OpenRaiser/Intern-Atlas huggingface dataset:
Robots Learn Social Skills: End-to-End Learning of Co-Speech Gesture Generation for Humanoid Robots
Venue: arXiv cs.RO 2018
Year: 2018

Co-speech gestures enhance interaction experiences between humans as well as between humans and robots. Existing robots use rule-based speech-gesture association, but this requires human labor and prior knowledge of experts to be implemented. We present a learning-based co-speech gesture generation that is learned from 52 h of TED talks. The proposed end-to-end neural network model consists of an encoder for speech text understanding and a decoder to generate a sequence of gestures. The model successfully produces various gestures including iconic, metaphoric, deictic, and beat gestures. In a subjective evaluation, participants reported that the gestures were human-like and matched the speech content. We also demonstrate a co-speech gesture with a NAO robot working in real time.
Learning-based predictive control for linear systems: a unitary approach
Venue: arXiv cs.SY 2018
Year: 2018

A comprehensive approach addressing identification and control for learningbased Model Predictive Control (MPC) for linear systems is presented. The design technique yields a data-driven MPC law, based on a dataset collected from the working plant. The method is indirect, i.e. it relies on a model learning phase and a model-based control design one, devised in an integrated manner. In the model learning phase, a twofold outcome is achieved: first, different optimal p-steps ahead prediction models are obtained, to be used in the MPC cost function; secondly, a perturbed state-space model is derived, to be used for robust constraint satisfaction. Resorting to Set Membership techniques, a characterization of the bounded model uncertainties is obtained, which is a key feature for a successful application of the robust control algorithm. In the control design phase, a robust MPC law is proposed, able to track piece-wise constant reference signals, with guaranteed recursive feasibility and convergence properties. The controller embeds multistep predictors in the cost function, it ensures robust constraints satisfaction thanks to the learnt uncertainty model, and it can deal with possibly unfeasible reference values. The proposed approach is finally tested in a numerical example.
VAPOR: a Value-Centric Blockchain that is Scale-out, Decentralized, and Flexible by Design
Venue: arXiv cs.DC 2018
Year: 2018

Blockchains is a special type of distributed systems that operates in unsafe networks. In most blockchains, all nodes should reach consensus on all state transitions with Byzantine fault tolerant algorithms, which creates bottlenecks in performance. In this paper, we propose a new type of blockchains, namely Value-Centric Blockchains (VCBs), in which the states are specified as values (or more comprehensively, coins) with owners and the state transition records are then specified as proofs of the ownerships of individual values. We then formalize the "rational" assumptions that have been used in most blockchains. We further propose a VCB, VAPOR, that guarantees secure value transfers if all nodes are rational and keep the proofs of the values they owned, which is merely parts of the whole state transition record. As a result, we show that VAPOR enjoys significant benefits in throughput, decentralization, and flexibility without compromising security.
Towards End-to-end Automatic Code-Switching Speech Recognition
Venue: arXiv cs.CL 2018
Year: 2018

Speech recognition in mixed language has difficulties to adapt end-to-end framework due to the lack of data and overlapping phone sets, for example in words such as "one" in English and "wàn" in Chinese. We propose a CTC-based end-to-end automatic speech recognition model for intra-sentential English-Mandarin code-switching. The model is trained by joint training on monolingual datasets, and fine-tuning with the mixed-language corpus. During the decoding process, we apply a beam search and combine CTC predictions and language model score. The proposed method is effective in leveraging monolingual corpus and detecting language transitions and it improves the CER by 5%.
An investigation on the skewness patterns and fractal nature of research productivity distributions at field and discipline level
Venue: arXiv cs.DL 2018
Year: 2018

The paper provides an empirical examination of how research productivity distributions differ across scientific fields and disciplines. Productivity is measured using the FSS indicator, which embeds both quantity and impact of output. The population studied consists of over 31,000 scientists in 180 fields (10 aggregate disciplines) of a national research system. The Characteristic Scores and Scale technique is used to investigate the distribution patterns for the different fields and disciplines. Research productivity distributions are found to be asymmetrical at the field level, although the degree of skewness varies substantially among the fields within the aggregate disciplines. We also examine whether the field productivity distributions show a fractal nature, which reveals an exception more than a rule. Differently, for the disciplines, the partitions of the distributions show skewed patterns that are highly similar.
The effect of multidisciplinary collaborations on research diversification
Venue: arXiv cs.DL 2018
Year: 2018

This work verifies whether research diversification by a scientist is in some measure related to their collaboration with multidisciplinary teams. The analysis considers the publications achieved by 5300 Italian academics in the sciences over the period 2004-2008. The findings show that a scientist's outputs resulting from research diversification are more often than not the result of collaborations with multidisciplinary teams. The effect becomes more pronounced with larger and particularly with more diversified teams. This phenomenon is observed both at the overall level and for the disciplinary macro-areas.
Prosodic entrainment in dialog acts
Venue: arXiv cs.CL 2018
Year: 2018

We examined prosodic entrainment in spoken dialogs separately for several dialog acts in cooperative and competitive games. Entrainment was measured for intonation features derived from a superpositional intonation stylization as well as for rhythm features. The found differences can be related to the cooperative or competitive nature of the game, as well as to dialog act properties as its intrinsic authority, supportiveness and distributional characteristics. In cooperative games dialog acts with a high authority given by knowledge and with a high frequency showed the most entrainment. The results are discussed amongst others with respect to the degree of active entrainment control in cooperative behavior.
The effect of a country's name in the title of a publication on its visibility and citability
Venue: arXiv cs.DL 2018
Year: 2018

The objective of this research is to determine if the reference to a country in the title, keywords or abstract of a publication can influence its visibility (measured by the impact factor of the publishing journal) and citability (measured by the citations received). The study is based on Italian scientific production indexed in the Web of Science over the period 2004-2011. The analysis is conducted by comparing the values of four impact indicators for two subsets: i) the indexed publications with a country's name in the title, keywords or abstract; ii) the remainder of the population, with no country' name. The results obtained both at the general level and by subject category show that publications with a country name systematically receive lower impact values, with the exception of a limited number of subject categories, Also, the incidence of highly-cited articles is lower for the first subset.
The combined effects of age and seniority on research performance of full professors
Venue: arXiv cs.DL 2018
Year: 2018

In this work we examine the relationship between research performance, age, and seniority in academic rank of full professors in the Italian academic system. Differently from a large part of the previous literature, our results generally show a negative monotonic relationship between age and research performance, in all the disciplines under analysis. We also highlight a positive relationship between seniority in rank and performance, occurring particularly in certain disciplines. While in Medicine, Biology and Chemistry this result could be explained by the "accumulative advantage" effect, in other disciplines, like Civil engineering and Pedagogy and Psychology, it could be due to the existence of a large performance differential between young and mature researchers, at the moment of the promotion to full professors. These results, witnessed both generally and at the level of the individual disciplines, offer useful insights for policy makers and academia administrators on the role of older professors.
A methodology to measure the effectiveness of academic recruitment and turnover
Venue: arXiv cs.DL 2018
Year: 2018

We propose a method to measure the effectiveness of the recruitment and turnover of professors, in terms of their research performance. The method presented is applied to the case of Italian universities over the period 2008-2012. The work then analyses the correlation between the indicators of effectiveness used, and between the indicators and the universities' overall research performance. In countries that conduct regular national assessment exercises, the evaluation of effectiveness in recruitment and turnover could complement the overall research assessments. In particular, monitoring such parameters could assist in deterring favoritism, in countries exposed to such practices.
Funnel plots for visualizing uncertainty in the research performance of institutions
Venue: arXiv cs.DL 2018
Year: 2018

Research performance values are not certain. Performance indexes should therefore be accompanied by uncertainty measures, to establish whether the performance of a unit is truly outstanding and not the result of random fluctuations. In this work we focus on the evaluation of research institutions on the basis of average individual performance, where uncertainty is inversely related to the number of research staff. We utilize the funnel plot, a tool originally developed in meta-analysis, to measure and visualize the uncertainty in the performance values of research institutions. As an illustrative example, we apply the funnel plot to represent the uncertainty in the assessed research performance for Italian universities active in biochemistry
Ranking research institutions by the number of highly-cited articles per scientist
Venue: arXiv cs.DL 2018
Year: 2018

In the literature and on the Web we can readily find research excellence rankings for organizations and countries by either total number of highly-cited articles (HCAs) or by ratio of HCAs to total publications. Neither are indicators of efficiency. In the current work we propose an indicator of efficiency, the number of HCAs per scientist, which can complement the productivity indicators based on impact of total output. We apply this indicator to measure excellence in the research of Italian universities as a whole, and in each field and discipline of the hard sciences.
Cluster Size Management in Multi-Stage Agglomerative Hierarchical Clustering of Acoustic Speech Segments
Venue: arXiv cs.LG 2018
Year: 2018

Agglomerative hierarchical clustering (AHC) requires only the similarity between objects to be known. This is attractive when clustering signals of varying length, such as speech, which are not readily represented in fixed-dimensional vector space. However, AHC is characterised by $O(N^2)$ space and time complexity, making it infeasible for partitioning large datasets. This has recently been addressed by an approach based on the iterative re-clustering of independent subsets of the larger dataset. We show that, due to its iterative nature, this procedure can sometimes lead to unchecked growth of individual subsets, thereby compromising its effectiveness. We propose the integration of a simple space management strategy into the iterative process, and show experimentally that this leads to no loss in performance in terms of F-measure while guaranteeing that a threshold space complexity is not breached.
Variability of research performance across disciplines within universities in non-competitive higher education systems
Venue: arXiv cs.DL 2018
Year: 2018

Many nations are adopting higher education strategies that emphasize the development of elite universities able to compete at the international level in the attraction of skills and resources. Elite universities pursue excellence in all their disciplines and fields of action. The impression is that this does not occur in "non-competitive" education systems, and that instead, within single universities excellent disciplines will coexist with mediocre ones. To test this, the authors measure research productivity in the hard sciences for all Italian universities over the period 2004-2008 at the levels of the institution, their individual disciplines and fields within them. The results show that the distribution of excellent disciplines is not concentrated in a few universities: top universities show disciplines and fields that are often mediocre, while generally mediocre universities will often include top disciplines.
Computational Intelligence in Sports: A Systematic Literature Review
Venue: arXiv cs.AI 2018
Year: 2018

Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
Venue: arXiv cs.CL 2018
Year: 2018

We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-of-the-art MRC systems fall far behind human performance. ReCoRD represents a challenge for future research to bridge the gap between human and machine commonsense reading comprehension. ReCoRD is available at http://nlp.jhu.edu/record.
Topic-Specific Sentiment Analysis Can Help Identify Political Ideology
Venue: arXiv cs.CL 2018
Year: 2018

Ideological leanings of an individual can often be gauged by the sentiment one expresses about different issues. We propose a simple framework that represents a political ideology as a distribution of sentiment polarities towards a set of topics. This representation can then be used to detect ideological leanings of documents (speeches, news articles, etc.) based on the sentiments expressed towards different topics. Experiments performed using a widely used dataset show the promise of our proposed approach that achieves comparable performance to other methods despite being much simpler and more interpretable.
MPNA: A Massively-Parallel Neural Array Accelerator with Dataflow Optimization for Convolutional Neural Networks
Venue: arXiv cs.DC 2018
Year: 2018

The state-of-the-art accelerators for Convolutional Neural Networks (CNNs) typically focus on accelerating only the convolutional layers, but do not prioritize the fully-connected layers much. Hence, they lack a synergistic optimization of the hardware architecture and diverse dataflows for the complete CNN design, which can provide a higher potential for performance/energy efficiency. Towards this, we propose a novel Massively-Parallel Neural Array (MPNA) accelerator that integrates two heterogeneous systolic arrays and respective highly-optimized dataflow patterns to jointly accelerate both the convolutional (CONV) and the fully-connected (FC) layers. Besides fully-exploiting the available off-chip memory bandwidth, these optimized dataflows enable high data-reuse of all the data types (i.e., weights, input and output activations), and thereby enable our MPNA to achieve high energy savings. We synthesized our MPNA architecture using the ASIC design flow for a 28nm technology, and performed functional and timing validation using multiple real-world complex CNNs. MPNA achieves 149.7GOPS/W at 280MHz and consumes 239mW. Experimental results show that our MPNA architecture provides 1.7x overall performance improvement compared to state-of-the-art accelerator, and 51% energy saving compared to the baseline architecture.
Weighted vertex cover on graphs with maximum degree 3
Venue: arXiv cs.DS 2018
Year: 2018

We give a parameterized algorithm for weighted vertex cover on graphs with maximum degree 3 whose time complexity is $O^*(1.402^t)$, where $t$ is the minimum size of a vertex cover of the input graph.
Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications
Venue: arXiv cs.IT 2018
Year: 2018

Scheduling fast uplink grant transmissions for machine type communications (MTCs) is one of the main challenges of future wireless systems. In this paper, a novel fast uplink grant scheduling method based on the theory of multi-armed bandits (MABs) is proposed. First, a single quality-of-service metric is defined as a combination of the value of data packets, maximum tolerable access delay, and data rate. Since full knowledge of these metrics for all machine type devices (MTDs) cannot be known in advance at the base station (BS) and the set of active MTDs changes over time, the problem is modeled as a sleeping MAB with stochastic availability and a stochastic reward function. In particular, given that, at each time step, the knowledge on the set of active MTDs is probabilistic, a novel probabilistic sleeping MAB algorithm is proposed to maximize the defined metric. Analysis of the regret is presented and the effect of the prediction error of the source traffic prediction algorithm on the performance of the proposed sleeping MAB algorithm is investigated. Moreover, to enable fast uplink allocation for multiple MTDs at each time, a novel method is proposed based on the concept of best arms ordering in the MAB setting. Simulation results show that the proposed framework yields a three-fold reduction in latency compared to a random scheduling policy since it prioritises the scheduling of MTDs that have stricter latency requirements. Moreover, by properly balancing the exploration versus exploitation tradeoff, the proposed algorithm can provide system fairness by allowing the most important MTDs to be scheduled more often while also allowing the less important MTDs to be selected enough times to ensure the accuracy of estimation of their importance.
Rate-Efficiency and Straggler-Robustness through Partition in Distributed Two-Sided Secure Matrix Computation
Venue: arXiv cs.IT 2018
Year: 2018

Computationally efficient matrix multiplication is a fundamental requirement in various fields, including and particularly in data analytics. To do so, the computation task of a large-scale matrix multiplication is typically outsourced to multiple servers. However, due to data misusage at the servers, security is typically of concern. In this paper, we study the two-sided secure matrix multiplication problem, where a user is interested in the matrix product $\boldsymbol{AB}$ of two finite field private matrices $\boldsymbol{A}$ and $\boldsymbol{B}$ from an information-theoretic perspective. In this problem, the user exploits the computational resources of $N$ servers to compute the matrix product, but simultaneously tries to conceal the private matrices from the servers. Our goal is twofold: (i) to maximize the communication rate, and, (ii) to minimize the effective number of server observations needed to determine $\boldsymbol{AB}$, while preserving security, where we allow for up to $\ell\leq N$ servers to collude. To this end, we propose a general aligned secret sharing scheme for which we optimize the matrix partition of matrices $\boldsymbol{A}$ and $\boldsymbol{B}$ in order to either optimize objective (i) or (ii) as a function of the system parameters (e.g., $N$ and $\ell$). A proposed inductive approach gives us analytical, close-to-optimal solutions for both (i) and (ii). With respect to (i), our scheme significantly outperforms the existing scheme of Chang and Tandon in terms of (a) communication rate, (b) maximum tolerable number of colluding servers and (c) computational complexity.
Towards End-to-End Code-Switching Speech Recognition
Venue: arXiv cs.CL 2018
Year: 2018

Code-switching speech recognition has attracted an increasing interest recently, but the need for expert linguistic knowledge has always been a big issue. End-to-end automatic speech recognition (ASR) simplifies the building of ASR systems considerably by predicting graphemes or characters directly from acoustic input. In the mean time, the need of expert linguistic knowledge is also eliminated, which makes it an attractive choice for code-switching ASR. This paper presents a hybrid CTC-Attention based end-to-end Mandarin-English code-switching (CS) speech recognition system and studies the effect of hybrid CTC-Attention based models, different modeling units, the inclusion of language identification and different decoding strategies on the task of code-switching ASR. On the SEAME corpus, our system achieves a mixed error rate (MER) of 34.24%.
Low-Rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling
Venue: arXiv cs.LG 2018
Year: 2018

Although the convolutional neural networks (CNNs) have become popular for various image processing and computer vision task recently, it remains a challenging problem to reduce the storage cost of the parameters for resource-limited platforms. In the previous studies, tensor decomposition (TD) has achieved promising compression performance by embedding the kernel of a convolutional layer into a low-rank subspace. However the employment of TD is naively on the kernel or its specified variants. Unlike the conventional approaches, this paper shows that the kernel can be embedded into more general or even random low-rank subspaces. We demonstrate this by compressing the convolutional layers via randomly-shuffled tensor decomposition (RsTD) for a standard classification task using CIFAR-10. In addition, we analyze how the spatial similarity of the training data influences the low-rank structure of the kernels. The experimental results show that the CNN can be significantly compressed even if the kernels are randomly shuffled. Furthermore, the RsTD-based method yields more stable classification accuracy than the conventional TD-based methods in a large range of compression ratios.