bluuebunny/crossref_metadata_2025 huggingface dataset:
Plan Reordering and Parallel Execution — A Parameterized Complexity View
Authors: Aghighi Meysam , Bäckström Christer
Published: June 2022

Bäckström has previously studied a number of optimization problems for partial-order plans, like finding a minimum deordering (MCD) or reordering (MCR), and finding the minimum parallel execution length (PPL), which are all NP-complete. We revisit these problems, but applying parameterized complexity analysis rather than standard complexity analysis. We consider various parameters, including both the original and desired size of the plan order, as well as its width and height. Our findings include that MCD and MCR are W[2]-hard and in W[P] when parameterized with the desired order size, and MCD is fixed-parameter tractable (fpt) when parameterized with the original order size. Problem PPL is fpt if parameterized with the size of the non-concurrency relation, but para-NP-hard in most other cases. We also consider this problem when the number (k) of agents, or processors, is restricted, finding that this number is a crucial parameter; this problem is fixed-parameter tractable with the order size, the parallel execution length and k as parameter, but para-NP-hard without k as parameter.
Human-Aware Plan Recognition
Authors: Zhuo Hankz
Published: June 2022

Plan recognition aims to recognize target plans given observed actions with history plan libraries ordomain models in hand. Despite of the success of previous plan recognition approaches, they all neglect the impact of human preferences on plans. For example, a kid in a shopping mall might prefer to "executing'' a plan of playing in water park, while an adult might prefer to "executing'' a plan of having a cup of coffee. It could be helpful for improving the plan recognition accuracy to consider human preferences on plans. We assume there are historical rating scores on a subset of plans given by humans, and action sequences observed on humans. We estimate unknown rating scores based on rating scores in hand using an off-the-shelf collaborative filtering approach. We then discover plans to best explain the estimated rating scores and observed actions using a skip-gram based approach. In the experiment, we evaluate our approach in three planning domains to demonstrate its effectiveness.
Logical Filtering and Smoothing: State Estimation in Partially Observable Domains
Authors: Mombourquette Brent , Muise Christian , McIlraith Sheila
Published: June 2022

State estimation is the task of estimating the state of a partially observable dynamical system given a sequence of executed actions and observations. In logical settings, state estimation can be realized via logical filtering, which is exact but can be intractable. We propose logical smoothing, a form of backwards reasoning that works in concert with approximated logical filtering to refine past beliefs in light of new observations.  We characterize the notion of logical smoothing together with an algorithm for backwards-forwards state estimation.  We also present an approximation of our smoothing algorithm that is space efficient. We prove properties of our algorithms, and experimentally demonstrate their behaviour, contrasting them with state estimation methods for planning. Smoothing and backwards-forwards reasoning are important techniques for reasoning about partially observable dynamical systems, introducing the logical analogue of effective techniques from control theory and dynamic programming.
A Diversified Generative Latent Variable Model for WiFi-SLAM
Authors: Xiong Hao , Tao Dacheng
Published: June 2022

WiFi-SLAM aims to map WiFi signals within an unknown environment while simultaneously determining the location of a mobile device. This localization method has been extensively used in indoor, space, undersea, and underground environments. For the sake of accuracy, most methods label the signal readings against ground truth locations. However, this is impractical in large environments, where it is hard to collect and maintain the data. Some methods use latent variable models to generate latent-space locations of signal strength data, an advantage being that no prior labeling of signal strength readings and their physical locations is required. However, the generated latent variables cannot cover all wireless signal locations and WiFi-SLAM performance is significantly degraded. Here we propose the diversified generative latent variable model (DGLVM) to overcome these limitations. By building a positive-definite kernel function, a diversity-encouraging prior is introduced to render the generated latent variables non-overlapping, thus capturing more wireless signal measurements characteristics. The defined objective function is then solved by variational inference. Our experiments illustrate that the method performs WiFi localization more accurately than other label-free methods.
Minimal Undefinedness for Fuzzy Answer Sets
Authors: Alviano Mario , Amendola Giovanni , Peñaloza Rafael
Published: June 2022

Fuzzy Answer Set Programming (FASP) combines the non-monotonic reasoning typical of Answer Set Programming with the capability of Fuzzy Logic to deal with imprecise information and paraconsistent reasoning. In the context of paraconsistent reasoning, the fundamental principle of minimal undefinedness states that truth degrees close to 0 and 1 should be preferred to those close to 0.5, to minimize the ambiguity of the scenario. The aim of this paper is to enforce such a principle in FASP through the minimization of a measure of undefinedness. Algorithms that minimize undefinedness of fuzzy answer sets are presented, and implemented.
Optimizing Expectation with Guarantees in POMDPs
Authors: Chatterjee Krishnendu , Novotný Petr , Pérez Guillermo , Raskin Jean-François , Žikelić Đorđe
Published: June 2022

A standard objective in partially-observable Markov decision processes (POMDPs) is to find a policy that maximizes the expected discounted-sum payoff. However, such policies may still permit unlikely but highly undesirable outcomes, which is problematic especially in safety-critical applications. Recently, there has been a surge of interest in POMDPs where the goal is to maximize the probability to ensure that the payoff is at least a given threshold, but these approaches do not consider any optimization beyond satisfying this threshold constraint. In this work we go beyond both the “expectation” and “threshold” approaches and consider a “guaranteed payoff optimization (GPO)” problem for POMDPs, where we are given a threshold t and the objective is to find a policy σ such that a) each possible outcome of σ yields a discounted-sum payoff of at least t, and b) the expected discounted-sum payoff of σ is optimal (or near-optimal) among all policies satisfying a). We present a practical approach to tackle the GPO problem and evaluate it on standard POMDP benchmarks.
Reasoning about Cognitive Trust in Stochastic Multiagent Systems
Authors: Huang Xiaowei , Kwiatkowska Marta
Published: June 2022

We consider the setting of stochastic multiagent systems and formulate an automated verification framework for quantifying and reasoning about agents' trust. To capture human trust, we work with a cognitive notion of trust defined as a subjective evaluation that agent A makes about agent B's ability to complete a task, which in turn may lead to a decision by A to rely on B. We propose a probabilistic rational temporal logic PRTL*, which extends the logic PCTL* with reasoning about mental attitudes (beliefs, goals and intentions), and includes novel operators that can express concepts of social trust such as competence, disposition and dependence. The logic can express, for example, that "agent A will eventually trust agent B with probability at least p that B will be have in a way that ensures the successful completion of a given task". We study the complexity of the automated verification problem and, while the general problem is undecidable, we identify restrictions on the logic and the system that result in decidable, or even tractable, subproblems.
The Kernel Kalman Rule — Efficient Nonparametric Inference with Recursive Least Squares
Authors: Gebhardt Gregor , Kupcsik Andras , Neumann Gerhard
Published: June 2022

Nonparametric inference techniques provide promising tools for probabilistic reasoning in high-dimensional nonlinear systems.Most of these techniques embed distributions into reproducing kernel Hilbert spaces (RKHS) and rely on the kernel Bayes' rule (KBR) to manipulate the embeddings. However, the computational demands of the KBR scale poorly with the number of samples and the KBR often suffers from numerical instabilities. In this paper, we present the kernel Kalman rule (KKR) as an alternative to the KBR.The derivation of the KKR is based on recursive least squares, inspired by the derivation of the Kalman innovation update.We apply the KKR to filtering tasks where we use RKHS embeddings to represent the belief state, resulting in the kernel Kalman filter (KKF).We show on a nonlinear state estimation task with high dimensional observations that our approach provides a significantly improved estimation accuracy while the computational demands are significantly decreased.
Misspecified Linear Bandits
Authors: Ghosh Avishek , Ray Chowdhury Sayak , Gopalan Aditya
Published: June 2022

We consider the problem of online learning in misspecified linear stochastic multi-armed bandit problems. Regret guarantees for state-of-the-art linear bandit algorithms such as Optimism in the Face of Uncertainty Linear bandit (OFUL) hold under the assumption that the arms expected rewards are perfectly linear in their features. It is, however, of interest to investigate the impact of potential misspecification in linear bandit models, where the expected rewards are perturbed away from the linear subspace determined by the arms features. Although OFUL has recently been shown to be robust to relatively small deviations from linearity, we show that any linear bandit algorithm that enjoys optimal regret performance in the perfectly linear setting (e.g., OFUL) must suffer linear regret under a sparse additive perturbation of the linear model. In an attempt to overcome this negative result,we define a natural class of bandit models characterized by a non-sparse deviation from linearity. We argue that the OFUL algorithm can fail to achieve sublinear regret even under models that have non-sparse deviation. We finally develop a novel bandit algorithm, comprising a hypothesis test for linearity followed by a decision to use either the OFUL or Upper Confidence Bound (UCB) algorithm. For perfectly linear bandit models, the algorithm provably exhibits OFULs favorable regret performance, while for misspecified models satisfying the non-sparse deviation property, the algorithm avoids the linear regret phenomenon and falls back on UCBs sublinear regret scaling. Numerical experiments on synthetic data, and on recommendation data from the public Yahoo! Learning toRank Challenge dataset, empirically support our findings.
Open-Universe Weighted Model Counting
Authors: Belle Vaishak
Published: June 2022

Weighted model counting (WMC) has recently emerged as an effective and general approach to probabilistic inference, offering a computational framework for encoding a variety of formalisms, such as factor graphs and Bayesian networks.The advent of large-scale probabilistic knowledge bases has generated further interest in relational probabilistic representations, obtained by according weights to first-order formulas, whose semantics is given in terms of the ground theory, and solved by WMC. A fundamental limitation is that the domain of quantification, by construction and design, is assumed to be finite, which is at odds with areas such as vision and language understanding, where the existence of objects must be inferred from raw data. Dropping the finite-domain assumption has been known to improve the expressiveness of a first-order language for open-universe purposes, but these languages, so far, have eluded WMC approaches. In this paper, we revisit relational probabilistic models over an infinite domain, and establish a number of results that permit effective algorithms. We demonstrate this language on a number of examples, including a parameterized version of Pearl's Burglary-Earthquake-Alarm Bayesian network.
Community-Based Question Answering via Contextual Ranking Metric Network Learning
Authors: Lu Hanqing , Kong Ming
Published: June 2022

The exponential growth of information on Community-based Question Answering (CQA) sites has raised the challenges for the accurate matching of high-quality answers to the given questions. Many existing approaches learn the matching model mainly based on the semantic similarity between questions and answers, which can not effectively handle the ambiguity problem of questions and the sparsity problem of CQA data. In this paper, we propose to solve these two problems by exploiting users' social contexts. Specifically, we propose a novel framework for CQA task by exploiting both the question-answer content in CQA site and users' social contexts. The experiment on real-world dataset shows the effectiveness of our method.
User Modeling Using LSTM Networks
Authors: Żołna Konrad , Romański Bartłomiej
Published: June 2022

The LSTM model presented is capable of describing a user of a particular website without human expert supervision. In other words, the model is able to automatically craft features which depict attitude, intention and the overall state of a user. This effect is achieved by projecting the complex history of the user (sequence data corresponding to his actions on the website) into fixed-size vectors of real numbers. The representation obtained may be used to enrich typical models used in e-commerce: click-through rate, conversion rate, recommender systems etc. The goal of this paper is to demonstrate a way of creating the mentioned projection, which we called user2vec, and present possible benefits of incorporating this solution to enhance conversion rate model. Thus enriched model’s superiority is due not only to its increased internal complexity but also to its capability of learning from wider data – it indirectly analyzes actions of all website users, rather than being limited to the users who clicked on an ad.
A Computational Assessment Model for the Adaptive Level of Rehabilitation Exergames for the Elderly
Authors: Zhang Hao , Miao Chunyan , Yu Han , Leung Cyril
Published: June 2022

Rehabilitation exergames can engage the elderly in physical activities and help them recover part of their deteriorating capabilities. However, most existing exergames lack measures of how suitable they are to specific individuals. In this paper, we propose the Computational Person-Environment Fit model to evaluate the adaptability of the exergames to each individual elderly user.
Audio Feature Learning with Triplet-Based Embedding Network
Authors: Qi Xiaoyu , Yang Deshun , Chen Xiaoou
Published: June 2022

We propose a triplet-based network for audio feature learning for version identification. Existing methods use hand-crafted features for a music as a whole while we learn features by a triplet-based neural network on segment-level, focusing on the most similar parts between music versions. We conduct extensive experiments and demonstrate our merits.
Participatory Art Museum: Collecting and Modeling Crowd Opinions
Authors: Zeng Xiaoyu , Zhang Ruohan
Published: June 2022

We collect public opinions on museum artworks using online crowdsourcing techniques. We ask two research questions. First, do crowd opinions on artworks differ from expert interpretations? Second, how can museum manage large amount of crowd opinions, such that users can efficiently retrieve useful information? We address these questions through opinion modeling via semantic embedding and dimension reduction.
Natural Language Person Retrieval
Authors: Zhou Tao , Yu Jie
Published: June 2022

Following the recent progress in image classification and image captioning using deep learning, we developed a novel person retrieval system using natural language, which to our knowledge is first of its kind. Our system employs a state-of-the-art deep learning based natural language object retrieval framework to detect and retrieve people in images. Quantitative experimental results show significant improvement over state-of-the-art meth- ods for generic object retrieval. This line of research provides great advantages for searching large amounts of video surveil- lance footage and it can also be utilized in other domains, such as human-robot interaction.
High-Resolution Mobile Fingerprint Matching via Deep Joint KNN-Triplet Embedding
Authors: Zhang Fandong , Feng Jufu
Published: June 2022

In mobile devices, the limited area of fingerprint sensors brings demand of partial fingerprint matching. Existing fingerprint authentication algorithms are mainly based on minutiae matching. However, their accuracy degrades significantly for partial-to-partial matching due to the lack of minutiae. Optical fingerprint sensor can capture very high-resolution fingerprints (2000dpi) with rich details as pores, scars, etc. These details can cover the shortage of minutiae insufficiency. In this paper, we propose a novel matching algorithm for such fingerprints, namely Deep Joint KNN-Triplet Embedding, by making good use of these subtle features. Our model employs a deep convolutional neural network (CNN) with a well-designed joint loss to project raw fingerprint images into an Euclidean space. Then we can use L2-distance to measure the similarity of two fingerprints. Experiments indicate that our model outperforms several state-of-the-art approaches.
Fast Electrical Demand Optimization Under Real-Time Pricing
Authors: He Shan , Wallace Mark , Wilson Campbell , Liebman Ariel
Published: June 2022

The introduction of smart meters has motivated the electricity industry to manage electrical demand, using dynamic pricing schemes such as real-time pricing. The overall aim of demand management is to minimize electricity generation and distribution costs while meeting the demands and preferences of consumers. However, rapidly scheduling consumption of large groups of households is a challenge. In this paper, we present a highly scalable approach to find the optimal consumption levels for households in an iterative and distributed manner. The complexity of this approach is independent of the number of households, which allows it to be applied to problems with large groups of households. Moreover, the intermediate results of this approach can be used by smart meters to schedule tasks with a simple randomized method.
Cycle-Based Singleton Local Consistencies
Authors: Woodward Robert , Choueiry Berthe , Bessiere Christian
Published: June 2022

We propose to exploit cycles in the constraint network of a Constraint Satisfaction Problem (CSP) to vehicle constraint propagation and improve the effectiveness of local consistency algorithms. We focus our attention on the consistency property Partition-One Arc-Consistency (POAC), which is a stronger variant of Singleton Arc-Consistency (SAC). We modify the algorithm for enforcing POAC to operate on a minimum cycle basis (MCB) of the incidence graph of the CSP. We empirically show that our approach improves the performance of problem solving and constitutes a novel and effective localization of consistency algorithms. Although this paper focuses on POAC, we believe that exploiting cycles, such as MCBs, is applicable to other consistency algorithms and that our study opens a new direction in the design of consistency algorithms. This research is documented in a technical report (Woordward, Choueiry, and Bessiere 2016). http://consystlab.unl.edu/our_work/StudentReports/TR-UNL-CSE-2016-0004.pdf
The Complexity of Succinct Elections
Authors: Fitzsimmons Zack , Hemaspaandra Edith
Published: June 2022

The computational study of elections generally assumes that the preferences of the electorate come in as a list of votes. Depending on the context, it may be much more natural to represent the preferences of the electorate succinctly, as the distinct votes and their counts. Though the succinct representation may be exponentially smaller than the nonsuccinct, we find only one natural case where the complexity increases, in sharp contrast to the case where each voter has a weight, where the complexity usually increases.
A SAT-Based Approach for Solving the Modal Logic S5-Satisfiability Problem
Authors: Caridroit Thomas , Lagniez Jean-Marie , Le Berre Daniel , De Lima Tiago , Montmirail Valentin
Published: June 2022

We present a SAT-based approach for solving the modal logic S5-satisfiability problem. That problem being NP-complete, the translation into SAT is not a surprise. Our contribution is to greatly reduce the number of propositional variables and clauses required to encode the problem. We first present a syntactic property called diamond degree. We show that the size of an S5-model satisfying a formula phi can be bounded by its diamond degree. Such measure can thus be used as an upper bound for generating a SAT encoding for the S5-satisfiability of that formula. We also propose a lightweight caching system which allows us to further reduce the size of the propositional formula.We implemented a generic SAT-based approach within the modal logic S5 solver S52SAT. It allowed us to compare experimentally our new upper-bound against previously known one, i.e. the number of modalities of phi and to evaluate the effect of our caching technique. We also compared our solver againstexisting modal logic S5 solvers. The proposed approach outperforms previous ones on the benchmarks used. These promising results open interesting research directions for the practical resolution of others modal logics (e.g. K, KT, S4)
RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
Authors: Shekarpour Saeedeh , Marx Edgard , Auer Sören , Sheth Amit
Published: June 2022

For non-expert users, a textual query is the most popular and simple means for communicating with a retrieval or question answering system.However, there is a risk of receiving queries which do not match with the background knowledge.Query expansion and query rewriting are solutions for this problem but they are in danger of potentially yielding a large number of irrelevant words, which in turn negatively influences runtime as well as accuracy.In this paper, we propose a new method for automatic rewriting input queries on graph-structured RDF knowledge bases.We employ a Hidden Markov Model to determine the most suitable derived words from linguistic resources.We introduce the concept of triple-based co-occurrence for recognizing co-occurred words in RDF data.This model was bootstrapped with three statistical distributions.Our experimental study demonstrates the superiority of the proposed approach to the traditional n-gram model.
Maximum Model Counting
Authors: Fremont Daniel , Rabe Markus , Seshia Sanjit
Published: June 2022

We introduce the problem Max#SAT, an extension of model counting (#SAT). Given a formula over sets of variables X, Y, and Z, the Max#SAT problem is to maximize over the variables X the number of assignments to Y that can be extended to a solution with some assignment to Z. We demonstrate that Max#SAT has applications in many areas, showing how it can be used to solve problems in probabilistic inference (marginal MAP), planning, program synthesis, and quantitative information flow analysis. We also give an algorithm which by making only polynomially many calls to an NP oracle can approximate the maximum count to within any desired multiplicative error. The NP queries needed are relatively simple, arising from recent practical approximate model counting and sampling algorithms, which allows our technique to be effectively implemented with a SAT solver. Through several experiments we show that our approach can be successfully applied to interesting problems.
Towards a Brain Inspired Model of Self-Awareness for Sociable Agents
Authors: Subagdja Budhitama , Tan Ah-Hwee
Published: June 2022

Self-awareness is a crucial feature for a sociable agent or robot to better interact with humans. In a futuristic scenario, a conversational agent may occasionally be asked for its own opinion or suggestion based on its own thought, feelings, or experiences as if it is an individual with identity, personality, and social life. In moving towards that direction, in this paper, a brain inspired model of self-awareness is presented that allows an agent to learn to attend to different aspects of self as an individual with identity, physical embodiment, mental states, experiences, and reflections on how others may think about oneself. The model is built and realized on a NAO humanoid robotic platform to investigate the role of this capacity of self-awareness on the robot's learning and interactivity.
Scanpath Complexity: Modeling Reading Effort Using Gaze Information
Authors: Mishra Abhijit , Kanojia Diptesh , Nagar Seema , Dey Kuntal , Bhattacharyya Pushpak
Published: June 2022

Measuring reading effort is useful for practical purposes such as designing learning material and personalizing text comprehension environment. We propose a quantification of reading effort by measuring the complexity of eye-movement patterns of readers. We call the measure Scanpath Complexity. Scanpath complexity is modeled as a function of various properties of gaze fixations and saccades- the basic parameters of eye movement behavior. We demonstrate the effectiveness of our scanpath complexity measure by showing that its correlation with different measures of lexical and syntactic complexity as well as standard readability metrics is better than popular baseline measures based on fixation alone.
Semantic Proto-Role Labeling
Authors: Teichert Adam , Poliak Adam , Van Durme Benjamin , Gormley Matthew
Published: June 2022

The semantic function tags of Bonial, Stowe, and Palmer (2013) and the ordinal, multi-property annotations of Reisinger et al. (2015) draw inspiration from Ddowty's semantic proto-role theory. We approach proto-role labeling as a multi-label classification problem and establish strong results for the task by adapting a successful model of traditional semantic role labeling. We achieve a proto-role micro-averaged F1 of 81.7 using gold syntax and explore joint and conditional models of proto-roles and categorical roles. In comparing the effect of Bonial, Stowe, and Palmer's tags to PropBank ArgN-style role labels, we are surprised that neither annotations greatly improve proto-role prediction; however, we observe that ArgN models benefit much from observed syntax and from observed or modeled proto-roles while our models of the semantic function tags do not.
Fast-Tracking Stationary MOMDPs for Adaptive Management Problems
Authors: Péron Martin , Becker Kai , Bartlett Peter , Chadès Iadine
Published: June 2022

Adaptive management is applied in conservation and natural resource management, and consists of making sequential decisions when the transition matrix is uncertain. Informally described as ’learning by doing’, this approach aims to trade off between decisions that help achieve the objective and decisions that will yield a better knowledge of the true transition matrix. When the true transition matrix is assumed to be an element of a finite set of possible matrices, solving a mixed observability Markov decision process (MOMDP) leads to an optimal trade-off but is very computationally demanding. Under the assumption (common in adaptive management) that the true transition matrix is stationary, we propose a polynomial-time algorithm to find a lower bound of the value function. In the corners of the domain of the value function (belief space), this lower bound is provably equal to the optimal value function. We also show that under further assumptions, it is a linear approximation of the optimal value function in a neighborhood around the corners. We evaluate the benefits of our approach by using it to initialize the solvers MO-SARSOP and Perseus on a novel computational sustainability problem and a recent adaptive management data challenge. Our approach leads to an improved initial value function and translates into significant computational gains for both solvers.
Fine-Grained Car Detection for Visual Census Estimation
Authors: Gebru Timnit , Krause Jonathan , Wang Yilun , Chen Duyun , Deng Jia , Fei-Fei Li
Published: June 2022

Targeted socio-economic policies require an accurate understanding of a country’s demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learning-driven approaches are cheaper and faster—with the potential ability to detect trends in close to real time. In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to predict demographic attributes using the detected cars. To facilitate our work, we have collected the largest and most challenging fine-grained dataset reported to date consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources, classified by car experts to account for even the most subtle of visual differences. We use this data to construct the largest scale fine-grained detection system reported to date. Our prediction results correlate well with ground truth income data (r=0.82), Massachusetts department of vehicle registration, and sources investigating crime rates, income segregation, per capita carbon emission, and other market research. Finally, we learn interesting relationships between cars and neighborhoods allowing us to perform the first large scale sociological analysis of cities using computer vision techniques.
Matrix Factorisation for Scalable Energy Breakdown
Authors: Batra Nipun , Wang Hongning , Singh Amarjeet , Whitehouse Kamin
Published: June 2022

Homes constitute more than one-thirds of the total energy consumption. Producing an energy breakdown for a home has been shown to reduce household energy consumption by up to 15%, among other benefits. However, existing approaches to produce an energy breakdown require hardware to be installed in each home and are thus prohibitively expensive. In this paper, we propose a novel application of feature-based matrix factorisation that does not require any additional hard- ware installation. The basic premise of our approach is that common design and construction patterns for homes create a repeating structure in their energy data. Thus, a sparse basis can be used to represent energy data from a broad range of homes. We evaluate our approach on 516 homes from a publicly available data set and find it to be more effective than five baseline approaches that either require sensing in each home, or a very rigorous survey across a large number of homes coupled with complex modelling. We also present a deployment of our system as a live web application that can potentially provide energy breakdown to millions of homes.
Healthy Cognitive Aging: A Hybrid Random Vector Functional-Link Model for the Analysis of Alzheimer’s Disease
Authors: Dai Peng , Gwadry-Sridhar Femida , Bauer Michael , Borrie Michael , Teng Xue
Published: June 2022

Alzheimer's disease (AD) is a genetically complex neurodegenerative disease, which leads to irreversible brain damage, severe cognitive problems and ultimately death. A number of clinical trials and study initiatives have been set up to investigate AD pathology, leading to large amounts of high dimensional heterogeneous data (biomarkers) for analysis. This paper focuses on combining clinical features from different modalities, including medical imaging, cerebrospinal fluid (CSF), etc., to diagnose AD and predict potential progression. Due to privacy and legal issues involved with clinical research, the study cohort (number of patients) is relatively small, compared to thousands of available biomarkers (predictors). We propose a hybrid pathological analysis model, which integrates manifold learning and Random Vector functional-link network (RVFL) so as to achieve better ability to extract discriminant information with limited training materials. Furthermore, we model (current and future) cognitive healthiness as a regression problem about age. By comparing the difference between predicted age and actual age, we manage to show statistical differences between different pathological stages. Verification tests are conducted based on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Extensive comparison is made against different machine learning algorithms, i.e. Support Vector Machine (SVM), Random Forest (RF), Decision Tree and Multilayer Perceptron (MLP). Experimental results show that our proposed algorithm achieves better results than the comparison targets, which indicates promising robustness for practical clinical implementation.
Vision-Language Fusion for Object Recognition
Authors: Shiang Sz-Rung , Rosenthal Stephanie , Gershman Anatole , Carbonell Jaime , Oh Jean
Published: June 2022

While recent advances in computer vision have caused object recognition rates to spike, there is still much room for improvement. In this paper, we develop an algorithm to improve object recognition by integrating human-generated contextual information with vision algorithms. Specifically, we examine how interactive systems such as robots can utilize two types of context information--verbal descriptions of an environment and human-labeled datasets. We propose a re-ranking schema, MultiRank, for object recognition that can efficiently combine such information with the computer vision results. In our experiments, we achieve up to 9.4% and 16.6% accuracy improvements using the oracle and the detected bounding boxes, respectively, over the vision-only recognizers. We conclude that our algorithm has the ability to make a significant impact on object recognition in robotics and beyond.
Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image
Authors: Zhu Xiaoke , Jing Xiao-Yuan , Wu Fei , Wang Yunhong , Zuo Wangmeng , Zheng Wei-Shi
Published: June 2022

Person re-identification (re-id) plays an important role in video surveillance and forensics applications. In many cases, person re-id needs to be conducted between image and video clip, e.g., re-identifying a suspect from large quantities of pedestrian videos given a single image of him. We call re-id in this scenario as image to video person re-id (IVPR). In practice, image and video are usually represented with different features, and there usually exist large variations between frames within each video. These factors make matching between image and video become a very challenging task. In this paper, we propose a joint feature projection matrix and heterogeneous dictionary pair learning (PHDL) approach for IVPR. Specifically, PHDL jointly learns an intra-video projection matrix and a pair of heterogeneous image and video dictionaries. With the learned projection matrix, the influence of variations within each video to the matching can be reduced. With the learned dictionary pair, the heterogeneous image and video features can be transformed into coding coefficients with the same dimension, such that the matching can be conducted using coding coefficients. Furthermore, to ensure that the obtained coding coefficients have favorable discriminability, PHDL designs a point-to-set coefficient discriminant term. Experiments on the public iLIDS-VID and PRID 2011 datasets demonstrate the effectiveness of the proposed approach.
Building an End-to-End Spatial-Temporal Convolutional Network for Video Super-Resolution
Authors: Guo Jun , Chao Hongyang
Published: June 2022

We propose an end-to-end deep network for video super-resolution. Our network is composed of a spatial component that encodes intra-frame visual patterns, a temporal component that discovers inter-frame relations, and a reconstruction component that aggregates information to predict details. We make the spatial component deep, so that it can better leverage spatial redundancies for rebuilding high-frequency structures. We organize the temporal component in a bidirectional and multi-scale fashion, to better capture how frames change across time. The effectiveness of the proposed approach is highlighted on two datasets, where we observe substantial improvements relative to the state of the arts.
Leveraging Saccades to Learn Smooth Pursuit: A Self-Organizing Motion Tracking Model Using Restricted Boltzmann Machines
Authors: Yogeswaran Arjun , Payeur Pierre
Published: June 2022

In this paper, we propose a biologically-plausible model to explain the emergence of motion tracking behaviour in early development using unsupervised learning. The model's training is biased by a concept called retinal constancy, which measures how similar visual contents are between successive frames. This biasing is similar to a reward in reinforcement learning, but is less explicit, as it modulates the model's learning rate instead of being a learning signal itself. The model is a two-layer deep network. The first layer learns to encode visual motion, and the second layer learns to relate that motion to gaze movements, which it perceives and creates through bi-directional nodes. By randomly generating gaze movements to traverse the local visual space, desirable correlations are developed between visual motion and the appropriate gaze to nullify that motion such that maximal retinal constancy is achieved. Biologically, this is similar to using saccades to look around and learning from moments where a target and the saccade move together such that the image stays the same on the retina, and developing smooth pursuit behaviour to perform this action in the future. Restricted Boltzmann machines are used to implement this model because they can form a deep belief network, perform online learning, and act generatively. These properties all have biological equivalents and coincide with the biological plausibility of using saccades as leverage to learn smooth pursuit. This method is unique because it uses general machine learning algorithms, and their inherent generative properties, to learn from real-world data. It also implements a biological theory, uses motion instead of recognition via local searches, without temporal filtering, and learns in a fully unsupervised manner. Its tracking performance after being trained on real-world images with simulated motion is compared to its tracking performance after being trained on natural video. Results show that this model is able to successfully follow targets in natural video, despite partial occlusions, scale changes, and nonlinear motion.
Text-Guided Attention Model for Image Captioning
Authors: Mun Jonghwan , Cho Minsu , Han Bohyung
Published: June 2022

Visual attention plays an important role to understand images and demonstrates its effectiveness in generating natural language descriptions of images. On the other hand, recent studies show that language associated with an image can steer visual attention in the scene during our cognitive process. Inspired by this, we introduce a text-guided attention model for image captioning, which learns to drive visual attention using associated captions. For this model, we propose an exemplar-based learning approach that retrieves from training data associated captions with each image, and use them to learn attention on visual features. Our attention model enables to describe a detailed state of scenes by distinguishing small or confusable objects effectively. We validate our model on MS-COCO Captioning benchmark and achieve the state-of-the-art performance in standard metrics.
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
Authors: Wang Yu , Xu Jixing , Wu Aohan , Li Mantian , He Yang , Hu Jinghe , Yan Weipeng
Published: June 2022

Designing an e-commerce recommender system that serves hundreds of millions of active users is a daunting challenge. To our best knowledge, the complex brain activity mechanism behind human shopping activities is never considered in existing recommender systems. From a human vision perspective, we found two key factors that affect users’ behaviors: items’ attractiveness and their matching degrees with users’ interests. This paper proposes Telepath, a vision-based bionic recommender system model, which simulates human brain activities in decision making of shopping, thus understanding users from such perspective. The core of Telepath is a complex deep neural network with multiple subnetworks. In practice, the Telepath model has been launched to JD’s recommender system and advertising system and outperformed the former state-of-the-art method. For one of the major item recommendation blocks on the JD app, click-through rate (CTR), gross merchandise value (GMV) and orders have been increased 1.59%, 8.16% and 8.71% respectively by Telepath. For several major ad publishers of JD demand-side platform, CTR, GMV and return on investment have been increased 6.58%, 61.72% and 65.57% respectively by the first launch of Telepath, and further increased 2.95%, 41.75% and 41.37% respectively by the second launch.
Joint Training for Neural Machine Translation Models with Monolingual Data
Authors: Zhang Zhirui , Liu Shujie , Li Mu , Zhou Ming , Chen Enhong
Published: June 2022

Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough. In this paper, we propose a novel approach to better leveraging monolingual data for neural machine translation by jointly learning source-to-target and target-to-source NMT models for a language pair with a joint EM optimization method. The training process starts with two initial NMT models pre-trained on parallel data for each direction, and these two models are iteratively updated by incrementally decreasing translation losses on training data.In each iteration step, both NMT models are first used to translate monolingual data from one language to the other, forming pseudo-training data of the other NMT model. Then two new NMT models are learnt from parallel data together with the pseudo training data. Both NMT models are expected to be improved and better pseudo-training data can be generated in next step. Experiment results on Chinese-English and English-German translation tasks show that our approach can simultaneously improve translation quality of source-to-target and target-to-source models, significantly outperforming strong baseline systems which are enhanced with monolingual data for model training including back-translation.
Improved English to Russian Translation by Neural Suffix Prediction
Authors: Song Kai , Zhang Yue , Zhang Min , Luo Weihua
Published: June 2022

Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages. To address this problem, previous work focused on adjusting translation granularity or expanding the vocabulary size. However, morphological information is relatively under-considered in NMT architectures, which may further improve translation quality. We propose a novel method, which can not only reduce data sparsity but also model morphology through a simple but effective mechanism. By predicting the stem and suffix separately during decoding, our system achieves an improvement of up to 1.98 BLEU compared with previous work on English to Russian translation. Our method is orthogonal to different NMT architectures and stably gains improvements on various domains.
Neural Link Prediction over Aligned Networks
Authors: Cao Xuezhi , Chen Haokun , Wang Xuejian , Zhang Weinan , Yu Yong
Published: June 2022

Link prediction is a fundamental problem with a wide range of applications in various domains, which predicts the links that are not yet observed or the links that may appear in the future. Most existing works in this field only focus on modeling a single network, while real-world networks are actually aligned with each other. Network alignments contain valuable additional information for understanding the networks, and provide a new direction for addressing data insufficiency and alleviating cold start problem. However, there are rare works leveraging network alignments for better link prediction. Besides, neural network is widely employed in various domains while its capability of capturing high-level patterns and correlations for link prediction problem has not been adequately researched yet. Hence, in this paper we target atlink prediction over aligned networks using neural networks. The major challenge is the heterogeneousness of the considered networks, as the networks may have different characteristics, link purposes, etc. To overcome this, we propose a novel multi-neural-network framework MNN, where we have one individual neural network for each heterogeneous target or feature while the vertex representations are shared. We further discuss training methods for the multi-neural-network framework. Extensive experiments demonstrate that MNN outperforms the state-of-the-art methods and achieves 3% to 5% relative improvement of AUC score across different settings, particularly over 8% for cold start scenarios.
Towards Efficient Detection of Overlapping Communities in Massive Networks
Authors: Sun Bing-Jie , Shen Huawei , Gao Jinhua , Ouyang Wentao , Cheng Xueqi
Published: June 2022

Community detection is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, few methods could be used as off-the-shelf tools to detect communities in real world networks for two reasons. On the one hand, most existing methods for community detection cannot handle massive networks that contain millions or even hundreds of millions of nodes. On the other hand, communities in real world networks are generally highly overlapped, requiring that community detection method could capture the mixed community membership. In this paper, we aim to offer an off-the-shelf method to detect overlapping communities in massive real world networks. For this purpose, we take the widely-used Poisson model for overlapping community detection as starting point and design two speedup strategies to achieve high efficiency. Extensive tests on synthetic and large scale real networks demonstrate that the proposed strategies speedup the community detection method based on Poisson model by 1 to 2 orders of magnitudes, while achieving comparable accuracy at community detection.