Reviewerly/exHarmony huggingface dataset:
SAR Image Denoising Algorithm Based on Bayes Wavelet Shrinkage and Fast Guided Filter

To remove the speckle noise of synthetic aperture radar (SAR) images, a novel denoising algorithm based on Bayes wavelet shrinkage and a fast guided filter is proposed. According to the statistical properties of SAR images, the noise-free signal and speckle noise in the wavelet domain are modeled as Laplace and Fisher-Tippett distributions respectively. Then a new wavelet shrinkage algorithm is obtained by adopting the Bayes maximum a posteriori estimation. Speckle noise in the high-frequency domain of SAR images is shrunk by this new wavelet shrinkage algorithm. As the wavelet coefficients of the low-frequency domain also contain some speckle noise, speckle noise in the low-frequency domain can be further filtered by the fast guided filter. The result of the denoising experiments of simulated SAR images and real SAR images demonstrate that the proposed algorithm has the ability to better denoise and preserve edge information.
Two-View Monocular Depth Estimation by Optic-Flow-Weighted Fusion

Depth estimation with monocular cameras is a cheap and promising solution for autonomous vehicles and robots. Even though there are many approaches in the literature, the issue of estimating depth of objects with low optic flow (low parallax) still remains. This work proposes a new two-view monocular depth estimation method that estimates depths with only a monocular camera using two optic flow directions based on the Flat Surface Model, fusing them with optic flow as weights. The proposed method achieves an average depth estimation error of 3.68 m and a maximum error of 107.34 m, which are smaller than those obtained by traditional techniques (22.90 and 9815.44 m, respectively).
Distance-Dependent Metric Learning

In most existing metric learning methods, the data pairs are equally treated without considering their diversity. In fact, for pairs with different distances, the main purpose of the metric acted on them has some differences. If the pairs have smaller distance, metric should focus more on expanding the negative pairs, which are more easily misjudged. While if the pairs have larger distance, metric should focus more on shrinking the positive pairs. However, most metric learning methods neglect these differences. In this letter, we propose a distance-dependent metric learning (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ML) method. It partitions data pairs into different clusters according to the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> distance between them. Each cluster is associated with a Mahalanobis metric that learns the pairs' distance. This not only allows us to make each metric more targeted and adapt to the data diversity flexibly, but also avoids the problem of computing the distance between points assigned to different clusters, which happens in some local metric learning methods. D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ML is further extended to D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ML to embrace the nonlinear capacity of neural network. Experiments on UCI datasets and speaker recognition i-vector machine learning challenge show that the proposed methods are superior to other metric learning methods.
Taming big data: Applying the experimental method to naturalistic data sets

Psychological researchers have traditionally focused on lab-based experiments to test their theories and hypotheses. Although the lab provides excellent facilities for controlled testing, some questions are best explored by collecting information that is difficult to obtain in the lab. The vast amounts of data now available to researchers can be a valuable resource in this respect. By incorporating this new realm of data and translating it into traditional laboratory methods, we can expand the reach of the lab into the wilderness of human society. This study demonstrates how the troves of linguistic data generated by humans can be used to test theories about cognition and representation. It also suggests how similar interpretations can be made of other research in cognition. The first case tests a long-standing prediction of Gentner's natural partition hypothesis: that verb meaning is more subject to change due to the textual context in which it appears than is the meaning of nouns. Within a diachronic corpus, verbs and other relational words indeed showed more evidence of semantic change than did concrete nouns. In the second case, corpus statistics were employed to empirically support the existence of phonesthemes-nonmorphemic units of sound that are associated with aspects of meaning. A third study also supported this measure, by demonstrating that it corresponds with performance in a lab experiment. Neither of these questions can be adequately explored without the use of big data in the form of linguistic corpora.
Lung tumour detection by fusing extended local binary patterns and weighted orientation of difference from computed tomography

Lung cancer is one of the leading causes of death in the world. Although early detection of lung tumours (nodules) can remarkably diminish the mortal rate, precise detection of them is not always possible by visual inspection of the computerised tomography images. Since nodules with different sizes have non-uniform shape and brightness, texture attributes and also the gradient of orientation can be good candidate features, which have been used for this purpose. They determined the co-occurrence matrix of the extended local binary pattern (ELBP) along with weighted orientation difference (WOD) for each sub-region of the lung area. Local binary pattern is a texture descriptor that can extract the discriminative features efficiently. The proposed ELBP is rotation invariant and suitable to describe non-uniform patterns. Moreover, WOD as a structural feature uses the magnitude of each edge as the weight of its orientation difference. After constructing the co-occurrence matrix, discriminative features were extracted from this matrix and fed into a support vector machine in order to classify each sub-region as a cancerous (nodule) or normal tissue. The proposed method was compared to some of state-of-the-art nodule detection methods and was assessed over several real datasets in terms of specificity, sensitivity and accuracy.
Agile Requirement Engineering Maturity Framework for Industry 4.0

Industry 4.0 (I4.0) is changing business models and processes. I4.0 uses innovative trends such as Big Data, Cloud Computing, and Internet of things (IOT) to maximize economic benefits and return on investments. However, developing an I4.0 project may be costly, complex and risky for some companies. Therefore, careful requirements definition and mature Requirement Engineering (RE) processes are necessary. Undeveloped RE processes and poorly defined business requirements will often result in an inferior or a cancelled project. In addition, to ensure agility and allow for iterative changes in developing projects for I4.0, agile methodologies should be applied and prudently assessed. Unfortunately, most of existing assessment models are narrow focused and lack theoretical foundation and proper validation. This research proposes a comprehensive maturity framework called agile Requirement Engineering Maturity Model for Industry 4.0 (ARE-MMI4.0). The framework provides assessment of the minimum maturity levels to start a project for I4.0. The framework integrates an I4.0 maturity model with RE and agile maturity models to ensure the ultimate maturity assessment for the business processes.
Deep Metadata Fusion for Traffic Light to Lane Assignment

We present a deep metadata fusion approach that connects image data and heterogeneous metadata inside a Convolutional Neural Network (CNN). This approach enables us to assign all relevant traffic lights to their associated lanes. To achieve this, a common CNN topology is trained by down-sampled and transformed input images to predict an indication vector. The indication vector contains the column positions of all the relevant traffic lights that are associated with lanes. In parallel, we fuse prepared and adaptively weighted Metadata Feature Maps (MFM) with the convolutional feature map input of a selected convolutional layer. The results are compared to rule-based, only-metadata, and only-vision approaches. In addition, human performance of the traffic light to ego-vehicle lane assignment has been measured by a subjective test. The proposed approach outperforms all other approaches. It achieves about 93.0% average precision for a real-world dataset. In a more complex dataset, 87.1% average precision is achieved. In particular, the new approach reaches significantly higher results with 93.7% to 91.0% average accuracy for a real-world dataset in contrast to lower human performance.
Are you magicians?

Based on fieldwork, we investigate the integration of an agricultural information service into a rural community in Bangladesh. We find that it takes work beyond the initial design and cursory introduction of the service to make the service work in a low-income rural community: it takes the strength of the farmers self-help groups to circulate the messages by word-of-mouth, it takes posters placed at key junctions according to a socio-geographic understanding of the village manifest in a map, and it (ideally) involves the support of the elite of the community via the broadcast of messages at places of high symbolic value. It takes all this in addition to a well-made information service delivering relevant and timely messages on for example agricultural matters. Hence, reducing the issue to one of technical delivery mechanism does not tell the full story.
Anonymous Bitcoin v enforcement law

Bitcoin is the most prominent cryptocurrency that is frequently debated nowadays, basically defined as decentralised 'currency', 'payment system' and 'investment tool' which is an opportunity offered by today's digital age. In this article, we aim to fulfil the analysis of the legal basis of the matter from both technical and legal point of view. Despite there are many legal issues related to Bitcoin, we will particularly draw attention to some of the fundamental legal problems caused by the anonymity feature of the Bitcoin. Among these problems that may arise, only the disputes that may fall within the scope of the cases relating to debt and asset which have an impact on enforcement law will be examined. We will discuss the anonymity feature, considering the possibility of accessing an anonymous Bitcoin wallet. The article examines the situation where a debtor or one of the parties in a lawsuit may conceal their assets unfairly via Bitcoin (with the anonymity feature) in civil disputes relating to debt and assets. Has Bitcoin turned into a tool that malevolent debtors can hide their wealth while at the same time, a secret place where they can invest their money? In this study, we will offer solutions on overcoming the anonymity feature in practice and how to reveal and reach the wealth that are stored via Bitcoin wallet. Likewise, it will be underlined what malevolent debtors or parties in a lawsuit who want to obscure their wealth via Bitcoin wallet can do to strengthen their anonymity. Finally, we provide a specific and practical guideline for judges and especially creditor's lawyers in order to reduce the potential adverse situation that Bitcoin's anonymity feature can cause.
Mitigating information leakage during critical communication using S*FSM

Security-centric components and systems, such as System-on-Chip early-boot communication protocols and ultra-specific lightweight devices, require a departure from minimalist design constructs. The need for built-in protection mechanisms, at all levels of design, is paramount to providing cost-effective, efficient, secure systems. In this work, Securely derived Finite State Machines (S*FSM) and power-aware S*FSM are proposed and studied. Overall results show that to provide an S*FSM, the typical FSM requires a 50% increase in the number of states and a 57% increase in the number of product terms needed to define the state transitions. These increases translate to a minimum encoding space increase of 70%, raising the average encoding length from 4.8 bits to 7.9 bits. When factoring in relaxed structural constraints for power and space mitigation, the respective increases of 53 and 67% raise the average number of bits needed to 7.3 and 7.9. Regarding power savings, current minimisation is possible for both FSMs and S*FSMs through the addition of encoding constraints with average current reductions of 30 and 70%, respectively. Overall, a power-constrained S*FSM consumes about 5% more power than insecure FSMs with binary encodings, though with a penalty of a 95% increase in layout area.
Beyond saving

This note presents an analysis of videos containing narrative stories of eight women in Nepal who had experienced domestic violence. The analysis highlights multifaceted issues and dependencies that coerced the survivors1 to stay in the abusive environment. Further, we present cases where the legal system failed to support the survivor because it overlooked the survivor's unique and complex situation. We contrast those cases with agency-supporting cases where the survivors had more control and could decide when and how they wanted to engage resources. We argue that designed systems, whether they be technologies or public policies, need to support the survivor's agency rather than seeing them as victims needing to be saved.
Ultra-Low-Power Gaze Tracking for Virtual Reality

LiGaze is a low-cost, low-power approach to gaze tracking tailored to virtual reality (VR). It relies on a few low-cost photodiodes, eliminating the need for cameras and active infrared emitters. Reusing light from the VR screen, LiGaze leverages photodiodes around a VR lens to measure reflected screen light in different directions. It then infers gaze direction by exploiting the pupil's light absorption property. The core of LiGaze is to deal with screen light dynamics and extract changes in reflected light related to pupil movement. LiGaze infers a 3D gaze vector on the fly using a lightweight regression algorithm. Compared to the eye tracker of an existing VR headset (FOVE), LiGaze achieves 6.3° and 10.1° mean within-user and cross-user accuracy. Its sensing and computation consume 791 W in total and thus can be completely powered by a credit card-size solar cell harvesting energy from indoor lighting. LiGaze's simplicity and ultra-low power make it applicable in a wide range of VR headsets to better unleash VR's potential.
VisminerTD: a tool for automatic identification and interactive monitoring of the evolution of technical debt items

Technical debt (TD) contextualizes problems faced during software evolution considering the tasks that are not carried out adequately during software development. Software TD is a type of debt that brings a short-term benefit, but which may have to be paid with interest later on in the software development life cycle. Its presence brings risks to the project and can reduce its quality. It is worthwhile to have automatic mechanisms to monitor it, as TD monitoring requires the analysis of large amounts of complex data. Therefore, the combination of software metrics and code comment analysis, in the identification, and information visualization techniques, in monitoring, present themselves as a promising strategy to manage TD. This work presents VisminerTD, a tool that allows the automatic identification and interactive monitoring of the evolution of TD items by combining software metrics, code comment analysis, and information visualization. To evaluate its applicability, a feasibility study was carried out considering JUnit 4 and Apache Ant software projects. The results indicated that VisminerTD can support software development teams in monitoring TD items. In addition, a second case study was performed to assess the feasibility of the proposed tool regarding its usefulness, ease of use, and self-predicted future use. The results provided positive evidence on the use of the proposed tool, indicating (i) that it can be useful in supporting TD Identification and TD monitoring activities and (ii) that it can bring gains in terms of comprehensiveness and efficacy when evaluating the desirable time to identify and monitor different types of debt. Given the current scenario characterized by limited options of tools that combine different information to support automatic identification and monitoring of the evolution of TD items in software projects, VisminerTD can approximate the state-of-the-art and the state-of-the-practice in the TD area, contributing to a wider dissemination of the concept.
The Wisdom of the Crowd: Reliable Deep Reinforcement Learning Through Ensembles of Q-Functions

Reinforcement learning (RL) agents learn by exploring the environment and then exploiting what they have learned. This frees the human trainers from having to know the preferred action or intrinsic value of each encountered state. The cost of this freedom is that RL is slower and more unstable than supervised learning. We explore the possibility that ensemble methods can remedy these shortcomings by investigating a novel technique which harnesses the wisdom of crowds by combining Q-function approximator estimates utilizing a simple combination scheme similar to the supervised learning approach known as bagging. Bagging approaches have not yet found widespread adoption in the RL literature nor has a comprehensive look at its performance been performed. Our results show that the proposed approach improves all three tasks and RL approaches attempted. The primary contribution of this work is a demonstration that the improvement is a direct result of the increased stability of the action portion of the state-action-value function. Subsequent experimentation demonstrates that the stability in learning allows an actor-critic method to find more efficient solutions. Finally we show that this approach can be used to decrease the amount of time necessary to solve problems which require a deep Q-learning (DQN) approach.
Bayesian Nonparametric Dynamic Methods: Applications to Linear and Nonlinear Advertising Models

Bayesian methods for dynamic models in marketing have so far been parametric. For instance, it is invariably assumed that model errors emerge from normal distributions. Yet using arbitrary distributional assumptions can result in false inference, which in turn misleads managers. The author therefore presents a set of flexible Bayesian nonparametric (NP) dynamic models that treat error densities as unknown but assume that they emerge from Dirichlet process mixtures. Although the methods address misspecification in dynamic linear models, the main innovation is a particle filter algorithm for nonlinear state-space models. The author used two advertising studies to confirm the benefits of the methods when strict error assumptions are untenable. In both studies, NP models markedly outperformed benchmarks in terms of fit and forecast results. In the first study, the benchmarks understated the effects of competitive advertising on own brand awareness. In the second study, the benchmark inflated ad quality, and consequently, the effects of past advertising appeared 36% higher than that predicted by the NP model. In general, these methods should be valuable wherever state-space models appear (e.g., brand and advertising dynamics, diffusion of innovation, dynamic discrete choice).
Human and non-human intermediation in rural agricultural markets

A central trope of the information society is that of ‘information flows.’ The implicit assumption underlying such a vision involves the removal of gatekeepers and intermediaries who are perceived to impede such flows. Drawing from field research on information circulation, trade, and money in rural markets in Myanmar and India, we show why intermediaries persist alongside information and communication technologies (ICTs) in trade and financial transactions in the ‘Information Age.’ We examine the range of roles, (human and non-human) actors, and material practices that are involved in conducting financial transactions, and we show the importance of historical legacies and politics in explaining why both cash and financial intermediaries persist in the digital age. Focusing on the different value that human and non-human intermediaries bring to financial encounters helps explain what characteristics make each resilient or replaceable in a time of change. By situating intermediaries and mediations in the social relations within which they operate, we bring back the role of power and politics – an element that is often missing in accounts focused on the unmediated and ‘free’ circulation of information using ICTs – in explaining processes of mediation and circulation.
Fresh insights

Agricultural market information (MI) services provide smallholder farmers with convenient access to price information and have demonstrated potential to improve their incomes. Some recent evaluations of MI systems, however, have shown disappointing results and brought forth many complicating factors. Cautious of the mixed literature, we investigate the potential effectiveness and likely limitations of an MI service for improving livelihoods of smallholder farmers in the context of Loop, a shared transport-to-market-service for farmers. We conducted interviews with 17 farmers and 3 commission agents in Buxar, Bihar (India). Consistent with "information scarcity" and "information asymmetry" theories, we report how many farmers in this area regularly use mobile phones to check prices for choosing markets and negotiating transactions. Participants reported increases in the numbers of traders and price stabilization since the arrival of mobile phones. However, we found many other diverse factors that often outweigh the importance of market prices and inhibit market access, including market capacity, time, unfamiliarity with new markets, personal relationships, attitudes towards risk, credit relationships, and physical danger. Finally, to probe which of these additional factors might be addressable using an MI service, we present exploratory findings from preliminary user-interface studies.
Implementation of a square‐root filtering approach in marginalized particle filters for mixed linear/nonlinear state‐space models

Summary Marginalized particle filter (MPF) takes advantage of both Kalman filter and particle filter frameworks to estimate nonlinear state‐space models with reduced number of calculations in comparison to particle filter. However, due to existence of Kalman filter framework inside MPF, some limitations are introduced in implementation of MPF especially in embedded systems with finite numerical accuracies. In this paper, for the first time, we propose a novel square‐root filtering strategy for MPFs to alleviate these restrictions using modified factorization. Typical square‐root Kalman filters cannot be employed inside MPF due to the presence of minus operations in some equations of MPF. However, our method can be easily implemented inside the MPF structure. The proposed method can be used in any application that employs MPFs to estimate the mixed linear/nonlinear state‐space models. In order to demonstrate its usefulness, we employed the proposed square‐root filtering method inside a marginalized particle extended Kalman filter (MP‐EKF) structure, which was specifically designed for ECG denoising. The experimental results showed that, in the field of ECG denoising, the square‐root MP‐EKF performs more consistently than MP‐EKF in white Gaussian noises.