pwc-archive/papers-with-abstracts huggingface dataset:
Granular conditional entropy-based attribute reduction for partially labeled data with proxy labels
Authors: Can Gao, Jie Zhoua, Duoqian Miao, Xiaodong Yue, Jun Wan
Date: 2021-01-23 00:00:00
Tasks: Attribute

Attribute reduction is one of the most important research topics in the theory of rough sets, and many rough sets-based attribute reduction methods have thus been presented. However, most of them are specifically designed for dealing with either labeled data or unlabeled data, while many real-world applications come in the form of partial supervision. In this paper, we propose a rough sets-based semi-supervised attribute reduction method for partially labeled data. Particularly, with the aid of prior class distribution information about data, we first develop a simple yet effective strategy to produce the proxy labels for unlabeled data. Then the concept of information granularity is integrated into the information-theoretic measure, based on which, a novel granular conditional entropy measure is proposed, and its monotonicity is proved in theory. Furthermore, a fast heuristic algorithm is provided to generate the optimal reduct of partially labeled data, which could accelerate the process of attribute reduction by removing irrelevant examples and excluding redundant attributes simultaneously. Extensive experiments conducted on UCI data sets demonstrate that the proposed semi-supervised attribute reduction method is promising and even compares favourably with the supervised methods on labeled data and unlabeled data with true labels in terms of classification performance.
Deep Learning for General Game Playing with Ludii and Polygames
Authors: Dennis J. N. J. Soemers, Vegard Mella, Cameron Browne, Olivier Teytaud
Date: 2021-01-23 00:00:00
Tasks: Board Games, Deep Learning

Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games. The training and search algorithms are not game-specific, but every individual game that these approaches are applied to still requires domain knowledge for the implementation of the game's rules, and constructing the neural network's architecture -- in particular the shapes of its input and output tensors. Ludii is a general game system that already contains over 500 different games, which can rapidly grow thanks to its powerful and user-friendly game description language. Polygames is a framework with training and search algorithms, which has already produced superhuman players for several board games. This paper describes the implementation of a bridge between Ludii and Polygames, which enables Polygames to train and evaluate models for games that are implemented and run through Ludii. We do not require any game-specific domain knowledge anymore, and instead leverage our domain knowledge of the Ludii system and its abstract state and move representations to write functions that can automatically determine the appropriate shapes for input and output tensors for any game implemented in Ludii. We describe experimental results for short training runs in a wide variety of different board games, and discuss several open problems and avenues for future research.
Improving Continuous-time Conflict Based Search
Authors: Anton Andreychuk, Konstantin Yakovlev, Eli Boyarski, Roni Stern
Date: 2021-01-24 00:00:00
Tasks: Multi-Agent Path Finding

Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time. However, the scalability of CCBS is limited because it does not include any known improvements of CBS. In this paper, we begin to close this gap and explore how to adapt successful CBS improvements, namely, prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics, to the continuous time setting of CCBS. These adaptions are not trivial, and require careful handling of different types of constraints, applying a generalized version of the Safe interval path planning (SIPP) algorithm, and extending the notion of cardinal conflicts. We evaluate the effect of the suggested enhancements by running experiments both on general graphs and $2^k$-neighborhood grids. CCBS with these improvements significantly outperforms vanilla CCBS, solving problems with almost twice as many agents in some cases and pushing the limits of multiagent path finding in continuous-time domains.
Context-Specific Likelihood Weighting
Authors: Nitesh Kumar, Ondřej Kuželka
Date: 2021-01-24 00:00:00

Sampling is a popular method for approximate inference when exact inference is impractical. Generally, sampling algorithms do not exploit context-specific independence (CSI) properties of probability distributions. We introduce context-specific likelihood weighting (CS-LW), a new sampling methodology, which besides exploiting the classical conditional independence properties, also exploits CSI properties. Unlike the standard likelihood weighting, CS-LW is based on partial assignments of random variables and requires fewer samples for convergence due to the sampling variance reduction. Furthermore, the speed of generating samples increases. Our novel notion of contextual assignments theoretically justifies CS-LW. We empirically show that CS-LW is competitive with state-of-the-art algorithms for approximate inference in the presence of a significant amount of CSIs.
AI-Empowered VNF Migration as a Cost-Loss-Effective Solution for Network Resilience
Authors: Amina Lejla Ibrahimpasic, Bin Han, Hans D. Schotten
Date: 2021-01-22 00:00:00
Tasks: Edge-computing

With a wide deployment of Multi-Access Edge Computing (MEC) in the Fifth Generation (5G) mobile networks, virtual network functions (VNF) can be flexibly migrated between difference locations, and therewith significantly enhances the network resilience to counter the degradation in quality of service (QoS) due to network function outages. A balance has to be taken carefully, between the loss reduced by VNF migration and the operations cost generated thereby. To achieve this in practical scenarios with realistic user behavior, it calls for models of both cost and user mobility. This paper proposes a novel cost model and a AI-empowered approach for a rational migration of stateful VNFs, which minimizes the sum of operations cost and potential loss caused by outages, and is capable to deal with the complex realistic user mobility patterns.
Machine Learning Based Early Fire Detection System using a Low-Cost Drone
Authors: Ayşegül Yanık, Mehmet Serdar Güzel, Mertkan Yanık, Erkan Bostanci
Date: 2021-01-21 00:00:00
Tasks: BIG-bench Machine Learning, Fire Detection

This paper proposes a new machine learning based system for forest fire earlier detection in a low-cost and accurate manner. Accordingly, it is aimed to bring a new and definite perspective to visual detection in forest fires. A drone is constructed for this purpose. The microcontroller in the system has been programmed by training with deep learning methods, and the unmanned aerial vehicle has been given the ability to recognize the smoke, the earliest sign of fire detection. The common problem in the prevalent algorithms used in fire detection is the high false alarm and overlook rates. Confirming the result obtained from the visualization with an additional supervision stage will increase the reliability of the system as well as guarantee the accuracy of the result. Due to the mobile vision ability of the unmanned aerial vehicle, the data can be controlled from any point of view clearly and continuously. System performance are validated by conducting experiments in both simulation and physical environments.
Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems
Authors: Joshua A. Kroll
Date: 2021-01-23 00:00:00

Accountability is widely understood as a goal for well governed computer systems, and is a sought-after value in many governance contexts. But how can it be achieved? Recent work on standards for governable artificial intelligence systems offers a related principle: traceability. Traceability requires establishing not only how a system worked but how it was created and for what purpose, in a way that explains why a system has particular dynamics or behaviors. It connects records of how the system was constructed and what the system did mechanically to the broader goals of governance, in a way that highlights human understanding of that mechanical operation and the decision processes underlying it. We examine the various ways in which the principle of traceability has been articulated in AI principles and other policy documents from around the world, distill from these a set of requirements on software systems driven by the principle, and systematize the technologies available to meet those requirements. From our map of requirements to supporting tools, techniques, and procedures, we identify gaps and needs separating what traceability requires from the toolbox available for practitioners. This map reframes existing discussions around accountability and transparency, using the principle of traceability to show how, when, and why transparency can be deployed to serve accountability goals and thereby improve the normative fidelity of systems and their development processes.
Symbiotic System of Systems Design for Safe and Resilient Autonomous Robotics in Offshore Wind Farms
Authors: Daniel Mitchell, Jamie Blanche, Osama Zaki, Joshua Roe, Leo Kong, Samuel Harper, Valentin Robu, Theodore Lim, David Flynn
Date: 2021-01-23 00:00:00
Tasks: Decision Making

To reduce Operation and Maintenance (O&M) costs on offshore wind farms, wherein 80% of the O&M cost relates to deploying personnel, the offshore wind sector looks to Robotics and Artificial Intelligence (RAI) for solutions. Barriers to Beyond Visual Line of Sight (BVLOS) robotics include operational safety compliance and resilience, inhibiting the commercialization of autonomous services offshore. To address safety and resilience challenges we propose a Symbiotic System Of Systems Approach (SSOSA), reflecting the lifecycle learning and co-evolution with knowledge sharing for mutual gain of robotic platforms and remote human operators. Our novel methodology enables the run-time verification of safety, reliability and resilience during autonomous missions. To achieve this, a Symbiotic Digital Architecture (SDA) was developed to synchronize digital models of the robot, environment, infrastructure, and integrate front-end analytics and bidirectional communication for autonomous adaptive mission planning and situation reporting to a remote operator. A reliability ontology for the deployed robot, based on our holistic hierarchical-relational model, supports computationally efficient platform data analysis. We demonstrate an asset inspection mission within a confined space through Cooperative, Collaborative and Corroborative (C3) governance (internal and external symbiosis) via decision-making processes and the associated structures. We create a hyper enabled human interaction capability to analyze the mission status, diagnostics of critical sub-systems within the robot to provide automatic updates to our AI-driven run-time reliability ontology. This enables faults to be translated into failure modes for decision-making during the mission.
Medical Information Retrieval and Interpretation: A Question-Answer based Interaction Model
Authors: Nilanjan Sinhababu, Rahul Saxena, Monalisa Sarma, Debasis Samanta
Date: 2021-01-24 00:00:00
Tasks: Information Retrieval, Recommendation Systems, Retrieval

The Internet has become a very powerful platform where diverse medical information are expressed daily. Recently, a huge growth is seen in searches like symptoms, diseases, medicines, and many other health related queries around the globe. The search engines typically populate the result by using the single query provided by the user and hence reaching to the final result may require a lot of manual filtering from the user's end. Current search engines and recommendation systems still lack real time interactions that may provide more precise result generation. This paper proposes an intelligent and interactive system tied up with the vast medical big data repository on the web and illustrates its potential in finding medical information.
Modern Techniques for Ancient Games
Authors: Cameron Browne
Date: 2020-12-30 00:00:00

Games potentially provide a wealth of knowledge about our shared cultural past and the development of human civilisation, but our understanding of early games is incomplete and often based on unreliable reconstructions. This paper describes the Digital Ludeme Project, a five-year research project currently underway that aims to address such issues using modern computational techniques.
The Slodderwetenschap (Sloppy Science) of Stochastic Parrots -- A Plea for Science to NOT take the Route Advocated by Gebru and Bender
Authors: Michael Lissack
Date: 2021-01-11 00:00:00
Tasks: Ethics, Position

This article is a position paper written in reaction to the now-infamous paper titled "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" by Timnit Gebru, Emily Bender, and others who were, as of the date of this writing, still unnamed. I find the ethics of the Parrot Paper lacking, and in that lack, I worry about the direction in which computer science, machine learning, and artificial intelligence are heading. At best, I would describe the argumentation and evidentiary practices embodied in the Parrot Paper as Slodderwetenschap (Dutch for Sloppy Science) -- a word which the academic world last widely used in conjunction with the Diederik Stapel affair in psychology [2]. What is missing in the Parrot Paper are three critical elements: 1) acknowledgment that it is a position paper/advocacy piece rather than research, 2) explicit articulation of the critical presuppositions, and 3) explicit consideration of cost/benefit trade-offs rather than a mere recitation of potential "harms" as if benefits did not matter. To leave out these three elements is not good practice for either science or research.
Obsolete Personal Information Update System for the Prevention of Falls among Elderly Patients
Authors: Salma Chaieb, Brahim Hnich, Ali Ben Mrad
Date: 2021-01-20 00:00:00

Falls are a common problem affecting the older adults and a major public health issue. Centers for Disease Control and Prevention, and World Health Organization report that one in three adults over the age of 65 and half of the adults over 80 fall each year. In recent years, an ever-increasing range of applications have been developed to help deliver more effective falls prevention interventions. All these applications rely on a huge elderly personal database collected from hospitals, mutual health, and other organizations in caring for elderly. The information describing an elderly is continually evolving and may become obsolete at a given moment and contradict what we already know on the same person. So, it needs to be continuously checked and updated in order to restore the database consistency and then provide better service. This paper provides an outline of an Obsolete personal Information Update System (OIUS) designed in the context of the elderly-fall prevention project. Our OIUS aims to control and update in real-time the information acquired about each older adult, provide on-demand consistent information and supply tailored interventions to caregivers and fall-risk patients. The approach outlined for this purpose is based on a polynomial-time algorithm build on top of a causal Bayesian network representing the elderly data. The result is given as a recommendation tree with some accuracy level. We conduct a thorough empirical study for such a model on an elderly personal information base. Experiments confirm the viability and effectiveness of our OIUS.
Analysis of Relation between Motor Activity and Imaginary EEG Records
Authors: Enver Kaan Alpturk, Yakup Kutlu
Date: 2021-01-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Relation

Electroencephalography (EEG) signals signals are often used to learn about brain structure and to learn what thinking. EEG signals can be easily affected by external factors. For this reason, they should be applied various pre-process during their analysis. In this study, it is used the EEG signals received from 109 subjects when opening and closing their right or left fists and performing hand and foot movements and imagining the same movements. The relationship between motor activities and imaginary of that motor activities were investigated. Algorithms with high performance rates have been used for feature extraction , selection and classification using the nearest neighbour algorithm.
The Third DIHARD Diarization Challenge
Authors: Neville Ryant, Prachi Singh, Venkat Krishnamohan, Rajat Varma, Kenneth Church, Christopher Cieri, Jun Du, Sriram Ganapathy, Mark Liberman
Date: 2020-12-02 00:00:00
Tasks: speaker-diarization, Speaker Diarization, valid

DIHARD III was the third in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variability in recording equipment, noise conditions, and conversational domain. Speaker diarization was evaluated under two speech activity conditions (diarization from a reference speech activity vs. diarization from scratch) and 11 diverse domains. The domains span a range of recording conditions and interaction types, including read audio-books, meeting speech, clinical interviews, web videos, and, for the first time, conversational telephone speech. A total of 30 organizations (forming 21teams) from industry and academia submitted 499 valid system outputs. The evaluation results indicate that speaker diarization has improved markedly since DIHARD I, particularly for two-party interactions, but that for many domains (e.g., web video) the problem remains far from solved.
The Agda Universal Algebra Library and Birkhoff's Theorem in Martin-Löf Dependent Type Theory
Authors: William DeMeo
Date: 2021-01-25 00:00:00

The Agda Universal Algebra Library (UALib) is a library of types and programs (theorems and proofs) we developed to formalize the foundations of universal algebra in Martin-Löf dependent type theory using the Agda programming language and proof assistant. This paper describes the UALib and demonstrates how it makes Agda more accessible to working mathematicians (like ourselves) as a tool for formally verifying "known" results in general algebra and related fields, as well as for discovering new theorems in these areas. The library already includes a substantial collection of definitions, theorems, and proofs from universal algebra and as such provides many examples that exhibit the power of inductive and dependent types for representing and reasoning about algebraic and relational structures.

The first major milestone of the UALib project is a complete proof of Birkhoff's HSP Theorem. To the best of our knowledge, this is the first time Birkhoff's Theorem has been formulated and proved in dependent type theory, and verified with a proof assistant.
In this paper we describe the UALib and the formal proof of Birkhoff's theorem, discussing some of the challenges we faced and how these hurdles were overcome. In so doing, we illustrate the effectiveness of dependent type theory, Agda, and the UALib for proving and verifying theorems in universal algebra.
A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method
Authors: SenZhao, YongZhang, ShangWang, BeitongZhou, ChengCheng
Date: 2019-06-04 00:00:00
Tasks: Prediction

Data-driven methods for remaining useful life (RUL) prediction normally learn features from a fixed window size of a priori of degradation, which may lead to less accurate prediction results on different datasets because of the variance of local features. This paper proposes a method for RUL prediction which depends on a trend feature representing the overall time sequence of degradation. Complete ensemble empirical mode decomposition, followed by a reconstruction procedure, is created to build the trend features. The probability distribution of sensors’ measurement learned by conditional neural processes is used to evaluate the trend features. With the best trend feature, a data-driven model using long short-term memory is developed to predict the RUL. To prove the effectiveness of the proposed method, experiments on a benchmark C-MAPSS dataset are carried out and compared with other state-of-the-art methods. Comparison results show that the proposed method achieves the smallest root mean square values in prediction of all RUL.
Learning Semantic Correspondences with Less Supervision
Authors: Percy Liang, Michael Jordan, Dan Klein
Date: 2009-08-01 00:00:00
Tasks: Language Acquisition

A central problem in grounded language acquisition is learning the correspondences between a
rich world state and a stream of text which references that world state. To deal with the high degree of ambiguity present in this setting, we present
a generative model that simultaneously segments
the text into utterances and maps each utterance
to a meaning representation grounded in the world
state. We show that our model generalizes across
three domains of increasing difficulty—Robocup
sportscasting, weather forecasts (a new domain),
and NFL recaps.
More Accurate Question Answering on Freebase
Authors: Hannah Bast, Elmar Haussmann
Date: 2015-10-01 00:00:00
Tasks: Learning-To-Rank, Question Answering

Real-world factoid or list questions often have a simple structure, yet are hard to match to facts in a given knowledge base due to high representational and linguistic variability. For example, to answer "who is the ceo of apple" on Freebase requires a match to an abstract "leadership" entity with three relations "role", "organization" and "person", and two other entities "apple inc" and "managing director". Recent years have seen a surge of research activity on learning-based solutions for this method. We further advance the state of the art by adopting learning-to-rank methodology and by fully addressing the inherent entity recognition problem, which was neglected in recent works.

We evaluate our system, called Aqqu, on two standard benchmarks, Free917 and WebQuestions, improving the previous best result for each benchmark considerably. These two benchmarks exhibit quite different challenges, and many of the existing approaches were evaluated (and work well) only for one of them. We also consider efficiency aspects and take care that all questions can be answered interactively (that is, within a second). Materials for full reproducibility are available on our website: http://ad.informatik.uni-freiburg.de/publications.
Learning Where to Sample in Structured Prediction
Authors: Tianlin Shi, Jacob Steinhardt, Percy Liang
Date: 2015-05-09 00:00:00
Tasks: Prediction, Reinforcement Learning (RL), Structured Prediction

In structured prediction, most inference algorithms allocate a homogeneous amount of computation to all parts of the output, which can be wasteful when different parts vary widely in terms of difficulty. In this paper, we propose a heterogeneous approach that dynamically allocates computation to the different parts. Given a pre-trained model, we tune its inference algorithm (a sampler) to increase test-time throughput. The inference algorithm is parametrized by a meta-model and trained via reinforcement learning, where actions correspond to sampling candidate parts of the output, and rewards are log-likelihood improvements. The meta-model is based on a set of domain-general meta-features capturing the progress of the sampler. We test our approach on five datasets and show that it attains the same accuracy as Gibbs sampling but is 2 to 5 times faster.