Emotion Aware Emotion Agnostic or Automatic Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotations

Appraisal theories explain how the cognitive evaluation of an event leads to a particular emotion . Smith andEllsworth (1985) showed that the appraisal dimensions attention, certainty,anticipated effort, pleasantness, responsibility/control and situationalcontrol discriminate between (at least) 15 emotion classes . We analyze two manual annotation settings: (1)showing the text to annotate while masking the experienced emotion label; (2) revealing the emotion associated with the text .…

Optimized Memoryless Fair Share HPC Resources Scheduling using Transparent Checkpoint Restart Preemption

Common resource management methods in supercomputing systems usually include hard divisions, capping, and quota allotment . Those methods involve bad supply-and-demand management rather than a free market playground that will eventually increase system utilization and productivity . In this work, we propose the newly Optimized Memoryless Fair-Share HPCResources Scheduling using Transparent Checkpoint-Restart Preemption, in which the social welfare increases using a free-of-cost interchangeable proprietarypossession scheme .…

Approximate Privacy Preserving Neighbourhood Estimations

Anonymous social networks present a number of new and challenging problems for existing Social Network Analysis techniques . Traditionally, existing methods for analysing graph structure, such as community detection, required global knowledge of the graph structure . Exchanging this data structure infuture decentralised learning deployments gives away no information about theneighbours of the node and therefore does preserve the privacy .…

Benchmarking and Survey of Explanation Methods for Black Box Models

The widespread adoption of black-box models in Artificial Intelligence has increased the need for explanation methods to reveal how these obscure models reach specific decisions . We provide acategorization of explanation methods based on the type of explanation returned . We present the most recent and widely used explainers, and we show avisual comparison among explanations and a quantitative benchmarking .…

Distributionally Robust Federated Averaging

In this paper, we study communication efficient distributed algorithms fordistributionally robust federated learning via periodic averaging with adaptivesampling . We propose a Distributionally Robust Federated Federated Averaging(DRFA) algorithm that employs a novel snapshotting scheme to approximate theaccumulation of history gradients of the mixing parameter .…

Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations

Deep learning semantic segmentation algorithms can localise abnormalities oropacities from chest radiographs . However the task of collecting andannotating training data is expensive and requires expertise which remains abottleneck for algorithm performance . We investigate the effect of imageaugmentations on reducing the requirement of labelled data in the semanticsegmentation of chest X-rays for pneumonia detection .…

Phragmén s Voting Methods and Justified Representation

Swedish mathematician Lars Edvard Phragm\'{e}n proposed a load-balancing approach for selecting committees based on approvalballots . We show that the sequential variant satisfiesproportional justified representation, which is a rare property for committeemonotonic methods . We also analyze the computational complexity of the methods and provide mixed-integer programming based algorithms for computing them.…

Generalized Parametric Path Problems

Parametric path problems arise independently in diverse domains, ranging from transportation to finance, where they are studied under various assumptions . We show that when the parametric weights are linear, algorithms remain tractable even under relaxed assumptions . If the weights are allowed to be non-linear, the problem becomes NP-hard.…

ZJUKLAB at SemEval 2021 Task 4 Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning

This paper presents our systems for the three Subtasks of SemEval Task4:Reading Comprehension of Abstract Meaning (ReCAM) We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model . Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and 92.8% .…

QNLP in Practice Running Compositional Models of Meaning on a Quantum Computer

Quantum Natural Language Processing (QNLP) deals with the design and implementation of NLP models intended to be run on quantum hardware . In thispaper, we present results on the first NLP experiments conducted on NoisyIntermediate-Scale Quantum (NISQ) computers for datasets of size < 100sentences . Exploiting the formal similarity of the compositional model of meaning by Coecke et al. with quantum theory, we create representationsfor sentences that have a natural mapping to quantum circuits . We use theserepresentations to implement and successfully train two models that solvesimple sentence classification tasks . …

Subcubic Certificates for CFL Reachability

Many problems in interprocedural program analysis can be modeled as the context-free language (CFL) reachability problem . Despite years of efforts, there are no known truly sub-cubical algorithms for this problem . We study the related certification task: given aninstance of CFL reachability, are there small and efficiently checkablecertificates for the existence and for the non-existence of a path?…

Deep Adversarial Learning on Google Home devices

Smart speakers and voice-based virtual assistants are vulnerable to various privacy threats exploiting machine learning to analyze the generated encrypted traffic . To cope with that, deep adversarial learning approaches can be used tobuild black-box countermeasures altering the network traffic (e.g.,…

Imitation Learning for Robust and Safe Real time Motion Planning A Contraction Theory Approach

Learning-based Autonomous Guidance with Robustness,Optimality, and Safety guarantees (LAG-ROS) is a real-time robust motion planning algorithm for safety-critical nonlinear systems perturbed by boundeddisturbances . For the CLF, we exploit a neural-network-based method of NeuralContraction Metrics (NCMs), which provides a differential Lyapunov function to minimize an upper bound of the steady-state Euclidean distance betweenperturbed and unperturbed system trajectories .…

Coalgebra Encoding for Efficient Minimization

Recently, we have developed an efficient generic partition refinementalgorithm, which computes behavioural equivalence on a state-based system givenas an encoded coalgebra, and implemented it in the tool CoPaR . Here we extendthis to a fully fledged minimization algorithm and tool by integrating two new aspects: (1) the computation of the transition structure on the minimized stateset, and (2) the computations of the reachable part of the given system .…

Scene Retrieval for Contextual Visual Mapping

Visual navigation localizes a query place image against a reference database of place images . Four different scene classes, including pedestrian crossings and stations, are identified in each of the Nordland andSt. Lucia datasets . Scene retrieval extends imageretrieval to classification of scenes defined at test time by associating asingle query image to reference images of scene classes .…

Combinatorial Bandits under Strategic Manipulations

We study combinatorial multi-armed bandits (CMAB) understrategic manipulations of rewards . We propose a strategic variant of thecombinatorial UCB algorithm, which has a regret of at most $O(m\log T + mB_{max)$ under strategic manipulations . We further providelower bounds on the strategic budgets for attackers to incur certain regret of the bandit algorithm .…

Swivel Hardening WebAssembly against Spectre

Swivel is a new compiler framework for hardening WebAssembly (Wasm) against Spectre attacks . Wasm has become a popular lightweight, in-process sandbox and is used in production to isolate different clients on edge clouds and function-as-a-service platforms . Spectre attacks can bypass Wasm’s isolation guarantees .…

Sentiment Analysis of Persian English Code mixed Texts

The rapid production of data on the internet and the need to understand howusers are feeling from a business and research perspective has prompted the need for an automatic monolingual sentiment detection systems . We then introduce a model which uses BERT pretrained embeddings as well as translation models to automaticallylearn the polarity scores of these Tweets .…

A Survey of RDF Stores SPARQL Engines for Querying Knowledge Graphs

Recent years have seen the growing adoption of non-relational data models forrepresenting diverse, incomplete data . The RDF graph-based datamodel has seen ever-broadening adoption, particularly on the Web . This adoption prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs .…

Random hypergraphs and property B

In 1964 Erd\H{o}s proved that $(1+\oh{1) is sufficient to build a $k$-graph which is not two colorable . To thisday, it is not known whether there exist such $k#graphs with smaller number ofedges . In the second part of the paper we consider the problem of algorithmmic coloring of random $k-graphs .…

Cloud Broker A Systematic Mapping Study

The current systematic review includes a comprehensive 3-tier strategy(manual search, backward snowballing, and database search). The accuracy of thesearch methodology has been analyzed in terms of extracting related studies and collecting comprehensive and complete information in a supplementary file . The current review includes .…

FASA Feature Augmentation and Sampling Adaptation for Long Tailed Instance Segmentation

Feature Augmentation and Sampling Adaptation (FASA) is a fast, generic method that can be easily plugged into standard or long-tailed segmentationframeworks, with consistent performance gains and little added cost . FASAdoes not require any elaborate loss design, and removes the need forinter-class transfer learning that often involves large cost andmanually-defined head/tail class groups .…

Fast Minimum norm Adversarial Attacks through Adaptive Norm Constraints

The inherent complexity of the optimization requires current gradient-based attacks to be carefullytuned, initialized, and possibly executed for many computationally-demandingiterations . In this work, we propose a fast minimum-norm (FMN) attack that works with different $ell_p$-norm perturbation models ($p=0, 1, 2, \infty$) FMN significantly outperforms existing attacks in termsof convergence speed and computation time .…

Blocks World Revisited The Effect of Self Occlusion on Classification by Convolutional Neural Networks

Despite recent successes in computer vision, there remain new avenues to explore . With TEOS (The Effect ofSelf-Occlusion), we propose a 3D blocks world dataset that focuses on thegeometric shape of 3D objects and their omnipresent challenge of self-occlusion . We present a dataset of twodifficulty levels (L1 and L2 ), containing 36 and 12 objects, respectively .…