Case Base Mining for Adaptation Knowledge Acquisition

In case-based reasoning, the adaptation of a source case in order to solve the target problem is at the same time crucial and difficult to implement. The reason for this difficulty is that, in general, adaptation strongly depends on domain-dependent knowledge. This fact motivates research on adaptation knowledge acquisition (AKA). This paper presents an approach to AKA based on the principles and techniques of knowledge discovery from databases and data-mining. It is implemented in CABAMAKA, a system that explores the variations within the case base to elicit adaptation knowledge. This system has been successfully tested in an application of case-based reasoning to decision support in the domain of breast cancer treatment

Model Driven Mutation Applied to Adaptative Systems Testing

Dynamically Adaptive Systems modify their behav- ior and structure in response to changes in their surrounding environment and according to an adaptation logic. Critical sys- tems increasingly incorporate dynamic adaptation capabilities; examples include disaster relief and space exploration systems. In this paper, we focus on mutation testing of the adaptation logic. We propose a fault model for adaptation logics that classifies faults into environmental completeness and adaptation correct- ness. Since there are several adaptation logic languages relying on the same underlying concepts, the fault model is expressed independently from specific adaptation languages. Taking benefit from model-driven engineering technology, we express these common concepts in a metamodel and define the operational semantics of mutation operators at this level. Mutation is applied on model elements and model transformations are used to propagate these changes to a given adaptation policy in the chosen formalism. Preliminary results on an adaptive web server highlight the difficulty of killing mutants for adaptive systems, and thus the difficulty of generating efficient tests.Comment: IEEE International Conference on Software Testing, Verification and Validation, Mutation Analysis Workshop (Mutation 2011), Berlin : Allemagne (2011

Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to a deep linear model. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on the Amazon review dataset and the spam dataset from the ECML/PKDD 2006 discovery challenge.Comment: ijca

Reusing adaptation strategies in adaptive educational hypermedia systems

Due to the difficulty and thus effort and expenses involved in creating them, personalization strategies in learning environments have to demonstrate a higher return-on-investment (ROI), if they are to be a viable component of the learning setting of the future. One feature that can increase this ROI is the reusability of adaptation strategies in Adaptive Educational Hypermedia Systems. This research looks into various ways of enhancing this reusability. Using multiple modular adaptation strategies (MAS) with a controlling meta-strategy is proposed as a more efficient way of authoring adaptation strategies. This renders the reuse of adaptation strategies faster and easier for course authors. A method for semi-automatically breaking down complex adaptation strategies into smaller modular adaptation strategies is described. Potential problems with using multiple strategies are described and ways to solve them are discussed. Finally, some evaluation points are illustrated, conclusions are drawn and further research areas are identified

Adaptation in Digital Games: The Effect of Challenge Adjustment on Player Performance and Experience

Good gaming experiences hinge on players being able to have a balance between challenge and skill. However, achieving that balance is challenging, so dynamic difficulty adjustment offers the opportunity to provide better gaming experiences through adapting the challenge in the game to suit an individual’s capabilities. The risk though is that in adapting the difficulty, players do not get a true sense of challenge, but rather some tailored, perhaps watered down experience. In this note, we report on a study, in which we used time manipulation as a method of simple adaptation in order to explore its effect on player experience (PX) and performance. Volunteers played a game in which the timer was adjusted based on their performance in the game, however they were not aware of the feature. The results showed that players in the experimental group found the game more immersive. This provides empirical support that dynamic difficulty adjustment could be used to improve the PX

Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation

Speech emotion recognition plays an important role in building more intelligent and human-like agents. Due to the difficulty of collecting speech emotional data, an increasingly popular solution is leveraging a related and rich source corpus to help address the target corpus. However, domain shift between the corpora poses a serious challenge, making domain shift adaptation difficult to function even on the recognition of positive/negative emotions. In this work, we propose class-wise adversarial domain adaptation to address this challenge by reducing the shift for all classes between different corpora. Experiments on the well-known corpora EMODB and Aibo demonstrate that our method is effective even when only a very limited number of target labeled examples are provided.Comment: 5 pages, 3 figures, accepted to ICASSP 201

Player experience and deceptive expectations of difficulty adaptation in digital games

Increasingly, digital games are including adaptive features that adjust the level of difficulty to match the skills of individual players. The intention is to improve and prolong the player experience by allowing the player to have the feeling of challenge without it being overwhelming and leading to repeated failure and frustration. Previous work has shown that player experience is indeed improved by such adaptations but also that the player experience can be improved by simply claiming such an adaptation is present even when it is not. It is therefore possible that claims about adaptations and the actual adaptations could interact and not lead to the intended outcomes for the players or worse disappoint players. This paper reports on two studies that were conducted to experimentally investigate the interaction between game adaptations and player information about adaptations on the player experience, specifically their sense of immersion in the game. For this, two games were developed using two different kinds of adaptations to adjust difficulty based on players’ performance in the game. Participants were provided with information about game adaptations independently of whether the adaptations were present. The results suggest that players felt more immersed in the game when told that the game adapts to them, regardless of whether the adaptation was present in the game or not. This effect was observed in both games despite their different adaptations and it remained prominent even during longer gaming sessions. These findings demonstrate that players’ knowledge of adaptations influences their experience independently of adaptations. In this particular context, the knowledge reinforced the experience of the adaptations. This suggests that, at least in some circumstances, developers do not need to be concerned about negative effects of telling players about in-game adaptations

Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos

Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of curating diverse large-scale video datasets. This paper addresses both of those challenges, through an image to video feature-level domain adaptation approach, to learn discriminative video frame representations. The framework utilizes large-scale unlabeled video data to reduce the gap between different domains while transferring discriminative knowledge from large-scale labeled still images. Given a face recognition network that is pretrained in the image domain, the adaptation is achieved by (i) distilling knowledge from the network to a video adaptation network through feature matching, (ii) performing feature restoration through synthetic data augmentation and (iii) learning a domain-invariant feature through a domain adversarial discriminator. We further improve performance through a discriminator-guided feature fusion that boosts high-quality frames while eliminating those degraded by video domain-specific factors. Experiments on the YouTube Faces and IJB-A datasets demonstrate that each module contributes to our feature-level domain adaptation framework and substantially improves video face recognition performance to achieve state-of-the-art accuracy. We demonstrate qualitatively that the network learns to suppress diverse artifacts in videos such as pose, illumination or occlusion without being explicitly trained for them.Comment: accepted for publication at International Conference on Computer Vision (ICCV) 201

Taking time to understand: articulating relationships between technologies and organizations

Dynamic relationships between technologies and organizations are investigated through research on digital visualization technologies and their use in the construction sector. Theoretical work highlights mutual adaptation between technologies and organizations but does not explain instances of sustained, sudden, or increasing maladaptation. By focusing on the technological field, I draw attention to hierarchical structuring around inter-dependent levels of technology; technological priorities of diverse groups; power asymmetries and disjunctures between contexts of development and use. For complex technologies, such as digital technologies, I argue these field-level features explain why organizations peripheral to the field may experience difficulty using emerging technology

Generate To Adapt: Aligning Domains using Generative Adversarial Networks

Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. We demonstrate the strength and generality of our approach by performing experiments on three different tasks with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. Our method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method that has been shown to work well across different datasets such as OFFICE and DIGITS.Comment: Accepted as spotlight talk at CVPR 2018. Code available here: https://github.com/yogeshbalaji/Generate_To_Adap