OpenRaiser/Intern-Atlas huggingface dataset:
A two-level solution to fight against dishonest opinions in recommendation-based trust systems
Venue: arXiv cs.IR 2020
Year: 2020

In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels. We consider a scenario in which an agent requests recommendations from multiple parties to build trust toward another agent. At the collection level, we propose to allow agents to self-assess the accuracy of their recommendations and autonomously decide on whether they would participate in the recommendation process or not. At the processing level, we propose a recommendations aggregation technique that is resilient to collusion attacks, followed by a credibility update mechanism for the participating agents. The originality of our work stems from its consideration of dishonest opinions at both the collection and processing levels, which allows for better and more persistent protection against dishonest recommenders. Experiments conducted on the Epinions dataset show that our solution yields better performance in protecting the recommendation process against Sybil attacks, in comparison with a competing model that derives the optimal network of advisors based on the agents' trust values.
Design and Development of an Automated Coimagination Support System
Venue: arXiv cs.HC 2020
Year: 2020

Coimagination method is a novel approach to support interactive communication for activating three (3) cognitive functions: episodic memory, division of attention, and planning. These cognitive functions are known to decline at an early stage of mild cognitive impairment (MCI). In previous studies about the coimagination method, experimenters tested different settings in different care institutions. Out of these experiments, various measures were introduced, analyzed, and presented. However, ease of changing configuration based on participants, and a quick assessment of captured data remained challenging. Also, several observers and measurers are needed to conduct the coimagination method. In this paper, we propose the initial design and development of an automated coimagination support system that can handle such challenges. We aim to have an automated coimagination support system that can be used easily either by healthy participants or elderly participants via a natural voice interface. In this paper, our focus is to measure how well our proposed features work with elderly participants. Preliminary experiments were conducted with healthy participants, and notably, with actual elder participants. Healthy participants experienced longer speaking round and question-and-answer round than with elderly participants; while, the latter had preparation time before the speaking round. In these preliminary experiments, our initial system showed the capability to handle different configurations. Healthy participants have operated the system using voice, while elderly participants managed to use the system with minimal assistance.
Calibrated neighborhood aware confidence measure for deep metric learning
Venue: arXiv cs.LG 2020
Year: 2020

Deep metric learning has gained promising improvement in recent years following the success of deep learning. It has been successfully applied to problems in few-shot learning, image retrieval, and open-set classifications. However, measuring the confidence of a deep metric learning model and identifying unreliable predictions is still an open challenge. This paper focuses on defining a calibrated and interpretable confidence metric that closely reflects its classification accuracy. While performing similarity comparison directly in the latent space using the learned distance metric, our approach approximates the distribution of data points for each class using a Gaussian kernel smoothing function. The post-processing calibration algorithm with proposed confidence metric on the held-out validation dataset improves generalization and robustness of state-of-the-art deep metric learning models while provides an interpretable estimation of the confidence. Extensive tests on four popular benchmark datasets (Caltech-UCSD Birds, Stanford Online Product, Stanford Car-196, and In-shop Clothes Retrieval) show consistent improvements even at the presence of distribution shifts in test data related to additional noise or adversarial examples.
An Improved and Parallel Version of a Scalable Algorithm for Analyzing Time Series Data
Venue: arXiv cs.DC 2020
Year: 2020

Today, very large amounts of data are produced and stored in all branches of society including science. Mining these data meaningfully has become a considerable challenge and is of the broadest possible interest. The size, both in numbers of observations and dimensionality thereof, requires data mining algorithms to possess time complexities with both variables that are linear or nearly linear. One such algorithm, see Comput. Phys. Commun. 184, 2446-2453 (2013), arranges observations into a sequence called the progress index. The progress index steps through distinct regions of high sampling density sequentially. By means of suitable annotations, it allows a compact representation of the behavior of complex systems, which is encoded in the original data set. The only essential parameter is a notion of distance between observations. Here, we present the shared memory parallelization of the key step in constructing the progress index, which is the calculation of an approximation of the minimum spanning tree of the complete graph of observations. We demonstrate that excellent parallel efficiencies are obtained for up to 72 logical (CPU) cores. In addition, we introduce three conceptual advances to the algorithm that improve its controllability and the interpretability of the progress index itself.
A Novel Navigation System for an Autonomous Mobile Robot in an Uncertain Environment
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we developed a new navigation system, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Mophin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The experiments on simulation and a robot for eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment.
Medidas de distanciamento social e mobilidade na América do Sul durante a pandemia por COVID-19: Condições necessárias e suficientes?
Venue: arXiv cs.SI 2020
Year: 2020

In a scenario where there is no vaccine for COVID-19, non-pharmaceutical interventions are necessary to contain the spread of the virus and the collapse of the health system in the affected regions. One of these measures is social distancing, which aims to reduce interactions in the community by closing public and private establishments that involve crowds of people. The lockdown presupposes a drastic reduction in community interactions, representing a more extreme measure of social distancing. Based on geolocation data provided by Google for six categories of physical spaces, this article identifies the variations in the circulation of people in South America for different types of social distancing measures adopted during the COVID-19 pandemic. In this study, population mobility trends for a group of countries between February 15, 2020 and May 16, 2020 were analyzed. To summarize these trends in a single metric, a general circulation index was created, and to identify regional mobility patterns, descriptive analyzes of spatial autocorrelation (global and local Moran index) were used. The first hypothesis of this study is that countries with a lockdown decree can achieve greater success in reducing the mobility of the population, and the second hypothesis is that Argentina, Brazil and Colombia have regional mobility patterns. The first hypothesis was partially confirmed (considering 10 countries in South America), and the results obtained in the spatial analyzes confirmed the second hypothesis. In general, the observed data shows that less rigid lockdown or social distancing measures are necessary, however, they are not sufficient to achieve a significant reduction in the circulation of people during the pandemic.
Deep Stock Predictions
Venue: arXiv cs.LG 2020
Year: 2020

Forecasting stock prices can be interpreted as a time series prediction problem, for which Long Short Term Memory (LSTM) neural networks are often used due to their architecture specifically built to solve such problems. In this paper, we consider the design of a trading strategy that performs portfolio optimization using the LSTM stock price prediction for four different companies. We then customize the loss function used to train the LSTM to increase the profit earned. Moreover, we propose a data driven approach for optimal selection of window length and multi-step prediction length, and consider the addition of analyst calls as technical indicators to a multi-stack Bidirectional LSTM strengthened by the addition of Attention units. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA, while the addition of analyst call does improve the performance for certain datasets.
Handover Rate and Sojourn Time Analysis in Mobile Drone-Assisted Cellular Networks
Venue: arXiv cs.NI 2020
Year: 2020

To improve capacity and overcome some of the limitations of cellular wireless networks, drones with aerial base stations can be deployed to assist the terrestrial cellular wireless networks. The mobility of drones allows flexible network reconfiguration to adapt to dynamic traffic and channel conditions. However, this is achieved at the expense of more handovers since even a static user may experience a handover when the drones are mobile. In this letter, we provide an exact analysis of the handover rate and sojourn time (time between two subsequent handovers) for a network of drone base stations. We also show that among different speed distributions with the same mean, the handover rate is minimum when all drone base stations move with same speed.
Online Page Migration with ML Advice
Venue: arXiv cs.DS 2020
Year: 2020

We consider online algorithms for the {\em page migration problem} that use predictions, potentially imperfect, to improve their performance. The best known online algorithms for this problem, due to Westbrook'94 and Bienkowski et al'17, have competitive ratios strictly bounded away from 1. In contrast, we show that if the algorithm is given a prediction of the input sequence, then it can achieve a competitive ratio that tends to $1$ as the prediction error rate tends to $0$. Specifically, the competitive ratio is equal to $1+O(q)$, where $q$ is the prediction error rate. We also design a ``fallback option'' that ensures that the competitive ratio of the algorithm for {\em any} input sequence is at most $O(1/q)$. Our result adds to the recent body of work that uses machine learning to improve the performance of ``classic'' algorithms.
Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining
Venue: arXiv cs.CY 2020
Year: 2020

Predicting students' academic performance has been a research area of interest in recent years with many institutions focusing on improving the students' performance and the education quality. The analysis and prediction of students' performance can be achieved using various data mining techniques. Moreover, such techniques allow instructors to determine possible factors that may affect the students' final marks. To that end, this work analyzes two different undergraduate datasets at two different universities. Furthermore, this work aims to predict the students' performance at two stages of course delivery (20% and 50% respectively). This analysis allows for properly choosing the appropriate machine learning algorithms to use as well as optimize the algorithms' parameters. Furthermore, this work adopts a systematic multi-split approach based on Gini index and p-value. This is done by optimizing a suitable bagging ensemble learner that is built from any combination of six potential base machine learning algorithms. It is shown through experimental results that the posited bagging ensemble models achieve high accuracy for the target group for both datasets.
Neural Physicist: Learning Physical Dynamics from Image Sequences
Venue: arXiv cs.LG 2020
Year: 2020

We present a novel architecture named Neural Physicist (NeurPhy) to learn physical dynamics directly from image sequences using deep neural networks. For any physical system, given the global system parameters, the time evolution of states is governed by the underlying physical laws. How to learn meaningful system representations in an end-to-end way and estimate accurate state transition dynamics facilitating long-term prediction have been long-standing challenges. In this paper, by leveraging recent progresses in representation learning and state space models (SSMs), we propose NeurPhy, which uses variational auto-encoder (VAE) to extract underlying Markovian dynamic state at each time step, neural process (NP) to extract the global system parameters, and a non-linear non-recurrent stochastic state space model to learn the physical dynamic transition. We apply NeurPhy to two physical experimental environments, i.e., damped pendulum and planetary orbits motion, and achieve promising results. Our model can not only extract the physically meaningful state representations, but also learn the state transition dynamics enabling long-term predictions for unseen image sequences. Furthermore, from the manifold dimension of the latent state space, we can easily identify the degree of freedom (DoF) of the underlying physical systems.
C-SL: Contrastive Sound Localization with Inertial-Acoustic Sensors
Venue: arXiv cs.SD 2020
Year: 2020

Human brain employs perceptual information about the head and eye movements to update the spatial relationship between the individual and the surrounding environment. Based on this cognitive process known as spatial updating, we introduce contrastive sound localization (C-SL) with mobile inertial-acoustic sensor arrays of arbitrary geometry. C-SL uses unlabeled multi-channel audio recordings and inertial measurement unit (IMU) readings collected during free rotational movements of the array to learn mappings from acoustical measurements to an array-centered direction-of-arrival (DOA) in a self-supervised manner. Contrary to conventional DOA estimation methods that require the knowledge of either the array geometry or source locations in the calibration stage, C-SL is agnostic to both, and can be trained on data collected in minimally constrained settings. To achieve this capability, our proposed method utilizes a customized contrastive loss measuring the spatial contrast between source locations predicted for disjoint segments of the input to jointly update estimated DOAs and the acoustic-spatial mapping in linear time. We provide quantitative and qualitative evaluations of C-SL comparing its performance with baseline DOA estimation methods in a wide range of conditions. We believe the relaxed calibration process offered by C-SL paves the way toward truly personalized augmented hearing applications.
Artificial Intelligence (AI)-Centric Management of Resources in Modern Distributed Computing Systems
Venue: arXiv cs.DC 2020
Year: 2020

Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Internet of Things (IoT)-driven applications are producing a huge amount of data that requires real-time processing and fast response. Managing these resources efficiently to provide reliable services to end-users or applications is a challenging task. The existing Resource Management Systems (RMS) rely on either static or heuristic solutions inadequate for such composite and dynamic systems. The advent of Artificial Intelligence (AI) due to data availability and processing capabilities manifested into possibilities of exploring data-driven solutions in RMS tasks that are adaptive, accurate, and efficient. In this regard, this paper aims to draw the motivations and necessities for data-driven solutions in resource management. It identifies the challenges associated with it and outlines the potential future research directions detailing where and how to apply the data-driven techniques in the different RMS tasks. Finally, it provides a conceptual data-driven RMS model for DCS and presents the two real-time use cases (GPU frequency scaling and data centre resource management from Google Cloud and Microsoft Azure) demonstrating AI-centric approaches' feasibility.
Reliable Detection for Spatial Modulation Systems
Venue: arXiv cs.IT 2020
Year: 2020

Spatial modulation (SM) is a promising multiple-input multiple-output system used to increase spectral efficiency. The maximum likelihood (ML) decoder jointly detects the transmitted SM symbol, which is of high complexity. In this paper, a novel reliable sphere decoder (RSD) algorithm based on tree-search is proposed for the SM system. The basic idea of the proposed RSD algorithm is to reduce the size of the tree-search, and then, a smart searching method inside the reduced tree-search is performed to find the solution. The proposed RSD algorithm provides a significant reduction in decoding complexity compared to the ML decoder and existent decoders as well. Moreover, the RSD algorithm provides a flexible trade-off between the bit error rate (BER) performance and decoding complexity, so as to be reliable for a wide range of practical hardware implementations. The BER performance and decoding complexity analysis for the RSD algorithm are studied, and Monte Carlo simulations are then provided to demonstrate the findings.
Enterprise Systems Lifecycle-wide Innovation Readiness
Venue: arXiv cs.CY 2020
Year: 2020

Enterprise Systems have been touted as a key driver of delivering benefits through innovation in corporate Information Systems. The advent of such systems expects to deliver best practices that improve organizational performance. Yet, most Enterprise System installations struggle to see lifecycle-wide value of it. Considering that Enterprise Systems deliver lifecycle-wide innovation; we observe organizational readiness for lifecycle-wide Enterprise Systems innovation. The A VICTORY apriori model compares contributions of eight constructs for organizational readiness for continuous Enterprise Systems innovation. The model is tested responses of both client and implementation partner. Results indicate that six of the eight constructs of readiness make significant contributions to organizational readiness for Enterprise Systems innovation.
ROSbag-based Multimodal Affective Dataset for Emotional and Cognitive States
Venue: arXiv cs.CY 2020
Year: 2020

This paper introduces a new ROSbag-based multimodal affective dataset for emotional and cognitive states generated using Robot Operating System (ROS). We utilized images and sounds from the International Affective Pictures System (IAPS) and the International Affective Digitized Sounds (IADS) to stimulate targeted emotions (happiness, sadness, anger, fear, surprise, disgust, and neutral), and a dual N-back game to stimulate different levels of cognitive workload. 30 human subjects participated in the user study; their physiological data was collected using the latest commercial wearable sensors, behavioral data was collected using hardware devices such as cameras, and subjective assessments were carried out through questionnaires. All data was stored in single ROSbag files rather than in conventional Comma-separated values (CSV) files. This not only ensures synchronization of signals and videos in a data set, but also allows researchers to easily analyze and verify their algorithms by connecting directly to this dataset through ROS. The generated affective dataset consists of 1,602 ROSbag files, and size of the dataset is about 787GB. The dataset is made publicly available. We expect that our dataset can be great resource for many researchers in the fields of affective computing, HCI, and HRI.
Over-crowdedness Alert! Forecasting the Future Crowd Distribution
Venue: arXiv cs.CV 2020
Year: 2020

In recent years, vision-based crowd analysis has been studied extensively due to its practical applications in real world. In this paper, we formulate a novel crowd analysis problem, in which we aim to predict the crowd distribution in the near future given sequential frames of a crowd video without any identity annotations. Studying this research problem will benefit applications concerned with forecasting crowd dynamics. To solve this problem, we propose a global-residual two-stream recurrent network, which leverages the consecutive crowd video frames as inputs and their corresponding density maps as auxiliary information to predict the future crowd distribution. Moreover, to strengthen the capability of our network, we synthesize scene-specific crowd density maps using simulated data for pretraining. Finally, we demonstrate that our framework is able to predict the crowd distribution for different crowd scenarios and we delve into applications including predicting future crowd count, forecasting high-density region, etc.
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
Venue: arXiv cs.LG 2020
Year: 2020

User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL). To remedy to this non-independent and identically distributed (non-IID) data problem, in this work we develop a privacy-preserving XOR based mixup data augmentation technique, coined XorMixup, and thereby propose a novel one-shot FL framework, termed XorMixFL. The core idea is to collect other devices' encoded data samples that are decoded only using each device's own data samples. The decoding provides synthetic-but-realistic samples until inducing an IID dataset, used for model training. Both encoding and decoding procedures follow the bit-wise XOR operations that intentionally distort raw samples, thereby preserving data privacy. Simulation results corroborate that XorMixFL achieves up to 17.6% higher accuracy than Vanilla FL under a non-IID MNIST dataset.
Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
Venue: arXiv cs.CV 2020
Year: 2020

We propose a framework that estimates inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learning and numerical simulation. A water and debris flow simulator generates training data for various artificial disaster scenarios. We show that regression models based on Attention U-Net and LinkNet architectures trained on such synthetic data can predict the maximum water level and topographic deformation from a remote sensing-derived change detection map and a digital elevation model. The proposed framework has an inpainting capability, thus mitigating the false negatives that are inevitable in remote sensing image analysis. Our framework breaks the limits of remote sensing and enables rapid estimation of inundation depth and topographic deformation, essential information for emergency response, including rescue and relief activities. We conduct experiments with both synthetic and real data for two disaster events that caused simultaneous flooding and debris flows and demonstrate the effectiveness of our approach quantitatively and qualitatively.