nils-herrmann/clean_scirepeval huggingface dataset:
An Analysis of the Fraud Triangle Incentive / Pressure Fraud Risk Opportunity

The concept of a “Fraud Triangle” is introduced to the professional literature in SAS No. 99, Consideration of Fraud in a Financial Statement Audit. The Fraud Triangle consists of three conditions generally present when fraud occurs: Incentive/Pressure, Opportunity, and Attitude/ Rationalizations. Input from forensic experts, academics and others consistently show that evaluation of information about fraud is enhanced when auditors evaluate in the context of these three conditions. To examine the impact of the fraud triangle on the audit process, this paper develops an evidential network that has two major sub-networks: one to capture risk and evidential relationships for a conventional financial statement audit and the other to capture the risk and evidential relationships for fraud risk assessment. These networks use the Belief Functions approach to express the uncertainties involved in the evidence in a financial statement audit. The results of the analyses support the concept of the fraud triangle in that the three components and the relationships between those components are shown to have a substantial impact on audit risk. AN ANALYSIS OF THE FRAUD TRIANGLE INTRODUCTION The main objectives of this research are to present a baseline model of audit risk that incorporates the risk of fraud; to determine reasonable combinations of audit evidence for this model that will result in an assessment of audit risk of approximately 0.05 or less; and most importantly, to enhance this model to incorporate the requirements of a recent Exposure Draft (AICPA 2002) intended to modify SAS No. 82. The characteristics of this enhanced model then are formally evaluated. These models are based on prior research into evidential audit networks (Srivastava and Shafer 1992), into audit planning (Mock et al.1998) and into fraud risk assessment (Turner et al.2002). To achieve these objectives, this paper develops an evidential network that has two major sub-networks: one to capture risk and evidential relationships for a conventional financial statement audit and the other to capture the risk and evidential relationships for fraud risk assessment. Given that prior research (Turner et al. 2002) has investigated the basic features of this type of framework, this paper investigates more complex relationships between the main factors that seem to affect fraud and audit risk. Specifically, the following research questions are investigated in this paper:  What is the effect on Audit Risk of an interaction between Incentives and Management Integrity?  What is the effect on Audit Risk of interactions between Incentives and Management Integrity and Opportunities and Management Integrity? RQ3: What is the effect on Audit Risk of Modified Audit Procedures?
Income inequality and health status : a nursing issue

Primary Argument Nursing has a rich heritage of advocating for a healthy society established on a foundation of social justice. The future legitimacy and success of public health nursing depends on recognising and appropriately addressing the social, economic and political determinants of health in the populations served. There is an incontrovertible association between population health status, absolute income levels and income inequality. Thus, along with other social determinants of health, income differentials within populations must be a fundamental consideration when planning and delivering nursing services. Ensuring that federal and state health policy explicitly addresses this key issue remains an important challenge for the nursing profession, the public health system and the Australian community.
Chemical scalp burns secondary to the hair highlighting process: clinical and histopathologic features.

Hair highlighting procedures are very common throughout the world. While rarely reported, potential adverse events to such procedures include allergic and irritant contact dermatitis, thermal burns, and chemical burns. Herein, we report two cases of female adolescents who underwent a hair highlighting procedure at local salons and sustained a chemical burn to the scalp. The burn etiology, clinical and histologic features, the expected sequelae, and a review of the literature are described.
Adult Content in Social Live Streaming Services: Characterizing Deviant Users and Relationships

Social Live Stream Services (SLSS) exploit a new level of social interaction. One of the main challenges in these services is how to detect and prevent deviant behaviors that violate community guidelines. In this work, we focus on adult content production and consumption in two widely used SLSS, namely Live.me and Loops Live, which have millions of users producing massive amounts of video content on a daily basis. We use a pre-trained deep learning model to identify broadcasters of adult content. Our results indicate that moderation systems in place are highly ineffective in suspending the accounts of such users. We create two large datasets by crawling the social graphs of these platforms, which we analyze to identify characterizing traits of adult content producers and consumers, and discover interesting patterns of relationships among them, evident in both networks.
Ensemble of feature selection methods: A hesitant fuzzy sets approach

Recently, there has been a great attention to develop feature selection methods on the microarray high dimensional datasets. In this paper, an innovative method based on Maximum Relevancy and Minimum Redundancy (MRMR) approach by using Hesitant Fuzzy Sets (HFSs) is proposed to deal with feature subset selection; the method is called MRMR-HFS. MRMR-HFS is a novel filterbased feature selection algorithm that selects features by ensemble of ranking algorithms (as the measure of feature-class relevancy that must be maximized) and similarity measures (as the measure of feature-feature redundancy that must be minimized). The combination of ranking algorithms and similarity measures are done by using the fundamental concepts of information energies of HFSs. The proposed method has been inspired from Correlation based Feature Selection (CFS) within the sequential forward search in order to present a robust feature selection tool to solve high dimensional problems. To evaluate the effectiveness of the MRMR-HFS, several experimental results are carried out on nine well-known microarray high dimensional datasets. The obtained results are compared with those of other similar state-of-the-art algorithms including Correlation-based Feature Selection (CFS), Fast Correlation-based Filter (FCBF), Intract (INT), and Maximum Relevancy Minimum Redundancy (MRMR). The outcomes of comparison carried out via some non-parametric statistical tests confirm that the MRMR-HFS is effective for feature subset selection in high dimensional datasets in terms of accuracy, sensitivity, specificity, G-mean, and number of selected features.
Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention

Currently, there are increasing interests in text-to-speech (TTS) synthesis to use sequence-to-sequence models with attention. These models are end-to-end meaning that they learn both co-articulation and duration properties directly from text and speech. Since these models are entirely data-driven, they need large amounts of data to generate synthetic speech with good quality. However, in challenging speaking styles, such as Lombard speech, it is difficult to record sufficiently large speech corpora. Therefore, in this study we propose a transfer learning method to adapt a sequence-to-sequence based TTS system of normal speaking style to Lombard style. Moreover, we experiment with a WaveNet vocoder in synthesis of Lombard speech. We conducted subjective evaluations to assess the performance of the adapted TTS systems. The subjective evaluation results indicated that an adaptation system with the WaveNet vocoder clearly outperformed the conventional deep neural network based TTS system in synthesis of Lombard speech.
A Stateful Intrusion Detection System for World-Wide Web Servers

Web servers are ubiquitous, remotely accessible, and often misconfigured. In addition, custom web-based applications may introduce vulnerabilities that are overlooked even by the most security-conscious server administrators. Consequently, web servers are a popular target for hackers. To mitigate the security exposure associated with web servers, intrusion detection systems are deployed to analyze and screen incoming requests. The goal is to perform early detection of malicious activity and possibly prevent more serious damage to the protected site. Even though intrusion detection is critical for the security of web servers, the intrusion detection systems available today only perform very simple analyses and are often vulnerable to simple evasion techniques. In addition, most systems do not provide sophisticated attack languages that allow a system administrator to specify custom, complex attack scenarios to be detected. This paper presents WebSTAT, an intrusion detection system that analyzes web requests looking for evidence of malicious behavior. The system is novel in several ways. First of all, it provides a sophisticated language to describe multistep attacks in terms of states and transitions. In addition, the modular nature of the system supports the integrated analysis of network traffic sent to the server host, operating system-level audit data produced by the server host, and the access logs produced by the web server. By correlating different streams of events, it is possible to achieve more effective detection of web-based attacks.
Effects of biochar and greenwaste compost amendments on mobility, bioavailability and toxicity of inorganic and organic contaminants in a multi-element polluted soil.

Applying amendments to multi-element contaminated soils can have contradictory effects on the mobility, bioavailability and toxicity of specific elements, depending on the amendment. Trace elements and PAHs were monitored in a contaminated soil amended with biochar and greenwaste compost over 60 days field exposure, after which phytotoxicity was assessed by a simple bio-indicator test. Copper and As concentrations in soil pore water increased more than 30 fold after adding both amendments, associated with significant increases in dissolved organic carbon and pH, whereas Zn and Cd significantly decreased. Biochar was most effective, resulting in a 10 fold decrease of Cd in pore water and a resultant reduction in phytotoxicity. Concentrations of PAHs were also reduced by biochar, with greater than 50% decreases of the heavier, more toxicologically relevant PAHs. The results highlight the potential of biochar for contaminated land remediation.
Hybrid thoracoscopic surgical and transvenous catheter ablation of atrial fibrillation.

The purpose of this study was to evaluate the feasibility, safety, and clinical outcomes up to 1 year in patients undergoing combined simultaneous thoracoscopic surgical and transvenous catheter atrial fibrillation (AF) ablation.



The combination of the transvenous endocardial approach with the thoracoscopic epicardial approach in a single AF ablation procedure overcomes the limitations of both techniques and should result in better outcomes.



A cohort of 26 consecutive patients with AF who underwent hybrid thoracoscopic surgical and transvenous catheter ablation were followed, with follow-up of up to 1 year.



Twenty-six patients (42% with persistent AF) underwent successful hybrid procedures. There were no complications. The mean follow-up period was 470 ± 154 days. In 23% of the patients, the epicardial lesions were not transmural, and endocardial touch-up was necessary. One-year success, defined according to the Heart Rhythm Society, European Heart Rhythm Association, and European Cardiac Arrhythmia Society consensus statement for the catheter and surgical ablation of AF, was 93% for patients with paroxysmal AF and 90% for patients with persistent AF. Two patients underwent catheter ablation for recurrent AF or left atrial flutter after the hybrid procedure.



A combined transvenous endocardial and thoracoscopic epicardial ablation procedure for AF is feasible and safe, with a single-procedure success rate of 83% at 1 year.
Exploring big graph computing - An empirical study from architectural perspective

Graph computing is widely applied in a large number of big data applications. Despite its importance, high performance graph computing remains a challenge, especially for large-scale graphs. In this paper, by analyzing from the architectural perspective, we study computational behaviors of graph computing in real-world use cases. We benchmark a set of representative graph algorithms implemented on a unified framework and conduct experiments to analyze comprehensive performance characteristics. In the characterization, we observed multiple insights, including irregular memory patterns, significant diverse behavior across different computations, highly data dependent behaviors, etc., using large-scale synthetic and real-world graphs. To the best of our knowledge, this is the first comprehensive architectural study on the full-scope of graph computing. It can improve our understanding on graph computing and help high performance computing research for graph-based big data applications.
A Critique of the Philosophical Underpinnings of Mainstream Social Science Research

The progress of the social science disciplines depends on conducting relevant research. However, research methodology adopted and choices made during the course of the research project are underpinned by varying ontological, epistemological and axiological positions that may be known or unknown to the researcher. This paper sought to critically explore the philosophical underpinnings of the social science research. It was suggested that a “multiversal” ontological position, positivist-hermeneutic epistemological position and value-laden axiological position should be adopted for social science research by non-western scholars as alternative to the dominant naïve realist, positivist, and value-free orientation. Against the backdrop of producing context-relevant knowledge, non-western scholars are encouraged to re-examine their philosophical positions in the conduct of social science research.
Quantifying supply chain disruption risk using Monte Carlo and discrete-event simulation

We present a model constructed for a large consumer products company to assess their vulnerability to disruption risk and quantify its impact on customer service. Risk profiles for the locations and connections in the supply chain are developed using Monte Carlo simulation, and the flow of material and network interactions are modeled using discrete-event simulation. Capturing both the risk profiles and material flow with simulation allows for a clear view of the impact of disruptions on the system. We also model various strategies for coping with the risk in the system in order to maintain product availability to the customer. We discuss the dynamic nature of risk in the network and the importance of proactive planning to mitigate and recover from disruptions.
Light-Triggered Soft Artificial Muscles: Molecular-Level Amplification of Actuation Control Signals

The principle of control signal amplification is found in all actuation systems, from engineered devices through to the operation of biological muscles. However, current engineering approaches require the use of hard and bulky external switches or valves, incompatible with both the properties of emerging soft artificial muscle technology and those of the bioinspired robotic systems they enable. To address this deficiency a biomimetic molecular-level approach is developed that employs light, with its excellent spatial and temporal control properties, to actuate soft, pH-responsive hydrogel artificial muscles. Although this actuation is triggered by light, it is largely powered by the resulting excitation and runaway chemical reaction of a light-sensitive acid autocatalytic solution in which the actuator is immersed. This process produces actuation strains of up to 45% and a three-fold chemical amplification of the controlling light-trigger, realising a new strategy for the creation of highly functional soft actuating systems.
Management and Education of the Risk of Insider Threat ( MERIT ) : System Dynamics Modeling of Computer System Sabotage

The Insider Threat Study, conducted by the U.S. Secret Service and Carnegie Mellon University’s Software Engineering Institute CERT Program, analyzed insider cyber crimes across U.S. critical infrastructure sectors. The study indicates that management decisions related to organizational and employee performance sometimes yield unintended consequences magnifying risk of insider attack. Lack of tools for understanding insider threat, analyzing risk mitigation alternatives, and communicating results exacerbates the problem. The goal of Carnegie Mellon University’s MERIT (Management and Education of the Risk of Insider Threat) project is to develop such tools. MERIT uses system dynamics to model and analyze insider threats and produce interactive learning environments. These tools can be used by policy makers, security officers, information technology, human resources, and management to understand the problem and assess risk from insiders based on simulations of policies, cultural, technical, and procedural factors. This paper describes the MERIT insider threat model and simulation results.
Extracting Chinese question-answer pairs from online forums

Extracting question-answer pairs from online forums is a meaningful work due to the huge amount of valuable user generated resource contained in forums. In this paper we consider the problem of extracting Chinese question-answer pairs for the first time. We present a strategy to detect Chinese questions and their answers. We propose a sequential rule based method to find questions in a forum thread, then we adopt nontextual features based on forum structure to improve the performance of answer detecting in the same thread. Experimental results show that our techniques are very effective.
Hierarchical Place Trees: A Portable Abstraction for Task Parallelism and Data Movement

Modern computer systems feature multiple homogeneous or heterogeneous computing units with deep memory hierarchies, and expect a high degree of thread-level parallelism from the software. Exploitation of data locality is critical to achieving scalable parallelism, but adds a significant dimension of complexity to performance optimization of parallel programs. This is especially true for programming models where locality is implicit and opaque to programmers. In this paper, we introduce the hierarchical place tree (HPT) model as a portable abstraction for task parallelism and data movement. The HPT model supports co-allocation of data and computation at multiple levels of a memory hierarchy. It can be viewed as a generalization of concepts from the Sequoia and X10 programming models, resulting in capabilities that are not supported by either. Compared to Sequoia, HPT supports three kinds of data movement in a memory hierarchy rather than just explicit data transfer between adjacent levels, as well as dynamic task scheduling rather than static task assignment. Compared to X10, HPT provides a hierarchical notion of places for both computation and data mapping. We describe our work-in-progress on implementing the HPT model in the Habanero-Java (HJ) compiler and runtime system. Preliminary results on general-purpose multicore processors and GPU accelerators indicate that the HPT model can be a promising portable abstraction for future multicore processors.
Bacteria and host interactions in the gut epithelial barrier.

The gut mucosa acts as a barrier against microbial invaders, whereas resident commensal and foreign invading bacteria interact intimately with the gut epithelium and influence the host cellular and immune systems. The epithelial barrier serves as an infectious foothold for many bacterial pathogens and as an entry port for pathogens to disseminate into deeper tissues. Enteric bacterial pathogens can efficiently infect the gut mucosa using highly sophisticated virulence mechanisms that allow bacteria to circumvent the defense barriers in the gut. We provide an overview of the components of the mucosal barrier and discuss the bacterial stratagems that circumvent these barriers with particular emphasis on the roles of bacterial effector proteins.
Parsing reward

Advances in neurobiology permit neuroscientists to manipulate specific brain molecules, neurons and systems. This has lead to major advances in the neuroscience of reward. Here, it is argued that further advances will require equal sophistication in parsing reward into its specific psychological components: (1) learning (including explicit and implicit knowledge produced by associative conditioning and cognitive processes); (2) affect or emotion (implicit 'liking' and conscious pleasure) and (3) motivation (implicit incentive salience 'wanting' and cognitive incentive goals). The challenge is to identify how different brain circuits mediate different psychological components of reward, and how these components interact.
Hanzi, Concept and Computation: A Preliminary Survey of Chinese Characters as a Knowledge Resource in NLP

This thesis deals with Chinese characters (Hanzi): their key characteristics and how they could be used as a kind of knowledge resource in the (Chinese) NLP. Part 1 deals with basic issues. In Chapter 1, the motivation and the reasons for reconsidering the writing system will be presented, and a short introduction to Chinese and its writing system will be given in Chapter 2. Part 2 provides a critical review of the current, ongoing debate about Chinese characters. Chapter 3 outlines some important linguistic insights from the vantage point of indigenous scriptological and Western linguistic traditions, as well as a new theoretical framework in contemporary studies of Chinese characters. The focus of Chapter 4 concerns the search for appropriate mathematical descriptions with regard to the systematic knowledge information hidden in characters. The subject matter of mathematical formalization of the shape structure of Chinese characters is depicted as well. Part 3 illustrates the representation issues. Chapter 5 addresses the design and construction of the HanziNet, an enriched conceptual network of Chinese characters. Topics that are covered in this chapter include the ideas, architecture, methods and ontology design. In Part 4, a case study based on the above mentioned ideas will be launched. Chapter 6 presents an experiment exploring the character-triggered semantic class of Chinese unknown words. Finally, Chapter 7 summarizes the major findings of this thesis. Next, it depicts some potential avenues in the future, and assesses the theoretical implications of these findings for computational linguistic theory.
Tools for teaching introductory programming: what works?

1.  In the past decade educators have developed a myriad of tools to help novices learn to program. Different tools emerge as new features or combinations of features are employed. In this panel we consider the features of recent tools that have garnered significant interest in the computer science education community. These including narrative tools which support programming to tell a story (e.g., Alice [6], Jeroo ), visual programming tools which support the construction of programs through a drag-and-drop interface (e.g., JPie , Alice [6], Karel Universe), flow-model tools (e.g., Raptor , Iconic Programmer , VisualLogic) which construct programs through connecting program elements to represent order of computation, specialized output realizations (e.g., Lego Mindstorms , JES [7]) that provide execution feedback in nontextual ways, like multimedia or kinesthetic robotics, and tiered language tools (e.g., ProfessorJ , RoboLab) in which novices can use more sophisticated versions of a language as their expertise develops.