Reviewerly/exHarmony huggingface dataset:
A Design of Smart Unmanned Vending Machine for New Retail Based on Binocular Camera and Machine Vision

The smart unmanned vending machine using machine vision technology suffers from the sharp decrease of detection accuracy due to the incomplete image collection of items by monocular camera in complex environment, and the lack of obvious features in dense stacking of items. In this article, a binocular camera system is designed to effectively solve the problems of distortion and coverage caused by monocular camera. Besides, an image-stitching algorithm is developed to splice the images captured by the camera, which reliefs the burden of computation for back-end recognition processing brought by the binocular camera. A new neural network structure-the YOLOv3-TinyE is proposed based on YOLOv3-tiny model. Based on the dataset of 21,000 images captured in real scenarios containing 20 different type of beverages, the comparison experimental results show that YOLOv3-TinyE model achieves the mean average precision of 99.15%, and the inference speed is 2.91 times faster than that of YOLOv3 model, and the detection accuracy of YOLOv3-TinyE model based on binocular vision is higher than that based on monocular vision. The results suggest that the designed method achieves the goal in terms of inference speed and average precision, that is, it is able to satisfy the requirements for real-world applications.
Generic visual data mining-based framework for revealing abnormal operation patterns in building energy systems

The abnormal operation patterns in building energy systems can be revealed by analyzing their historical operational data. In practice, the amount of data is so tremendous that manual data analysis is challenging. Visual data mining is a promising solution to this problem. This study proposes a generic visual data mining-based framework for extracting abnormal operation patterns in building energy systems from their historical operational data. The framework consists of three steps. First, a kernel density estimation-based approach is utilized to preprocess the raw data. Then, a decision tree-based approach is adopted to identify the system operation conditions. Finally, a maximal frequent subgraph mining-based approach is developed to reveal the system operation patterns. The framework is applied to analyze the one-year operational data of a chiller plant. This study proves that the framework can appropriately interpret the data mining results, and can make the analysis of the results more convenient.
Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management

Task-oriented dialogue systems typically rely on large amounts of high-quality training data or require complex handcrafted rules. However, existing datasets are often limited in size con- sidering the complexity of the dialogues. Additionally, conventional training signal in- ference is not suitable for non-deterministic agent behavior, namely, considering multiple actions as valid in identical dialogue states. We propose the Conversation Graph (ConvGraph), a graph-based representation of dialogues that can be exploited for data augmentation, multi- reference training and evaluation of non- deterministic agents. ConvGraph generates novel dialogue paths to augment data volume and diversity. Intrinsic and extrinsic evaluation across three datasets shows that data augmentation and/or multi-reference training with ConvGraph can improve dialogue success rates by up to 6.4%.
A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery

Bayesian networks (BNs) are a widely used graphical model in machine learning. As learning the structure of BNs is NP-hard, high-performance computing methods are necessary for constructing large-scale networks. In this paper, we present a parallel framework to scale BN structure learning algorithms to tens of thousands of variables. Our framework is applicable to learning algorithms that rely on the discovery of Markov blankets (MBs) as an intermediate step. We demonstrate the applicability of our framework by parallelizing three different algorithms: Grow-Shrink (GS), Incremental Association MB (IAMB), and Interleaved IAMB (Inter-IAMB). Our implementations are able to construct BNs from real data sets with tens of thousands of variables and thousands of observations in less than a minute on 1024 cores, with a speedup of up to 845X and 82.5% efficiency. Furthermore, we demonstrate using simulated data sets that our proposed parallel framework can scale to BNs of even higher dimensionality.
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation

To satisfy the stringent requirements for computational resources in the field of real-time semantic segmentation, most approaches focus on the hand-crafted design of light-weight segmentation networks. To enjoy the ability of model auto-design, Neural Architecture Search (NAS) has been introduced to search for the optimal building blocks of networks automatically. However, the network depth, downsampling strategy, and feature aggregation method are still set in advance and nonadjustable during searching. Moreover, these key properties are highly correlated and essential for a remarkable real-time segmentation model. In this paper, we propose a joint search framework, called AutoRTNet, to automate all the aforementioned key properties in semantic segmentation. Specifically, we propose hyper-cells to jointly decide the network depth and the downsampling strategy via a novel cell-level pruning process. Furthermore, we propose an aggregation cell to achieve automatic multi-scale feature aggregation. Extensive experimental results on Cityscapes and CamVid datasets demonstrate that the proposed AutoRTNet achieves the new state-of-the-art trade-off between accuracy and speed. Notably, our AutoRTNet achieves 73.9% mIoU on Cityscapes and 110.0 FPS on an NVIDIA TitanXP GPU card with input images at a resolution of $$768 \times 1536$$ .
Lab-scale error analysis on X-ray fluorecence sensing for bulk ore sorting

X-Ray Fluorescence is one of the sensing technologies with potential in bulk ore sorting application. However, its reliability is often questioned since X-Ray Fluorescence does not sufficiently penetrate and errors are associated with surface measurements representing the volumetric grade. In this paper, error analysis was conducted on the XRF measurements on bulk ore materials at lab scale. Three main sources of the errors were identified, termed as instrumental error, heterogeneity error and the sampling error. Among the errors, the heterogeneity error was identified as the most significant due to its irrelevance with measuring the concentration and the unpredictability of that value. The influence of the errors on the conventional linear regression approach was investigated in terms of the sorting results and economics. Results showed that the linear regression approach was expected suitable for cases when sufficient analysis interval time could be ensured. It this was allowed for, the applicability of XRF in the bulk ore sorting was fundamentally justified.
Membership Inference Attack with Multi-Grade Service Models in Edge Intelligence

Edge intelligence (EI), integrated with the merits of both edge computing and artificial intelligence, has been proposed recently to realize intensive computation and low delay inference in the edge of the Internet of Things (IoT). However, the constrained energy and computation ability in edge devices become the main obstacle for EI application in IoT. There is a flexible multi-grade EI deployment scheme in which multiple machine models are provided to meet the different requirements of edge users (inference accuracy, inference delay, inference cost, etc.). In the multi-grade EI model, low-grade models contain information of high-grade ones, and the inference cost increases with the inference accuracy. Thus, some attackers or malicious users may want to obtain some private information of those high-grade models by querying low-grade ones at low cost. Specifically, in this article we study the vulnerability of the multi-grade EI model against membership inference attack (MIA). First, we propose an attack model for multi-grade EI. Second, we reveal different grades of EI on vulnerability by comparing inference accuracy and cost in different datasets. The experiment results show that in multi-grade EI, a low-grade model would leak privacy information of a high-grade model under MIA.
Software defect prediction using Binary Particle Swarm Optimization with Binary Cross Entropy as the fitness function

Abstract Software is a consequential asset because concrete software is needed in virtually every industry, in every business, and for every function. It becomes more paramount as time goes on – if something breaks within your application portfolio and expeditious, efficient, and efficacious fine-tune needs to transpire as anon as possible. Therefore, recognizing the faults in the early phase of the software development lifecycle is essential for both, diminishing the cost in terms of efforts and money Similarly, It is important to find out features which could be redundant or features which are highly correlated with each other, as it could largely affect the model’s learning process. This analysis refers to the use of crossover Artificial Neural Network (ANN) and Binary Particle Swarm Optimization (BPSO) with Binary Cross-Entropy (BCE) loss for the fitness function. The conclusion of proposed paper is to provide the significance and opportunity of using Binary Cross-Entropy (BCE) in Binary Particle Swarm Optimization (BPSO) for the feature reduction process in order to minimize the developer’s effort and costs for software development as well as for its maintenance.
Planning-based knowing how: A unified approach

Various logical notions of know-how have been recently proposed and studied in the literature based on different types of epistemic planning in different frameworks. This paper proposes a unified logical framework to incorporate the existing and some new notions of know-how. We define the semantics of the know-how operator using a unified notion of epistemic planning with parameters of different types of plans specified by a programming language. Surprisingly, via a highly unified completeness proof, we show that all the ten intuitive notions of plans discussed in this paper lead to exactly the same know-how logic, which is proven to be decidable. We also show that over finite models, the know-how logic based on knowledge-based plans requires an extension with an axiom capturing the compositionality of the plans. In the context of epistemic planning, our axiomatization results reveal the core principles behind the very idea of epistemic planning, independent of the particular notion of plans. Moreover, since epistemic planning can be expressed by the know-how modality in our object language, we can greatly generalize the planning problems that can be solved formally by model checking various formulas in our language.
Verbal Communication in Robotics: A Study on Salient Terms, Research Fields and Trends in the Last Decades Based on a Computational Linguistic Analysis

Verbal communication is an expanding field in robotics showing a significant increase in both the industrial and research field. The application of verbal communication in robotics aims to reach a natural human-like interaction with robots. In this study, we investigated how salient terms related to verbal communication in robotics have evolved over the years, what are the topics that recur in the related literature, and what are their trends. The study is based on a computational linguistic analysis conducted on a database of 7,435 scientific publications over the last 2 decades. This comprehensive dataset was extracted from the Scopus database using specific key-words. Our results show how relevant terms of verbal communication evolved, which are the main coherent topics and how they have changed over the years. We highlighted positive and negative trends for the most coherent topics and the distribution over the years for the most significant ones. In particular, verbal communication resulted in being highly relevant for social robotics. Potentially, achieving natural verbal communication with a robot can have a great impact on the scientific, societal, and economic role of robotics in the future.
Isolation Forest Based Anomaly Detection Framework on Non-IID Data

Anomaly detection is a significant but challenging data mining task in a wide range of applications. Different domains usually use different ways to measure the characteristics of data and to define the anomaly types. As a result, it is a big challenge to develop a versatile anomaly detection framework that can be universally applied with satisfactory performance in most, if not all, applications. In this article, we propose a generic isolation forest based ensemble framework named EDBHiForest, which can be universally applied to data spaces with arbitrary distance measures. It is realized through embedding the isolation forest structure with extended distance-based hashing (EDBH), which can significantly enhance the versatility and applicability of isolation forest based anomaly detection. This framework overcomes the limitations of existing isolation forest based methods that can only be applied to datasets with a very limited range of distance measure types. Extensive experiments on various non-independent and identically distributed datasets demonstrate the effectiveness and efficiency of our approach.
A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences

Event suffix and remaining time prediction are sequence to sequence learning tasks.They have wide applications in different areas such as economics, digital health, business process management and IT infrastructure monitoring.Timestamped event sequences contain ordered events which carry at least two attributes: the event's label and its timestamp.Suffix and remaining time prediction are about obtaining the most likely continuation of event labels and the remaining time until the sequence finishes, respectively.Recent deep learning-based works for such predictions are prone to potentially large prediction errors because of closed-loop training (i.e., the next event is conditioned on the ground truth of previous events) and open-loop inference (i.e., the next event is conditioned on previously predicted events).In this work, we propose an encoder-decoder architecture for open-loop training to advance the suffix and remaining time prediction of event sequences.To capture the joint temporal dynamics of events, we harness the power of adversarial learning techniques to boost prediction performance.We consider four real-life datasets and three baselines in our experiments.The results show improvements up to four times compared to the state of the art in suffix and remaining time prediction of event sequences, specifically in the realm of business process executions.We also show that the obtained improvements of adversarial training are superior compared to standard training under the same experimental setup.
Exploration Versus Exploitation in Reinforcement Learning: A Stochastic Control Approach

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Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo for Industrial Applications

Abstract Gaussian process (GP) regression or kriging has been extensively applied in the engineering literature for the purposes of building a cheap-to-evaluate surrogate, within the contexts of multi-fidelity modeling, model calibration, and design optimization. With the ongoing automation of manufacturing and industrial practices as a part of Industry 4.0, there has been a greater need for advancing GP regression techniques to handle challenges such as high input dimensionality, data paucity or big data problems, these consist primarily of proposing efficient design of experiments, optimal data acquisition strategies, sparsifying covariance kernels, and other mathematical tricks. In this work, our attention is focused on the challenges of efficiently training a GP model, which, to the authors opinion, has attracted very little attention and is to-date poorly addressed. The performance of widely used training approaches such as maximum likelihood estimation and Markov Chain Monte Carlo (MCMC) sampling can deteriorate significantly in high-dimensional and big data problems and can lead to cost deficient implementations of critical importance to many industrial applications. Here, we compare an Adaptive Sequential Monte Carlo (ASMC) sampling algorithm to classic MCMC sampling strategies and we demonstrate the effectiveness of our implementation on several mathematical problems and challenging industry applications of varying complexity. The computational time savings of the ASMC approach manifest in large-scale problems helping us to push the boundaries of applicability and scalability of GPs for model calibration in various domains of the industry, including but not limited to design automation, design engineering, smart manufacturing, predictive maintenance, and supply chain manufacturing.
When Measurements Meet Blockchain: On Behalf of an Inter-NMI Network

The growing demand for solutions related to measurement (e.g., digital sensors, smart meters, distributed measuring systems) imposes several concerns about information and process reliability. In this context, blockchain can play a crucial role as a platform to implement applications and activities in the context of legal metrology. In most countries, the National Metrology Institutes (NMIs) are responsible for promoting these initiatives. Thus, in this paper, we present a functional architecture to integrate NMIs in a collaborative blockchain network. We discuss the main aspects and features that an inter-NMI blockchain network must deliver. Furthermore, we implement our proposal using the Hyperledger Fabric platform. We connect peers from Physikalisch-Technische Bundesanstalt (PTB) (German NMI) and the National Institute of Metrology, Quality, and Technology (Inmetro) (Brazilian NMI) in a useful application that consists of a blockchain-based public-key infrastructure to identify and authenticate smart meters. Our preliminary results demonstrate that the proposed architecture meets the main requirements imposed by applications involving measurements. Furthermore, it opens the opportunity to integrate NMIs from other countries into the project, constituting an important global initiative in the metrology field.
A deep‐learning‐ and reinforcement‐learning‐based system for encrypted network malicious traffic detection

Traditional network intrusion detection methods lack the ability of automatic feature extraction for encrypted network malicious traffic, and thus, the detection rates are low. Moreover, the means of this malicious traffic are concealed, and the key malicious features are usually hidden in many normal data packets, so fewer encrypted malicious traffic samples can be captured. This easily leads to insufficient system training, low detection rate, and high false alarm rate. This letter proposes an encrypted network malicious traffic detection model based on deep learning, in which automatic feature extraction is performed against encrypted network malicious traffic. The proposed model has self-learning and self-adaption abilities. Furthermore, a sample generation method of encrypted traffic based on deep Q-networks and deep convolution generative adversarial networks is proposed, in which new samples are learned from the training samples of encrypted traffic and solves problems, such as insufficient original training samples and unbalanced samples. In a validation experiment, the proposed model could distinguish between normal and abnormal encrypted network traffic, and the accuracy rate reached 99.94%. Experimental results show that the proposed model in this letter can provide a new and better solution for an encrypted network malicious traffic detection system.
Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

With the rapid development of social networks, users pay more and more attention to the protection of personal information. However, the transmission of users’ personal information through social networks will inevitably lead to privacy leakage and make users attacked. A large amount of privacy information can be inferred from the content and social traces published by users, which leads to the rise of privacy inference technology for users in social networks. Social relationship inference and attribute inference are two basic attacks on users’ privacy in social networks. This is the first systematic review of privacy inference attacks in social networks. The purpose of this retrospective study is to provide a global summary, summarizing the time trend of the topic, and show the evolution of the topic in the past 15 years. In addition, this paper discusses the trend and development of this topic and finally looks forward to future work. The contribution of our work is helpful for researchers to continue their research in this field.
Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis

Deep neural networks can achieve impressive performance in the regime where they are massively over-parameterized. Consequently, over the past year, there has been a growing interest in analyzing optimization and generalization properties of over-parameterized networks. However, the majority of existing work only applies to supervised learning. The role of over-parameterization in the unsupervised setting has by contrast gained far less attention. In this paper, we study the inductive bias of gradient descent for two-layer over-parameterized autoencoders with ReLU activation. We first provide theoretical evidence for the memorization phenomena observed in recent work using the property that infinitely wide neural networks under gradient descent evolve as linear models. We also analyze the gradient dynamics of the autoencoders in the finite-width setting. Starting from a randomly initialized autoencoder network, we rigorously prove the linear convergence of gradient descent in two weakly-trained and jointly-trained regimes. Our results indicate the considerable benefits of joint training over weak training in finding global optima, achieving a dramatic decrease in the required level of over-parameterization. Finally, we analyze the case of weight-tied autoencoders and prove that in the over-parameterized setting, training such networks from randomly initialized points leads to certain unexpected degeneracies.