Reviewerly/exHarmony huggingface dataset:
Troubling Design

The euphemism “female trouble” (discreetly referring to women's health experiences) suggests that trouble is linked to women and bodily transitions women can go through. However, trouble is not only a noun; it is also a verb with a strong feminist tradition. In this article, I present troubling design ; a theoretically grounded and practice-oriented design program for designing with women's health. Troubling design brings to matter how trouble is an implicit condition of designing ethical and responsible technologies for women's health. By designing with trouble, troubling design encourages designers and researchers to develop knowledge embedded in criticality and questioning status quo, in order to expand their perspectives on designing with intimate and tabooed bodily experiences. Grounded in feminist theory and research-through-design, troubling design contributes with analytical and generative design knowledge articulated through design examples and three practices: staying with the wrong, curious visiting, and collective imagining.
Massive Exploration of Pseudo Data for Grammatical Error Correction

Collecting a large amount of training data for grammatical error correction (GEC) models has been an ongoing challenge in the field of GEC. Recently, it has become common to use data demanding deep neural models such as an encoder-decoder for GEC; thus, tackling the problem of data collection has become increasingly important. The incorporation of pseudo data in the training of GEC models is one of the main approaches for mitigating the problem of data scarcity. However, a consensus is lacking on experimental configurations, namely, (i) the methods for generating pseudo data, (ii) the seed corpora used as the source of the pseudo data, and (iii) the means of optimizing the model. In this study, these configurations are thoroughly explored through massive amount of experiments, with the aim of providing an improved understanding of pseudo data. Our main experimental finding is that pretraining a model with pseudo data generated by back-translation-based method is the most effective approach. Our findings are supported by the achievement of state-of-the-art performance on multiple benchmark test sets (the CoNLL-2014 test set and the official test set of the BEA-2019 shared task) without requiring any modifications to the model architecture. We also perform an in-depth analysis of our model with respect to the grammatical error type and proficiency level of the text. Finally, we suggest future directions for further improving model performance.
Knowing your Bitcoin Customer: Money Laundering in the Bitcoin Economy

Cryptocurrencies like Bitcoin have the potential advantages to break traditional financial barriers, which have attracted great interests from civilian users, financial and online commercial industry, and researchers. However, a recent study [1] reported that approximately one-quarter of Bitcoin users and one-half of Bitcoin transactions are associated with illicit activity. Around US$72 billion of unlawful activity per year involves Bitcoin, which is close to the scale of the U.S. and European markets for illegal drugs. We have made an effort to understand and try our best to exhaustively discover Bitcoin mixing or tumbling services (essentially money laundering mechanisms) which exist or had existed. In our study, 69 services were identified, and evaluation of the public discussion around these services reveals certain trends in Bitcoin user understanding of privacy issues and enforcement of anti-money laundering regulation. So far, Law enforcement interference with Bitcoin laundering services is uncommon, while our study showed that most services failed due to lack of user trust. Trust is perhaps the greatest challenge amongst Bitcoin anonymization services, as many services that have existed appear to be outright scams, and even legitimate services sometimes disappear with user funds. We will report other observations and discussions at the end of the paper.
BCONV - ELM: Binary Weights Convolutional Neural Network Simulator based on Keras/Tensorflow, for Low Complexity Implementations

A deep investigation of the possibility to minimize complexity, while keeping accuracy at its highest values is considered, centered around the idea of building specialized convolutional neural network architectures, tailored for resource-constrained platforms. Starting from the idea used in extreme learning machines where a nonlinear expander with random weights is combined with a rapidly trained linear adaptive unit as the output layer, a simulator working under Keras/Tensorflow is developed. As a consequence of using Keras backend operators, GPU or CPU support is available using the same code. The resulting simulator (made freely available) is composed of three main modules: i) A convolutional expander, using kernels with randomly generated binary weights; ii) An extreme learning machine for rapidly tuning the random kernels for the best performance; iii) A Keras based multi-layer perceptron (usually operated as Adaline or 0 hidden layer with weights quantization) as a compact output layer of BCONV-ELM. Performance evaluation on several benchmark datasets show that with a proper tuning of the convolutional layers structure, good accuracies are achieved without additional dense hidden layers thus minimizing the required computational resources.
Intertemporal asset pricing with bitcoin

This paper develops and tests an intertemporal regime-switching asset pricing model characterized by heterogeneous agents that have different expectations about the persistence and volatility of bitcoin prices. The model is estimated using daily bitcoin price data from 2013 until 2020 whereby three types of agents are considered: mean–variance optimizers, speculators and fundamentalists, respectively. While mean–variance optimizers trade on the basis of conditional first and second moments of the return distribution, speculators engage in trend chasing and buy when prices are rising and sell when prices are declining. Fundamentalists trade on the basis of fundamental factors that can impact the value of bitcoin. The fractions of agents engaging in one strategy over another shows statistically substantial variation during high and low bitcoin price volatility regimes. Estimation results reveal the following. First, unlike in traditional asset classes, there is evidence of mean–variance optimizers. Second, there is evidence of speculators who engage in ‘bandwagon behavior’ and buy bitcoins during price appreciations and sell bitcoins during price declines. Finally, there is evidence of fundamentalists who trade bitcoins when fundamental factors deviate from their long-run trends. Remarkably, these fundamentalists exhibit contrarian-type behaviors during low price volatility regimes while behaving more like fundamental traders during high price volatility regimes.
CAB U-Net: An end-to-end category attention boosting algorithm for segmentation

With the development of machine learning and artificial intelligence, many convolutional neural networks (CNNs) based segmentation methods have been proposed for 3D cardiac segmentation. In this paper, we propose the category attention boosting (CAB) module, which combines the deep network calculation graph with the boosting method. On the one hand, we add the attention mechanism into the gradient boosting process, which enhances the information of coarse segmentation without high computation cost. On the other hand, we introduce the CAB module into the 3D U-Net segmentation network and construct a new multi-scale boosting model CAB U-Net which strengthens the gradient flow in the network and makes full use of the low resolution feature information. Thanks to the advantage that end-to-end networks can adaptively adjust the internal parameters, CAB U-Net can make full use of the complementary effects among different base learners. Extensive experiments on public datasets show that our approach can achieve superior performance over the state-of-the-art methods.
Bloccess: Towards Fine-Grained Access Control Using Blockchain in a Distributed Untrustworthy Environment

Access control plays a crucial role in constructing trust in a system. Particularly, it is imperative to enforce a fine-grained access control mechanism to make the access control framework flexible due to the high complexity of untrustworthy environments such as the Internet of Things (IoT) environments. However, traditional access control techniques can be hardly trusted on account of their centralized enforcements and improper distributed computing mechanisms while facing diverse and intricate threats. Although existing solutions based on public blockchain technology have addressed some issues, new challenges derived from public blockchain technology become noticeable such as low consensus efficiency and delicate incentive mechanism. In this paper, we propose Bloccess, a fine-grained access control framework using permissioned blockchain techniques, which enhances the trust in untrustworthy environments by enforcing a trustworthy access control mechanism. Bloccess provides a unified and user-centric solution for access control in distributed untrustworthy environments and optimizes the decentralized access control management, which significantly ensures the security properties of protected environments in terms of the threat model structured in this paper. We also prove the feasibility and effectiveness of Bloccess by security analysis and the comparison with some related frameworks.
Auto-Encoders for Content-based Image Retrieval with its Implementation Using Handwritten Dataset

Image retrieval technology is a very fast-growing digital technology for researchers in the field of computer science from a very long period. It is a system for retrieving digital images from a large database. The well-known organizations that are using this system are Google and Pinterest. In this conference paper, a content-based image retrieval system that uses an innovative type of neural network known as autoencoder is discussed and developed a basic system to understand it. The methodology that has been used is an unsupervised method which is a machine learning algorithm in which the system retrieves images without searching about its name, labels, and tags. This system retrieves images just by its visual information. This approach of image retrieval is known as Content-Based Image Retrieval (CBIR).
Ontology-based data acquisition model development for agricultural open data platforms and implementation of OWL2MVC tool

In the open data world, it is difficult to collect data in compliance with a certain data model that is of interest to different types of stakeholders within a domain like agriculture. Ontologies that provide broad vocabularies and metadata with respect to a given domain can be used to create various data models. We consider that while creating data acquisition forms to gather data related to an agricultural product, which is hazelnut in this study, from stakeholders of the relevant domain, the traits can be modeled as attributes of the data models. We propose a generic ontology-based data acquisition model to create data acquisition forms based on model-view-controller (MVC) design pattern, to publish and make use of on the agricultural open data platforms. We develop a tool called OWL2MVC that integrates the Hazelnut Ontology, which illustrates the effectiveness of the proposed model for generating data acquisition forms. Because model creation is implemented in compliance with the selection of ontology classes, stakeholders; in other words, the users of OWL2MVC Tool could generate data acquisition forms quickly and independently. OWL2MVC Tool was evaluated in terms of usability by fifty-three respondents implementing the case-study scenario. Among others the findings show that the tool has satisfactory usability score overall and is promising to provide stakeholders with required support for agricultural open data platforms.
Time and Singular Causation—A Computational Model

Abstract Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co‐occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a computational model that combines information about the causal strengths of the potential causes with information about their temporal relations to derive answers to singular causation queries. The relative causal strengths of the potential cause factors are relevant because weak causes are more likely to fail to generate effects than strong causes. But even a strong cause factor does not necessarily need to be causal in a singular case because it could have been preempted by an alternative cause. We here show how information about causal strength and about two different temporal parameters, the potential causes' onset times and their causal latencies, can be formalized and integrated into a computational account of singular causation. Four experiments are presented in which we tested the validity of the model. The results showed that people integrate the different types of information as predicted by the new model.
Deep Learning based Smart Garbage Classifier for Effective Waste Management

India is the second most populated country in the world and it is still facing hindrances to its development on waste management. It is believed that 10 million tons of waste is produced just by the metropolitan cities in India. In this work, a way to classify the waste and find the category of it is proposed with a well-defined and labelled data set of images consisting of categories (plastic, paper, cardboard, metals) using Convolutional Neural Network (CNN). Images are categorized based on their properties by the help of a self-learning neural network. The designed classifier learns from the image data provided for training purpose. The classifier uses the method of supervised learning where the algorithm learns from a labelled data set. With this method a testing accuracy of 76% is achieved.
Responsible processing of crowdsourced tourism data

Online tourism crowdsourcing platforms, such as AirBnB, Expedia or TripAdvisor, rely on the continuous data sharing by tourists and businesses to provide free or paid value-added services. When adequately processed, these data streams can be used to explain and support businesses in the early identification of trends as well as prospective tourists in obtaining tailored recommendations, increasing the confidence in the platform and empowering further end-users. However, existing platforms still do not embrace the desired accountability, responsibility and transparency (ART) design principles, underlying to the concept of sustainable tourism. The objective of this work is to study this problem, identify the most promising techniques which follow these principles and design a novel ART-compliant processing pipeline. To this end, this work surveys: (i) real-time data stream mining techniques for recommendation and trend identification; (ii) trust and reputation (T&R) modelling of data contributors; (iii) chained-based storage of trust models as smart contracts for traceability and authenticity; and (iv) trust- and reputation-based explanations for a transparent and satisfying user experience. The proposed pipeline redesign has implications both to digital and to sustainable tourism since it advances the current processing of tourism crowdsourcing platforms and impacts on the three pillars of sustainable tourism.
Enabling Quantum Key Distribution Networks via Software-Defined Networking

Quantum Key Distribution (QKD) is one of the major cryptographic solutions to tackle the security threats associated to future computational advances, in particular those coming from quantum computing. At the most simple level, QKD can be seen as a highly secure source of symmetric secret keys in two separated places. Nonetheless, the lack of standardization and the strong requirements from the physical layer makes this technology very difficult to integrate in existing infrastructure.However, the success of QKD networks radically depends on the degree in which they can be adopted in the existing infrastructure, and it is a must that it happens via standard protocols and interfaces. The flexibility of novel network paradigms, like Software-Defined Networking (SDN), allows for a faster adoption of new concepts, hardware and services in telecommunications networks. This paradigm can be used for quantum communications, allowing for a fast and scalable deployment of quantum technologies and services in the telecommunications network to a degree that was simply impossible in previous schemes, where the different network devices and their connections should be modified, one by one, to create a quantum communications channel.This tutorial provides an introduction to existing QKD technologies and networks, followed by a descriptive explanation on how SDN principles can be utilized to abstract and integrate QKD systems as part of the network management. This will help to reduce the time-to-market for quantum technologies, and also will make quantum cryptography available as a service to be capitalized by the network operators.
Large Factor Image Super-Resolution With Cascaded Convolutional Neural Networks

Recently, convolutional neural networks (CNNs) have attracted considerable attention in single image super-resolution (SISR) and have enabled great performance improvements. However, most of the existing methods super-resolve input images to the desired size with an interpolation operation during the beginning stage, which brings about heavy aliasing artifacts and high computational costs. Especially for large upsampling factors (e.g., 8×), it remains a challenge to restore high-quality results for deeply degraded images. To tackle this problem, we propose a cascaded super-resolution convolutional neural network (CSRCNN), which takes a single low-resolution (LR) image as an input and reconstructs high-resolution (HR) images in a progressive way. At each cascaded level, to help converge and improve the accuracy, a novel U-net based block with backprojection is first introduced, which exploits the mutual relation between HR and LR feature spaces. A refined block following the U-net block is also used to reconstruct the realistic texture details. In addition, we naturally utilize the strategy of curriculum learning, organizing the learning process from easy (small factors) to hard (large factors). Comprehensive experiments on benchmark datasets demonstrate that the proposed network achieves superior results compared with those of other state-of-the-art methods, particularly with the 8× upsampling factor.
Privacy-Preserving Blockchain-Based Systems for Car Sharing Leveraging Zero-Knowledge Protocols

Blockchain-based systems open new opportunities for the IT business and society if designed and implemented properly. One of the essential challenges on their way to production readiness are severe privacy concerns and hence stringent compliance regulations. In this paper, we investigate how different privacy-preserving techniques leveraging zero-knowledge proof protocols and anonymous credentials can be used to tackle this issue on the example of a car sharing use case. Our approach considers a blockchain ecosystem enabling different parties including governmental authorities and automobile industry partners to cooperate ensuring end user privacy protection. The implemented proof-of-concept is leveraging Ethereum ZoKrates and Indy technologies respectively. This project is a joint initiative of T-Systems Multimedia Solutions GmbH and BMW Group.
Malicious Spam Distribution: A Routine Activities Approach

The distribution of malicious spam occurs in substantial numbers around the globe and can function as a precursor to serious forms of cybercrime. Yet to date, no known study has employed criminological theory to gain insight into the macro-correlates of malicious spam victimization. To address this gap in the literature, the current study utilizes data gathered from multiple sources to assess whether variables derived from routine activity theory are associated with malicious spam victimization at the country-level. Findings indicate that multiple measures of target suitability increase the amount of malicious spam a country receives. Specifically, corruption, political freedom, gross domestic product, and being an Asian nation are associated with an increase in malicious spam victimization. Capable guardianship, however, does not decrease the amount of malicious spam a country receives, and in some instances, is associated with an increase. Theoretical and policy implications are discussed.
Modified Multi-Verse Optimizer for Solving Numerical Optimization Problems

The hybrid version of a multi-verse optimizer (MVO) namely the modified multi-verse optimizer (mMVO) is developed in this paper by modifying the position updating equation of MVO. Here two modifications are proposed in the standard MVO. Firstly, an average position selection mechanism is proposed for solving the local optima problem and secondly, the MVO algorithm is hybrid with another metaheuristics algorithm namely the Sine Cosine Algorithm (SCA) for better balancing the exploration and exploitation of standard MVO algorithm so that it can improve its searching capability. The proposed version of MVO has been evaluated on 23 well known benchmark functions namely unimodal, multimodal and fixed-dimension multimodal benchmark functions and the results are then verified with the standard MVO algorithm. Experimental results demonstrate that the proposed mMVO algorithm gives much better improvement than the standard MVO in the optimization problems in the sense of preventing local optima and increasing the search capability.
Breast Cancer Classification using Deep Convolutional Neural Network

Abstract Over the last decade, the demand for early diagnosis of breast cancer has resulted in new research avenues. According to the world health organization (WHO), a successful treatment plan can be provided to individuals suffering from breast cancer once the non-communicable disease is diagnosed at an early stage. An early diagnosis of cure disease can reduce mortality all over the world. Computer-Aided Diagnosis (CAD) tools are widely implemented to diagnose and detect different kinds of abnormalities. In the last few years, the use of the CAD system has become common to increase the accuracy in different research areas. The CAD systems have minimum human intervention and producing accurate results. In this study, we proposed a CAD technique for the diagnosis of breast cancer using a Deep Convolutional Neural Network followed by Softmax classifier. The proposed technique was tested on the Wisconsin Breast Cancer Datasets (WBCD). The proposed classifier produced an accuracy of 100% and 99.1% for two different datasets, which indicates effective diagnostic capabilities and promising results. Moreover, we test our proposed architecture with different train-test partitions.