scholarly journals Interacting with Explanations through Critiquing

2021 ◽  
Author(s):  
Diego Antognini ◽  
Claudiu Musat ◽  
Boi Faltings
Keyword(s):  
State Of The Art ◽  
Perceived Quality ◽  
User Model ◽  
Empirical Results ◽  
Novel Technique ◽  
Aspect Markers ◽  
Art Techniques

Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting with the explanation. We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer these explanations over those produced by state-of-the-art techniques. Our work's most important innovation is that it allows users to react to a recommendation by critiquing the textual explanation: removing (symmetrically adding) certain aspects they dislike or that are no longer relevant (symmetrically that are of interest). The system updates its user model and the resulting recommendations according to the critique. This is based on a novel unsupervised critiquing method for single- and multi-step critiquing with textual explanations. Empirical results show that our system achieves good performance in adapting to the preferences expressed in multi-step critiquing and generates consistent explanations.

scholarly journals Gamma-ray Spectrometry in Geothermal Exploration: State of the Art Techniques

Energies ◽  
2014 ◽  
Vol 7 (8) ◽  
pp. 4757-4780 ◽  
Author(s):  
Alistair McCay ◽  
Thomas Harley ◽  
Paul Younger ◽  
David Sanderson ◽  
Alan Cresswell
Keyword(s):  
Gamma Ray ◽  
State Of The Art ◽  
Gamma Ray Spectrometry ◽  
Geothermal Exploration ◽  
Art Techniques

scholarly journals Utterance Level Feature Aggregation with Deep Metric Learning for Speech Emotion Recognition

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4233
Author(s):  
Bogdan Mocanu ◽  
Ruxandra Tapu ◽  
Titus Zaharia
Keyword(s):  
Emotion Recognition ◽  
Loss Function ◽  
State Of The Art ◽  
Disease Diagnosis ◽  
Data Representation ◽  
Speech Emotion Recognition ◽  
Audio Features ◽  
Global Accuracy ◽  
Space Data ◽  
Art Techniques

Emotion is a form of high-level paralinguistic information that is intrinsically conveyed by human speech. Automatic speech emotion recognition is an essential challenge for various applications; including mental disease diagnosis; audio surveillance; human behavior understanding; e-learning and human–machine/robot interaction. In this paper, we introduce a novel speech emotion recognition method, based on the Squeeze and Excitation ResNet (SE-ResNet) model and fed with spectrogram inputs. In order to overcome the limitations of the state-of-the-art techniques, which fail in providing a robust feature representation at the utterance level, the CNN architecture is extended with a trainable discriminative GhostVLAD clustering layer that aggregates the audio features into compact, single-utterance vector representation. In addition, an end-to-end neural embedding approach is introduced, based on an emotionally constrained triplet loss function. The loss function integrates the relations between the various emotional patterns and thus improves the latent space data representation. The proposed methodology achieves 83.35% and 64.92% global accuracy rates on the RAVDESS and CREMA-D publicly available datasets, respectively. When compared with the results provided by human observers, the gains in global accuracy scores are superior to 24%. Finally, the objective comparative evaluation with state-of-the-art techniques demonstrates accuracy gains of more than 3%.

scholarly journals Reconfigurable Intelligent Surface Aided Multi-User Communications: State-of-the-Art Techniques and Open Issues

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 118584-118605
Author(s):  
Munyaradzi Munochiveyi ◽  
Arjun Chakravarthi Pogaku ◽  
Dinh-Thuan Do ◽  
Anh-Tu Le ◽  
Miroslav Voznak ◽  
...  
Keyword(s):  
State Of The Art ◽  
Open Issues ◽  
Art Techniques

Revolutions in algal biochar for different applications: State-of-the-art techniques and future scenarios

2020 ◽  
Vol 31 (10) ◽  
pp. 2591-2602
Author(s):  
Yi-Di Chen ◽  
Feiyu Liu ◽  
Nan-Qi Ren ◽  
Shih-Hsin Ho
Keyword(s):  
State Of The Art ◽  
Future Scenarios ◽  
Algal Biochar ◽  
Art Techniques

scholarly journals On the Properties of Non-Media Digital Watermarking: A Review of State of the Art Techniques

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 2670-2704 ◽  
Author(s):  
Arezou Soltani Panah ◽  
Ron Van Schyndel ◽  
Timos Sellis ◽  
Elisa Bertino
Keyword(s):  
Digital Watermarking ◽  
State Of The Art ◽  
Art Techniques

scholarly journals Coinductive Algorithms for Büchi Automata

2021 ◽  
Vol 180 (4) ◽  
pp. 351-373
Author(s):  
Denis Kuperberg ◽  
Laureline Pinault ◽  
Damien Pous
Keyword(s):  
State Space ◽  
State Of The Art ◽  
The State ◽  
Specific Structure ◽  
Büchi Automata ◽  
Deterministic Automata ◽  
Buchi Automata ◽  
Language Equivalence ◽  
Art Techniques

We propose a new algorithm for checking language equivalence of non-deterministic Büchi automata. We start from a construction proposed by Calbrix, Nivat and Podelski, which makes it possible to reduce the problem to that of checking equivalence of automata on finite words. Although this construction generates large and highly non-deterministic automata, we show how to exploit their specific structure and apply state-of-the art techniques based on coinduction to reduce the state-space that has to be explored. Doing so, we obtain algorithms which do not require full determinisation or complementation.

scholarly journals On μe-scattering at NNLO in QED

2018 ◽  
Vol 179 ◽  
pp. 01014 ◽  
Author(s):  
P. Mastrolia ◽  
M. Passera ◽  
A. Primo ◽  
U. Schubert ◽  
W. J. Torres Bobadilla
Keyword(s):  
Quantum Electrodynamics ◽  
State Of The Art ◽  
Current Status ◽  
Analytic Evaluation ◽  
Art Techniques

We report on the current status of the analytic evaluation of the two-loop corrections to the μescattering in Quantum Electrodynamics, presenting state-of-the art techniques which have been developed to address this challenging task.

scholarly journals HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

2021 ◽  
Author(s):  
Leila Zahedi ◽  
Farid Ghareh Mohammadi ◽  
M. Hadi Amini
Keyword(s):  
Parameter Optimization ◽  
Real World ◽  
Optimization Problems ◽  
State Of The Art ◽  
Parameter Tuning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Wide Range ◽  
Extreme Gradient Boosting ◽  
Art Techniques

Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application, a large number of hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance (accuracy and run-time). However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally challenging. Existing automated hyper-parameter tuning techniques suffer from high time complexity. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms, namely random forest, extreme gradient boosting, and support vector machine. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned, making it worthwhile for real-world hyper-parameter optimization problems. We further compare our proposed HyP-ABC algorithm with state-of-the-art techniques. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.

scholarly journals A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration

2021 ◽  
Vol 2 ◽  
Author(s):  
Sidrah Liaqat ◽  
Kia Dashtipour ◽  
Adnan Zahid ◽  
Kamran Arshad ◽  
Sana Ullah Jan ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
State Of The Art ◽  
The State ◽  
Skin Hydration ◽  
Hydration Level ◽  
Independent Review ◽  
Intelligent Models ◽  
Electrocardiogram Ecg ◽  
Fast Heart Rate ◽  
Art Techniques

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, with a prevalence of 1–2% in the community, increasing the risk of stroke and myocardial infarction. Early detection of AF, typically causing an irregular and abnormally fast heart rate, can help reduce the risk of strokes that are more common among older people. Intelligent models capable of automatic detection of AF in its earliest possible stages can improve the early diagnosis and treatment. Luckily, this can be made possible with the information about the heart's rhythm and electrical activity provided through electrocardiogram (ECG) and the decision-making machine learning-based autonomous models. In addition, AF has a direct impact on the skin hydration level and, hence, can be used as a measure for detection. In this paper, we present an independent review along with a comparative analysis of the state-of-the-art techniques proposed for AF detection using ECG and skin hydration levels. This paper also highlights the effects of AF on skin hydration level that is missing in most of the previous studies.

scholarly journals Generalized and Sub-Optimal Bipartite Constraints for Conflict-Based Search

2020 ◽  
Vol 34 (05) ◽  
pp. 7277-7284
Author(s):  
Thayne T. Walker ◽  
Nathan R. Sturtevant ◽  
Ariel Felner
Keyword(s):  
Bipartite Graph ◽  
Continuous Time ◽  
State Of The Art ◽  
Main Idea ◽  
Constraint Generation ◽  
Empirical Results ◽  
Generation Technique ◽  
Unit Costs ◽  
Previous State ◽  
Multi Agent

The main idea of conflict-based search (CBS), a popular, state-of-the-art algorithm for multi-agent pathfinding is to resolve conflicts between agents by systematically adding constraints to agents. Recently, CBS has been adapted for new domains and variants, including non-unit costs and continuous time settings. These adaptations require new types of constraints. This paper introduces a new automatic constraint generation technique called bipartite reduction (BR). BR converts the constraint generation step of CBS to a surrogate bipartite graph problem. The properties of BR guarantee completeness and optimality for CBS. Also, BR's properties may be relaxed to obtain suboptimal solutions. Empirical results show that BR yields significant speedups in 2k connected grids over the previous state-of-the-art for both optimal and suboptimal search.

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