Reviewerly/exHarmony huggingface dataset:
Quantum speed limits and the maximal rate of information production

The Bremermann-Bekenstein bound sets a fundamental upper limit on the rate with which information can be processed. However, the original treatment heavily relies on cosmological properties and plausibility arguments. In the present analysis, we derive equivalent statements by relying on only two fundamental results in quantum information theory and quantum dynamics -- Fannes inequality and the quantum speed limit. As main results, we obtain Bremermann-Bekenstein-type bounds for the rate of change of the von Neumann entropy in quantum systems undergoing open system dynamics, and for the rate of change of the Shannon information over some logical basis in unitary quantum evolution.
Generation of Highly Nonlinear and Dynamic AES Substitution-Boxes (S-Boxes) Using Chaos-Based Rotational Matrices

This work reports a novel chaos-based affine transformation generation method, which is based on rotational matrices to design strong key-based S-boxes.Chaotic logistic map's nonlinear trajectories are used to generate rotational matrices under given design conditions.Thus, the inherent logic is to generate key-based S-boxes, as strong as AES S-box, in terms of cryptographic properties using chaos in affine transformation.The randomness of chaotic sequences is tested using the National Institute of Standard and Technology (NIST) Statistical Test Suit (STS) 800-22 that validates the generated sequences for S-box design.The results show that methodology adapted to design proposed key-based dynamic S-boxes entails near-optimal cryptographic properties so that proposed S-boxes are as stronger as AES S-box.
Interpretable and Accurate Convolutional Neural Networks for Human Activity Recognition

With the advances of sensing technology and deep learning, deep learning based human activity recognition from sensor signal data has been actively studied. While deep neural networks can automatically extract features appropriate for the target task and focus on increasing the recognition performance, they cannot select important input sensor signals, which leads to the lack of interpretability. Since not all signals from wearable sensors are important for the target task, sensor signal importance will be insightful information for practitioners. In this article, we propose an interpretable and accurate convolutional neural network capable of select important sensor signals. This is enabled by spatially sparse convolutional filters whose sparsity is imposed by spatial group lasso. While there is a tradeoff between accuracy and interpretability in a model, experimental results on the opportunity activity recognition dataset show that the proposed model can help improve recognition performance and select important sensor signals, providing interpretability.
Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Handwritten numeral recognition is a classical and important task in the computer vision area. We propose two novel deep learning models for this task, which combine the edge extraction method and Siamese/Triple network structures. We evaluate the models on seven handwritten numeral datasets and the results demonstrate both the simplicity and effectiveness of our models, comparing to baseline methods.
Topic-Aware Pointer-Generator Networks for Summarizing Spoken Conversations

Due to the lack of publicly available resources, conversation summarization has received far less attention than text summarization. As the purpose of conversations is to exchange information between at least two interlocutors, key information about a certain topic is often scattered and spanned across multiple utterances and turns from different speakers. This phenomenon is more pronounced during spoken conversations, where speech characteristics such as backchanneling and false-starts might interrupt the topical flow. Moreover, topic diffusion and (intra-utterance) topic drift are also more common in human-to-human conversations. Such linguistic characteristics of dialogue topics make sentence-level extractive summarization approaches used in spoken documents ill-suited for summarizing conversations. Pointer-generator networks have effectively demonstrated its strength at integrating extractive and abstractive capabilities through neural modeling in text summarization. To the best of our knowledge, to date no one has adopted it for summarizing conversations. In this work, we propose a topic-aware architecture to exploit the inherent hierarchical structure in conversations to further adapt the pointer-generator model. Our approach significantly outperforms competitive baselines, achieves more efficient learning outcomes, and attains more robust performance.
AUV 3D Path Planning Based on the Improved Hierarchical Deep Q Network

This study proposed the 3D path planning of an autonomous underwater vehicle (AUV) by using the hierarchical deep Q network (HDQN) combined with the prioritized experience replay. The path planning task was divided into three layers, which realized the dimensionality reduction of state space and solved the problem of dimension disaster. An artificial potential field was used to design the positive rewards of the algorithm to shorten the training time. According to the different requirements of the task, this study modified the rewards in the training process to obtain different paths. The path planning simulation and field tests were carried out. The results of the tests corroborated that the training time of the proposed method was shorter than that of the traditional method. The path obtained by simulation training was proved to be safe and effective.
Learning-Based Proxy Collision Detection for Robot Motion Planning Applications

This article demonstrates that collision detection-intensive applications such as robotic motion planning may be accelerated by performing collision checks with a machine learning model. We propose Fastron, a learning-based algorithm, to model a robot's configuration space to be used as a proxy collision detector in place of standard geometric collision checkers. We demonstrate that leveraging the proxy collision detector results in up to an order of magnitude faster performance in robot simulation and planning than state-of-the-art collision detection libraries. Our results show that Fastron learns a model more than 100 times faster than a competing C-space modeling approach, while also providing theoretical guarantees of learning convergence. Using the open motion planning libraries (OMPLs), we were able to generate initial motion plans across all experiments with varying robot and environment complexities and workspace obstacle locations. With Fastron, we can repeatedly generate new motion plans at a 56 Hz rate, showing its application toward autonomous surgical assistance task in shared environments with human-controlled manipulators. All performance gains were achieved despite using only CPU-based calculations, suggesting further computational gains with a GPU approach that can parallelize tensor algebra. Code is available online.
Large-Scale Question Tagging via Joint Question-Topic Embedding Learning

Recent years have witnessed a flourishing of community-driven question answering (cQA), like Yahoo! Answers and AnswerBag, where people can seek precise information. After 2010, some novel cQA systems, including Quora and Zhihu, gained momentum. Besides interactions, the latter enables users to label the questions with topic tags that highlight the key points conveyed in the questions. In this article, we shed light on automatically annotating a newly posted question with topic tags that are predefined and preorganized into a directed acyclic graph. To accomplish this task, we present an end-to-end deep interactive embedding model to jointly learn the embeddings of questions and topics by projecting them into the same space for a similarity measure. In particular, we first learn the embeddings of questions and topic tags by two deep parallel models. Thereinto, we regularize the embeddings of topic tags via fully exploring their hierarchical structures, which is able to alleviate the problem of imbalanced topic distribution. Thereafter, we interact each question embedding with the topic tag matrix, i.e., all the topic tag embeddings. Following that, a sigmoid cross-entropy loss is appended to reward the positive question-topic pairs and penalize the negative ones. To justify our model, we have conducted extensive experiments on an unprecedented large-scale social QA dataset obtained from Zhihu.com, and the experimental results demonstrate that our model achieves superior performance to several state-of-the-art baselines.
Detection of Cyberattacks in Industrial Control Systems Using Enhanced Principal Component Analysis and Hypergraph-Based Convolution Neural Network (EPCA-HG-CNN)

The automated operations of industrial control systems (ICSs) highly rely on the interconnected devices, sensors, and actuators that are monitored and controlled by the supervisory control and data acquisition (SCADA) systems. Despite the numerous benefits of unifying the networking technologies with SCADA systems, ICSs are more susceptible to cyberattacks that can disrupt the secure operations of the critical infrastructures. Thus, the design and development of an efficient attack detection approach has become a complex task. Hence, this research work presents a novel hypergraph-based anomaly detection technique with enhanced principal component analysis and convolution neural network (EPCA-HG-CNN) to detect deviant behaviors of such systems. The proposed EPCA-HG-CNN algorithm involves two phases: 1) dimensionality reduction using enhanced PCA and 2) anomaly detection with HG-based CNN. Furthermore, the performance of EPCA-HG-CNN is evaluated with Singapore University of Technology and Design secure water treatment system and the experimental results show that the proposed EPCA-HG-CNN has identified anomalous behavior of the data with high detection rate, low false positives, and better classification accuracy.
Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

The evaluation of kidney biopsies performed by expert pathologists is a crucial process for assessing if a kidney is eligible for transplantation. In this evaluation process, an important step consists of the quantification of global glomerulosclerosis, which is the ratio between sclerotic glomeruli and the overall number of glomeruli. Since there is a shortage of organs available for transplantation, a quick and accurate assessment of global glomerulosclerosis is essential for retaining the largest number of eligible kidneys. In the present paper, the authors introduce a Computer-Aided Diagnosis (CAD) system to assess global glomerulosclerosis. The proposed tool is based on Convolutional Neural Networks (CNNs). In particular, the authors considered approaches based on Semantic Segmentation networks, such as SegNet and DeepLab v3+. The dataset has been provided by the Department of Emergency and Organ Transplantations (DETO) of Bari University Hospital, and it is composed of 26 kidney biopsies coming from 19 donors. The dataset contains 2344 non-sclerotic glomeruli and 428 sclerotic glomeruli. The proposed model consents to achieve promising results in the task of automatically detecting and classifying glomeruli, thus easing the burden of pathologists. We get high performance both at pixel-level, achieving mean F-score higher than 0.81, and Weighted Intersection over Union (IoU) higher than 0.97 for both SegNet and Deeplab v3+ approaches, and at object detection level, achieving 0.924 as best F-score for non-sclerotic glomeruli and 0.730 as best F-score for sclerotic glomeruli.
Deliberation Model Based Two-Pass End-To-End Speech Recognition

End-to-end (E2E) models have made rapid progress in automatic speech recognition (ASR) and perform competitively relative to conventional models. To further improve the quality, a two-pass model has been proposed to rescore streamed hypotheses using the non-streaming Listen, Attend and Spell (LAS) model while maintaining a reasonable latency. The model attends to acoustics to rescore hypotheses, as opposed to a class of neural correction models that use only first-pass text hypotheses. In this work, we propose to attend to both acoustics and first-pass hypotheses using a deliberation network. A bidirectional encoder is used to extract context information from first-pass hypotheses. The proposed deliberation model achieves 12% relative WER reduction compared to LAS rescoring in Google Voice Search (VS) tasks, and 23% reduction on a proper noun test set. Compared to a large conventional model, our best model performs 21% relatively better for VS. In terms of computational complexity, the deliberation decoder has a larger size than the LAS decoder, and hence requires more computations in second-pass decoding.
Semantic-k-NN algorithm: An enhanced version of traditional k-NN algorithm

The k-NN algorithm is one of the most renowned ML algorithms widely used in the area of data classification research. With the emergence of big data, the performance and the efficiency of the traditional k-NN algorithm is fast becoming a critical issue. The traditional k-NN algorithm is inefficient to solve the high volume multi-categorical training datasets Traditional k-NN algorithm has a constraint in filtering the training dataset to yield training data that are most relevant to the intended or the targeted test dataset/file. It has to scan through all the training datasets categories to classify the intended/targeted data. As such, traditional k-NN is considered not intelligent and consequently is suffering poor accuracy performance with high computational complexity. A Semantic-kNN (Sk-NN) algorithm for ML is thus proposed in this paper to address the limitations in the traditional k-NN. The proposed Sk-NN deploys a process by leveraging on the semantic itemization and bigram model to filter the training dataset in accordance with the relevant information engaged in the test dataset. It is aimed for general security applications such as finding (the confidentiality level of the data when the algorithm is trained with multiple training categories during the data classification phase. Ultimately, Sk-NN is to elevate the ML performance in pattern extraction and labeling in the big data context.
A Machine-Learning-Based Auction for Resource Trading in Fog Computing

Fog computing is considered to be a key enabling technology for future networks. By broadening the cloud computing services to the network edge, fog computing can support various emerging applications such as IoT, big data, and blockchain with low latency and low bandwidth consumption cost. To achieve the full potential of fog computing, it is essential to design an incentive mechanism for fog computing service providers. Auction is a promising solution for the incentive mechanism design. However, it is challenging to design an optimal auction that maximizes the revenue for the providers while holding important properties: IR and IC. Therefore, this article introduces the design of an optimal auction based on deep learning for the resource allocation in fog computing. The proposed optimal auction is developed specifically to support blockchain applications. In particular, we first discuss resource management issues in fog computing. Second, we review economic and pricing models for resource management in fog computing. Third, we introduce fog computing and blockchain. Fourth, we present how to design the optimal auction by using deep learning for the fog resource allocation in the blockchain network. Simulation results demonstrate that the proposed scheme outperforms the baseline scheme (i.e., the greedy algorithm) in terms of revenue, and IC and IR violations. Thus, the proposed scheme can be used as a useful tool for the optimal resource allocation in general fog networks.
Optimal design of planar steel frame structures utilizing meta-heuristic optimization algorithms

Meta-heuristic algorithms are applied in optimization problems in a variety of fields, including engineering, economics, and computer science. In this paper, seven population-based meta-heuristic algorithms are employed for size optimization of two-dimensional steel frame structures. These algorithms consist of the Artificial Bee Colony algorithm, Big Bang-Big Crunch algorithm, Cyclical Parthenogenesis Algorithm, Cuckoo Search algorithm, Thermal Exchange Optimization algorithm, Teaching-Learning-Based Optimization algorithm, and Water Evaporation Optimization algorithm. Optimization aims to minimize the weight of rigid-jointed steel frame structures while satisfying some constraints on displacement and stress limits. The design is based on the requirements of the AISC Load and Resistance Factor Design (LRFD). The capability and robustness of the algorithms are investigated through three well-known steel frame benchmarks.
STDnet: Exploiting high resolution feature maps for small object detection

The accuracy of small object detection with convolutional neural networks (ConvNets) lags behind that of larger objects. This can be observed in popular contests like MS COCO. This is in part caused by the lack of specific architectures and datasets with a sufficiently large number of small objects. Our work aims at these two issues. First, this paper introduces STDnet, a convolutional neural network focused on the detection of small objects that we defined as those under 16 × 16 pixels. The high performance of STDnet is built on a novel early visual attention mechanism, called Region Context Network (RCN), to choose the most promising regions, while discarding the rest of the input image. Processing only specific areas allows STDnet to keep high resolution feature maps in deeper layers providing low memory overhead and higher frame rates. High resolution feature maps were proved to be key to increasing localization accuracy in such small objects. Second, we also present USC-GRAD-STDdb, a video dataset with more than 56,000 annotated small objects in challenging scenarios. Experimental results over USC-GRAD-STDdb show that STDnet improves the AP@.5 of the best state-of-the-art object detectors for small target detection from 50.8% to 57.4%. Performance has also been tested in MS COCO for objects under 16 × 16 pixels. In addition, a spatio-temporal baseline network, STDnet-bST, has been proposed to make use of the information of successive frames, increasing the AP@.5 of STDnet in 2.3%. Finally, optimizations have been carried out to be fit on embedded devices such as Jetson TX2.
An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine

Over the past years, the surge in the necessity for early detection and diagnosis of breast cancer has resulted in many innovative research directions. According to the World Health Organization, an early and accurate detection of breast cancer successfully leads to a correct decision towards its treatment. Development of computer-aided diagnosis (CAD) system is considered to be a major stead in current research practice to abet medical practitioners in decision-making. This paper proposes an improved CAD framework to correctly classify the digital mammograms into normal or abnormal, and further, benign or malignant. The proposed scheme employs a block-based discrete wavelet packet transform (BDWPT) to extract the features, namely, the Shannon entropy, Tsallis entropy, Renyi entropy, and energy. Then, principal component analysis (PCA) technique is utilized to extract the discriminating features from the original feature vector. Subsequently, an optimized wrapper-based kernel extreme learning machine (KELM) using a weighted chaotic salp swarm algorithm (WC-SSA) is proposed as classifier to classify the digital mammograms. Since the efficacy of KELM algorithm depends on its two important parameters, namely, the penalty parameter and the kernel parameter, the prime objective of the proposed work is to obtain the optimized value of the aforementioned parameters as well as to select the most relevant features from the reduced feature vector simultaneously. The proposed scheme is evaluated on three publicly available standard datasets, namely, MIAS, DDSM, and BCDR to validate the efficacy of the proposed BDWPT+PCA+WC-SSA-KELM scheme. The performance of the proposed model is evaluated in terms of different metrics, namely, classification accuracy, sensitivity, specificity, area under curve (AUC), Matthew’s correlation coefficient (MCC), and F-measure via a 5 × 5 stratified cross-validation approach. From the experimental results and their analysis, it is observed that for the normal–abnormal category, the proposed technique results in an accuracy of 99.62% and 99.92% for MIAS and DDSM, respectively, whereas in the case of benign–malignant classification, the proposed method yields an accuracy of 99.28%, 99.63%, 99.60% for MIAS, DDSM, and BCDR datasets, respectively. Further, it is also observed that the proposed WC-SSA-KELM scheme exhibits superior performance as compared to that of its counterparts. Additionally, two well-known statistical analyses, namely, ANOVA and Friedman tests are performed to demonstrate that the performance of the proposed scheme is significantly better than that of the other existing schemes.
Privacy-Preserving in Healthcare Blockchain Systems Based on Lightweight Message Sharing

Electronic medical records (EMRs) are extremely important for patients’ treatment, doctors’ diagnoses, and medical technology development. In recent years, the distributed healthcare blockchain system has been researched for solving the information isolated island problem in centralized healthcare service systems. However, there still exists a series of important problems such as the patients’ sensitive information security, cross-institutional data sharing, medical quality, and efficiency. In this paper, we establish a lightweight privacy-preserving mechanism for a healthcare blockchain system. First, we apply an interleaving encoder to encrypt the original EMRs. This can hide the sensitive information of EMRs to protect the patient’s privacy security. Second, a ( t , n )-threshold lightweight message sharing scheme is presented. The EMRs are mapped to n different short shares, and it can be reconstructed by at least t shares. The EMR shares rather than the original EMRs are stored in the blockchain nodes. This can guarantee high security for EMR sharing and improve the data reconstruction efficiency. Third, the indexes of the stored EMR shares are employed to generate blocks that are chained together and finally form a blockchain. The authorized data users or institutions can recover an EMR by requesting at least t shares of the EMR from the blockchain nodes. In this way, the healthcare blockchain system can not only facilitate the cross-institution sharing process, but also provide proper protections for the EMRs. The security proof and analysis indicate that the proposed scheme can protect the privacy and security of patients’ medical information. The simulation results show that our proposed scheme is more efficient than similar literature in terms of energy consumption and storage space, and the healthcare blockchain system is more stable with the proposed message sharing scheme.