Reviewerly/exHarmony huggingface dataset:
The effects of class rarity on the evaluation of supervised healthcare fraud detection models

The United States healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions (documented and readily available) than non-fraudulent. The ability to successfully detect fraudulent activities in healthcare, given such discrepancies, can garner up to $350 billion in recovered monetary losses. In machine learning, when one class has a substantially larger number of instances (majority) compared to the other (minority), this is known as class imbalance. In this paper, we focus specifically on Medicare, utilizing three 'Big Data' Medicare claims datasets with real-world fraudulent physicians. We create a training and test dataset for all three Medicare parts, both separately and combined, to assess fraud detection performance. To emulate class rarity, which indicates particularly severe levels of class imbalance, we generate additional datasets, by removing fraud instances, to determine the effects of rarity on fraud detection performance. Before a machine learning model can be distributed for real-world use, a performance evaluation is necessary to determine the best configuration (e.g. learner, class sampling ratio) and whether the associated error rates are low, indicating good detection rates. With our research, we demonstrate the effects of severe class imbalance and rarity using a training and testing (Train_Test) evaluation method via a hold-out set, and provide our recommendations based on the supervised machine learning results. Additionally, we repeat the same experiments using Cross-Validation, and determine it is a viable substitute for Medicare fraud detection. For machine learning with the severe class imbalance datasets, we found that, as expected, fraud detection performance decreased as the fraudulent instances became more rare. We apply Random Undersampling to both Train_Test and Cross-Validation, for all original and generated datasets, in order to assess potential improvements in fraud detection by reducing the adverse effects of class imbalance and rarity. Overall, our results indicate that the Train_Test method significantly outperforms Cross-Validation.
Lightweight Assessment of Test-Case Effectiveness Using Source-Code-Quality Indicators

Test cases are crucial to help developers preventing the introduction of software faults. Unfortunately, not all the tests are properly designed or can effectively capture faults in production code. Some measures have been defined to assess test-case effectiveness: the most relevant one is the mutation score, which highlights the quality of a test by generating the so-called mutants, i.e., variations of the production code that make it faulty and that the test is supposed to identify. However, previous studies revealed that mutation analysis is extremely costly and hard to use in practice. The approaches proposed by researchers so far have not been able to provide practical gains in terms of mutation testing efficiency. This leaves the problem of efficiently assessing test-case effectiveness as still open. In this paper, we investigate a novel, orthogonal, and lightweight methodology to assess test-case effectiveness: in particular, we study the feasibility to exploit production and test-code-quality indicators to estimate the mutation score of a test case. We first select a set of 67 factors and study their relation with test-case effectiveness. Then, we devise a mutation score estimation model exploiting such factors and investigate its performance as well as its most relevant features. The key results of the study reveal that our estimation model only based on static features has 86 percent of both F-Measure and AUC-ROC. This means that we can estimate the test-case effectiveness, using source-code-quality indicators, with high accuracy and without executing the tests. As a consequence, we can provide a practical approach that is beyond the typical limitations of current mutation testing techniques.
Characterizing Activity on the Deep and Dark Web

The deep and darkweb (d2web) refers to limited access web sites that require registration, authentication, or more complex encryption protocols to access them. These web sites serve as hubs for a variety of illicit activities: to trade drugs, stolen user credentials, hacking tools, and to coordinate attacks and manipulation campaigns. Despite its importance to cyber crime, the d2web has not been systematically investigated. In this paper, we study a large corpus of messages posted to 80 d2web forums over a period of more than a year. We identify topics of discussion using LDA and use a non-parametric HMM to model the evolution of topics across forums. Then, we examine the dynamic patterns of discussion and identify forums with similar patterns. We show that our approach surfaces hidden similarities across different forums and can help identify anomalous events in this rich, heterogeneous data.
Exploring the Opportunities of the Emojis in Brand Communication: The Case of the Beer Industry

This study shows that emojis are a significant element in brand communications, which still requires attention from researchers. Specifically, it describes the use of emojis by the four companies with the largest audiences on Twitter in the Spanish beer industry. Through a correspondence analysis, we found that those emojis were not a mere occasional resource within a message but rather a differentiating element for brand positioning. Likewise, we analyzed the existing relationship between the way in which they were used and the engagement generated. In this regard, we concluded that communications using emojis aimed at customer service and care, as well as those used in positive contexts and for emphasis, were the ones related to higher user engagement. We discuss herein the practical implications of these findings for businesses.
Integrated platforms for blockchain enablement

The Internet of Things (IoT) is experiencing an exponential growth in a wide variety of use-cases in multiple application domains, such as healthcare, agriculture, smart cities, smart homes, supply chain, and so on. To harness its full potential, it must be based upon a resilient network architecture with strong support for security, privacy, and trust. Most of these issues still remain to be addressed carefully for the IoT systems. Blockchain technology has recently emerged as a breakthrough technology with the potential to deliver some valuable properties such as resiliency, support for integrity, anonymity, decentralization, and autonomous control. A number of blockchain platforms are proposed that may be suitable for different use-cases including IoT applications. In such, the possibility to integrate the IoT and blockchain technology is seen as a potential solution to address some crucial issues. However, to achieve this, there must be a clear understanding of the requirements of different IoT applications and the suitability of a blockchain platform for a particular application satisfying its underlying requirements. This chapter aims to achieve this goal by describing an evaluation framework which can be utilized to select a suitable blockchain platform for a given IoT application.
A Privacy-Preserving E-Commerce System Based on the Blockchain Technology

With the increasing popularity of online shopping, privacy concerns in E-commerce are attracting more and more attention. Existing E-commerce models are trapped in a dilemma between the proof of ownership and privacy protection. To address this issue, in this paper we design a privacy-preserving business protocol by employing private smart contracts in the negotiation phase. The protocol allows counterparties make deals without the disclosure of private information such as identities, addresses, and phone numbers. Moreover, we employ the zero-knowledge proof to guarantee the ownership. To understand the feasibility for implementing the proposed model, we also conduct extensive experiments to evaluate the performance of existing blockchain development platforms, Ethereum Quorum and SERO.
Efficient Scheduling of Weighted Coflows in Data Centers

Traditional network resource management mechanisms are mainly flow or packet based.Recently, coflow has been proposed as a new abstraction to capture the communication patterns in a rich set of data parallel applications in data centers.Coflows effectively model the application-level semantics of network resource usage, so high-level optimization goals, such as reducing the transfer latency of applications, can be better achieved by taking coflows as the basic elements in network resource allocation or scheduling.Although efficient coflow scheduling methods have been studied, in this paper, we advocate to schedule weighted coflows as a further step in this direction, where weights are used to express the importances or priorities of different coflows or their corresponding applications.We propose the Weighted Coflow Completion Time (WCCT) minimization problem and a ð2 À 2 nþ1 Þ-approximate optimal offline algorithm, where n is the concurrent number of coflows.We then design an information-agnostic online algorithm named IAOA to dynamically schedule coflows according to their weights and the instantaneous network condition.We also design and implement a coflow scheduling system named FlyTransfer, which can use the online algorithm as its scheduling method.We test the performance of FlyTransfer by trace-driven simulations as well as real deployment in openstack.Our evaluation results show that, compared to the latest information-agnostic coflow scheduling algorithms, FlyTransfer can reduce more than 40 percent of the WCCT, and more than 30 percent of the completion time for coflows with above-the-average level of importance.It even outperforms the most efficient clairvoyant coflow scheduling method by reducing around 30 percent WCCT, and 25-30 percent of the completion time for coflows with above-the-average importance, respectively.
Predicting bioavailability change of complex chemical mixtures in contaminated soils using visible and near-infrared spectroscopy and random forest regression

Abstract A number of studies have shown that visible and near infrared spectroscopy (VIS-NIRS) offers a rapid on-site measurement tool for the determination of total contaminant concentration of petroleum hydrocarbons compounds (PHC), heavy metals and metalloids (HM) in soil. However none of them have yet assessed the feasibility of using VIS-NIRS coupled to random forest (RF) regression for determining both the total and bioavailable concentrations of complex chemical mixtures. Results showed that the predictions of the total concentrations of polycyclic aromatic hydrocarbons (PAH), PHC, and alkanes (ALK) were very good, good and fair, and in contrast, the predictions of the bioavailable concentrations of the PAH and PHC were only fair, and poor for ALK. A large number of trace elements, mainly lead (Pb), aluminium (Al), nickel (Ni), chromium (Cr), cadmium (Cd), iron (Fe) and zinc (Zn) were predicted with very good or good accuracy. The prediction results of the total HMs were also better than those of the bioavailable concentrations. Overall, the results demonstrate that VIS-NIR DRS coupled to RF is a promising rapid measurement tool to inform both the distribution and bioavailability of complex chemical mixtures without the need of collecting soil samples and lengthy extraction for further analysis.
Publishing Social Network Graph Eigenspectrum With Privacy Guarantees

Online social networks (OSNs) often refuse to publish their social network graphs due to privacy concerns. Recently, differential privacy has become the widely accepted criteria for privacy preserving data publishing. Although some work has been done on publishing matrices with differential privacy, they are computationally unpractical as they are not designed to handle large matrices such as adjacency matrices of OSN graphs. In this paper, we propose a random matrix approach to OSN data publishing, which achieves storage and computational efficiency by reducing dimensions of adjacency matrices and achieves differential privacy by adding a small amount of noise. Our key idea is to first project each row of an adjacency matrix into a low-dimensional space using random projection, and then perturb the projected matrix with random noise, and finally publish the perturbed and projected matrix. In this paper, we first prove that random projection plus random perturbation preserve differential privacy, and also that the random noise required to achieve differential privacy is small. We validate the proposed approach and evaluate the utility of the published data for three different applications, namely node clustering, node ranking, and node classification, using publicly available OSN graphs of Facebook, LiveJournal, and Pokec.
WindowWall

As architects usually decide on the shape and look of windows during the design of buildings, opportunities for interactive windows have not been systematically explored yet. In this work, we extend the vision of sustainable and comfortable adaptive buildings using interactive smart windows. We systematically explore the design space of interactive windows to chart requirements, constraints, and challenges. To that end, we built proof-of-concept prototypes of smart windows with fine-grained control of transparency. In two studies, we explored user attitudes towards interactive windows and elicited control methods. We found that users understand and see potential for interactive windows at home. We provide specific usage contexts and specify interactions that may facilitate domestic applications. Our work illustrates the concept of interactive smart windows and provides insights regarding their design, development, and user controls for adaptive walls. We identify design dimensions and challenges to stimulate further development in the domain of adaptive buildings.
SCC: Storage Compression Consensus for Blockchain in Lightweight IoT Network

A centralized IoT network has a problem of data centralization because data is stored in a center. A blockchain is emerging as a solution to solve the problem in the IoT network by storing blocks to each device. However, lightweight IoT devices cannot store all of the blockchain due to their low storage capacity. We propose storage compression consensus (SCC) algorithm which compresses a blockchain in each device to ensure the storage capacity. When a lightweight device does not have enough storage space, the device processes the SCC to compress the blockchain. We implemented simulations to compare between the SCC and the existing consensus algorithm. As a result, the proposed SCC reduced the storage capacity of stored blockchain by 63% compared to the existing algorithm.
An Empirical Study of Learning to Rank Techniques for Effort-Aware Defect Prediction

Effort-Aware Defect Prediction (EADP) ranks software modules based on the possibility of these modules being defective, their predicted number of defects, or defect density by using learning to rank algorithms. Prior empirical studies compared a few learning to rank algorithms considering small number of datasets, evaluating with inappropriate or one type of performance measure, and non-robust statistical test techniques. To address these concerns and investigate the impact of learning to rank algorithms on the performance of EADP models, we examine the practical effects of 23 learning to rank algorithms on 41 available defect datasets from the PROMISE repository using a module-based effort-aware performance measure (FPA) and a source lines of code (SLOC) based effort-aware performance measure (Norm(P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> ). In addition, we compare the performance of these algorithms when they are trained on a more relevant feature subset selected by the Information Gain feature selection method. In terms of FPA and Norm(P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> ), statistically significant differences are observed among these algorithms with BRR (Bayesian Ridge Regression) performing best in terms of FPA, and BRR and LTR (Learning-to-Rank) performing best in terms of Norm (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> ). When these algorithms are trained on a more relevant feature subset selected by Information Gain, LTR and BRR still perform best with significant differences in terms of FPA and Norm(P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> ). Therefore, we recommend BRR and LTR for building the EADP model in order to find more defects by inspecting a certain number of modules or lines of codes.
Understanding the gap between perceived threats to and preparedness for cybersecurity

This study aims to investigate the influences on perceived threat to and preparedness for cybersecurity, as well as what causes the gap between these two perceptions regarding public infrastructure and business systems in the United States. To that end, the study considers Cybersecurity Survey data from the Pew Research Center. Using ordered logistic regression analysis, the significant determinants of perceived threat and preparedness are determined. Personal experience in and awareness of cybersecurity breaches increased the level of perceived cyber threats, but reduced that of perceived preparedness. In contrast, confidence in organizational cybersecurity capacity, social trust, and liberalism exhibited opposite trends regarding these two outcomes. By employing multinomial logistic regression analysis, the study investigates the unexplored relationships of various theoretical determinants with the gap between perceived threat and preparedness. The analysis results show that the effects of these determinants differ with the type of gap. The determinants for perceived overperformance (good preparedness relative to low threat) and perceived underperformance (poor preparedness relative to high threat) are significantly different from those for perceived fair performance (matching levels of threat and preparedness).
Developer Reading Behavior While Summarizing Java Methods: Size and Context Matters

An eye-tracking study of 18 developers reading and summarizing Java methods is presented. The developers provide a written summary for methods assigned to them. In total, 63 methods are used from five different systems. Previous studies on this topic use only short methods presented in isolation usually as images. In contrast, this work presents the study in the Eclipse IDE allowing access to all the source code in the system. The developer can navigate via scrolling and switching files while writing the summary. New eye-tracking infrastructure allows for this improvement in the study environment. Data collected includes eye gazes on source code, written summaries, and time to complete each summary. Unlike prior work that concluded developers focus on the signature the most, these results indicate that they tend to focus on the method body more than the signature. Moreover, both experts and novices tend to revisit control flow terms rather than reading them for a long period. They also spend a significant amount of gaze time and have higher gaze visits when they read call terms. Experts tend to revisit the body of the method significantly more frequently than its signature as the size of the method increases. Moreover, experts tend to write their summaries from source code lines that they read the most.
Early virtual reality adopters in Spain: sociodemographic profile and interest in the use of virtual reality as a learning tool

This study describes the social and demographic profile of the first generation of users of marketed virtual reality (VR) viewers in Spain and, subsequently, it assesses the interest in its use as a learning tool. For that purpose, an online questionnaire created ad hoc was administered to a sample of 117 participants. The relationship between twelve variables was analysed comparing means through the Snedecor's F distribution and the contingency tables through the Chi-squared test and Somers' D. Among other issues, it was concluded that the virtual reality user profile at present corresponds to a person older than 36, mainly men, with higher education and having acquired their viewer no longer than one year ago. Concerning the interests of virtual reality users as a learning tool, only a few of them currently use virtual reality for this aim, but they mainly show an interest in using the virtual reality as a learning method and they feel optimism regarding the future use of this technology as a learning tool. However, this is not the case among users of video game consoles (PSVR), who are mainly men not interested in their use as a learning tool at present. Finally, it can be stated that current use as a learning tool among teachers and students is occasional and preferably via smartphones.
A modified particle swarm optimization algorithm based on velocity updating mechanism

Due to less parameters and simple operations in PSO, PSO has attracted the attention of many researchers. However, it amy fall into local optimum and the search precision is not high. Therefore, this paper introduces an improved PSO (CNPSO). There are two new formulas: (1) when the individual is not the best particle, the gbest is replaced by a particle, which is selected from a set based on the probability calculation. Such mechanism can help the algorithm to escape local position. (2) when the individual is the best particle, it is combined with a randomly selected particle to generate a convex combination, after that opposite learning is adopted to get a reverse solution. This operation can maintain the diversity of population. Finally, CNPSO is compared with several algorithms in three experiments, and used to optimize the spring design problem. The results indicate CNPSO has good performance and high search precision.
A Preliminary Study of Machine Learning Workload Prediction Techniques for Cloud Applications

Cloud computing has transformed the means of computing in recent years with several benefits over traditional systems, like scalability and high availability. However, there are still some opportunities, especially in the area of resource provisioning and scaling [13]. Since workload may fluctuate a lot in certain environments, over-provisioning is a common practice to avoid abrupt Quality of Service (QoS) drops that may result in Service Level Agreement (SLA) violations, but at the price of an increase in provisioning costs and energy consumption. Workload prediction is one of the strategies by which efficiency and operational cost of a cloud can be improved [13]. Knowing demand in advance allows the previous allocation of sufficient resources to maintain QoS and avoid SLA violations [1]. This paper presents the advantages and disadvantages of three workload prediction techniques when applied in the context of cloud computing. Our preliminary results compare ARIMA, MLP, and GRU under different cloud configurations to help administrators choose the more appropriate and efficient predictive model for their specific problem.