Reviewerly/exHarmony huggingface dataset:
Cognitive Optimal-Setting Control of AIoT Industrial Applications With Deep Reinforcement Learning

For industrial applications of the artificial intelligence of things, mechanical control usually affects the overall product output and production schedule. Recently, more and more engineers have applied the deep reinforcement learning method to mechanical control to improve the company's profit. However, the problem of deep reinforcement learning training stage is that overfitting often occurs, which results in accidental control and increases the risk of overcontrol. In order to address this problem, in this article, an expected advantage learning method is proposed for moderating the maximum value of expectation-based deep reinforcement learning for industrial applications. With the tanh softmax policy of the softmax function, we replace the sigmod function with the tanh function as the softmax function activation value. It makes it so that the proposed expectation-based method can successfully decrease the value overfitting in cognitive computing. In the experimental results, the performance of the Deep Q Network algorithm, advantage learning algorithm, and propose expected advantage learning method were evaluated in every episodes with the four criteria: the total score, total step, average score, and highest score. Comparing with the AL algorithm, the total score of the proposed expected advantage learning method is increased by 6% in the same number of trainings. This shows that the action probability distribution of the proposed expected advantage learning method has better performance than the traditional soft-max strategy for the optimal setting control of industrial applications.
Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

The works presented in this paper addresses the robust population-based global optimization that is influenced by the simplicity and efficiency principles introduced in two new generation optimization algorithms.Galactic Swarm Optimization is inspired by the motion of stars, galaxies, and superclusters of galaxies under the influence of gravity.It acts well as a global controller of the whole optimization process by employing multiple flexible cycles of exploration and exploitation phases to find new, better solutions.However, the optimization process still suffers poverty in the exploitation phase, which is improved in this work by its hybridization with our evolution version of the Whale Optimization Algorithm.Concretely, the exploitation phase of Galactic Swarm Optimization is replaced by our Evolution Whale Optimization Algorithm to avoid early convergence.The Whale Optimization Algorithm mimics the unusual social behaviors and the hunting activities of humpback whales.However, it is not optimized for global optimization when the number of dimensions is increased.Hence its is evolved in our works by Levy-Flight trajectory for faster local search with adaptive step lengths and two-point crossover operator to reduce bias in the offspring creation procedure.The achieved results through extensive and careful experiments showed that our hybridization and evolution enhancements bring outstanding performance in terms of accuracy, convergence speed, and stability.
Maximizing Camera Coverage in Multicamera Surveillance Networks

Efficient placement of cameras to perform surveillance tasks has a significant impact on the overall performance as well as cost of video surveillance systems. Identifying an optimal configuration for cameras in a surveillance system to maximize coverage is a combinatorial optimization problem. This paper proposes two algorithms for identifying optimal camera configuration for a multi-camera network with predefined camera locations. We propose an Alternate Global Greedy (AGG) algorithm, a novel variant of the Global Greedy algorithm by making significant modifications to the traditional execution strategy to produce better coverage results. Additionally, we propose an innovative Greedy Grid Voting (GGV) algorithm, which gives preference to cover unique as well as critical regions and effectively addresses a broad range of coverage scenarios mandated by the application. The proposed algorithms are validated on map images across diverse scenarios with predefined camera locations. Experimental results show that our algorithms maximize the coverage results with minimal overlap, compared to the existing greedy techniques.
Blockchain, consent and prosent for medical research

Recent advances in medical and information technologies, the availability of new types of medical data, the requirement of increasing numbers of study participants, as well as difficulties in recruitment and retention, all present serious problems for traditional models of specific and informed consent to medical research. However, these advances also enable novel ways to securely share and analyse data. This paper introduces one of these advances—blockchain technologies—and argues that they can be used to share medical data in a secure and auditable fashion. In addition, some aspects of consent and data collection, as well as data access management and analysis, can be automated using blockchain-based smart contracts. This paper demonstrates how blockchain technologies can be used to further all three of the bioethical principles underlying consent requirements: the autonomy of patients, by giving them much greater control over their data; beneficence, by greatly facilitating medical research efficiency and by reducing biases and opportunities for errors; and justice, by enabling patients with rare or under-researched conditions to pseudonymously aggregate their data for analysis. Finally, we coin and describe the novel concept of prosent, by which we mean the blockchain-enabled ability of all stakeholders in the research process to pseudonymously and proactively consent to data release or exchange under specific conditions, such as trial completion.
Intuitionistic based segmentation of thyroid nodules in ultrasound images

Accurate delineation of thyroid nodules in ultrasound images is vital for computer-aided diagnosis. Most segmentation methods are semi-automated for thyroid nodules and require manual intervention, which increases the processing time and errors. We propose an automated intuitionistic fuzzy active contour method (IFACM) that integrates intuitionistic fuzzy clustering with an active contour for thyroid nodule segmentation using ultrasound images. Intuitionistic fuzzy clustering is used for the initialization of an active contour and estimation of the parameters required to automatically control the curve evolution. The IFACM was tested extensively on both artificial and real ultrasound images. The IFACM obtained a higher value of true positive (95.1% ± 2.86%), overlap metric (93.1 ± 2.95%), and dice coefficient (90.90 ± 3.08),indicating that the boundary delineated by the IFACM fits best to true nodules. Moreover, it obtained a lower value of false positive (04.1% ± 3.24%) and Hausdorff distance (0.50 ± 0.21 in pixels), further verifying the higher similarity of shape and boundary, respectively. According to the significance test, the results of the proposed method were more significant than those of the other segmentation methods. The main benefit of the IFACM is the automatic identification of nodules on the basis of image characteristics, which eliminates manual intervention. In all the experiments, all initial contours were automatically defined closer to the boundaries of the nodule, which is a benefit of the IFACM. Moreover, this method can segment multiple nodules in a single image efficiently.
Encoding Pathlet and SIFT Features With Bagged VLAD for Historical Writer Identification

Offline writer identification plays an important role in forensic document examination and historical document analysis. Today, challenges still exist in historical writer identification (WI), where documents may present very complex handwriting styles. In this paper, we propose novel techniques for a detailed description and accurate identification of handwriting in historical documents. Because handwriting contours are one of the most salient components to characterize one's handwriting style, a novel pathlet feature is proposed to describe their rich properties beyond slant and curvature in a principled way; these properties can be exploited in a VLAD-like encoding framework for fine-grained handwriting description. Besides the pathlet feature, we extract unidirectional SIFT feature to describe handwriting corners and junctions. To effectively encode the pathlet and SIFT features, a novel encoding method, named bagged VLAD, is further proposed to address the problem that a large codebook sparsely spreads out the data points and leads to a degraded performance, allowing a much larger codebook for improved encoding performance. Our proposed method achieves state-of-the-art performance on ICDAR2017 Historical-WI database and ICDAR2019 HDRC-IR database, and has won the first place in ICDAR2019 HDRC-IR competition.
Local keypoint-based Faster R-CNN

Region-based Convolutional Neural Network (R-CNN) detectors have achieved state-of-the-art results on various challenging benchmarks. Although R-CNN has achieved high detection performance, the research of local information in producing candidates is insufficient. In this paper, we design a Keypoint-based Faster R-CNN (K-Faster) method for object detection. K-Faster incorporates local keypoints in Faster R-CNN to improve the detection performance. In detail, a sparse descriptor, which first detects the points of interest in a given image and then samples a local patch and describes its invariant features, is first employed to produce keypoints. All 2-combinations of the produced keypoints are second selected to generate keypoint anchors, which are helpful for object detection. The heterogeneously distributed anchors are then encoded in feature maps based on their areas and center coordinates. Finally, the keypoint anchors are coupled with the anchors produced by Faster R-CNN, and the coupled anchors are used for Region Proposal Network (RPN) training. Comparison experiments are implemented on PASCAL VOC 07/12 and MS COCO. The experimental results show that our K-Faster approach not only increases the mean Average Precision (mAP) performance but also improves the positioning precision of the detected boxes.
A Linux in unikernel clothing

Unikernels leverage library OS architectures to run isolated workloads on the cloud. They have garnered attention in part due to their promised performance characteristics such as small image size, fast boot time, low memory footprint and application performance. However, those that aimed at generality fall short of the application compatibility, robustness and, more importantly, community that is available for Linux. In this paper, we describe and evaluate Lupine Linux, a standard Linux system that---through kernel configuration specialization and system call overhead elimination---achieves unikernel-like performance, in fact outperforming at least one reference unikernel in all of the above dimensions. At the same time, Lupine can run any application (since it is Linux) when faced with more general workloads, whereas many unikernels simply crash. We demonstrate a graceful degradation of unikernel-like performance properties.
A Study of Chaotic Maps Producing Symmetric Distributions in the Fish School Search Optimization Algorithm with Exponential Step Decay

Inspired by the collective behavior of fish schools, the fish school search (FSS) algorithm is a technique for finding globally optimal solutions. The algorithm is characterized by its simplicity and high performance; FSS is computationally inexpensive, compared to other evolution-inspired algorithms. However, the premature convergence problem is inherent to FSS, especially in the optimization of functions that are in very-high-dimensional spaces and have plenty of local minima or maxima. The accuracy of the obtained solution highly depends on the initial distribution of agents in the search space and on the predefined initial individual and collective-volitive movement step sizes. In this paper, we provide a study of different chaotic maps with symmetric distributions, used as pseudorandom number generators (PRNGs) in FSS. In addition, we incorporate exponential step decay in order to improve the accuracy of the solutions produced by the algorithm. The obtained results of the conducted numerical experiments show that the use of chaotic maps instead of other commonly used high-quality PRNGs can speed up the algorithm, and the incorporated exponential step decay can improve the accuracy of the obtained solution. Different pseudorandom number distributions produced by the considered chaotic maps can positively affect the accuracy of the algorithm in different optimization problems. Overall, the use of the uniform pseudorandom number distribution generated by the tent map produced the most accurate results. Moreover, the tent-map-based PRNG achieved the best performance when compared to other chaotic maps and nonchaotic PRNGs. To demonstrate the effectiveness of the proposed optimization technique, we provide a comparison of the tent-map-based FSS algorithm with exponential step decay (ETFSS) with particle swarm optimization (PSO) and with the genetic algorithm with tournament selection (GA) on test functions for optimization.
In-depth comparison of deep artificial neural network architectures on seismic events classification

One of the most challenging tasks in volcanic data analysis is the classification of seismic events. By knowing them, it is possible to take decisions in advance, providing benefits for the neighboring societies as, for instance, how such events may impact users' life and cropland areas. Although there are several approaches to perform such task, Deep Neural Networks (DNN) have been barely considered to deal with seismic signals, as discussed in our related work. In this sense, we started our research with a wide set of experiments to analyze the DNN performance while discriminating seismic activities through two common network architectures: a 2D Convolutional Neural Networks (CNN) and the Long Short-Term Memory (LSTM) network. In attempt to draw a parallel of DNN and classical supervised learning strategies, we extended our study by comparing those two architectures against the Multilayer Perceptron (MLP), which would be the simplest and most common baseline to take into account. Our MLP network was implemented after combining a basic architecture with elements from DNN models as dropout, flatten, and batch normalization layers. From this first analysis, we confirmed the need of additional neural network layers to obtain good classification results for seismic events, given the MLP required more DNN-based pipeline operations to improve its overall performance, making it comparable to the previous results obtained by our research group. As a natural extension, we designed a new DNN architecture by assessing additional network layers specifically devoted to extract features from seismic signals, thus improving the overall classification of Volcano activities involving the following events: Volcano-Tectonic (VT), Long Period (LP), Tremor (TR) and Tectonic (TC). Such design motivated the study of current DNN architectures tackling similar problems to classify raw signals from which SoundNet was taken as the most prominent candidate, thus leading to a new CNN architecture here proposed and referred to as SeismicNet. As our final contribution, SeismicNet provided classification results among the best in the literature without demanding explicit signal pre-processing steps though.
Generalizable Layered Blockchain Architecture for Health Care Applications: Development, Case Studies, and Evaluation

Background Data coordination across multiple health care facilities has become increasingly important for many emerging health care applications. Distrust has been recognized as a key barrier to the success of such applications. Leveraging blockchain technology could provide potential solutions tobuild trust between data providers and receivers by taking advantage of blockchain properties such as security, immutability, anonymity, decentralization, and smart contracts. Many health technologies have empirically proven that blockchain designs fit well with the needs of health care applications with certain degrees of success. However, there is a lack of robust architecture to provide a practical framework for developers to implement applications and test the performance of stability, efficiency, and scalability using standard blockchain designs. A generalized blockchain model is needed for the health care community to adopt blockchain technology and develop applications in a timely fashion. Objective This study aimed at building a generalized blockchain architecture that provides data coordination functions, including data requests, permission granting, data exchange, and usage tracking, for a wide spectrum of health care application developments. Methods An augmented, 3-layered blockchain architecture was built on a private blockchain network. The 3 layers, from bottom to top, are as follows: (1) incorporation of fundamental blockchain settings and smart contract design for data collection; (2) interactions between the blockchain and health care application development environment using Node.js and web3.js; and (3) a flexible development platform that supports web technologies such as HTML, https, and various programing languages. Two example applications, health information exchange (HIE) and clinical trial recruitment, were developed in our design to demonstrate the feasibility of the layered architecture. Case studies were conducted to test the performance in terms of stability, efficiency, and scalability of the blockchain system. Results A total of 331,142 simulated HIE requests from accounts of 40,000 patients were successfully validated through this layered blockchain architecture with an average exchange time of 11.271 (SD 2.208) seconds. We also simulated a clinical trial recruitment scenario with the same set of patients and various recruitment criteria to match potential subjects using the same architecture. Potential subjects successfully received the clinical trial recruitment information and granted permission to the trial sponsors to access their health records with an average time of 3.07 seconds. Conclusions This study proposes a generalized layered blockchain architecture that offers health technology community blockchain features for application development without requiring developers to have extensive experience with blockchain technology. The case studies tested the performance of our design and empirically proved the feasibility of the architecture in 2 relevant health application domains.
Rhythm

Cloud service providers improve resource utilization by co-locating latency-critical (LC) workloads with best-effort batch (BE) jobs in datacenters. However, they usually treat an LC workload as a whole when allocating resources to BE jobs and neglect the different features of components of an LC workload. This kind of coarse-grained co-location method leaves a significant room for improvement in resource utilization.
A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases

Analyzing customer transactions to discover high-utility itemsets is a popular task, which consists of finding the sets of items that are purchased together and yield a high profit. However, many studies assume that transactional data is static while in real-life, it changes over time. For example, the unit profits of items may vary from one week to another because sale prices and production costs may change. Many algorithms for mining high-utility itemsets (HUI) ignore this important property and thus are inapplicable or generate inaccurate results on real data. To address this issue, this paper proposes a novel algorithm named Multi-Core HUI Miner (MCH-Miner). It adapts techniques introduced in the iMEFIM algorithm to run on a parallel multi-core architecture to efficiently mine HUIs in dynamic transaction databases. An empirical evaluation shows that in most cases, MCH-Miner is significantly faster than iMEFIM, and that the cost of database scans is reduced.
Every node counts: Self-ensembling graph convolutional networks for semi-supervised learning

Graph convolutional network (GCN) provides a powerful means for graph-based semi-supervised tasks. However, as a localized first-order approximation of spectral graph convolution, the classic GCN can not take full advantage of unlabeled data, especially when the unlabeled node is far from labeled ones. To capitalize on the information from unlabeled nodes to boost the training for GCN, we propose a novel framework named Self-Ensembling GCN (SEGCN), which marries GCN with Mean Teacher – a powerful self-ensemble learning mechanism for semi-supervised task. SEGCN contains a student model and a teacher model. As a student, it not only learns to correctly classify the labeled nodes, but also tries to be consistent with the teacher on unlabeled nodes in more challenging situations, such as a high dropout rate and graph corrosion. As a teacher, it averages the student model weights and generates more accurate predictions to lead the student. In such a mutual-promoting process, both labeled and unlabeled samples can be fully utilized for backpropagating effective gradients to train GCN. In a variety of semi-supervised classification benchmarks, i.e. Citeseer, Cora, Pubmed and NELL, we validate that the proposed method matches the state of the arts in the classification accuracy. The code is publicly available at https://github.com/RoyalVane/SEGCN.
Covid-19 contact tracing: a briefing

The UK has plunged into a new programme of contact tracing—three months after it abandoned its original test and trace strategy for covid-19. <b>Chris Baraniuk</b> explains what we know about current global efforts and why they are is crucial during a pandemic
Relevant region prediction for crowd counting

Crowd counting is a concerned and challenging task in computer vision. Existing density map based methods excessively focus on the individuals’ localization which harms the crowd counting performance in highly congested scenes. In addition, the dependency between the regions of different density is also ignored. In this paper, we propose Relevant Region Prediction (RRP) for crowd counting, which consists of the Count Map and the Region Relation-Aware Module (RRAM). Each pixel in the count map represents the number of heads falling into the corresponding local area in the input image, which discards the detailed spatial information and forces the network pay more attention to counting rather than localizing individuals. Based on the Graph Convolutional Network (GCN), Region Relation-Aware Module is proposed to capture and exploit the important region dependency. The module builds a fully connected directed graph between the regions of different density where each node (region) is represented by weighted global pooled feature, and GCN is learned to map this region graph to a set of relation-aware regions representations. Experimental results on three datasets show that our method obviously outperforms other existing state-of-the-art methods.