Regulatory Compliance through Doc2Doc Information Retrieval A case study in EU UK legislation where text similarity has limitations

Regulatory Technology (RegTech) is being used to keep track of constantly changing legislation . Organizations need to ensure that their controls (processes)comply with relevant laws, regulations, and policies . To this end, we introduce regulatory information retrieval (REG-IR), an application of document-to-document information retrieval .…

Data Driven simulation of inelastic materials using structured data sets tangent space information and transition rules

Data-driven computational mechanics replaces phenomenological constitutive functions by performing numerical simulations based on data sets ofrepresentative samples in stress-strain space . The distance of modeling values,e.g. stresses and strains in integration points of a finite elementcalculation, from the data set is minimized with respect to an appropriatemetric, subject to equilibrium and compatibility constraints .…

On formal concepts of random formal contexts

In formal concept analysis, it is well-known that the number of formal concepts can be exponential in the worst case . To analyze the average case, we introduce a probabilistic model for random formal contexts and prove that theaverage number offormal concepts has a superpolynomial asymptotic lower bound .…

Non Monotone Energy Aware Information Gathering for Heterogeneous Robot Teams

This paper considers the problem of planning trajectories for a team of sensors-equipped robots to reduce uncertainty about a dynamical process . Methods based on localsearch provide performance guarantees for optimizing a non-monotone submodularfunction . Our results show up to 60% communication reduction and 80-92%computation reduction on average when coordinating up to 10 robots, while outperforming the coordinate descent based algorithm in achieving a desirable trade-off between sensing and energy expenditure .…

Toward Personalized Affect Aware Socially Assistive Robot Tutors in Long Term Interventions for Children with Autism

Affect-aware socially assistive robotics (SAR) has shown great potential for augmenting interventions for children with autism spectrum disorders . Current SAR cannot yet perceive the unique and diverse set of atypical cognitive-affective behaviors from children with ASD in an automatic and personalized fashion in long-term (multi-session) real-world interactions .…

A Survey on Data Plane Programming with P4 Fundamentals Advances and Applied Research

Programmingprotocol-independent packet processors (P4) has emerged as the currently most widespread abstraction, programming language, and concept for data planeprogramming . With SDN, users may providetheir own control plane, that can control network devices through their dataplane APIs . Programmable data planes allow users to define their own data planealgorithms for network devices including appropriate data plane APIs which maybe leveraged by user-defined SDN control .…

How Good is a Video Summary A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization

For long videos, human referencesummaries necessary for supervised video summarization techniques are difficult to obtain . We show that thesesummaries are at par with human summaries . We also present a study of differentdesired characteristics of a good summary and demonstrate how it is normal tohave two good summaries with different characteristics .…

Impact of Explanation on Trust of a Novel Mobile Robot

An experiment to test whether the presence of an explanation of expected robot behavior affected asupervisor’s trust in an autonomous robot . Participants who received anexplanation of the robot’s behavior were more likely to focus on their own task at the risk of neglecting their robot supervision task during the first trial .…

Can Predominant Credible Information Suppress Misinformation in Crises Empirical Studies of Tweets Related to Prevention Measures during COVID 19

During COVID-19, misinformation on social media affects the adoption of appropriate prevention behaviors . It is urgent to suppress the misinformationto prevent negative public health consequences . Research findings provide empirical evidence for suppressing misinformation with credible information in complex online environments andsuggest practical strategies for future information management during crisesand emergencies .…

Human Centric Accessibility Graph For Environment Analysis

Spatial Human Accessibility graph for Planning andEnvironment Analysis (SHAPE) is introduced that brings together the technical challenges of discrete representations of digital models, with human-basedmetrics for evaluating the environment . SHAPE: does not need labeled geometryas input, works with multi-level buildings, captures surface variations (e.g.,slopes…

Accurate and Efficient Simulations of Hamiltonian Mechanical Systems with Discontinuous Potentials

This article considers Hamiltonian mechanical systems with potential functions admitting jump discontinuities . The focus is on accurate and efficient numerical approximations of their solutions . Various numerical evidence, on theorder of convergence, long time performance, momentum map conservation, and consistency with the computationally-expensive penalty method, are supplied .…

Separation of Control and Data Transmissions in 5G Networks may not be Beneficial

The logical separation of control signaling from data transmission in amobile cellular network has been shown to have significant energy saving potential compared with the legacy systems . However, the energy savings of separationarchitecture remain under 16-17% when compared with legacy systems and thisgain falls to a mere 7% when both architectures are realized under a CloudRAN(CRAN) setting .…

EDGF Empirical dataset generation framework for wireless network networks

In wireless sensor networks, simulation practices, system models, and protocols have been published worldwide based on the assumption of randomness . In this proposal, we show how the results are affected by the generalized assumption made on randomness. Insensor node deployment, ambiguity arises due to node error-value ($\epsilon$), and it’s upper bound in the relative position is estimated to understand thedelicacy of diminutives changes.…

Behavioral Mereology A Modal Logic for Passing Constraints

Mereology is the study of parts and the relationships that hold between them . We introduce a behavioral approach to mereology, in which systems and their parts are known only by the types of behavior they can exhibit . We give an inter-modal logic that generalizes the usual alethic modalities in the setting of symmetricaccessibility .…

Autonomous Off road Navigation over Extreme Terrains with Perceptually challenging Conditions

We propose a framework for resilient autonomous navigation in perceptuallychallenging unknown environments . Environments are GPS-denied andperceptually-degraded with variable lighting from dark to lit and obscurants . The proposed approach was deployed on multiple physical systems including skid-steer and tracked robots, a high-speed RC car and legged robots, as a part of Team CoSTAR’s effort to the DARPA Subterranean Challenge, where the team won 2nd and 1st place in the Tunnel and Urban Circuits, respectively .…

Temporal Latent Auto Encoder A Method for Probabilistic Multivariate Time Series Forecasting

Probabilistic forecasting of high dimensional multivariate time series is anotoriously challenging task . Most previous work either makes simple distributionassumptions or abandons modeling cross-series correlations . We introduce a noveltemporal latent auto-encoder method which enables nonlinear factorization ofmultivariary time series, learned end-to-end with a temporal deep learninglatent space forecast model .…

Design analysis and control of the series parallel hybrid RH5 humanoid robot

Humanoid robots built with a purely tree typearchitecture tend to be bulky and usually suffer from velocity and force/torquelimitations . This paper presents a novel series-parallel hybrid humanoid calledRH5 which is 2 m tall and weighs only 62.5 kg . The analysis and control of thishumanoid is performed with whole-body trajectory optimization technique based on differential dynamic programming (DDP) We present an improvedcontact stability soft-constrained DDP algorithm which is able to generatephysically consistent walking trajectories for the humanoid that can be tracked via a simple PD position control in a physics simulator .…

Liberalism rationality and Pareto optimality

Classical liberalism argues that people should be constrained by no-harm principle when they act . We show that rational players constrained by the NHP will produce Pareto efficient outcomes in n-person non-cooperative games . We alsoshow that both rationality and NHP are required for this result .…

Compositional Game Theory Compositionally

We present a new compositional approach to compositional game theory (CGT) based upon Arrows . Arrows, a concept originally from functional programming, and operators to build new Arrows from old . We also model strategiesas graded Arrows and define an operator which builds a new Arrow by taking thecolimit of a graded Arrow .…

Spectral Leakage and Rethinking the Kernel Size in CNNs

Convolutional layers in CNNs implement linear filters which decompose the input into different frequency bands . We show that the small size of CNN kernels make them susceptible to spectral leakage . To address this issue, we propose theuse of larger kernel sizes along with the Hamming window function to alleviateleakage in CNN architectures .…

A Two stage Framework for Compound Figure Separation

Scientific literature contains large volumes of complex, unstructured figuresthat are compound in nature . Separation of these compound figures is critical for informationretrieval from these figures . In this paper, we propose a new strategy forcompound figure separation, which decomposes the compound figures intoconstituent subfigures .…

Weakly supervised Video Anomaly Detection with Contrastive Learning of Long and Short range Temporal Features

Multiple-scale TemporalNetwork trained with top-K Contrastive Multiple Instance Learning (MTN-KMIL) The code is available athttps://://://github.com/tianyu0207/MIL . The method outperforms several state-of-the-art methods by a large margin on three benchmark data sets (ShanghaiTech, UCF-Crimeand XD-Violence) The method is a novelsynthesis of a pyramid of dilated convolutions and a self-attention mechanism, with the former capturing the multi-scale short-range temporal dependencies between snippets and the latter capturing long-range time-relationship of snippets .…

Weakly Supervised Learning for Facial Behavior Analysis A Review

In recent years, there has been a shift in facial behavior analysis from the laboratory-controlled conditions to the challenging in-the-wild conditions due to superior performance of deep learning based approaches for many realworld applications . Labeling process of huge training data demands lot of human support with strong domainexpertise for facial expressions or action units, which is difficult to obtainin real-time environments .…

A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms

In stable coronary artery disease, reduction in mortality and/ormyocardial infarction with revascularization over medical therapy has not beenreliably achieved . We aim to develop an automatic algorithm by deep learning to extractcoronary arteries from ICAs . After segmentation, an arterial stenosis detection algorithm was developed to extract vascular centerlines and calculate arterial diameter to evaluate stenotic level .…

Weakly Supervised Thoracic Disease Localization via Disease Masks

Using disease masks, we propose a spatial attention method using disease masks thatdescribe the areas where diseases mainly occur . We then apply the spatialattention to find the precise disease area by highlighting the highestprobability of disease occurrence . We show that the proposed method results in superior localization performances compared to state-of-the-art methods .…

Spatio temporal Data Augmentation for Visual Surveillance

Visual surveillance aims to stably detect a foreground object using acontinuous image acquired from a fixed camera . Recent deep learning methods based on supervised learning show superior performance compared to classicalbackground subtraction algorithms . However, there is still a room for improvement in static foreground, dynamic background, hard shadow, illumination changes, camouflage, etc.…

Creating a Virtuous Cycle in Performance Testing at MongoDB

Performance testing is part of the development process at MongoDB, and integrated into our continuous integration system . We believe that we have created and exploited a virtuous cycle:performance test improvements drive impact, which drives more use . Overall, MongoDB is gettingfaster and we avoid shipping major performance regressions to our customers because of this infrastructure .…

Unsupervised Anomaly Detection and Localisation with Multi scale Interpolated Gaussian Descriptors

Current unsupervised anomaly detection and localisation systems are commonlyformulated as one-class classifiers that depend on an effective estimation of the distribution of normal images and robust criteria to identify anomalies . However, the current systems tendsto be unstable for classes that are under-represented in the training set, and the anomaly identification criteria commonly explored in thefield does not work well for multi-scale structural and non-structuralanomalies .…

Novel Dynamic Load Balancing Algorithm for Cloud Based Big Data Analytics

The proposedload balancer reduces the execution response time in big data applicationsperformed on clouds . Scheduling, in general, is an NP-hard problem . We recommend two mathematicaloptimization models to perform dynamic resource allocation to virtual machines and task scheduling . We evaluate the performance of proposed algorithms in terms of response time, turnaround time, throughput metrics, andrequest distribution with some of the existing algorithms that show significantimprovements .…