nils-herrmann/clean_scirepeval huggingface dataset:
An Incentive Mechanism for Decongesting the Roads : A Pilot Program in Bangalore

We describe an experiment conducted in Bangalore, India, for incentivizing a population of commuters to travel at less congested times. The goals were to reduce the travel times and increase the travel comfort of commuters, and to reduce congestion, fuel consumption and pollution. The project, called INSTANT (for the Infosys-Stanford Traffic project), ran for six months from Oct 6, 2008, to April 10, 2009 and incentivized the employees of Infosys Technologies, Bangalore. We describe the background, the incentive mechanism and the results. The INSTANT project involved about 14,000 commuters and succeeded in incentivizing many commuters to travel at uncongested times, thereby significantly reducing their commute times. Our approach of providing incentives to decongestors contrasts with the current practice of charging congestors. It also contrasts with prior work on “selfish routing” which focusses on the reaction of commuters to a spatial choice of routes (same time, different routes) as opposed to a temporal choice (same route, different times).
Our algorithm for nasal reconstruction.

Nasal reconstruction is always challenging for plastic surgeons. Its midfacial localisation and the relationship between convexities and concavities of nasal subunits make impossible to hide any sort of deformity without a proper reconstruction. Nasal tissue defects can be caused by tumor removal, trauma or by any other insult to the nasal pyramid, like cocaine abuse, developing an irreversible sequela. Due to the special characteristics of the nasal pyramid surface, the removal of the lesion or the debridement must be performed according to nasal subunits as introduced by Burget. Afterwards, the reconstructive technique or a combination of them must be selected according to the size and the localisation of the defect created, and tissue availability to fulfil the procedure. An anatomical reconstruction must be completed as far as possible, trying to restore the nasal lining, the osteocartilaginous framework and the skin cover. In our department, 35 patients were operated on between 2000 and 2002: three bilobed flaps, five nasolabial flaps, two V-Y advancement flaps from the sidewall, three dorsonasal flaps modified by Ohsumi, 19 paramedian forehead flaps, three cheek advancement flaps, three costocondral grafts, two full-thickness skin grafts and two auricular helix free flaps for alar reconstruction. All flaps but one free flap survived with no postoperative complications. After 12-24 months of follow-up, all reconstructions remained stable from cosmetic and functional point of view. Our aim is to present our choice for nasal reconstruction according to the size and localization of the defect, and donor tissue availability.
SCOPE: parallel databases meet MapReduce

Companies providing cloud-scale data services have increasing needs to store and analyze massive data sets, such as search logs, click streams, and web graph data. For cost and performance reasons, processing is typically done on large clusters of tens of thousands of commodity machines. Such massive data analysis on large clusters presents new opportunities and challenges for developing a highly scalable and efficient distributed computation system that is easy to program and supports complex system optimization to maximize performance and reliability. In this paper, we describe a distributed computation system, Structured Computations Optimized for Parallel Execution (Scope), targeted for this type of massive data analysis. Scope combines benefits from both traditional parallel databases and MapReduce execution engines to allow easy programmability and deliver massive scalability and high performance through advanced optimization. Similar to parallel databases, the system has a SQL-like declarative scripting language with no explicit parallelism, while being amenable to efficient parallel execution on large clusters. An optimizer is responsible for converting scripts into efficient execution plans for the distributed computation engine. A physical execution plan consists of a directed acyclic graph of vertices. Execution of the plan is orchestrated by a job manager that schedules execution on available machines and provides fault tolerance and recovery, much like MapReduce systems. Scope is being used daily for a variety of data analysis and data mining applications over tens of thousands of machines at Microsoft, powering Bing, and other online services.
Prescience: Probabilistic Guidance on the Retraining Conundrum for Malware Detection

Malware evolves perpetually and relies on increasingly so- phisticated attacks to supersede defense strategies. Data-driven approaches to malware detection run the risk of becoming rapidly antiquated. Keeping pace with malware requires models that are periodically enriched with fresh knowledge, commonly known as retraining. In this work, we propose the use of Venn-Abers predictors for assessing the quality of binary classification tasks as a first step towards identifying antiquated models. One of the key benefits behind the use of Venn-Abers predictors is that they are automatically well calibrated and offer probabilistic guidance on the identification of nonstationary populations of malware. Our framework is agnostic to the underlying classification algorithm and can then be used for building better retraining strategies in the presence of concept drift. Results obtained over a timeline-based evaluation with about 90K samples show that our framework can identify when models tend to become obsolete.
Cost-Sensitive Learning with Neural Networks

In the usual setting of Machine Learning, classifiers are typically evaluated by estimating their error rate (or equi valently, the classification accuracy) on the test data. However, this mak es sense only if all errors have equal (uniform) costs. When the costs of errors differ between each other, the classifiers should be eva luated by comparing the total costs of the errors. Classifiers are typically designed to minimize the number of errors (incorrect classifications) made. When misclassification c sts vary between classes, this approach is not suitable. In this case the total misclassification cost should be minimized. In Machine Learning, only little work for dealing with nonuniform misclassification costs has been done. This paper pr esents a few different approaches for cost-sensitive modifications f the backpropagation learning algorithm for multilayered feedforw a d neural networks . The described approaches are thoroughly tested a nd evaluated on several standard benchmark domains.
Enhancing IT governance practices: A model and case study of an organization's efforts

For many organizations, Information Technology (IT) enabled business initiatives and IT infrastructure constitute major investments that, if not managed properly, may impair rather than enhance the organization's competitive position. Especially since the advent of Sarbanes–Oxley (SOX), both management and IT professionals are concerned with design, implementation, and assessment of IT governance strategies to ensure that technology truly serves the needs of the business. Via an in-depth study within one organisation, this research explores the factors influencing IT governance structures, processes, and outcome metrics. Interview responses to open-ended questions indicated that more effective IT governance performance outcomes are associated with a shared understanding of business and IT objectives; active involvement of IT steering committees; a balance of business and IT representatives in IT decisions; and comprehensive and well-communicated IT strategies and policies. IT governance also plays a prominent role in fostering project success and delivering business value.
Fake Account Detection in Twitter Based on Minimum Weighted Feature set

Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting the fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, and then the determined factors are applied using different classification techniques. A comparison of the results of these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent researches in the same area; this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts; moreover, the study can be applied on different social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper. Fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques.
The Uncertainty Bellman Equation and Exploration

We consider the exploration/exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the expected value at subsequent time-steps. In this paper we consider a similar uncertainty Bellman equation (UBE), which connects the uncertainty at any time-step to the expected uncertainties at subsequent time-steps, thereby extending the potential exploratory benefit of a policy beyond individual time-steps. We prove that the unique fixed point of the UBE yields an upper bound on the variance of the posterior distribution of the Q-values induced by any policy. This bound can be much tighter than traditional count-based bonuses that compound standard deviation rather than variance. Importantly, and unlike several existing approaches to optimism, this method scales naturally to large systems with complex generalization. Substituting our UBE-exploration strategy for -greedy improves DQN performance on 51 out of 57 games in the Atari suite.
Mitigation of Inrush Currents in Network Transformers by Reducing the Residual Flux With an Ultra-Low-Frequency Power Source

A methodology for the reduction of the residual flux in network transformers is proposed in this paper. The purpose is the mitigation of large inrush currents taken by numerous transformers when a long feeder is energized. Time-domain simulations are used to prove that a small-power device can substantially reduce the residual flux of all transformers simultaneously. The device consists of a low-voltage dc source, a suitable power-electronic switching unit, and a simple controller. Before a feeder is re-energized, the residual flux is reduced to a minimum and, as a consequence, the large inrush currents are reduced to an acceptable level. This greatly enhances the probability for the feeder to be successfully energized when otherwise a false trip would have occurred. Inrush current reductions of more than 60% are obtained at the head of the feeder.
Rapid MIMO-OFDM software defined radio system prototyping

Combining MIMO with OFDM, it is possible to significantly reduce receiver complexity as OFDM greatly simplifies equalization at the receiver. MIMO-OFDM is currently being considered for a number of developing wireless standards; consequently, the study of MIMO-OFDM in realistic environments is of great importance. This paper describes an approach for prototyping a MIMO-OFDM systems using a flexible software defined radio (SDR) system architecture in conjunction with commercially available hardware. An emphasis on software permits a focus on algorithm and system design issues rather than implementation and hardware configuration. The penalty of this flexibility, however, is that the ease of use comes at the expense of overall throughput. To illustrate the benefits of the proposed architecture, applications to MIMO-OFDM system prototyping and preliminary MIMO channel measurements are presented. A detailed description of the hardware is provided along with downloadable software to reproduce the system.
Endocrine disruptors in bottled mineral water: total estrogenic burden and migration from plastic bottles.

Food consumption is an important route of human exposure to endocrine-disrupting chemicals. So far, this has been demonstrated by exposure modeling or analytical identification of single substances in foodstuff (e.g., phthalates) and human body fluids (e.g., urine and blood). Since the research in this field is focused on few chemicals (and thus missing mixture effects), the overall contamination of edibles with xenohormones is largely unknown. The aim of this study was to assess the integrated estrogenic burden of bottled mineral water as model foodstuff and to characterize the potential sources of the estrogenic contamination.



In the present study, we analyzed commercially available mineral water in an in vitro system with the human estrogen receptor alpha and detected estrogenic contamination in 60% of all samples with a maximum activity equivalent to 75.2 ng/l of the natural sex hormone 17beta-estradiol. Furthermore, breeding of the molluskan model Potamopyrgus antipodarum in water bottles made of glass and plastic [polyethylene terephthalate (PET)] resulted in an increased reproductive output of snails cultured in PET bottles. This provides first evidence that substances leaching from plastic food packaging materials act as functional estrogens in vivo.



Our results demonstrate a widespread contamination of mineral water with xenoestrogens that partly originates from compounds leaching from the plastic packaging material. These substances possess potent estrogenic activity in vivo in a molluskan sentinel. Overall, the results indicate that a broader range of foodstuff may be contaminated with endocrine disruptors when packed in plastics.
Conflict-Probability-Estimation-Based Overtaking for Intelligent Vehicles

Overtaking is a complex and hazardous driving maneuver for intelligent vehicles. When to initiate overtaking and how to complete overtaking are critical issues for an overtaking intelligent vehicle. We propose an overtaking control method based on the estimation of the conflict probability. This method uses the conflict probability as the safety indicator and completes overtaking by tracking a safe conflict probability. The conflict probability is estimated by the future relative position of intelligent vehicles, and the future relative position is estimated by using the dynamics models of the intelligent vehicles. The proposed method uses model predictive control to track a desired safe conflict probability and synthesizes decision making and control of the overtaking maneuver. The effectiveness of this method has been validated in different experimental configurations, and the effects of some parameters in this control method have also been investigated.
Control of single-phase cascaded H-bridge multilevel inverter with modified MPPT for grid-connected photovoltaic systems

This paper presents a control technique of cascaded H-bridge multilevel voltage source inverter (CHB-MLI) for a grid-connected photovoltaic system (GCPVS). The proposed control technique is the modified ripple-correlation control maximum power point tracking (MRCC-MPPT). This algorithm has been developed using the mean function concept to continuously correct the maximum power point (MPP) of power transferring from each PV string and to speedily reach the MPP in rapidly shading irradiance. Additionally, It can reduce a PV voltage harmonic filter in the dc-link voltage controller. In task of injecting the quality current to the utility grid, the current control technique based-on the principle of rotating reference frame is proposed. This method can generate the sinusoidal current and independently control the injection of active and reactive power to the utility grid. Simulation results for two H-bridge cells CHB-MLI 4000W/220V/50Hz GCPVS are presented to validate the proposed control scheme.
Synthetic sub-flow pairs for timely and stable IP traffic identification

Literature on the use of Machine Learning (ML) algorithms for classifying IP traffic has relied on bi-directional full-flow statistics while assuming that flows have explicit directionality implied by the first packet captured or the Client-toServer direction. In contrast, many real-world classifiers may miss an arbitrary number of packets from the start of a flow, and be unsure in which direction the flow started. This would lead to degradation in classification performance for application with asymmetric traffic characteristics. We propose a novel approach to train the ML classifier using statistical features calculated over multiple short sub-flows extracted from full-flow generated by the target application and their mirror-imaged replicas as if the flow is in the reverse direction. We demonstrate our optimisation when applied to the Naive Bayes and Decision Tree algorithms. Our approach results in excellent performance even when classification is initiated mid-way through a flow, without prior knowledge of the flow’s direction and using windows as small as 25 packets long.
A 50-kVA Three-Phase Solid-State Transformer Based on the Minimal Topology: Dyna-C

The Dynamic Current or Dyna-C is a minimal topology for implementing the bidirectional three-phase solid-state transformer (SST). While only two current-source power conversion stages are employed, the Dyna-C SST has features of voltage step up/down, arbitrary power factors, and frequencies between the input and output terminals. In this paper, a compact 50-kVA three-phase SST based on this minimal topology is designed. More specifically, design considerations and practical implementation techniques are presented. Results from experimental measurements are shown and discussed.
Passivity-Based Control of DC Microgrid for Self-Disciplined Stabilization

DC microgrids may have time-varying system structures and operation patterns due to the flexibility and uncertainty of distributed resources. This feature poses a challenge to conventional stability analysis methods, which are based on fixed and complete system models. To solve this problem, the concept of self-disciplined stabilization is introduced in this paper. A common stability discipline is established using the passivity-based control theory, which ensures that a microgrid is always stable as long as this discipline is complied by each individual converter. In this way, the stabilization task is localized to avoid investigating the entire microgrid, thereby providing immunity against system variations. Moreover, a passivity margin criterion is proposed to further enhance the stability margin of the self-disciplined control. The modified criterion imposes a tighter phase restriction to provide explicit phase margins and prevent under-damped transient oscillations. In line with this criterion, a practical control algorithm is also derived, which increases the converter's passivity through voltage feed forward. The major theoretical conclusions are verified by a laboratory DC microgrid test bench.
Strategies that fit emerging markets.

It's no easy task to identify strategies for entering new international markets or to decide which countries to do business with. Many firms simply go with what they know-and fall far short of their goals. Part of the problem is that emerging markets have "institutional voids": They lack specialized intermediaries, regulatory systems, and contract-enforcing methods. These gaps have made it difficult for multinationals to succeed in developing nations; thus, many companies have resisted investing there. That may be a mistake. If Western companies don't come up with good strategies for engaging with emerging markets, they are unlikely to remain competitive. Many firms choose their markets and strategies for the wrong reasons, relying on everything from senior managers' gut feelings to the behaviors of rivals. Corporations also depend on composite indexes for help making decisions. But these analyses can be misleading; they don't account for vital information about the soft infrastructures in developing nations. A better approach is to understand institutional variations between countries. The best way to do this, the authors have found, is by using the five contexts framework. The five contexts are a country's political and social systems, its degree of openness, its product markets, its labor markets, and its capital markets. By asking a series of questions that pertain to each ofthe five areas, executives can map the institutional contexts of any nation. When companies match their strategies to each country's contexts, they can take advantage of a location's unique strengths. But first firms should weigh the benefits against the costs. If they find that the risks of adaptation are too great, they should try to change the contexts in which they operate or simply stay away.
Fuzzy Sets as a Basis for a Theory of Possibility*

The theory of possibility described in this paper is related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable. More specifically, if F is a fuzzy subset of a universe of discourse U = {u} which is characterized by its membership function/~r, then a proposition of the form "X is F," where X is a variable taking values in U, induces a possibility distribution Hx which equates the possibility of X taking the value u to/~r.(u)--the compatibility of u with F. In this way, X becomes a fuzzy variable which is associated with the possibility distribution Fix in much the same way as a random variable is associated with a probability distribution. In general, a variable may be associated both with a possibility distribution and a probability distribution, with the weak connection between the two expressed as the possibility/probability consistency principle. A thesis advanced in this paper is that the imprecision that is intrinsic in natural languages is, in the main, possibilistic rather than probabilistic in nature. Thus, by employing the concept of a possibility distribution, a proposition, p, in a natural language may be translated into a procedure which computes the probability distribution of a set of attributes which are implied by p. Several types of conditional translation rules are discussed and, in particular, a translation rule r,~r propositions of the form"X is F is ~-possible, "~ where ~ is a number in the interval [0, ! ], is formulate~ and illustrated by examples.
Understanding the recognition of facial identity and facial expression

Faces convey a wealth of social signals. A dominant view in face-perception research has been that the recognition of facial identity and facial expression involves separable visual pathways at the functional and neural levels, and data from experimental, neuropsychological, functional imaging and cell-recording studies are commonly interpreted within this framework. However, the existing evidence supports this model less strongly than is often assumed. Alongside this two-pathway framework, other possible models of facial identity and expression recognition, including one that has emerged from principal component analysis techniques, should be considered.