OpenRaiser/Intern-Atlas huggingface dataset:
Presence-absence estimation in audio recordings of tropical frog communities
Venue: arXiv cs.SD 2019
Year: 2019

One non-invasive way to study frog communities is by analyzing long-term samples of acoustic material containing calls. This immense task has been optimized by the development of Machine Learning tools to extract ecological information. We explored a likelihood-ratio audio detector based on Gaussian mixture model classification of 10 frog species, and applied it to estimate presence-absence in audio recordings from an actual amphibian monitoring performed at Yasuní National Park in the Ecuadorian Amazonia. A modified filter-bank was used to extract 20 cepstral features that model the spectral content of frog calls. Experiments were carried out to investigate the hyperparameters and the minimum frog-call time needed to train an accurate GMM classifier. With 64 Gaussians and 12 seconds of training time, the classifier achieved an average weighted error rate of 0.9% on the 10-fold cross-validation for nine species classification, as compared to 3% with MFCC and 1.8% with PLP features. For testing, 10 GMMs were trained using all the available training-validation dataset to study 23.5 hours in 141, 10-minute long samples of unidentified real-world audio recorded at two frog communities in 2001 with analog equipment. To evaluate automatic presence-absence estimation, we characterized the audio samples with 10 binary variables each corresponding to a frog species, and manually labeled a sub-set of 18 samples using headphones. A recall of 87.5% and precision of 100% with average accuracy of 96.66% suggests good generalization ability of the algorithm, and provides evidence of the validity of this approach to study real-world audio recorded in a tropical acoustic environment. Finally, we applied the algorithm to the available corpus, and show its potentiality to gain insights into the temporal reproductive behavior of frogs.
New approach for a stable multi-criteria ridesharing system
Venue: arXiv cs.CY 2019
Year: 2019

The witnessed boom in mobility results in many problems such as urbanization, costly construction of many highways and air pollution. In an attempt to address these problems, in this master, we are interested in the implementation of a ridesharing system. Ridesharing is recognized as a highly effective means of transport to solve energy consumption, environmental pollution and traffic congestion issues. Indeed, ridesharing can reduce the number of vehicles on the roads to avoid traffic jams and thus it contributes to a reduction in greenhouse gas emissions. Its main thrust resides in sharing transport expenses, meeting different people and making traveling more enjoyable. In this respect, we introduce in this dissertation an effective ridesharing system, called the Stable Multi-Criteria Rideshare Matching (SMRM) system, that (i) considers users' personal preferences when sharing a private space with others and (ii) enables a stable matching between driver and passenger sets. The performed experiments show that the introduced system outperforms its competitors in terms of stability quality and cost. Keywords: Smart cities, Social sustainability, Ridesharing , Social preferences , TOPSIS , Stable marriage .
Construction and Analysis of Posterior Matching in Arbitrary Dimensions via Optimal Transport
Venue: arXiv cs.IT 2019
Year: 2019

The posterior matching scheme, for feedback encoding of a message point lying on the unit interval over memoryless channels, maximizes mutual information for an arbitrary number of channel uses. However, it in general does not always achieve any positive rate; so far, elaborate analyses have been required to show that it achieves any positive rate below capacity. More recent efforts have introduced a random "dither" shared by the encoder and decoder to the problem formulation, to simplify analyses and guarantee that the randomized scheme achieves any rate below capacity. Motivated by applications (e.g. human-computer interfaces) where (a) common randomness shared by the encoder and decoder may not be feasible and (b) the message point lies in a higher dimensional space, we focus here on the original formulation without common randomness, and use optimal transport theory to generalize the scheme for a message point in a higher dimensional space. By defining a stricter, almost sure, notion of message decoding, we use classical probabilistic techniques (e.g. change of measure and martingale convergence) to establish succinct necessary and sufficient conditions on when the message point can be recovered from infinite observations: Birkhoff ergodicity of a random process sequentially generated by the encoder. We also show a surprising "all or nothing" result: the same ergodicity condition is necessary and sufficient to achieve any rate below capacity. We provide applications of this message point framework in human-computer interfaces and multi-antenna communications.
Analysis of Non-Pilot Interference on Link Adaptation and Latency in Cellular Networks
Venue: arXiv cs.NI 2019
Year: 2019

Modern wireless systems such as the Long-Term Evolution (LTE) and 5G New Radio (5G NR) use pilot-aided SINR estimates to adapt the transmission mode and the modulation and coding scheme (MCS) of data transmissions, maximizing the utility of the wireless channel capacity. However, when interference is localized exclusively on non-pilot resources, pilot-aided SINR estimates become inaccurate. We show that this leads to congestion due to retransmissions, and in the worst case, outage due to very high block error rate (BLER). We demonstrate this behavior through numerical as well as experimental results with the 4G LTE downlink, which show high BLER and significant throughput detriment in the presence of non-pilot interference (NPI). To provide useful insights on the impact of NPI on low-latency communications, we derive an approximate relation between the retransmission-induced latency and BLER. Our results show that NPI can severely compromise low-latency applications such as vehicle-to-vehicle (V2V) communications and 5G NR. We identify robust link adaptation schemes as the key to reliable communications.
UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition
Venue: arXiv cs.LG 2019
Year: 2019

Current UAV-recorded datasets are mostly limited to action recognition and object tracking, whereas the gesture signals datasets were mostly recorded in indoor spaces. Currently, there is no outdoor recorded public video dataset for UAV commanding signals. Gesture signals can be effectively used with UAVs by leveraging the UAVs visual sensors and operational simplicity. To fill this gap and enable research in wider application areas, we present a UAV gesture signals dataset recorded in an outdoor setting. We selected 13 gestures suitable for basic UAV navigation and command from general aircraft handling and helicopter handling signals. We provide 119 high-definition video clips consisting of 37151 frames. The overall baseline gesture recognition performance computed using Pose-based Convolutional Neural Network (P-CNN) is 91.9 %. All the frames are annotated with body joints and gesture classes in order to extend the dataset's applicability to a wider research area including gesture recognition, action recognition, human pose recognition and situation awareness.
Segmentation-Discarding Ordered-Statistic Decoding for Linear Block Codes
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we propose an efficient reliability based segmentation-discarding decoding (SDD) algorithm for short block-length codes. A novel segmentation-discarding technique is proposed along with the stopping rule to significantly reduce the decoding complexity without a significant performance degradation compared to ordered statistics decoding (OSD). In the proposed decoder, the list of test error patterns (TEPs) is divided into several segments according to carefully selected boundaries and every segment is checked separately during the reprocessing stage. Decoding is performed under the constraint of the discarding rule and stopping rule. Simulations results for different codes show that our proposed algorithm can significantly reduce the decoding complexity compared to the existing OSD algorithms in literature.
Sequential Attention-based Network for Noetic End-to-End Response Selection
Venue: arXiv cs.CL 2019
Year: 2019

The noetic end-to-end response selection challenge as one track in Dialog System Technology Challenges 7 (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context. This paper describes our systems that are ranked the top on both datasets under this challenge, one focused and small (Advising) and the other more diverse and large (Ubuntu). Previous state-of-the-art models use hierarchy-based (utterance-level and token-level) neural networks to explicitly model the interactions among different turns' utterances for context modeling. In this paper, we investigate a sequential matching model based only on chain sequence for multi-turn response selection. Our results demonstrate that the potentials of sequential matching approaches have not yet been fully exploited in the past for multi-turn response selection. In addition to ranking the top in the challenge, the proposed model outperforms all previous models, including state-of-the-art hierarchy-based models, and achieves new state-of-the-art performances on two large-scale public multi-turn response selection benchmark datasets.
Dynamic Mobility-Aware Interference Avoidance for Aerial Base Stations in Cognitive Radio Networks
Venue: arXiv cs.NI 2019
Year: 2019

Aerial base station (ABS) is a promising solution for public safety as it can be deployed in coexistence with cellular networks to form a temporary communication network. However, the interference from the primary cellular network may severely degrade the performance of an ABS network. With this consideration, an adaptive dynamic interference avoidance scheme is proposed in this work for ABSs coexisting with a primary network. In the proposed scheme, the mobile ABSs can reconfigure their locations to mitigate the interference from the primary network, so as to better relay the data from the designated source(s) to destination(s). To this end, the single/multi-commodity maximum flow problems are formulated and the weighted Cheeger constant is adopted as a criterion to improve the maximum flow of the ABS network. In addition, a distributed algorithm is proposed to compute the optimal ABS moving directions. Moreover, the trade-off between the maximum flow and the shortest path trajectories is investigated and an energy-efficient approach is developed as well. Simulation results show that the proposed approach is effective in improving the maximum network flow and the energy-efficient approach can save up to 39% of the energy for the ABSs with marginal degradation in the maximum network flow.
Collaborative 3D modeling system based on blockchain
Venue: arXiv cs.GR 2019
Year: 2019

We propose a collaborative 3D modeling system, which is based on the blockchain technology. Our approach uses the blockchain to communicate with modeling tools and to provide them a decentralized database of the mesh modification history. This approach also provides a server-less version control system: users can commit their modifications to the blockchain and checkout others' modifications from the blockchain. As a result, our system enables users to do collaborative modeling without any central server.
What do Language Representations Really Represent?
Venue: arXiv cs.CL 2019
Year: 2019

A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just like it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations. We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, while genetic relationships---a convenient benchmark used for evaluation in previous work---appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another.
Incentivising Privacy in Cryptocurrencies
Venue: arXiv cs.CR 2019
Year: 2019

Privacy was one of the key points mentioned in Nakamoto's Bitcoin whitepaper, and one of the selling points of Bitcoin in its early stages. In hindsight, however, de-anonymising Bitcoin users turned out to be more feasible than expected. Since then, privacy focused cryptocurrencies such as Zcash and Monero have surfaced. Both of these examples cannot be described as fully successful in their aims, as recent research has shown. Incentives are integral to the security of cryptocurrencies, so it is interesting to investigate whether they could also be aligned with privacy goals. A lack of privacy often results from low user counts, resulting in low anonymity sets. Could users be incentivised to use the privacy preserving implementations of the systems they use? Not only is Zcash much less used than Bitcoin (which it forked from), but most Zcash transactions are simply transparent transactions, rather than the (at least intended to be) privacy-preserving shielded transactions. This paper and poster briefly discusses how incentives could be incorporated into systems like cryptocurrencies with the aim of achieving privacy goals. We take Zcash as example, but the ideas discussed could apply to other privacy-focused cryptocurrencies. This work was presented as a poster at OPERANDI 2018, the poster can be found within this short document.
Dirichlet Variational Autoencoder
Venue: arXiv cs.LG 2019
Year: 2019

This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior for a continuous latent variable that exhibits the characteristic of the categorical probabilities. To infer the parameters of DirVAE, we utilize the stochastic gradient method by approximating the Gamma distribution, which is a component of the Dirichlet distribution, with the inverse Gamma CDF approximation. Additionally, we reshape the component collapsing issue by investigating two problem sources, which are decoder weight collapsing and latent value collapsing, and we show that DirVAE has no component collapsing; while Gaussian VAE exhibits the decoder weight collapsing and Stick-Breaking VAE shows the latent value collapsing. The experimental results show that 1) DirVAE models the latent representation result with the best log-likelihood compared to the baselines; and 2) DirVAE produces more interpretable latent values with no collapsing issues which the baseline models suffer from. Also, we show that the learned latent representation from the DirVAE achieves the best classification accuracy in the semi-supervised and the supervised classification tasks on MNIST, OMNIGLOT, and SVHN compared to the baseline VAEs. Finally, we demonstrated that the DirVAE augmented topic models show better performances in most cases.
On Optimal Proactive and Retention-Aware Caching with User Mobility
Venue: arXiv cs.DC 2019
Year: 2019

Caching popular contents at edge devices is an effective solution to alleviate the burden of the backhaul networks. Earlier investigations commonly neglected the storage cost in caching. More recently, retention-aware caching, where both the downloading cost and storage cost are accounted for, is attracting attention. Motivated by this, we address proactive and retention-aware caching with user mobility, optimizing the sum of the two types of costs. This is a combinatorial optimization problem. However, we derive a stream of analytical results and they together lead to an algorithm that guarantees global optimum with polynomial-time complexity. Numerical results show significant improvements in comparison to popular caching and random caching.
Sentiment Analysis of Czech Texts: An Algorithmic Survey
Venue: arXiv cs.CL 2019
Year: 2019

In the area of online communication, commerce and transactions, analyzing sentiment polarity of texts written in various natural languages has become crucial. While there have been a lot of contributions in resources and studies for the English language, "smaller" languages like Czech have not received much attention. In this survey, we explore the effectiveness of many existing machine learning algorithms for sentiment analysis of Czech Facebook posts and product reviews. We report the sets of optimal parameter values for each algorithm and the scores in both datasets. We finally observe that support vector machines are the best classifier and efforts to increase performance even more with bagging, boosting or voting ensemble schemes fail to do so.
SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild
Venue: arXiv cs.HC 2019
Year: 2019

Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are increasingly becoming an indispensable part of our life. Accurately annotated real-world data are the crux in devising such systems. However, existing databases usually consider controlled settings, low demographic variability, and a single task. In this paper, we introduce the SEWA database of more than 2000 minutes of audio-visual data of 398 people coming from six cultures, 50% female, and uniformly spanning the age range of 18 to 65 years old. Subjects were recorded in two different contexts: while watching adverts and while discussing adverts in a video chat. The database includes rich annotations of the recordings in terms of facial landmarks, facial action units (FAU), various vocalisations, mirroring, and continuously valued valence, arousal, liking, agreement, and prototypic examples of (dis)liking. This database aims to be an extremely valuable resource for researchers in affective computing and automatic human sensing and is expected to push forward the research in human behaviour analysis, including cultural studies. Along with the database, we provide extensive baseline experiments for automatic FAU detection and automatic valence, arousal and (dis)liking intensity estimation.
Learning by Fictitious Play in Large Populations
Venue: arXiv cs.GT 2019
Year: 2019

We consider learning by fictitious play in a large population of agents engaged in single-play, two-person rounds of a symmetric game, and derive a mean-filed type model for the corresponding stochastic process. Using this model, we describe qualitative properties of the learning process and discuss its asymptotic behavior. Of the special interest is the comparative characteristics of the fictitious play learning with and without a memory factor. As a part of the analysis, we show that the model leads to the continuous, best-response dynamics equation of Gilboa and Matsui (1991), when all agents have similar empirical probabilities.
Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard for quantifying myocardial infarction (MI), demanding most algorithms to be expert dependent. Objectives and Methods: In this work a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular-obstructed areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of CNNs. For its validation, reproducibility and further comparison against other methods, we tested the method on a big multi-field expert annotated LGE-MRI database including healthy and diseased cases. Results and Conclusion: In an exhaustive comparison against nine reference algorithms, the proposal achieved state-of-the-art segmentation performances and showed to be the only method agreeing in volumetric scar quantification with the expert delineations. Moreover, the method was able to reproduce the intra- and inter-observer variability ranges. It is concluded that the method could suitably be transferred to clinical scenarios.
On the Distance Between the Rumor Source and Its Optimal Estimate in a Regular Tree
Venue: arXiv cs.IT 2019
Year: 2019

This paper addresses the rumor source identification problem, where the goal is to find the origin node of a rumor in a network among a given set of nodes with the rumor. In this paper, we focus on a network represented by a regular tree which does not have any cycle and in which all nodes have the same number of edges connected to a node. For this network, we clarify that, with quite high probability, the origin node is within the distance 3 from the node selected by the optimal estimator, where the distance is the number of edges of the unique path connecting two nodes. This is clarified by the probability distribution of the distance between the origin and the selected node.
Towards Optimised Data Transport and Analytics for Edge Computing
Venue: arXiv cs.DC 2019
Year: 2019

Industrial organisations, particularly Small and Medium-sized Enterprises (SME), face a number of challenges with regard to the adoption of Industrial Internet of Things (IIoT) technologies and methods. The scope of analytics processing that can be performed on data from IIoT-enabled industrial processes is typically limited by the compute and storage resources that are available, and any investment in additional hardware that is sufficiently flexible and scalable is difficult to justify in terms of Return On Investment (ROI). We describe a distributed model of data transport and processing that eases the take-up of IIoT, whilst also enabling a capability to securely deliver more complex analysis and future insight discovery, than would be possible with traditional network architectures.