OpenRaiser/Intern-Atlas huggingface dataset:
Genetic Programming visitation scheduling solution can deliver a less austere COVID-19 pandemic population lockdown
Venue: arXiv cs.NE 2020
Year: 2020

A computational methodology is introduced to minimize infection opportunities for people suffering some degree of lockdown in response to a pandemic, as is the 2020 COVID-19 pandemic. Persons use their mobile phone or computational device to request trips to places of their need or interest indicating a rough time of day: `morning', `afternoon', `night' or `any time' when they would like to undertake these outings as well as the desired place to visit. An artificial intelligence methodology which is a variant of Genetic Programming studies all requests and responds with specific time allocations for such visits that minimize the overall risks of infection, hospitalization and death of people. A number of alternatives for this computation are presented and results of numerical experiments involving over 230 people of various ages and background health levels in over 1700 visits that take place over three consecutive days. A novel partial infection model is introduced to discuss these proof of concept solutions which are compared to round robin uninformed time scheduling for visits to places. The computations indicate vast improvements with far fewer dead and hospitalized. These auger well for a more realistic study using accurate infection models with the view to test deployment in the real world. The input that drives the infection model is the degree of infection by taxonomic class, such as the information that may arise from population testing for COVID-19 or, alternatively, any contamination model. The taxonomy class assumed in the computations is the likely level of infection by age group.
SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic
Venue: arXiv cs.SI 2020
Year: 2020

Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories. As of June 18, 2020, in total, there are now over 8,400,000 cases and over 450,000 related deaths. This causes massive losses in the economy and jobs globally and confining about 58% of the global population. In this paper, we introduce SenWave, a novel sentimental analysis work using 105+ million collected tweets and Weibo messages to evaluate the global rise and falls of sentiments during the COVID-19 pandemic. To make a fine-grained analysis on the feeling when we face this global health crisis, we annotate 10K tweets in English and 10K tweets in Arabic in 10 categories, including optimistic, thankful, empathetic, pessimistic, anxious, sad, annoyed, denial, official report, and joking. We then utilize an integrated transformer framework, called simpletransformer, to conduct multi-label sentimental classification by fine-tuning the pre-trained language model on the labeled data. Meanwhile, in order for a more complete analysis, we also translate the annotated English tweets into different languages (Spanish, Italian, and French) to generated training data for building sentiment analysis models for these languages. SenWave thus reveals the sentiment of global conversation in six different languages on COVID-19 (covering English, Spanish, French, Italian, Arabic and Chinese), followed the spread of the epidemic. The conversation showed a remarkably similar pattern of rapid rise and slow decline over time across all nations, as well as on special topics like the herd immunity strategies, to which the global conversation reacts strongly negatively. Overall, SenWave shows that optimistic and positive sentiments increased over time, foretelling a desire to seek, together, a reset for an improved COVID-19 world.
New Bounds for the Vertices of the Integer Hull
Venue: arXiv cs.DS 2020
Year: 2020

The vertices of the integer hull are the integral equivalent to the well-studied basic feasible solutions of linear programs. In this paper we give new bounds on the number of non-zero components -- their support -- of these vertices matching either the best known bounds or improving upon them. While the best known bounds make use of deep techniques, we only use basic results from probability theory to make use of the concentration of measure effect. To show the versatility of our techniques, we use our results to give the best known bounds on the number of such vertices and an algorithm to enumerate them. We also improve upon the known lower bounds to show that our results are nearly optimal. One of the main ingredients of our work is a generalization of the famous Hoeffding bound to vector-valued random variables that might be of general interest.
N=1 Modelling of Lifestyle Impact on SleepPerformance
Venue: arXiv cs.CY 2020
Year: 2020

Sleep is critical to leading a healthy lifestyle. Each day, most people go to sleep without any idea about how their night's rest is going to be. For an activity that humans spend around a third of their life doing, there is a surprising amount of mystery around it. Despite current research, creating personalized sleep models in real-world settings has been challenging. Existing literature provides several connections between daily activities and sleep quality. Unfortunately, these insights do not generalize well in many individuals. For these reasons, it is important to create a personalized sleep model. This research proposes a sleep model that can identify causal relationships between daily activities and sleep quality and present the user with specific feedback about how their lifestyle affects their sleep. Our method uses N-of-1 experiments on longitudinal user data and event mining to generate understanding between lifestyle choices (exercise, eating, circadian rhythm) and their impact on sleep quality. Our experimental results identified and quantified relationships while extracting confounding variables through a causal framework. These insights can be used by the user or a personal health navigator to provide guidance in improving sleep.
Learn to Earn: Enabling Coordination within a Ride Hailing Fleet
Venue: arXiv cs.AI 2020
Year: 2020

The problem of optimizing social welfare objectives on multi sided ride hailing platforms such as Uber, Lyft, etc., is challenging, due to misalignment of objectives between drivers, passengers, and the platform itself. An ideal solution aims to minimize the response time for each hyper local passenger ride request, while simultaneously maintaining high demand satisfaction and supply utilization across the entire city. Economists tend to rely on dynamic pricing mechanisms that stifle price sensitive excess demand and resolve the supply demand imbalances emerging in specific neighborhoods. In contrast, computer scientists primarily view it as a demand prediction problem with the goal of preemptively repositioning supply to such neighborhoods using black box coordinated multi agent deep reinforcement learning based approaches. Here, we introduce explainability in the existing supply repositioning approaches by establishing the need for coordination between the drivers at specific locations and times. Explicit need based coordination allows our framework to use a simpler non deep reinforcement learning based approach, thereby enabling it to explain its recommendations ex post. Moreover, it provides envy free recommendations i.e., drivers at the same location and time do not envy one another's future earnings. Our experimental evaluation demonstrates the effectiveness, the robustness, and the generalizability of our framework. Finally, in contrast to previous works, we make available a reinforcement learning environment for end to end reproducibility of our work and to encourage future comparative studies.
Simulation-Based Digital Twin Development for Blockchain Enabled End-to-End Industrial Hemp Supply Chain Risk Management
Venue: arXiv cs.CR 2020
Year: 2020

With the passage of the 2018 U.S. Farm Bill, Industrial Hemp production is moved from limited pilot programs to a regulated agriculture production system. However, Industrial Hemp Supply Chain (IHSC) faces critical challenges, including: high complexity and variability, very limited production knowledge, lack of data and information tracking. In this paper, we propose blockchain-enabled IHSC and develop a preliminary simulation-based digital twin for this distributed cyber-physical system (CPS) to support the process learning and risk management. Basically, we develop a two-layer blockchain with proof of authority smart contract, which can track the data and key information, improve the supply chain transparency, and leverage local authorities and state regulators to ensure the quality control verification. Then, we introduce a stochastic simulation-based digital twin for IHSC risk management, which can characterize the process spatial-temporal causal interdependencies and dynamic evolution to guide risk control and decision making. Our empirical study demonstrates the promising performance of proposed platform.
Pervasive Lying Posture Tracking
Venue: arXiv cs.LG 2020
Year: 2020

There exist significant gaps in research about how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article, we propose a comprehensive approach to design a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are most effective in accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F-Score that ranges from 95.2% to 97.8% with 0.03 to 0.05 coefficient of variation. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.
Stateless Distributed Ledgers
Venue: arXiv cs.CR 2020
Year: 2020

In public distributed ledger technologies (DLTs), such as Blockchains, nodes can join and leave the network at any time. A major challenge occurs when a new node joining the network wants to retrieve the current state of the ledger. Indeed, that node may receive conflicting information from honest and Byzantine nodes, making it difficult to identify the current state. In this paper, we are interested in protocols that are stateless, i.e., a new joining node should be able to retrieve the current state of the ledger just using a fixed amount of data that characterizes the ledger (such as the genesis block in Bitcoin). We define three variants of stateless DLTs: weak, strong, and probabilistic. Then, we analyze this property for DLTs using different types of consensus.
Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
Venue: arXiv cs.AI 2020
Year: 2020

This paper introduces for the first time a framework to obtain provable worst-case guarantees for neural network performance, using learning for optimal power flow (OPF) problems as a guiding example. Neural networks have the potential to substantially reduce the computing time of OPF solutions. However, the lack of guarantees for their worst-case performance remains a major barrier for their adoption in practice. This work aims to remove this barrier. We formulate mixed-integer linear programs to obtain worst-case guarantees for neural network predictions related to (i) maximum constraint violations, (ii) maximum distances between predicted and optimal decision variables, and (iii) maximum sub-optimality. We demonstrate our methods on a range of PGLib-OPF networks up to 300 buses. We show that the worst-case guarantees can be up to one order of magnitude larger than the empirical lower bounds calculated with conventional methods. More importantly, we show that the worst-case predictions appear at the boundaries of the training input domain, and we demonstrate how we can systematically reduce the worst-case guarantees by training on a larger input domain than the domain they are evaluated on.
Extracting Topics from Open Educational Resources
Venue: arXiv cs.CY 2020
Year: 2020

In recent years, Open Educational Resources (OERs) were earmarked as critical when mitigating the increasing need for education globally. Obviously, OERs have high-potential to satisfy learners in many different circumstances, as they are available in a wide range of contexts. However, the low-quality of OER metadata, in general, is one of the main reasons behind the lack of personalised services such as search and recommendation. As a result, the applicability of OERs remains limited. Nevertheless, OER metadata about covered topics (subjects) is essentially required by learners to build effective learning pathways towards their individual learning objectives. Therefore, in this paper, we report on a work in progress project proposing an OER topic extraction approach, applying text mining techniques, to generate high-quality OER metadata about topic distribution. This is done by: 1) collecting 123 lectures from Coursera and Khan Academy in the area of data science related skills, 2) applying Latent Dirichlet Allocation (LDA) on the collected resources in order to extract existing topics related to these skills, and 3) defining topic distributions covered by a particular OER. To evaluate our model, we used the data-set of educational resources from Youtube, and compared our topic distribution results with their manually defined target topics with the help of 3 experts in the area of data science. As a result, our model extracted topics with 79% of F1-score.
Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
Venue: arXiv cs.NE 2020
Year: 2020

Exploratory landscape analysis (ELA) supports supervised learning approaches for automated algorithm selection and configuration by providing sets of features that quantify the most relevant characteristics of the optimization problem at hand. In black-box optimization, where an explicit problem representation is not available, the feature values need to be approximated from a small number of sample points. In practice, uniformly sampled random point sets and Latin hypercube constructions are commonly used sampling strategies. In this work, we analyze how the sampling method and the sample size influence the quality of the feature value approximations and how this quality impacts the accuracy of a standard classification task. While, not unexpectedly, increasing the number of sample points gives more robust estimates for the feature values, to our surprise we find that the feature value approximations for different sampling strategies do not converge to the same value. This implies that approximated feature values cannot be interpreted independently of the underlying sampling strategy. As our classification experiments show, this also implies that the feature approximations used for training a classifier must stem from the same sampling strategy as those used for the actual classification tasks. As a side result we show that classifiers trained with feature values approximated by Sobol' sequences achieve higher accuracy than any of the standard sampling techniques. This may indicate improvement potential for ELA-trained machine learning models.
REBD:A Conceptual Framework for Big Data Requirements Engineering
Venue: arXiv cs.SE 2020
Year: 2020

Requirements engineering (RE), as a part of the project development life cycle, has increasingly been recognized as the key to ensuring on-time, on-budget, and goal-based delivery of software projects;compromising this vital phase is nothing but project failures. RE of big data projects is even more crucial because of the main characteristics of big data, including high volume, velocity, and variety. As the traditional RE methods and tools are user-centric rather than data-centric, employing these methodologies is insufficient to fulfill the RE processes for big data projects. Because of the importance of RE and limitations of traditional RE methodologies in the context of big data software projects, in this paper, a big data requirements engineering framework, named REBD, has been proposed. This conceptual framework describes the systematic plan to carry out big data projects starting from requirements engineering to the development, assuring successful execution, and increased productivity of the big data projects.
Frustratingly Simple Domain Generalization via Image Stylization
Venue: arXiv cs.CV 2020
Year: 2020

Convolutional Neural Networks (CNNs) show impressive performance in the standard classification setting where training and testing data are drawn i.i.d. from a given domain. However, CNNs do not readily generalize to new domains with different statistics, a setting that is simple for humans. In this work, we address the Domain Generalization problem, where the classifier must generalize to an unknown target domain. Inspired by recent works that have shown a difference in biases between CNNs and humans, we demonstrate an extremely simple yet effective method, namely correcting this bias by augmenting the dataset with stylized images. In contrast with existing stylization works, which use external data sources such as art, we further introduce a method that is entirely in-domain using no such extra sources of data. We provide a detailed analysis as to the mechanism by which the method works, verifying our claim that it changes the shape/texture bias, and demonstrate results surpassing or comparable to the state of the arts that utilize much more complex methods.
Rumor source detection with multiple observations under adaptive diffusions
Venue: arXiv cs.SI 2020
Year: 2020

Recent work, motivated by anonymous messaging platforms, has introduced adaptive diffusion protocols which can obfuscate the source of a rumor: a "snapshot adversary" with access to the subgraph of "infected" nodes can do no better than randomly guessing the entity of the source node. What happens if the adversary has access to multiple independent snapshots? We study this question when the underlying graph is the infinite $d$-regular tree. We show that (1) a weak form of source obfuscation is still possible in the case of two independent snapshots, but (2) already with three observations there is a simple algorithm that finds the rumor source with constant probability, regardless of the adaptive diffusion protocol. We also characterize the tradeoff between local spreading and source obfuscation for adaptive diffusion protocols (under a single snapshot). These results raise questions about the robustness of anonymity guarantees when spreading information in social networks.
Counting Risk Increments to Make Decisions During an Epidemic
Venue: arXiv cs.CY 2020
Year: 2020

I propose a smartphone app that will allow people to participate in the management of their own safety during an epidemic or pandemic such as COVID-19 by enabling them to view, in advance, the risks they would take if they visit some given venue (a cafe, the gym, the workplace, the park,...) and, furthermore, track the accumulation of such risks during the course of any given day or week. This idea can be presented to users of the app as counting points. One point represents some constant probability, $p_\text{point}$, of infection. Then the app would work in a similar way to a calorie counting app (instead of counting calories we count probability increments of being infected). Government could set a maximum recommended number of daily (or weekly) points available to each user in accord with its objectives (bringing the disease under control, allowing essential workers to work, protecting vulnerable individuals, ...). It is posited that this, along with other proposed "levers" would allow government to manage a gradual transition to normalcy. I discuss a circuit framework with wires running between boxes. In this framework the wires represent possible sources of infection, namely individuals and the venues themselves (through deposits of pathogens left at the venue). The boxes represent interactions of these sources (when individuals visit a venue). This circuit framework allows (i) calculation of points cost for visiting venues and (ii) probabilistic contact tracing. The points systems proposed here could complement existing contact tracing apps by adding functionality to permit users to participate in decision making up front.
SkyHaul: An Autonomous Gigabit Network Fabric in the Sky
Venue: arXiv cs.NI 2020
Year: 2020

We design and build SKYHAUL, the first large-scale, autonomous, self-organizing network of Unmanned Aerial Vehicles (UAVs) that are connected using a mmWave wireless mesh backhaul. While the use of a mmWave backhaul paves the way for a new class of bandwidth-intensive, latency-sensitive cooperative applications (e.g., LTE coverage during disasters, surveillance during rescue in challenging terrains), the network of UAVs allows these applications to be executed at operating ranges that are far beyond the line-of-sight distances that limit individual UAVs today. To realize the challenging vision of deploying and maintaining an airborne mmWave mesh backhaul to cater to dynamic applications, SKYHAUL's design incorporates various elements: (1) Role-specific UAV operations that simultaneously address application tracking and backhaul connectivity (2) Novel algorithms to jointly address the problem of deployment (position, yaw of UAVs) and traffic routing across the UAV network; and (3) A provably optimal solution for fast and safe reconfiguration of UAV backhaul during application dynamics. We implement SKYHAUL on four DJI Matrice 600 Pros to demonstrate its practicality and performance through autonomous flight operations, complemented by large scale simulations.
Real-time Pupil Tracking from Monocular Video for Digital Puppetry
Venue: arXiv cs.CV 2020
Year: 2020

We present a simple, real-time approach for pupil tracking from live video on mobile devices. Our method extends a state-of-the-art face mesh detector with two new components: a tiny neural network that predicts positions of the pupils in 2D, and a displacement-based estimation of the pupil blend shape coefficients. Our technique can be used to accurately control the pupil movements of a virtual puppet, and lends liveliness and energy to it. The proposed approach runs at over 50 FPS on modern phones, and enables its usage in any real-time puppeteering pipeline.