Reviewerly/exHarmony huggingface dataset:
Non-Autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition

Non-autoregressive (NAR) transformer models have been studied intensively in automatic speech recognition (ASR), and many NAR transformer models is to use the causal mask to limit token dependencies. However, the causal mask is designed for the left-to-right decoding process of the non-parallel autoregressive (AR) transformer, which is inappropriate for the parallel NAR transformer since it ignores the right-to-left contexts. Some methods are proposed to utilize right-to-left contexts with an extra decoder, but these methods increase the model complexity. To tackle the above problems, we propose a new non-autoregressive transformer with a unified bidirectional decoder (NAT-UBD), which can simultaneously utilize left-to-right and right-to-left contexts for ASR. However, direct use of bidirectional contexts will cause information leakage, which means the decoder output can be affected by the character information of the input in the same position. To avoid information leakage, we propose a novel attention mask and modify vanilla queries, keys, and values matrices for NAT-UBD. Experimental results verify that NAT-UBD can achieve character error rates (CERs) of 5.0%/5.5% on the Aishell-1 dev/test sets, outperforming all previous NAR transformer models. Moreover, NAT-UBD can run 49.8× faster than the AR transformer baseline when decoding in a single step.
Disparity Between Batches as a Signal for Early Stopping

We propose a metric for evaluating the generalization ability of deep neural networks trained with mini-batch gradient descent. Our metric, called gradient disparity, is the \(\ell _2\) norm distance between the gradient vectors of two mini-batches drawn from the training set. It is derived from a probabilistic upper bound on the difference between the classification errors over a given mini-batch, when the network is trained on this mini-batch and when the network is trained on another mini-batch of points sampled from the same dataset. We empirically show that gradient disparity is a very promising early-stopping criterion (i) when data is limited, as it uses all the samples for training and (ii) when available data has noisy labels, as it signals overfitting better than the validation data. Furthermore, we show in a wide range of experimental settings that gradient disparity is strongly related to the generalization error between the training and test sets, and that it is also very informative about the level of label noise.
Persuasive Contrastive Explanations for Bayesian Networks

Explanation in Artificial Intelligence is often focused on providing reasons for why a model under consideration and its outcome are correct. Recently, research in explainable machine learning has initiated a shift in focus on including so-called counterfactual explanations. In this paper we propose to combine both types of explanation in the context of explaining Bayesian networks. To this end we introduce persuasive contrastive explanations that aim to provide an answer to the question Why outcome t instead of \(t^\prime ?\) posed by a user. In addition, we propose an algorithm for computing persuasive contrastive explanations. Both our definition of persuasive contrastive explanation and the proposed algorithm can be employed beyond the current scope of Bayesian networks.
Parsing Tools for Italian Phraseological Units

Phraseological complexity plays a critical role in assessing the language competence level necessary to understand or produce text by a language learner and automated tools supporting international certifications for second languages. Appropriate syntactic-based parsing tools for pos/tagging texts are required to efficiently and correctly identify the components of phraseological units to apply computational and corpus-based measures. In this paper, we consider some of the main syntactic parsing tools publicly available for the Italian language and compare them in the framework of a project aiming at characterizing the phraseological complexity of a corpus produced by learners of Italian as a second language.
AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning

The process of capturing a well-composed photo is difficult and it takes years of experience to master. We propose a novel pipeline for an autonomous agent to automatically capture an aesthetic photograph by navigating within a local region in a scene. Instead of classical optimization over heuristics such as the rule-of-thirds, we adopt a data-driven aesthetics estimator to assess photo quality. A reinforcement learning framework is used to optimize the model with respect to the learned aesthetics metric. We train our model in simulation with indoor scenes, and we demonstrate that our system can capture aesthetic photos in both simulation and real world environments on a ground robot. To our knowledge, this is the first system that can automatically explore an environment to capture an aesthetic photo with respect to a learned aesthetic estimator. Source code is at https://github.com/HadiZayer/AutoPhoto
Predicting Task-Driven Attention via Integrating Bottom-Up Stimulus and Top-Down Guidance

Task-free attention has gained intensive interest in the computer vision community while relatively few works focus on task-driven attention (TDAttention). Thus this paper handles the problem of TDAttention prediction in daily scenarios where a human is doing a task. Motivated by the cognition mechanism that human attention allocation is jointly controlled by the top-down guidance and bottom-up stimulus, this paper proposes a cognitively-explanatory deep neural network model to predict TDAttention. Given an image sequence, bottom-up features, such as human pose and motion, are firstly extracted. At the same time, the coarse-grained task information and fine-grained task information are embedded as a top-down feature. The bottom-up features are then fused with the top-down feature to guide the model to predict TDAttention. Two public datasets are re-annotated to make them qualified for TDAttention prediction, and our model is widely compared with other models on the two datasets. In addition, some ablation studies are conducted to evaluate the individual modules in our model. Experiment results demonstrate the effectiveness of our model.
Inference Engines Performance in Reasoning Tasks for Intelligent Tutoring Systems

The use of formal knowledge representation models in intelligent tutoring systems often requires logical reasoning on these models by predefined rules. This process can be time and memory consuming, so finding effective software reasoners for different applications is an important research field. This problem is relevant for cognitive and constraint-based intelligent tutoring systems. We performed a comparative study of various software reasoners (Pellet, Apache Jena inference subsystem, Apache Jena SPARQL query processor, SWI-Prolog with semweb package, Closed World Machine, and Answer Set Programming solvers Clingo and DLV) for solving tasks specific to intelligent tutoring systems using three formal models with different properties and corresponding rule sets created for intelligent tutoring systems in introductory programming education domain. We compared features of rule-definition formalisms for different approaches and measured run and wall time, average CPU load, and peak RAM usage based on the count of inferred RDF triples. The experiments show that Apache Jena infers the solution quicker than other reasoners on the majority of tasks but consumes a significant amount of memory, while Clingo performs significantly better for combinatorial problems.
Change Detection in Multivariate Datastreams Controlling False Alarms

We introduce QuantTree Exponentially Weighted Moving Average (QT-EWMA), a novel change-detection algorithm for multivariate datastreams that can operate in a nonparametric and online manner. QT-EWMA can be configured to yield a target Average Run Length (ARL\(_0\)), thus controlling the expected time before a false alarm. Control over false alarms has many practical implications and is rarely guaranteed by online change-detection algorithms that can monitor multivariate datastreams whose distribution is unknown. Our experiments, performed on synthetic and real-world datasets, demonstrate that QT-EWMA controls the ARL\(_0\) and the false alarm rate better than state-of-the-art methods operating in similar conditions, achieving comparable detection delays.
RoCo-NAS: Robust and Compact Neural Architecture Search

Deep model compression has been studied widely, and state-of-the-art methods can now achieve high compression ratios with minimum accuracy loss. Recent advances in adversarial attacks reveal the inherent vulnerability of deep neural networks to slightly perturbed images called adversarial examples. Since then, extensive efforts have been performed to enhance deep networks' robustness via specialized loss functions and learning algorithms. Previous works suggest that network size and robustness against adversarial examples contradict on most occasions. In this paper, we investigate how to optimize compactness and robustness to adversarial attacks of neural network architectures while maintaining the accuracy using multi-objective neural architecture search. We propose the use of previously generated adversarial examples as an objective to evaluate the robustness of our models in addition to the number of floating-point operations to assess model complexity i.e. compactness. Experiments on some recent neural architecture search algorithms show that due to their limited search space they fail to find robust and compact architectures. By creating a novel neural architecture search (RoCo-NAS), we were able to evolve an architecture that is up to 7% more accurate against adversarial samples than its more complex architecture counterpart. Thus, the results show inherently robust architectures regardless of their size. This opens up a new range of possibilities for the exploration and design of deep neural networks using automatic architecture search.
Large Scale Double Density Dual Tree Complex Wavelet Transform Based Robust Feature Extraction for Face Recognition

Varying effects in face recognition often causes intrapersonal variations due to which efficient feature extraction is desirable. In this work, a significant feature extraction technique based on Double Density Dual Tree Complex Wavelet Transform (DD_DTCWT) is proposed for face recognition. DD_DTCWT is a variant of wavelet transformation which provides better multiresolution sub-band spectral analysis of face images with good shift invariance and directional selectivity. Extensive experiments are performed with slight pose variations on ORL database and on images with illumination and expression variations in YALE database. It has been depicted from experimental results that the proposed technique for large-scale Double Density Dual Tree Complex Wavelet Transform-based robust feature extraction yields accurate results on Yale database and better results on ORL database as compared with state-of-the-art techniques. The classification is performed on training and testing face vectors based on DD_DTCWT using K-nearest neighbor classifier. Several experiments are performed to analyze significant features by varying decomposition levels for sub-band selection and number of images in training set.
Classification and Diagnosis of Lymphoma’s Histopathological Images Using Transfer Learning

Current cancer diagnosis procedure requires expert knowledge and is time-consuming, which raises the need to build an accurate diagnosis support system for lymphoma identification and classification.Many studies have shown promising results using Machine Learning and, recently, Deep Learning to detect malignancy in cancer cells.However, the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem.In literature, many attempts were made to classify up to four simple types of lymphoma.This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive lymphoma.These Lymphoma types are Classical Hodgkin Lymphoma, Nodular Lymphoma Predominant, Burkitt Lymphoma, Follicular Lymphoma, Mantle Lymphoma, Large B-Cell Lymphoma, and T-Cell Lymphoma.Our proposed approach uses Residual Neural Networks, ResNet50, with a Transfer Learning for lymphoma's detection and classification.The model used results are validated according to the performance evaluation metrics: Accuracy, precision, recall, F-score, and kappa score for the seven multi-classes.Our algorithms are tested, and the results are validated on 323 images of 224 × 224 pixels resolution.The results are promising and show that our used model can classify and predict the correct lymphoma subtype with an accuracy of 91.6%.
Virtual Reconstruction System of Building Spatial Structure Based on Laser 3D Scanning under Multivariate Big Data Fusion

Abstract Existing systems have disadvantages such as slow running speed, long time-consuming, and poor rendering effect in virtual reconstruction of architectural spatial structure. In order to solve such problems, virtual reconstruction system of building space structure is designed using laser 3D scanning technology under condition of fusion of multiple big data. The system was equipped with a 3D laser scanner and connected to computer, and the noise interference was reduced by image preprocessing module to complete the hardware design. The system improved user interface and maintenance module. Eventually, 3D model reconstruction was realized via data acquisition, data registration, coordinate transformation and 3D rendering. The results show that the system designed in this paper runs fast, and color of reconstruction results is consistent, which indicates that reconstruction results of building space structure obtained by the system are conducive to in-depth study of building space.
User-Centred Design without Involving Users: A Longitudinal Case Study in a Human-Centred-Design–Mature Company

Human-centred design has grown into a widely applied field that has produced a large number of standards, methods and guidelines for designing meaningful and usable products and services and direct contact to users seems to define whether a project is considered human-centric or not. However, as the field has grown more mature, companies have also matured in human-centredness, and thus, they have already accumulated user knowledge and may not need to start from the beginning in each project. This paper presents a case study of a human-centred-design–mature company, where first-hand access to users was blocked due to confidentiality. The project team had to rely on other sources of user knowledge. They utilized user representations that were based on earlier user studies and other sources, and the company also employed in-house users who gave their input in the product development process. Together these resulted in a successful design project.
KLM-GOMS Detection of Interaction Patterns Through the Execution of Unplanned Tasks

The availability of software applications has contributed to the increase in user demand, which has increased the complexity of these applications. This contributed to the adoption of automation mechanisms for the software testing process, in order to reduce coding errors and shorten the time needed to deploy a new version of the application to the user. Currently, automating the application testing process is a well-established reality and supported by many tools. However, the usability evaluation of an application requires solutions that allow to determine, in advance, the type of improvements that may be necessary in the application without the need for intensive user testing. This work deals with the automatic analysis of the impact on the user of changes in the design of an application, through the implementation of the Keystroke Level Model (KLM). Based on the execution of unplanned user interactions in a web interface, a KLM string is obtained and evaluated, providing a model that converts KLM operators and the execution time of each operator into information for designers. Moreover, performance indicators are obtained and interaction patterns are automatically defined.
Enhancing the Interpretability of Deep Models in Healthcare Through Attention: Application to Glucose Forecasting for Diabetic People

The adoption of deep learning in healthcare is hindered by their “black box” nature. In this paper, we explore the RETAIN architecture for the task of glucose forecasting for diabetic people. By using a two-level attention mechanism, the recurrent-neural-network-based RETAIN model is interpretable. We evaluate the RETAIN model on the type-2 IDIAB and the type-1 OhioT1DM datasets by comparing its statistical and clinical performances against two deep models and three models based on decision trees. We show that the RETAIN model offers a very good compromise between accuracy and interpretability, being almost as accurate as the LSTM and FCN models while remaining interpretable. We show the usefulness of its interpretable nature by analyzing the contribution of each variable to the final prediction. It revealed that signal values older than 1[Formula: see text]h are not used by the RETAIN model for 30[Formula: see text]min ahead of time prediction of glucose. Also, we show how the RETAIN model changes its behavior upon the arrival of an event such as carbohydrate intakes or insulin infusions. In particular, it showed that the patient’s state before the event is particularly important for the prediction. Overall the RETAIN model, thanks to its interpretability, seems to be a very promising model for regression or classification tasks in healthcare.
Challenges Experienced in Public Service Media Recommendation Systems

After multiple years of successfully applying recommendation algorithms at ZDF, a German Public Service Media provider, we have faced certain challenges in regards to the optimization of our systems and the resulting recommendations. The design and the optimization of our systems are guided by various, partially competing objectives and are, therefore, influenced by various factors. Similarly to commercial video on demand services, ZDF is interested in binding its audience by providing personalized recommendations in its streaming media service. However, more importantly, as a Public Service Media provider, we are committed to offer diverse, universal, unbiased, and transparent recommendations while following established editorial guidelines and strict privacy regulations. Additionally, we are committed to provide environmentally-friendly or green recommendations optimizing our systems for run time and power consumption. With the intent to start a public discussion, we describe the challenges that arise when optimizing Public Service Media recommendation systems towards machine learning metrics, business Key Performance Indicators, Public Service Media values, and run-time simultaneously, while aiming to keep the results transparent.
Automated trade study analysis based on dynamic requirements verification in the model‐based system engineering

Abstract In this complex world, where evolving systems communicate independently to achieve a common goal, the ability for an engineer to develop systems with traceability to requirements, using one integrated architecture model that enables all types of automated analysis (e.g., impact analysis, gap analysis, trade study analysis, and simulation) is becoming more and more vital. Today, the core enabler for automated analysis is the application of model‐based systems engineering practices. Model‐based systems engineering is used to capture system or systems of systems architecture as descriptive and analytical system models, which relate text‐based requirements to the architecture and provide an infrastructure to support trade study analysis. One of the core techniques to perform such analysis is requirements verification. This paper proposes an approach for an automated trade study analysis based on dynamic requirements verification in the model‐based systems engineering environment, with a goal to support trade of analysis both in the system of systems and systems engineering domains.
Probing Spatial Clues: Canonical Spatial Templates for Object Relationship Understanding

Humans often leverage spatial clues to categorize scenes in a fraction of a second.This form of intelligence is very relevant in time-critical situations (e.g., when driving a car) and valuable to transfer to automated systems.This work investigates the predictive power of solely processing spatial clues for scene understanding in 2D images and compares such an approach with the predictive power of visual appearance.To this end, we design the laboratory task of predicting the identity of two objects (e.g., "man" and "horse") and their relationship or predicate (e.g., "riding") given exclusively the ground truth bounding box coordinates of both objects.We also measure the performance attainable in Human Object Interaction (HOI) detection, a real-world spatial task, which includes a setting where ground truth boxes are not available at test time.An additional goal is to identify the principles necessary to effectively represent a spatial template, that is, the visual region in which two objects involved in a relationship expressed by a predicate occur.We propose a scale-, mirror-, and translation-invariant representation that captures the spatial essence of the relationship, i.e., a canonical spatial representation.Tests in two benchmarks reveal:(1) High performance is attainable by using exclusively spatial information in all tasks.(2) In HOI detection, the canonical template outperforms the rest of spatial, visual, and several state-of-the-art baselines.(3) Simple fusion of visual and spatial features substantially improves performance.(4) Our methods fare remarkably well with a small amount of data and rare categories.Our results obtained on the Visual Genome (VG) and the Humans Interacting with Common Objects -Detection (HICO-DET) datasets indicate that great predictive power can be obtained from spatial clues alone, opening up possibilities for performing fast scene understanding at a glance.
User Interaction Based Recommender System Using Machine Learning

In the present scenario of electronic commerce (E-Commerce), the in-depth knowledge of user interaction with resources has become a significant research concern that impacts more on analytical evaluations of recommender systems. For staying in aggressive E-Commerce, various products and services regarding distinctive requirements must be provided on time. Moreover, because of the large amount of product information available online, Recommender Systems (RS) are required to analyze the availability of consumers, which improves the decision-making of customers with detailed product knowledge and reduces time consumption. With that note, this paper derives a new model called User Interaction based Recommender System (UI-RS) that utilizes the data from multiple sources and opinion-based analysis for sensing the consumer needs and interests. For that, Content-Based Filtering (CBF) analyses various products and determines the likeliness of products based on User Interaction to recommend that to consumers. Then, the product information from multiple sources is combined with Dempster-Shafer (D-S) evidence theory, and then, decision making for product recommendation is performed with CBF. Moreover, the modified Radial Basis Function Neural Networks (RBFNN) technique has been incorporated for measuring product recommendations. The results show that the proposed model produces better results in providing accurate recommendations to Consumers with a higher rate of coverage and precision, thereby enhancing significant growth in E-Commerce.