Reviewerly/exHarmony huggingface dataset:
A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

Image enhancement is an integral component of face recognition systems and other image processing tasks such as in medical and satellite imaging. Among a number of existing image enhancement methods, metaheuristic-based approaches have gained popularity owing to their highly effective performance rates. However, the need for improved evaluation functions is a major research concern in the study of metaheuristic-based image enhancement methods. Thus, in this paper, we present a new evaluation function for improving the performance of metaheuristic-based image enhancement methods. Essentially, we applied our new evaluation function in conjunction with metaheuristic-based optimization algorithms in order to select automatically the best enhanced face image based on a linear combination of different key quantitative measures. Furthermore, different from other existing evaluation functions, our evaluation function is finitely bounded to determine easily whether an image is either too dark or too bright. This makes it better suited to find optimal solutions (best enhanced images) during the search process. Our method was compared with existing metaheuristic-based methods and other state-of-the-art image enhancement techniques. Based on the qualitative and quantitative measures obtained, our approach is shown to enhance facial images in unconstrained environments significantly.
Gaussian Target Tracking With Direction-of-Arrival von Mises–Fisher Measurements

This paper proposes a novel algorithm for target tracking with direction-of-arrival measurements, modeled by von Mises-Fisher distributions. The algorithm makes use of the assumed density framework with Gaussian distributions, in which the posterior probability density of the target state is approximated by a Gaussian density. A key component of this algorithm is that the proposed Bayesian model of the measurements takes into account the specific characteristics of angular measurements by using a von Mises-Fisher distribution. We propose two implementations of the algorithm, one based on first-order Taylor series expansion and another one based on sigma points. Simulation results show the benefits of the proposed algorithms in relation to other Gaussian filters in the literature.
Identification of the state of an object under conditions of fuzzy input data

The modernization of the methods for identification of the state of objects under conditions of fuzzy input data, described by their membership functions, was performed. The selected direction of improvement of traditional methods is associated with the fundamental features of solving this problem under actual conditions of a small source data sample. Under these conditions, to solve the problem of state identification, it is advisable to transfer to the technology of description of source data, based on the mathematical apparatus of fuzzy mathematics and less demanding in terms of information. This transition required the development of new formal methods for solving specific tasks. In this case, the procedure for solution of the fuzzy system of linear algebraic equations was developed for multidimensional discriminant analysis. To solve the clustering problem, a special procedure of comparison of fuzzy distances between objects of clustering and centers of grouping was proposed. The selected direction of improvement of the traditional method for regression analysis was determined by impossibility of using the classical least squares method under conditions when all variables are described fuzzily. This fact led to the need to construct a special two-step procedure for solving the problem. In this case, the linear combination of the measure of distance of the sought-for solution from the modal one and the measures of compactness of membership function of the explained variable are minimized. The technology of fuzzy regressive analysis was implemented in the important practical case when the source fuzzy data are described by general membership functions of the (L-R) type. In addition, the analytic solution to the problem in the form of calculation formulas was obtained. The discussion showed that the modernization of the classical methods for solving the problem of the state identification, considering the fuzzy nature of representation of source data, made it possible to identify objects under actual conditions of a small sample of fuzzy source data
Control over spatial representation format enhances information extraction but prevents long-term learning.

Previous research has demonstrated that cognitive offloading (i.e., externalizing mental processes) is useful for immediate problem solving. However, long-term effects of cognitive offloading on subsequent problem solving without offloading are remarkably understudied. Our main goal was to investigate the effects of representation control (i.e., adjusting the format of the representation to task requirements) on incidental procedural learning in an information extraction task with interactive visualizations. More specifically, we tested how the availability of representation control for solving tasks in a practice phase affects procedural learning measured in a testing phase in which representation control is no longer available. In both phases, we explored time on task as well as proportion correct as proxies of task performance and analyzed how often participants used representation control. We conducted three experiments in which participants could modify and reorganize information displays in the practice phase, whereas in the testing phase, they had to solve equivalent and near transfer problems without this offloading opportunity. We show that representation control is beneficial for immediate task solution, particularly for problems that require a spatial transformation. This benefit was more pronounced for automated (i.e., system-controlled) types of control (Experiment 3). In contrast, in subsequent problem solving without representation control (up to 24 hr later), participants who had been using representation control previously fell back to the level of an untrained baseline condition (Experiments 1 and 2). However, the detrimental effect of representation control was confined to equivalent tasks and did not generalize to a near transfer task. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
Analysis of projects funded by the National Natural Science Foundation of China during the years of 2014–2018

The National Natural Science Foundation of China (NSFC) plays an important role in supporting scientific research. And numerous scientists and researchers are concerned about the applications and funding.Annual reports of 2014-2018 were searched respectively from the NSFC official website. Further analysis was made to discover the regularity and trend of projects funded by NSFC.The funding by NSFC continuously increased rapidly since its establishment. Recently, the annual amount, which is still on the rise, has reached more than 20 billion CNY. From the year 2014 to 2018 multifarious projects types were set up by NSFC to support scientific research of different level and multiple dimensions.In the past years, NSFC had provides strong support to basic scientific research in all fronts and provided a mechanism for fair competition.
No, That's Not My Feedback: TV Show Recommendation Using Watchable Interval

As the number of TV channels increases, it is becoming important to recommend TV shows that users prefer to watch. To this end, we investigate the inherent characteristics of implicit feedback given in the TV show domain, and identify the challenges for building an effective TV show recommendation. Based on the unique characteristics, we define a user's watchable interval, the most important and novel concept in understanding users' true preferences. In order to reflect this new concept into the TV show recommendation, we propose a novel framework based on collaborative filtering. Our framework is composed of (1) preference estimation based on a user's watchable interval, (2) preference prediction based on confidence exploiting watchable episodes, and (3) top-N recommendation considering TV show's staying and remaining times. Using a real-world TV show dataset, we demonstrate that our framework effectively solves the challenges and significantly outperforms other existing state-of-the-art methods.
Robust and Efficient Harmonics Denoising in Large Dataset Based on Random SVD and Soft Thresholding

The Hankel matrix of harmonic signals has the important low-rank property, based on which the principal components (or the eigenvectors) extracted from the matrix by singular value decomposition (SVD) could be applied for harmonic signal denoising. However, SVD is time-consuming, and may even fail to converge when the data matrix is too large. To overcome the computational difficulties of SVD for the big dataset, dimension reduction of the matrix is necessary, but it results in a significant reduction on signal intensities. In this paper, we proposed an efficient and robust denoising method for harmonic signals with large data. First, the Hankel matrix of the harmonic signal is constructed and randomly projected onto a lower dimensional subspace with a Gaussian matrix. Second, SVD on the matrix with reduced dimension is performed to extract essential eigenvectors, applying which a smooth signal with simplified compositions is reconstructed from the original noisy signal. Third, the threshold of signal to noise is analyzed on the smooth signal, then a soft thresholding algorithm is performed to obtain a denoised result from the original noisy signal. The simulation and experimental results have proved the robustness and effectiveness of this method.
A game theoretic analysis on block withholding attacks using the zero-determinant strategy

In Bitcoin's incentive system that supports open mining pools, block withholding attacks incur huge security threats. In this paper, we investigate the mutual attacks among pools as this determines the macroscopic utility of the whole distributed system. Existing studies on pools' interactive attacks usually employ the conventional game theory, where the strategies of the players are considered pure and equal, neglecting the existence of powerful strategies and the corresponding favorable game results. In this study, we take advantage of the Zero-Determinant (ZD) strategy to analyze the block withholding attack between any two pools, where the ZD adopter has the unilateral control on the expected payoffs of its opponent and itself. In this case, we are faced with the following questions: who can adopt the ZD strategy? individually or simultaneously? what can the ZD player achieve? In order to answer these questions, we derive the conditions under which two pools can individually or simultaneously employ the ZD strategy and demonstrate the effectiveness. To the best of our knowledge, we are the first to use the ZD strategy to analyze the block withholding attack among pools.
Influence of Blockchain in the Real Estate Sector

The two primary characteristics of real estate assets are their heterogeneity and immobility. Because of these two factors, the market for buying real estate tends to be illiquid, localized and highly segmented, with privately negotiated transactions and high transaction costs due to the involvement of a vast amount of trusted third parties (Ling &amp; Archer, 2013). Because of these characteristics, and the opportunities Blockchain offers, the buying process of commercial real estate, and the involved stakeholders could be affected majorly by this technological phenomena. The lack of transparency, high transaction costs and the need for digitalization in commercial real estate companies give Blockchain its game-changing potential. The results have shown that the pre-marketing phase and due diligence phase are most suitable for the implementation of Blockchain. This due to the characteristics of the phases, characteristics of the stakeholders and the characteristics of Blockchain. The main aspect here can be focused on the added value of Blockchain as a data sharing program which could add value creating a more safe and secure way of sharing data. What should be mentioned is that the technology is in an early stage of development and therefore not (yet) suitable for the implementation in the real estate sector. Although multiple pilots and user cases could be mentioned, the technology needs to overcome some obstacles to be a beter success in the current buying process of commercial real estate.
Forensic Analysis of Mobile Banking Apps

Over the years, the proliferation of mobile banking applications has been on the increase. Financial institutions are taking advantage of mobile technology to provide accessible, ubiquitous, user-friendly, convenient, and cost-effective services to their customers. The mobile banking applications access and process sensitive user data. As such, they are required to manage such data in a high secure manner and run in secure environment. This study conducts a forensic investigation of twelve popular Android m-banking apps in Nigeria to determine if the generated backups by the mobile OS do not save sensitive data; the application removes sensitive data from view when backgrounded; sensitive data are not held longer than necessary in the memory, with the memory cleared after use; minimum device access security policies are enforced by the app, and users are educated by the app about the type of PII processed and security best practices in using the app. Our findings revealed that while none of the apps saved sensitive data in generated backup, all except one held data of sensitive value in the memory of the test device and did not enforce any device access security policy. Also, none of the apps removed sensitive data when backgrounded. In addition to serving as a source of information for forensic investigators, we believe our study could assist mobile banking app developers in identifying aspects of the development process that need attention, which would lead to better secured apps.
Transparent Logging with Hyperledger Fabric

Today a massive amount of data is used for machine learning (ML) and artificial intelligence (AI) to derive knowledge about the world and do automated decision making. All this data contains personal and/or sensitive data, and thus privacy is becoming more and more important. The European Union's GDPR [9] recognizes the importance of transparency with respect to the handling of personal data. Hence it is desirable to devise means for increasing the transparency of personal data handling within an organization besides documenting processes of personal data handling. We employ blockchains as a basic building block to increase transparency with respect to personal data handling. A public blockchain serves as a trust anchor for a private blockchain, whereas the private blockchain serves as a log storage for the handling (i.e., access to and processing of) of personal and/or sensitive data. By giving the customer access to the private blockchain the customer's trust in the enterprise is increased.
How do we feel when a robot dies? Emotions expressed on Twitter before and after hitch

In 2014, a chatty but immobile robot called hitchBOT set out to hitchhike across Canada.It similarly made its way across Germany and the Netherlands, and had begun a trip across the USA when it was destroyed by vandals.In this work, we analyze the emotions and sentiments associated with words in tweets posted before and after hitchBOT's destruction to answer two questions: Were there any differences in the emotions expressed across the different countries visited by hitchBOT?And how did the public react to the demise of hitch-BOT?Our analyses indicate that while there were few cross-cultural differences in sentiment towards hitchBOT, there was a significant negative emotional reaction to its destruction, suggesting that people had formed an emotional connection with hitchBOT and perceived its destruction as morally wrong.We discuss potential implications of anthropomorphism and emotional attachment to robots from the perspective of robot ethics.
Sampling Techniques to Overcome Class Imbalance in a Cyberbullying Context

&lt;div data-canvas-width="705.3003252350338"&gt;The majority of datasets suffer from class imbalance where samples of a dominant class significantly outnumber the samples available for the minority class that is to be detected. Prediction and classification machine learning models work best when there are roughly equal numbers of each class type. This paper explores sampling techniques that can be used to overcome this class imbalance problem in a cyberbullying context. A newly classified cyberbullying dataset, including detailed descriptions of the criteria used in its classification, was used to examine the feasibility of applying text mining techniques, to automate the detection of cyberbullying text when the dataset shows a significant class imbalance between the positive, cyberbullying, sample and the negative, not cyberbullying, samples. In this paper, we will investigate if oversampling the minority positive class or undersampling the majority negative class affects the performance of a prediction model. A compromise solution where the positive class is partially oversampled, and the negative class is partially undersampled is also examined. Although not strictly a class imbalance solution, sampling using the most frequently observed features was also explored.&lt;/div&gt;&lt;p&gt; &lt;/p&gt;
Compressing Gradient Optimizers via Count-Sketches

Many popular first-order optimization methods (e.g., Momentum, AdaGrad, Adam) accelerate the convergence rate of deep learning models. However, these algorithms require auxiliary parameters, which cost additional memory proportional to the number of parameters in the model. The problem is becoming more severe as deep learning models continue to grow larger in order to learn from complex, large-scale datasets. Our proposed solution is to maintain a linear sketch to compress the auxiliary variables. We demonstrate that our technique has the same performance as the full-sized baseline, while using significantly less space for the auxiliary variables. Theoretically, we prove that count-sketch optimization maintains the SGD convergence rate, while gracefully reducing memory usage for large-models. On the large-scale 1-Billion Word dataset, we save 25% of the memory used during training (8.6 GB instead of 11.7 GB) by compressing the Adam optimizer in the Embedding and Softmax layers with negligible accuracy and performance loss. For an Amazon extreme classification task with over 49.5 million classes, we also reduce the training time by 38%, by increasing the mini-batch size 3.5x using our count-sketch optimizer.
Detecting Anomalies of Satellite Power Subsystem via Stage-Training Denoising Autoencoders

Satellite telemetry data contains satellite status information, and ground-monitoring personnel need to promptly detect satellite anomalies from these data. This paper takes the satellite power subsystem as an example and presents a reliable anomaly detection method. Due to the lack of abnormal data, the autoencoder is a powerful method for unsupervised anomaly detection. This study proposes a novel stage-training denoising autoencoder (ST-DAE) that trains the features, in stages. This novel method has better reconstruction capabilities in comparison to common autoencoders, sparse autoencoders, and denoising autoencoders. Meanwhile, a cluster-based anomaly threshold determination method is proposed. In this study, specific methods were designed to evaluate the autoencoder performance in three perspectives. Experiments were carried out on real satellite telemetry data, and the results showed that the proposed ST-DAE generally outperformed the autoencoders, in comparison.
Genetic programming in the twenty-first century: a bibliometric and content-based analysis from both sides of the fence

In this work we present an extensive bibliometric and content-based analysis of the scientific literature about genetic programming in the twenty-first century. Our work has two key peculiarities. First, we revealed the topics emerging from the literature based on an unsupervised analysis of the textual content of titles and abstracts. Second, we executed all of our analyses twice, once on the papers published in the venues that are typical of the evolutionary computation research community and once on those published in all the other venues. This view from "both sides of the fence" allows us to gain broader and deeper insights into the actual contributions of our community.
One format to rule them all – The emtsv pipeline for Hungarian

We present a more efficient version of the e-magyar NLP pipeline for Hungarian called emtsv.It integrates Hungarian NLP tools in a framework whose individual modules can be developed or replaced independently and allows new ones to be added.The design also allows convenient investigation and manual correction of the data flow from one module to another.The improvements we publish include effective communication between the modules and support of the use of individual modules both in the chain and standing alone.Our goals are accomplished using extended tsv (tab separated values) files, a simple, uniform, generic and selfdocumenting input/output format.Our vision is maintaining the system for a long time and making it easier for external developers to fit their own modules into the system, thus sharing existing competencies in the field of processing Hungarian, a mid-resourced language.
On a Quest for Cultural Change - Surveying Research Data Management Practices at Delft University of Technology

The Data Stewardship project is a new initiative from the Delft University of Technology (TU Delft) in the Netherlands. Its aim is to create mature working practices and policies regarding research data management across all TU Delft faculties. The novelty of this project relies on having a dedicated person, the so-called ‘Data Steward’, embedded in each faculty to approach research data management from a more discipline-specific perspective. It is within this framework that a research data management survey was carried out at the faculties that had a Data Steward in place by July 2018. The goal was to get an overview of the general data management practices, and use its results as a benchmark for the project. The total response rate was 11 to 37% depending on the faculty. Overall, the results show similar trends in all faculties, and indicate lack of awareness regarding different data management topics such as automatic data backups, data ownership, relevance of data management plans, awareness of FAIR data principles and usage of research data repositories. The results also show great interest towards data management, as more than ~80% of the respondents in each faculty claimed to be interested in data management training and wished to see the summary of survey results. Thus, the survey helped identified the topics the Data Stewardship project is currently focusing on, by carrying out awareness campaigns and providing training at both university and faculty levels.