nils-herrmann/clean_scirepeval huggingface dataset:
A fuel economy optimization system with applications in vehicles with human drivers and autonomous vehicles

Vehicle fuel economy Eco-driving Human–machine interface Autonomous vehicle Driving simulator analysis a b s t r a c t Motor vehicle powered by regular gasoline is one of major sources of pollutants for local and global environment. The current study developed and validated a new fuel-economy optimization system (FEOS), which receives input from vehicle variables and environment variables (e.g., headway spacing) as input, mathematically computes the optimal acceler-ation/deceleration value with Lagrange multipliers method, and sends the optimal values to drivers via a human-machine interface (HMI) or automatic control systems of autonomous vehicles. FEOS can be used in both free-flow and car-following traffic conditions. An experimental study was conducted to evaluate FEOS. It was found that without sacrificing driver safety, drivers with the aid of FEOS consumed significant less fuel than those without FEOS in all acceleration conditions (22–31% overall gas saving) and the majority of deceleration conditions (12–26% overall gas saving). Compared to relative expensive vehicle engineering system design and improvement, FEOS provides a feasible way to minimize fuel consumptions considering human factors. Applications of the optimal model in the design of both HMI for vehicles with human drivers and autonomous vehicles were discussed. A number of alternatives have been put forward to improve fuel economy of motor vehicles and recently driving behaviors and energy efficient technologies have been seen to offer considerable potential for reducing fuel consumption. Additional while the exploitation of energy efficient technologies may take time to implement and be costly in terms of continuously having to satisfy consumer demands for safety, comfort, space and adequate acceleration and performance encouraging changes in driving behavior can be accomplished relatively quickly. One method to help drivers form appropriate driving behaviors is via the in-vehicle human–machine interface (HMI). For example, van der Voort et al. (2001) develop a fuel-efficiency support tool to present visual advice on optimal gear shifting to maximize fuel economy. Appropriate vehicle pedal operations, however, may contribute more than manual shifting operations to fuel economy (Brundell-Freij and Ericsson, 2005). Further pedal operations are applied for both manual-transmission and automatic-transmission vehicles with human drivers as well as autonomous vehicles, while gear shifting operations are only used for manual-transmission ones. Fuel consumption models have been developed to quantify the relationship between fuel consumption and vehicle characteristics , traffic or road conditions but these, in general, are only able to provide approximate fuel consumption estimates. As the model accuracy …
Comparison of energy consumption of SRM and IPMSM in automotive driving schedules

Permanent magnets with rare earth materials are widely used in interior permanent magnet synchronous motors (IPMSMs) in Hybrid Electric Vehicles (HEVs). The recent price rise of rare earth materials has become a serious concern. A Switched Reluctance Motor (SRM) is one of the candidates for HEV rare-earth-free-motors. An SRM has been developed with dimensions, maximum torque, operating area, and maximum efficiency that all compete with the IPMSM. The efficiency map of the SRM is different from that of the IPMSM; thus, direct comparison has been rather difficult. In this paper, a comparison of energy consumption between the SRM and the IPMSM using four standard driving schedules is carried out. In HWFET and NEDC driving schedules, the SRM is found to have better efficiency because its efficiency is high at the high-rotational-speed region.
Analysis and Design Strategy of UHF Micro-Power CMOS Rectifiers for Micro-Sensor and RFID Applications

Design strategy and efficiency optimization of ultrahigh-frequency (UHF) micro-power rectifiers using diode-connected MOS transistors with very low threshold voltage is presented. The analysis takes into account the conduction angle, leakage current, and body effect in deriving the output voltage. Appropriate approximations allow analytical expressions for the output voltage, power consumption, and efficiency to be derived. A design procedure to maximize efficiency is presented. A superposition method is proposed to optimize the performance of multiple-output rectifiers. Constant-power scaling and area-efficient design are discussed. Using a 0.18-mum CMOS process with zero-threshold transistors, 900-MHz rectifiers with different conversion ratios were designed, and extensive HSPICE simulations show good agreement with the analysis. A 24-stage triple-output rectifier was designed and fabricated, and measurement results verified the validity of the analysis
Predicting students marks in Hellenic Open University

The ability to provide assistance for a student at the appropriate level is invaluable in the learning process. Not only does it aids the student's learning process but also prevents problems, such as student frustration and floundering. Students' key demographic characteristics and their marks in a small number of written assignments can constitute the training set for a regression method in order to predict the student's performance. The scope of this work compares some of the state of the art regression algorithms in the application domain of predicting students' marks. A number of experiments have been conducted with six algorithms, which were trained using datasets provided by the Hellenic Open University. Finally, a prototype version of software support tool for tutors has been constructed implementing the M5rules algorithm, which proved to be the most appropriate among the tested algorithms.
On Calibration of Modern Neural Networks

Confidence calibration – the problem of predicting probability estimates representative of the true correctness likelihood – is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-ofthe-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling – a singleparameter variant of Platt Scaling – is surprisingly effective at calibrating predictions.
Enterprise Information Systems Architecture—Analysis and Evaluation

Numerous software architecture proposals are available to industrial information engineers in developing their enterprise information systems. While those proposals and corresponding methodologies are helpful to engineers in determining appropriate architecture, the systematic methods for the evaluation of software architecture are scarce. To select appropriate software architecture from various alternatives appropriately, a scenario-based method has been proposed to assess how software architecture affects the fulfillment of business requirements. The empirical evaluation on the selection of a supply chain software tool has shown that the developed method offers remarkable insights of software development and can be incorporated into the industrial informatics practice of an organization with a moderate cost.
Mocking the Embedded World : Test-Driven Development , Continuous Integration , and Design Patterns Embedded Systems Conference Silicon Valley

Despite a prevalent industry perception to the contrary, the agile practices of Test-Driven Development and Continuous Integration can be successfully applied to embedded software. We present here a holistic set of practices, platform independent tools, and a new design pattern (Model Conductor Hardware MCH) that together produce: good design from tests programmed first, logic decoupled from hardware, and systems testable under automation. Ultimately, this approach yields an order of magnitude or more reduction in software flaws, predictable progress, and measurable velocity for data-driven project management. We use the approach discussed herein for real-world production systems and have included a full C-based sample project (using an Atmel AT91SAM7X ARM7) to illustrate it. This example demonstrates transforming requirements into test code, system, integration, and unit tests driving development, daily “micro design” fleshing out a system’s architecture, the use of the MCH itself, and the use of mock functions in tests.
Learning sentiment-inherent word embedding for word-level and sentence-level sentiment analysis

Vector-based word representations have made great progress on many Natural Language Processing tasks. However, due to the lack of sentiment information, the traditional word vectors are insufficient to settle sentiment analysis tasks. In order to capture the sentiment information, we extended Continuous Skip-gram model (Skip-gram) and presented two sentiment word embedding models by integrating sentiment information into semantic word representations. Experimental results showed that the sentiment word embeddings learned by two models indeed capture sentiment and semantic information as well. Moreover, the proposed sentiment word embedding models outperform traditional word vectors on both Chinese and English corpora.
A 500 mW ADC-Based CMOS AFE With Digital Calibration for 10 Gb/s Serial Links Over KR-Backplane and Multimode Fiber

This paper presents the design of an analog-front-end (AFE) integrated into a DSP-based transceiver for both serial 10 Gbps KR-backplane and long-reach-multimode-fiber (LRM) applications. The receiver consists of a programmable gain amplifier (PGA) and a 6-bit 4-way time-interleaved ADC, which is digitally calibrated to compensate for the offset, gain and phase mismatches between the interleaved channels. With a 5 GHz input signal, the ADC achieves overall SNDR of 29 dB, while the measured SNDR of flash sub-ADC is 31.6 dB. The power efficiency FoM of the complete interleaved ADC is 1.4 pJ per conversion step. The PLL uses a calibrated LC-VCO and the TX features a full-rate 3-tap de-emphasis at the output. Inductively tuned buffers connected in tandem are employed to distribute the 10 GHz clock. Random and deterministic jitter measured at the TX output are 0.38 psrms and 2.65 pspp, respectively. Implemented in 65 nm CMOS technology, the AFE occupies an area of 3 mm2 and consumes 500 mW from a 1 V supply. BER of less than 10-15 is measured over legacy backplanes with 26 dB loss at Nyquist and the measured transceiver optical sensitivity is less than -13 dBm for all four LRM stressors, exceeding both the KR and the LRM specifications.
The Impact of Individual , Competitive , and Collaborative Mathematics Game Play on Learning , Performance , and Motivation

The present research examined how mode of play in an educational mathematics video game impacts learning, performance, and motivation. The game was designed for the practice and automation of arithmetic skills to increase fluency and was adapted to allow for individual, competitive, or collaborative game play. Participants (N 58) from urban middle schools were randomly assigned to each experimental condition. Results suggested that, in comparison to individual play, competition increased in-game learning, whereas collaboration decreased performance during the experimental play session. Although out-of-game math fluency improved overall, it did not vary by condition. Furthermore, competition and collaboration elicited greater situational interest and enjoyment and invoked a stronger mastery goal orientation. Additionally, collaboration resulted in stronger intentions to play the game again and to recommend it to others. Results are discussed in terms of the potential for mathematics learning games and technology to increase student learning and motivation and to demonstrate how different modes of engagement can inform the instructional design of such games.
ABox Abduction in the Description Logic ALC

Due to the growing popularity of Description Logics-based knowledge representation systems, predominantly in the context of Semantic Web applications, there is a rising demand for tools offering non-standard reasoning services. One particularly interesting form of reasoning, both from the user as well as the ontology engineering perspective, is abduction. In this paper we introduce two novel reasoning calculi for solving ABox abduction problems in the Description Logic ALC, i.e. problems of finding minimal sets of ABox axioms, which when added to the knowledge base enforce entailment of a requested set of assertions. The algorithms are based on regular connection tableaux and resolution with set-of-support and are proven to be sound and complete. We elaborate on a number of technical issues involved and discuss some practical aspects of reasoning with the methods.
Tactical cooperative planning for autonomous highway driving using Monte-Carlo Tree Search

Human drivers use nonverbal communication and anticipation of other drivers' actions to master conflicts occurring in everyday driving situations. Without a high penetration of vehicle-to-vehicle communication an autonomous vehicle has to have the possibility to understand intentions of others and share own intentions with the surrounding traffic participants. This paper proposes a cooperative combinatorial motion planning algorithm without the need for inter vehicle communication based on Monte Carlo Tree Search (MCTS). We motivate why MCTS is particularly suited for the autonomous driving domain. Furthermore, adoptions to the MCTS algorithm are presented as for example simultaneous decisions, the usage of the Intelligent Driver Model as microscopic traffic simulation, and a cooperative cost function. We further show simulation results of merging scenarios in highway-like situations to underline the cooperative nature of the approach.
Design of a Broadband Eight-Way Coaxial Waveguide Power Combiner

In this paper, a broadband high-power eight-way coaxial waveguide power combiner with axially symmetric structure is proposed. A combination of circuit model and full electromagnetic wave methods is used to simplify the design procedure by increasing the role of the circuit model and, in contrast, reducing the amount of full wave optimization. The presented structure is compact and easy to fabricate. Keeping its return loss greater than 12 dB, the constructed combiner operates within 112% bandwidth from 520 to 1860 MHz.
Analytical Calculation of the Magnetic Field Created by Permanent-Magnet Rings

We present analytical formulations, based on a coulombian approach, of the magnetic field created by permanent-magnet rings. For axially magnetized magnets, we establish the expressions for the three components. We also give the analytical 3-D formulation of the created magnetic field for radially magnetized rings. We compare the results determined by a 2-D analytical approximation to those for the 3-D analytical formulation, in order to determine the range of validity of the 2-D approximation.
Neural Network-based Question Answering over Knowledge Graphs on Word and Character Level

Answering (QA) systems over Knowledge Graphs (KG) automatically answer natural language questions using facts contained in a knowledge graph. Simple questions, which can be answered by the extraction of a single fact, constitute a large part of questions asked on the web but still pose challenges to QA systems, especially when asked against a large knowledge resource. Existing QA systems usually rely on various components each specialised in solving different sub-tasks of the problem (such as segmentation, entity recognition, disambiguation, and relation classification etc.). In this work, we follow a quite different approach: We train a neural network for answering simple questions in an end-to-end manner, leaving all decisions to the model. It learns to rank subject-predicate pairs to enable the retrieval of relevant facts given a question. The network contains a nested word/character-level question encoder which allows to handle out-of-vocabulary and rare word problems while still being able to exploit word-level semantics. Our approach achieves results competitive with state-of-the-art end-to-end approaches that rely on an attention mechanism.
Boolean Searchable Symmetric Encryption with Worst-Case Sub-Linear Complexity

Recent work on searchable symmetric encryption (SSE) has focused on increasing its expressiveness. A notable example is the OXT construction (Cash et al., CRYPTO ’13 ) which is the first SSE scheme to support conjunctive keyword queries with sub-linear search complexity. While OXT efficiently supports disjunctive and boolean queries that can be expressed in searchable normal form, it can only handle arbitrary disjunctive and boolean queries in linear time. This motivates the problem of designing expressive SSE schemes with worst-case sub-linear search; that is, schemes that remain highly efficient for any keyword query. In this work, we address this problem and propose non-interactive highly efficient SSE schemes that handle arbitrary disjunctive and boolean queries with worst-case sub-linear search and optimal communication complexity. Our main construction, called IEX, makes black-box use of an underlying single keyword SSE scheme which we can instantiate in various ways. Our first instantiation, IEX-2Lev, makes use of the recent 2Lev construction (Cash et al., NDSS ’14 ) and is optimized for search at the expense of storage overhead. Our second instantiation, IEX-ZMF, relies on a new single keyword SSE scheme we introduce called ZMF and is optimized for storage overhead at the expense of efficiency (while still achieving asymptotically sub-linear search). Our ZMF construction is the first adaptively-secure highly compact SSE scheme and may be of independent interest. At a very high level, it can be viewed as an encrypted version of a new Bloom filter variant we refer to as a Matryoshka filter. In addition, we show how to extend IEX to be dynamic and forward-secure. To evaluate the practicality of our schemes, we designed and implemented a new encrypted search framework called Clusion. Our experimental results demonstrate the practicality of IEX and of its instantiations with respect to either search (for IEX-2Lev) and storage overhead (for IEX-ZMF). ∗seny@brown.edu. Work done in part at Microsoft Research. †tarik_moataz@brown.edu. Work done in part at IMT Atlantique and Colorado State.
A New Lane Line Segmentation and Detection Method based on Inverse Perspective Mapping

Lane line detection plays an important role in vehicle autonomous driving, however non lane line markings and vehicle shadows seriously influence lane line detection results. In this paper, we propose a new lane line segmentation and detection algorithm based on inverse perspective mapping (IPM). Firstly, by using the restriction of the consistent lane line width after IPM, we segment the lane line and project the segmentation results back to the original coordinate system; then detect the right and left two lane lines by the gray scale weighted Hough transform method. Experimental results show that the proposed algorithm has strong robustness to non lane line markings and vehicle shadows, and the lane line can be accurately detected under the complex pavement conditions.
Blockchain Multiparty Computation Markets at Scale

We explore ways of allowing for the offloading of computationally rigorous tasks from devices with slow logical processors onto a network of anonymous peer-processors. Recent advances in secret sharing schemes, decentralized consensus mechanisms, and multiparty computation (MPC) protocols are combined to create a P2P MPC market. Unlike other computational ”clouds”, ours is able to generically compute any arithmetic circuit, providing a viable platform for processing on the semantic web. Finally, we show that such a system works in a hostile environment, that it scales well, and that it adapts very easily to any future advances in the complexity theoretic cryptography used. Specifically, we show that the feasibility of our system can only improve, and is historically guaranteed to do so.
Visual Sentiment Prediction with Deep Convolutional Neural Networks

Images have become one of the most popular types of media through which users convey their emotions within online social networks. Although vast amount of research is devoted to sentiment analysis of textual data, there has been very limited work that focuses on analyzing sentiment of image data. In this work, we propose a novel visual sentiment prediction framework that performs image understanding with Convolutional Neural Networks (CNN). Specifically, the proposed sentiment prediction framework performs transfer learning from a CNN with millions of parameters, which is pre-trained on large-scale data for object recognition. Experiments conducted on two real-world datasets from Twitter and Tumblr demonstrate the effectiveness of the proposed visual sentiment analysis framework.
Confidence and Risk Estimation Plugins for Multi-Modal Authentication on Mobile Devices using CORMORANT

Mobile devices, ubiquitous in modern lifestyle, embody and provide convenient access to our digital lives. Being small and mobile, they are easily lost or stole, therefore require strong authentication to mitigate the risk of unauthorized access. Common knowledge-based mechanism like PIN or pattern, however, fail to scale with the high frequency but short duration of device interactions and ever increasing number of mobile devices carried simultaneously. To overcome these limitations, we present CORMORANT, an extensible framework for risk-aware multi-modal biometric authentication across multiple mobile devices that offers increased security and requires less user interaction.
CIRL: Controllable Imitative Reinforcement Learning for Vision-Based Self-driving

Autonomous urban driving navigation with complex multi-agent dynamics is under-explored due to the difficulty of learning an optimal driving policy. The traditional modular pipeline heavily relies on hand-designed rules and the pre-processing perception system while the supervised learning-based models are limited by the accessibility of extensive human experience. We present a general and principled Controllable Imitative Reinforcement Learning (CIRL) approach which successfully makes the driving agent achieve higher success rates based on only vision inputs in a high-fidelity car simulator. To alleviate the low exploration efficiency for large continuous action space that often prohibits the use of classical RL on challenging real tasks, our CIRL explores over a reasonably constrained action space guided by encoded experiences that imitate human demonstrations, building upon Deep Deterministic Policy Gradient (DDPG). Moreover, we propose to specialize adaptive policies and steering-angle reward designs for different control signals (i.e. follow, straight, turn right, turn left) based on the shared representations to improve the model capability in tackling with diverse cases. Extensive experiments on CARLA driving benchmark demonstrate that CIRL substantially outperforms all previous methods in terms of the percentage of successfully completed episodes on a variety of goal-directed driving tasks. We also show its superior generalization capability in unseen environments. To our knowledge, this is the first successful case of the learned driving policy by reinforcement learning in the high-fidelity simulator, which performs better than supervised imitation learning.