Reviewerly/exHarmony huggingface dataset:
Reinforced quasi-random forest

We propose a reinforced quasi-random forest for classification task. Reinforcement is performed iteratively by adding new trees to the forest. Our method assigns an importance to each of the attributes and identifies the attributes that causes the mis-classification of data points during training. The new trees are constructed using the mis-classified data points with reduced set of attributes. The attributes for splitting the nodes of the reinforced trees are found in a deterministic manner. Hence the new trees are quasi-random in nature. The best out of all the new trees are found using a novel electrostatic model. These trees are termed as reinforced trees. Additions of reinforced trees to the existing forest ensure maximum reduction in classification error. The efficacy of the proposed method is established through experiments on breast cancer datasets for detecting mitotic nuclei. Results of our method show significant improvement compared to other state-of-the-art approaches. Results on benchmark datasets show as much as 14% reduction in classification error.
Prediction of time series for several hours of surface solar irradiance using one-granule cloud property data from satellite observations

Satellite observation is one of the most useful methods for monitoring the power generated by photovoltaic systems. We propose a method for predicting time series of the surface solar irradiance (SSI) from one-granule cloud properties data obtained by satellite observation. This method has two parts. One is prediction of time series features from cloud properties, which is based on a previous study. The other is prediction of a time series from time series features, which is developed in this work. Seven time series features are used: mean, standard deviation, skewness, kurtosis, linear regression coefficient, autocorrelation coefficient with lag-1, and sample entropy. The prediction skill is characterized in two ways: by the distance between the predicted and observed time series (metric D), and by the similarity of the spectrum between the two time series (metric S). Verifying the time series prediction method from time series features shows that the mean and linear regression coefficient are the first and second most influential factors for metric D, and standard deviation and autocorrelation coefficient with lag-1 are those for metric S. The prediction skill of the prediction method for the time series features from cloud properties is also verified, indicating that predictors for the mean, standard deviation lag-1 autocorrelation coefficient, and sample entropy have sufficient prediction skill, measured by the standardized root mean square error. Finally, the two methods are integrated to construct the prediction system using four time series features. The overall skill of the prediction system for the time series is evaluated. The proposed system provides information about the strength of the SSI time series and its variation for several hours over the satellite observation area.
Signature handwriting identification based on generative adversarial networks

Handwritten signature has been an important identity-verification method since ancient times. Compared with manual handwriting verification, the use of computer image recognition technology for handwriting verification is faster and avoids subjectivity. However, there are still some challenges in traditional image recognition methods, such as feature selection, lack of a standard basis, and low accuracy. For the first time, generative adversarial nets (GAN) technology is adopted to study the task of handwritten signature identification. A special network SIGAN (Signature Identification GAN, SIGAN)is proposed based on the idea of dual learning. The loss value of the trained discriminator of SIGAN is used as the identification threshold. The authenticity of the test handwritten signature is determined by comparing the threshold and loss value of the test image obtained through the network. The experimental data set in this study consists of five hard pen-type signatures, which include some real signatures and some deliberate imitations. The experimental results show that the average accuracy of the SIGAN-based signature identification model is 91.2%, which is 3.6% higher than that of the traditional image classification method.
Ink Removal from Histopathology Whole Slide Images by Combining Classification, Detection and Image Generation Models

Histopathology slides are routinely marked by pathologists using permanent ink markers that should not be removed as they form part of the medical record. Often tumour regions are marked up for the purpose of highlighting features or other downstream processing such an gene sequencing. Once digitised there is no established method for removing this information from the whole slide images limiting its usability in research and study. Removal of marker ink from these high-resolution whole slide images is non-trivial and complex problem as they contaminate different regions and in an inconsistent manner. We propose an efficient pipeline using convolution neural networks that results in ink-free images without compromising information and image resolution. Our pipeline includes a sequential classical convolution neural network for accurate classification of contaminated image tiles, a fast region detector and a domain adaptive cycle consistent adversarial generative model for restoration of foreground pixels. Both quantitative and qualitative results on four different whole slide images show that our approach yields visually coherent ink-free whole slide images.
An Attempt at Improving Density-based Clustering Algorithms

Clustering is an unsupervised analytical technique for processing data that works by grouping elements of a set in order to form clusters of similar items. This task lies at the base level of many other tasks including machine vision and artificial intelligence. Big data sets present challenges for clustering due to the size and complexity of the data to be processed. Previous work in this domain resulted in an algorithm called Fast Density-Grid Clustering, which is designed to create a grid structure on the data and then merging cells based on local density. In this paper, we focus on an ongoing modification to this algorithm that could also be used for other density-based clustering algorithms, based on adaptive grids that have an irregular spacing. Testing shows that while the initial results for the first stage of implementation resulted in an average 29.728% loss in accuracy with no significant speed increase, there is a lot of further room for experimentation and development for this approach.
Experimental generation of entanglement-assisted quantum random access code

$n\ensuremath{\rightarrow}m$ random access code (RAC) is an information task to encode an $n$-bit random message into a shorter $m$-bit string. Resulting from the compression loss from $n$ to $m$ bits, there exists success probabilities for the string receiver to recover any bits of the initial message. It has been shown that success probabilities of bit recovery can be enhanced by employing quantum resources. Here we present experimental realizations of $2\ensuremath{\rightarrow}1$ and $3\ensuremath{\rightarrow}1$ distributed entanglement-assisted random access codes (EARACs) with a two-photon entangled source. The average success probabilities obtained in our experiment are $0.8491(41)$ for a $2\ensuremath{\rightarrow}1$ EARAC and $0.7724(34)$ for a $3\ensuremath{\rightarrow}1$ EARAC, exceeding the classical counterparts, respectively. Moreover, through the existing experimental data, success probabilities for $n$ varying from 4 to 10 ($m=1$) are estimated.
Parametric model for microwave filter by using multiple hidden layer output matrix extreme learning machine

Smart tuning of a filter depends very much on an accurate parametric model. Here, the authors develop a parametric model for a microwave filter based on an improved extreme learning machine (ELM). First, a coupling matrix is extracted from scattering parameters in undesirable states. The automatic decomposition and modular training strategy greatly reduces the network complexity. Next, by increasing the size of the output matrix of the hidden-layer, the number of hidden layer nodes can be changed. Finally, the model parameters are optimised by combining the particle swarm optimisation (PSO) algorithm and the differential evolution (DE) algorithm. The presented parametric model benefits from the information of the fusion mechanism with a hybrid optimisation algorithm and succeeds in avoiding slow convergence and premature influence on the system. It also achieves the desired training accuracy within the target time. Compared with prediction based on back-propagation (BP) neural networks and least-squares support vector machines (LS-SVMs), the proposed method can be generalised and has a better modelling accuracy.
Effective Vehicle Tracking Algorithm for Smart Traffic Networks

A smart traffic analysis system could be used to reduce congestion, prevent accidents, as well as to control traffic flow. Such a system would need to make use of many technologies, such as computer networking, communication, image processing, object detection, and tracking. In this paper, we introduce an efficient vehicle tracking algorithm which could be used to help create a smart city traffic control system. Our algorithm is targeted to solve the multi-object tracking (MOT) problem. Our method follows a track-by-detection paradigm, i.e., it relies on vehicle detection results to decide whether a detected vehicle in sequential frames belong to the same track. Our vehicle tracker extends the intersection over union (IOU) tracker and improves upon it by fusing historical tracking information, taking into consideration the balance between tracking efficiency and effectiveness. We demonstrate the effectiveness and efficiency of our approach using the UA-DETRAC benchmark dataset. Our proposed method runs at an average speed of 1,264 frames per second. We conclude that our tracker could be useful for applications running in a real-time environment.
On the Convergence of Fuzzy Grey Cognitive Maps

Fuzzy grey cognitive maps (FGCMs) are extensions of fuzzy cognitive maps (FCMs), applying uncertain weights between the concepts. This uncertainty is expressed by so-called grey numbers. Similarly to FCMs, the inference is determined by an iteration process, which may converge to an equilibrium point, but limit cycles or chaotic behaviour may also turn up. In this paper, based on the grey weighted connections between the concepts and the parameter of the sigmoid threshold function, we give sufficient conditions for the existence and uniqueness of fixed points for sigmoid FGCMs.
An Information-Theoretic Analysis of Deduplication

Deduplication finds and removes long-range data duplicates. It is commonly used in cloud and enterprise server settings and has been successfully applied to primary, backup, and archival storage. Despite its practical importance as a source-coding technique, its analysis from the point of view of information theory is missing. This paper provides such an information-theoretic analysis of data deduplication. It introduces a new source model adapted to the deduplication setting. It formalizes the two standard fixed-length and variable-length deduplication schemes, and it introduces a novel multi-chunk deduplication scheme. It then provides an analysis of these three deduplication variants, emphasizing the importance of boundary synchronization between source blocks and deduplication chunks. In particular, under fairly mild assumptions, the proposed multi-chunk deduplication scheme is shown to be order optimal.
Augmented Reality Application for Aiding Tumor Resection in Skull-Base Surgery

Treatment of cancer patients has improved with advances in tumor resection techniques for skull-base surgery. However, a secondary procedure or chemotherapy is often required to treat residual tumor to prevent recurrence. With the advent of assistive technology, such as augmented reality, a myriad of possibilities have been facilitated for the field of surgery. This work explores the development of an augmented reality application to improving the tumor margin excised during surgical procedures, with a focus on skull-base surgery. An isosurface reconstruction algorithm was integrated with the Microsoft HoloLens, a self-contained holographic computer, to enable visualization of Computed Tomography (CT) imaging superimposed in 3D on the patient. The results suggest that though the device has limitations at its current stage, the Microsoft HoloLens could be used for planning and overlaying the imaging information on the patient for removal of lesions in real-time. The modules developed could also be extended to other types of surgery involving visualization of Digital Imaging and Communication in Medicine (DICOM) files.
Managing Distributed Ledgers: Blockchain and Beyond

The Internet of Value based on Distributed Ledger Technology strives for a strictly decentralised organisation of interactivities between peers without any centralised platform or intermediary. The technology is disruptive because core elements of the current organisation of value exchange will change radically. This applies in particular to four areas: (1) Proof of identity of customers, of clients, of users, of patients and the associated handling of private data; (2) Recording, documenting and certifying transactions, the change of value and entrepreneurial success; (3) Organisation of the value exchange and the transfer of values and utilities; (4) Integration of ob-jects, of machines and of robots in communication and transaction processes.Distributed Ledger Technology is therefore not an innovation which comes overnight. The diffusion period takes longer – probably years or a decade – as radical changes within society are needed before distributed and shared ledgers become standard. Many technological aspects are not yet fully developed, so that the DLT is currently still in experimental mode. But the cases of use so far already show that the technology has the potential to revolutionize the nominal world of registration, certification, accounting and exchange of digital value and thereby enable completely new forms of collaboration and organization.
Adapting Financial Technology Standards to Blockchain Platforms

Traditional payment systems have standards designed to keep transaction data secure, but blockchain systems are not in scope for such security standards. We compare the Payment Application Data Security Standard's (PA-DSS) applicability towards transaction-supported blockchain platforms to test the standard's applicability. By highlighting the differences in implementation on traditional and decentralized transaction platforms, we critique and adapt the standards to fit the decentralized model. In two case studies, we analyze the QTUM and Ethereum blockchain platforms' industry compliance, as their payment platforms support transactions equivalent to that of applications governed by the PA-DSS. We determine QTUM's and Ethereum's capabilities to properly ensure secure data handling with respect to current security standards. After adapting the PA-DSS and analyzing the QTUM and Ethereum platforms, we revise the new set of standards to create a set of best-practices for ensuring data security on both traditional and blockchain payment systems. We report the security gaps identified on each platform based on the final revision of the standards, presenting a conclusive perspective that neither platform is suitable for business adoption based on the PA-DSS standard's results. Finally, we discuss open research issues.
Agile Practices in Practice: Towards a Theory of Agile Adoption and Process Evolution

Abstract As teams and organisations make the difficult shift to agile ways of working, there has been relatively little investigation of how they adopt and use agile practices. To aid those teams looking to move to agile we should examine how others have done so and what practical value they found. We studied teams which adopted agile practices across a spectrum from taking on a whole methodology to a couple of practices at a time, and then committed to continuous assessment and improvement of their ways of working. Those teams favoured adapting agile-based, team-oriented practices suited to their particular needs over technical practices and defined methodologies.
Depth-aware image vectorization and editing

Image vectorization is one of the primary means of creating vector graphics. The quality of a vectorized image depends crucially on extracting accurate features from input raster images. However, correct object edges can be difficult to detect when color gradients are weak. We present an image vectorization technique that operates on a color image augmented with a depth map and uses both color and depth edges to define vectorized paths. We output a vectorized result as a diffusion curve image. The information extracted from the depth map allows us more flexibility in the manipulation of the diffusion curves, in particular permitting high-level object segmentation. Our experimental results demonstrate that this method achieves high reconstruction quality and provides greater control in the organization and editing of vectorized images than existing work based on diffusion curves.
A Fast and Efficient Local Outlier Detection in Data Streams

Outlier detection in data streams is used in many applications, such as network flow monitoring, stock trading fluctuation detection and network intrusion detection [1]. These applications require that the algorithms finish outlier detection effectively in a limited amount of time and memory space. Local Outlier Factor (LOF) is a fundamental density-based outlier detection algorithm [2], it determines whether an object is an outlier by calculating LOF score of each observer. There are many LOF-based algorithms that have achieved excellent results with respect to outlier detection in data streams, while most of existing LOF-based algorithms have problems with excessive computation. In this paper, we propose a fast outlier detection algorithm in data streams, the algorithm effectively reduces the LOF calculation of the whole data by Z-score pruning. The algorithm consists of three phases. Firstly, generate the prediction data through the generator. Secondly, judge whether the observation object is a potential outlier by the Z-score of the residual from the origin value and the prediction value. Finally, calculate the LOF of the observation object in the current time window according to the judgment result of the previous step. It is proved by experiments that our algorithm effectively reduces the detection time consumption through Z-score pruning under the condition of ensuring the detection accuracy.
An Improved Confidence Connected Liver Segmentation Method Based on Three Views of CT Images

To enhance the accuracy of liver segmentation, we present an improved confidence connected liver segmentation method, which combines the liver segmentation results obtained from three views is proposed.First, to reduce noise, an improved curvature anisotropic diffusion filter is applied, which simultaneously stores edge information.Second, seed points located in the liver are selected automatically using statistics and analysis of the liver intensity.We extract the liver contours from three views of computed tomography (CT) images using the confidence connected method and improve the contours by the cavity filling method.Finally, we combine the liver contours extracted from the coronal, sagittal, and cross section.In our experiments, clinical validation is performed using ten abdominal CT datasets.The results show that our proposed method can extract liver contours quickly and accurately, achieving an overall true positive rate (TPR) of 0.97.In addition, this method is useful for clinical diagnosis of liver disorders and virtual surgical planning.
Perceptually lossless compression of Mastcam images with error recovery

We present a high performance image compression framework for Mastcam images in the Mars rover Curiosity. First, we aim at achieving perceptually lossless compression. Four well-known image codecs in the literature have been evaluated and the performance was assessed using four well-known performance metrics. Second, we investigated the impact of error concealment algorithms for handling corrupted pixels due to transmission errors in communication channels. Extensive experiments using actual Mastcam images have been performed to demonstrate the proposed framework.
Security analysis and classification of image secret sharing

Image secret sharing (ISS) mainly focuses on image protection through splitting a secret image into n shares distributed to n associated participants. The secret image can be decoded by collecting sufficient shares. In recent years, many ISS schemes are introduced, among which polynomial-based ISS scheme and visual secret sharing (VSS) also called visual cryptography scheme (VCS) are the primary branches. However, as the basic research problems, the security analysis and security classification of ISS are merely discussed. In this paper, based on the study of image characteristic and typical ISS schemes, four security Levels are classified as well as the security of typical ISS schemes is analyzed. Furthermore, experiments are carried out to evaluate the efficiency of our analysis in terms of illustrations and evaluation metrics.
Towards Consistency Checking Between Software Architecture and Informal Documentation

In the development process, documenting the software architecture is important to capture all reasoning and design decisions. Without a good and complete documentation, there is a lot of tacit knowledge that easily can get lost resulting in threats for success and increased costs. However, software architecture documentation is often missing or outdated. One reason for it is the tedious and costly process of creating and updating documentation in comparison to (perceived) low benefits. In this paper, we first present our long-term vision, where all information from any sources are persisted to avoid losing crucial information about a system. A base problem in this vision is keeping information from different sources consistent, with a major challenge of keeping consistency between models and informal documentation. We plan to address checking the consistency between models and textual natural language artefacts using natural language understanding. For this, we plan to break down the task into smaller subtasks that should provide a better understanding of the documents and their semantics. The extracted information should then be used to create traceability links and to check whether statements within the textual documentation are consistent with the software architecture models.