Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering

Multi-view clustering aims to partition data collected from diverse sources based on the assumption that all views are complete. However, such prior assumption is hardly satisfied in many real-world applications, resulting in the incomplete multi-view learning problem. The existing attempts on this problem still have the following limitations: 1) the underlying semantic information of the missing views is commonly ignored; 2) The local structure of data is not well explored; 3) The importance of different views is not effectively evaluated. To address these issues, this paper proposes a Unified Embedding Alignment Framework (UEAF) for robust incomplete multi-view clustering. In particular, a locality-preserved reconstruction term is introduced to infer the missing views such that all views can be naturally aligned. A consensus graph is adaptively learned and embedded via the reverse graph regularization to guarantee the common local structure of multiple views and in turn can further align the incomplete views and inferred views. Moreover, an adaptive weighting strategy is designed to capture the importance of different views. Extensive experimental results show that the proposed method can significantly improve the clustering performance in comparison with some state-of-the-art methods.

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Adversarial Oracular Seq2seq Learning for Sequential Recommendation

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/264 ◽

2020 ◽

Author(s):

Pengyu Zhao ◽

Tianxiao Shui ◽

Yuanxing Zhang ◽

Kecheng Xiao ◽

Kaigui Bian

Keyword(s):

Recommender Systems ◽

Real World ◽

State Of The Art ◽

Large Datasets ◽

Time Varying ◽

Learning Problem ◽

Adversarial Learning ◽

Real World Applications ◽

Art Methods ◽

And Behavior

Recently, sequential recommendation has become a significant demand for many real-world applications, where the recommended items would be displayed to users one after another and the order of the displays influences the satisfaction of users. An extensive number of models have been developed for sequential recommendation by recommending the next items with the highest scores based on the user histories while few efforts have been made on identifying the transition dependency and behavior continuity in the recommended sequences. In this paper, we introduce the Adversarial Oracular Seq2seq learning for sequential Recommendation (AOS4Rec), which formulates the sequential recommendation as a seq2seq learning problem to portray time-varying interactions in the recommendation, and exploits the oracular learning and adversarial learning to enhance the recommendation quality. We examine the performance of AOS4Rec over RNN-based and Transformer-based recommender systems on two large datasets from real-world applications and make comparisons with state-of-the-art methods. Results indicate the accuracy and efficiency of AOS4Rec, and further analysis verifies that AOS4Rec has both robustness and practicability for real-world scenarios.

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A Survey on Bias and Fairness in Machine Learning

ACM Computing Surveys ◽

10.1145/3457607 ◽

2021 ◽

Vol 54 (6) ◽

pp. 1-35

Author(s):

Ninareh Mehrabi ◽

Fred Morstatter ◽

Nripsuta Saxena ◽

Kristina Lerman ◽

Aram Galstyan

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Real World ◽

State Of The Art ◽

Future Directions ◽

Discriminatory Behavior ◽

Real World Applications ◽

Near Future ◽

Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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Sliding-Window Thompson Sampling for Non-Stationary Settings

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.11407 ◽

2020 ◽

Vol 68 ◽

pp. 311-364

Author(s):

Francesco Trovo ◽

Stefano Paladino ◽

Marcello Restelli ◽

Nicola Gatti

Keyword(s):

Real World ◽

State Of The Art ◽

Sliding Window ◽

Upper Bounds ◽

Decision Problems ◽

Sequential Decision ◽

Thompson Sampling ◽

The Past ◽

Real World Applications ◽

Window Approach

Multi-Armed Bandit (MAB) techniques have been successfully applied to many classes of sequential decision problems in the past decades. However, non-stationary settings -- very common in real-world applications -- received little attention so far, and theoretical guarantees on the regret are known only for some frequentist algorithms. In this paper, we propose an algorithm, namely Sliding-Window Thompson Sampling (SW-TS), for nonstationary stochastic MAB settings. Our algorithm is based on Thompson Sampling and exploits a sliding-window approach to tackle, in a unified fashion, two different forms of non-stationarity studied separately so far: abruptly changing and smoothly changing. In the former, the reward distributions are constant during sequences of rounds, and their change may be arbitrary and happen at unknown rounds, while, in the latter, the reward distributions smoothly evolve over rounds according to unknown dynamics. Under mild assumptions, we provide regret upper bounds on the dynamic pseudo-regret of SW-TS for the abruptly changing environment, for the smoothly changing one, and for the setting in which both the non-stationarity forms are present. Furthermore, we empirically show that SW-TS dramatically outperforms state-of-the-art algorithms even when the forms of non-stationarity are taken separately, as previously studied in the literature.

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Particle Swarm Contour Search Algorithm

Entropy ◽

10.3390/e22040407 ◽

2020 ◽

Vol 22 (4) ◽

pp. 407 ◽

Cited By ~ 1

Author(s):

Dominik Weikert ◽

Sebastian Mai ◽

Sanaz Mostaghim

Keyword(s):

Image Processing ◽

Real World ◽

State Of The Art ◽

Search Algorithm ◽

Particle Swarm ◽

Search Space ◽

Local Information ◽

The State ◽

Complete Knowledge ◽

Real World Applications

In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.

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Design and implementation of aggregate functions in the DLV system

Theory and Practice of Logic Programming ◽

10.1017/s1471068408003323 ◽

2008 ◽

Vol 8 (5-6) ◽

pp. 545-580 ◽

Cited By ~ 41

Author(s):

WOLFGANG FABER ◽

GERALD PFEIFER ◽

NICOLA LEONE ◽

TINA DELL'ARMI ◽

GIUSEPPE IELPA

Keyword(s):

Computational Complexity ◽

Logic Programming ◽

Real World ◽

State Of The Art ◽

Complexity Class ◽

Natural Manner ◽

Design And Implementation ◽

Finite Structures ◽

Real World Applications ◽

Aggregate Functions

AbstractDisjunctive logic programming (DLP) is a very expressive formalism. It allows for expressing every property of finite structures that is decidable in the complexity class ΣP2(=NPNP). Despite this high expressiveness, there are some simple properties, often arising in real-world applications, which cannot be encoded in a simple and natural manner. Especially properties that require the use of arithmetic operators (like sum, times, or count) on a set or multiset of elements, which satisfy some conditions, cannot be naturally expressed in classic DLP. To overcome this deficiency, we extend DLP by aggregate functions in a conservative way. In particular, we avoid the introduction of constructs with disputed semantics, by requiring aggregates to be stratified. We formally define the semantics of the extended language (called ), and illustrate how it can be profitably used for representing knowledge. Furthermore, we analyze the computational complexity of , showing that the addition of aggregates does not bring a higher cost in that respect. Finally, we provide an implementation of in DLV—a state-of-the-art DLP system—and report on experiments which confirm the usefulness of the proposed extension also for the efficiency of computation.

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AN ANALYSIS OF COMMON APPLICATIONS ON HEDONIC PRICING MODEL IN VALUING ECOSYSTEM SERVICES: SIMILARITIES AND DIFFERENCE BETWEEN EUROPE, US, AND CHINA

International Journal of Engineering Technologies and Management Research ◽

10.29121/ijetmr.v8.i1.2021.849 ◽

2021 ◽

Vol 8 (1) ◽

pp. 1-11

Author(s):

Xuanfei Zhang

Keyword(s):

United States ◽

Ecosystem Services ◽

Real World ◽

Housing Prices ◽

The United States ◽

Hedonic Pricing ◽

Pricing Model ◽

Hedonic Pricing Model ◽

Real World Applications ◽

The Common

The study made a comparison with the common applications on the hedonic pricing model that valuing ecosystem services between Europe, the United States, and China. By analyzing various reasons impacting housing prices, cultural and historical backgrounds played roles in the real-world applications.

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Data based intervention approach for Complexity-Causality measure

10.7287/peerj.preprints.27416v1 ◽

2018 ◽

Author(s):

Aditi Kathpalia ◽

Nithin Nagaraj

Keyword(s):

Real World ◽

State Of The Art ◽

Dynamical Evolution ◽

Long Term Memory ◽

Uniform Sampling ◽

Cause And Effect ◽

Model Free ◽

Real World Applications ◽

Causality Testing

Causality testing methods are being widely used in various disciplines of science. Model-free methods for causality estimation are very useful as the underlying model generating the data is often unknown. However, existing model-free measures assume separability of cause and effect at the level of individual samples of measurements and unlike model-based methods do not perform any intervention to learn causal relationships. These measures can thus only capture causality which is by the associational occurrence of ‘cause’ and ‘effect’ between well separated samples. In real-world processes, often ‘cause’ and ‘effect’ are inherently inseparable or become inseparable in the acquired measurements. We propose a novel measure that uses an adaptive interventional scheme to capture causality which is not merely associational. The scheme is based on characterizing complexities associated with the dynamical evolution of processes on short windows of measurements. The formulated measure, Compression- Complexity Causality is rigorously tested on simulated and real datasets and its performance is compared with that of existing measures such as Granger Causality and Transfer Entropy. The proposed measure is robust to presence of noise, long-term memory, filtering and decimation, low temporal resolution (including aliasing), non-uniform sampling, finite length signals and presence of common driving variables. Our measure outperforms existing state-of-the-art measures, establishing itself as an effective tool for causality testing in real world applications.

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Unsupervised Quality Estimation for Neural Machine Translation

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00330 ◽

2020 ◽

Vol 8 ◽

pp. 539-555

Author(s):

Marina Fomicheva ◽

Shuo Sun ◽

Lisa Yankovskaya ◽

Frédéric Blain ◽

Francisco Guzmán ◽

...

Keyword(s):

Machine Translation ◽

Real World ◽

State Of The Art ◽

Black Box ◽

Test Time ◽

Quality Estimation ◽

Neural Machine Translation ◽

Real World Applications ◽

Unsupervised Approach

Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time. Existing approaches require large amounts of expert annotated data, computation, and time for training. As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. Different from most of the current work that treats the MT system as a black box, we explore useful information that can be extracted from the MT system as a by-product of translation. By utilizing methods for uncertainty quantification, we achieve very good correlation with human judgments of quality, rivaling state-of-the-art supervised QE models. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE.

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Understanding Generalization in Neural Networks for Robustness against Adversarial Vulnerabilities

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7129 ◽

2020 ◽

Vol 34 (10) ◽

pp. 13714-13715

Author(s):

Subhajit Chaudhury

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Real World ◽

Speech Processing ◽

State Of The Art ◽

Feature Learning ◽

Equivalent Problem ◽

Tremendous Progress ◽

Real World Applications ◽

Out Of Sample

Neural networks have contributed to tremendous progress in the domains of computer vision, speech processing, and other real-world applications. However, recent studies have shown that these state-of-the-art models can be easily compromised by adding small imperceptible perturbations. My thesis summary frames the problem of adversarial robustness as an equivalent problem of learning suitable features that leads to good generalization in neural networks. This is motivated from learning in humans which is not trivially fooled by such perturbations due to robust feature learning which shows good out-of-sample generalization.

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EHANet: An Effective Hierarchical Aggregation Network for Face Parsing

Applied Sciences ◽

10.3390/app10093135 ◽

2020 ◽

Vol 10 (9) ◽

pp. 3135 ◽

Cited By ~ 3

Author(s):

Ling Luo ◽

Dingyu Xue ◽

Xinglong Feng

Keyword(s):

Neural Networks ◽

Real World ◽

State Of The Art ◽

Contextual Information ◽

Semantic Gap ◽

Deep Convolutional Neural Networks ◽

Multi Scale ◽

Hierarchical Aggregation ◽

Real World Applications ◽

Weighted Boundary

In recent years, benefiting from deep convolutional neural networks (DCNNs), face parsing has developed rapidly. However, it still has the following problems: (1) Existing state-of-the-art frameworks usually do not satisfy real-time while pursuing performance; (2) similar appearances cause incorrect pixel label assignments, especially in the boundary; (3) to promote multi-scale prediction, deep features and shallow features are used for fusion without considering the semantic gap between them. To overcome these drawbacks, we propose an effective and efficient hierarchical aggregation network called EHANet for fast and accurate face parsing. More specifically, we first propose a stage contextual attention mechanism (SCAM), which uses higher-level contextual information to re-encode the channel according to its importance. Secondly, a semantic gap compensation block (SGCB) is presented to ensure the effective aggregation of hierarchical information. Thirdly, the advantages of weighted boundary-aware loss effectively make up for the ambiguity of boundary semantics. Without any bells and whistles, combined with a lightweight backbone, we achieve outstanding results on both CelebAMask-HQ (78.19% mIoU) and Helen datasets (90.7% F1-score). Furthermore, our model can achieve 55 FPS on a single GTX 1080Ti card with 640 × 640 input and further reach over 300 FPS with a resolution of 256 × 256, which is suitable for real-world applications.

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