OpenRaiser/Intern-Atlas huggingface dataset:
Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
Venue: arXiv cs.MA 2021
Year: 2021

With the growing reliability of modern Ad Hoc Networks, it is encouraging to analyze potential involvement of autonomous Ad Hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster where timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a near optimal path to a trapped victim, followed by the application of Dijkstra's algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for a faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision making model of the swarm, segmented into two primary phases, responsible for the search and rescue respectively. Different aspects of the performance of the agent swarm are analyzed, as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area.
Alternative Paradigms of Computation
Venue: arXiv cs.ET 2021
Year: 2021

With Moore's law coming to a close it is useful to look at other forms of computer hardware. In this paper we survey what is known about several modes of computation: Neuromorphic, Custom Logic, Quantum, Optical, Spintronics, Reversible, Many-Valued Logic, Chemical, DNA, Neurological, Fluidic, Amorphous, Thermodynamic, Peptide, and Membrane. For each of these modes of computing we discuss pros, cons, current work, and metrics. After surveying these alternative modes of computation we discuss two aread where they may useful: data analytics and graph processing.
Achievable Rate of Near-Field Communications Based on Physically Consistent Models
Venue: arXiv cs.IT 2021
Year: 2021

This paper introduces a novel information-theoretic approach for studying the effects of mutual coupling (MC), between the transmit and receive antennas, on the overall performance of single-input-single-output (SISO) near-field communications. By incorporating the finite antenna size constraint using Chu's theory and under the assumption of canonical-minimum scattering, we derive the MC between two radiating volumes of fixed sizes. Expressions for the self and mutual impedances are obtained by the use of the reciprocity theorem. Based on a circuit-theoretic two-port model for SISO radio communication systems, we establish the achievable rate for a given pair of transmit and receive antenna sizes, thereby providing an upper bound on the system performance under physical size constraints. Through the lens of these findings, we shed new light on the influence of MC on the information-theoretic limits of near-field communications using compact antennas.
CAMIG: Concurrency-Aware Live Migration Management of Multiple Virtual Machines in SDN-enabled Clouds
Venue: arXiv cs.NI 2021
Year: 2021

By integrating Software-Defined Networking and cloud computing, virtualized networking and computing resources can be dynamically reallocated through live migration of Virtual Machines (VMs). Dynamic resource management such as load balancing and energy-saving policies can request multiple migrations when the algorithms are triggered periodically. There exist notable research efforts in dynamic resource management that alleviate single migration overheads, such as single migration time and co-location interference while selecting the potential VMs and migration destinations. However, by neglecting the resource dependency among potential migration requests, the existing solutions of dynamic resource management can result in the Quality of Service (QoS) degradation and Service Level Agreement (SLA) violations during the migration schedule. Therefore, it is essential to integrate both single and multiple migration overheads into VM reallocation planning. In this paper, we propose a concurrency-aware multiple migration selector that operates based on the maximal cliques and independent sets of the resource dependency graph of multiple migration requests. Our proposed method can be integrated with existing dynamic resource management policies. The experimental results demonstrate that our solution efficiently minimizes migration interference and shortens the convergence time of reallocation by maximizing the multiple migration performance while achieving the objective of dynamic resource management.
An energy-efficient scheduling algorithm for shared facility supercomputer centers
Venue: arXiv cs.DC 2021
Year: 2021

The evolution of high-performance computing is associated with the growth of energy consumption. Performance of cluster computes (is increased via rising in performance and the number of used processors, GPUs, and coprocessors. An increment in the number of computing elements results in significant growth of energy consumption. Power grids limits for supercomputer centers (SCC) are driving the transition to more energy-efficient solutions. Often upgrade of computing resources is done step-by-step, i.e. parts of older supercomputers are removed from service and replaced with newer ones. A single SCC at any time can operate several computing systems with different performance and power consumption. That is why the problem of scheduling parallel programs execution on SCC resources to optimize energy consumption and minimize the increase in execution time (energy-efficient scheduling) is important. The goal of the presented work was the development of a new energy-efficient algorithm for scheduling computing resources at SCC. To reach the goal the authors analyzed methods of scheduling computing resources in a shared facility, including energy consumption minimizing methods. The study made it possible to formulate the problem of energy-efficient scheduling for a set of CCs and propose an algorithm for its solution. Experiments on NPB benchmarks allowed achieving significant reduction in energy consumption with a minor increase of runtime.
Towards Interpretable and Reliable Reading Comprehension: A Pipeline Model with Unanswerability Prediction
Venue: arXiv cs.CL 2021
Year: 2021

Multi-hop QA with annotated supporting facts, which is the task of reading comprehension (RC) considering the interpretability of the answer, has been extensively studied. In this study, we define an interpretable reading comprehension (IRC) model as a pipeline model with the capability of predicting unanswerable queries. The IRC model justifies the answer prediction by establishing consistency between the predicted supporting facts and the actual rationale for interpretability. The IRC model detects unanswerable questions, instead of outputting the answer forcibly based on the insufficient information, to ensure the reliability of the answer. We also propose an end-to-end training method for the pipeline RC model. To evaluate the interpretability and the reliability, we conducted the experiments considering unanswerability in a multi-hop question for a given passage. We show that our end-to-end trainable pipeline model outperformed a non-interpretable model on our modified HotpotQA dataset. Experimental results also show that the IRC model achieves comparable results to the previous non-interpretable models in spite of the trade-off between prediction performance and interpretability.
Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction
Venue: arXiv cs.CL 2021
Year: 2021

Natural language understanding's relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.
Dimensionless formulation and similarity to assess the main phenomena of heat and mass transfer in building porous material
Venue: arXiv cs.CE 2021
Year: 2021

Within the environmental context, several tools based on simulations have been proposed to analyze the physical phenomena of heat and mass transfer in porous materials. However, it is still an open challenge to propose tools that do not require to perform computations to catch the dominant processes. Thus, this article proposes to explore advantages of using a dimensionless analysis by scaling the governing equations of heat and mass transfer. Proposed methodology introduces dimensionless numbers and their nonlinear distortions. The relevant investigation enables to enhance the preponderant phenomena to \emph{(i)} compare different categories of materials, \emph{(ii)} evaluate the competition between heat and mass transfer for each material or \emph{(iii)} describe the transfer in multi-layered wall configurations. It also permits to define hygrothermal kinetic, geometric and dynamic similarities among different physical materials. Equivalent systems can be characterized in the framework of experimental or wall designs. Three cases are presented for similarity studies in terms of \emph{(i)} equivalent material length, \emph{(ii)} time of heat and mass transfer and \emph{(iii)} experimental configurations. All these advantages are illustrated in the given article considering $49$ building materials separated in $7$ categories.
The Polygon Burning Problem
Venue: arXiv cs.CG 2021
Year: 2021

Motivated by the $k$-center problem in location analysis, we consider the \emph{polygon burning} (PB) problem: Given a polygonal domain $P$ with $h$ holes and $n$ vertices, find a set $S$ of $k$ vertices of $P$ that minimizes the maximum geodesic distance from any point in $P$ to its nearest vertex in $S$. Alternatively, viewing each vertex in $S$ as a site to start a fire, the goal is to select $S$ such that fires burning simultaneously and uniformly from $S$, restricted to $P$, consume $P$ entirely as quickly as possible. We prove that PB is NP-hard when $k$ is arbitrary. We show that the discrete $k$-center of the vertices of $P$ under the geodesic metric on $P$ provides a $2$-approximation for PB, resulting in an $O(n^2 \log n + hkn \log n)$-time $3$-approximation algorithm for PB. Lastly, we define and characterize a new type of polygon, the sliceable polygon. A sliceable polygon is a convex polygon that contains no Voronoi vertex from the Voronoi diagram of its vertices. We give a dynamic programming algorithm to solve PB exactly on a sliceable polygon in $O(kn^2)$ time.
Trajectory Prediction & Path Planning for an Object Intercepting UAV with a Mounted Depth Camera
Venue: arXiv cs.RO 2021
Year: 2021

A novel control & software architecture using ROS C++ is introduced for object interception by a UAV with a mounted depth camera and no external aid. Existing work in trajectory prediction focused on the use of off-board tools like motion capture rooms to intercept thrown objects. The present study designs the UAV architecture to be completely on-board capable of object interception with the use of a depth camera and point cloud processing. The architecture uses an iterative trajectory prediction algorithm for non-propelled objects like a ping-pong ball. A variety of path planning approaches to object interception and their corresponding scenarios are discussed, evaluated & simulated in Gazebo. The successful simulations exemplify the potential of using the proposed architecture for the on-board autonomy of UAVs intercepting objects.
Parametric PGD model used with orthogonal polynomials to assess efficiently the building's envelope thermal performance
Venue: arXiv cs.CE 2021
Year: 2021

Estimating the temperature field of a building envelope could be a time-consuming task. The use of a reduced-order method is then proposed: the Proper Generalized Decomposition method. The solution of the transient heat equation is then re-written as a function of its parameters: the boundary conditions, the initial condition, etc. To avoid a tremendous number of parameters, the initial condition is parameterized. This is usually done by using the Proper Orthogonal Decomposition method to provide an optimal basis. Building this basis requires data and a learning strategy. As an alternative, the use of orthogonal polynomials (Chebyshev, Legendre) is here proposed.
2nd Place Solution to Facebook AI Image Similarity Challenge Matching Track
Venue: arXiv cs.CV 2021
Year: 2021

This paper presents the 2nd place solution to the Facebook AI Image Similarity Challenge : Matching Track on DrivenData. The solution is based on self-supervised learning, and Vision Transformer(ViT). The main breaktrough comes from concatenating query and reference image to form as one image and asking ViT to directly predict from the image if query image used reference image. The solution scored 0.8291 Micro-average Precision on the private leaderboard.
Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records
Venue: arXiv cs.CL 2021
Year: 2021

Dementia is a neurodegenerative disorder that causes cognitive decline and affects more than 50 million people worldwide. Dementia is under-diagnosed by healthcare professionals - only one in four people who suffer from dementia are diagnosed. Even when a diagnosis is made, it may not be entered as a structured International Classification of Diseases (ICD) diagnosis code in a patient's charts. Information relevant to cognitive impairment (CI) is often found within electronic health records (EHR), but manual review of clinician notes by experts is both time consuming and often prone to errors. Automated mining of these notes presents an opportunity to label patients with cognitive impairment in EHR data. We developed natural language processing (NLP) tools to identify patients with cognitive impairment and demonstrate that linguistic context enhances performance for the cognitive impairment classification task. We fine-tuned our attention based deep learning model, which can learn from complex language structures, and substantially improved accuracy (0.93) relative to a baseline NLP model (0.84). Further, we show that deep learning NLP can successfully identify dementia patients without dementia-related ICD codes or medications.
Airport Taxi Time Prediction and Alerting: A Convolutional Neural Network Approach
Venue: arXiv cs.LG 2021
Year: 2021

This paper proposes a novel approach to predict and determine whether the average taxi- out time at an airport will exceed a pre-defined threshold within the next hour of operations. Prior work in this domain has focused exclusively on predicting taxi-out times on a flight-by-flight basis, which requires significant efforts and data on modeling taxiing activities from gates to runways. Learning directly from surface radar information with minimal processing, a computer vision-based model is proposed that incorporates airport surface data in such a way that adaptation-specific information (e.g., runway configuration, the state of aircraft in the taxiing process) is inferred implicitly and automatically by Artificial Intelligence (AI).
Tiny Obstacle Discovery by Occlusion-Aware Multilayer Regression
Venue: arXiv cs.RO 2021
Year: 2021

Edges are the fundamental visual element for discovering tiny obstacles using a monocular camera. Nevertheless, tiny obstacles often have weak and inconsistent edge cues due to various properties such as small size and similar appearance to the free space, making it hard to capture them. ...
Semantic Foundations of Seraph Continuous Graph Query Language
Venue: arXiv cs.DB 2021
Year: 2021

The scientific community has been studying graph data models for decades. Their high expressiveness and elasticity led the scientific community to design a variety of graph data models and graph query languages, and the practitioners to use them to model real-world cases and extract useful information. Recently, property graphs and, in particular, Cypher 9 (the first open version of the well-known Neo4j Inc.'s language) are gaining popularity. Practitioners find Cypher useful and applicable in many scenarios. However, we are living in a streaming world where data continuously flows. A growing number of Cypher's users show interest in continuously querying graph data to act in a timely fashion. Indeed, Cypher lacks the features for dealing with streams of (graph) data and continuous query evaluation. In this work, we propose Seraph, an extension of Cypher, as a first attempt to introduce streaming features in the context of property graph query languages. Specifically, we define Seraph semantics, we propose a first version of Seraph syntax, and we discuss the potential impacts from a user perspective.
An Investigation into Keystroke Dynamics and Heart Rate Variability as Indicators of Stress
Venue: arXiv cs.HC 2021
Year: 2021

Lifelogging has become a prominent research topic in recent years. Wearable sensors like Fitbits and smart watches are now increasingly popular for recording ones activities. Some researchers are also exploring keystroke dynamics for lifelogging. Keystroke dynamics refers to the process of measuring and assessing a persons typing rhythm on digital devices. A digital footprint is created when a user interacts with devices like keyboards, mobile phones or touch screen panels and the timing of the keystrokes is unique to each individual though likely to be affected by factors such as fatigue, distraction or emotional stress. In this work we explore the relationship between keystroke dynamics as measured by the timing for the top-10 most frequently occurring bi-grams in English, and the emotional state and stress of an individual as measured by heart rate variability (HRV). We collected keystroke data using the Loggerman application while HRV was simultaneously gathered. With this data we performed an analysis to determine the relationship between variations in keystroke dynamics and variations in HRV. Our conclusion is that we need to use a more detailed representation of keystroke timing than the top-10 bigrams, probably personalised to each user.
MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System
Venue: arXiv cs.CL 2021
Year: 2021

We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed and evaluated specifically for the history taking task. It integrates the advantage of a traditional modular approach to incorporate (medical) domain knowledge with modern deep learning techniques to generate flexible, human-like natural language expressions. Two key aspects of MEDCOD's natural language output are described in detail. First, the generated sentences are emotive and empathetic, similar to how a doctor would communicate to the patient. Second, the generated sentence structures and phrasings are varied and diverse while maintaining medical consistency with the desired medical concept (provided by the dialogue manager module of MEDCOD). Experimental results demonstrate the effectiveness of our approach in creating a human-like medical dialogue system. Relevant code is available at https://github.com/curai/curai-research/tree/main/MEDCOD
ORPHEUS: Living Labs for End-to-End Data Infrastructures for Digital Agriculture
Venue: arXiv cs.NI 2021
Year: 2021

IoT networks are being used to collect, analyze, and utilize sensor data. There are still some key requirements to leverage IoT networks in digital agriculture, e.g., design and deployment of energy saving and ruggedized sensor nodes (SN), reliable and long-range wireless network connectivity, end-to-end data collection pipelines for batch and streaming data. Thus, we introduce our living lab ORPHEUS and its design and implementation trajectory to showcase our orchestrated testbed of IoT sensors, data connectivity, database orchestration, and visualization dashboard. We deploy light-weight energy saving SNs in the field to collect data, using LoRa (Long Range wireless) to transmit data from the SNs to the Gateway node, upload all the data to the database server, and finally visualize the data. For future exploration, we also built a testbed of embedded devices using four different variants of NVIDIA Jetson development modules (Nano, TX2, Xavier NX, AGX Xavier) to benchmark the potential upgrade choices for SNs in ORPHEUS. Based on our deployment in multiple farms in a 3-county region around Purdue University, and on the Purdue University campus, we present analyses from our living lab deployment and additional components of the next-generation IoT farm.
Monitoring COVID-19-induced gender differences in teleworking rates using Mobile Network Data
Venue: arXiv cs.SI 2021
Year: 2021

The COVID-19 pandemic has created a sudden need for a wider uptake of home-based telework as means of sustaining the production. Generally, teleworking arrangements impacts directly worker's efficiency and motivation. The direction of this impact, however, depends on the balance between positive effects of teleworking (e.g. increased flexibility and autonomy) and its downsides (e.g. blurring boundaries between private and work life). Moreover, these effects of teleworking can be amplified in case of vulnerable groups of workers, such as women. The first step in understanding the implications of teleworking on women is to have timely information on the extent of teleworking by age and gender. In the absence of timely official statistics, in this paper we propose a method for nowcasting the teleworking trends by age and gender for 20 Italian regions using mobile network operators (MNO) data. The method is developed and validated using MNO data together with the Italian quarterly Labour Force Survey. Our results confirm that the MNO data have the potential to be used as a tool for monitoring gender and age differences in teleworking patterns. This tool becomes even more important today as it could support the adequate gender mainstreaming in the ``Next Generation EU'' recovery plan and help to manage related social impacts of COVID-19 through policymaking.
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines
Venue: arXiv cs.AI 2021
Year: 2021

Continuously learning new tasks using high-level ideas or knowledge is a key capability of humans. In this paper, we propose Lifelong reinforcement learning with Sequential linear temporal logic formulas and Reward Machines (LSRM), which enables an agent to leverage previously learned knowledge to fasten learning of logically specified tasks. For the sake of more flexible specification of tasks, we first introduce Sequential Linear Temporal Logic (SLTL), which is a supplement to the existing Linear Temporal Logic (LTL) formal language. We then utilize Reward Machines (RM) to exploit structural reward functions for tasks encoded with high-level events, and propose automatic extension of RM and efficient knowledge transfer over tasks for continuous learning in lifetime. Experimental results show that LSRM outperforms the methods that learn the target tasks from scratch by taking advantage of the task decomposition using SLTL and knowledge transfer over RM during the lifelong learning process.