Reviewerly/exHarmony huggingface dataset:
Summary study of data-driven photometric stereo methods

A photometric stereo method aims to recover the surface normal of a 3D object observed under varying light directions. It is an ill-defined problem because the general reflectance properties of the surface are unknown. This paper reviews existing data-driven methods, with a focus on their technical insights into the photometric stereo problem. We divide these methods into two categories, per-pixel and all-pixel, according to how they process an image. We discuss the differences and relationships between these methods from the perspective of inputs, networks, and data, which are key factors in designing a deep learning approach. We demonstrate the performance of the models using a popular benchmark dataset. Data-driven photometric stereo methods have shown that they possess a superior performance advantage over traditional methods. However, these methods suffer from various limitations, such as limited generalization capability. Finally, this study suggests directions for future research.
Applying Model-Driven Engineering to Distributed Ledger Deployment

Distributed Ledger Technology (DLT) enables data storage in a decentralized manner among collaborating parties. The software architecture of such solutions encompasses models placed in the relevant architectural views. A lot of research is devoted to smart contracts and consensus algorithms, which are realized by distributed applications and can be positioned within the Logical view. However, we see the need to provide modeling support for the Deployment view of distributed ledger solutions. Especially since the chosen DLT framework has a significant impact on implementation and deployment. Besides, consistency between models and configuration deployment scripts should be ensured. So, we have applied Model-Driven Engineering (MDE) that allows on the transformation of models into more detailed models, source code, or tests. We have proposed Unified Modeling Language (UML) stereotypes and tagged values for distributed ledger deployment modeling and placed them in the UML Profile for Distributed Ledger Deployment. We have also designed the UML2Deployment model-to-code transformation for the R3 Corda DLT framework. A UML Deployment model is the source whereas a Gradle Groovy deployment script is the target of the transformation. We have provided the complete solution by incorporating the transformation into the Visual Paradigm modeling tool. Furthermore, we have designed a dedicated plug-in to validate generated deployment scripts. In the paper, we have shown how to design transformation for generating deployment scripts for the R3 Corda DLT framework with the ability to switch to another one.
Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm

Abstract The backpropagation (BP) algorithm is a gradient-based algorithm used for training a feedforward neural network (FNN). Despite the fact that BP is still used today when FNNs are trained, it has some disadvantages, including the following: (i) it fails when non-differentiable functions are addressed, (ii) it can become trapped in local minima, and (iii) it has slow convergence. In order to solve some of these problems, metaheuristic algorithms have been used to train FNN. Although they have good exploration skills, they are not as good as gradient-based algorithms at exploitation tasks. The main contribution of this article lies in its application of novel memetic approaches based on the Gravitational Search Algorithm (GSA) and Chaotic Gravitational Search Algorithm (CGSA) algorithms, called respectively Memetic Gravitational Search Algorithm (MGSA) and Memetic Chaotic Gravitational Search Algorithm (MCGSA), to train FNNs in three classical benchmark problems: the XOR problem, the approximation of a continuous function, and classification tasks. The results show that both approaches constitute suitable alternatives for training FNNs, even improving on the performance of other state-of-the-art metaheuristic algorithms such as ParticleSwarm Optimization (PSO), the Genetic Algorithm (GA), the Adaptive Differential Evolution algorithm with Repaired crossover rate (Rcr-JADE), and the Covariance matrix learning and Bimodal distribution parameter setting Differential Evolution (COBIDE) algorithm. Swarm optimization, the genetic algorithm, the adaptive differential evolution algorithm with repaired crossover rate, and the covariance matrix learning and bimodal distribution parameter setting differential evolution algorithm.
The Impact of Scope Creep on Project Success: An Empirical Investigation

Advocates of software engineering and software project management stated in the literature that creeping of scope is one of the most common causes for the failure of software projects.Also, advocates believed that it could occur in almost every software project, which leads to compromise in quality, delayed schedules, increase cost and decreased customer satisfaction.However, the lack of empirical evidence demands a comprehensive investigation to identify the factors of scope creep and to propose a conceptual framework to empirically evaluate the impact of scope creep on software project success.To determine the scope creep factors in this study, two exploratory methods, i.e. a Systematic Literature Review (SLR) and interview from experts are performed.Following the analysis of these methods, a conceptual framework is proposed.To empirically evaluate the proposed conceptual framework, data is collected through a survey method.Next, the collected data is analyzed through Partial Least Squares' Structural Equation Modelling (PLS-SEM).From the results, it is evident that the identified factors of scope creep are negatively associated with software project success.The results of empirical evaluation also second the findings of SLR.The outcome of the study may help the practitioners to understand the dynamics of factors, which undermine scope creep in software SMEs and to assist them in the development of effective control and mitigation strategies, therefore, to increase the project success rate.
Haze Removal of Railway Monitoring Images Using Multi-Scale Residual Network

As one of the main pollution sources in China, haze can blur the railway monitoring and threaten railway safety. In this paper, we propose an end-to-end multi-scale residual network (MSRN) which can achieve remarkable dehazing effect on railway monitoring images. The method optimizes the image dehazing algorithm in three aspects: network structure, loss function and hazy dataset. Firstly, inspired by the residual network, the paper presents a method of fusing multi-scale feature information based on the residual network, which can extract more effective information at different scales. Secondly, a combined loss function is designed to achieve better convergent results by balancing training time, training calculations, and precision. Thirdly, the paper synthesizes an outdoor dataset specifically for railway scenarios, which relies on real depth maps and various outdoor images. Extensive experimental results on both full reference image quality assessment and no reference image quality assessment of image restoration demonstrate that the proposed algorithm shows higher performance than the state-of-the-art algorithms. Moreover, the haze of railway monitoring images is removed under hazy weather, and the detection algorithm achieved higher detection accuracy on the images after dehazing. The proposed network structure, loss function, and hazy dataset are discussed and analyzed in detail to verify the effectiveness of the proposed method.
Plotting with Thread

Punch needle embroidery is a unique type of embroidery that uses loops of threads to create designs. Technology for punch needle embroidery ranges from popular handheld manual tools to high-cost industrial tufting machines. Computer-controlled punch needle fabrication tools remain out-of-reach for most practitioners. In this work, we describe how a low-cost X-Y plotter can be repurposed to support punch needle embroidery fabrication. By adding easy-to-make physical accessories coupled with a novel software toolkit, we support the production of delicate and precise punch needle embroideries with minimal manual labor. After examining and evaluating the potential and challenges of converting X-Y plotters into punch needle embroidery fabricators, we propose design and fabrication guidelines that are specific to plotter-based punch needle embroideries. We demonstrate how this novel fabrication approach enables the production of a wide range of artifacts and textures.
Achieving Heisenberg scaling with maximally entangled states: An analytic upper bound for the attainable root-mean-square error

In this paper we explore the possibility of performing Heisenberg limited quantum metrology of a phase, without any prior, by employing only maximally entangled states. Starting from the estimator introduced by Higgins et al. in New J. Phys. 11, 073023 (2009), the main result of this paper is to produce an analytical upper bound on the associated Mean Squared Error which is monotonically decreasing as a function of the square of the number of quantum probes used in the process. The analysed protocol is non-adaptive and requires in principle (for distinguishable probes) only separable measurements. We explore also metrology in presence of a limitation on the entanglement size and in presence of loss.
GAN Tunnel: Network Traffic Steganography by Using GANs to Counter Internet Traffic Classifiers

In this paper, we introduce a novel traffic masking method, called Generative Adversarial Network (GAN) tunnel, to protect the identity of applications that generate network traffic from classification by adversarial Internet traffic classifiers (ITCs).Such ITCs have been used in the past for website fingerprinting and detection of network protocols.Their use is becoming more ubiquitous than before for inferring user information.ITCs based on machine learning can identify user applications by analyzing the statistical features of encrypted packets.Our proposed GAN tunnel generates traffic that mimics a decoy application and encapsulates actual user traffic in the GAN-generated traffic to prevent classification from adversarial ITCs.We show that the statistical distributions of the generated traffic features closely resemble those of the actual network traffic.Therefore, the actual user applications and information associated with the user remain anonymous.We test the GAN tunnel traffic against high-performing ITCs, such as Random Forest and eXtreme Gradient Boosting (XGBoost), and we show that the GAN tunnel protects the identity of the source applications effectively.
Multiscale superpixel method for segmentation of breast ultrasound

In medical diagnostics, breast ultrasound is an inexpensive and flexible imaging modality. The segmentation of breast ultrasounds to identify tumour regions is a challenging and complex task. The major problems of effective tumour identification are speckle noise, artefacts and low contrast. The gold standard for segmentation is manual processing; however, manual segmentation is a cumbersome task. To address this problem, the automatic multiscale superpixel method for the segmentation of breast ultrasounds is proposed. The original breast ultrasound image was transformed into multiscaled images, and then, the multiscaled images were preprocessed. Next, a boundary efficient superpixel decomposition of the multiscaled images was created. Finally, the tumour region was generated by the boundary graph cut segmentation method. The proposed method was evaluated with 120 images from the Thammassat University Hospital database. The dataset consists of 30 malignant, 30 benign tumors, 60 fibroadenoma, and 60 cyst images. Popular metrics, such as the accuracy, sensitivity, specificity, Dice index, Jaccard index and Hausdorff distance, were used for the evaluation. The results indicate that the proposed method achieves segmentation accuracy of 97.3% for benign tumors, 94.2% for malignant, 96.4% for cysts and 96.7% for fibroadenomas. The results validate that the proposed model outperforms selected state-of-the-art segmentation methods. The proposed method outperforms selected state-of-the-art segmentation methods with an average segmentation accuracy of 94%.
A Cheap Feature Selection Approach for the <i>K</i>-Means Algorithm

The increase in the number of features that need to be analyzed in a wide variety of areas, such as genome sequencing, computer vision, or sensor networks, represents a challenge for the K-means algorithm. In this regard, different dimensionality reduction approaches for the K-means algorithm have been designed recently, leading to algorithms that have proved to generate competitive clusterings. Unfortunately, most of these techniques tend to have fairly high computational costs and/or might not be easy to parallelize. In this article, we propose a fully parallelizable feature selection technique intended for the K-means algorithm. The proposal is based on a novel feature relevance measure that is closely related to the K-means error of a given clustering. Given a disjoint partition of the features, the technique consists of obtaining a clustering for each subset of features and selecting the m features with the highest relevance measure. The computational cost of this approach is just <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (m·max{n·K,logm}) per subset of features. We additionally provide a theoretical analysis on the quality of the obtained solution via our proposal and empirically analyze its performance with respect to well-known feature selection and feature extraction techniques. Such an analysis shows that our proposal consistently obtains the results with lower K-means error than all the considered feature selection techniques: Laplacian scores, maximum variance, multicluster feature selection, and random selection while also requiring similar or lower computational times than these approaches. Moreover, when compared with feature extraction techniques, such as random projections, the proposed approach also shows a noticeable improvement in both error and computational time.
More efficient processes for creating automated essay scoring frameworks: A demonstration of two algorithms

Automated essay scoring (AES) has emerged as a secondary or as a sole marker for many high-stakes educational assessments, in native and non-native testing, owing to remarkable advances in feature engineering using natural language processing, machine learning, and deep-neural algorithms. The purpose of this study is to compare the effectiveness and the performance of two AES frameworks, each based on machine learning with deep language features, or complex language features, and deep neural algorithms. More specifically, support vector machines (SVMs) in conjunction with Coh-Metrix features were used for a traditional AES model development, and the convolutional neural networks (CNNs) approach was used for more contemporary deep-neural model development. Then, the strengths and weaknesses of the traditional and contemporary models under different circumstances (e.g., types of the rubric, length of the essay, and the essay type) were tested. The results were evaluated using the quadratic weighted kappa (QWK) score and compared with the agreement between the human raters. The results indicated that the CNNs model performs better, meaning that it produced more comparable results to the human raters than the Coh-Metrix + SVMs model. Moreover, the CNNs model also achieved state-of-the-art performance in most of the essay sets with a high average QWK score.
The Logic of Interactive Biorobotics

In recent studies, robots are used to stimulate living systems in controlled experimental settings. This research strategy is here called interactive biorobotics, to distinguish it from classical biorobotics, in which robots are used to simulate, rather than to stimulate, living system behavior. This article offers a methodological analysis of interactive biorobotics and has two goals. The first one is to argue that interactive biorobotics is methodologically different, in some important respects, from classical biorobotics and from countless instances of model-based science. It will be shown that interactive biorobotics does not conform to the so-called "understanding by building" approach or synthetic method, and that it illustrates a novel use of models in science. The second goal is to reflect on the logic of interactive biorobotics. A distinction will be made between two classes of studies, which will be called "proximal" and "distal." In proximal studies, experiments involving robot-animal interaction are brought to bear on theoretical hypotheses on robot-animal interaction. In distal studies, experiments involving robot-animal interaction are brought to bear on theoretical hypotheses on animal-animal interaction. Distal studies involve logical steps which may be particularly hard to justify. This distinction, together with a methodological reflection on the relationship between the context in which the experiments are carried out and the context in which the conclusions are expected to hold, will lead to a checklist of questions which may be useful to justify and evaluate the validity of interactive biorobotics studies. The reconstruction of the logic of interactive biorobotics made here, though preliminary, may contribute to justifying the important role that robots, as tool for stimulating living systems, can play in the contemporary life sciences.
Don't lose your coin! Investigating Security Practices of Cryptocurrency Users

In recent years, cryptocurrencies have increasingly gained interest. The underlying technology, Blockchain, shifts the responsibility for securing assets to the end-user and requires them to manage their (private) keys. Little attention has been given to how cryptocurrency users handle the challenges of key management in practice and how they select the tools to do so. To close this gap, we conducted semi-structured interviews (N=10). Our thematic analysis revealed prominent themes surrounding motivation, risk assessment, and coin management tool usage in practice. We found that the choice of tools is driven by how users assess and balance the key risks that can lead to loss: the risk of (1) human error, (2) betrayal, and (3) malicious attacks. We derive a model, explaining how risk assessment and intended usage drive the decision which tools to use. Our work is complemented by discussing design implications for building systems for the crypto economy.
Robust Motion Planning for Uncertain Systems With Disturbances Using the Invariant-Set Motion Planner

The invariant-set motion planner uses a collection of safe sets to find a collision-free path through an obstacle-filled environment [1]-[4]. This article extends the invariant-set motion planner to systems with persistently varying disturbances and parametric model uncertainty. This is accomplished by replacing the previously used positive invariant sets with robust positive invariant sets. Since the model uncertainty obfuscates the relationship between the invariant sets in the state space, and the references and obstacles in the output space, we reformulate the dynamics in velocity form so that the system output appears directly in the modified system state. Since the persistently varying disturbances will prevent the closed-loop system from converging to the desired reference, we introduce a new robust connection rule where references are connected when the invariant set of one reference contains the minimal volume robust invariant-set of another. In addition, we bound the time required to transition between invariant sets to ensure safety when the obstacles are moving. By parameterizing the invariant sets using a precomputed input-to-state Lyapunov function, we reduce the real-time computational complexity of our motion planner. The robust invariant-set motion planner is demonstrated for an automated highway driving case study.
The Evolution of Marketing in the Context of Voice Commerce: A Managerial Perspective

The world is confronted with the rise of voice assistants, increasingly used for shopping activities. This paper examines managers’ perceptions of the evolution of voice assistants and their potential effects on the marketing practice. Shopping-related voice assistants are likely to radically change the way consumers search and purchase products with severe impact on brands. However, the behavior of these AI-enabled machines represents a “black box” for brand owners. The study of the managers’ interpretation of a voice-enabled marketplace is critical as it may influence future marketing choices. The authors use an inductive theory construction process to study the phenomenon of voice commerce through the eyes of AI experts and voice-aware managers. A mixed-method approach paced three distinct data collection phases. First, systematic machine behavior observations (Amazon Alexa) unfolded the unique characteristics of voice shopping. Second, in-depth interviews with 30 executives drew the current brand owner’s challenges and opportunities in the context of voice commerce. Third, an expert survey with international managers (N = 62) revealed the expected impact of voice assistants on the shopping process. Findings show that managers consider voice assistants a disruptive technology assuming a central relational role in the consumer market. However, they often divergence in opinions across industry, function, and seniority level. Besides, managers’ familiarity with voice commerce is correlated to a higher optimism towards voice technologies (opportunity for brands) but also a greater sense of urgency (short-term focus) with implications for marketing strategy. This article offers support to brand owners explaining how voice assistants work and examining their effects on consumption. The authors discuss empirical results while providing managerial guidelines to create resilient and sustainable brands in the era of voice commerce.
Security of Distributed Intelligence in Edge Computing: Threats and Countermeasures

Rapid growth in the amount of data produced by IoT sensors and devices has led to the advent of edge computing wherein the data is processed at a point at or near to its origin. This facilitates lower latency, as well as data security and privacy by keeping the data localized to the edge node. However, due to the issues of resource-constrained hardware and software heterogeneities, most edge computing systems are prone to a large variety of attacks. Furthermore, the recent trend of incorporating intelligence in edge computing systems has led to its own security issues such as data and model poisoning, and evasion attacks. This chapter presents a discussion on the most pertinent threats to edge intelligence. Countermeasures to deal with the threats are then discussed. Lastly, avenues for future research are highlighted.
Attention based consistent semantic learning for micro-video scene recognition

Micro-videos are one of the most popular multimedia forms in mobile internet domain, and scene recognition is important for micro-video semantic analyses and understanding. Compared with traditional videos, scene recognition in micro-videos is subject to content inconsistency for the same scene owing to the subjectivity of photographers. Moreover, the importance of frames for semantic representation differs in the same micro-video. These phenomenons limit micro-video scene recognition. To address these issues, in this paper, attention-based consistent semantic learning (ACSL) is proposed for micro-video scene recognition; this consists of a two-branch framework combined with an attention mechanism for the maintenance of the semantic consistency within classes. The experiments conducted in this study on multiple datasets revealed that the proposed ACSL achieves a better performance than other video scene recognition methods.