Text analysis and deep learning A network approach

Deep language models such as BERT have achievedunprecedented success in many applications of computational linguistics . However, much less is known about how these models can be used to analyze existing text . We propose a novel method that combines transformer models withnetwork analysis to form a self-referential representation of language use within a corpus of interest .…

Appearance

ACM “The Handbook on Socially Interactive Agents” reviews research on and technologies involving socially interactive agents . It outlines the design space for the appearance of agents, including what appearance comprises, modalities in which agents are presented, and how agents are constructed .…

Social Groups Based Content Caching in Wireless Networks

A promising approach to reduce the backhaul load is toproactively cache content at the network edge, taking into account the overlaid social network . Known caching schemes require complete knowledge of the socialgraph and mainly focus on one-to-one interactions forgoing the prevalent mode of content sharing among circles of ‘friends’ We propose Bingo, a proactive content caching scheme that leverages the presence of interest groups in onlinesocial networks .…

Contextual Sentence Classification Detecting Sustainability Initiatives in Company Reports

We introduce the novel task of detecting sustainability initiatives incompany reports . Given a full report, the aim is to automatically identifymentions of practical activities that a company has performed in order totackle specific societal issues . As a single initiative can often be described over multiples sentences, new methods for identifying continuous sentence spans needs to be developed .…

A New Data Integration Framework for Covid 19 Social Media Information

Current research on Covid-19 is typically based on a single source of information, specifically on structured historical data . Other studies are exclusively focused on unstructured onlineretrieved insights, such as data available from social media . The proposed approach is based on vine copulas, which allow us to improve predictions byexploiting the dependencies between different sources of information .…

A Mining Software Repository Extended Cookbook Lessons learned from a literature review

The main purpose of Mining Software Repositories (MSR) is to discover the latest enhancements and provide an insight into how to make improvements in asoftware project . This paper updates the MSR findings of the original MSR Cookbook, by first conducting a systematic mapping study to elicitand analyze the state-of-the-art, and then proposing an extended version of the Cookbook .…

Towards Creating a Standardized Collection of Simple and Targeted Experiments to Analyze Core Aspects of the Recommender Systems Problem

In Recommender Systems, more often than not, we design evaluations to measure an algorithm’sability to optimize goals in complex scenarios . We believe the RS community wouldgreatly benefit from creating a collection of standardized, simple, andtargeted experiments, which, much like a suite of “unit tests”, would assess algorithm’s ability to tackle core challenges that makeup complex RS tasks .…

Parallel Quantum Pebbling Analyzing the Post Quantum Security of iMHFs

The classical (parallel) black pebbling game is a useful abstraction which allows us to analyze the resources (space, space-time, cumulative space)necessary to evaluate a function $f$ with a static data-dependency graph $G$ . The pebbled complexity of thegraph $G  characterized the amortized cost of evaluating $f $f_{G,H$ multipletimes or the total cost to run a brute-force preimage attack over a fixed domain $mathcal{X}$ While a classical attacker will need to evaluate thefunction $f .…

A Decision Model for Selecting Patterns and Strategies to Decompose Applications into Microservices

Microservices Architecture (MSA) style is a promising design approach todevelop software applications consisting of multiple small and independently deployable services . We usedpeer-reviewed and grey literature to collect the patterns, strategies, and quality attributes for creating this decision model . The study proposes a decision model for selectingpatterns and strategies to decompose applications into microservices.…

GEO satellites on orbit repairing mission planning with mission deadline constraint using a large neighborhood search genetic algorithm

This paper proposed a novel large neighborhood search-adaptive genetical algorithm (LNS-AGA) for many-to-many on-orbit repairing mission planning ofgeosynchronous orbit (GEO) satellites with mission deadline constraint . The missionobjective is to find the optimal servicing sequence and orbit rendezvous time of every servicing spacecraft to minimize total cost of all servicingspacecrafts with all target satellites repaired .…

RelaySum for Decentralized Deep Learning on Heterogeneous Data

In decentralized machine learning, workers compute model updates on their local data . Because the workers only communicate with few neighbors without central coordination, these updates propagate progressively over the network . This paradigm enables distributed training on networks without all-to-allconnectivity, helping to protect data privacy as well as to reduce thecommunication cost of distributed training in data centers .…

Knowledge Enhanced Hierarchical Graph Transformer Network for Multi Behavior Recommendation

Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT) tackles multi-typed interactive patterns between users and items in recommendation systems . KHGT is built upon a graph-structured neuralarchitecture to capture type-specific behavior characteristics . Extensive experiments conducted on threereal-world datasets show that KHGT consistently outperforms many state-of-the-art recommendation methods .…

Knowledge aware Coupled Graph Neural Network for Social Recommendation

Social recommendation task aims to predict users’ preferences over items with the incorporation of social connections among users . KCGN enables the high-order user- and item-wise relation encoding byexploiting the mutual information for global graph structure awareness . We further augment KCGN with the capability of capturing dynamicmulti-typed user-item interactive patterns .…

Chromatic Aberration Recovery on Arbitrary Images

Digital imaging sensor technology has continued to outpace development inoptical technology in modern imaging systems . The resulting quality lossattributable to lateral chromatic aberration is becoming increasinglysignificant as sensor resolution increases . The goals ofhigher-performance and lighter lens systems drive a recent need to find new ways to overcome resulting image quality limitations .…

Polygon Area Decomposition Using a Compactness Metric

A new algorithm is proposed to solve the problem of partitioning a polygon into a set of connected disjoint sub-polygons, each of which covers an area of a specificsize . The work is motivated by terrain covering applications in robotics, where the goal is to find an efficient plans for a team of heterogeneous robotsto cover a given area .…

Nash Convergence of Mean Based Learning Algorithms in First Price Auctions

We consider repeated first price auctions where each bidder, having adeterministic type, learns to bid using a mean-based learning algorithm . We characterize the Nash convergence property of the bidding dynamics in two senses: (1) time-average: the fraction of rounds where bidders play aNash equilibrium approaches to 1 in the limit; (2) last-iterate: the mixedstrategy profile of bidder approaches to a Nash equilibrium in limit .…

Nash Convergence of Mean Based Learning Algorithms in First Price Auctions

We consider repeated first price auctions where each bidder, having adeterministic type, learns to bid using a mean-based learning algorithm . We characterize the Nash convergence property of the bidding dynamics in two senses: (1) time-average: the fraction of rounds where bidders play aNash equilibrium approaches to 1 in the limit; (2) last-iterate: the mixedstrategy profile of bidder approaches to a Nash equilibrium in limit .…

Pyxis An Open Source Performance Dataset of Sparse Accelerators

Specialized accelerators provide gains of performance and efficiency inspecific domains of applications . Sparse data structures or/and representationsexist in a wide range of applications. PYXIS collects accelerator designs andreal execution performance statistics. Currently, there are 73.8 K instances in PYxIS. PyXIS is open-source, and we are constantly growing with new accelerator designs and performance statistics .…

Bounds for the Twin width of Graphs

Bonnet, Kim, Thomass\'{e, and Watrigant (2020) introduced the twin-width of a graph . We show that the twin width of an $n$-vertex graph is less than $(n+\sqrt{n\ln n}+\qrt {n}+2\lt n}$ asymptotically almost surely for any positive $varepsilon$ We also calculate the twinwidth of randomgraphs $G(n,p)$ with $p\leq c/n$ for a constant $c <1$ and $1$ to $2$ . …

Multifocal Stereoscopic Projection Mapping

Stereoscopic projection mapping (PM) allows a user to see a 3D computer-generated (3D) object floating over physical surfaces ofarbitrary shapes around us . The current technology only satisfies binocular cues and is not capable of providing correct focus cues, which causes a vergence–accommodation conflict(VAC) We propose a multifocal approach to mitigate VAC instereoscopic PM .…

Nash Convergence of Mean Based Learning Algorithms in First Price Auctions

We consider repeated first price auctions where each bidder, having adeterministic type, learns to bid using a mean-based learning algorithm . We characterize the Nash convergence property of the bidding dynamics in two senses: (1) time-average: the fraction of rounds where bidders play aNash equilibrium approaches to 1 in the limit; (2) last-iterate: the mixedstrategy profile of bidder approaches to a Nash equilibrium in limit .…

Computing an Optimal Pitching Strategy in a Baseball At Bat

The field of quantitative analytics has transformed the world of sports over the last decade . We often view teamsports, such as soccer, hockey, and baseball, as pairwise win-lose encounters . We propose a novel model of this encounter as a zero-sum stochastic game, in which the goal of the batter is toget on base, an outcome the pitcher aims to prevent .…

A Framework for Aspectual Requirements Validation An Experimental Study

Aspect-Oriented Requirements Engineering (AORE) extends the existing software engineering approaches to cope with the issue of tangling andscattering resulted from crosscutting concerns . The proposed framework comprises a high-level and low-levelvalidation to implement on software requirements specification (SRS) Validation of requirements artefacts is an essential task in software development .…

Constraint Aware Deep Reinforcement Learning for End to End Resource Orchestration in Mobile Networks

Network slicing is a promising technology that allows mobile network operators to efficiently serve various emerging use cases in 5G . It is challenging to optimize the utilization of network infrastructures while ensuring the performance of network slices according to service levelagreements (SLAs) To solve this problem, we propose SafeSlicing that introduces a new constraint-aware deep reinforcement learning (CaDRL) algorithm to learn the optimal resource orchestration policy within two steps, i.e.,offline…

Graph Meta Network for Multi Behavior Recommendation

Modern recommender systems often embed users and items into low-dimensionallatent representations, based on their observed interactions . Exploring multi-typed behavior patterns is of great importance torecommendation systems, yet is very challenging because of two aspects: i. The complex dependencies across different types of user-item interactions; ii)Diversity of such multi-behavior patterns may vary by users due to their personalized preference .…