The proposed descriptors are evaluated on the RobotVision@ImageCLEF-2013 benchmark dataset, remarkably outperforming state-of-the-art 3D local and global descriptors.