Miracle-dz/UniFAR-FTUs huggingface dataset:
CollabLoc: Privacy-Preserving Multi-Modal Collaborative Mobile Phone Localization

Mobile location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed—the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. A privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi Received Signal Strength Indicator (RSSI) (existing infrastructure), Cellular RSSI, sound, light, and geo-magnetic levels, that enables sub-room level localization. The solution is fully based on mobile phones and existing Wi-Fi infrastructure, and has privacy inherently built into it via cryptographically-secured onion routing and perturbation/randomization techniques. It also exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The solution has been analyzed in terms of latency overhead due to onion-routing, request load on phones, privacy-accuracy tradeoffs, optimum parameters, granularity, different classification algorithms using real location data collected at multiple indoor and outdoor locations via an Android application. The additional features other than Wi-Fi RSSI values are shown to increase the accuracy to a maximum of 15 percent, while considering Geo-magnetic field is shown to enhance the granularity from <inline-formula><tex-math notation="LaTeX">$2.5~\mathrm {m}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>. </mml:mo><mml:mn>5</mml:mn><mml:mspace width="3.33333pt"/><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="sadhu-ieq1-2937775.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$\approx 1~\mathrm {m}$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>≈</mml:mo><mml:mn>1</mml:mn><mml:mspace width="3.33333pt"/><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="sadhu-ieq2-2937775.gif"/></alternatives></inline-formula>, a 60 percent improvement.
A New Study On Wood Fibers Textures: Documents Authentication Through LBP Fingerprint

The authentication of printed material based on textures is a critical and challenging problem for many security agencies in many contexts: valuable documents, banknotes, tickets or rare collectible cards are often targets for forgery. This motivates the study of low-cost, fast and reliable approaches for documents authenticity analysis. In this paper, we present a new approach based on the extraction of translucent patterns from paper sheet by means of a specific-built framework. A fingerprint is obtained by computing a Local Binary Pattern descriptor on the digital image. To validate the robustness of the proposed method for authentication analysis, we introduce a novel dataset and perform retrieval tests under both, ideal and noisy conditions. Experimental results prove the validity of the proposed strategy.
Video Processing and Learning in Assistive Robotic Applications

The integration of visual perception to robotic systems is a key research area in recent years. Advances in modern computer vision techniques along with the development of faster and more accurate visual sensors led to the emergence of new methods for robotic visual perception [1]. One area of research is the development of robotic assistive vision for Human-Robot Interaction (HRI) systems [2], [3]. The rapid increase of people with special needs, such as the elderly population, and the simultaneous reduction of personal care staff, reinforce the need for robotic assistants [4], [5]. There are many challenges in this area including the familiarity of these users with new technologies and the domain specific datasets, which are required for training user oriented models. Nowadays, modern assistive and social human-robot interaction requires the multimodal communication with speech, gestures and human movements so as to enhance the classic interaction with only spoken commands.
Verifying the Impact of the FV3-GFS Upgrade on LAMP Statistical Guidance

As part of its ​Next Generation Global Prediction System (NGGPS) initiative, the National Weather Service (NWS) is upgrading the Global Forecast System (GFS) from a spectral model to a Finite Volume Cubed Sphere Dynamical Core (FV3) based model (Lin 2004). ​The FV3-GFS upgrade is the first step towards a fully integrated and flexible modeling framework which will optimize available computing capabilities. Changes to the dynamical core do not only impact the GFS itself, but also affect the multitude of post-processed products that utilize the GFS as an input. One such product is the Localized Aviation MOS Program (LAMP) which is run hourly (and sub-hourly for some elements), providing rapidly updating short-term guidance in the 1 to 25-hour timeframe for aviation forecasters while simultaneously supporting the National Blend of Models (NBM) (Ghirardelli et al. 2010). LAMP uses numerical model output and observations to provide guidance for weather elements that are particularly impactful for aviation, such as ceiling height and visibility. Through statistical post-processing using multiple linear regression techniques, LAMP aims to mitigate the systematic biases that are inherent in modeled forecasts. However, upgrading the GFS could degrade the LAMP guidance by altering the characteristics of the bias profile that LAMP was developed to account for.
Mental Health Practitioners' Professional Opinions of Etiology of Eating Disorders

Cross-cultural exploration of eating disorders has focused on comparing eating behaviors across cultures. However, studies investigating the opinions of professionals regarding various aspects of eating disorders have been limited. This study seeks to understand the professional opinions of mental health providers from Turkey and from the United States concerning the etiology of eating disorders and their conceptualizations of the disorder. A total of 128 mental health practitioners participated in the study. Of the total participants, 74 were from Turkey and 54 were from the United States; 103 participants were female and 25 were male. The results of this study show that mental health professionals from Turkey perceive the causes of eating disorders on the microsystemic level, whereas U. S. practitioners perceive the causes on the macrosystemic level. However, a common definition of eating disorders emerged from the open-ended responses of mental health practitioners from both countries. Implications for research and practice are discussed.
Geographical Routing Protocols in Wireless Sensor Networks

: Wireless sensor networks offer inexpensive solutions in a wide variety of applications ranging from health, home, roads and public area monitoring to industry and aircraft control. As sensors proliferate, scalable communication protocols with low implementation requirements are pursued. Another key issue for deployment of these solutions is security since the wireless sensor networks themselves are prone to security attacks. In this paper, we review geographical routing protocols, which allow for high scalability due to their localized operation while they also support mobility consuming less node and network resources since they operate on very limited overhead. We also investigate their vulnerabilities to security attacks and report efficient counter measures.
A Discrete CVAE for Response Generation on Short-Text Conversation

Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder (CVAE) which maximizes the lower bound on the conditional log-likelihood on a continuous latent variable. With different sampled latent variables, the model is expected to generate diverse responses. Although the CVAE-based models have shown tremendous potential, their improvement of generating high-quality responses is still unsatisfactory. In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation. A major advantage of our model is that we can exploit the semantic distance between the latent variables to maintain good diversity between the sampled latent variables. Accordingly, we propose a two-stage sampling approach to enable efficient diverse variable selection from a large latent space assumed in the short-text conversation task. Experimental results indicate that our model outperforms various kinds of generation models under both automatic and human evaluations and generates more diverse and informative responses.
モデル予測制御とスライディングモード制御による四輪操舵駆動車両のロバスト経路追従制御;モデル予測制御とスライディングモード制御による四輪操舵駆動車両のロバスト経路追従制御;Robust Path Tracking Control for Four-wheel Driving and Steering Vehicles Using Model Predictive Control and Sliding Mode Control

This paper investigates a robust path tracking control for four-wheel driving and steering vehicles. The proposed integrated control system aims at high maneuverability and robustness. The proposed controller is applied to the path tracking problem which is formulated utilizing time state control form. The inputs for path tracking are calculated by model predictive control considering the friction circle to realize the path-following. In addition, using sliding mode control, the proposed control system is robust against external disturbance or uncertainties which satisfy the matching condition. The approximated tire force usage rate is introduced in order to balance path-following performance and the robustness. The eﬃciency of the proposed control system is demonstrated through numerical simulations.
Möbius Transformation for Fast Inner Product Search on Graph

We present a fast search on graph algorithm for Maximum Inner Product Search (MIPS). This optimization problem is challenging since traditional Approximate Nearest Neighbor (ANN) search methods may not perform efficiently in the non-metric similarity measure. Our proposed method is based on the property that Mobius transformation introduces an isomorphism between a subgraph of l^2-Delaunay graph and Delaunay graph for inner product. Under this observation, we propose a simple but novel graph indexing and searching algorithm to find the optimal solution with the largest inner product with the query. Experiments show our approach leads to significant improvements compared to existing methods.
Machine Teaching of Active Sequential Learners

Machine teaching addresses the problem of finding the best training data that can guide a learning algorithm to a target model with minimal effort. In conventional settings, a teacher provides data that are consistent with the true data distribution. However, for sequential learners which actively choose their queries, such as multi-armed bandits and active learners, the teacher can only provide responses to the learner’s queries, not design the full data. In this setting, consistent teachers can be sub-optimal for finite horizons. We formulate this sequential teaching problem, which current techniques in machine teaching do not address, as a Markov decision process, with the dynamics nesting a model of the learner and the actions being the teacher's responses. Furthermore, we address the complementary problem of learning from a teacher that plans: to recognise the teaching intent of the responses, the learner is endowed with a model of the teacher. We test the formulation with multi-armed bandit learners in simulated experiments and a user study. The results show that learning is improved by (i) planning teaching and (ii) the learner having a model of the teacher. The approach gives tools to taking into account strategic (planning) behaviour of users of interactive intelligent systems, such as recommendation engines, by considering them as boundedly optimal teachers.
LIDA: Lightweight Interactive Dialogue Annotator

Dialogue systems have the potential to change how people interact with machines but are highly dependent on the quality of the data used to train them.It is therefore important to develop good dialogue annotation tools which can improve the speed and quality of dialogue data annotation. With this in mind, we introduce LIDA, an annotation tool designed specifically for conversation data. As far as we know, LIDA is the first dialogue annotation system that handles the entire dialogue annotation pipeline from raw text, as may be the output of transcription services, to structured conversation data. Furthermore it supports the integration of arbitrary machine learning mod-els as annotation recommenders and also has a dedicated interface to resolve inter-annotator disagreements such as after crowdsourcing an-notations for a dataset. LIDA is fully open source, documented and publicly available. [https://github.com/Wluper/lida] –> Screen Cast: https://vimeo.com/329824847
A Simple Algorithm for Non-Negative Sparse Principal Component Analysis

A novel adaptive method that computes sparse and non-negative eigenvectors is proposed. The proposed method achieves the sparsity in the derived eigenvectors implicitly through non-negative constraints and does not include any additional parameters except the learning rate. Although adding constraints to the standard Principal Component Analysis (PCA) leads to a reduction in the explained variance, the proposed method performs competitively with the standard PCA and other PCA variants. The assessment of the proposed method is conducted by performing a quantitative and qualitative evaluation.
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery

This paper develops a new class of nonconvex regularizers for low-rank matrix recovery. Many regularizers are motivated as convex relaxations of the \emph{matrix rank} function. Our new factor group-sparse regularizers are motivated as a relaxation of the \emph{number of nonzero columns} in a factorization of the matrix. These nonconvex regularizers are sharper than the nuclear norm; indeed, we show they are related to Schatten-$p$ norms with arbitrarily small $0 < p \leq 1$. Moreover, these factor group-sparse regularizers can be written in a factored form that enables efficient and effective nonconvex optimization; notably, the method does not use singular value decomposition. We provide generalization error bounds for low-rank matrix completion which show improved upper bounds for Schatten-$p$ norm reglarization as $p$ decreases. Compared to the max norm and the factored formulation of the nuclear norm, factor group-sparse regularizers are more efficient, accurate, and robust to the initial guess of rank. Experiments show promising performance of factor group-sparse regularization for low-rank matrix completion and robust principal component analysis.
Choice of the Number of Hidden Layers for Back Propagation Neural Network Driven by Stock Price Data and Application to Price Prediction

Since the stock market is dynamic and nonlinear, we adopt the neural network to forecast the stock price. We construct the single hidden layer prediction model firstly, and analyse the effect of prediction accuracy on neurons amount and epochs. To improve the prediction accuracy and operating rate, we then construct the multiple hidden layers prediction model, and provide some theory guide on setting the number of each hidden layer for neural network with multiple hidden layers. Finally, we make a choice of the number of hidden layers by analysing the effect of stock price prediction, and the empirical results obtained demonstrate that the prediction performance of two hidden layers prediction model is better than that of the single hidden layer prediction model. Additionally, the empirical results obtained also demonstrate that the more epochs of training network, the better the results obtained with using the same number of neurons.
DEBUG: A Dense Bottom-Up Grounding Approach for Natural Language Video Localization

In this paper, we focus on natural language video localization: localizing (ie, grounding) a natural language description in a long and untrimmed video sequence. All currently published models for addressing this problem can be categorized into two types: (i) top-down approach: it does classification and regression for a set of pre-cut video segment candidates; (ii) bottom-up approach: it directly predicts probabilities for each video frame as the temporal boundaries (ie, start and end time point). However, both two approaches suffer several limitations: the former is computation-intensive for densely placed candidates, while the latter has trailed the performance of the top-down counterpart thus far. To this end, we propose a novel dense bottom-up framework: DEnse Bottom-Up Grounding (DEBUG). DEBUG regards all frames falling in the ground truth segment as foreground, and each foreground frame regresses the unique distances from its location to bi-directional ground truth boundaries. Extensive experiments on three challenging benchmarks (TACoS, Charades-STA, and ActivityNet Captions) show that DEBUG is able to match the speed of bottom-up models while surpassing the performance of the state-of-the-art top-down models.
End-To-End Person Search Sequentially Trained On Aggregated Dataset

In video surveillance applications, person search is a challenging task consisting in detecting people and extracting features from their silhouette for re-identification (re-ID) purpose. We propose a new end-to-end model that jointly computes detection and feature extraction steps through a single deep Convolutional Neural Network architecture. Sharing feature maps between the two tasks for jointly describing people commonalities and specificities allows faster runtime, which is valuable in real-world applications. In addition to reaching state-of-the-art accuracy, this multi-task model can be sequentially trained task-by-task, which results in a broader acceptance of input dataset types. Indeed, we show that aggregating more pedestrian detection datasets without costly identity annotations makes the shared feature maps more generic, and improves re-ID precision. Moreover, these boosted shared feature maps result in re-ID features more robust to a cross-dataset scenario.
Gradient-based Adaptive Markov Chain Monte Carlo

We introduce a gradient-based learning method to automatically adapt Markov chain Monte Carlo (MCMC) proposal distributions to intractable targets. We define a maximum entropy regularised objective function, referred to as generalised speed measure, which can be robustly optimised over the parameters of the proposal distribution by applying stochastic gradient optimisation. An advantage of our method compared to traditional adaptive MCMC methods is that the adaptation occurs even when candidate state values are rejected. This is a highly desirable property of any adaptation strategy because the adaptation starts in early iterations even if the initial proposal distribution is far from optimum. We apply the framework for learning multivariate random walk Metropolis and Metropolis-adjusted Langevin proposals with full covariance matrices, and provide empirical evidence that our method can outperform other MCMC algorithms, including Hamiltonian Monte Carlo schemes.
The impact of household characteristics, socio-cultural factors and HIV/aids on child welfare in Kenya

This thesis investigates socio-economic and health economic issues related to the Kenyan people using the 2003 Kenya Demographic and Health Survey Data. The studies give empirical evidence that helps to understand the impact of socio-economic and health factors, including HIV/AIDS, on child schooling and health status. The thesis examines the factors contributing to an individual acquiring HIV, factors affecting child schooling and the factors having impact on child health in Kenya. Each of these studies employs the econometric tools necessary for reliable estimates. The econometric tools address issues with endogeneity, sample selection and missing data. The thesis is comprised of five chapters with three major ones. The first Chapter gives an introduction and overview of the thesis. This chapter covers the motivation and objectives of the thesis. It also gives some historical background about Kenya. Chapter 2 examines the factors contributing to an individual acquiring HIV, with a focus on behavioural and personal characteristics, household characteristics and other socio-economic factors. Chapter 3 deals with children’s education outcomes, specifically examining school attendance, school attainment, and rates of grade progression. The study focuses on the impact of individual and household characteristics. It incorporates the effect of socio-cultural factors as well as HIV/AIDS on child schooling. Extensive work is done on relevant econometric and statistical tools designed to address the difficulties associated with the variables used in the analysis. The discussion and conclusion gives some guidance to education policy makers in Kenya and other parts of the world which face similar conditions.
A Topic Augmented Text Generation Model: Joint Learning of Semantics and Structural Features

Text generation is among the most fundamental tasks in natural language processing. In this paper, we propose a text generation model that learns semantics and structural features simultaneously. This model captures structural features by a sequential variational autoencoder component and leverages a topic modeling component based on Gaussian distribution to enhance the recognition of text semantics. To make the reconstructed text more coherent to the topics, the model further adapts the encoder of the topic modeling component for a discriminator. The results of experiments over several datasets demonstrate that our model outperforms several states of the art models in terms of text perplexity and topic coherence. Moreover, the latent representations learned by our model is superior to others in a text classification task. Finally, given the input texts, our model can generate meaningful texts which hold similar structures but under different topics.
Dynamic Adaptive Piecewise Linear Representation Approach Based on Streaming Time Series

With the burgeoning of IoE (Internet of Everything), massive numbers of IoT devices in extensive fields are continuously producing huge number of streaming time series. The high dimensionality and dynamic uncertainty of this kind of data lead to the main challenge on traditional time series data mining research. Accordingly, time series representation methods have been regarded as a necessary pre-processing tool to provide data support for the follow-up time series data mining research. In this paper, we propose a novel dynamic time series representation approach called dynamic adaptive piecewise linear representation (DAPLR) for streaming time series, which can automatically provide a series of piecewise linear representation results to meet the diverse needs of different users.