Reviewerly/exHarmony huggingface dataset:
An Information Entropy Method to Quantify the Degrees of Decentralization for Blockchain Systems

Decentralization is a key selling point of most public blockchain platforms. However, despite the widely acknowledged importance of this property, most researches on this topic lack quantification, and none of them performs a calculation on the degrees of decentralization they achieve in practice. In this paper, taking Bitcoin and Ethereum for instances, we propose an entropy method in information theory to quantify the decentralization for them. Using the information entropy, we calculate the discrete degrees of blocks mined and address balances to quantify the degrees of decentralization for Bitcoin and Ethereum systems, and the results of calculations indicate that Bitcoin's mining is more approximately 12% decentralized than Ethereum with full samples, and Bitcoin's wealth is more approximately 9.2% decentralized than Ethereum with 10,000 samples. Our method can be used to evaluate the degree of decentralization for any blockchain system.
Whole Heart Segmentation from CT images Using 3D U-Net architecture

Recent studies have demonstrated the importance of neural networks in medical image processing and analysis. However, their great efficiency in segmentation tasks is highly dependent on the amount of training data. When these networks are used on small datasets, the process of data augmentation can be very significant. We propose a convolutional neural network approach for the whole heart segmentation which is based upon the 3D U-Net architecture and incorporates principle component analysis as an additional data augmentation technique. The network is trained end-to-end i.e. no pre-trained network is required. Evaluation of the proposed approach is performed on 20 3D CT images from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, divided into 15 training and 5 validation images. Final segmentation results show a high Dice coefficient overlap to ground truth, indicating that the proposed approach is competitive to state-of-the-art Additionally, we provide the discussion of the influence of different learning rates on the final segmentation results.
A Node Density Control Learning Method for the Internet of Things

When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%.
Methodology for generating high time resolution typical meteorological year data for accurate photovoltaic energy yield modelling

Accurate energy yield prediction is of utmost importance for commercial scale photovoltaic systems. One key parameter crucial to the prediction accuracy is the quality of solar radiation data. Most energy yield prediction models rely on Typical Meteorological Year data with maximum temporal resolution of one hour. In this work we develop a methodology to generate Typical Meteorological Year data with much higher time resolution using gap filling methods that aim to maintain high-quality solar radiation data for photovoltaic yield modelling. We demonstrate our method using ground-based one-minute solar radiation measurements available in Australia and find that hourly averaging reduces the share of irradiance values exceeding 1000 W/m2 by 4.1% to 10.8% compared to our one-minute resolution dataset. Such high irradiance values usually result from the cloud enhancement effect, which is filtered out by averaging. We estimate that the hourly averaging can lead to an underestimation of inverter clipping losses by 0.4% to 2.2% and an overestimation of the performance ratio by on average 1.1% for a common DC-to-AC ratio of 1.2. One potential issue is the limited availability of high-resolution radiation data with broad geographic coverage. However, new satellite-based irradiance products and stochastic models can overcome this limitation.
Indoor Navigation with Augmented Reality

This paper is a survey of types of Augmented Reality or AR interfaces and different indoor positioning and navigation techniques. Various existing methods are briefly elaborated and compared to build a set of techniques which can be used to develop a visual positioning and navigation system to implement handheld, mobile indoor navigation using augmented reality. The paper also illustrates about the newest and most trending technological advancements and available tools that enhance and ease the implementation of Augmented Reality applications.
Secure and Scalable Trust Management Model for IoT P2P Network

IoT trust management is a security solution that assures the trust between different IoT entities before establishing any relationship with other anonymous devices. Recent researches presented in the literature tend to use a Blockchain-based trust management model for IoT besides the fog node approach in order to address the constraints of IoT resources. Actually, Blockchain has solved many drawbacks of centralized models. However, it is still not preferable for dealing with massive data produced by IoT because of its drawbacks such as delay, network overhead, and scalability issues. Therefore, in this paper we define some factors that should be considered when designing scalable models, and we propose a fully distributed trust management model for IoT that provide a large-scale trust model and address the limitations of Blockchain. We design our model based on a new approach called Holochain considering some security issues, such as detecting misbehaviors, data integrity and availability.
Unsupervised Learning for Optical Flow Estimation Using Pyramid Convolution LSTM

Most of current Convolution Neural Network (CNN) based methods for optical flow estimation focus on learning optical flow on synthetic datasets with groundtruth, which is not practical. In this paper, we propose an unsupervised optical flow estimation framework named PCLNet. It uses pyramid Convolution LSTM (ConvLSTM) with the constraint of adjacent frame reconstruction, which allows flexibly estimating multi-frame optical flows from any video clip. Besides, by decoupling motion feature learning and optical flow representation, our method avoids complex short-cut connections used in existing frameworks while improving accuracy of optical flow estimation. Moreover, different from those methods using specialized CNN architectures for capturing motion, our framework directly learns optical flow from the features of generic CNNs and thus can be easily embedded in any CNN based frameworks for other tasks. Extensive experiments have verified that our method not only estimates optical flow effectively and accurately, but also obtains comparable performance on action recognition.
Multi-Scale Adaptive Dehazing Network

Since haze degrades an image including contrast decreasing and color lost, which has a negative effect on the subsequent object detection and recognition. single image dehazing is a challenging visual task. Most existing dehazing methods are not robust to uneven haze. In this paper, we developed an adaptive distillation network to solve the dehaze problem with non-uniform haze, which does not rely on the physical scattering model. The proposed model consists of two parts: an adaptive distillation module and a multi-scale enhancing module. The adaptive distillation block reassigns the channel feature response via adaptively weighting the input maps. And then the important feature maps are separated from the trivial for further focused learning. After that, a multi-scale enhancing module containing two pyramid downsampling layers is employed to fuse the context features for haze-free images restoration in a coarse-to-fine way. Extensive experimental results on synthetic and real datasets demonstrates that the proposed approach outperforms the state-of-the-arts in both quantitative and qualitative evaluations.
Collaborative Visual Analysis with Multi-level Information Sharing Using a Wall-Size Display and See-Through HMDs

Solving complex data analysis problems can often benefit a collaborative effort. For synchronous co-located collaboration, a well-recognized challenge is to deliver different contents to people with different privileges and different responsibilities. This challenge is becoming more obvious with the use of a shared display space such as a wall-size display. In particular, scenarios often arise that a privileged participant needs to access sensitive information that other participants are not permitted to view. This is nearly impossible to achieve with only a single display. As a result, it becomes clear that additional devices are needed to provide some of the participants the capability to access and manage certain information in a private space. In this work, we investigate incorporating optical see-through head-mounted displays (OST-HMDs) with a wall-size display to deliver sensitive information in a synchronous co-located, collaborative setting. With our prototype system, we conduct a user study to observe the collaboration styles under this unique setup. We also present the lessons learned by reflecting on the iterative design process of our prototype system.
An Improved Dijkstra's Algorithm for Shortest Path Planning on 2D Grid Maps

Finding a shortest path from an arbitrarily selected initial position to a single goal position is very useful in some domains, such as in partially-known environments or control many robotics from different initial positions to move to the goal position. This paper analyzes a property on 2D eight-neighbor grid map and introduces an improved Dijkstra's algorithm (or IDA for short) to solve the problem. The property we analyzed ensures that each position only needs to calculate once to get the shortest distance to the goal position in IDA, so it can save much time with regard to the Dijkstra's algorithm. We test the performance of IDA on different sizes of 2D eight-neighbor gird maps, the results show that IDA can speed up the Dijkstra's algorithm by several orders of magnitude and more. Meanwhile, the results get by our algorithm can easily be used by the A* algorithm for partially-known environments as Path Adaptive A* and Tree Adaptive A* does.
Detecting Shilling Attacks with Automatic Features from Multiple Views

Due to the openness of the recommender systems, the attackers are likely to inject a large number of fake profiles to bias the prediction of such systems. The traditional detection methods mainly rely on the artificial features, which are often extracted from one kind of user-generated information. In these methods, fine-grained interactions between users and items cannot be captured comprehensively, leading to the degradation of detection accuracy under various types of attacks. In this paper, we propose an ensemble detection method based on the automatic features extracted from multiple views. Firstly, to collaboratively discover the shilling profiles, the users’ behaviors are analyzed from multiple views including ratings, item popularity, and user-user graph. Secondly, based on the data preprocessed from multiple views, the stacked denoising autoencoders are used to automatically extract user features with different corruption rates. Moreover, the features extracted from multiple views are effectively combined based on principal component analysis. Finally, according to the features extracted with different corruption rates, the weak classifiers are generated and then integrated to detect attacks. The experimental results on the MovieLens, Netflix, and Amazon datasets indicate that the proposed method can effectively detect various attacks.
Cryptanalysis and Improvement of a Color Image Encryption Scheme Based on DNA Sequences and Multiple 1D Chaotic Maps

This paper presents cryptanalysis of a color image encryption scheme. DNA encoding and multiple 1D chaotic maps are used in the encryption process which increases its computational speed. The key streams generated in this scheme are dependent on secret keys, updated using the sum of pixel intensities of plain image of size [Formula: see text]. This paper analyzes the security of encryption scheme against the chosen plaintext attack and finds that only [Formula: see text] different key matrices for diffusion are possible, an equivalent version of which can be revealed with [Formula: see text] chosen plain images. Experimental results are presented to prove that equivalent diffusion keys and block permutation sequence can be effectively revealed through the attack. In addition, low sensitivity of keys towards changes in plaintext along with insecure diffusion process involved in encryption process is also reported. Finally, to remedy the shortcomings of the original encryption scheme, an enhanced encryption scheme is generated that can resist chosen/known plaintext attack while maintaining the merits of the original encryption scheme.
Cryptocurrencies and the Denationalization of Money

The theoretical foundations of bitcoin have been frequently traced back to the Austrian school of economics. To the extent that cryptocurrencies are not issued by a centralized authority and do not rely on an official legal tender status for their acceptance, they may indeed appear as a dramatic departure from the historical trend that has led, over the past few centuries, to the making of national money and as a decisive step toward the “denationalization of money” advocated by F. A. von Hayek. This article investigates to what extent bitcoin truly embodies the principles of stable money prescribed by Hayek and whether the proliferation of cryptocurrencies constitutes a Hayekian monetary competition.
Prediction of Cloud Resources Demand Based on Hierarchical Pythagorean Fuzzy Deep Neural Network

Having stepped into the era of information explosion, storing, processing and analyzing the vast data sometimes are quite intractable problems. However, it is impossible for personal computer or devices to tackle with such heavy workloads. Then, companies that provides cloud computational services come into business. From the perspective of companies, the cost for providing fog computing services is much higher than the traditional computing services. Consequently, the price for real-time requests is more expensive than the reserved services. Aiming at minimizing the expenditures, the most important part is how many cloud services the customers should reserve in advance because different amounts they consume will yield different expenses and both of insufficient and excess consumption result in wastes. The emerging machine learning method provides a powerful tool to address such a prediction problem. In this paper, we propose a hierarchical Pythagorean fuzzy deep neural network (HPFDNN) to forecast the quantity of requisite cloud services. On account of obtaining the better interpretations of original data, beyond the employment of fuzzy logic, the neural representation is also utilized as a complementary method. The information or the knowledge acquired from fuzzy and neural perspectives are coalesced as the final transformed data to be put into the learning systems, so that the useful information concealed in the enormous contents can be effectively described. On the basis of the anticipation of the deep neural network, the consumers are able to decide the amount of cloud services to purchase. Numerical results based on the real data set from Carnegie Mellon University demonstrate that the proposed model yields the economical predictions and outperforms the prediction by the traditional deep neural network.
Sequential Fusion Estimation for Networked Multisensor Nonlinear Systems

This article presents a sequential fusion approach for state estimation of networked multisensor nonlinear systems, where sensors and estimators are allowed to work asynchronously. Both sequential measurement fusion (SMF) and sequential state fusion (SSF) estimators are designed, where the unscented filtering method is used in the design of the local SMF estimator, and the matrix weighting method is applied in the design of the SSF estimator. It is shown that the proposed SSF estimators provide a satisfactory estimation precision that is close to the centralized batch state fusion (BSF) estimator, while requiring smaller computation burden as compared with the BSF estimator. Both simulations and experiments of a moving target tracking system are presented to show the effectiveness of the proposed sequential fusion methods.
Learned Scalable Image Compression with Bidirectional Context Disentanglement Network

In this paper, we propose a learned scalable/progressive image compression scheme based on deep neural networks (DNN), named Bidirectional Context Disentanglement Network (BCD-Net). For learning hierarchical representations, we first adopt bit-plane decomposition to decompose the information coarsely before the deep-learning-based transformation. However, the information carried by different bit-planes is not only unequal in entropy but also of different importance for reconstruction. We thus take the hidden features corresponding to different bit-planes as the context and design a network topology with bidirectional flows to disentangle the contextual information for more effective compressed representations. Our proposed scheme enables us to obtain the compressed codes with scalable rates via a one-pass encoding-decoding. Experiment results demonstrate that our proposed model outperforms the state-of-the-art DNN-based scalable image compression methods in both PSNR and MS-SSIM metrics. In addition, our proposed model achieves better performance in MS-SSIM metric than conventional scalable image codecs. Effectiveness of our technical components is also verified through sufficient ablation experiments.
Deformation measurement of high-speed rotating drone blades based on digital image correlation combined with ring projection transform and orientation codes

Digital image correlation (DIC) is a versatile non-contact optical measurement method, but it still has many shortcomings in theory and application. For example, the traditional DIC method has difficulty obtaining the desired result when the surface of the test object is rotated substantially or the deformation involves large rotation. Some scholars argue that a rotation angle greater than 7° will not work with the DIC algorithm, and this phenomenon is called decorrelation. To solve this problem, this study proposes the rotation-invariant DIC (RI-DIC) method that combines rotation-invariant template matching with traditional DIC. A pre-matching method based on ring projection transform (RPT) and orientation codes (OC) is proposed to provide the initial values for the DIC iteration process. The algorithm consists of three stages. In the first stage, the RPT process is used to convert the 2D template in a circular region into a 1D grey-level signal as a function of radius, and the template matching based on RPT is used to obtain a series of candidate points. The advantages of RPT process are the characterisation of its rotation invariance and the reduction of computational complexity. In the second stage, a fine matching process based on OC is performed on a limited number of candidate points to obtain the integer pixel matching position and the initial value. Finally, the inverse compositional Gauss–Newton (IC-GN) iteration method is used to further calculate the displacement and strain information accurately on the basis of the initial values. The novelty of this paper is to fuse the ring projection transform and orientation codes in machine vision into the traditional DIC algorithm, finally propose the RI-DIC algorithm. By the proposed method, we can measure the strain generated by the centrifugal force of a drone blade rotating at a high speed. Experimental results indicate that the method can accurately measure the surface deformation of a test object at any rotation angle.
Principles Underpinning Innovative Mobile Learning: Stakeholders’ Priorities

This article discusses the results of a survey that measured school teachers' and mobile learning (m-learning) experts' perceptions of the relative importance and effectiveness of various pedagogical principles underpinning m-learning designs. A scan of relevant literature produced a set of articles describing effective innovative m-learning. Principles underpinning the learning activities in these articles were identified. These principles were then provided to respondents so that they could identify the most important ones relative to the others for designing effective and innovative m-learning tasks. A rigorous Best/Worst Scaling (BWS) survey was used to collect these data. This is the first time that a BWS has been conducted with regard to mobile pedagogical principles. Findings showed that principles related to authenticity were rated most important relative to other principles by the m-learning experts and principles related to personalisation and customisation were rated most important by practising teachers. Other principles that have been used in innovative m-learning designs, such as gamification and intergenerational learning, were seen as least important by all respondents. The findings will inform design of professional development activities, in particular, those pertaining to an app being developed in an Erasmus + project, Developing and Evaluating Innovative Mobile Pedagogies (DEIMP).
Toward Virtual Stress Inoculation Training of Prehospital Healthcare Personnel: A Stress-Inducing Environment Design and Investigation of an Emotional Connection Factor

Prehospital emergency healthcare personnel are responsible for finding, rescuing, and taking prehospital care of emergency patients. They are regularly exposed to stressful and traumatic lifesaving situations. The stress involved can impact their performance and can cause mental disorders in the long term. Stress inoculation training (SIT) inoculates individuals to potential stressors by letting them practice stress-coping skills in a controlled environment. Our work explores a story-driven stressful virtual environment design that can potentially be used for SIT in the new context of emergency healthcare personnel. Users role-play a first-time emergency worker on a rescue mission. The interactive storytelling is designed to engage users and elicit strong emotional responses, and follows the three-act structure commonly found in films and video games. To understand the stress-inducing and sense of presence qualities of our approach including the previously untested impact of an emotional connection factor, we conduct a between-subjects experiment involving 60 subjects. Results show that the approach successfully induces stress by increasing heart rate, galvanic skin response, and subjective stress rating. Questionnaire results indicate positive presence. One subject group engages in an initial friendly conversation with a virtual co-worker to establish an emotional connection. Another group includes no such conversation. The group with the emotional connection shows higher physiological stress levels and more occurrences of subject behaviors reflecting presence. Medical experts review our approach and suggest several applications that can benefit from its stress inducing ability.