Reviewerly/exHarmony huggingface dataset:
A Quantum Access Network Suitable for Internetworking Optical Network Units

Quantum access networks allow a large number of users to access quantum networks, which can be regarded as an important part of the practical application for quantum communication. However, current quantum access networks have not considered as the peer-to-peer multimedia service between optical network units (ONUs) that have become the dominant service in the access networks. Thus, we propose a quantum access network that can support direct quantum and classical ONU-ONU communication by using an N:N splitter. In addition, we improve the efficiency of quantum key distribution (QKD) from both the physical and the data link layer. For the former, we analyze spontaneous Raman scattering (SRS) that is the main impairment source to QKD due to the co-propagation of the classical and quantum signal. Then, we propose a collision-avoiding scheme to reduce the influence of the SRS. For the latter, a dynamic time slot assignment protocol is proposed to reduce discarding secure keys greatly. The two methods are verified by simulations, and the results show that each method can effectively optimize QKD performance.
A Multivariate Outlier Detection Algorithm for Wireless Sensor Networks

In wireless sensor networks, an outlier detection algorithm removes from sensed data any possible error, redundancy and malicious injection of fake data. Therefore, such algorithms improve considerably the reliability and the accuracy of the collected data and also reduce the overall energy consumption in the context of large-scale and dense networks. Yet, the proposed algorithms generate huge communication costs due to information exchanges among neighbors and cannot detect outliers for more than one data type. In this paper, we propose a new outlier detection algorithm that is capable of detecting outliers even in the presence of multiple types of data and that does not require any information about the neighborhood. The proposed approach relies on a set of classifiers implemented in each node of a wireless sensor network. The sensed data is therefore classified in either outlier or normal data in a distributed manner. The extensive simulations of the proposed algorithm confirm its outperforming characteristics in terms of detection accuracy, false alarm rate and energy consumption.
The Lions' Gate

In light of the devastating ecological effects of climate breakdown and with human well-being under severe stress, this paper presents The Lions' Gate - an in-development, urban, interactive permaculture project on-campus in Edinburgh, Scotland. We argue that to address limits to computing, a radical framework is needed. We position permaculture as an alternative to the economic growth model, and it's associated temporal controls. Our work is an attempt at shifting our thinking and doing by employing a blended spaces perspective to permaculture, university campuses and digital media. By starting from a permaculture view we have the ecological and ethical tools to practice alternatives to growth. As far as Sustainable Interaction Design (SiD) is concerned - in today's climate, actions must ultimately speak louder than words.
Stroke Data Analysis through a HVN Visual Mining Platform

Today there are abounding collected data in cases of various diseases in medical sciences. Physicians can access new findings about diseases and procedures in dealing with them by probing these data. Clinical data is a collection of large and complex datasets that commonly appear in multidimensional data formats. It has been recognized as a big challenge in modern data analysis tasks. Therefore, there is an urgent need to find new and effective techniques to deal with such huge datasets. This paper presents an application of a new visual data mining platform for visual analysis of the stroke data for predicting the levels of risk to those people who have the similar characteristics of the stroke patients. The visualization platform uses a hierarchical clustering algorithm to aggregate the data and map coherent groups of data-points to the same visual elements - curved 'super-polylines' that significantly reduces the visual complexity of the visualization. On the other hand, to enable users to interactively manipulate data items (super-polylines) in the parallel coordinates geometry through the mouse rollover and clicking, we created many 'virtual nodes' along the multi-axis of the visualization based on the hierarchical structure of the value range of selected data attributes. The experimental result shows that we can easily verify research hypothesis and reach to the conclusion of research questions through human-data & human-algorithm interactions by using this visual platform with a fully transparency manner of data processing.
Random Forest-Based Sarcastic Tweet Classification Using Multiple Feature Collection

Sarcasm is primary reason behind the faulty classification of the tweets. The tweets of sarcastic nature appear in the different compositions, but mainly deflect the meaning different than their actual composition. This confuses the classification models and produces false results. In the paper, the primary focus remains upon the classification of sarcastic tweets, which has been accomplished using the textual structure. This involves the expressions of speech, part of speech features, punctuations, term sentiment, affection, etc. All of the features are extracted individually from the target tweet and combined altogether to create the cumulative feature for the target tweet. The proposed model has been observed with accuracy slightly higher than 84%, which depicts the clear improvement in comparison with existing models. The random forest-based classification model has outperformed all other candidates deployed under the experiment. The random forest classifier is observed with accuracy of 84.7, which outperforms the SVM (78.6%), KNN (73.1%), and Maximum entropy (80.5%).
Re-staining Pathology Images by FCNN

In histopathology, pathologic tissue samples are stained using one of various techniques according to the desired features to be observed in microscopic examination. One problem is that staining is irreversible. Once a tissue slice is stained using a technique, it cannot be re-stained using another. In this work, we propose a method for simulated re-staining using a Fully Convolutional Neural Network (FCNN). We convert a digitally scanned pathology image of a sample, stained using one technique, into another image with a different simulated stain. The challenge is that the ground truth cannot be obtained: the network needs training data, which in this case would be pairs of images of a sample stained in two different techniques. We overcome this problem by using the images of consecutive slices that are stained using the two distinct techniques, screening for morphological similarity by comparing their density components in the HSD color space. We demonstrate the effectiveness of the method in the case of converting hematoxylin and eosin-stained images into Masson's trichrome-stained images.
A Privacy-Preserving and Fully Decentralized Storage and Sharing System on Blockchain

With the blockchain receiving extensive attention in recent years, many storage schemes based on the blockchain have been proposed as alternative means of cloud storage for data outsourcing. However, the conventional access control methods in the current sharing schemes require either individual permission granting via symmetric keys or a trusted central attribute authority for ciphertext-policy attribute-based encryption (CP-ABE). Moreover, due to the transparency of the blockchain, the data query logs are recorded in public ledgers. Nevertheless, the problem of privacy protection for data consumers has not been properly addressed. In this paper, we propose a fully decentralized data storage and sharing system on a blockchain by using multi-authority CP-ABE and decentralized multi-authority attribute-based signatures (DMA-ABSs). In our system, every party can securely store and share its data with a set of individuals satisfying a policy with no need to grant separate permissions individually. Additionally, the data owners can fully control their data, know how their data are accessed due to the nature of the blockchain and have the ability to opt-out at any time. The public ledger of the blockchain provides immutable logs of data address pointers, access policies, attribute public keys and data queries. In addition, data consumers' attributes are publicly verifiable through the DMA-ABS scheme without revealing more private information. Finally, the combination of the multi-authority CP-ABE with the blockchain guarantees the integrity, confidentiality, and accessibility of the data without the need for trusted third parties, such as a central authority or a data center.
Facilitating Manual Segmentation of 3D Datasets Using Contour And Intensity Guided Interpolation

Manual segmentation of anatomical structures in 3D imaging datasets is a highly time-consuming process. This process can be sped up using interslice interpolation techniques, which require only a small subset of slices to be manually segmented. In this paper, we propose a two-step interpolation approach that utilizes a “binary weighted averaging” algorithm to interpolate contour information, and the random forest framework to perform intensity-based label classification. We present the results of experiments performed in the context of hippocampal segmentations in ex vivo MRI scans. Compared to the random walker algorithm and morphology-based interpolation, the proposed method produces more accurate segmentations and smoother 3D reconstructions.
An Entropy Regularization k-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering

Although within-cluster information is commonly used in most clustering approaches, other important information such as between-cluster information is rarely considered in some cases. Hence, in this study, we propose a new novel measure of between-cluster distance in subspace, which is to maximize the distance between the center of a cluster and the points that do not belong to this cluster. Based on this idea, we firstly design an optimization objective function integrating the between-cluster distance and entropy regularization in this paper. Then, updating rules are given by theoretical analysis. In the following, the properties of our proposed algorithm are investigated, and the performance is evaluated experimentally using two synthetic and seven real-life datasets. Finally, the experimental studies demonstrate that the results of the proposed algorithm (ERKM) outperform most existing state-of-the-art k-means-type clustering algorithms in most cases.
Design Thinking Versus Design Sprint: A Comparative Study

Design Thinking methodology is being increasingly used by the software industry in an attempt to reduce the problems with requirements elicitation. In the literature it is possible to find several papers addressing the use of this methodology in the analysis and specification of software requirements. In order to help reduce existing problems that have not yet been fully resolved through the use of Design Thinking, Google has launched Design Sprint with the goal of eliciting requirements quickly and efficiently. There are very few academic papers reporting on the use of this technique and what its advantages and disadvantages are. This paper describes a comparative study of the two methodologies used to minimize the problems faced by companies in eliciting the requirements of their software, presenting their phases and how they can be used.
Scalable Probabilistic Data Association with Extended Objects

Situation-aware technologies will create new services and applications in emerging fields such as autonomous transportation, home automation, and smart cities. A main challenge in situation-aware applications is data association with &#x0022;extended&#x0022; objects that originate an unknown number of measurements. To address this challenge, we introduce a new method based on factor graphs and the sum-product algorithm. Our approach makes it possible to reduce computational complexity in a principled manner due to an overcomplete description of data association uncertainty that enables a &#x0022;stretching&#x0022; of the factor graph. The complexity of the resulting sum-product algorithm for data association with extended objects (SPADA-X), only scales quadratically in the number of objects and linearly in the number of measurements. Without relying on heuristic preprocessing steps, it is suitable for localization and tracking of multiple objects that potentially generate a large number of measurements. Simulation results demonstrate the excellent estimation accuracy and scalability of SPADA-X.
Construction of special entangled basis based on generalized weighing matrices

A special entangled basis (SEBk) is a set with $dd'$ orthonormal entangled pure states in $\mathbb{C}^d \otimes \mathbb{C}^{d'}$ whose nonzero Schmidt coefficients are all equal to $1/\sqrt{k}$. When $k$ is equal to the minimum of $d$ and $d'$, we get a maximally entangled basis. In this paper, we present how to construct a special entangled basis via some special matrices which are known as weighing matrices. Specially, using a skew Hadamard matrix of order $k+1$, we derive a weighing matrix which is useful for constructing SEBk in $\mathbb{C}^d \otimes \mathbb{C}^{d'}$ whenever $\min\{d,d'\}\geq k$. These results are further progress of those studied by Guo \emph{et al.} in [\textcolor[rgb]{0.00,0.07,1.00}{J. Phys. A: Math. Theor. \textbf{48} 245301(2015)}]. We also disprove two conjectures proposed by them.
Despeckling of 3D ultrasound image using tensor low rank approximation

In this paper, a new method for despeckling of 3D ultrasound images based on tensor low rank approximation technique is presented. The main motive of this paper is to establish a new denoising method which can incorporate the redundancy present in 3D ultrasound image in all directions unlike the existing techniques. Hence, a substantial improvement in the overall performance can be achieved by preserving the edges and other finer details of the image. In order to achieve this goal, the 3D ultrasound image is modeled in terms of low rank tensor approximation of grouped tensors which is solved by minimizing the Tensor Nuclear Norm (TNN) in tensor-Singular Value Decomposition (t-SVD) framework. The resulting optimization problem is solved by the method of inexact Augmented Lagrangian Multipliers (ALM). To the best of our understanding, this is the first attempt to model the 3D ultrasound image despeckling problem as tensor low rank recovery technique. The proposed algorithm is compared with popular state-of-art works in literature including a low rank based approach termed as DLRA [1] designed for 2D ultrasound images, and it is found that our scheme offers superior performance than its counterparts.
What are the Characteristics of Reopened Pull Requests? A Case Study on Open Source Projects in GitHub

In GitHub, integrators inspect submitted code changes, make evaluation decision, and close pull requests. Some pull requests may be reopened for further modification and code review. It remains unknown why some pull requests are reopened, and how they affect software development. In this paper, we conduct a case study to understand reopened pull requests. We collect 100,622 pull requests from 7 popular projects in GitHub. We study the impacts of reopened pull requests on the code review process. We then qualitatively analyze the root reasons that lead to reopening pull requests. Finally, we examine the characteristics of pull requests caused by various reasons. Our main observations are: 1) Reopened pull requests have lower acceptance rates, more comments, and longer evaluation time than non-reopened pull requests. 2) Several key reasons that lead to reopened pull request are identified such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bugs</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">change minds</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">insufficient tests</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incompatible version</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tests fails</i> , and so on. 3) Reopened pull requests in different categories have various acceptance rates, comment amounts, and evaluation time. Reopened pull requests in the category <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rebase and change branch</i> has the largest number of comments and the longest evaluation time. These findings provide insights to decrease reopened pull requests, develop a variety of assistant tools, and reduce the maintenance cost of open source projects.
Video Prediction with Bidirectional Constraint Network

Future frame prediction in videos is promising avenue for unsupervised video representation learning. However video prediction has the huge solution space since the high-dimensionality and inherent uncertainty of the future video frames. Existing approaches impose weak constraints on the predictions, which results in motion confusion. To alleviate this problem, we propose a novel model named Bidirectional Constraint Network (BCnet). BCnet consists of forward prediction module and backward prediction module. The forward prediction module learns to predict the future sequence from the present sequence, while the backward prediction module learns to invert the task. The closed loop of the two modules allows that the backward prediction module generates informative feedback signals. The feedback signals clamp down the solution space of forward prediction module. Therefore, our approach can effectively alleviate the motion confusion. We further evaluate BCnet by fine-tuning it for a supervised learning problem: human action recognition on the UCF-101 dataset. We show that the representation help improve classification accuracy. Extensive experiments on several challenging public datasets show that our approach significantly outperforms state-of-the-art approaches, which demonstrates the effectiveness and generalization ability of our approach.
The infinite extendibility problem for exchangeable real-valued random vectors

We survey known solutions to the infinite extendibility problem for (necessarily exchangeable) probability laws on R d , which is: Can a given random vector X = (X 1 , . . ., X d ) be represented in distribution as the first d members of an infinite exchangeable sequence of random variables?This is the case if and only if X has a stochastic representation that is "conditionally iid" according to the seminal de Finetti's Theorem.Of particular interest are cases in which the original motivation behind the model X is not one of conditional independence.After an introduction and some general theory, the survey covers the traditional cases when X takes values in {0, 1} d , has a spherical law, a law with 1 -norm symmetric survival function, or a law with ∞-norm symmetric density.The solutions in all these cases constitute analytical characterizations of mixtures of iid sequences drawn from popular, one-parametric probability laws on R, like the Bernoulli, the normal, the exponential, or the uniform distribution.The survey further covers the less traditional cases when X has a Marshall-Olkin distribution, a multivariate wide-sense geometric distribution, a multivariate extreme-value distribution, or is defined as a certain exogenous shock model including the special case when its components are samples from a Dirichlet prior.The solutions in these cases correspond to iid sequences drawn from random distribution functions defined in terms of popular families of non-decreasing stochastic processes, like a Lévy subordinator, a random walk, a process that is strongly infinitely divisible with respect to time, or an additive process.The survey finishes with a list of potentially interesting open problems.In comparison to former literature on the topic, this survey purposely dispenses with generalizations to the related and larger concept of finite exchangeability or to more general state spaces than R. Instead, it aims to constitute an up-to-date comprehensive collection of known and compelling solutions of the real-valued extendibility problem, accessible for both applied and theoretical probabilists, presented in a lecture-like fashion.
K2SN-MSS

With the rapid development of quantum technologies, quantum-safe cryptography has found significant attention. Hash-based signature schemes have been in particular of interest because of (i) the importance of digital signature as the main source of trust on the Internet, (ii) the fact that the security of these signatures relies on existence of one-way functions, which is the minimal assumption for signature schemes, and (iii) they can be efficiently implemented. Basic hash-based signatures are for a single message, but have been extended for signing multiple messages. In this paper we design a Multi-message Signature Scheme (MSS) based on an existing One-Time Signature (OTS) that we refer to as KSN-OTS. KSN uses SWIFFT, an additive homomorphic lattice-based hash function family with provable one-wayness property, as the one-way-function and achieves a short signature. We prove security of our proposed signature scheme in a new strengthened security model (multi-target multi-function) of MSS, determine the system parameters for 512 bit classical (256 bit quantum) security, and compare parameter sizes of our scheme against XMSS, a widely studied hash based MSS that has been a candidate for NIST standardization of post-quantum signature scheme. We give an efficient implementation of our scheme using Intel SIMD (Single Instruction Multiple Data) instruction set. For this, we first implement SWIFFT computation using a SIMD parallelization of Number Theoretic Transform (NTT) of elements of the ring Zp[X]/(Xn+1), that can support different levels of parallelization. We compare efficiency of this implementation with a comparable (security level) implementation of XMSS and show its superior performance on a number of efficiency parameters.
Efficient Feature Selection Based on Modified Cuckoo Search Optimization Problem for Classifying Web Text Documents

The continuous increase of information in the web with varying dimensions is becoming difficult for users to filter and analyse them efficiently as it incorporates redundant and irrelevant terms. Managing, filtering and organizing such huge datasets need the classification of text documents to be performed. Text classification is the process of assigning the text documents to their predefined text categories based on the content. The aim of this paper is to explore Cuckoo search optimization (CSO) problem established from the behaviour of cuckoo birds for selection of relevant features by modifying the algorithm. The revised algorithm is named as modified Cuckoo search (MCS) optimization algorithm that can be proved to be useful for developing an efficient text classification system. The proposed method is generated by combining the ability of MCS with the sharpness of Naive Bayes Multinomial (NBM) algorithm for generating proper feature which increases the rate of success. The approach adopted here is tested on 9000 text documents that cover eight different domains fetched from several web sources and obtains encouraging outcome. The results compared with the results from other well-known approaches for text classification task show the effectiveness of the proposed approach as an automatic Bangla text classification system.