nils-herrmann/clean_scirepeval huggingface dataset:
VALUE CREATION FROM EXPERT SYSTEMS: AN ECONOMIC APPROACH WITH APPLICATIONS IN ACCOUNTING, AUDITING AND FINANCE

The purpose of this paper is to explore some of the value creation processes associated with expert systems. The paper employs a number of different applications of economics to explore the issues. Cost-benefit analysis, based in microeconomics and the economics of defense and government, is used to provide a basic measure of value. Contributions of value for expert systems are explored: economics of strategy/industrial organization, economics of production and innovation, industrial economics, economics of information and economic theory of teams. The paper has a number of conclusions including the following. First, if expert systems can be used to reduce the risk of doing business or develop barriers to entry then those reductions and barriers may be the source of additional value. Second, diffusion of innovations can also lead to additional value through the use of the system by others in the organization for additional purposes or the use of the system by others from different organizations. Third, industrial economics learning processes suggest that building one expert system makes building other expert systems easier and more inexpensive, thus adding value. Fourth, information economics indicates that information has "fleeting" value so the use of expert systems for some applications has limited applicability. Fifth, the economics of teams suggests that expert systems be used to coordinate the efforts of multiple actors. Sixth, the summary suggests that this paper provides a basis for the study of the economics of knowledge and expert systems.
Development and natural history of mood disorders

To expand and accelerate research on mood disorders, the National Institute of Mental Health (NIMH) developed a project to formulate a strategic research plan for mood disorder research. One of the areas selected for review concerns the development and natural history of these disorders. The NIMH convened a multidisciplinary Workgroup of scientists to review the field and the NIMH portfolio and to generate specific recommendations. To encourage a balanced and creative set of proposals, experts were included within and outside this area of research, as well as public stakeholders. The Workgroup identified the need for expanded knowledge of mood disorders in children and adolescents, noting important gaps in understanding the onset, course, and recurrence of early-onset unipolar and bipolar disorder. Recommendations included the need for a multidisciplinary research initiative on the pathogenesis of unipolar depression encompassing genetic and environmental risk and protective factors. Specifically, we encourage the NIMH to convene a panel of experts and advocates to review the findings concerning children at high risk for unipolar depression. Joint analyses of existing data sets should examine specific risk factors to refine models of pathogenesis in preparation for the next era of multidisciplinary research. Other priority areas include the need to assess the long-term impact of successful treatment of juvenile depression and known precursors of depression, in particular, childhood anxiety disorders. Expanded knowledge of pediatric-onset bipolar disorder was identified as a particularly pressing issue because of the severity of the disorder, the controversies surrounding its diagnosis and treatment, and the possibility that widespread use of psychotropic medications in vulnerable children may precipitate the condition. The Workgroup recommends that the NIMH establish a collaborative multisite multidisciplinary Network of Research Programs on Pediatric-Onset Bipolar Disorder to achieve a better understanding of its causes, course, treatment, and prevention. The NIMH should develop a capacity-building plan to ensure the availability of trained investigators in the child and adolescent field. Mood disorders are among the most prevalent, recurrent, and disabling of all illnesses. They are often disorders of early onset. Although the NIMH has made important strides in mood disorders research, more data, beginning with at-risk infants, children, and adolescents, are needed concerning the etiology and developmental course of these disorders. A diverse program of multidisciplinary research is recommended to reduce the burden on children and families affected with these conditions.
A hybrid HMM/DNN approach to keyword spotting of short words

An HMM/DNN framework is proposed to address the issues of short-word detection. The first-stage keyword hypothesizer is redesigned with a context-aware keyword model and a 9state filler model to reduce the miss rate from 80% to 6% and increase the figure-of-merit (FOM) from 6.08% to 21.88% for short words. The hypothesizer is followed by a MLP-based second-stage keyword verifier to further reduce its putative hits. To enhance short word detection, three new techniques, including an HMM-based feature transformation for the MLPs, knowledge-based features, and deep neural networks, are incorporated into redesigning the verifier. With a set of nine short keywords from the TIMIT set the best FOM we had achieved for the proposed KWS system was 42.79%, which is comparable with that of 42.6% for long content words and much better than the FOM of 18.4% for short keywords reported in previous research .
A Method for Building Personalized Ontology Summaries

In the context of ontology engineering, the ontology understanding is the basis for its further development and reuse. One intuitive effective approach to support ontology understanding is the process of ontology summarization which highlights the most important concepts of an ontology. Ontology summarization identifies an excerpt from an ontology that contains the most relevant concepts and produces an abridged ontology. In this article, we present a method for summarizing ontologies that represent a data source schema or describe a knowledge domain. We propose an algorithm to produce a personalized ontology summary based on user-defined parameters, e.g. summary size. The relevance of a concept is determined through user indication or centrality measures, commonly used to determine the relative importance of a vertex within a graph. The algorithm searches for the best ontology summary, i.e., the one containing the most relevant ontology concepts respecting all the original relationships between concepts and the parameters set by the user. Experiments were done comparing our generated ontology summaries against golden standard summaries as well as summaries produced by methods available in related work. We achieved in average more than 62.5% of similarity with golden standard summaries.
A convenient method for securely managing passwords

Computer users are asked to generate, keep secret, and recall an increasing number of passwords for uses including host accounts, email servers, e-commerce sites, and online financial services. Unfortunately, the password entropy that users can comfortably memorize seems insufficient to store unique, secure passwords for all these accounts, and it is likely to remain constant as the number of passwords (and the adversary's computational power) increases into the future. In this paper, we propose a technique that uses a strengthened cryptographic hash function to compute secure passwords for arbitrarily many accounts while requiring the user to memorize only a single short password. This mechanism functions entirely on the client; no server-side changes are needed. Unlike previous approaches, our design is both highly resistant to brute force attacks and nearly stateless, allowing users to retrieve their passwords from any location so long as they can execute our program and remember a short secret. This combination of security and convenience will, we believe, entice users to adopt our scheme. We discuss the construction of our algorithm in detail, compare its strengths and weaknesses to those of related approaches, and present Password Multiplier, an implementation in the form of an extension to the Mozilla Firefox web browser.
Dedoop: Efficient Deduplication with Hadoop

We demonstrate a powerful and easy-to-use tool called Dedoop (Deduplication with Hadoop) for MapReduce-based entity resolution (ER) of large datasets. Dedoop supports a browser-based specification of complex ER workflows including blocking and matching steps as well as the optional use of machine learning for the automatic generation of match classifiers. Specified workflows are automatically translated into MapReduce jobs for parallel execution on different Hadoop clusters. To achieve high performance Dedoop supports several advanced load balancing strategies.
Ontology-based traffic scene modeling, traffic regulations dependent situational awareness and decision-making for automated vehicles

This paper presents a modular framework for traffic regulations based decision-making of automated vehicles. It builds on a semantic traffic scene representation formulated as ontology and includes knowledge about traffic regulations. The semantic representation supports traffic situation classification by reasoning, providing improved situational awareness for the automated vehicle. Decision-making rules are directly derived from traffic regulations and concepts used in the ontology are harmonized with concepts used in traffic regulations. Due to the modular structure of the developed ontology, switching between different sets of national traffic regulations becomes a simple process. The methodology is evaluated for a variety of traffic scenarios, building up from basic to complex urban scenarios containing intersections, traffic regulating police officers and crossing street railways.
License Plate Recognition From Still Images and Video Sequences: A Survey

License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment.
Identifying careless responses in survey data.

When data are collected via anonymous Internet surveys, particularly under conditions of obligatory participation (such as with student samples), data quality can be a concern. However, little guidance exists in the published literature regarding techniques for detecting careless responses. Previously several potential approaches have been suggested for identifying careless respondents via indices computed from the data, yet almost no prior work has examined the relationships among these indicators or the types of data patterns identified by each. In 2 studies, we examined several methods for identifying careless responses, including (a) special items designed to detect careless response, (b) response consistency indices formed from responses to typical survey items, (c) multivariate outlier analysis, (d) response time, and (e) self-reported diligence. Results indicated that there are two distinct patterns of careless response (random and nonrandom) and that different indices are needed to identify these different response patterns. We also found that approximately 10%-12% of undergraduates completing a lengthy survey for course credit were identified as careless responders. In Study 2, we simulated data with known random response patterns to determine the efficacy of several indicators of careless response. We found that the nature of the data strongly influenced the efficacy of the indices to identify careless responses. Recommendations include using identified rather than anonymous responses, incorporating instructed response items before data collection, as well as computing consistency indices and multivariate outlier analysis to ensure high-quality data.
Conceptual modeling: Definition, purpose and benefits

Over the last decade there has been a growing interest in `conceptual modeling' for simulation. This is signified by a greater intensity of research and volume of papers on the topic. What is becoming apparent, however, is that when it comes to conceptual modeling there are quite different views and opinions. These differences may be beneficial for creating a debate that takes the field forward, but they can also lead to confusion. The purpose of this panel is for leading researchers to identify and discuss their views on conceptual modeling. In particular we will debate the definition, purpose and benefits of conceptual modeling for the field of simulation. Through the discussion we hope to highlight common ground and key areas of difference.
A Study on False Channel Condition Reporting Attacks in Wireless Networks

Wireless networking protocols are increasingly being designed to exploit a user's measured channel condition; we call such protocols channel-aware. Each user reports the measured channel condition to a manager of wireless resources and a channel-aware protocol uses these reports to determine how resources are allocated to users. In a channel-aware protocol, each user's reported channel condition affects the performance of every other user. The deployment of channel-aware protocols increases the risks posed by false channel-condition feedback. In this paper, we study what happens in the presence of an attacker that falsely reports its channel condition. We perform case studies on channel-aware network protocols to understand how an attack can use false feedback and how much the attack can affect network performance. The results of the case studies show that we need a secure channel condition estimation algorithm to fundamentally defend against the channel-condition misreporting attack. We design such an algorithm and evaluate our algorithm through analysis and simulation. Our evaluation quantifies the effect of our algorithm on system performance as well as the security and the performance of our algorithm.
Presence detection, identification and tracking in smart homes utilizing bluetooth enabled smartphones

Advances in ubiquitous computing over the last decade have allowed us to inch closer to the realization of true smart homes. Many sensors are already embedded in our living environments which can monitor several environmental parameters such as temperature, humidity, brightness and appliance-level power consumption. However, in order to achieve the primary goal of the smart home, we should be able to detect, identify, and localize the entities inside it. Therefore, the user detection, identification and localization problems represent a crucial facet of the challenges introduced by the smart home problem. Our approach towards solving these challenges entailed the usage of Bluetooth technology for user identification and tracking, alongside a Wireless Local Area Network setup to collate the sensor data at a centralized server such as a home gateway which subsequently processed and stored the entries. Moreover, we have studied the efficacy of various pattern recognition algorithms for real time processing and decision modeling on the received data. We have hence demonstrated our solution represents a non-intrusive, inexpensive and energy-conserving methodology to solve an essential part of the smart home problem by integrating already existent devices and infrastructure in an innocuous manner to obtain good results with minimum overhead.
Stochastic computation

Stochastic computation, as presented in this paper, exploits the statistical nature of application-level performance metrics, and matches it to the statistical attributes of the underlying device and circuit fabrics. Nanoscale circuit fabrics are viewed as noisy communication channels/networks. Communications-inspired design techniques based on estimation and detection theory are proposed. Stochastic computation advocates an explicit characterization and exploitation of error statistics at the architectural and system levels. This paper traces the roots of stochastic computing from the Von Neumann era into its current form. Design and CAD challenges are described.
Learning Morphology with Morfette

Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system is composed of two learning modules which are trained to predict morphological tags and lemmas using the Maximum Entropy classifier. The third module dynamically combines the predictions of the Maximum-Entropy models and outputs a probability distribution over tag-lemma pair sequences. The lemmatization module exploits the idea of recasting lemmatization as a classification task by using class labels which encode mappings from wordforms to lemmas. Experimental evaluation results and error analysis on three morphologically rich languages show that the system achieves high accuracy with no language-specific feature engineering or additional resources.
Development and analysis of a folded shorted-patch antenna with reduced size

The length of a wall-shorted rectangular patch antenna can be reduced from /spl sim//spl lambda//sub 0//4 to /spl sim//spl lambda//sub 0//8 by a simple folding operation, which results in a stacked shorted-patch (S-P) structure with a resonant frequency that can be controlled by modifying the distance between the stacked (lower and upper) shorted-patches. A theoretical analysis based on a simple transmission-line model is presented and compared with numerical simulations, showing good agreement if the height of the folded patch is much smaller than the patch length. The physical insight of the variation of the resonant frequency for this reduced-size antenna can be understood by considering the antenna as a shorted patch loaded with a capacitor. An experimental verification is carried out for a 15 mm/spl times/15 mm/spl times/6 mm folded S-P antenna prototype designed for the 2.4 GHz ISM band that can achieve a 10-dB return loss bandwidth of 4% and results in a nearly omni-directional radiation pattern.
An estimation of the number of cells in the human body.

All living organisms are made of individual and identifiable cells, whose number, together with their size and type, ultimately defines the structure and functions of an organism. While the total cell number of lower organisms is often known, it has not yet been defined in higher organisms. In particular, the reported total cell number of a human being ranges between 10(12) and 10(16) and it is widely mentioned without a proper reference.



To study and discuss the theoretical issue of the total number of cells that compose the standard human adult organism.



A systematic calculation of the total cell number of the whole human body and of the single organs was carried out using bibliographical and/or mathematical approaches.



A current estimation of human total cell number calculated for a variety of organs and cell types is presented. These partial data correspond to a total number of 3.72 × 10(13).



Knowing the total cell number of the human body as well as of individual organs is important from a cultural, biological, medical and comparative modelling point of view. The presented cell count could be a starting point for a common effort to complete the total calculation.
A Java API and Web Service Gateway for wireless M-Bus

M-Bus Systems are used to support the remote data exchange with meter units through a network. They provide an easy extend able and cost effective method to connect many meter devices to one coherent network. Master applications operate as concentrated data collectors and are used to process measured values further. As modern approaches facilitate the possibility of a Smart Grid, M-Bus can be seen as the foundation of this technology. With the current focus on a more effective power grid, Smart Meters and Smart Grids are an important research topic. This bachelor thesis first gives an overview of the M-Bus standard and then presents a Java library and API to access M-Bus devices remotely in a standardized way through Web Services. Integration into common IT applications requires interoperable interfaces which also facilitate automated machine-to-machine communication. The oBIX (Open Building Information Exchange) standard provides such standardized objects and thus is used to create a Web Service gateway for the Java API.
An Open Benchmark Suite for Evaluating Computer Architecture on Bioinformatics and Life Science Applications

In this paper, we propose BIOPERF, a definitive benchmark suite of representative applications from the biology and life sciences community, where the codes are carefully selected to span a breadth of algorithms and performance characteristics. The BIOPERF suite is available from www.bioperf. org and includes benchmark source code, input datasets of various sizes, and information for compiling and using the benchmarks. We include parallel codes where available.
RACS: a case for cloud storage diversity

The increasing popularity of cloud storage is leading organizations to consider moving data out of their own data centers and into the cloud. However, success for cloud storage providers can present a significant risk to customers; namely, it becomes very expensive to switch storage providers. In this paper, we make a case for applying RAID-like techniques used by disks and file systems, but at the cloud storage level. We argue that striping user data across multiple providers can allow customers to avoid vendor lock-in, reduce the cost of switching providers, and better tolerate provider outages or failures. We introduce RACS, a proxy that transparently spreads the storage load over many providers. We evaluate a prototype of our system and estimate the costs incurred and benefits reaped. Finally, we use trace-driven simulations to demonstrate how RACS can reduce the cost of switching storage vendors for a large organization such as the Internet Archive by seven-fold or more by varying erasure-coding parameters.
A Top-Down Compiler for Sentential Decision Diagrams

The sentential decision diagram (SDD) has been recently proposed as a new tractable representation of Boolean functions that generalizes the influential ordered binary decision diagram (OBDD). Empirically, compiling CNFs into SDDs has yielded significant improvements in both time and space over compiling them into OBDDs, using a bottomup compilation approach. In this work, we present a top-down CNF to SDD compiler that is based on techniques from the SAT literature. We compare the presented compiler empirically to the state-ofthe-art, bottom-up SDD compiler, showing ordersof-magnitude improvements in compilation time.