scholarly journals Human-Machine Collaborative Optimization via Apprenticeship Scheduling

2018 ◽  
Vol 63 ◽  
pp. 1-49 ◽  
Author(s):  
Matthew Gombolay ◽  
Reed Jensen ◽  
Jessica Stigile ◽  
Toni Golen ◽  
Neel Shah ◽  
...  
Keyword(s):  
Decision Making ◽  
Domain Knowledge ◽  
Resource Constraints ◽  
Optimization Technique ◽  
Collaborative Optimization ◽  
Data Sets ◽  
Scheduling Problems ◽  
Real World Data ◽  
Domain Experts ◽  
Model Free

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the "single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes. We propose a new approach for capturing this decision-making process through counterfactual reasoning in pairwise comparisons. Our approach is model-free and does not require iterating through the state space. We demonstrate that this approach accurately learns multifaceted heuristics on a synthetic and real world data sets. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of schedule optimization. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates optimal solutions up to 9.5 times faster than a state-of-the-art optimization algorithm.

scholarly journals Preventing Disparate Treatment in Sequential Decision Making

2018 ◽  
Author(s):  
Hoda Heidari ◽  
Andreas Krause
Keyword(s):  
Decision Making ◽  
Learning Algorithm ◽  
Feature Space ◽  
Sequential Decision Making ◽  
Data Sets ◽  
Sequential Decision ◽  
Real World Data ◽  
Time Step ◽  
Job Application ◽  
Disparate Treatment

We study fairness in sequential decision making environments, where at each time step a learning algorithm receives data corresponding to a new individual (e.g. a new job application) and must make an irrevocable decision about him/her (e.g. whether to hire the applicant) based on observations made so far. In order to prevent cases of disparate treatment, our time-dependent notion of fairness requires algorithmic decisions to be consistent: if two individuals are similar in the feature space and arrive during the same time epoch, the algorithm must assign them to similar outcomes. We propose a general framework for post-processing predictions made by a black-box learning model, that guarantees the resulting sequence of outcomes is consistent. We show theoretically that imposing consistency will not significantly slow down learning. Our experiments on two real-world data sets illustrate and confirm this finding in practice.

scholarly journals Penalty-Enhanced Utility-Based Multi-Criteria Recommendations

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 551
Author(s):  
Yong Zheng
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Real World ◽  
User Preferences ◽  
Experimental Results ◽  
Data Sets ◽  
Real World Data ◽  
Promising Solution ◽  
Multiple Domains

Recommender systems have been successfully applied to assist decision making in multiple domains and applications. Multi-criteria recommender systems try to take the user preferences on multiple criteria into consideration, in order to further improve the quality of the recommendations. Most recently, the utility-based multi-criteria recommendation approach has been proposed as an effective and promising solution. However, the issue of over-/under-expectations was ignored in the approach, which may bring risks to the recommendation model. In this paper, we propose a penalty-enhanced model to alleviate this issue. Our experimental results based on multiple real-world data sets can demonstrate the effectiveness of the proposed solutions. In addition, the outcomes of the proposed solution can also help explain the characteristics of the applications by observing the treatment on the issue of over-/under-expectations.

scholarly journals SCRO: A Domain Ontology for Describing Steel Cold Rolling Processes towards Industry 4.0

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 304
Author(s):  
Sadeer Beden ◽  
Qiushi Cao ◽  
Arnold Beckmann
Keyword(s):  
Cold Rolling ◽  
Domain Knowledge ◽  
Detection System ◽  
Data Access ◽  
Data Sets ◽  
Domain Experts ◽  
Data Querying ◽  
Set Up ◽  
Integration Data

This paper introduces the Steel Cold Rolling Ontology (SCRO) to model and capture domain knowledge of cold rolling processes and activities within a steel plant. A case study is set up that uses real-world cold rolling data sets to validate the performance and functionality of SCRO. This includes using the Ontop framework to deploy virtual knowledge graphs for data access, data integration, data querying, and condition-based maintenance purposes. SCRO is evaluated using OOPS!, the ontology pitfall detection system, and feedback from domain experts from Tata Steel.

scholarly journals An Interaction-Based Convolutional Neural Network (ICNN) Toward a Better Understanding of COVID-19 X-ray Images

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 337
Author(s):  
Shaw-Hwa Lo ◽  
Yiqiao Yin
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Explanatory Power ◽  
Prediction Performance ◽  
Data Sets ◽  
Real World Data ◽  
Data Set ◽  
Model Free

The field of explainable artificial intelligence (XAI) aims to build explainable and interpretable machine learning (or deep learning) methods without sacrificing prediction performance. Convolutional neural networks (CNNs) have been successful in making predictions, especially in image classification. These popular and well-documented successes use extremely deep CNNs such as VGG16, DenseNet121, and Xception. However, these well-known deep learning models use tens of millions of parameters based on a large number of pretrained filters that have been repurposed from previous data sets. Among these identified filters, a large portion contain no information yet remain as input features. Thus far, there is no effective method to omit these noisy features from a data set, and their existence negatively impacts prediction performance. In this paper, a novel interaction-based convolutional neural network (ICNN) is introduced that does not make assumptions about the relevance of local information. Instead, a model-free influence score (I-score) is proposed to directly extract the influential information from images to form important variable modules. This innovative technique replaces all pretrained filters found by trial-and-error with explainable, influential, and predictive variable sets (modules) determined by the I-score. In other words, future researchers need not rely on pretrained filters; the suggested algorithm identifies only the variables or pixels with high I-score values that are extremely predictive and important. The proposed method and algorithm were tested on real-world data set and a state-of-the-art prediction performance of 99.8% was achieved without sacrificing the explanatory power of the model. This proposed design can efficiently screen patients infected by COVID-19 before human diagnosis and can be a benchmark for addressing future XAI problems in large-scale data sets.

scholarly journals A Model-Free Approach for Detecting Genomic Regions of Deep Divergence Using the Distribution of Haplotype Distances

2017 ◽  
Author(s):  
Mats E. Pettersson ◽  
Marcin Kierczak ◽  
Markus Sällman Almén ◽  
Sangeet Lamichhaney ◽  
Leif Andersson
Keyword(s):  
Evolutionary History ◽  
Balancing Selection ◽  
A Priori ◽  
R Package ◽  
Data Sets ◽  
Proof Of Concept ◽  
Real World Data ◽  
Genome Scanning ◽  
Model Free ◽  
Genomic Regions

AbstractRecent advances in comparative genomics have revealed that divergence between populations is not necessarily uniform across all parts of the genome. There are examples of regions with divergent haplotypes that are substantially more different from each other that the genomic average.Typically, these regions are of interest, as their persistence over long periods of time may reflect balancing selection. However, they are hard to detect unless the divergent sub-populations are known prior to analysis.Here, we introduce HaploDistScan, an R-package implementing model-free detection of deep-divergence genomic regions based on the distribution of pair-wise haplotype distances, and show that it can detect such regions without use of a priori information about population sub-division. We apply the method to real-world data sets, from ruff and Darwin’s finches, and show that we are able to recover known instances of balancing selection – originally identified in studies reliant on detailed phenotyping – using only genotype data. Furthermore, in addition to replicating previously known divergent haplotypes as a proof-of-concept, we identify novel regions of interest in the Darwin’s finch genome and propose a plausible, data-driven evolutionary history for each novel locus individually.In conclusion, HaploDistScan requires neither phenotypic nor demographic input data, thus filling a gap in the existing set of methods for genome scanning, and provides a useful tool for identification of regions under balancing selection or similar evolutionary processes.

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

scholarly journals A top-level model of case-based argumentation for explanation: Formalisation and experiments

2021 ◽  
pp. 1-36
Author(s):  
Henry Prakken ◽  
Rosa Ratsma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Linear Models ◽  
Evaluation Studies ◽  
Data Sets ◽  
Machine Learning Applications ◽  
Level Model ◽  
Similarities And Differences ◽  
Further Development ◽  
Case Based

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

scholarly journals Hfinger: Malware HTTP Request Fingerprinting

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 507
Author(s):  
Piotr Białczak ◽  
Wojciech Mazurczyk
Keyword(s):  
Real World ◽  
Network Traffic ◽  
Experimental Evaluation ◽  
Data Sets ◽  
Real World Data ◽  
Malicious Software ◽  
Default Mode ◽  
World Data ◽  
Effectiveness Analysis ◽  
Http Protocol

Malicious software utilizes HTTP protocol for communication purposes, creating network traffic that is hard to identify as it blends into the traffic generated by benign applications. To this aim, fingerprinting tools have been developed to help track and identify such traffic by providing a short representation of malicious HTTP requests. However, currently existing tools do not analyze all information included in the HTTP message or analyze it insufficiently. To address these issues, we propose Hfinger, a novel malware HTTP request fingerprinting tool. It extracts information from the parts of the request such as URI, protocol information, headers, and payload, providing a concise request representation that preserves the extracted information in a form interpretable by a human analyst. For the developed solution, we have performed an extensive experimental evaluation using real-world data sets and we also compared Hfinger with the most related and popular existing tools such as FATT, Mercury, and p0f. The conducted effectiveness analysis reveals that on average only 1.85% of requests fingerprinted by Hfinger collide between malware families, what is 8–34 times lower than existing tools. Moreover, unlike these tools, in default mode, Hfinger does not introduce collisions between malware and benign applications and achieves it by increasing the number of fingerprints by at most 3 times. As a result, Hfinger can effectively track and hunt malware by providing more unique fingerprints than other standard tools.

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

scholarly journals Different algorithms, different models

2021 ◽  
Author(s):  
Martyna Daria Swiatczak
Keyword(s):  
Comparative Analysis ◽  
Real World ◽  
Qualitative Comparative Analysis ◽  
Comparative Methods ◽  
Data Sets ◽  
Simulation Studies ◽  
Threshold Values ◽  
Real World Data ◽  
Software Packages ◽  
Methodological Approaches

AbstractThis study assesses the extent to which the two main Configurational Comparative Methods (CCMs), i.e. Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), produce different models. It further explains how this non-identity is due to the different algorithms upon which both methods are based, namely QCA’s Quine–McCluskey algorithm and the CNA algorithm. I offer an overview of the fundamental differences between QCA and CNA and demonstrate both underlying algorithms on three data sets of ascending proximity to real-world data. Subsequent simulation studies in scenarios of varying sample sizes and degrees of noise in the data show high overall ratios of non-identity between the QCA parsimonious solution and the CNA atomic solution for varying analytical choices, i.e. different consistency and coverage threshold values and ways to derive QCA’s parsimonious solution. Clarity on the contrasts between the two methods is supposed to enable scholars to make more informed decisions on their methodological approaches, enhance their understanding of what is happening behind the results generated by the software packages, and better navigate the interpretation of results. Clarity on the non-identity between the underlying algorithms and their consequences for the results is supposed to provide a basis for a methodological discussion about which method and which variants thereof are more successful in deriving which search target.

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