Reviewerly/exHarmony huggingface dataset:
SETA and Security Behavior

This article contends that information security education, training and awareness programs can improve employee security behavior. Empirical studies have analyzed the direct effects of employee security training on security behavior without taking into account the mediating role of employee relations, monitoring, and accountability. Based on employee relations and accountability theories, this study proposes and tests a causal model that estimates the direct effect of employee security training on security behavior as well as its indirect effects as mediated by employee relations, monitoring, and accountability. The empirical analysis relies on a survey data from a cross section of employees from five major industry sectors and a structural equation modeling approach via SmartPLS 3.0. The results show that employee security training has indirect and significant effects on security behavior through its influence on employee relations, monitoring, and accountability. However, the result does not indicate direct and significant effect of security training on employee security behavior.
Sparse Feature Learning for Correlation Filter Tracking Toward 5G-Enabled Tactile Internet

Fifth generation with high dimensions and capabilities is expected to fulfil the requirements of the Tactile Internet. Tracking provides strong support for intuitive interaction with interfaces by hands, eyes, bodies, etc. Such interfaces can be used in the Tactile Internet for interaction with real and virtual objects. The trackers based on tracking-by-detection framework rely on manual feature detectors for robust tracking, which is particularly useful for specific objects like humans but cannot handle generic tracking problems. Therefore, a sparse feature learning method beyond manual design is proposed to learn features from the samples sampled during tracking. The basic idea is to learn a dictionary from the samples in the previous frames and construct feature representations to represent the object for detection of the location in the current frame. The samples are patches centered at the keypoints based on an adaptive features from accelerated segment test (FAST) detector with local threshold. The dictionary is learned with sparse coding for sparse representations and atoms of the dictionary are grouped to describe the local orientation of these samples. The integrated features are built after rectification of the sparse representations. The correlation filter is used to infer the object location from the sparse features. The qualitative and quantitative experimental results on OTB100 show the advantage of the proposed tracker against current state-of-the-art trackers in terms of accuracy.
In no uncertain terms: a dataset for monolingual and multilingual automatic term extraction from comparable corpora

Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in low inter-annotator agreement. There is a large need for well-documented, manually validated datasets, especially in the rising field of multilingual term extraction from comparable corpora, which presents a unique new set of challenges. In this paper, a new approach is presented for both monolingual and multilingual term annotation in comparable corpora. The detailed guidelines with different term labels, the domain- and language-independent methodology and the large volumes annotated in three different languages and four different domains make this a rich resource. The resulting datasets are not just suited for evaluation purposes but can also serve as a general source of information about terms and even as training data for supervised methods. Moreover, the gold standard for multilingual term extraction from comparable corpora contains information about term variants and translation equivalents, which allows an in-depth, nuanced evaluation.
Practices to Improve Risk Management in Agile Projects

Risk management contributes to software projects success, but agile software development methods do not offer specific activities to manage risks. Therefore, this study aims to propose a list of risk management practices for agile projects, aiming to increase their chances of success. We analyzed 129 works on agile methods that afforded 127 risk management practices. We categorized and ranked practices using the AHP multi-criteria method with the participation of experts in the subject. The study presents risk management practices for daily meetings, increment, prototype, product backlog and Sprint planning as the most important for the risk management effectiveness. This study identified specific risk management practices for agile methods, not converging with other studies. Results contribute to the risk management improvement in agile projects and, consequently, increase their chances of success.
Robust Human Targets Tracking for MIMO Through-Wall Radar via Multi-Algorithm Fusion

The detection and tracking of human targets behind the wall is of great importance in urban sensing. The random and high maneuvering behaviors of moving human targets and the clutter diversity lead to high missed detection and false-alarm probability. In this paper, we consider two-dimensional human-target tracking problem, and a multi-algorithm fusion (MAF) framework exploiting the mean-shift algorithm and the Kalman filter is proposed. Compared with the mean-shift algorithm, the proposed MAF framework has robust tracking performance, especially in the presence of multiple targets. Finally, the proposed MAF framework is evaluated by simulations and real data.
An enhanced time efficient technique for image watermarking using ant colony optimization and light gradient boosting algorithm

For a secure multimedia data exchange, watermarking is used. Generally, meta-heuristic optimization is required to optimize scheme parameters in the watermarking embedding. Even though meta-heuristic methods have been widely used because of their enhancing capability, due to their large time consumption nature they are not applicable for time-sensitive applications. This paper proposes a time efficient optimization method based on machine learning algorithms to detect the best embedding parameter for image watermarking with both robustness and imperceptibility. Initially, for providing robustness against watermarking attacks, a method for watermarking embedding is designed in the domain of Discrete Cosine Transform. After that, Ant Colony optimization is used for finding the optimization parameters. Then an observation data is obtained, which includes the feature vector and optimum strength values of the training images. The image features are extracted at different embedded strength values by the calculation of optimum fitness function. Finally Light Gradient Boosting algorithm (LGBA) is applied to predict the optimum embedding parameters of the set of new images which are to be watermarked. When compared with the existing optimization methods, it has been found that the proposed method consumes very less time for the evaluation of optimum solutions. From the results, it has been identified that the proposed algorithm satisfies the image watermarking with the improvement in time enhancement. The effectiveness of the proposed method is analyzed using MATLAB 2018b.
Canonical Correlation Analysis With <i>L</i> <sub>2,1</sub>-Norm for Multiview Data Representation

For many machine learning algorithms, their success heavily depends on data representation. In this paper, we present an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm constrained canonical correlation analysis (CCA) model, that is, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -CCA, toward discovering compact and discriminative representation for the data associated with multiple views. To well exploit the complementary and coherent information across multiple views, the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm is employed to constrain the canonical loadings and measure the canonical correlation loss term simultaneously. It enables, on the one hand, the canonical loadings to be with the capacity of variable selection for facilitating the interpretability of the learned canonical variables, and on the other hand, the learned canonical common representation keeps highly consistent with the most canonical variables from each view of the data. Meanwhile, the proposed L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -CCA can also be provided with the desired insensitivity to noise (outliers) to some degree. To solve the optimization problem, we develop an efficient alternating optimization algorithm and give its convergence analysis both theoretically and experimentally. Considerable experiment results on several realworld datasets have demonstrated that L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -CCA can achieve competitive or better performance in comparison with some representative approaches for multiview representation learning.
Multi-layer boosting sparse convolutional model for generalized nuclear segmentation from histopathology images

It is a challenging problem to achieve generalized nuclear segmentation in digital histopathology images. Existing techniques, using either handcrafted features in learning-based models or traditional image analysis-based approaches, do not effectively tackle the challenging cases, such as crowded nuclei, chromatin-sparse, and heavy background clutter. In contrast, deep networks have achieved state-of-the-art performance in modeling various nuclear appearances. However, their success is limited due to the size of the considered networks. We solve these problems by reformulating nuclear segmentation in terms of a cascade 2-class classification problem and propose a multi-layer boosting sparse convolutional (ML-BSC) model. In the proposed ML-BSC model, discriminative probabilistic binary decision trees (PBDTs) are designed as weak learners in each layer to cope with challenging cases. A sparsity-constrained cascade structure enables the ML-BSC model to improve representation learning. Comparing to the existing techniques, our method can accurately separate individual nuclei in complex histopathology images, and it is more robust against chromatin-sparse and heavy background clutter. An evaluation carried out using three disparate datasets demonstrates the superiority of our method over the state-of-the-art supervised approaches in terms of segmentation accuracy.
Distributed semantic representations for modeling human judgment

People make judgments about thousands of different objects and concepts on a day-to-day basis; however, capturing the knowledge that subserves these judgments has been difficult. Recent advances in computational linguistics are filling this gap, as the statistics of language use yield rich, distributed semantic representations for natural objects and concepts. These representations have been shown to predict semantic and linguistic judgments, such as judgments of meaning and relatedness, and more recently, high-level judgments, including probability judgment and forecasting, stereotyping and various types of social judgment, consumer choice, and perceptions of risk. Distributed semantic representations are now a key component of computational models that represent knowledge, make evaluations and attributions, and give responses, in a human-like manner.
The relationship between personality and decision-making: A Systematic literature review

From a point of view, software development is a set of decisions that need to be made while the software is developed. Many alternatives should be considered, such as the technology to employ, or the most important features to implement. However, many factors can influence one’s decision-making, such as the decision maker’s personality. This paper reports the state of the art with regard to the relationship between decision-makers’ personality and decision-making aspects. We conducted a Systematic Literature Review to search and analyze published primary studies that discuss the abovementioned relationship in the context of companies that develop any kind of product or service. Despite the recognized influence of personality in decision-making activities, we were not able to find any study in Software Engineering field that discusses this relationship. We included 15 studies and most of them are from Management field, excluding one from Information System field. From these studies, we identified 75 reported relationships between 28 different personality aspects and 30 different decision-making aspects. The interest in this topic born on 80’s and it has grown after 2002. However, despite the number of reported relationships, and the number of personalities and decision-making aspects investigated, more research on this topic is necessary. In particular, it is important to verify how someone’s personality influences the decision-making considering the software development context. This can help in improving how a decision is made in software engineering context.
Convergence analysis of beetle antennae search algorithm and its applications

In this paper, we focuses on the theoretical analysis on the convergence of the beetle antennae search (BAS) algorithm. For the first time, we prove that the BAS algorithm is convergent with probability 1, which provides some insights about why the BAS algorithm works and also brings us some ideas about how to improve the BAS algorithm in the future. We also test the performance of the BAS algorithm via some representative benchmark functions. Meanwhile, some applications of the BAS algorithm are also presented, showing the advantages of the algorithm over other algorithms.
The impact of globally diverse GHI training data: Evaluation through application of a simple Markov chain downscaling methodology

Synthetic solar irradiance models that generate irradiance time series are often trained on too few sites with limited climatic diversity. This often results in models which are overfit and cannot be globally applied. The impact of training data for solar energy applications is relatively unexplored and, therefore, of importance. A new Markov-downscaling methodology is proposed to test diverse training datasets. In terms of synthetic global horizontal irradiance (GHI) generation, occasionally, Markov downscaling was found to validate excellently, whereas linear interpolation failed resoundingly at all sites in this application. Markov downscaling was unsuitable if the aim was to obtain the actual result in cases such as historic gap filling, interpolation, or forecasting; thus, it is purely a synthetic model. A significant influence of training data is found when comparing 100 repetitions of all 752 combinations of training and testing data. Using different training data in the model significantly influenced the accuracy of the downscaled GHI. We find that climate similarity is not a fundamental driver for GHI similarities and that some form of site index derived from kc must have an influence. A particular site at Izana, Tenerife, performs consistently poor across all but 10 testing sites, exemplifying how idiosyncrasies of a model can produce unexpected global behaviour with minimal training data exploration. A considerable performance variation was observed resulting only from training data selection, as such warrants careful attention. It is concluded that all GHI methodological approaches that require training data must undertake a similar global investigation before they can be accepted as globally applicable.
Legal ontologies over time: A systematic mapping study

Over the last 30 years, AI & Law has provided breakthroughs in studies involving case-based reasoning, rule-based reasoning, information retrieval and, most recently, conceptual models for knowledge representation and reasoning, known as Legal Ontologies. Ontologies have been widely used by legal practitioners, scholars, and lay people in a variety of situations, such as simulating legal actions, semantic search and indexing, and to keep up-to-date with the continual change of laws and regulations. Given the high number of legal ontologies produced, the need to summarize this research realm through a well-defined methodological procedure is urgent need. This study presents the results of a systematic mapping of the literature, aiming at categorizing legal ontologies along certain dimensions, such as purpose, level of generality, underlying legal theories, among other aspects. The reasons to carry out a systematic mapping are twofold: in addition to explaining the maturation of the area over recent decades, it helps to avoid the old problem of reinventing the wheel. Through organizing and classifying what has already been produced, it is possible to realize that the development of legal ontologies can rise to the level of reusability where prefabricated models might be coupled with new and more complex ontologies for practical law.
Ant Colony Optimizer: Theory, Literature Review, and Application in AUV Path Planning

This chapter starts with the inspiration and main mechanisms of one of the most well-regarded combinatorial optimization algorithms called Ant Colony Optimizer (ACO). This algorithm is then employed to find the optimal path for an AUV. In fact, the problem investigated is a real-world application of the Traveling Salesman Problem (TSP).
Mediated Semi‐Quantum Key Distribution Using Single Photons

Abstract A new mediated semi‐quantum key distribution (SQKD) protocol is proposed, allowing two classical participants to share a secret key with the help of an untrusted third party, who only needs to generate single photons and perform Bell measurements. This is the first work attempting to reduce the quantum overhead of the untrusted third party, which makes the mediated SQKD even more practical. The proposed protocol is shown to be free from several well‐known attacks.
Applying a methodology to develop user eXperience heuristics

A heuristic evaluation method allows the evaluation of the usability of application domains. To evaluate applications that have specific domain features, researchers can use sets of specific usability heuristics in addition to the well-known (usually Nielsen's) heuristics. Heuristics can also focus on the User eXperience (UX) aspects other than the usability. In a previous work, we proposed a formal methodology for establishing usability/UX heuristics. The methodology has 8 stages including activities to formulate, specify, validate and refine a new set of heuristics for a specific application domain. The methodology was validated through expert opinion and several case studies. Although when specifying the methodology, we explained each of its stages in detail, some activities can be difficult to perform without a guide that helps the researcher determine how the stages should be carried out. This article presents a detailed explanation regarding how to apply each stage of the methodology to create a new set of heuristics for a specific domain. Additionally, this paper explains how to iterate the methodology's stages and when to stop the process of developing new heuristics.
Efficient multi-kernel DCNN with pixel dropout for stroke MRI segmentation

As manual delineation of lesions in medical image is a very tedious and time consuming process, accurate and automatic segmentation of medical images can assist diagnosis and treatment. In this study, we propose a deep convolution neural network for stroke magnetic resonance imaging(MRI) segmentation. The main structure of our network consists of two symmetrical deep sub-networks, in which dense blocks are embedded for extracting effective features from sparse pixels to alleviate the over-fitting problem of deep networks. We use the multi-kernel to divide the network into two sub-networks for acquiring more receptive fields, and the dropout regularization method to achieve an effective feature mapping. For the post-processing of the soft segmentation, we use image median filtering to alleviate noises and preserve the edge details of images. Our network is evaluated on two public benchmark segmentation challenges (SISS: sub-acute ischemic stroke lesion segmentation and SPES: acute stroke outcome/penumbra estimation) with multi-modality MRI sequences. According to the results of the public benchmark reports, among 9 teams participating in both SISS and SPES challenges at the same time, our network achieves the top performance on SISS challenge, and the top 3 performance on the SPES challenge. In addition, our network also exhibits state-of-the-art performance compared with other segmentation methods. Finally, we extensively evaluate our network with an ablation experiment. The experimental results show that both multi-kernel and dropout strategies can improve the segmentation accuracy of our proposed network.
Do banking sector and stock market development matter for economic growth?

Financial development as a concept is multifaceted with no clear measurement or definition. Inference via individual proxies may result in an incomplete understanding of the relationship between financial development and economic growth, since sole proxies are unlikely to capture the true capacity of financial development. To address this issue, this paper utilizes a multiple indicators multiple causes (MIMIC) model to create a more complete measure of financial development. In doing this, we treat banking sector and stock market developments as two latent indicators of financial development and use the MIMIC model to predict them which are used as their proxies. Using data from 101 countries over the period 1990–2014, we use the predicted values of the two latent variables as regressors, among other controls, in the growth regression. We find a robust negative relationship between banking sector development and economic growth, whereas the effect of stock market development on economic growth is positive up to a threshold after which the effect becomes negative.
Is the introduction of futures responsible for the crash of Bitcoin?

The price of Bitcoin reached its peak a mere few days after the introduction of Bitcoin futures and suffered an 80% loss in the following year. In this paper, we find a significant and negative relationship between the introduction of Bitcoin futures and Bitcoin returns, and an insignificant or positive relationship for other 7 major non-Bitcoin cryptocurrencies. Within the first 45 days after the futures launch, Bitcoin suffered a –26.50% loss, while other cryptocurrencies could still provide positive returns. We presume that the launch of Bitcoin futures was to an extent responsible for the crash of Bitcoin.
Direct-to-Consumer Genetic Testing and Potential Loopholes in Protecting Consumer Privacy and Nondiscrimination

This Viewpoint discusses privacy and discrimination issues raised when direct-to-consumer (DTC) genetic testing companies give law enforcement agencies access to their data, and gaps in nondiscrimination and HIPAA regulations protecting consumers of DTC genetic testing more generally.