Putting Humans in the Natural Language Processing Loop A Survey

There is a growing research body of Human-in-the-loop (HITL)NLP frameworks that continuously integrate human feedback to improve the model itself . HITL NLP research is nascent but multifarious — solving various NLPproblems, collecting diverse feedback from different people, and applyingdifferent methods to learn from collected feedback .…

Towards Automated Benchmark Support for Multi Blockchain Interoperability Facilitating Platforms

Since the introduction of the first Bitcoin blockchain in 2008, differentdecentralized blockchain systems have emerged with public and private accessibility . It has been widelyacknowledged that no single blockchain network will fit all use cases . Inrealization of this multi-blockchain paradigm, initiatives in buildingInteroperability-Facilitating Platforms (IFPs) that aim at bridging differentblockchains have come to the fore .…

Intelligent Reflecting Surface Enhanced D2D Cooperative Computing

This paper investigates a device-to-device (D2D) cooperative computingsystem . An user can offload part of its computation task to nearby idle users with the aid of an intelligent reflecting surface (IRS) We propose tominimize the total computing delay via jointly optimizing the computation taskassignment, transmit power, bandwidth allocation, and phase beamforming of theIRS .…

Quartermaster A Tool for Modeling and Simulating System Degradation

There are patterns and techniques which software engineers use to ensure their systems gracefully degrade . But tuning and configuration is hard to get right and it is expensive to explore possible changes to components andtechniques in complex systems . To fill these gaps, we propose Quartermaster to model and simulate systems and fault-tolerant techniques .…

Make Your Autonomous Vehicle Deliver And Earn Money for You

The drone-based last-mile delivery is an emerging technology designed toautomate the delivery process by utilizing drones loaded on a truck totransport parcels to customers . A novel greedy algorithm is proposed to solve the problem that incorporates the real-world operational cost of AVs, traveling distances, and varying load capacities of .…

Matching Algorithms Fundamentals Applications and Challenges

In economics, matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state . In computer science, all branches of matching problems have emerged, such as the question-answer matching in informationretrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph .…

Block Randomized Gradient Descent Methods with Importance Sampling for CP Tensor Decomposition

This work considers the problem of computing the CANDECOMP/PARAFAC (CP)decomposition of large tensors . One popular way is to translate the problem into a sequence of overdetermined least squares subproblems with Khatri-Raoproduct (KRP) structure . In this work, for tensor with different levels ofimportance in each fiber, we present a mini-batch stochastic gradient descent algorithm with .importance…

Selective Encryption of the Versatile Video Coding Standard

Versatile video coding (VVC) is the next generation video coding standard developed by the joint video experts team (JVET) and released in July 2020 . VVCintroduces several new coding tools providing a significant coding gain over the HEVC standard . A new algorithm is proposed to encrypt informat-compliant and constant bitrate the transform coefficients (TCs) together with other syntax elements at the level of the entropy encoder .…

The whole brain architecture approach Accelerating the development of artificial general intelligence by referring to the brain

The vastness of the design space created by the combination of a large number of computational mechanisms, including machine learning, is an obstacle to creating an artificial general intelligence (AGI) The whole-brain architecture approachdivides the brain-inspired AGI development process into the task of designing the brain reference architecture and developing each component using the BRA .…

Smart Speakers the Next Frontier in Computational Health

The rapid dissemination and adoption of smart speakers has enabled substantial opportunities to improve human health . Smart speakers carry several unique advantages that have the potential to catalyze newfields of health research, particularly in out-of-hospital environments . Therecent rise and ubiquity of these smart computing systems hold significantpotential for enhancing chronic disease management, enabling passiveidentification of unwitnessed medical emergencies, detecting subtle changes in behavior and cognition, limiting isolation, and potentially allowing widespread, passive, remote monitoring of respiratory diseases that impact the public health .…

Consensus Maximisation Using Influences of Monotone Boolean Functions

Consensus maximisation (MaxCon) aims to find the largest subset of data that fits the model . This is particularly important where there are large number of outliers (grossor pseudo) in the observed data . Results forboth synthetic and real visual data experiments show that the MBF based algorithm is capable of generating a near optimal solution relatively quickly .…

New Separations Results for External Information

The external information complexity of afunction $f(x,y)$ is the minimum amount of information a two-party protocolcomputing $f$ must reveal to an outside observer about the input . We obtain new separation results for the two party external informationcomplexity of boolean functions .…

Clifford wavelets for fetal ECG extraction

Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus . The challenge lies in the extraction ofthe fetal ECG from the mother’s ECG during pregnancy . This approach has theadvantage of being a reliable and non-invasive technique .…

End to end optimized image compression for multiple machine tasks

An increasing share of captured images and videos are transmitted for storage and remote analysis by computer vision algorithms, rather than to be viewed by humans . We introduce ‘Connectors’ that are inserted betweenthe decoder and the task algorithms to enable a direct transformation of the compressed content, which was previously optimized for a specific task, tomultiple other machine tasks .…

Simplicial Complex Representation Learning

Simplicial complexes form an important class of topological spaces that are frequently used to in many applications areas such as computer-aided design, computer graphics, and simulation . The representation learning on graphs, whichare just 1-d simplicial complexes, has witnessed a great attention and success in the past few years .…

A Framework for Measuring Compositional Inductive Bias

We devise corruptedcompositional grammars that probe for limitations in the compositionalinductive bias of frequently used models . We propose a hierarchicalmodel which might show an inductive bias towards relocatable atomic groups oftokens, thus potentially encouraging the emergence of words . We experiment withprobing for the .…

HexDom Polycube Based Hexahedral Dominant Mesh Generation

Given the boundary representation of a solid model, HexDom creates a hex-dominant mesh by using a semi-automated polycube-based mesh generation method . The resulting hexahedral dominant mesh includeshexahedra, tetrahedra and triangular prisms . We also apply our software to a number of other complex models totest their robustness .…

Automatic Exploration Process Adjustment for Safe Reinforcement Learning with Joint Chance Constraint Satisfaction

In reinforcement learning (RL) algorithms, exploratory control inputs are used during learning to acquire knowledge for decision making and control, while the true dynamics of a controlled object is unknown . In this paper, we propose an automatic exploration process adjustment method for safe RL incontinuous state and action spaces utilizing a linear nominal model of thecontrolled object .…

Data Driven Short Term Voltage Stability Assessment Based on Spatial Temporal Graph Convolutional Network

Post-fault dynamics of short-term voltage stability (SVS) presentspatial-temporal characteristics, but existing data-driven methods for online SVS assessment fail to incorporate such characteristics into their models effectively . The proposed STGCN utilizes graph convolution to integrate network topologyinformation into the learning model to exploit spatial information .…

Control Barrier Functions in Sampled Data Systems

This paper presents conditions for ensuring forward invariance of safe setsunder sampled-data system dynamics with piecewise-constant controllers and fixed time-steps . We show that the proposed conditions are lessconservative than those in earlier studies . The advantages of the new approaches are demonstrated via simulations on an obstacle-avoidance problemfor a unicycle agent and on a spacecraft attitude reorientation problem, inwhich the new approach achieve the mission objectives for cases where the existing methods fail .…

Polyhedral Lyapunov Functions with Fixed Complexity

Polyhedral Lyapunov functions can approximate any norm arbitrarily well . They are used to study stability of linear time varying systems without being conservative . However the computational cost associated with using them grows unbounded as the size of their representation increases .…

Neuromorphic Computing with Deeply Scaled Ferroelectric FinFET in Presence of Process Variation Device Aging and Flicker Noise

This paper reports a comprehensive study on the applicability of ultra-scaledferroelectric FinFETs with 6 nm thick hafnium zirconium oxide layer forneuromorphic computing . Process variation, flicker noise, and device agingcharacterization have been performed . A pre-trained neuralnetwork with 97.5% inference accuracy on the MNIST dataset has been adopted asthe baseline .…

A Multi Stage Stochastic Programming Approach to Epidemic Resource Allocation with Equity Considerations

Existing compartmental models in epidemiology are limited in terms ofoptimizing the resource allocation to control an epidemic outbreak underdisease growth uncertainty . The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution oftreatment centers and resources while minimizing the total expected number of infections and funerals .…

Solving Linear Equations with Separable Problem Data over Directed Networks

This paper deals with linear algebraic equations where the global coefficientmatrix and constant vector are given respectively . We propose two sets of exponentiallystable continuous-time distributed algorithms that do not require the individual agent matrices to be invertible . The first algorithm works for time-varying weight-balanced directednetworks, and the second algorithm is based on estimatingnon-distributed terms in the centralized algorithm using dynamic averageconsensus .…