Reviewerly/exHarmony huggingface dataset:
Silicon nitride Mach-Zehnder interferometer for on-chip quantum random number generation

Abstract In this work, we experimentally studied silicon nitride Mach-Zehnder interferometer (MZI) with two directional couplers and 400 ps optical delay line for telecom wavelength 1550 nm. We achieved the extinction ratio in a range of 0.76-13.86 dB and system coupling losses of 28-44 dB, depending on the parameters of directional couplers. The developed interferometer is promising for the use in a compact random number generator for the needs of a fully integrated quantum cryptography system, where compact design, as well as high generation speed, are needed.
Cybersecurity Policy and the Legislative Context of the Water and Wastewater Sector in South Africa

The water and wastewater sector is an important lifeline upon which other economic sectors depend. Securing the sector’s critical infrastructure is therefore important for any country’s economy. Like many other nations, South Africa has an overarching national cybersecurity strategy aimed at addressing cyber terrorism, cybercriminal activities, cyber vandalism, and cyber sabotage. The aim of this study is to contextualise the water and wastewater sector’s cybersecurity responsibilities within the national cybersecurity legislative and policy environment. This is achieved by conducting a detailed analysis of the international, national and sector cybersecurity stakeholders; legislation and policies; and challenges pertaining to the protection of the water and wastewater sector. The study found some concerning challenges and improvement gaps regarding the complex manner in which the national government is implementing the cybersecurity strategy. The study also found that, along with the National Cybersecurity Policy Framework (the national cybersecurity strategy of South Africa), the Electronic Communications and Transactions Act, Critical Infrastructure Protection Act, and other supporting legislation and policies make provision for the water and wastewater sector’s computer security incidents response team to be established without the need to propose any new laws or amend existing ones. This is conducive for the immediate development of the sector-specific cybersecurity governance framework and resilience strategy to protect the water and wastewater assets.
Exploring State Transition Uncertainty in Variational Reinforcement Learning

Model-free agent in reinforcement learning (RL) generally performs well but inefficient in training process with sparse data. A practical solution is to incorporate a model-based module in model-free agent. State transition can be learned to make desirable prediction of next state based on current state and action at each time step. This paper presents a new learning representation for variational RL by introducing the so-called transition uncertainty critic based on the variational encoder-decoder network where the uncertainty of structured state transition is encoded in a model-based agent. In particular, an action-gating mechanism is carried out to learn and decode the trajectory of actions and state transitions in latent variable space. The transition uncertainty maximizing exploration (TUME) is performed according to the entropy search by using the intrinsic reward based on the uncertainty measure corresponding to different states and actions. A dedicate latent variable model with a penalty using the bias of state-action value is developed. Experiments on Cart Pole and dialogue system show that the proposed TUME considerably performs better than the other exploration methods for reinforcement learning.
Human‐like motion planning for autonomous parking based on revised bidirectional rapidly‐exploring random tree* with Reeds‐Shepp curve

Abstract Autonomous parking is one significant autonomous application and will be implemented in daily life in the near future. Due to encountered narrow environments, the issues related to autonomous parking, such as path quality requirements, strict collision avoidance, and motion direction changes, must be overcome properly. Moreover, to be applied in daily driving activities, real‐time planning and human preference should be fulfilled by the designed motion planners. Therefore, an efficient and human‐like motion planning method based on the revised Bidirectional Rapidly‐Exploring Random Tree* (Bi‐RRT*) with Reeds‐Shepp curve is presented. The proposed method results in human‐like paths which have high trajectory quality and consistency for parking scenarios due to the revised Bi‐RRT* framework. Strict collision checking model guarantees the resulting paths to be collision‐free and even leaves safe distance from obstacles and uncertainties. State space adjustment makes the path optimization more efficient and effective. On the other hand, the cost function revision makes the resulting paths meet human driving behavior, such as less backward driving and motion direction changes. In addition, rigorous simulations and analysis demonstrate the effectiveness of the cost function revision and the state space adjustment and illustrate good performance of the proposed approach in common and even complex parking scenarios.
Multi-perspective cross-class domain adaptation for open logo detection

Existing logo detection methods mostly rely on supervised learning with a large quantity of labelled training data in limited classes. This restricts their scalability to a large number of logo classes subject to limited labelling budget. In this work, we consider a more scalable open logo detection problem where only a fraction of logo classes are fully labelled whilst the remaining classes are only annotated with a clean icon image (e.g. 1-shot icon supervised). To generalise and transfer knowledge of fully supervised logo classes to other 1-shot icon supervised classes, we propose a Multi-Perspective Cross-Class (MPCC) domain adaptation method. In a data augmentation principle, MPCC conducts feature distribution alignment in two perspectives. Specifically, we align the feature distribution between synthetic logo images of 1-shot icon supervised classes and genuine logo images of fully supervised classes, and that between logo images and non-logo images, concurrently. This allows for mitigating the domain shift problem between model training and testing on 1-shot icon supervised logo classes, simultaneously reducing the model overfitting towards fully labelled logo classes. Extensive comparative experiments show the advantage of MPCC over existing state-of-the-art competitors on the challenging QMUL-OpenLogo benchmark (Su et al., 2018).
Good practices for the adoption of DataOps in the software industry

Abstract The increasing adoption of DevOps, the growing availability of data concerning data development processes gives rise to the need for a systematic process for collecting, processing and using data into companies. Enterprises are making significant investments in data science applications while still struggling to realize the value of this effort. Data science is emerging as a fast-growing practice within enterprises. Several tools and platforms are being continuously introduced that support data science models while managing large data sets used to train data science models. Such a scenario lead to the emergence of DataOps. This paper summarises some of the good practices in the DataOps from the literature, offering guidelines intended to approach an organizational shift towards better data-driven decision making. This study presents a picture of the definition, the steps for adopting and challenges of the adoption of DataOps.
A Context-aware Hybrid Framework for Human Behavior Analysis

In Ambient Assisted Living (AAL) systems and personal assistive robots, human behavior analysis is essential to provide intelligent services intended to improve people's quality of lives in terms of autonomy, well-being, and safety. Human behavior analysis allows discovering people's preferences, activities, and habits. While human behavior analysis has been explored in several domains, there are still some challenges for developing efficient approaches dealing with the limitations of data-driven approach to analyze human behaviors. In this paper, a framework is proposed to better characterize the human context by inferring new knowledge about his/her behaviors using commonsense reasoning and exploiting contextual information. Human activities are firstly recognized using a CNN-LSTM model. Different contexts of human activities are then extracted to analyze human behaviors. The obtained activity contexts are mapped to an ontology, called Human AcTivity (HAT) ontology, conceptualizing the human activities and their contexts. Answer Set Programming (ASP), a high-level expressive logic-based formalism, is then used to represent human behaviors and carry out commonsense reasoning to infer new knowledge about these behaviors. The proposed framework is evaluated using the Orange4Home dataset. Moreover, two quantitative experiments are carried out to demonstrate the ability of the proposed framework to better characterize human behaviors.
Automatic Text Summarization using Soft-Cosine Similarity and Centrality Measures

The exponentially evolving size of information today has made it difficult to find relevant information quickly and efficiently. A good extractive text summarizer not only provides the most significant information from the document but also helps the user to decide the relevance of the information. The proposed method is a knowledge-based, generic, extractive text summarization technique. Our approach is based on the centrality of a sentences in the graphical representation of the documents. The graph is constructed using the pair-wise soft-cosine similarity measures between the sentences derived using the S semantic relations presented in WordNet lexical database. Eigenvector centrality measure outperforms the weighted degree, betweenness and closeness centrality measures. The resultant summary is compared against the gold-standard summaries of BBC news articles from year 2004 to 2005 and DUC 2007 datasets. The ROUGE-I, -2 and -Lmetrices are used to evaluate the results and found that our approach performs better than LexRank, TextRank, Luhn and LSA baseline text summarizers.
Teager Energy Cepstral Coefficients for Classification of Normal vs. Whisper Speech

The whispered speech is quite different from natural speech in the context of nature, acoustic characteristics, and generation mechanism. In order to improve the robustness of Automatic Speech Recognition (ASR) system, it is very important to analyze the mismatched training and testing situations and propose a robust acoustic features to enhance the whisper recognition. In this paper we propose to use Teager Energy Cepstral Coefficients (TECC) which uses Teager Energy Operator (TEO) for estimating "true" total energy of the signal, i.e., the sum of kinetic and potential energies which is contradictory to the traditional signal energy approximation, which only takes kinetic energy into account, i.e., L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> norm of the signal. In this study, experiments are performed on wTIMIT and CHAINS corpus. For wTIMIT corpus, frame-level accuracy of 92.22 % is obtained and for CHAINS corpus, it is 95.61 %. We have also estimated the performance measure of the classifier by using Matthew Correlation Coefficient (MCC), F-measure, and J-statistics. Furthermore, experiments are performed by considering latency period from a practical deployment viewpoint, and the trade-off between latency period vs. accuracy is discussed for both the corpora.
Research on basketball sports neural network model based on nonlinear classification

Intelligent video analysis has broad application prospects. How to automatically analyze and identify human behavior in video has attracted extensive attention from researchers at home and abroad. Moreover, researching effective video behavior recognition algorithms and designing efficient behavior recognition systems has important theoretical and practical value. This paper studies the nonlinear classification technique and applies the video behavior recognition algorithm to basketball recognition. Moreover, this paper studies the classical convolutional neural network model and several improvements. In addition, this paper explains the advantages of convolutional neural networks in feature extraction compared with traditional neural networks and analyzes the performance of the algorithm by designing actual experiments. The research results show that the algorithm can quickly identify multiple players on the field, and the method can effectively deal with occlusion and other issues with high accuracy and real-time.
Proposed Artificial Bee Colony Algorithm as Feature Selector to Predict the Leadership Perception of Site Managers

Abstract Datasets have relevant and irrelevant features whose evaluations are fundamental for classification or clustering processes. The effects of these relevant features make classification accuracy more accurate and stable. At this point, optimization methods are used for feature selection process. This process is a feature reduction process finding the most relevant feature subset without decrement of the accuracy rate obtained by original feature sets. Varied nature inspiration-based optimization algorithms have been proposed as feature selector. The density of data in construction projects and the inability of extracting these data cause various losses in field studies. In this respect, the behaviors of leaders are important in the selection and efficient use of these data. The objective of this study is implementing Artificial Bee Colony (ABC) algorithm as a feature selection method to predict the leadership perception of the construction employees. When Random Forest, Sequential Minimal Optimization and K-Nearest Neighborhood (KNN) are used as classifier, 84.1584% as highest accuracy result and 0.805 as highest F-Measure result were obtained by using KNN and Random Forest classifier with proposed ABC Algorithm as feature selector. The results show that a nature inspiration-based optimization algorithm like ABC algorithm as feature selector is satisfactory in prediction of the Construction Employee’s Leadership Perception.
Visual Servoing with Deep Learning and Data Augmentation for Robotic Manipulation

We propose a visual servoing (VS) approach with deep learning to perform precise, robust, and real-time six degrees of freedom (6DOF) control of robotic manipulation to ease the extraction of image features and estimate the nonlinear relationship between the two-dimensional image space and the three-dimensional Cartesian space in traditional VS tasks. Owing to the superior learning capabilities of convolutional neural networks (CNNs), autonomous learning to select and extract image features from images and fitting the nonlinear mapping is achieved. A method for designing and generating a dataset from few or one image, by simulating the motion of an eye-in-hand robotic system is described herein. Therefore, network training requiring a large amount of data and difficult data collection occurring in actual situations can be solved. A dataset is utilized to train our VS convolutional neural network. Subsequently, a two-stream network is designed and the corresponding control approach is presented. This method converges robustly with the experimental results, in that the position error is less than 3 mm and the rotation error is less than 2.5° on average.
Fisheye-Based Smart Control System for Autonomous UAV Operation

Recently, as UAVs (unmanned aerial vehicles) have become smaller and higher-performance, they play a very important role in the Internet of Things (IoT). Especially, UAVs are currently used not only in military fields but also in various private sectors such as IT, agriculture, logistics, construction, etc. The range is further expected to increase. Drone-related techniques need to evolve along with this change. In particular, there is a need for the development of an autonomous system in which a drone can determine and accomplish its mission even in the absence of remote control from a GCS (Ground Control Station). Responding to such requirements, there have been various studies and algorithms developed for autonomous flight systems. Especially, many ML-based (Machine-Learning-based) methods have been proposed for autonomous path finding. Unlike other studies, the proposed mechanism could enable autonomous drone path finding over a large target area without size limitations, one of the challenges of ML-based autonomous flight or driving in the real world. Specifically, we devised Multi-Layer HVIN (Hierarchical VIN) methods that increase the area applicable to autonomous flight by overlaying multiple layers. To further improve this, we developed Fisheye HVIN, which applied an adaptive map compression ratio according to the drone’s location. We also built an autonomous flight training and verification platform. Through the proposed simulation platform, it is possible to train ML-based path planning algorithms in a realistic environment that takes into account the physical characteristics of UAV movements.
Artificial Neural Networks to Impute Rounded Zeros in Compositional Data

Methods of deep learning have become increasingly popular in recent years, but they have not arrived in compositional data analysis. Imputation methods for compositional data are typically applied on additive, centered, or isometric log-ratio representations of the data. Generally, methods for compositional data analysis can only be applied to observed positive entries in a data matrix. Therefore, one tries to impute missing values or measurements that were below a detection limit. In this paper, a new method for imputing rounded zeros based on artificial neural networks is shown and compared with conventional methods. We are also interested in the question whether for ANNs, a representation of the data in log-ratios for imputation purposes is relevant. It can be shown that ANNs are competitive or even performing better when imputing rounded zeros of data sets with moderate size. They deliver better results when data sets are big. Also, we can see that log-ratio transformations within the artificial neural network imputation procedure nevertheless help to improve the results. This proves that the theory of compositional data analysis and the fulfillment of all properties of compositional data analysis is still very important in the age of deep learning.
TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling

This work introduces TrimTuner, the first system for optimizing machine learning jobs in the cloud to exploit sub-sampling techniques to reduce the cost of the optimization process, while keeping into account user-specified constraints. TrimTuner jointly optimizes the cloud and application-specific parameters and, unlike state of the art works for cloud optimization, eschews the need to train the model with the full training set every time a new configuration is sampled. Indeed, by leveraging sub-sampling techniques and data-sets that are up to 60 x smaller than the original one, we show that TrimTuner can reduce the cost of the optimization process by up to 50 x. Further, TrimTuner speeds-up the recommendation process by 65 x with respect to state of the art techniques for hyperparameter optimization that use sub-sampling techniques. The reasons for this improvement are twofold: i) a novel domain specific heuristic that reduces the number of configurations for which the acquisition function has to be evaluated; ii) the adoption of an ensemble of decision trees that enables boosting the speed of the recommendation process by one additional order of magnitude.
Towards a Characterization of Safe Driving Behavior for Automated Vehicles Based on Models of “Typical” Human Driving Behavior

Automated driving is expected to play a central role in future mobility systems by enabling, among other benefits, mobility-as-a-service schemes and better road utilization. To this end, automated vehicles must not only be functionally safe. They should also be perceived as driving safely by other traffic participants and have a positive impact on traffic safety. However, to the best of our knowledge, there is no consensus yet on what “driving safely” means. This article proposes a new characterization of safe driving behavior for automated vehicles based on models of “typical” human driving behavior. Such behavior (specially from attentive, experienced drivers) is known to lead to interactions of mid to low severity (i.e., low collision risk). Automated vehicles displaying similar behavior would interact with other traffic participants in a recognizable, predictable, and safe way. As a first step towards this characterization, machine-learning-based models (autoencoders) were developed from longitudinal, naturalistic driving data (from NGSIM). Autoencoders are relatively inexpensive computationally and can monitor whether a vehicle behaves “typically” or not based on anomaly detection principles. Our initial results show that the proposed approach can readily separate typical (safe) from anomalous (unsafe) driving behavior in the considered data set.
A Hybrid Deep Learning and Handcrafted Features based Approach for Thyroid Nodule Classification in Ultrasound Images

Abstract With the increasing incidence rate of thyroid cancer, the diagnosis of thyroid nodules has become an important task. In this paper, we designed a deep neural network (DNN) to classify whether a thyroid nodule is benign or malignant, and proposed a structure which combines local binary pattern (LBP) with deep learning. Our method mitigates the effects of overfitting in medical image diagnosis tasks. With well-designed transfer leaning, we achieve an accuracy of 85% on our own ultrasound thyroid dataset. To ensure the reliability of our experiments, all examples are estimated by experts in Shanghai Tenth People’s Hospital using fine needle analysis (FNA), which is a gold standard for thyroid nodules diagnosis. The experimental results show that combinations of the traditional medial image features can help the deep learning network get more semantic information from low-level inputs.
Modeling physician variability to prioritize relevant medical record information

Patient information can be retrieved more efficiently in electronic medical record (EMR) systems by using machine learning models that predict which information a physician will seek in a clinical context. However, information-seeking behavior varies across EMR users. To explicitly account for this variability, we derived hierarchical models and compared their performance to nonhierarchical models in identifying relevant patient information in intensive care unit (ICU) cases.Critical care physicians reviewed ICU patient cases and selected data items relevant for presenting at morning rounds. Using patient EMR data as predictors, we derived hierarchical logistic regression (HLR) and standard logistic regression (LR) models to predict their relevance.In 73 pairs of HLR and LR models, the HLR models achieved an area under the receiver operating characteristic curve of 0.81, 95% confidence interval (CI) [0.80-0.82], which was statistically significantly higher than that of LR models (0.75, 95% CI [0.74-0.76]). Further, the HLR models achieved statistically significantly lower expected calibration error (0.07, 95% CI [0.06-0.08]) than LR models (0.16, 95% CI [0.14-0.17]).The physician reviewers demonstrated variability in selecting relevant data. Our results show that HLR models perform significantly better than LR models with respect to both discrimination and calibration. This is likely due to explicitly modeling physician-related variability.Hierarchical models can yield better performance when there is physician-related variability as in the case of identifying relevant information in the EMR.