nils-herrmann/clean_scirepeval huggingface dataset:
Accounting Information System Risk Assessment Algorithm Based on Analytic Hierarchy Process

So far, there is little research on accounting information system risk assessment in our country. In order to provide the basis to meet the security needs of the accounting information system and reduce the risk of accounting information system security, reduce financial loses and improve the work efficiency, a model of enterprise accounting information system risk assessment method based on Analytic Hierarchy Process is proposed. The analytic hierarchy process model is applied to one corporate accounting information system for risk assessment. It can be concluded that the proposed method get better result of risk assessment, and have strong operability and effectiveness of risk assessment in the accounting information system for enterprise.
Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain

Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Although polypectomy at early stage reduces CRC incidence, 90% of the polyps are small and diminutive, where removal of them poses risks to patients that may outweigh the benefits. Correctly detecting and predicting polyp type during colonoscopy allows endoscopists to resect and discard the tissue without submitting it for histology, saving time, and costs. Nevertheless, human visual observation of early stage polyps varies. Therefore, this paper aims at developing a fully automatic algorithm to detect and classify hyperplastic and adenomatous colorectal polyps. Adenomatous polyps should be removed, whereas distal diminutive hyperplastic polyps are considered clinically insignificant and may be left in situ . A novel transfer learning application is proposed utilizing features learned from big nonmedical datasets with 1.4–2.5 million images using deep convolutional neural network. The endoscopic images we collected for experiment were taken under random lighting conditions, zooming and optical magnification, including 1104 endoscopic nonpolyp images taken under both white-light and narrowband imaging (NBI) endoscopy and 826 NBI endoscopic polyp images, of which 263 images were hyperplasia and 563 were adenoma as confirmed by histology. The proposed method identified polyp images from nonpolyp images in the beginning followed by predicting the polyp histology. When compared with visual inspection by endoscopists, the results of this study show that the proposed method has similar precision (87.3% versus 86.4%) but a higher recall rate (87.6% versus 77.0%) and a higher accuracy (85.9% versus 74.3%). In conclusion, automatic algorithms can assist endoscopists in identifying polyps that are adenomatous but have been incorrectly judged as hyperplasia and, therefore, enable timely resection of these polyps at an early stage before they develop into invasive cancer.
Simplifying Lexical Simplification: Do We Need Simplified Corpora?

Simplification of lexically complex texts, by replacing complex words with their simpler synonyms, helps non-native speakers, children, and language-impaired people understand text better. Recent lexical simplification methods rely on manually simplified corpora, which are expensive and time-consuming to build. We present an unsupervised approach to lexical simplification that makes use of the most recent word vector representations and requires only regular corpora. Results of both automated and human evaluation show that our simple method is as effective as systems that rely on simplified corpora.
Adaptation and coaching of periodic motion primitives through physical and visual interaction

In this paper we propose and evaluate a control system to (1) learn and (2) adapt robot motion for continuous non-rigid contact with the environment. We present the approach in the context of wiping surfaces with robots. Our approach is based on learning by demonstration. First an initial periodicmotion, covering the essence of the wiping task, is transferred from a human to a robot. The system extracts and learns one period of motion. Once the user/demonstrator is content with the motion, the robot seeks and establishes contactwith a given surface,maintaining a predefined force of contact through force feedback. The shape of the surface is encoded for the complete period ofmotion, but the robot can adapt to a different surface, perturbations or obstacles. The novelty stems from the fact that the feedforward component is learned and encoded in a dynamic movement primitive. By using the feedforward component, the feedback component is greatly reduced if not completely canceled. Finally, if the user is not satisfied with the periodic pattern, he/she can change parts of motion through predefined gestures or through physical contact in a manner of a tutor or a coach. The complete system thus allows not only a transfer ofmotion, but a transfer ofmotionwithmatching correspondences, i.e. wiping motion is constrained to maintain physical contact with the surface to be wiped. The interface for both learning and adaptation is simple and intuitive and allows for fast and reliable knowledge transfer to the robot. Simulated and real world results in the application domain of wiping a surface are presented on three different robotic platforms. Results of the three robotic platforms, namely a 7 degree-of-freedom Kuka LWR-4 robot, the ARMAR-IIIa humanoid platform and the Sarcos CB-i humanoid robot, depict different methods of adaptation to the environment and coaching.  E-mail addresses: andrej.gams@ijs.si (A. Gams), tadej.petric@ijs.si (T. Petri£), martin.do@kit.edu (M. Do), bojan.nemec@ijs.si (B. Nemec), xmorimo@atr.jp (J. Morimoto), asfour@kit.edu (T. Asfour), ales.ude@ijs.si (A. Ude).
Hybrid Recommendation Approaches: Collaborative Filtering via Valuable Content Information

Collaborative filtering (CF) method has been successfully used in recommender systems to support product recommendation, but it has several limitations. This work uses customer demands derived from the frequent purchased products in each industry as valuable content information. Accordingly, this work explores two hybrid approaches each of which combines CF and customer demands to improve quality of recommendation. Valuable content information is also included as a factor in making recommendations for re-ranking candidate products. The experimental results indicate that the quality of recommendation obtained by the combined methods is promising.
A Fourth Order Accurate Discretization for the Laplace and Heat Equations on Arbitrary Domains , with Applications to the Stefan Problem ∗

In this paper, we first describe a fourth order accurate finite difference discretization for both the Laplace equation and the heat equation with Dirichlet boundary conditions on irregular domains. In the case of the heat equation we use an implicit time discretization to avoid the stringent time step restrictions associated with explicit schemes. We then turn our focus to the Stefan problem and construct a third order accurate method that also includes an implicit time discretization. Multidimensional computational results are presented to demonstrate the order accuracy of these numerical methods. ∗Research supported in part by an ONR YIP and PECASE award (N00014-01-1-0620), a Packard Foundation Fellowship, a Sloan Research Fellowship, ONR N00014-03-1-0071, ONR N00014-02-1-0720, NSF DMS-0106694 and NSF ACI-0323866. In addition, the first author was supported in part by an NSF postdoctoral fellowship (DMS-0102029). †Mathematics Department & Computer Science Department, Stanford University, Stanford, CA 94305. ‡Computer Science Department, Stanford University, Stanford, CA 94305.
Performance analysis of mesh routing protocols for UAV swarming applications

Unmanned Aerial Vehicles (UAVs) are an emerging technology offering new opportunities for innovative applications and efficient overall process management in the areas of public security, cellular networks and surveying. A key factor for the optimizations yielded by this technology is an advanced mesh network design for fast and reliable information sharing between UAVs. In this paper, we analyze the performance of four available mesh routing protocol implementations (open80211s, BATMAN, BATMAN Advanced and OLSR) in the context of swarming applications for UAVs. The protocols are analyzed by means of goodput in one static and one mobile scenario using the same embedded hardware platform installed at UAVs in current research projects. Our results show that layer-2 protocols suit better for mobile applications in comparison to layer-3. On the other hand, they often cause routing flippings, which are unwanted route changes, in static scenarios imposing a small performance decrease. Hence, given the aforementioned routing protocols, we recommend to currently use open80211s or batman-advanced to establish a reliable multi-hop mesh network for swarming applications.
Transmit/receive (T/R) modules architectures for dual-polarized weather phased array radars

A discussion of dual-polarized T/R module architectures and technologies for active phased array radar (PAR) for remote sensing application is presented in this paper. Three types of T/R modules developed for weather phased array radar show the evolution and trend of T/R module technology for civil applications. Factors influencing system performance are discussed in terms of the allowable technology and tradeoffs that includes cost. Performance is considered for deployable dual-polarized PAR radar systems. T/R module and antenna aperture Line Replacement Units (LRU) have been evaluated. Performance and cost are derived from affordability constraints imposed by the radar application.
Distance Metric Learning with Application to Clustering with Side-Information

Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many “plausible” ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for the user to manually tweak the metric until sufficiently good clusters are found. For these and other applications requiring good metrics, it is desirable that we provide a more systematic way for users to indicate what they consider “similar.” For instance, we may ask them to provide examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in , learns a distance metric over that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows us to give efficient, local-optima-free algorithms. We also demonstrate empirically that the learned metrics can be used to significantly improve clustering performance.
ManiWordle: Providing Flexible Control over Wordle

Among the multifarious tag-clouding techniques, Wordle stands out to the community by providing an aesthetic layout, eliciting the emergence of the participatory culture and usage of tag-clouding in the artistic creations. In this paper, we introduce ManiWordle, a Wordle-based visualization tool that revamps interactions with the layout by supporting custom manipulations. ManiWordle allows people to manipulate typography, color, and composition not only for the layout as a whole, but also for the individual words, enabling them to have better control over the layout result. We first describe our design rationale along with the interaction techniques for tweaking the layout. We then present the results both from the preliminary usability study and from the comparative study between ManiWordle and Wordle. The results suggest that ManiWordle provides higher user satisfaction and an efficient method of creating the desired "art work," harnessing the power behind the ever-increasing popularity of Wordle.
Narcissists' social pain seen only in the brain.

Narcissism is a complex phenomenon, involving a level of defensive self-enhancement. Narcissists have avoidant attachment styles, maintain distance in relationships and claim not to need others. However, they are especially sensitive to others' evaluations, needing positive reflected appraisals to maintain their inflated self-views, and showing extreme responses (e.g. aggression) when rejected. The current study tested the hypothesis that narcissists also show hypersensitivity in brain systems associated with distress during exclusion. We measured individual differences in narcissism (Narcissistic Personality Inventory) and monitored neural responses to social exclusion (Cyberball). Narcissism was significantly associated with activity in an a priori anatomically defined social pain network (anterior insula, dorsal anterior cingulate cortex and subgenual anterior cingulate cortex) during social exclusion. Results suggest hypersensitivity to exclusion in narcissists may be a function of hypersensitivity in brain systems associated with distress, and suggests a potential pathway that connects narcissism to negative consequences for longer-term physical and mental health-findings not apparent with self-report alone.
CyberCIEGE TM : An Information Assurance Teaching Tool for Training and Awareness

Good security is not intrusive and can be almost invisible to typical users, who are often unaware of or take it for granted. However, good security practice by user populations is a critical element of an organization’s information assurance strategy. This is reflected in government information assurance teaching mandates such as DoD Directive 8570.1, which outlines objectives and requirements for information assurance (IA) education, training and awareness. Although mundane education, training and awareness programs may temporarily raise user interest, for many, mandatory education is considered a distracting waste of time. A new approach is needed to convey IA concepts that will engage the user’s imagination. CyberCIEGE is an innovative computer-based tool to teach information assurance concepts. The tool enhances information assurance education and training through the use of computer gaming techniques. In the CyberCIEGE virtual world, students spend virtual money to operate and defend their networks, and can watch the consequences of their choices, while under attack. This paper describes CyberCIEGE and will present ways in which this tool can be used to achieve Federal and DoD information assurance teaching objectives.  Development of CyberCIEGE was sponsored by the US Navy, the Naval Education and Training Command, the Office of Naval Research, and the Office of the Secretary of Defense. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsors. + CyberCIEGE is a Trademark of Rivermind, Inc.
An integrated theory of the mind.

Adaptive control of thought-rational (ACT-R; J. R. Anderson & C. Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how these modules are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time, a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. A number of simple and complex empirical examples are described to illustrate how these modules function singly and in concert.
Information-rich virtual environments: theory, tools, and research agenda

Virtual environments (VEs) allow users to experience and interact with a rich sensory environment, but most virtual worlds contain only sensory information similar to that which we experience in the physical world. Information-rich virtual environments (IRVEs) combine the power of VEs and information visualization, augmenting VEs with additional abstract information such as text, numbers, or graphs. IRVEs can be useful in many contexts, such as education, medicine, or construction. In our work, we are developing a theoretical foundation for the study of IRVEs and tools for their development and evaluation. We present a working definition of IRVEs, a discussion of information display and interaction in IRVEs. We also describe a software framework for IRVE development and a testbed enabling evaluation of text display techniques for IRVEs. Finally, we present a research agenda for this area.
Maximum Entropy Deep Inverse Reinforcement Learning

This paper presents a general framework for employing deep architectures in particular neural networks to solve the inverse reinforcement learning (IRL) problem. Specifically, we propose to exploit the representational capacity and favourable computational complexity of deep networks to approximate complex, nonlinear reward functions. We show that the Maximum Entropy paradigm for IRL lends itself naturally to the efficient training of deep architectures. At test time, the approach leads to a computational complexity independent of the number of demonstrations. This makes it especially well-suited for applications in life-long learning scenarios commonly encountered in robotics. We demonstrate that our approach achieves performance commensurate to the state-of-the-art on existing benchmarks already with simple, comparatively shallow network architectures while significantly outperforming the stateof-the-art on an alternative benchmark based on more complex, highly varying reward structures representing strong interactions between features. Furthermore, we extend the approach to include convolutional layers in order to eliminate the dependency on precomputed features of current algorithms and to underline the substantial gain in flexibility in framing IRL in the context of deep learning.
Environmentally Concerned Logistics Operations in Fuzzy Environment : A Literature Survey

Growing environmental awareness coupled with stricter governmental regulations has fueled the need for integrating sustainability into supply chain and logistics activities. Accordingly, recent studies in the literature have emphasized the significance of environmentally concerned logistics operations (ECLO). Research in the broad area of ECLO encompasses a wide range of topics including sustainable supply chain, green supply chain, closed-loop supply chain, low-carbon logistics, and waste management. In this paper, a comprehensive content analysis and area review is presented. Over 800 papers published between 1994 and 2017 in peer-reviewed journals, proceedings, and book chapters are utilized. These papers are analyzed in consecutive stages after being reviewed under a structural dimension process that addresses the fields of environmentally concerned logistics operations. Following the state-of-the-art review, a detailed analysis of ECLO research with a special emphasis on fuzzy applications is provided. The findings clearly indicate that the fuzzy multi-criteria decision making technique is a frequently used hybrid method, whereas fuzzy sets theory and other fuzzy hybrid techniques identify a gap in the related literature. This paper provides further critical analysis and other research suggestions in order to clarify these gaps and offer additional research perspectives. This information may provide extensive data that will enable future researchers to fill these gaps within this field.
GeoDa: An Introduction to Spatial Data Analysis

This article presents an overview of GeoDa, a free software program intended to serve as a user-friendly and graphical introduction to spatial analysis for nongeographic information systems (GIS) specialists. It includes functionality ranging from simple mapping to exploratory data analysis, the visualization of global and local spatial autocorrelation, and spatial regression. A key feature of GeoDa is an interactive environment that combines maps with statistical graphics, using the technology of dynamically linked windows. A brief review of the software design is given, as well as some illustrative examples that highlight distinctive features of the program in applications dealing with public health, economic development, real estate analysis, and criminology.
Neuropsychologic Theory and Findings in Attention-Deficit/Hyperactivity Disorder: The State of the Field and Salient Challenges for the Coming Decade

The past decade has witnessed the establishment of several now well-replicated findings in the neuropsychology of attention-deficit/hyperactivity disorder (ADHD), which have been confirmed by meta-analyses. Progress has been notable from the importing of cognitive science and neuroscience paradigms. Yet these findings point to many neural networks being involved in the syndrome and to modest effect sizes suggesting that any one neuropsychologic deficit will not be able to explain the disorder. In this article, leading theories and key findings are briefly reviewed in four key domains: attention, executive functions, state regulation and motivation, and temporal information processing. Key issues facing the field of neuropsychologic research and theory in ADHD include 1) the need for more integrative developmental accounts that address both multiple neural systems and the socialization processes that assure their development; 2) consideration of multiple models/measures in the same study so as to examine relative contributions, within-group heterogeneity, and differential deficit; and 3) better integration of cognitive process models with affective and temperament theories so that early precursors to ADHD can be better understood. Overall, the field has witnessed notable progress as it converges on an understanding of ADHD in relation to disruption of a multicomponent self-regulatory system. The next era must articulate multipathway, multilevel developmental accounts of ADHD that incorporate neuropsychologic effects.
STATOR WINDING FAULT DIAGNOSISOF THREE-PHASE INDUCTION MOTORBY PARKS VECTOR APPROACH

Through this paper issue of on-line detection of short winding fault in three-phase induction motors is presented, and a technique, based on the computer-aided monitoring of the stator current Park’s Vector, is proposed. The park's vector model is realized by employing Virtual instrumentation. The Virtual Instrumentation is acquired by programming in Lab-view software. Laboratory analysis was performed on a 0.5 hp three phase induction motor. The motor was originally evaluated under optimum circumstances and Park's vector model was sketched. Subsequently, the short winding fault is reproduced in the motor. The motor was scrutinized again under erroneous condition and Park's vector model was sketched. Both plots were then scrutinized. It is ascertained that current park's vector pattern of healthy motor was perfect circle meanwhile current park's vector pattern under faulty condition was elliptical in shape. Experimental conclusion, derived by employing a distinct fault producing test rig, illustrate the efficiency of the suggested modus operandi, for determining the presence of short winding fault in running three-phase induction machines.