nils-herrmann/clean_scirepeval huggingface dataset:
Uncertainty and Risk Analysis in Petroleum Exploration and Production

30 cally viable reservoirs, technology and oil price. Even at the development and production stage the engineering parameters embody a high level of uncertainties in relation to their critical variables (infrastructure, production schedule, quality of oil, operational costs, reservoir characteristics etc.). These uncertainties originated from geological models and coupled with economic and engineering models involve high-risk decision scenarios, with no guarantee of successfully discovering and developing hydrocarbons resources. Corporate managers continuously face important decisions regarding the allocation of scarce resources among investments that are characterized by substantial geological and financial risk. For instance, in the petroleum industry, managers are increasingly using decision analysis techniques to aid in making these decisions. In this sense, the petroleum industry is a classic case of uncertainty in decision-making; it provides an ideal setting for the investigation of corporate risk behavior and its effects on the firm’s performance. The wildcat drilling decision has long been a typical example of the application of decision analysis in classical textbooks. Future trends in oil resource availability will depend largely on the balance between the outcome of the cost-increasing effects of depletion and the cost-reducing effects of new technology. Based upon that scenario, new forms of reservoir Abstract During the past decades, there have been some significant improvements in uncertainty and risk analysis applied to petroleum exploration and production. This paper presents a brief overview of the main contributions made in the exploration and production stages, followed by a summary of the main trends in the context of an exhaustible resource. Decisions related to petroleum exploration and production are still very complex because of the high number of issues involved in the process. However, uncertainty and risk analysis are becoming more popular as new hardware and software advances appear, contributing in an important manner to clarify the range and the impacts of new discoveries as well as development and production assets.
The dynamic kinetochore-microtubule interface.

The kinetochore is a control module that both powers and regulates chromosome segregation in mitosis and meiosis. The kinetochore-microtubule interface is remarkably fluid, with the microtubules growing and shrinking at their point of attachment to the kinetochore. Furthermore, the kinetochore itself is highly dynamic, its makeup changing as cells enter mitosis and as it encounters microtubules. Active kinetochores have yet to be isolated or reconstituted, and so the structure remains enigmatic. Nonetheless, recent advances in genetic, bioinformatic and imaging technology mean we are now beginning to understand how kinetochores assemble, bind to microtubules and release them when the connections made are inappropriate, and also how they influence microtubule behaviour. Recent work has begun to elucidate a pathway of kinetochore assembly in animal cells; the work has revealed that many kinetochore components are highly dynamic and that some cycle between kinetochores and spindle poles along microtubules. Further studies of the kinetochore-microtubule interface are illuminating: (1) the role of the Ndc80 complex and components of the Ran-GTPase system in microtubule attachment, force generation and microtubule-dependent inactivation of kinetochore spindle checkpoint activity; (2) the role of chromosomal passenger proteins in the correction of kinetochore attachment errors; and (3) the function of microtubule plus-end tracking proteins, motor depolymerases and other proteins in kinetochore movement on microtubules and movement coupled to microtubule poleward flux.
Restaurant Rating: Industrial Standard and Word-of-Mouth -- A Text Mining and Multi-dimensional Sentiment Analysis

AAA Restaurant Diamond Rating Guidelines (which is regarded as industry standards) rate a restaurant in three aspects: food, service, and décor/ambience. Drawing upon extant literature, we argue that special contexts and pricing are two other major aspects in restaurant rating in addition to aforementioned three aspects. We tested our hypotheses based on our text mining and sentiment analysis of 268, 442 customer reviews of 7, 508 restaurants on Yelp.com, A form of digital word-of-mouth. Results from fitting a multilevel model showed that the sentiments about each of these five aspects alone explained about 28% of the explainable between-restaurant variances, and 12% of the explainable within-restaurant variances of the restaurants' star ratings. With other level and control variables, the multilevel model can explain more than 53% between-restaurant variances and 28% within-restaurant variances.
An Adaptive Impedance-Matching Network Based on a Novel Capacitor Matrix for Wireless Power Transfer

In a wireless power transfer (WPT) system via the magnetic resonant coupling, one of the most challenging design issues is to maintain a reasonable level of power transfer efficiency (PTE), even when the distance between the transmitter and the receiver changes. When the distance varies, the PTE drastically decreases due to the impedance mismatch between the resonator of the transmitter and that of the receiver. This paper presents a novel serial/parallel capacitor matrix in the transmitter, where the impedance can be automatically reconfigured to track the optimum impedance-matching point in the case of varying distances. The dynamic WPT matching system is enabled by changing the combination of serial and parallel capacitors in the capacitor matrix. An interesting observation in the proposed capacitor matrix is that the resonant frequency is not shifted, even with capacitor-matrix tuning. In order to quickly find the best capacitor combination that achieves maximum power transfer, a window-prediction-based search algorithm is also presented in this paper. The proposed resonance WPT system is implemented using a resonant frequency of 13.56 MHz, and the experimental results with 1W power transfer show that the transfer efficiency increases up to 88 % when the distance changes from 0 to 1.2 m.
The Gamification of Learning and Instruction: Game-Based Methods and Strategies for Training and Education

Praise for The Gamification of Learning and Instruction "Kapp argues convincingly that gamification is not just about adding points, levels and badges to an eLearning program, but about fundamentally rethinking learning design. He has put together a brilliant primer for learning professionals on how to gamify learning, packed with useful advice and examples." ANDERS GRONSTEDT, president, Gronstedt Group "After reading this book, you'll never be able to design boring learning again." CONNIE MALAMED, author, Visual Language For Designers; author/creator of The eLearning Coach Blog "Engaging, informative and complete; if you need to understand anything about instructional game design, this is the book you need. It provides the right amount of academic evidence, practical advice and insightful design tips to have you creating impactful learning in no time." SHERRY ENGEL, associate director learning technology, Penn Medicine Center for Innovation and Learning "What Karl Kapp has done with this book is looked at games and learning from every possible angle....he provocatively asks questions that the learning community needs to answer, like 'Do our design processes still work?' and 'Are we really meeting the needs of today's learners?' This book may make you anxious, make you laugh, or make you angry. But one thing it will definitely do is make you think." RICH MESCH, experiential learning guru, Performance Development Group
Proving the TLS Handshake Secure (As It Is)

The TLS Internet Standard features a mixed bag of cryptographic algorithms and constructions, letting clients and servers negotiate their use for each run of the handshake. Although many ciphersuites are now well-understood in isolation, their composition remains problematic, and yet it is critical to obtain practical security guarantees for TLS. We experimentally confirm that all mainstream implementations of TLS share key materials between different algorithms, some of them of dubious strength. We outline attacks in their handling of resumption and renegotiation, stressing the need to model multiple related instances of the handshake. We study the provable security of the TLS handshake, as it is implemented and deployed. To capture the details of the standard and its main extensions, we rely on miTLS, a verified reference implementation of the protocol. miTLS inter-operates with mainstream browsers and servers for many protocol versions, configurations, and ciphersuites; and it provides application-level, provable security for some. We propose new agile security definitions and assumptions for the signatures, key encapsulation mechanisms (KEM), and key derivation algorithms used by the TLS handshake. By necessity, our definitions are stronger than those expected with simple modern protocols. To validate our model of key encapsulation, we prove that both RSA and Diffie-Hellman ciphersuites satisfy our definition for the KEM. In particular, we formalize the use of PKCS#1v1.5 encryption in TLS, including recommended countermeasures against Bleichenbacher attacks, and build a 3,000-line EasyCrypt proof of the security of the resulting master secret KEM against replayable chosen-ciphertext attacks under the assumption that ciphertexts are hard to re-randomize. Based on our new agile definitions, we construct a modular proof of security for the miTLS reference implementation of the handshake, including ciphersuite negotiation, key exchange, renegotiation, and resumption, treated as a detailed 3,600-line executable model. We present our main definitions, constructions, and proofs for an abstract model of the protocol, featuring series of related runs of the handshake with different ciphersuites. We also describe its refinement to account for the whole reference implementation, based on automated verification tools.
A gateway system for an automotive system: LIN, CAN, and FlexRay

In the automotive industry, the usage of microcontrollers has been increasing rapidly. Many mechanical parts of automobiles are being replaced with microcontrollers, and these microcontrollers are controlling various parts of the automobile, so communications between microcontrollers must be reliable. Until now, several protocols for automotive communications have been introduced, and LIN, CAN, and FlexRay are the most widely known protocols. Different vendors of automobiles use different protocols and each protocol possess different features. This paper presents a network gateway system between LIN, low-speed CAN, high-speed CAN, and FlexRay.
Control of a Three-Phase Boost PFC Converter Using a Single DC-Link Voltage Sensor

This paper proposes a new methodology to control a three-phase boost power factor correction (PFC) using a single dc output voltage sensor. Typically, a PFC control technique requires measurement from five independent sensors, i.e., two input voltages, one output dc voltage, and two input phase currents. Elimination of four sensors in the control system of a PFC converter is theoretically feasible and implementable without compromising stability and power quality of the converter, as analyzed and presented in this paper. The proposed control technique uses the ripple information of the measured dc-link voltage, converter dynamics, and switching states at a preceding sample in order to estimate the present state of four other unknown state variables and, thus, establishes the control method. A 2.2-kW experimental prototype of a three-phase boost PFC is developed and tested to verify the accuracy and applicability of the proposed control logic at different line and load conditions. According to the experimental measurements, conversion efficiency more than 98%, total harmonic distortion as low as 4.3%, and an output voltage ripple of ±2% are achieved at 2.2-kW output power.
A compact dual-band half-mode SIW based semi-circular antenna

In this paper, a half mode SIW based semi-circular antenna with co-axial feed has been designed for dual band operation. The fundamental resonating frequency is found to operate at 4.66 GHz while the next higher mode is at 8.3 GHz. The antenna performance is optimized through parametric studies and the dual band antenna is designed using Arlon AD270 substrate. Simulation studies have been carried out to study the various antenna parameters.
A Low-Power Class-AB Gm-Based Amplifier With Application to an 11-bit Pipelined ADC

A Gm-based amplifier is proposed for the use in low-voltage and low-power switched-capacitor circuits. At the input stage of the amplifier, a common-mode current-suppression scheme effectively suppresses the common-mode current with the differential-mode current even amplified, resulting in decrease in dc power consumption. At the output stage of the amplifier, a self-biased cascode configuration adaptively changes bias voltages to achieve both the cascode operation for high gain and digital switching operation for fast transients. The proposed amplifier is applied to a design of an 11-bit pipelined ADC with a 0.13-μm CMOS process. The implemented analog-to-digital converter consumes 80 μW from a single 0.7 V supply at 2.5 MS/s. It achieves an effective number of bit of 9.72 bit without any calibration scheme and a figure of merit of 37.8 fJ/c-s at near Nyquist rate.
Token-based chunking of turn-internal dialogue act sequences

In this study we compare two sequence learning approaches to chunk dialogue acts within a speaker’s turn. We assign a dialogue act label to each token in the transcribed speech stream of a dialogue participant, additionally classifying if the token is at the beginning of, inside, or outside that specific dialogue act. Experimental findings show that both our approaches – conditional random fields and memory-based tagging – largely improve over local classification methods, obtaining comparable scores on distinct datasets. We discuss the interplay between transcription granularity of turns and dialogue act chunking.
Fake News in Social Networks

We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors’ past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in accordance with the veracity of claims. Our framework yields strategies with agent utility close to a theoretical, Bayes optimal benchmark, while remaining flexible to model re-specification. Optimized strategies allow agents to correctly identify most false claims, when all agents receive unbiased private signals. However, an adversary’s attempt to spread fake news by targeting a subset of agents with a biased private signal can be successful. Even more so when the adversary has information about agents’ network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary’s attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users’ private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected.
Operations for Learning with Graphical Models

This paper is a multidisciplinary review of empirical, statistical learning from a graph-ical model perspective. Well-known examples of graphical models include Bayesian networks , directed graphs representing a Markov chain, and undirected networks representing a Markov eld. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, diierentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation max-imization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical speciication. This includes versions of linear regression , techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.
Learning from Millions of 3 D Scans for Large-scale 3 D Face Recognition

Deep networks trained on millions of facial images are believed to be closely approaching human-level performance in face recognition. However, open world face recognition still remains a challenge. Although, 3D face recognition has an inherent edge over its 2D counterpart, it has not benefited from the recent developments in deep learning due to the unavailability of large training as well as large test datasets. Recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans cannot be sourced from the web causing a bottleneck in the development of deep 3D face recognition networks and datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing. We also propose the first deep CNN model designed specifically for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises 1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this dataset, our network already outperforms state of the art face recognition by over 10%. We fine tune our network on the gallery set to perform end-to-end large scale 3D face recognition which further improves accuracy. Finally, we show the efficacy of our method for the open world face recognition problem.
Predicting the Price of Used Cars using Machine Learning Techniques

In this paper, we investigate the application of supervised machine learning techniques to predict the price of used cars in Mauritius. The predictions are based on historical data collected from daily newspapers. Different techniques like multiple linear regression analysis, k-nearest neighbours, naïve bayes and decision trees have been used to make the predictions. The predictions are then evaluated and compared in order to find those which provide the best performances. A seemingly easy problem turned out to be indeed very difficult to resolve with high accuracy. All the four methods provided comparable performance. In the future, we intend to use more sophisticated algorithms to make the predictions. Keywords-component; formatting; style; styling; insert (key words)
Learned Step Size Quantization

We present here Learned Step Size Quantization, a method for training deep networks such that they can run at inference time using low precision integer matrix multipliers, which offer power and space advantages over high precision alternatives. The essence of our approach is to learn the step size parameter of a uniform quantizer by backpropagation of the training loss, applying a scaling factor to its learning rate, and computing its associated loss gradient by ignoring the discontinuity present in the quantizer. This quantization approach can be applied to activations or weights, using different levels of precision as needed for a given system, and requiring only a simple modification of existing training code. As demonstrated on the ImageNet dataset, our approach achieves better accuracy than all previous published methods for creating quantized networks on several ResNet network architectures at 2-, 3and 4-bits of precision.
Development of an underwater gravimeter and the first observation by using autonomous underwater vehicle

We developed an underwater gravimeter for exploration of a seafloor hydrothermal deposit. Our hybrid gravimeter system consists of an underwater gravimeter and an underwater gravity gradiometer. We adopted Micro-g LaCoste S-174 as a gravity sensor. The sensor is mounted on a gimbal mechanism with a fiber gyroscope. A titanium sphere contains the sensor system. Maximum depth rating is 4,200 m. The data are sent to a recording system housed in another cylinder-shape capsule. The whole system is controlled and monitored via acoustic link of the AUV. In September 2012, the first practical measurement in marine area was carried out by using JAMSTEC's AUV URASHIMA to evaluate performance of the system. The gravimeter and gravity gradiometer were simultaneously mounted on the URASHIMA and the first measurement was performed in Sagami-Bay. From the survey, we obtained the gravity data and supplemental data for compensation of the gravity data with good quality. According to preliminary analyses, the resolution of the gravity data from the first practical measurement is estimated to reach 0.1 mgal.
Wideband and Unidirectional Cavity-Backed Folded Triangular Bowtie Antenna

In this paper, a cavity-backed folded triangular bowtie antenna (FTBA) is proposed and investigated. Comparisons show that it has much smaller fluctuation of input impedance and much larger bandwidth (BW) for stable radiation patterns than cavity-backed ordinary TBA. The proposed antenna is fed by a conventional balun composed of a transition from a microstrip line to a parallel stripline. It achieves an impedance BW of 92.2% for |S11| les -10 dB, stable gain of around 9.5 dBi and unidirectional radiation patterns over the whole operating band. A close examination of the antenna aperture efficiency and the electric field distribution in the aperture provides an explanation for the stable gain across such large frequency band. Comparisons are made against several conventional antennas, including short backfire and microstrip patch antennas, to illustrate the salient features of the proposed antenna. A design guideline is also given for practical applications.
Crash to not crash: Playing video games to predict vehicle collisions

Today’s vehicle collision prediction algorithms are rule-based, and have not benefited from the recent developments in deep learning. This is because it is almost impossible to collect a large amount of collision data from the real world. To address this challenge, we collect a large accident data set using a popular video game named GTA V. Using this accident data set, we develop efficient prediction algorithms based on modern CNN architectures. The performances of our algorithms are compared with the simple rule-based algorithms. We observe that the best CNN-based algorithm among several variants achieves the prediction accuracy of 96.2% while the best rule-based one achieves the accuracy of 89.6%. We also show that our approaches can identify the source of danger when a collision is predicted. Moreover, our approaches are shown to learn how the wheel angles, vehicle orientations, and distances affect the collision probability.