Reviewerly/exHarmony huggingface dataset:
Introduction to neural network‐based question answering over knowledge graphs

Abstract Question answering has emerged as an intuitive way of querying structured data sources and has attracted significant advancements over the years. A large body of recent work on question answering over knowledge graphs (KGQA) employs neural network‐based systems. In this article, we provide an overview of these neural network‐based methods for KGQA. We introduce readers to the formalism and the challenges of the task, different paradigms and approaches, discuss notable advancements, and outline the emerging trends in the field. Through this article, we aim to provide newcomers to the field with a suitable entry point to semantic parsing for KGQA, and ease their process of making informed decisions while creating their own QA systems. This article is categorized under: Technologies &gt; Machine Learning Technologies &gt; Prediction Technologies &gt; Artificial Intelligence
Steganalysis of Digital Images Using Deep Fractal Network

In the recent literature on steganalysis, it has been observed that a deeper network is, in general, preferred for detecting low tone embedding noise, e.g., SRNet. However, very recently, a deep model, called FractalNet, became popular, which is based on self-similarity and grows deeper and wider by maintaining a balance between depth and width using a recurrent adaptation of a fundamental building block. In this work, the concept of the FractalNet model has been exploited for steganalytic detection, where the embedded image has been used as input. In a practical scenario, it has been observed that steganalytic detection for test images is increasing if the width of the network can be increased with a certain proportion to the depth. The proposed deep network is designed by repeating a basic fractal block in such a way that a balance between the depth and width of the overall network can be maintained. A comprehensive set of experiments reveals that the proposed model outperformed the state-of-the-art results. An ablation study is also included to justify the proposed architecture in favor of its performance.
Predictive learning of multi-channel isochronal chaotic synchronization by utilizing parallel optical reservoir computers based on three laterally coupled semiconductor lasers with delay-time feedback

In this work, we utilize three parallel optical reservoir computers to model three optical dynamic systems, respectively. Here, the three laser-elements in the response laser array with both delay-time feedback and optical injection are utilized as nonlinear nodes to realize three optical chaotic reservoir computers (RCs). The nonlinear dynamics of three laser-elements in the driving laser array are predictively learned by these three parallel RCs. We show that these three parallel reservoir computers can reproduce the nonlinear dynamics of the three laser-elements in the driving laser array with self-feedback. Very small training errors for their predictions can be realized by the optimization of two key parameters such as the delay-time and the interval of the virtual nodes. Moreover, these three parallel RCs to be trained will well synchronize with three chaotic laser-elements in the driving laser array, respectively, even when there are some parameter mismatches between the response laser array and the driving laser array. Our findings show that optical reservoir computing approach possibly provide a successful path for the realization of the high-quality chaotic synchronization between the driving laser and the response laser when their rate-equations imperfectly match each other.
Traffic Sign Recognition Based on Convolutional Neural Network Model

Traffic sign recognition (TSR) is a significance research branch in the field of unmanned driving, which is very important for driverless driving and is often used to read permanent or temporary road signs on the roadside. Traffic sign detection (TSD) and traffic sign classification (TSC) constitute a complete recognition system. The paper mainly studies the traffic sign recognition. Traffic sign recognition is mostly applied to portable devices, so the size and detection speed of the model are important factors to be considered. Under the condition of ensuring the speed, the detection accuracy of the model is guaranteed. The accuracy of the model designed in this paper on the German traffic sign recognition benchmark (GTSRB) is 99.30%, the parameter size is only 1.3M, and the trained network model is 4.0M. The results of final experiment show that the network is valid for the classification of traffic signs.
Unsupervised cross-modal similarity via Latent Structure Discrete Hashing Factorization

To date, large amounts of discriminative semantics-preserving discrete hash models are enjoying great popularity in cross-modal hashing community. Most of them, however, distill the shared hash codes from semantic tags, which ignores the intrinsic structure of features. Therefore, this paper proposes a direct extraction of discrete hash representation framework by factorizing latently similar structures for cross-modal retrieval in an unsupervised manner, which we refer to as Latent Structure Discrete Hashing Factorization (LSDHF). Concretely, for different modalities, assisted by the Hadamard matrix, LSDHF aligns all eigenvalues of the similarity matrix to generate a hash dictionary, and then straightly distills the shared hash codes from modalities' intrinsic structure rather than just preserving the original geometry, so as to strengthen modal connection. In addition, a hyperbolic tangent kernel function is exploited to make the original feature more close to the hash code, thus reducing the mapping loss from the original space to the Hamming space. In the optimization phase, a discrete iterative algorithm is designed for binary optimization without introducing any intermediate variables, or utilizing relaxation strategies. The simulation results on widely used data sets demonstrate the superiority of the proposed strategy competing with state-of-the-art methods, including some supervised cross-modal hashing methods.
A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.
Online End-To-End Neural Diarization with Speaker-Tracing Buffer

This paper proposes a novel online speaker diarization algorithm based on a fully supervised self-attention mechanism (SA-EEND). Online diarization inherently presents a speaker's permutation problem due to the possibility to assign speaker regions incorrectly across the recording. To circumvent this inconsistency, we proposed a speaker-tracing buffer mechanism that selects several input frames representing the speaker permutation information from previous chunks and stores them in a buffer. These buffered frames are stacked with the input frames in the current chunk and fed into a self-attention network. Our method ensures consistent diarization outputs across the buffer and the current chunk by checking the correlation between their corresponding outputs. Additionally, we trained SA-EEND with variable chunk-sizes to mitigate the mismatch between training and inference introduced by the speaker-tracing buffer mechanism. Experimental results, including online SA-EEND and variable chunk-size, achieved DERs of 12.54% for CALLHOME and 20.77% for CSJ with 1.4 s actual latency.
Optimized Bezier Curve Based Intensity Mapping Scheme for Low Light Image Enhancement

Low light enhancement has been very challenging for images acquired under extremely-low light circumstances. In this article, an optimized Bezier curve based intensity mapping scheme for low light image enhancement is proposed. The proposed algorithm is employed to adjust the intensity mapping curve in both dark and bright regions. To enhance the low visibility details and suppress the noise, the control points of the Bezier curve transformation are automatically obtained by the optimal weighting process. The optimal constraints of the weighted probability distribution function are calculated by whale optimization algorithm to solve the problem of under and over enhancement. Tests on different low light images are conducted to expose the efficiency and superiority of the proposed method over other existing methods in terms of improving the visual quality and bringing out the hidden details. The proposed method can preserve some minor details of the brighter portions and significantly reduce the intensive noise hidden in the low light scenes without any color distortion.
Rethink the IoU-based loss functions for bounding box regression

The ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> -norm loss is widely used as the bounding box regression loss function in the existing object detectors, but Intersection over Union (IoU) is used to evaluate the accuracy of the bounding box locating. There is no inevitable correlation between low ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> -norm loss and high IoU, so IoU-based loss functions represented by IoU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+1</sup> , Generalized IoU (GIoU), Distance IoU (DIoU) and Complete IoU (CIoU) appeared. Based on the simulation experiment of bounding box regression, we point out that the existing IoU-based loss functions have the problems of slow convergence speed, large training error, and poor training result. From the specific bounding box regression process, the causes of the problems are explored, and a new loss function LIoU combining smooth ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm loss and IoU is proposed. The experimental results on the two-stage object detectors Faster R-CNN, Mask R-CNN and the single-stage object detector RetinaNet consistently show that LIoU gets rid of the problems of the existing IoU-based loss functions and can further improve the performance of the object detectors. The source code and model training weights are open source on https://github.com/iamRyanChia/LIoU.https://github.com/iamRyanChia/LIoU.
Minimalistic fuzzy ontology reasoning: An application to Building Information Modeling

This paper presents a minimalistic reasoning algorithm to solve imprecise instance retrieval in fuzzy ontologies with application to querying Building Information Models (BIMs)—a knowledge representation formalism used in the construction industry. Our proposal is based on a novel lossless reduction of fuzzy to crisp reasoning tasks, which can be processed by any Description Logics reasoner. We implemented the minimalistic reasoning algorithm and performed an empirical evaluation of its performance in several tasks: interoperation with classical reasoners (Hermit and TrOWL), initialization time (comparing TrOWL and a SPARQL engine), and use of different data structures (hash tables, databases, and programming interfaces). We show that our software can efficiently solve very expressive queries not available nowadays in regular or semantic BIMs tools.
Bound estimation-based safe acceleration for maximum margin of twin spheres machine with pinball loss

Maximum margin of twin spheres support vector machine (MMTSSVM) is an efficient method for imbalanced data classification. As an extension to enhance noise insensitivity of MMTSSVM, MMTSSVM with pinball loss (Pin-MMTSM) has a good generalization performance. However, it is not efficient enough for large-scale data. Inspired by the sparse solution of SVMs, in this paper, we propose a safe accelerative approach to reduce the computational cost. Unlike the existing safe screening rules, where only one variable changes with the parameters. We utilize bound estimation-based to derive the upper and lower bounds of center and radius. With our approach, the inactive samples are discarded before solving the problem, thus it can reduce the computational cost. One important advantage of our approach is safety, i.e., we can obtain the same solution as solving original problem both in linear and non-linear cases. Moreover, it is obvious that our acceleration approach is independent of the solver. To further accelerate the computational speed, a decomposition method is employed. Experiments on three artificial datasets and twelve benchmark datasets clearly demonstrate the effectiveness of our approach. At last, we extend bound estimation-based method to ν-SVM, theoretical analysis and experimental results both verify its feasibility and effectiveness.
A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

Selecting internal hyperparameters, which can be set by the automatic search algorithm, is important to improve the generalization performance of machine learning models. In this study, the geological, remote sensing and geochemical data of the Lalingzaohuo area in Qinghai province were researched. A multi-source metallogenic information spatial data set was constructed by calculating the Youden index for selecting potential evidence layers. The model for mapping mineral prospectivity of the study area was established by combining two swarm intelligence optimization algorithms, namely the bat algorithm (BA) and the firefly algorithm (FA), with different machine learning models. The receiver operating characteristic (ROC) and prediction-area (P-A) curves were used for performance evaluation and showed that the two algorithms had an obvious optimization effect. The BA and FA differentiated in improving multilayer perceptron (MLP), AdaBoost and one-class support vector machine (OCSVM) models; thus, there was no optimization algorithm that was consistently superior to the other. However, the accuracy of the machine learning models was significantly enhanced after optimizing the hyperparameters. The area under curve (AUC) values of the ROC curve of the optimized machine learning models were all higher than 0.8, indicating that the hyperparameter optimization calculation was effective. In terms of individual model improvement, the accuracy of the FA-AdaBoost model was improved the most significantly, with the AUC value increasing from 0.8173 to 0.9597 and the prediction/area (P/A) value increasing from 3.156 to 10.765, where the mineral targets predicted by the model occupied 8.63% of the study area and contained 92.86% of the known mineral deposits. The targets predicted by the improved machine learning models are consistent with the metallogenic geological characteristics, indicating that the swarm intelligence optimization algorithm combined with the machine learning model is an efficient method for mineral prospectivity mapping.
Too Informal? How a Chatbot’s Communication Style Affects Brand Attitude and Quality of Interaction

This study investigated the effects of (in)formal chatbot responses and brand familiarity on social presence, appropriateness, brand attitude, and quality of interaction. An online experiment using a 2 (Communication Style: Informal vs. Formal) by 2 (Brand: Familiar vs. Unfamiliar) between subject design was conducted in which participants performed customer service tasks with the assistance of chatbots developed for the study. Subsequently, they filled out an online questionnaire. An indirect effect of communication style on brand attitude and quality of interaction through social presence was found. Thus, a chatbot's informal communication style induced a higher perceived social presence which in turn positively influenced quality of the interaction and brand attitude. However, brand familiarity did not enhance perceptions of appropriateness, indicating participants do not assign different roles to chatbots as communication partner.
Quantum Convolutional Neural Network for Resource-Efficient Image Classification: A Quantum Random Access Memory (QRAM) Approach

Convolutional Neural Network (CNN) is a breakthrough learning model that shows outstanding performance in computer vision and deep learning applications. However, it is a relatively burdened model in terms of learning speed and resource usage compared to other learning models when the learning scale becomes large. Quantum Convolutional Neural Network (QCNN) is a novel model as a potential solution using quantum computers to handle this problem. Quantum computers with a limited number of usable qubits needs a resource-efficient method to process large-scale data at once. In addition, Quantum Random Access Memory (QRAM) can store the large data to qubits logarithmically using superposition and entanglement. The QRAM algorithm can design a new QCNN model that can efficiently process in massive data. This paper proposes a more resource and depth efficient model for larger-sized input data and the number of output channels using the QRAM algorithm and efficiently extracting features.
Performance of Ag–Ag<sub>2</sub>S core–shell nanoparticle-based random network reservoir computing device

Abstract Reservoir computing (RC), a low-power computational framework derived from recurrent neural networks, is suitable for temporal/sequential data processing. Here, we report the development of RC devices utilizing Ag–Ag 2 S core–shell nanoparticles (NPs), synthesized by a simple wet chemical protocol, as the reservoir layer. We examined the NP-based reservoir layer for the required properties of RC hardware, such as echo state property, and then performed the benchmark tasks. Our study on NP-based reservoirs highlighted the importance of the dynamics between the NPs as indicated by the rich high dimensionality due to the echo state property. These dynamics affected the accuracy (up to 99%) of the target waveforms that were generated with a low number of readout channels. Our study demonstrates the great potential of Ag–Ag 2 S NPs for the development of next-generation RC hardware.
Automatic Report Generation for Chest X-Ray Images via Adversarial Reinforcement Learning

An adversarial reinforced report-generation framework for chest x-ray images is proposed. Previous medical-report-generation models are mostly trained by minimizing the cross-entropy loss or further optimizing the common image-captioning metrics, such as CIDEr, ignoring diagnostic accuracy, which should be the first consideration in this area. Inspired by the generative adversarial network, an adversarial reinforcement learning approach is proposed for report generation of chest x-ray images considering both diagnostic accuracy and language fluency. Specifically, an accuracy discriminator (AD) and fluency discriminator (FD) are built that serve as the evaluators by which a report based on these two aspects is scored. The FD checks how likely a report originates from a human expert, while the AD determines how much a report covers the key chest observations. The weighted score is viewed as a “reward” used for training the report generator via reinforcement learning, which solves the problem that the gradient cannot be passed back to the generative model when the output is discrete. Simultaneously, these two discriminators are optimized by maximum-likelihood estimation for better assessment ability. Additionally, a multi-type medical concept fused encoder followed by a hierarchical decoder is adopted as the report generator. Experiments on two large radiograph datasets demonstrate that the proposed model outperforms all methods to which it is compared.
A Pursuit of Sustainable Privacy Protection in Big Data Environment by an Optimized Clustered-Purpose Based Algorithm

Achievement of sustainable privacy preservation is mostly very challenging in a resource shared computer environment. This challenge demands a dedicated focus on the exponential growth of big data. Despite the existence of sp... | Find, read and cite all the research you need on Tech Science Press
Performance Evaluation of Consensus Protocols in Blockchain-based Audit Systems

Blockchain-based audit systems use “Practical Byzantine Fault Tolerance” (PBFT) consensus protocol which suffers from a high message complexity and low scalability. Alternatives to PBFT have not been tested in blockchain-based audit systems since no blockchain testbed supports the execution and benchmarking of different consensus protocols in a unified testing environment. In this paper, we address this gap by developing a blockchain testbed capable of executing and testing five consensus protocols in a blockchain network; namely PBFT, Proof-of-Work (PoW), Proof-of-Stake (PoS), Proof-of-Elapsed Time (PoET), and Clique. We carry out performance evaluation of those consensus algorithms using data from a real-world audit system. Our results show that the Clique protocol is best suited for blockchain-based audit systems, based on scalability features.
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition

The lack of large-scale real datasets with annotations makes transfer learning a necessity for video activity understanding. We aim to develop an effective method for few-shot transfer learning for first-person action classification. We leverage independently trained local visual cues to learn representations that can be transferred from a source domain, which provides primitive action labels, to a different target domain - using only a handful of examples. Visual cues we employ include object-object interactions, hand grasps and motion within regions that are a function of hand locations. We employ a framework based on meta-learning to extract the distinctive and domain invariant components of the deployed visual cues. This enables transfer of action classification models across public datasets captured with diverse scene and action configurations. We present comparative results of our transfer learning methodology and report superior results over state-of-the-art action classification approaches for both inter-class and inter-dataset transfer.