Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognizing and predicting activities in smart homes

Understanding the context of a home is essential in order to provide services to occupants that fit their situations and thus fulfil their needs. One example of service that such a context-aware smart home could provide is that of a communication assistant, which can for example advise correspondents outside the home on the availability for communication of occupants. In order to implement such a service, it is indeed required that the home understands the situations of occupants, in order to derive their availability.In this thesis, we first propose a definition of context in homes. We argue that one of the primary context dimensions necessary for a system to be context-aware is the activity of occupants. As such, we then study the problem of recognizing activities, from ambient smart home sensors. We propose a new supervised place-based approach which both improves activity recognition accuracy as well as computing times compared to standard approaches.Smart home services, such as our communication assistance example, may often need to anticipate future situations. In particular, they need to anticipate future activities of occupants. Therefore, we design a new supervised activity prediction model, based on previous state-of-the-art work. We propose a number of extensions to improve prediction accuracy based on the specificities of smart home environments.Finally, we study the problem of inferring the availability of occupants for communication, in order to illustrate the feasibility of our communication assistant example. We argue that availability can be inferred from primary context dimensions such as place and activity (which can be recognized or predicted using our previous contributions), and by taking into consideration the correspondent initiating the communication as well as the modality of communication used. We discuss the impact of the activity recognition step on availability inference.We evaluate those contributions on various state-of-the-art datasets, as well as on a new dataset of activities and availabilities in homes which we constructed specifically for the purposes of this thesis: Orange4Home. Through our contributions to these 3 problems, we demonstrate the way in which an example context-aware communication assistance service can be implemented, which can advise on future availability for communication of occupants. More generally, we show how secondary context dimensions such as availability can be inferred from other context dimensions, in particular from activity. Highly accurate activity recognition and prediction are thus mandatory for a smart home to achieve context awareness.
Migration and Refugee Crisis: a Critical Analysis of Online Public Perception

The migration rate and the level of resentments towards migrants are an important issue in modern civilisation. The infamous EU refugee crisis caught many countries unprepared, leading to sporadic and rudimentary containment measures that, in turn, led to significant public discourse. Decades of offline data collected via traditional survey methods have been utilised earlier to understand public opinion to foster peaceful coexistence. Capturing and understanding online public opinion via social media is crucial towards a joint strategic regulation spanning safety, rights of migrants and cordial integration for economic prosperity. We present a analysis of opinions on migrants and refugees expressed by the users of a very popular social platform, Twitter. We analyse sentiment and the associated context of expressions in a vast collection of tweets related to the EU refugee crisis. Our study reveals a marginally higher proportion of negative sentiments vis-a-vis migrants and a large proportion of the negative sentiments is more reflected among the ordinary users. Users with many followers and non-governmental organisations (NGO) tend to tweet favourably about the topic, offsetting the distribution of negative sentiment. We opine that they can be encouraged to be more proactive in neutralising negative attitudes that may arise concerning similar incidences.
An Overview of Natural Language State Representation for Reinforcement Learning

A suitable state representation is a fundamental part of the learning process in Reinforcement Learning. In various tasks, the state can either be described by natural language or be natural language itself. This survey outlines the strategies used in the literature to build natural language state representations. We appeal for more linguistically interpretable and grounded representations, careful justification of design decisions and evaluation of the effectiveness of different approaches.
Jupyter Notebooks on GitHub: Characteristics and Code Clones

Jupyter notebooks have emerged as a standard tool for data science programming. Programs in Jupiter notebooks are different from typical programs by dividing code into snippets interleaved with text and visualisation and allowing interactive exploration and execution of snippets in different order. Previous studies have shown the presence of considerable code duplication in sources of traditional programs, in both so-called systems programming languages and so-called scripting languages. In this paper we present the first large-scale study of code cloning in Jupyter notebooks. We analyse a corpus of 2.7 million Jupyter notebooks hosted on GitHJub, representing over 36974714 individual snippets and 226744094 lines of code. We study clones at the level of individual snippets, and study the extent to which snippets are recurring across multiple notebooks. We study both identical clones clones (with possible differences in whitespaces) and approximate clones and conduct a small-scale ocular inspection of the most common clones. We find that that code cloning is common in Jupyter notebooks -- more than 70% of all code snippets are exact copies of other snippets, and around 50% of all notebooks do not have a unique snippet, but consists solely of snippets that are also found elsewhere. In notebooks written in Python, around 80% of all snippets are near-miss clones and the prevalence of code cloning is higher in Python than in other languages. We further find that clones between different repositories are far more common than clones within the same repository. However, the most common individual repository from which a Jupyter notebook contains clones is the repository in which itself resides.
Lagrangian Duality in Reinforcement Learning

Although duality is used extensively in certain fields, such as supervised learning in machine learning, it has been much less explored in others, such as reinforcement learning (RL). In this paper, we show how duality is involved in a variety of RL work, from that which spearheaded the field, such as Richard Bellman's value iteration, to that which was done within just the past few years yet has already had significant impact, such as TRPO, A3C, and GAIL. We show that duality is not uncommon in reinforcement learning, especially when value iteration, or dynamic programming, is used or when first or second order approximations are made to transform initially intractable problems into tractable convex programs.
Supervised Learning Using a Dressed Quantum Network with "Super Compressed Encoding": Algorithm and Quantum-Hardware-Based Implementation

Implementation of variational Quantum Machine Learning (QML) algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices is known to have issues related to the high number of qubits needed and the noise associated with multi-qubit gates. In this paper, we propose a variational QML algorithm using a dressed quantum network to address these issues. Using the "super compressed encoding" scheme that we follow here, the classical encoding layer in our dressed network drastically scales down the input-dimension, before feeding the input to the variational quantum circuit. Hence, the number of qubits needed in our quantum circuit goes down drastically. Also, unlike in most other existing QML algorithms, our quantum circuit consists only of single-qubit gates, making it robust against noise. These factors make our algorithm suitable for implementation on NISQ hardware. To support our argument, we implement our algorithm on real NISQ hardware and thereby show accurate classification using popular machine learning data-sets like Fisher's Iris, Wisconsin's Breast Cancer (WBC), and Abalone. Then, to provide an intuitive explanation for our algorithm's working, we demonstrate the clustering of quantum states, which correspond to the input-samples of different output-classes, on the Bloch sphere (using WBC and MNIST data-sets). This clustering happens as a result of the training process followed in our algorithm. Through this Bloch-sphere-based representation, we also show the distinct roles played (in training) by the adjustable parameters of the classical encoding layer and the adjustable parameters of the variational quantum circuit. These parameters are adjusted iteratively during training through loss-minimization.
Privacy-Preserving Multi-Operator Contact Tracing for Early Detection of Covid19 Contagions

The outbreak of coronavirus disease 2019 (Covid-19) is imposing a severe worldwide lock-down. Contact tracing based on smartphones’ applications (apps) has emerged as a possible solution to trace contagions and enforce a more sustainable selective quarantine. However, a massive adoption of these apps is required to reach the critical mass needed for effective contact tracing. As an alternative, geo-location technologies in next generation networks (e.g., 5G) can enable Mobile Operators (MOs) to perform passive tracing of users’ mobility and contacts with a promised accuracy of down to one meter. To effectively detect contagions, the identities of positive individuals, which are known only by a Governmental Authority (GA), are also required. Note that, besides being extremely sensitive, these data might also be critical from a business perspective. Hence, MOs and the GA need to exchange and process users’ geo-locations and infection status data in a privacy-preserving manner. In this work, we propose a privacy-preserving protocol that enables multiple MOs and the GA to share and process users’ data to make only the final users discover the number of their contacts with positive individuals. The protocol is based on existing privacy-enhancing strategies that guarantee that users’ mobility and infection status are only known to their MOs and to the GA, respectively. From extensive simulations, we observe that the cost to guarantee total privacy (evaluated in terms of data overhead introduced by the protocol) is acceptable, and can also be significantly reduced if we accept a negligible compromise in users’ privacy.
Biased measures for random constraint satisfaction problems: larger interaction range and asymptotic expansion

We investigate the clustering transition undergone by an exemplary random constraint satisfaction problem, the bicoloring of k-uniform random hypergraphs, when its solutions are weighted non-uniformly, with a soft interaction between variables belonging to distinct hyperedges. We show that the threshold α d(k) for the transition can be further increased with respect to a restricted interaction within the hyperedges, and perform an asymptotic expansion of α d(k) in the large k limit. We find that αd(k)=2k−1k(lnk+lnlnk+γd+o(1)) , where the constant γ d is strictly larger than for the uniform measure over solutions.
NGC 90: a hidden jellyfish galaxy?

We study a peculiar galaxy NGC 90, a pair member of interacting system Arp 65 (NGC 90/93), using the long-slit spectral observations carried out at the Russian 6m telescope BTA and the available SDSS photometric data. This galaxy demonstrates two tidal tails containing young stellar population, being an extension of its `Grand Design' spiral arms. We obtained the distribution of velocity and oxygen abundance of emission gas (O/H) for two slit orientations. In the central part of the galaxy a significant role belongs to non-photoionization mechanism of line emission probably caused by shocks due to LINER-like activity of the nucleus. The O/H has a shallow abundance gradient, typical for interacting galaxies. The most intriguing peculiarity of the galaxy is the presence of the discovered earlier huge HI `cloud' containing about half of total mass of galaxy gas, which is strongly displaced outwards and has a velocity exceeding at about 340 km/s the central velocity of the main galaxy. We found traces of current star formation in the `cloud', even though the cloud is apparently not gravitationally bound with the galaxy. A possible nature of the `cloud' is discussed. We argue that it presents a flow of gas sweeping by ram pressure and elongated along a line of sight.
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning

Recent work has demonstrated the promise of combining local explanations with active learning for understanding and supervising black-box models. Here we show that, under specific conditions, these algorithms may misrepresent the quality of the model being learned. The reason is that the machine illustrates its beliefs by predicting and explaining the labels of the query instances: if the machine is unaware of its own mistakes, it may end up choosing queries on which it performs artificially well. This biases the "narrative" presented by the machine to the user.We address this narrative bias by introducing explanatory guided learning, a novel interactive learning strategy in which: i) the supervisor is in charge of choosing the query instances, while ii) the machine uses global explanations to illustrate its overall behavior and to guide the supervisor toward choosing challenging, informative instances. This strategy retains the key advantages of explanatory interaction while avoiding narrative bias and compares favorably to active learning in terms of sample complexity. An initial empirical evaluation with a clustering-based prototype highlights the promise of our approach.
Wide-Scanning Circularly Polarized Reflector-Based Modulated Metasurface Antenna Enabled by a Broadband Polarizer

A circularly polarized leaky-wave antenna capable of frequency scanning is proposed in this article. The main objective is to achieve high gain and polarization purity without the need for a complex feed network. The antenna consists of two independent modules: 1) anisotropic-modulated metasurface antenna (MoMetA) for generating vertically polarized radiation with an operational scanning range of 19°–47° in elevation and 2) wideband polarization converter with high angular stability and capable of converting vertically linear polarization into circular polarization (CP). The aperture field estimation (AFE) method is used to design the MoMetA with high accuracy in predicting far-field pattern without the need for full-wave simulations. The gain of antenna in the bandwidth of 16–21 GHz is obtained well than 18 dBic. Simulation results show that the axial ratio in the maximum gain direction is lower than 3 dB all over its operational frequency bandwidth, demonstrating the proper operation of polarizer. In order to verify the simulation results, one prototype of the antenna is fabricated and its radiation patterns are measured in an anechoic chamber.
Dark Matter Deficient Galaxies Produced via High-velocity Galaxy Collisions in High-resolution Numerical Simulations

The recent discovery of diffuse dwarf galaxies that are deficient in dark matter appears to challenge the current paradigm of structure formation in our universe. We describe numerical experiments to determine if so-called dark matter deficient galaxies (DMDGs) could be produced when two gas-rich, dwarf-sized galaxies collide with a high relative velocity of ∼300 km s−1. Using idealized high-resolution simulations with both mesh-based and particle-based gravito-hydrodynamics codes, we find that DMDGs can form as high-velocity galaxy collisions and separate dark matter from the warm disk gas, which subsequently is compressed by shock and tidal interaction to form stars. Then using the large simulated universe IllustrisTNG, we discover a number of high-velocity galaxy collision events in which DMDGs are expected to form. However, we did not find evidence that these types of collisions actually produced DMDGs in the TNG100-1 run. We argue that the resolution of the numerical experiment is critical to realizing the “collision-induced” DMDG formation scenario. Our results demonstrate one of many routes in which galaxies could form with unconventional dark matter fractions.
A 3D Tractable Model for UAV-Enabled Cellular Networks With Multiple Antennas

This paper aims to propose a three-dimensional (3D) point process that can be employed to generally deploy unmanned aerial vehicles (UAVs) in a large-scale 3D cellular network and to tractably analyze the fundamental network-wide performances of the network. The proposed 3D point process is devised based on a 2D marked Poisson point process in which each point and its random mark uniquely correspond to the projection and the altitude of each point in the 3D point process, respectively. We study some of the important statistical properties of the proposed 3D point process and shed light on some crucial insights into them that facilitate the analyses of a UAV-enabled cellular network wherein all UAVs equipped with multiple antennas are deployed by the proposed 3D point process to serve as aerial base stations. The salient features of the proposed 3D point process lie in its suitability in practical 3D channel modeling and tractability in analysis. The downlink coverages of the UAV-enabled cellular network are found and their closed-form results for some special cases are also derived. Most importantly, their fundamental limits achieved by cell-free massive antenna array are characterized when coordinating all the UAVs to jointly perform non-coherent downlink transmission. These key findings and observations are numerically validated in this paper.
It’s LeVAsa not LevioSA! Latent Encodings for Valence-Arousal Structure Alignment

In recent years, great strides have been made in the field of affective computing. Several models have been developed to represent and quantify emotions. Two popular ones include (i) categorical models which represent emotions as discrete labels, and (ii) dimensional models which represent emotions in a Valence-Arousal (VA) circumplex domain. However, there is no standard for annotation mapping between the two labelling methods. We build a novel algorithm for mapping categorical and dimensional model labels using annotation transfer across affective facial image datasets. Further, we utilize the transferred annotations to learn rich and interpretable data representations using a variational autoencoder (VAE). We present “LeVAsa”, a VAE model that learns implicit structure by aligning the latent space with the VA space. We evaluate the efficacy of LeVAsa by comparing performance with the Vanilla VAE using quantitative and qualitative analysis on two benchmark affective image datasets. Our results reveal that LeVAsa achieves high latent-circumplex alignment which leads to improved downstream categorical emotion prediction. The work also demonstrates the trade-off between degree of alignment and quality of reconstructions.
Improving the Long-Range Performance of Gated Graph Neural Networks

Many popular variants of graph neural networks (GNNs) that are capable of handling multi-relational graphs may suffer from vanishing gradients. In this work, we propose a novel GNN architecture based on the Gated Graph Neural Network with an improved ability to handle long-range dependencies in multi-relational graphs. An experimental analysis on different synthetic tasks demonstrates that the proposed architecture outperforms several popular GNN models.
User-Oriented Multi-Task Federated Deep Learning for Mobile Edge Computing

Federated Learning (FL) is a recent approach for collaboratively training Machine Learning models on mobile edge devices, without private user data leaving the devices. The popular FL algorithm, Federated Averaging (FedAvg), suffers from poor convergence speed given non-iid user data. Furthermore, most existing work on FedAvg measures central-model accuracy, but in many cases, such as user content-recommendation, improving individual User model Accuracy (UA) is the real objective. To address these issues, we propose a Multi-Task Federated Learning (MTFL) system, which converges faster than FedAvg by using distributed Adam optimization (FedAdam), and benefits UA by introducing personal, non-federated 'patch' Batch-Normalization (BN) layers into the model. Testing FedAdam on the MNIST and CIFAR10 datasets show that it converges faster (up to 5x) than FedAvg in non-iid scenarios, and experiments using MTFL on the CIFAR10 dataset show that MTFL significantly improves average UA over FedAvg, by up to 54%. We also analyse the affect that private BN patches have on the MTFL model during inference, and give evidence that MTFL strikes a better balance between regularization and convergence in FL. Finally, we test the MTFL system on a mobile edge computing testbed, showing that MTFL's convergence and UA benefits outweigh its overhead.
Attention Sequence to Sequence Model for Machine Remaining Useful Life Prediction

Accurate estimation of remaining useful life (RUL) of industrial equipment can enable advanced maintenance schedules, increase equipment availability and reduce operational costs. However, existing deep learning methods for RUL prediction are not completely successful due to the following two reasons. First, relying on a single objective function to estimate the RUL will limit the learned representations and thus affect the prediction accuracy. Second, while longer sequences are more informative for modelling the sensor dynamics of equipment, existing methods are less effective to deal with very long sequences, as they mainly focus on the latest information. To address these two problems, we develop a novel attention-based sequence to sequence with auxiliary task (ATS2S) model. In particular, our model jointly optimizes both reconstruction loss to empower our model with predictive capabilities (by predicting next input sequence given current input sequence) and RUL prediction loss to minimize the difference between the predicted RUL and actual RUL. Furthermore, to better handle longer sequence, we employ the attention mechanism to focus on all the important input information during training process. Finally, we propose a new dual-latent feature representation to integrate the encoder features and decoder hidden states, to capture rich semantic information in data. We conduct extensive experiments on four real datasets to evaluate the efficacy of the proposed method. Experimental results show that our proposed method can achieve superior performance over 13 state-of-the-art methods consistently.
Molecular Systematics and Plumage Evolution in the Monotypic Obligate Army-Ant-Following Genus Skutchia (Thamnophilidae)

Abstract. We obtained 1403 base pairs of mtDNA sequences of cyt b and ND2 from four specimens of the Pale-faced Bareeye (Skutchia borbae, representing a monotypic genus of the Thamnophilidae) and evaluated for the first time the species' phylogenetic position among core obligate army-ant-following antbirds. All trees placed Skutchia borbae consistently and with high statistical support as sister to the Black-spotted Bare-eye (Phlegopsis nigromaculata) in a clade with the two known species of Phlegopsis. This result supports the return of S. borbae to the genus Phlegopsis, where it was originally placed. When the plumage characters originally used to diagnose Skutchia are mapped onto the phylogeny, it is evident that they have evolved convergently multiple times in core obligate army-ant-following antbirds. This discovery further supports merging Skutchia with Phlegopsis.