We study the (parameterized) complexity of SHIFT BRIBERY for multiwinner voting rules. We focus on SNTV, Bloc, k-Borda, and Chamberlin-Courant, as well as on approximate variants of Chamberlin-Courant, since the original rule is NP-hard to compute. We show that SHIFT BRIBERY tends to be significantly harder in the multiwinner setting than in the single-winner one by showing settings where SHIFT BRIBERY is easy in the single-winner cases, but is hard (and hard to approximate) in the multiwinner ones. Moreover, we show that the non-monotonicity of those rules which are based on approximation algorithms for the Chamberlin-Courant rule sometimes affects the complexity of SHIFT BRIBERY.
The developpment of the Internet of Things (IoT) concept revives Responsive Environments (RE) technologies. Nowadays, the idea of a permanent connection between physical and digital world is technologically possible. The capillar Internet relates to the Internet extension into daily appliances such as they become actors of Internet like any hu-man. The parallel development of Machine-to-Machine communications and Arti cial Intelligence (AI) technics start a new area of cybernetic. This paper presents an approach for Cybernetic Organism (Cyborg) for RE based on Organic Computing (OC). In such approach, each appli-ance is a part of an autonomic system in order to control a physical environment. The underlying idea is that such systems must have self-x properties in order to adapt their behavior to external disturbances with a high-degree of autonomy.
The concepts of fuzzy objects and their classes are described that make it possible to structurally represent knowledge about fuzzy and partially-defined objects and their classes. Operations over such objects and classes are also proposed that make it possible to obtain sets and new classes of fuzzy objects and also to model variations in object structures under the influence of external factors.
Popular online enrichment analysis tools from the field of molecular systems biology provide users with the ability to submit their experimental results as gene sets for individual analysis. Such queries are kept private, and have never before been considered as a resource for integrative analysis. By harnessing gene set query submissions from thousands of users, we aim to discover biological knowledge beyond the scope of an individual study. In this work, we investigated a large collection of gene sets submitted to the tool Enrichr by thousands of users. Based on co-occurrence, we constructed a global gene-gene association network. We interpret this inferred network as providing a summary of the structure present in this crowdsourced gene set library, and show that this network recapitulates known protein-protein interactions and functional associations between genes. This finding implies that this network also offers predictive value. Furthermore, we visualize this gene-gene association network using a new edge-pruning algorithm that retains both the local and global structures of large-scale networks. Our ability to make predictions for currently unknown gene associations, that may not be captured by individual researchers and data sources, is a demonstration of the potential of harnessing collective knowledge from users of popular tools in the field of molecular systems biology.
In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an MHDP-based active perception method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback--Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive an efficient Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The results support our theoretical outcomes.
This paper introduces MazeBase: an environment for simple 2D games, designed as a sandbox for machine learning approaches to reasoning and planning. Within it, we create 10 simple games embodying a range of algorithmic tasks (e.g. if-then statements or set negation). A variety of neural models (fully connected, convolutional network, memory network) are deployed via reinforcement learning on these games, with and without a procedurally generated curriculum. Despite the tasks' simplicity, the performance of the models is far from optimal, suggesting directions for future development. We also demonstrate the versatility of MazeBase by using it to emulate small combat scenarios from StarCraft. Models trained on the MazeBase version can be directly applied to StarCraft, where they consistently beat the in-game AI.
We present a new concept - Wikiometrics - the derivation of metrics and indicators from Wikipedia. Wikipedia provides an accurate representation of the real world due to its size, structure, editing policy and popularity. We demonstrate an innovative mining methodology, where different elements of Wikipedia - content, structure, editorial actions and reader reviews - are used to rank items in a manner which is by no means inferior to rankings produced by experts or other methods. We test our proposed method by applying it to two real-world ranking problems: top world universities and academic journals. Our proposed ranking methods were compared to leading and widely accepted benchmarks, and were found to be extremely correlative but with the advantage of the data being publically available.
Financial fraud detection is an important problem with a number of design aspects to consider. Issues such as algorithm selection and performance analysis will affect the perceived ability of proposed solutions, so for auditors and re-searchers to be able to sufficiently detect financial fraud it is necessary that these issues be thoroughly explored. In this paper we will revisit the key performance metrics used for financial fraud detection with a focus on credit card fraud, critiquing the prevailing ideas and offering our own understandings. There are many different performance metrics that have been employed in prior financial fraud detection research. We will analyse several of the popular metrics and compare their effectiveness at measuring the ability of detection mechanisms. We further investigated the performance of a range of computational intelligence techniques when applied to this problem domain, and explored the efficacy of several binary classification methods.
We train a reinforcement learner to play a simplified version of the game Angry Birds. The learner is provided with a game state in a manner similar to the output that could be produced by computer vision algorithms. We improve on the efficiency of regular {\epsilon}-greedy Q-Learning with linear function approximation through more systematic exploration in Randomized Least Squares Value Iteration (RLSVI), an algorithm that samples its policy from a posterior distribution on optimal policies. With larger state-action spaces, efficient exploration becomes increasingly important, as evidenced by the faster learning in RLSVI.
This paper presents a framework for exact discovery of the most interesting sequential patterns. It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact discovery of top-k sequential patterns under a given measure of interest. Our interestingness measure is based on comparing the pattern support with the average support of its sister patterns, obtained by permuting (to certain extent) the items of the pattern. The larger the support compared to the expectation, the more interesting is the pattern. We build on these two elements to exactly extract the k sequential patterns with highest leverage, consistent with our definition of expected support. We conduct experiments on both synthetic data with known patterns and real-world datasets; both experiments confirm the consistency and relevance of our approach with regard to the state of the art.
The paper proposes a novel cognitive architecture (CA) for computational creativity based on the Psi model and on the mechanisms inspired by dual process theories of reasoning and rationality. In recent years, many cognitive models have focused on dual process theories to better describe and implement complex cognitive skills in artificial agents, but creativity has been approached only at a descriptive level. In previous works we have described various modules of the cognitive architecture that allows a robot to execute creative paintings. By means of dual process theories we refine some relevant mechanisms to obtain artworks, and in particular we explain details about the resolution level of the CA dealing with different strategies of access to the Long Term Memory (LTM) and managing the interaction between S1 and S2 processes of the dual process theory. The creative process involves both divergent and convergent processes in either implicit or explicit manner. This leads to four activities (exploratory, reflective, tacit, and analytic) that, triggered by urges and motivations, generate creative acts. These creative acts exploit both the LTM and the WM in order to make novel substitutions to a perceived image by properly mixing parts of pictures coming from different domains. The paper highlights the role of the interaction between S1 and S2 processes, modulated by the resolution level, which focuses the attention of the creative agent by broadening or narrowing the exploration of novel solutions, or even drawing the solution from a set of already made associations. An example of artificial painter is described in some experimentations by using a robotic platform.
An important problem for both graphics and vision is to synthesize novel views of a 3D object from a single image. This is particularly challenging due to the partial observability inherent in projecting a 3D object onto the image space, and the ill-posedness of inferring object shape and pose. However, we can train a neural network to address the problem if we restrict our attention to specific object categories (in our case faces and chairs) for which we can gather ample training data. In this paper, we propose a novel recurrent convolutional encoder-decoder network that is trained end-to-end on the task of rendering rotated objects starting from a single image. The recurrent structure allows our model to capture long-term dependencies along a sequence of transformations. We demonstrate the quality of its predictions for human faces on the Multi-PIE dataset and for a dataset of 3D chair models, and also show its ability to disentangle latent factors of variation (e.g., identity and pose) without using full supervision.
We propose a formal mathematical model for sparse representations in neocortex based on a neuron model and associated operations. The design of our model neuron is inspired by recent experimental findings on active dendritic processing and NMDA spikes in pyramidal neurons. We derive a number of scaling laws that characterize the accuracy of such neurons in detecting activation patterns in a neuronal population under adverse conditions. We introduce the union property which shows that synapses for multiple patterns can be randomly mixed together within a segment and still lead to highly accurate recognition. We describe simulation results that provide overall insight into sparse representations as well as two primary results. First we show that pattern recognition by a neuron can be extremely accurate and robust with high dimensional sparse inputs even when using a tiny number of synapses to recognize large patterns. Second, equations representing recognition accuracy of a dendrite predict optimal NMDA spiking thresholds under a generous set of assumptions. The prediction tightly matches NMDA spiking thresholds measured in the literature. Our model neuron matches many of the known properties of pyramidal neurons. As such the theory provides a unified and practical mathematical framework for understanding the benefits and limits of sparse representations in cortical networks.
Multi-tenancy hosting of users in cloud NoSQL data stores is favored by cloud providers because it enables resource sharing at low operating cost. Multi-tenancy takes several forms depending on whether the back-end file system is a local file system (LFS) or a parallel file system (PFS), and on whether tenants are independent or share data across tenants. In this thesis I focus on and propose solutions to two cases: independent data-local file system, and shared data-parallel file system.
In the independent data-local file system case, resource contention occurs under certain conditions in Cassandra and HBase, two state-of-the-art NoSQL stores, causing performance degradation for one tenant by another. We investigate the interference and propose two approaches. The first provides a scheduling scheme that can approximate resource consumption, adapt to workload dynamics and work in a distributed fashion. The second introduces a workload-aware resource reservation approach to prevent interference. The approach relies on a performance model obtained offline and plans the reservation according to different workload resource demands. Results show the approaches together can prevent interference and adapt to dynamic workloads under multi-tenancy.
In the shared data-parallel file system case, it has been shown that running a distributed NoSQL store over PFS for shared data across tenants is not cost effective. Overheads are introduced due to the unawareness of the NoSQL store of PFS. This dissertation targets the key-value store (KVS), a specific form of NoSQL stores, and proposes a lightweight KVS over a parallel file system to improve efficiency. The solution is built on an embedded KVS for high performance but uses novel data structures to support concurrent writes. Results show the proposed system outperforms Cassandra and Voldemort in several different workloads.
We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than simple geometric constraints on trajectories; they are rather governed by the surrounding context of various objects and human interactions in the environment. We propose a coactive online learning framework for teaching preferences in contextually rich environments. The key novelty of our approach lies in the type of feedback expected from the user: the human user does not need to demonstrate optimal trajectories as training data, but merely needs to iteratively provide trajectories that slightly improve over the trajectory currently proposed by the system. We argue that this coactive preference feedback can be more easily elicited than demonstrations of optimal trajectories. Nevertheless, theoretical regret bounds of our algorithm match the asymptotic rates of optimal trajectory algorithms.
We implement our algorithm on two high degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings -- household chores and grocery store checkout -- and show that users are able to train the robot with just a few feedbacks (taking only a few minutes).\footnote{Parts of this work has been published at NIPS and ISRR conferences~\citep{Jain13,Jain13b}. This journal submission presents a consistent full paper, and also includes the proof of regret bounds, more details of the robotic system, and a thorough related work.}
This article discusses open scientific challenges for understanding development and evolution of speech forms, as a commentary to Moulin-Frier et al. (Moulin-Frier et al., 2015). Based on the analysis of mathematical models of the origins of speech forms, with a focus on their assumptions , we study the fundamental question of how speech can be formed out of non--speech, at both developmental and evolutionary scales. In particular, we emphasize the importance of embodied self-organization , as well as the role of mechanisms of motivation and active curiosity-driven exploration in speech formation. Finally , we discuss an evolutionary-developmental perspective of the origins of speech.
Semantic parsing methods are used for capturing and representing semantic meaning of text. Meaning representation capturing all the concepts in the text may not always be available or may not be sufficiently complete. Ontologies provide a structured and reasoning-capable way to model the content of a collection of texts. In this work, we present a novel approach to joint learning of ontology and semantic parser from text. The method is based on semi-automatic induction of a context-free grammar from semantically annotated text. The grammar parses the text into semantic trees. Both, the grammar and the semantic trees are used to learn the ontology on several levels -- classes, instances, taxonomic and non-taxonomic relations. The approach was evaluated on the first sentences of Wikipedia pages describing people.
We consider the problem of learning user preferences over robot trajectories for environments rich in objects and humans. This is challenging because the criterion defining a good trajectory varies with users, tasks and interactions in the environment. We represent trajectory preferences using a cost function that the robot learns and uses it to generate good trajectories in new environments. We design a crowdsourcing system - PlanIt, where non-expert users label segments of the robot's trajectory. PlanIt allows us to collect a large amount of user feedback, and using the weak and noisy labels from PlanIt we learn the parameters of our model. We test our approach on 122 different environments for robotic navigation and manipulation tasks. Our extensive experiments show that the learned cost function generates preferred trajectories in human environments. Our crowdsourcing system is publicly available for the visualization of the learned costs and for providing preference feedback: \url{this http URL}
