Reviewerly/exHarmony huggingface dataset:
Joint Deep Networks Based Multi-Source Feature Learning for QoS Prediction

The ever-increasing diversity and dynamic of cloud environment pose many challenges on QoS prediction in service recommendation. One such challenge is how to extract and learn deep features of users/services from multi-source information to improve prediction accuracy. In this article, we propose a novel Joint Deep Networks based Multi-source Feature Learning(JDNMFL) framework for QoS prediction. JDNMFL has two parts: Multi-source Feature Extraction and Feature Interaction Learning. In the first part, a latent factor embedding method is first proposed to capture implicit features from QoS matrix, and then the multi-source features, combined by explicit features from WSDL(Web Services Description Language) document and contextual data as well as implicit features, are extracted based on the combination of matrix factorization and neural networks. In the second part, the CNN(Convolutional Neural Network)-based joint deep networks are built to learn both local and global high-order feature interactions, and to complete the final QoS prediction based on mixed features. Experimental results demonstrate that our JDNMFL approach can not only extract and integrate implicit and explicit features of various multi-source data, but also can learn feature sequence and feature interactions, so that it is very effective in improving the accuracy of QoS prediction with sparse data.
Mutual Information Maximization on Disentangled Representations for Differential Morph Detection

In this paper, we present a novel differential morph detection framework, utilizing landmark and appearance disentanglement. In our framework, the face image is represented in the embedding domain using two disentangled but complementary representations. The network is trained by triplets of face images, in which the intermediate image inherits the landmarks from one image and the appearance from the other image. This initially trained network is further trained for each dataset using contrastive representations. We demonstrate that, by employing appearance and landmark disentanglement, the proposed frame-work can provide state-of-the-art differential morph detection performance. This functionality is achieved by the using distances in landmark, appearance, and ID domains. The performance of the proposed framework is evaluated using three morph datasets generated with different methodologies.
Visual Goal Human-Robot Communication Framework With Few-Shot Learning: A Case Study in Robot Waiter System

A conventional adopted method for operating a waiter robot is based on the static position control, where predefined goal positions are marked on a map. However, this solution is not optimal in a dynamic setting, such as in a coffee shop or an outdoor catering event, because the customers often change their positions. This article explores an alternative human-robot interface design where a human operator communicates the identity of the customer to the robot instead. Inspired by how human communicates, we propose a framework for communicating a visual goal to the robot, through interactive two-way communications. The framework exploits concepts from two machine learning domains: human-in-the-loop machine learning, where active learning is used to acquire informative data, and deep metric learning, where a suitable embedding can improve the learning ability of a classifier. We also propose novel class imbalance handling techniques, which aim to actively alleviate the class imbalance problem found to be important in this mode of communication. The framework is evaluated using publicly available pedestrian datasets. We demonstrate that the proposed framework can help reduce the number of required two-way interactions and increases the robustness of the predictive model. We successfully implement the framework on a mobile robot for a delivery service in a cafe-like environment. Through the online visual goal human-robot communication, the robot can detect, recognize, and autonomously navigate to the target customer.
Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach

Modern complex systems include products and services that comprise many interconnected pieces of integrated hardware and software, which are expected to serve humans interacting with them. As technology advances, expectations of a smooth, flawless system operation grow. Model-based systems engineering, an approach based on conceptual models, copes with this challenge. Models help construct formal system representations, visualize them, understand the design, simulate the system, and discover design flaws early on. Modeling tools can benefit tremendously from querying capabilities that enable gaining deep insights into system aspects that direct model observations do not reveal. Querying mechanisms can unveil and explain cause-and-effect phenomena, identify central components, and estimate impacts or risks associated with changes. Being connected networks of system elements, models can be effectively represented as graphs, to which queries are applied. Capitalizing on established graph-theoretic algorithms to solve a large variety of problems can elevate the modeling experience to new levels. To utilize this rich set of capabilities, one must convert the model into a graph and store it in a graph database with no significant loss of information. Applying the appropriate algorithms and translating the query response back to the original intelligible and meaningful diagrammatic and textual model representation is most valuable. We present and demonstrate a querying approach of converting Object-Process Methodology (OPM) ISO 19450 models into graphs, storing them in a Neo4J graph database, and performing queries that answer complex questions on various system aspects, providing key insights into the modeled system or phenomenon and helping to improve the system design.
Detecting and explaining self-admitted technical debts with attention-based neural networks

Self-Admitted Technical Debt (SATD) is a sub-type of technical debt. It is introduced to represent such technical debts that are intentionally introduced by developers in the process of software development. While being able to gain short-term benefits, the introduction of SATDs often requires to be paid back later with a higher cost, e.g., introducing bugs to the software or increasing the complexity of the software.
Data Pricing for Blockchain-based Car Sharing: A Stackelberg Game Approach

With the increasing popularity of car sharing, a large amount of vehicle data has been generated which has great potential values for various applications (e.g., analyzing user habits for more economic benefits). These valuable data can be traded among owners and buyers on a data trading platform. Traditionally, data is traded in a centralized market which requires data exchange by trustworthy authorities. In this work, to address the potential unreliable issues (e.g., data loss and leakage), we design a consortium blockchain-based data trading framework to create a P2P trading market and enhance the security of data trading. We classify the data into five types to distinguish data with different values. Specifically, we investigate the pricing issue in the proposed car-sharing data market, which consists of data owner, service provider and data buyer. The data owner gives the pricing strategy of original data, and then the service provider processes the raw data and provides hierarchical quality of data with different data accuracy and privacy levels to the buyer who determines the data purchase strategy. Based on the interactions among these three parties, we formulate the problem as a three-layer Stackelberg game. Backward induction is applied to analyze the solution of the problem, and we conduct theoretical analysis to show the existence of Stackelberg game equilibrium. Numerical results evaluate the performance of our system under different settings.
CSAERNet: An Efficient Deep Learning Architecture for Image Classification

Deep learning performs a great role in solving many problems, particularly with image classification. Still, the performance of many architectures is often limited at some points, especially with regular architecture. In this paper, we propose an efficient and noncomplex image classification architecture using deep learning based on the most popular algorithms: Convolutional Neural Network (CNN) for Feature extraction, Stacked AutoEncoder (SAE) for reducing the dimensionality, and Recurrent Neural Network (RNN) for increasing the accuracy. The proposed network, called Convolutional Stacked AutoEncoder Recurrent Neural Network (CSAERNet), that takes the input to be features-extracted by CNN then put as input to SAE for dimensionality reduction after that the RNN takes it as input to increase the accuracy. The proposed CSAERNet has been evaluated on two widely-used benchmark datasets; CIFAR-10 and CIFAR-100. The obtained results are compared with the latest state-of-the-art approaches, the results showed that the proposed CSAERNet perform well on both datasets and outperforms on other models.
<i>dynoNet</i>: A neural network architecture for learning dynamical systems

Summary This article introduces a network architecture, called dynoNet , utilizing linear dynamical operators as elementary building blocks. Owing to the dynamical nature of these blocks, dynoNet networks are tailored for sequence modeling and system identification purposes. The back‐propagation behavior of the linear dynamical operator with respect to both its parameters and its input sequence is defined. This enables end‐to‐end training of structured networks containing linear dynamical operators and other differentiable units, exploiting existing deep learning software. Examples show the effectiveness of the proposed approach on well‐known system identification benchmarks.
On Linear Codes With One-Dimensional Euclidean Hull and Their Applications to EAQECCs

The Euclidean hull of a linear code <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> is the intersection of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> with its Euclidean dual <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C^\perp $ </tex-math></inline-formula> . The hull with low dimensions gets much interest due to its crucial role in determining the complexity of algorithms for computing the automorphism group of a linear code and for checking permutation equivalence of two linear codes. The Euclidean hull of a linear code has been applied to the so-called entanglement-assisted quantum error-correcting codes (EAQECCs) via classical error-correcting codes. In this paper, we firstly consider linear codes with one-dimensional Euclidean hull from algebraic geometry codes, and then present a general method to construct linear codes with arbitrary dimensional Euclidean hull. Some new EAQECCs are presented.
Leveraging neighborhood session information with dual attentive neural network for session-based recommendation

In the context of user uncertainty and limited information, predicting user preference is a challenging work in many online services, e.g., e-commerce and media streaming. Recent advances in session-based recommendation mostly focus on mining more available information within the current session. However, those methods ignored the sessions with similar context for the current session, which contains rich collaborative information. Therefore, in this study, we proposed a novel Leveraging Neighborhood Session Information with Dual Attentive Neural Network (LNIDA) for session-based recommendation. Specifically, LNIDA contains two main components, i.e., Current Session Encoder (CSE) and Neighborhood Session Encoder (NSE). The CSE module exploits an item-level attention mechanism to model user’s own information in the current session, and the NSE module further captures neighborhood collaborative information via a session-level attention. Then, a simple co-attention fusion mechanism is used to dynamically combine information from the CSE and NSE. Finally, to verify the performance of LNIDA, we conduct extensive experiments on three benchmark datasets, YOOCHOOSE and DIGINETICA, and the experiment results clearly show the effectiveness of LNIDA. Furthermore, we find out that LNIDA can improve performance when modeling the current session information and the neighborhood session information simultaneously.
From <i>F</i> to <i>A</i> on the New York Regents Science Exams — An Overview of the Aristo Project

Artificial intelligence has achieved remarkable mastery over games such as Chess, Go, and poker, and even Jeopardy!, but the rich variety of standardized exams has remained a landmark challenge. Even as recently as 2016, the best artificial intelligence system could only achieve 59.3 percent on an eighth‐grade science exam (Schoenick et al. 2017). This article reports success on the Grade 8 New York Regents Science Exam, where, for the first time, a system scores more than ninety percent on the exam's non‐diagram, multiple‐choice questions. In addition, our Aristo system, building upon the success of recent language models, exceeded eighty‐three percent on the corresponding Grade 12 Science Exam's non‐diagram, multiple‐choice questions. The results, on unseen test questions, are robust across different test years and different variations of this kind of test. They demonstrate that modern natural language processing methods can result in mastery on this task. While not a full solution to general question answering (the questions are limited to eighth‐grade multiple‐choice science), it represents a significant milestone for the field.
Automatic medical protocol classification using machine learning approaches

Background and objective: Assignment of medical imaging procedure protocols requires extensive knowledge about patient’s data, usually included in radiological request forms and radiological reports. Assignment of protocol is required prior to radiological study acquisition, determining procedure for each patient. The automation of this protocol assignment process could improve the efficiency of patient’s diagnosis. Artificial intelligence has proven to be of great help in these healthcare-related problems, and specifically the application of Natural Language Processing (NLP) techniques for extracting information from text reports has been successfully used in automatic text classification tasks. Methods: In this paper, machine learning classification models based on NLP have been developed using patient’s data present in radiological reports and radiological imaging protocols. We have used a real corpus provided by the private medical center “HT medica” composed of almost 700,000 Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) examinations obtained during routine clinical use. We have compared several models including traditional machine learning methods such as support vector machine and random forest, neural networks and transfer language techniques. Results: The results obtained are encouraging taking into account that the system is performing a complex text multiclass classification task. Specifically, for the best proposed system we obtain 92.2% accuracy in the CT dataset and 86.9% in the MRI dataset. Conclusions: The best machine learning system is potentially efficient, quality and cost effective. For this reason it is currently used in real scenarios by radiologists as decision support tool for assigning protocols of CT and MRI studies.
Coarse-to-fine: A dual-view attention network for click-through rate prediction

Click-Through Rate (CTR) prediction is one of the most challenging tasks in the advertising systems, which aims to estimate the probability of a candidate user clicking on a candidate item. Recently, CTR prediction has observed remarkable progress, due to the advances of the Deep Neural network (DNN) in learning representation. However, most of the existing DNN-based methods focus on modeling implicit information from the user aspect, while ignoring the correlations between the user and other objects in the historical interactive behaviors (e.g., user→items or user→users). Additionally, the large-scale historical interactive behaviors also bring the model a lot of difficulty in finding precise relations from redundant and noisy data. Targeted at the above issues, our paper introduces a simple yet effective model, which boosts the user-view and item-view (i.e., dual-view) to extract various interactive relationships for CTR prediction. Therefore, we name our model as Dual-View Attention Network (DVAN). More specifically, we propose a cross-domain attention module (i.e., user→items and item→users) to extract the coarse-level correlations, and a local homogeneous-domain attention module (i.e., user→users and item→items) to extract the fine-level correlations adaptively. In order to extract the accurate relationship from the large-scale historical behaviors, a novel selection mechanism is introduced to connect the cross-domain attention module and the local homogeneous-domain attention module, and thus builds representation from the coarse-level to the fine-level gradually. Experimental results on four public datasets have demonstrated the effectiveness of our method, which outperforms existing state-of-the-art methods.
Extracting and inserting knowledge into stacked denoising auto-encoders

Deep neural networks (DNNs) with a complex structure and multiple nonlinear processing units have achieved great successes for feature learning in image and visualization analysis. Due to interpretability of the "black box" problem in DNNs, however, there are still many obstacles to applications of DNNs in various real-world cases. This paper proposes a new DNN model, knowledge-based deep stacked denoising auto-encoders (KBSDAE), which inserts the knowledge (i.e., confidence and classification rules) into the deep network structure. This model not only can offer a good understanding of the representations learned by the deep network but also can produce an improvement in the learning performance of stacked denoising auto-encoder (SDAE). The knowledge discovery algorithm is proposed to extract confidence rules to interpret the layerwise network (i.e., denoising auto-encoder (DAE)). The symbolic language is developed to describe the deep network and shows that it is suitable for the representation of quantitative reasoning in a deep network. The confidence rule insertion to the deep network is able to produce an improvement in feature learning of DAEs. The classification rules extracted from the data offer a novel method for knowledge insertion to the classification layer of SDAE. The testing results of KBSDAE on various benchmark data indicate that the proposed method not only effectively extracts knowledge from the deep network, but also shows better feature learning performance than that of those typical DNNs (e.g., SDAE).
Interactive prostate MR image segmentation based on ConvLSTMs and GGNN

Accurate segmentation of the prostate on magnetic resonance (MR) images plays an important role for prostate cancer diagnosis and treatment. Although many automated prostate segmentation methods have been proposed, the performance still faces several challenges, which includes large variability in prostate shape, unclear boundary, and complex intensity distribution. Therefore, the results obtained from the automated methods should be further refined by users to get a more accurate and reliable segmentation. In this paper, we propose an end-to-end interactive segmentation method to refine the automated results. A convolutional long short term memory (convLSTM) module and a gated graph neural network (GGNN) are presented in the proposed method for prostate segmentation in both automated and interactive manners. A boundary loss is proposed to train our model. We evaluated the proposed method on two public available datasets and one in–house dataset. Experimental results show that the proposed convLSTM module could obtain a DSC of 91.78% on the test dataset, which outperforms eight state-of-the-art methods. A further 1.5% improvements can be obtained by user interactions based on the GGNN. The segmentation time including user interactions and inference time was 2.3 min on average for segmenting one volume.
Reasoning about Cognitive Trust in Stochastic Multiagent Systems

We consider the setting of stochastic multiagent systems modelled as stochastic multiplayer games 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 probabilistic computation tree 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 behave 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.
Continuous Tongue Robot Mapping for Paralyzed Individuals Improves the Functional Performance of Tongue-Based Robotic Assistance

Individuals with tetraplegia have a challenging life due to a lack of independence and autonomy. Assistive robots have the potential to assist with the activities of daily living and thus improve the quality of life. However, an efficient and reliable control interface for severely disabled individuals is still missing. An intraoral tongue-computer interface (ITCI) for people with tetraplegia has previously been introduced and tested for controlling a robotic manipulator in a study deploying discrete tongue robot mapping. To improve the efficiency of the interface, the current study proposed the use of virtual buttons based on the ITCI and evaluated them in combination with a joystick-like control implementation, enabling continuous control commands. Twelve able-bodied volunteers participated in a three-day experiment. They controlled an assistive robotic manipulator through the tongue to perform two tasks: Pouring water in a cup (PW) and picking up a roll of tape (PUT). Four different tongue-robot mapping methods were compared. The results showed that using continuous commands reduced the task completion time by 16% and the number of commands of the PUT test by 20% compared with discrete commands. The highest success rate for completing the tasks was 77.8% for the PUT test and 100% for the PW test, both achieved by the control methods with continuous commands. Thus, the study demonstrated that incorporating continuous commands can improve the performance of the ITCI system for controlling robotic manipulators.