Reviewerly/exHarmony huggingface dataset:
Learning-Based Mobile Edge Computing Resource Management to Support Public Blockchain Networks

We consider a public blockchain realized in the mobile edge computing (MEC) network, where the blockchain miners compete against each other to solve the proof-of-work puzzle and win a mining reward. Due to limited computing capabilities of their mobile terminals, miners offload computations to the MEC servers. The MEC servers are maintained by the service provider (SP) that sells its computing resources to the miners. The SP aims at maximizing its long-term profit subject to miners' budget constraints. The miners decide on their hash rates, i.e., computing powers, simultaneously and independently, to maximize their payoffs without revealing their decisions to other miners. As such, the interactions between the SP and miners are modeled as a stochastic Stackelberg game under private information, where the SP assigns the price per unit hash rate, and miners select their actions, i.e., hash rate decisions, without observing actions of other miners. We develop a hierarchical learning framework for this game based on fully- and partially-observable Markov decision models of the decision processes of the SP and miners. We show that the proposed learning algorithms converge to stable states in which miners' actions are the best responses to the optimal price assigned by the SP.
Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search

Research on metaheuristics has focused on (novel) algorithmic development and on competitive testing, both of which have been frequently argued to yield little generalizable knowledge. The main goal of this paper is to promote meta-analysis — a systematic statistical examination that combines the results of several independent studies — as a more suitable way to obtain problem- and implementation-independent insights on metaheuristics. Meta-analysis is widely used in several scientific domains, most notably the medical sciences (e.g., to establish the efficacy of a certain treatment). To the best of our knowledge, this is the first meta-analysis in the field of metaheuristics. To illustrate the approach, we carry out a meta-analysis to gain insights into the importance of the adaptive layer in adaptive large neighborhood search (ALNS). Although ALNS has been widely used to solve a broad range of problems, it has not yet been established whether or not adaptiveness actually contributes to the performance of an ALNS algorithm. A total of 134 studies were identified through Google Scholar or personal e-mail correspondence with researchers in the domain, 63 of which fit our eligibility criteria. After sending requests for data to the authors of the eligible studies, we obtained results for 25 different implementations of ALNS, which were analysed using a random effects model. On average, the addition of an adaptive layer in an ALNS algorithm improves the objective function value by 0.14% (95% confidence interval 0.06–0.21%). Although the adaptive layer can (and in a limited number of studies does) have an added value, it also adds complexity and can therefore only be recommended in some specific situations. These findings underline the importance of evaluating the contribution of metaheuristic components.
A Smart IoT Device for Detecting and Responding to Earthquakes

The advancement of hardware and software technologies makes it possible to use smartphones or Internet of things for monitoring environments in realtime. In recent years, much effort has been made to develop a smartphone based earthquake early warning system, where low-cost acceleration sensors inside a smartphones are used for capturing earthquake signals. However, because a smartphone comes with a powerful CPU, spacious memory, and several sensors, it is waste of such resources to use it only for detecting earthquakes. Furthermore, because a smartphone is mostly in use during the daytime, the acquired data cannot be used for detecting earthquakes due to human activities. Therefore, in this article, we introduce a stand-alone device equipped with a low-cost acceleration sensor and least computing resources to detect earthquakes. To that end, we first select an appropriate acceleration sensor by assessing the performance and accuracy of four different sensors. Then, we design and develop an earthquake alert device. To detect earthquakes, we employ a simple machine learning technique which trains an earthquake detection model with daily motions, noise data recorded in buildings, and earthquakes recorded in the past. Furthermore, we evaluate the four acceleration sensors by recording two realistic earthquakes on a shake-table. In the experiments, the results show that the developed earthquake alert device can successfully detect earthquakes and send a warning message to nearby devices, thereby enabling proactive responses to earthquakes.
Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification

Big data is progressively being used in various areas, such as industry, financial dealing, medicine, and so on, as it can handle the challenges in processing large amounts of data. One of the data mining techniques used widely and effectively to classify big data is the MapReduce model. In this paper, an approach for the classification of big data is developed using Cuckoo–Grey wolf based Correlative Naive Bayes classifier and MapReduce Model (CGCNB-MRM). Accordingly, a novel classifier, named Cuckoo–Grey wolf based Correlative Naive Bayes classifier (CG-CNB), is designed by modifying CNB classifier with a newly developed optimization algorithm, Cuckoo–Grey Wolf based Optimization (CGWO). CGWO algorithm is designed by the effective integration of Cuckoo Search (CS) Algorithm into Grey Wolf Optimizer (GWO), to optimize the CNB model by the optimal selection of the model parameters. Finally, the proposed CGCNB-MRM approach performs the classification for each data samples based on the probability index table and the posterior probability of the data. Three metrics, such as accuracy, sensitivity, and specificity, are utilized for the performance evaluation of the proposed CGCNB-MRM approach, where it could achieve 80.7% accuracy with 84.5% sensitivity and 76.9% specificity and thus, prove its effectiveness in big data classification.
Simultaneous Segmentation and Classification of Mass Region From Mammograms Using a Mixed-Supervision Guided Deep Model

Automatic diagnosis based on medical imaging necessitates both lesion segmentation and disease classification. Lesion segmentation requires pixel-level annotations while disease classification only requires image-level annotations. The two tasks are usually studied separately despite the latter problem relies on the former. Motivated by the close correlation between them, we propose a mixed-supervision guided method and a residual-aided classification U-Net model (ResCU-Net) for joint segmentation and benign-malignant classification. By coupling the strong supervision in the form of segmentation mask and weak supervision in the form of benign-malignant label through a simple annotation procedure, our method efficiently segments tumor regions while simultaneously predicting a discriminative map for identifying the benign-malignant types of tumors. Our network, ResCU-Net, extends U-Net by incorporating the residual module and the SegNet architecture to exploit multilevel information for achieving improved tissue identification. With experiments on a public mammogram database of INbreast, we validate the effectiveness of our method and achieve consistent improvements over state-of-the-art models.
Practical Automated Video Analytics for Crowd Monitoring and Counting

Video surveillance is gaining popularity in numerous applications, including facility management, traffic monitoring, crowd analysis, and urban security. Despite the increasing demand for closed-circuit television (CCTV) and related infrastructure in public spaces, there remains a notable lack of readily-deployable automated surveillance systems. In this study, we present a low-cost and efficient approach that integrates the use of computational object recognition to perform fully-automated identification, tracking, and counting of human traffic on camera video streams. Two software implementations are explored and the performance of these schemes is compared. Validation against controlled and non-controlled real-world environments is also demonstrated. The implementation provides automated video analytics for medium crowd density monitoring and tracking, eliminating labor-intensive tasks traditionally requiring human operation, with results indicating great reliability in real-life scenarios.
Asynchronous Episodic Deep Deterministic Policy Gradient: Toward Continuous Control in Computationally Complex Environments

Deep deterministic policy gradient (DDPG) has been proved to be a successful reinforcement learning (RL) algorithm for continuous control tasks. However, DDPG still suffers from data insufficiency and training inefficiency, especially, in computationally complex environments. In this article, we propose asynchronous episodic DDPG (AE-DDPG), as an expansion of DDPG, which can achieve more effective learning with less training time required. First, we design a modified scheme for data collection in an asynchronous fashion. Generally, for asynchronous RL algorithms, sample efficiency or/and training stability diminish as the degree of parallelism increases. We consider this problem from the perspectives of both data generation and data utilization. In detail, we redesign experience replay by introducing the idea of episodic control so that the agent can latch on good trajectories rapidly. In addition, we also inject a new type of noise in action space to enrich the exploration behaviors. Experiments demonstrate that our AE-DDPG achieves higher rewards and requires less time consumption than most popular RL algorithms in learning to run task which has a computationally complex environment. Not limited to the control tasks in the computationally complex environments, AE-DDPG also achieves higher rewards and two-fold to four-fold improvement in sample efficiency on average compared with other variants of DDPG in MuJoCo environments. Furthermore, we verify the effectiveness of each proposed technique component through abundant ablation study.
How a Computer Should Think

I propose that a certain four-valued logic should sometimes be used. It is to be understood that I use “logic” in a narrow sense, the old sense: a logic as an organon, a tool, a canon of inference. And it is also to be understood that I use “should” in a straightforward normative sense.
Reversible data hiding for JPEG images based on pairwise nonzero AC coefficient expansion

JPEG (Joint Photographic Experts Group) is the most common format in our daily life for storing and transmitting photographic images. Reversible data hiding (RDH) for JPEG images currently is one of the hotspots in the field of data hiding. However, different from RDH in uncompressed images, any modification in the compressed domain may produce distinct visual distortion and significantly increase the file size of the marked image. In this paper, a new JPEG RDH scheme based on pairwise nonzero AC coefficient expansion (pairwise NACE) is proposed. Firstly, by taking every two adjacent nonzero alternating current (AC) coefficients together to generate the nonzero-AC-coefficient pairs (NACPs), a sharp two-dimensional (2D) histogram can be constructed. Then a new reversible 2D mapping is carefully designed, and data are embedded based on the modification of these NACPs according to the mapping. Furthermore, an adaptive embedding strategy based on block and DCT frequency selection is proposed in order to preserve the visual quality and reduce the file size increase of the marked JPEG image. Experimental results demonstrate that our proposed method can achieve better performance both in visual quality and file size preservation compared to the previous works.
Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework

Segmenting gland instances in histology images is highly challenging as it requires not only detecting glands from a complex background but also separating each individual gland instance with accurate boundary detection. However, due to the boundary uncertainty problem in manual annotations, pixel-to-pixel matching based loss functions are too restrictive for simultaneous gland detection and boundary detection. State-of-the-art approaches adopted multi-model schemes, resulting in unnecessarily high model complexity and difficulties in the training process. In this paper, we propose to use one single deep learning model for accurate gland instance segmentation. To address the boundary uncertainty problem, instead of pixel-to-pixel matching, we propose a segment-level shape similarity measure to calculate the curve similarity between each annotated boundary segment and the corresponding detected boundary segment within a fixed searching range. As the segment-level measure allows location variations within a fixed range for shape similarity calculation, it has better tolerance to boundary uncertainty and is more effective for boundary detection. Furthermore, by adjusting the radius of the searching range, the segment-level shape similarity measure is able to deal with different levels of boundary uncertainty. Therefore, in our framework, images of different scales are down-sampled and integrated to provide both global and local contextual information for training, which is helpful in segmenting gland instances of different sizes. To reduce the variations of multi-scale training images, by referring to adversarial domain adaptation, we propose a pseudo domain adaptation framework for feature alignment. By constructing loss functions based on the segment-level shape similarity measure, combining with the adversarial loss function, the proposed shape-aware adversarial learning framework enables one single deep learning model for gland instance segmentation. Experimental results on the 2015 MICCAI Gland Challenge dataset demonstrate that the proposed framework achieves state-of-the-art performance with one single deep learning model. As the boundary uncertainty problem widely exists in medical image segmentation, it is broadly applicable to other applications.
Data Resource Profile: Regional healthcare information platform in Halland, Sweden

Accurate and comprehensive healthcare data coupled with modern analytical tools can play a vital role in enabling care providers to make better-informed decisions, leading to effective and cost-eff ...
Universal approximation with quadratic deep networks

Recently, deep learning has achieved huge successes in many important applications. In our previous studies, we proposed quadratic/second-order neurons and deep quadratic neural networks. In a quadratic neuron, the inner product of a vector of data and the corresponding weights in a conventional neuron is replaced with a quadratic function. The resultant quadratic neuron enjoys an enhanced expressive capability over the conventional neuron. However, how quadratic neurons improve the expressing capability of a deep quadratic network has not been studied up to now, preferably in relation to that of a conventional neural network. Specifically, we ask four basic questions in this paper: (1) for the one-hidden-layer network structure, is there any function that a quadratic network can approximate much more efficiently than a conventional network? (2) for the same multi-layer network structure, is there any function that can be expressed by a quadratic network but cannot be expressed with conventional neurons in the same structure? (3) Does a quadratic network give a new insight into universal approximation? (4) To approximate the same class of functions with the same error bound, could a quantized quadratic network have a lower number of weights than a quantized conventional network? Our main contributions are the four interconnected theorems shedding light upon these four questions and demonstrating the merits of a quadratic network in terms of expressive efficiency, unique capability, compact architecture and computational capacity respectively.
Learning gains and attitudes of students performing chemistry experiments in an immersive virtual world

Virtual worlds are increasingly popular in educational settings. In order to further usefulness in providing a laboratory education, almost 300 students participated in a study to determine the relative learning gains and attitudes related to performing two chemistry experiments in either the virtual world of Second Life (SL) or a real world laboratory. The participants were traditional college students enrolled in a General Chemistry 2 course at a large public university. Student grades, responses to multiple surveys, and written comments were collected over two spring semesters that the experiments were implemented. Students performing the Second Life experiments performed as well or better than students conducting equivalent experiments in a real world laboratory based on quiz and lab report grades. Students held a favourable view of the experiences in Second Life, noting that the SL setting minimized distractions and made certain aspects of the experiments easier to perform. A large majority of students performing the SL experiments enjoyed and recommended them. Male and female students performed equally well and showed equivalent attitudes in all aspects of this study.
A Two-Stream Approach to Fall Detection With MobileVGG

The existing deep learning methods for human fall detection have difficulties to distinguish falls from similar daily activities such as lying down because of not using the 3D network.Meanwhile, they are not suitable for mobile devices because they are heavyweight methods and consume a large number of memories.In order to alleviate these problems, a two-stream approach to fall detection with the MobileVGG is proposed in this paper.One stream is based on the motion characteristics of the human body for detection of falls, while the other is an improved lightweight VGG network, named the MobileVGG, put forward in the paper.The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution, depth convolution and point convolution.The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model.The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory.Therefore, it is suitable for mobile devices.
An overview of deep learning methods for image registration with focus on feature-based approaches

Image registration is an essential pre-processing step for several computer vision problems like image reconstruction and image fusion. In this paper, we present a review on image registration approaches using deep learning. The focus of the survey presented is on how conventional image registration methods such as area-based and feature-based methods are addressed using deep net architectures. Registration approach adopted depends on type of images and type of transformation used to describe the deformation between the images in an application. We then present a comparative performance analysis of convolutional neural networks that have shown good performance across feature extraction, matching and transformation estimation in featured-based registration. Experimentation is done on each of these approaches using a dataset of aerial images generated by inducing deformations such as scale.
A MultiKernel Domain Adaptation Method for Unsupervised Transfer Learning on Cross-Source and Cross-Region Remote Sensing Data Classification

Labeling remote sensing data for classification is labor-intensive and time-consuming. Transfer learning (TL), under such context, is attracting increasing attention as it aims to harness information from data set of other regions where labels are readily available. The central topic of concern is to homogenize the large disparities of feature distribution of different data set through domain adaptation (DA). This article proposes a novel DA method for unsupervised TL, namely, multikernel jointly domain matching (MKJDM), which by definition considers multiple kernels as opposed to the currently popular single-kernel methods for measuring the distances between distributions. The single-kernel methods minimize the distances of feature distribution between the source domain (data set with training labels) and the target domain (data set to be classified) through, for example, maximum mean discrepancy (MMD) metric, formed under a kernel function mapping, while the multikernel version (MK-MMD) uses different kernel functions to encapsulate multiple aspects of distribution discrepancies, and is, therefore, more capable of distance minimization. Our MKJDM implementation also considers simultaneously aligning marginal and class conditional distributions and reweight for each instance, which further improves the performance. Two experiments performed on remote sensing images and multi-modal data sets (i.e., Orthophoto and Digital Surface Models), with regions of different countries with distinctly different land patterns serving as source and target domain data, show that the overall accuracies are improved by 37.28% and 46.62% after applications of our MKJDM method. An additional comparative experiment with five state-of-the-art DA methods also demonstrates that our method achieves the best performance.
The Modal Age of Statistics

Summary Recently, a number of statistical problems have found an unexpected solution by inspecting them through a ‘modal point of view'. These include classical tasks such as clustering or regression. This has led to a renewed interest in estimation and inference for the mode. This paper offers an extensive survey of the traditional approaches to mode estimation and explores the consequences of applying this modern modal methodology to other, seemingly unrelated, fields.
A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics

This letter presents a novel median filtering forensics approach, based on a convolutional neural network (CNN) with an adaptive filtering layer (AFL), which is built in the discrete cosine transform (DCT) domain. Using the proposed AFL, the CNN can determine the main frequency range closely related with the operational traces. Then, to automatically learn the multi-scale manipulation features, a multi-scale convolutional block is developed, exploring a new multi-scale feature fusion strategy based on the maxout function. The resultant features are further processed by a convolutional stream with pooling and batch normalization operations, and finally fed into the classification layer with the Softmax function. Experimental results show that our proposed approach is able to accurately detect the median filtering manipulation and outperforms the state-of-the-art schemes, especially in the scenarios of low image resolution and serious compression loss.