OpenRaiser/Intern-Atlas huggingface dataset:
Toward Multimodal Model-Agnostic Meta-Learning
Venue: arXiv cs.LG 2018
Year: 2018

Gradient-based meta-learners such as MAML are able to learn a meta-prior from similar tasks to adapt to novel tasks from the same distribution with few gradient updates. One important limitation of such frameworks is that they seek a common initialization shared across the entire task distribution, substantially limiting the diversity of the task distributions that they are able to learn from. In this paper, we augment MAML with the capability to identify tasks sampled from a multimodal task distribution and adapt quickly through gradient updates. Specifically, we propose a multimodal MAML algorithm that is able to modulate its meta-learned prior according to the identified task, allowing faster adaptation. We evaluate the proposed model on a diverse set of problems including regression, few-shot image classification, and reinforcement learning. The results demonstrate the effectiveness of our model in modulating the meta-learned prior in response to the characteristics of tasks sampled from a multimodal distribution.
D{é}tection de locuteurs dans les s{é}ries TV
Venue: arXiv cs.MM 2018
Year: 2018

Speaker diarization of audio streams turns out to be particularly challenging when applied to fictional films, where many characters talk in various acoustic conditions (background music, sound effects, variations in intonation...). Despite this acoustic variability, such movies exhibit specific visual patterns, particularly within dialogue scenes. In this paper, we introduce a two-step method to achieve speaker diarization in TV series: speaker diarization is first performed locally within scenes visually identified as dialogues; then, the hypothesized local speakers are compared to each other during a second clustering process in order to detect recurring speakers: this second stage of clustering is subject to the constraint that the different speakers involved in the same dialogue have to be assigned to different clusters. The performances of our approach are compared to those obtained by standard speaker diarization tools applied to the same data.
Audiovisual speaker diarization of TV series
Venue: arXiv cs.MM 2018
Year: 2018

Speaker diarization may be difficult to achieve when applied to narrative films, where speakers usually talk in adverse acoustic conditions: background music, sound effects, wide variations in intonation may hide the inter-speaker variability and make audio-based speaker diarization approaches error prone. On the other hand, such fictional movies exhibit strong regularities at the image level, particularly within dialogue scenes. In this paper, we propose to perform speaker diarization within dialogue scenes of TV series by combining the audio and video modalities: speaker diarization is first performed by using each modality, the two resulting partitions of the instance set are then optimally matched, before the remaining instances, corresponding to cases of disagreement between both modalities, are finally processed. The results obtained by applying such a multi-modal approach to fictional films turn out to outperform those obtained by relying on a single modality.
Motivations, Classification and Model Trial of Conversational Agents for Insurance Companies
Venue: arXiv cs.HC 2018
Year: 2018

Advances in artificial intelligence have renewed interest in conversational agents. So-called chatbots have reached maturity for industrial applications. German insurance companies are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance companies by determining which classes of agents are of interest to insurance companies, finding relevant use cases and requirements, and developing a prototype for an exemplary insurance scenario. Based on this approach, we derive key findings for conversational agent implementation in insurance companies.
Assistive robotic device: evaluation of intelligent algorithms
Venue: arXiv cs.RO 2018
Year: 2018

Assistive robotic devices can be used to help people with upper body disabilities gaining more autonomy in their daily life. Although basic motions such as positioning and orienting an assistive robot gripper in space allow performance of many tasks, it might be time consuming and tedious to perform more complex tasks. To overcome these difficulties, improvements can be implemented at different levels, such as mechanical design, control interfaces and intelligent control algorithms. In order to guide the design of solutions, it is important to assess the impact and potential of different innovations. This paper thus presents the evaluation of three intelligent algorithms aiming to improve the performance of the JACO robotic arm (Kinova Robotics). The evaluated algorithms are 'preset position', 'fluidity filter' and 'drinking mode'. The algorithm evaluation was performed with 14 motorized wheelchair's users and showed a statistically significant improvement of the robot's performance.
Probabilistic Inference on Noisy Time Series (PINTS)
Venue: arXiv cs.MS 2018
Year: 2018

Time series models are ubiquitous in science, arising in any situation where researchers seek to understand how a system's behaviour changes over time. A key problem in time series modelling is \emph{inference}; determining properties of the underlying system based on observed time series. For both statistical and mechanistic models, inference involves finding parameter values, or distributions of parameters values, for which model outputs are consistent with observations. A wide variety of inference techniques are available and different approaches are suitable for different classes of problems. This variety presents a challenge for researchers, who may not have the resources or expertise to implement and experiment with these methods. PINTS (Probabilistic Inference on Noisy Time Series - https://github.com/pints-team/pints is an open-source (BSD 3-clause license) Python library that provides researchers with a broad suite of non-linear optimisation and sampling methods. It allows users to wrap a model and data in a transparent and straightforward interface, which can then be used with custom or pre-defined error measures for optimisation, or with likelihood functions for Bayesian inference or maximum-likelihood estimation. Derivative-free optimisation algorithms - which work without harder-to-obtain gradient information - are included, as well as inference algorithms such as adaptive Markov chain Monte Carlo and nested sampling which estimate distributions over parameter values. By making these statistical techniques available in an open and easy-to-use framework, PINTS brings the power of modern statistical techniques to a wider scientific audience.
Performance Analysis of NOMA with Fixed Gain Relaying over Nakagami-m Fading Channels
Venue: arXiv cs.IT 2018
Year: 2018

This paper studies the application of cooperative techniques for non-orthogonal multiple access (NOMA). More particularly, the fixed gain amplify-and-forward (AF) relaying with NOMA is investigated over Nakagami-$m$ fading channels. Two scenarios are considered insightfully. 1) The first scenario is that the base station (BS) intends to communicate with multiple users through the assistance of AF relaying, where the direct links are existent between the BS and users; and 2) The second scenario is that the AF relaying is inexistent between the BS and users. To characterize the performance of the considered scenarios, new closed-form expressions for both exact and asymptomatic outage probabilities are derived. Based on the analytical results, the diversity orders achieved by the users are obtained. For the first and second scenarios, the diversity order for the $n$-th user are $μ(n+1)$ and $μn$, respectively. Simulation results unveil that NOMA is capable of outperforming orthogonal multiple access (OMA) in terms of outage probability and system throughput. It is also worth noting that NOMA can provide better fairness compared to conventional OMA. By comparing the two scenarios, cooperative NOMA scenario can provide better outage performance relative to the second scenario.
A Software Ecosystem Reshaped by a Paradigm Shift: the CSI-Piemonte Case
Venue: arXiv cs.SE 2018
Year: 2018

Context: Changes in the software development paradigm, when operated by entities with a pivotal role, have the power to affect a number of groups and entities in their sphere of influence, changing both their working habits and relations. Objective: In this paper we present the organizational changes occurred in a software ecosystem as consequence of a technological change. In particular we examine the evolution of an MDD solution and the changing roles of the company promoting it, the public administrations and the sub-contractors. Method: The paper focuses on a single case study that encompasses the six years long evolution of a Model-driven development solution, starting from its conception until is recent open-source release, across five distinct phases. The history was analyzed jointly by software engineering academics and industrial managers directly involved in the case study. Results: A report of the ecosystem evolution from an idiographic perspective is reported. An analysis of the history allowed an abstraction that led to the identification of several distinct ecosystem evolution motifs. Conclusion: The motifs represent a set of key process areas for the evolution of a software ecosystem. They are potentially generalizable to other similar ecosystems. As such, they can be used by researchers to evaluate existing in-progress case studies, and by practitioners as a set of guidelines.
Superconducting Neuromorphic Computing Using Quantum Phase-Slip Junctions
Venue: arXiv cs.ET 2018
Year: 2018

Superconducting circuits based on quantum phase-slip junctions (QPSJs) can conduct quantized charge pulses, which naturally resemble action potentials generated by biological neurons. A corresponding synaptic circuit, which works as a weighted connection between two neurons, can also be realized by circuits comprised of QPSJs and magnetic Josephson junctions (MJJs) as a means of charge modulation for quantized charge propagation. In this paper, we present basic neuromorphic components such as neuron and synaptic circuits based on superconducting QPSJs and MJJs. Using a SPICE model developed for QPSJs, neuron and synaptic circuits have been simulated in WRSPICE to demonstrate possible operation. We provide estimates for QPSJ energy dissipation and operation speed based on calculations using simple models. The challenges for implementation of this technology are also briefly discussed.
Inputs from Hell: Generating Uncommon Inputs from Common Samples
Venue: arXiv cs.SE 2018
Year: 2018

Generating structured input files to test programs can be performed by techniques that produce them from a grammar that serves as the specification for syntactically correct input files. Two interesting scenarios then arise for effective testing. In the first scenario, software engineers would like to generate inputs that are as similar as possible to the inputs in common usage of the program, to test the reliability of the program. More interesting is the second scenario where inputs should be as dissimilar as possible from normal usage. This is useful for robustness testing and exploring yet uncovered behavior. To provide test cases for both scenarios, we leverage a context-free grammar to parse a set of sample input files that represent the program's common usage, and determine probabilities for individual grammar production as they occur during parsing the inputs. Replicating these probabilities during grammar-based test input generation, we obtain inputs that are close to the samples. Inverting these probabilities yields inputs that are strongly dissimilar to common inputs, yet still valid with respect to the grammar. Our evaluation on three common input formats (JSON, JavaScript, CSS) shows the effectiveness of these approaches in obtaining instances from both sets of inputs.
Modular meta-learning in abstract graph networks for combinatorial generalization
Venue: arXiv cs.LG 2018
Year: 2018

Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways. In this work we propose abstract graph networks: using graphs as abstractions of a system's subparts without a fixed assignment of nodes to system subparts, for which we would need supervision. We combine this idea with modular meta-learning to get a flexible framework with combinatorial generalization to new tasks built in. We then use it to model the pushing of arbitrarily shaped objects from little or no training data.
Two-stage Combinatorial Optimization Problems under Risk
Venue: arXiv cs.DS 2018
Year: 2018

In this paper a class of combinatorial optimization problems is discussed. It is assumed that a solution can be constructed in two stages. The current first-stage costs are precisely known, while the future second-stage costs are only known to belong to an uncertainty set, which contains a finite number of scenarios with known probability distribution. A partial solution, chosen in the first stage, can be completed by performing an optimal recourse action, after the true second-stage scenario is revealed. A solution minimizing the Conditional Value at Risk (CVaR) measure is computed. Since expectation and maximum are boundary cases of CVaR, the model generalizes the traditional stochastic and robust two-stage approaches, previously discussed in the existing literature. In this paper some new negative and positive results are provided for basic combinatorial optimization problems such as the selection or network problems.
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Venue: arXiv cs.CV 2018
Year: 2018

Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require resource-intensive expert annotation. Semi-supervised generative adversarial network (GAN) approaches offer a means to learn from limited labeled data alongside larger unlabeled datasets, but have not been applied to discern fine-scale, sparse or localized features that define medical abnormalities. To overcome these limitations, we propose a patch-based semi-supervised learning approach and evaluate performance on classification of diabetic retinopathy from funduscopic images. Our semi-supervised approach achieves high AUC with just 10-20 labeled training images, and outperforms the supervised baselines by upto 15% when less than 30% of the training dataset is labeled. Further, our method implicitly enables interpretation of the SSL predictions. As this approach enables good accuracy, resolution and interpretability with lower annotation burden, it sets the pathway for scalable applications of deep learning in clinical imaging.
Mapping excellence in national research systems: the case of Italy
Venue: arXiv cs.DL 2018
Year: 2018

The study of the concept of "scientific excellence" and the methods for its measurement and evaluation is taking on increasing importance in the development of research policies in many nations. However, scientific excellence results as difficult to define in both conceptual and operational terms, because of its multi-dimensional and highly complex character. The literature on the theme is limited to few studies of an almost pioneering character. This work intends to contribute to the state of the art by exploring a bibliometric methodology which is effective, simple and inexpensive, and which further identifies "excellent" centers of research by beginning from excellence of the individual researchers affiliated with such centers. The study concentrates on the specific case of public research organizations in Italy, analyzing 109 scientific categories of research in the so called "hard" sciences and identifying 157 centers of excellence operating in 60 of these categories. The findings from this first application of the methodology should be considered exploratory and indicative. With a longer period of observation and the addition of further measurements, making the methodology more robust, it can be extended and adapted to a variety of national and supranational contexts, aiding with policy decisions at various levels.
Allocative efficiency in public research funding:can bibliometrics help?
Venue: arXiv cs.DL 2018
Year: 2018

The use of outcome control modes of research evaluation exercises is ever more frequent. They are conceived as tools to stimulate increased levels of research productivity, and to guide choices in allocating components of government research budgets for publicly funded institutions. There are several contributions in the literature that compare the different methodological approaches that policy makers could adopt for these exercises, however the comparisons are limited to only a few disciplines. This work, examining the case of the whole of the "hard sciences" of the Italian academic system, makes a comparison between results obtained from peer review type of evaluations (as adopted by the Ministry of Universities and Research) and those possible from a bibliometric approach (as developed by the authors). The aim is to understand to what extent bibliometric methodology, which is noted as relatively inexpensive, time-saving and exhaustive, can complement and integrate peer review methodology in research evaluation.
Research collaboration and productivity: is there correlation?
Venue: arXiv cs.DL 2018
Year: 2018

The incidence of extramural collaboration in academic research activities is increasing as a result of various factors. These factors include policy measures aimed at fostering partnership and networking among the various components of the research system, policies which are in turn justified by the idea that knowledge sharing could increase the effectiveness of the system. Over the last two decades, the scientific community has also stepped up activities to assess the actual impact of collaboration intensity on the performance of research systems. This study draws on a number of empirical analyses, with the intention of measuring the effects of extramural collaboration on research performance and, indirectly, verifying the legitimacy of policies that support this type of collaboration. The analysis focuses on the Italian academic research system. The aim of the work is to assess the level of correlation, at institutional level, between scientific productivity and collaboration intensity as a whole, both internationally and with private organizations. This will be carried out using a bibliometric type of approach, which equates collaboration with the co-authorship of scientific publications.
Hierarchical Macro Strategy Model for MOBA Game AI
Venue: arXiv cs.MA 2018
Year: 2018

The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution. Recently, great progress has been made in micro level execution, while complete solutions for macro strategies are still lacking. In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy model, agents explicitly make macro strategy decisions and further guide their micro level execution. Moreover, each of the agents makes independent strategy decisions, while simultaneously communicating with the allies through leveraging a novel imitated cross-agent communication mechanism. We perform comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena (MOBA) game. Our 5-AI team achieves a 48% winning rate against human player teams which are ranked top 1% in the player ranking system.
Preventing Attacks on Anonymous Data Collection
Venue: arXiv cs.CR 2018
Year: 2018

Anonymous data collection systems allow users to contribute the data necessary to build services and applications while preserving their privacy. Anonymity, however, can be abused by malicious agents aiming to subvert or to sabotage the data collection, for instance by injecting fabricated data. In this paper we propose an efficient mechanism to rate-limit an attacker without compromising the privacy and anonymity of the users contributing data. The proposed system builds on top of Direct Anonymous Attestation, a proven cryptographic primitive. We describe how a set of rate-limiting rules can be formalized to define a normative space in which messages sent by an attacker can be linked, and consequently, dropped. We present all components needed to build and deploy such protection on existing data collection systems with little overhead. Empirical evaluation yields performance up to 125 and 140 messages per second for senders and the collector respectively on nominal hardware. Latency of communication is bound to 4 seconds in the 95th percentile when using Tor as network layer.
Learning Political DNA in the Italian Senate
Venue: arXiv cs.SI 2018
Year: 2018

Motivated by the increasing interest of the control community towards social sciences and the study of opinion formation and belief systems, in this paper we address the problem of exploiting voting data for inferring the underlying affinity of individuals to competing ideology groups. In particular, we mine key voting records of the Italian Senate during the XVII legislature, in order to extract the hidden information about the closeness of senators to political parties, based on a parsimonious feature extraction method that selects the most relevant bills. Modeling the voting data as outcomes of a mixture of random variables and using sparse learning techniques, we cast the problem in a probabilistic framework and derive an information theoretic measure, which we refer to as Political Data-aNalytic Affinity (Political DNA). The advantages of this new affinity measure are discussed in the paper. The results of the numerical analysis on voting data unveil underlying relationships among political exponents of the Italian Senate.
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templates, latent patterns, and neural units. The AOG associates each object part with certain neural units in feature maps of conv-layers. The AOG is constructed in a weakly-supervised manner, i.e., very few annotations (e.g., 3-20) of object parts are used to guide the learning of AOGs. We develop a question-answering (QA) method that uses active human-computer communications to mine patterns from a pre-trained CNN, in order to incrementally explain more features in conv-layers. During the learning process, our QA method uses the current AOG for part localization. The QA method actively identifies objects, whose feature maps cannot be explained by the AOG. Then, our method asks people to annotate parts on the unexplained objects, and uses answers to discover CNN patterns corresponding to the newly labeled parts. In this way, our method gradually grows new branches and refines existing branches on the AOG to semanticize CNN representations. In experiments, our method exhibited a high learning efficiency. Our method used about 1/6-1/3 of the part annotations for training, but achieved similar or better part-localization performance than fast-RCNN methods.