Reviewerly/exHarmony huggingface dataset:
The myths and legends of king Satoshi and the knights of blockchain

In this paper, we present an ethnographic account of the quasi-religious romanticism of the crypto-community towards blockchain technologies. To do so, we explore the cultural significance of phenomena such as myth, faith, and ritual, without excluding both the realms of technological practices and techno-scientific narrative. Drawing on a comparison with the legend of King Arthur, we analyse how the legendary creator of Bitcoin, Satoshi Nakamoto, translates contemporary anxieties resulting from the financial crisis and the centralisation of power. By analysing white papers, we further explore the persuasive narratives which convey how ethics and virtue can be encoded into software, and, finally, we describe the secular rituals that reinforce cohesion among the community – in moments which are often guided by charismatic preachers and specialists. We argue that blockchain technologies have had a symbolic impact in re-invigorating enchantment and material romanticism towards finance and technology, which has had a wider impact on the social perception and acceptance of the transition to a digital society.
Ship Model Identification with Genetic Algorithm Tuning

Modeling is the most important component in predictive controller design. It should predict outputs precisely and fast. Thus, it must be adequate for the ship dynamics while having as simple a structure as possible. In a good ship model the standard deviation of a particular coefficient should not exceed 10% of its value. Fitting the validation data to 80% for short-term prediction and 65% for long-term prediction is treated as a declared benchmark for model usage in ship course predictive controller. Regularization was proposed to ensure better state-space models to fit the real ship dynamics and more accurate standard deviation value control. Usage of the simulation results and real-time trials, as model estimation and validation data, respectively, during the identification procedure is proposed. In the first step a predictive linear model is identified conventionally, and then coefficients are regularized, based on the validation data, using a genetic algorithm. Particular linearized model coefficient standard deviations were decreased from more than 100% of their values to approximately 5% of them using genetic algorithm tuning. Moreover, the proposed method eliminated model output signal oscillations, which were observed during the validation process based on experimental data, gained during ship trials. Improved mapping of ship dynamics was achieved. Fit to validation data increased from 71% and 54% to 89% and 76%, respectively, for short-term and long-term prediction. The proposed method, which may be applied to real applications, is easily applicable and reliable. The tuned model is sufficiently suited to plant dynamics and may be used for future predictive control purposes.
Effects of an integrated STEAM approach on the development of competence in primary education students (<i>Efectos de una propuesta STEAM integrada en el desarrollo competencial del alumnado de Educación Primaria</i>)

The multidimensional character of competence development involves a profound methodological change in how the teaching process is conceived. An integrated STEAM education is one potentially useful methodological approach to assist in this matter. Using design research, an integrated STEAM approach was designed, implemented and assessed with 121 students in their sixth year of primary education. Its effect was analysed through a mixed methods study. The results show that the students obtain a high level of competence after the approach is applied in all of the key competences, with an emphasis on the level attained by girls in scientific skills. These results seem to reinforce the hypothesis that an integrated STEAM education is effective as a possible avenue for improving competence development in the primary education years as it didactically responds to the complexity of today’s world.
Question-aware transformer models for consumer health question summarization

Searching for health information online is becoming customary for more and more consumers every day, which makes the need for efficient and reliable question answering systems more pressing. An important contributor to the success rates of these systems is their ability to fully understand the consumers' questions. However, these questions are frequently longer than needed and mention peripheral information that is not useful in finding relevant answers. Question summarization is one of the potential solutions to simplifying long and complex consumer questions before attempting to find an answer. In this paper, we study the task of abstractive summarization for real-world consumer health questions. We develop an abstractive question summarization model that leverages the semantic interpretation of a question via recognition of medical entities, which enables generation of informative summaries. Towards this, we propose multiple Cloze tasks (i.e. the task of filing missing words in a given context) to identify the key medical entities that enforce the model to have better coverage in question-focus recognition. Additionally, we infuse the decoder inputs with question-type information to generate question-type driven summaries. When evaluated on the MeQSum benchmark corpus, our framework outperformed the state-of-the-art method by 10.2 ROUGE-L points. We also conducted a manual evaluation to assess the correctness of the generated summaries.
An Res-Unet Method for Pulmonary Artery Segmentation of CT Images

Abstract In the recognition of pulmonary embolism, the accuracy of pulmonary artery segmentation plays a key role. Due to the irregular shape of pulmonary artery and the complex adjacent tissues, it is very challenging to segment pulmonary artery using traditional convolutional neural network. Therefore, an improved Res-Unet method for pulmonary artery segmentation is proposed in this paper. To begin with, the U-net structure is used as the basis structure to allow efficient information flow. Secondly, in order to improve the gradient circulation of the network, our model introduces residual connections based on the U-net structure, that is, adding a connection from the input to the output of the two convolutions and performing a convolution operation. Finally, to quick converge, we use a hybrid loss function, which is linearly combined by Dice loss and Cross Entropy loss. The experimental results show that the proposed framework ranks higher than U-net on recall, precision and Dice, yielding results comparable to that of manual segmentation.
Soft Computing and Decision Support System for Software Process Improvement: A Systematic Literature Review

Software project development is very crucial, and measuring the exact cost and effort of development is becoming tedious and challenging. Organizations are trying to wind up their project of software development within the agreed budget and schedule successfully. Traditional practices are inadequate to achieve the current needs of the software industry. Underestimation and overestimation of software development effort lead to financial implications in the form of resources, cost of staffing, and budget of developing the software project. Soft computing (SC) approaches and tools deliver an addition of techniques for anticipating resistance to the deception, defect, incomplete truth for traceability and ambiguity, low arrangement cost, and strength. A large amount of SC approaches is prevailing in the literature to accomplish way-out to difficulties precisely, practically, and speedily. The approaches of SC can give better prediction, high performance, and dynamic behavior. SC deals with computational intelligence which integrates the concept of agent paradigm and SC. The proposed study presents a systematic literature review (SLR) of the approaches, tools, and techniques of SC used in the literature. The study presented a comprehensive review by searching the defined keywords in the popular libraries, filtered the paper, and obtained most relevant papers. After the selection of the papers, the quality assessment process of the included papers has been done in order to determine the relevancy of the papers. The study will help researchers in the area of research to devise novel ideas and solutions to overcome the existing issue on the basis of this study as evidence of the literature.
ANEGMA: an automated negotiation model for e-markets

Abstract We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.
SETA++: Real-Time Scalable Encrypted Traffic Analytics in Multi-Gbps Networks

The security and privacy of the end-users are a few of the most important components of a communication network. Though end-to-end encryption (e.g., TLS/SSL) fulfils this requirement, it makes inspecting network traffic with legacy solutions such as Deep Packet Inspection difficult. Recent Machine Learning techniques have shown outstanding performance in encrypted traffic classification. Nevertheless, such approaches require efficient flow sampling at real enterprise-scale networks due to the sheer volume of transferred data. Through this paper, we propose a holistic architecture to extract flow information of encrypted data at multi Gbps line rate using sampling and sketching mechanisms, enabling network operators to estimate flow size distribution accurately and understand the behavior of VPN-obfuscated traffic. Using over 6000 video traffic traces, under three main evaluation scenarios based on trace duration and starting time point, we show that it is possible to achieve 99% accuracy for service provider classification and over 90% accuracy for content classification for a given service provider in the best case. We also deploy our solution at an operational enterprise-scale network leveraging kernel bypassing to demonstrate its capability to efficiently sample live traffic for analytics.
Comparison of Hybrid ACO-k-means algorithm and Grub cut for MRI images segmentation

Image segmentation is the process of dividing image into homogenous regions by some charasteristics and is widely used in medical diagnostics. Segmentation algorithms are used for anatomical features extraction from medical images. The Hybrid Ant Colony Optimization (ACO) – k-means and Grub Cut image segmentation algorithms for MRI images segmentation are considered in this paper. The proposed algorithms and sub-system for the medical image segmentation have been implemented. As there is no universal algorithm for medical image segmentation, image segmentation is still a challenging problem in image processing and computer vision in many real time applications and hence more research work is required. The experimental results show that the proposed algorithm has good accuracy in comparison to Grub cut.
Indirect Invisible Poisoning Attacks on Domain Adaptation

Unsupervised domain adaptation has been successfully applied across multiple high-impact applications, since it improves the generalization performance of a learning algorithm when the source and target domains are related. However, the adversarial vulnerability of domain adaptation models has largely been neglected. Most existing unsupervised domain adaptation algorithms might be easily fooled by an adversary, resulting in deteriorated prediction performance on the target domain, when transferring the knowledge from a maliciously manipulated source domain.
Mapping and correcting the distortion of 3D structured light scanners

We describe the use of an automated co-ordinate measurement machine (CMM) directly or in concert with a three-axis robot arm to map the distortion of a 3D structured light scanner's field of view. This enables us to compare the claimed accuracy and resolution of different structured light scanners. In addition, we can also use the distortion mapping to generate a correction mechanism to improve the performance of the scanner. We present the results of experiments obtained using this novel approach on three commercially available 3D scanners.
Partial Multi-Label Learning with Meta Disambiguation

In partial multi-label learning (PML) problems, each instance is partially annotated with a candidate label set, which consists of multiple relevant labels and some noisy labels. To solve PML problems, existing methods typically try to recover the ground-truth information from partial annotations based on extra assumptions on the data structures. While the assumptions hardly hold in real-world applications, the trained model may not generalize well to varied PML tasks. In this paper, we propose a novel approach for partial multi-label learning with meta disambiguation (PML-MD). Instead of relying on extra assumptions, we try to disambiguate between ground-truth and noisy labels in a meta-learning fashion. On one hand, the multi-label classifier is trained by minimizing a confidence-weighted ranking loss, which distinctively utilizes the supervised information according to the label quality; on the other hand, the confidence for each candidate label is adaptively estimated with its performance on a small validation set. To speed up the optimization, these two procedures are performed alternately with an online approximation strategy. Comprehensive experiments on multiple datasets and varied evaluation metrics validate the effectiveness of the proposed method.
First step towards parameters estimation of image operator chain

Many effective techniques have recently been proposed to estimate the parameters of specific tampering operations. Most of them consider the situation in which an image is tampered with by only a single operation. However, in reality, multiple operations are used to falsify an image. Because the tampering traces of previous operations may be weakened or eliminated by later operations, it is difficult for the prior algorithms, each of which was developed for a single operation, to detect all tampering operations. In this paper, we propose a new method for estimating the parameters of operations in different manipulation chains. A framework is presented to investigate the correlation between multiple operations, which divides the degree of correlation into uncoupled and coupled operations. Then, two cases of certain operator chains with different degrees of correlation are adopted to reveal the assessment framework. Under this framework, we design well-directed features to estimate the parameters for each operator chain. Finally, the experimental results demonstrate the effectiveness of the proposed framework.
Self-adversarial variational autoencoder with spectral residual for time series anomaly detection

Detecting anomalies accurately in time series data has been receiving considerable attention due to its enormous potential for a wide array of applications. Numerous unsupervised anomaly detection methods for time series have been developed because of the difficulty of obtaining accurate labels. However, most existing unsupervised approaches suffer from the problem of anomaly contamination, which results in models that are unable to learn the normal pattern well and further deteriorate the performance of detection methods. To this end, a novel unsupervised method, called Self-adversarial Variational Autoencoder with Spectral Residual (SaVAE-SR), is introduced for time series anomaly detection in this paper. The SaVAE-SR first produces labels for unlabeled training data using the spectral residual technique to identify the most critical anomalies. A VAE model with a modified loss that can leverage label information to remove the influence of anomalous points is then trained in a self-adversarial manner, enabling the model to self-evaluate the learning of complex data distribution and improve itself accordingly. Specifically, the encoder acts as an encoder to approximate the posterior of latent variables and as a discriminator to evaluate the generative ability of the generator and improve itself accordingly. The generator is trained to capture the underlying data distribution and attempts to produce real samples to deceive the discriminator. The encoder and generator of the model compete with each other just like the behavior of GANs but work together under the theoretical framework of VAEs. As a result, the SaVAE-SR model combines the respective strengths of the VAE and adversarial training but does not require an additional discriminator, which makes the whole model very compact. Extensive experiments on five datasets demonstrate the superiority of the proposed method over the existing state-of-the-art methods.
Robust Zero Watermarking Algorithm for Encrypted Medical Images Based on DWT-Gabor

To ensure the safety of medical images and patient information, this paper proposes a robust algorithm based on encrypted medical images. Firstly, the medical image is encrypted by combining DWT-DCT and tent mapping. Then, the features of the encrypted medical image are extracted by DWT-Gabor transform. Finally, the scrambled watermark is embedded into the encrypted medical image combined with zero watermark technology. Experiments show that the algorithm in this paper has good robustness and invisibility, and can resist a certain degree of conventional attacks and geometric attacks, especially against the translation and crop attacks.
Amended Convolutional Neural Network with Global Average Pooling for Image Classification

Image classification is playing a vital role in several computer vision and pattern recognition applications. Multi-class, corruptions and heterogeneous and complex shapes make the image classification task is extremely challenging. In this article, we introduce a new Convolutional Neural Network (CNN) design that combines several concepts including parallel convolutional layers with different filter sizes and a global average pooling layer (GAP). One of the deep learning limitations is overfitting. To diminish this issue, we have applied a GAP layer at the end of the mode. Different challenging benchmarks are used for evaluation. Specifically, CIFAR-10, CIFAR100, and MNIST are used in our final experiments. We showed that our model surpasses many former models evaluated on the same datasets. It has been proven the proposed model is active in phases of feature extraction and classification.
DDAT: Dual domain adaptive translation for low-resolution face verification in the wild

Low-resolution (LR) face verification has received much attention because of its wide applicability in real scenarios, especially in long-distance surveillance. However, the poor quality and scarcity of training data make the accuracy far from satisfactory. In this paper, we propose an end-to-end LR face translation and verification framework to improve the generation quality of face images and face verification accuracy simultaneously. We design a dual domain adaptive structure to generate high-quality images. On one hand, the structure can reduce the domain gap between training data and test data. On the other hand, the structure preserves identity consistency and low-level attributes. Meanwhile, in order to make the whole model more robust, we treat the generated images of the target domain as an extension of the training data. We conduct extensive comparative experiments on multiple benchmark data sets. Experimental results verify that our method achieves improved results in high-quality face generation and LR face verification. In particular, our model DDAT reduces FID to 18.63 and 39.55 on the source and the target domain from 254.7 and 206.19 of the up-sampling results, respectively. Our method outperforms competing approaches by more than 10 percentage points in terms of face verification accuracy on multiple surveillance benchmarks.
EZAC: Encrypted Zero-day Applications Classification using CNN and K-Means

With the rapid development of traffic encryption technology and the continuous emergence of various network services, the classification of encrypted zero-day applications has become a major challenge in network supervision. More seriously, many attackers will utilize zero-day applications to hide their attack behaviors and make attack undetectable. However, there are very few existing studies on zero-day applications. Existing works usually select and label zero-day applications from unlabeled datasets, and these are not true zero-day applications classification. To address the classification of zero-day applications, this paper proposes an Encrypted Zero-day Applications Classification (EZAC) method that combines Convolutional Neural Network (CNN) and K-Means, which can effectively classify zero-day applications. We first use CNN to classify the flows, and for the flows that may be zero-day applications, we use K-Means to divide them into several categories, which are then manually labeled. Experimental results show that the EZAC achieves 97.4% accuracy on a public dataset (CIC-Darknet2020), which outperforms the state-of-the-art methods.
‘GoLand Registry’ Case Study

research-article Share on ‘GoLand Registry’ Case Study: Blockchain/DLT Adoption in Land Administration in Afghanistan Author: Oleksii Konashevych Idependent researcher, Australia Idependent researcher, AustraliaView Profile Authors Info & Claims DG.O'21: DG.O2021: The 22nd Annual International Conference on Digital Government ResearchJune 2021 Pages 489–494https://doi.org/10.1145/3463677.3463720Published:09 June 2021Publication History 2citation86DownloadsMetricsTotal Citations2Total Downloads86Last 12 Months40Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
GeoGraph

In many applications of graph processing, the input data is often generated from an underlying geometric point data set. However, existing high-performance graph processing frameworks assume that the input data is given as a graph. Therefore, to use these frameworks, the user must write or use external programs based on computational geometry algorithms to convert their point data set to a graph, which requires more programming effort and can also lead to performance degradation. In this paper, we present our ongoing work on the Geo- Graph framework for shared-memory multicore machines, which seamlessly supports routines for parallel geometric graph construction and parallel graph processing within the same environment. GeoGraph supports graph construction based on k-nearest neighbors, Delaunay triangulation, and b-skeleton graphs. It can then pass these generated graphs to over 25 graph algorithms. GeoGraph contains highperformance parallel primitives and algorithms implemented in C++, and includes a Python interface. We present four examples of using GeoGraph, and some experimental results showing good parallel speedups and improvements over the Higra library. We conclude with a vision of future directions for research in bridging graph and geometric data processing.