nils-herrmann/clean_scirepeval huggingface dataset:
A review of semantic segmentation using deep neural networks

During the long history of computer vision, one of the grand challenges has been semantic segmentation which is the ability to segment an unknown image into different parts and objects (e.g., beach, ocean, sun, dog, swimmer). Furthermore, segmentation is even deeper than object recognition because recognition is not necessary for segmentation. Specifically, humans can perform image segmentation without even knowing what the objects are (for example, in satellite imagery or medical X-ray scans, there may be several objects which are unknown, but they can still be segmented within the image typically for further investigation). Performing segmentation without knowing the exact identity of all objects in the scene is an important part of our visual understanding process which can give us a powerful model to understand the world and also be used to improve or augment existing computer vision techniques. Herein this work, we review the field of semantic segmentation as pertaining to deep convolutional neural networks. We provide comprehensive coverage of the top approaches and summarize the strengths, weaknesses and major challenges.
Estimating flight-level price elasticities using online airline data : A first step toward integrating pricing , demand

We estimate flight-level price elasticities using a database of online prices and seat map displays. In contrast to market-level and route-level elasticities reported in the literature, flight-level elasticities can forecast responses in demand due to day-to-day price fluctuations. Knowing how elasticities vary by flight and booking characteristics and in response to competitors’ pricing actions allows airlines to design better promotions. It also allows policy makers the ability to evaluate the impacts of proposed tax increases or time-of-day congestion pricing policies. Our elasticity results show how airlines can design optimal promotions by considering not only which departure dates should be targeted, but also which days of the week customers should be allowed to purchase. Additionally, we show how elasticities can be used by carriers to strategically match a subset of their competitors’ sale fares. Methodologically, we use an approach that corrects for price endogeneity; failure to do so results in biased estimates and incorrect pricing recommendations. Using an instrumental variable approach to address this problem we find a set of valid instruments that can be used in future studies of air travel demand. We conclude by describing how our approach contributes to the literature, by offering an approach to estimate flight-level demand elasticities that the research community needs as an input to more advanced optimization models that integrate demand forecasting, price optimization, and revenue optimization models.
A Single-Chip Dual-Band 22–29-GHz/77–81-GHz BiCMOS Transceiver for Automotive Radars

Integration of multi-mode multi-band transceivers on a single chip will enable low-cost millimeter-wave systems for next-generation automotive radar sensors. The first dual-band millimeter-wave transceiver operating in the 22-29-GHz and 77-81-GHz short-range automotive radar bands is designed and implemented in 0.18-¿ m SiGe BiCMOS technology with fT/fmax of 200/180 GHz. The transceiver chip includes a dual-band low noise amplifier, a shared downconversion chain, dual-band pulse formers, power amplifiers, a dual-band frequency synthesizer and a high-speed highly-programmable baseband pulse generator. The transceiver achieves 35/31-dB receive gain, 4.5/8-dB double side-band noise figure, >60/30-dB cross-band isolation, -114/-100.4-dBc/Hz phase noise at 1-MHz offset, and 14.5/10.5-dBm transmit power in the 24/79-GHz bands. Radar functionality is also demonstrated using a loopback measurement. The 3.9 × 1.9-mm2 24/79-GHz transceiver chip consumes 0.51/0.615 W.
Unveiling turbo codes: some results on parallel concatenated coding schemes

A parallel concatenated coding scheme consists of two simple constituent systematic encoders linked by an interleaver. The input bits to the first encoder are scrambled by the interleaver before entering the second encoder. The codeword of the parallel concatenated code consists of the input bits to the first encoder followed by the parity check bits of both encoders. This construction can be generalized to any number of constituent codes. Parallel concatenated schemes employing two convolutional codes as constituent codes, in connection with an iterative decoding algorithm of complexity comparable to that of the constituent codes, have been recently shown to yield remarkable coding gains close to theoretical limits. They have been named, and are known as, “turbo codes.” We propose a method to evaluate an upper bound to the bit error probability of a parallel concatenated coding scheme averaged over all interleavers of a given length. The analytical bounding technique is then used to shed some light on some crucial questions which have been floating around in the communications community since the proposal of turbo codes.
Leveraging wearables for steering and driver tracking

Given the increasing popularity of wearable devices, this paper explores the potential to use wearables for steering and driver tracking. Such capability would enable novel classes of mobile safety applications without relying on information or sensors in the vehicle. In particular, we study how wrist-mounted inertial sensors, such as those in smart watches and fitness trackers, can track steering wheel usage and angle. In particular, tracking steering wheel usage and turning angle provide fundamental techniques to improve driving detection, enhance vehicle motion tracking by mobile devices and help identify unsafe driving. The approach relies on motion features that allow distinguishing steering from other confounding hand movements. Once steering wheel usage is detected, it further uses wrist rotation measurements to infer steering wheel turning angles. Our on-road experiments show that the technique is 99% accurate in detecting steering wheel usage and can estimate turning angles with an average error within 3.4 degrees.
The BellKor Solution to the Netflix Grand Prize

This article describes part of our contribution to the “Bell Kor’s Pragmatic Chaos” final solution, which won the Netflix Grand Prize. The other portion of the contribution was creat ed while working at AT&T with Robert Bell and Chris Volinsky, as reported in our 2008 Progress Prize report . The final solution includes all the predictors described there. In th is article we describe only the newer predictors. So what is new over last year’s solution? First we further improved the baseline predictors (Sec. III). This in turn impr oves our other models, which incorporate those predictors, like the matrix factorization model (Sec. IV). In addition, an exten sion of the neighborhood model that addresses temporal dynamics was introduced (Sec. V). On the Restricted Boltzmann Machines (RBM) front, we use a new RBM model with superior accuracy by conditioning the visible units (. The fin al addition is the introduction of a new blending algorithm, wh ich is based on gradient boosted decision trees (GBDT) (Sec. VII ).
SDWS: Semantic Description of Web Services

This paper introduces SDWS (Semantic Description of Web Services), a Web tool which generates semantic descriptions from collections of Web services. The fundamental approach of SDWS consists of the integration of a set of ontological models for the representation of different Web service description languages and models. The main contributions of this proposal are (i) a general ontological model for the representation of Web services, (ii) a set of language-specific ontological models for the representation of different Web service descriptions implementations, and (iii) a set of software modules that automatically parse Web service descriptions and produce their respective ontological representation. The design of the generic service model incorporates the common elements that all service descriptions share: a service name, a set of operations, and input and output parameters; together with other important elements that semantic models define: preconditions and effects. Experimental results show that the automatic generation of semantic descriptions from public Web services is feasible and represents an important step towards the integration of a general semantic service registry.
High-Efficiency Hybrid Full-Bridge–Half-Bridge Converter With Shared ZVS Lagging Leg and Dual Outputs in Series

A novel soft-switching hybrid converter combining the phase-shift full-bridge (FB) and half-bridge (HB) LLC resonant converters' configuration with shared zero-voltage switching (ZVS) lagging leg is proposed to ensure the switches in the lagging leg operating at fully ZVS condition. The dual outputs of the proposed hybrid FB-HB converter are connected in series and the whole dc-output voltage can be regulated by the PWM phase-shift control within the desired voltage range. A resonant circuit is used in the secondary side of the FB converter to reset the primary current during the freewheeling period, as well as to transfer more input energy and clamp secondary rectifier voltage. The proposed converter is attractive for hybrid electric vehicle/electric vehicle on-board charger applications. The principle of operation, the validity, and performance are illustrated and verified on a 3.7-kW experimental circuit. Experimental results show that the proposed converter can get good efficiency curves at different operation points, and the maximum efficiency is 98.30%.
On a Formal Model of Safe and Scalable Self-driving Cars

In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of this paper is to add to the equation two additional crucial parameters. The first is standardization of safety assurance — what are the minimal requirements that every self-driving car must satisfy, and how can we verify these requirements. The second parameter is scalability — engineering solutions that lead to unleashed costs will not scale to millions of cars, which will push interest in this field into a niche academic corner, and drive the entire field into a “winter of autonomous driving”. In the first part of the paper we propose a white-box, interpretable, mathematical model for safety assurance, which we call Responsibility-Sensitive Safety (RSS). In the second part we describe a design of a system that adheres to our safety assurance requirements and is scalable to millions of cars.
Electrostatic chuck with a thin ceramic insulation layer for wafer holding

An electrostatic chuck is one of the useful device holding a thin object flat on a bed by electrostatic force. The authors have investigated the fundamental characteristics of an electrostatic chuck consisted of a pair of comb type electrodes and a thin insulation layer between the electrodes and an object. When a thin polymer film is used as an insulation, the holding force for a wafer was large enough in practical use, while the large residual force remains after removing the DC applied voltage. Thus, it was concluded that AC applied voltage will be more preferable than DC, though the electrostatic force for DC applied voltage is somewhat greater than that for AC voltage. Since the electrostatic chuck is generally used in high temperature atmosphere, for example plasma etching, in the semiconductor industry, the insulating layer must be heat resistant. By using a thin ceramic plate, which was made specially for this purpose, the fundamental characteristics of the electrostatic chuck has been investigated. The greater holding force was obtained with a ceramic plate than that with a polymer film. Furthermore, almost no residual force was observed even for the DC applied voltage. The experimental results are reported both in air and in vacuum condition.
On distinguishing prime numbers from composite numbers

A new algorithm for testing primality is presented. The algorithm is distinguishable from the lovely algorithms of Solvay and Strassen [36], Miller [27] and Rabin [32] in that its assertions of primality are certain (i.e., provable from Peano's axioms) rather than dependent on unproven hypothesis (Miller) or probability (Solovay-Strassen, Rabin). An argument is presented which suggests that the algorithm runs within time c1ln(n)c2ln(ln(ln(n))) where n is the input, and C1, c2 constants independent of n. Unfortunately no rigorous proof of this running time is yet available.
Automatic Essay Grading System for Short Answers in English Language

Automatic Essay Grading (AEG) system is defined as the computer technology that evaluates and grades written prose. The short essay answer, where the es say i written in short sentences where it has two types the open ended short answer and the close ended sho rt answer where it is our research domain based on the computer subject. The Marking of short essay answer automatically is one of the most complicated domains because it is relying heavily on the semant ic similarity in meaning refers to the degree to wh ich two sentences are similar in the meaning where both used similar words in the meaning, in this case Humans are able to easily judge if a concepts are r elated to each other, there for is a problem when S tudent use a synonym words during the answer in case they forget the target answer and they use their alterna ive words in the answer which will be different from th e Model answer that prepared by the structure. The Standard text similarity measures perform poorly on such tasks. Short answer only provides a limited content, because the length of the text is typicall y short, ranging from a single word to a dozen word s. This research has two propose; the first propose is Alternative Sentence Generator Method in order to generate the alternative model answer by connecting the method with the synonym dictionary. The second proposed three algorithms combined together in matching phase, Commons Words (COW), Longest Common Subsequence (LCS) and Semantic Dista nce (SD), these algorithms have been successfully used in many Natural Language Processi ng ystems and have yielded efficient results. The system was manually tested on 40 questions answ ered by three students and evaluated by teacher in class. The proposed system has yielded %82 corre lation-style with human grading, which has made the system significantly better than the other stat e of the art systems.
Prediction during language comprehension: benefits, costs, and ERP components.

Because context has a robust influence on the processing of subsequent words, the idea that readers and listeners predict upcoming words has attracted research attention, but prediction has fallen in and out of favor as a likely factor in normal comprehension. We note that the common sense of this word includes both benefits for confirmed predictions and costs for disconfirmed predictions. The N400 component of the event-related potential (ERP) reliably indexes the benefits of semantic context. Evidence that the N400 is sensitive to the other half of prediction--a cost for failure--is largely absent from the literature. This raises the possibility that "prediction" is not a good description of what comprehenders do. However, it need not be the case that the benefits and costs of prediction are evident in a single ERP component. Research outside of language processing indicates that late positive components of the ERP are very sensitive to disconfirmed predictions. We review late positive components elicited by words that are potentially more or less predictable from preceding sentence context. This survey suggests that late positive responses to unexpected words are fairly common, but that these consist of two distinct components with different scalp topographies, one associated with semantically incongruent words and one associated with congruent words. We conclude with a discussion of the possible cognitive correlates of these distinct late positivities and their relationships with more thoroughly characterized ERP components, namely the P300, P600 response to syntactic errors, and the "old/new effect" in studies of recognition memory.
A Revised Comparison of Crossover and Mutation in Genetic Programming

In [Luke and Spector 1997] we presented a comprehensive suite of data comparing GP crossover and point mutation over four domains and a wide range of parameter settings. Unfortunately, the results were marred by statistical flaws. This revision of the study eliminates these flaws, with three times as much the data as the original experiments had. Our results again show that crossover does have some advantage over mutation given the right parameter settings (primarily larger population sizes), though the difference between the two surprisingly small. Further, the results are complex, suggesting that the big picture is more complicated than is commonly believed.
Techniques for efficiently querying scientific workflow provenance graphs

A key advantage of scientific workflow systems over traditional scripting approaches is their ability to automatically record data and process dependencies introduced during workflow runs. This information is often represented through provenance graphs, which can be used by scientists to better understand, reproduce, and verify scientific results. However, while most systems record and store data and process dependencies, few provide easy-to-use and efficient approaches for accessing and querying provenance information. Instead, users formulate provenance graph queries directly against physical data representations (e.g., relational, XML, or RDF), leading to queries that are difficult to express and expensive to evaluate. We address these problems through a high-level query language tailored for expressing provenance graph queries. The language is based on a general model of provenance supporting scientific workflows that process XML data and employ update semantics. Query constructs are provided for querying both structure and lineage information. Unlike other languages that return sets of nodes as answers, our query language is closed, i.e., answers to lineage queries are sets of lineage dependencies (edges) allowing answers to be further queried. We provide a formal semantics for the language and present novel techniques for efficiently evaluating lineage queries. Experimental results on real and synthetic provenance traces demonstrate that our lineage based optimizations outperform an in-memory and standard database implementation by orders of magnitude. We also show that our strategies are feasible and can significantly reduce both provenance storage size and query execution time when compared with standard approaches.
Vehicle Logo Recognition System Based on Convolutional Neural Networks With a Pretraining Strategy

Since a vehicle logo is the clearest indicator of a vehicle manufacturer, most vehicle manufacturer recognition (VMR) methods are based on vehicle logo recognition. Logo recognition can be still a challenge due to difficulties in precisely segmenting the vehicle logo in an image and the requirement for robustness against various imaging situations simultaneously. In this paper, a convolutional neural network (CNN) system has been proposed for VMR that removes the requirement for precise logo detection and segmentation. In addition, an efficient pretraining strategy has been introduced to reduce the high computational cost of kernel training in CNN-based systems to enable improved real-world applications. A data set containing 11 500 logo images belonging to 10 manufacturers, with 10 000 for training and 1500 for testing, is generated and employed to assess the suitability of the proposed system. An average accuracy of 99.07% is obtained, demonstrating the high classification potential and robustness against various poor imaging situations.
Depth of field postprocessing for layered scenes using constant-time rectangle spreading

Control over what is in focus and what is not in focus in an image is an important artistic tool. The range of depth in a 3D scene that is imaged in sufficient focus through an optics system, such as a camera lens, is called depth of field. Without depth of field, the entire scene appears completely in sharp focus, leading to an unnatural, overly crisp appearance. Current techniques for rendering depth of field in computer graphics are either slow or suffer from artifacts, or restrict the choice of point spread function (PSF). In this paper, we present a new image filter based on rectangle spreading which is constant time per pixel. When used in a layered depth of field framework, our filter eliminates the intensity leakage and depth discontinuity artifacts that occur in previous methods. We also present several extensions to our rectangle spreading method to allow flexibility in the appearance of the blur through control over the PSF.
Linearized vegetation indices based on a formal statistical framework

Vegetation indices have been used extensively to estimate the vegetation density from satellite and airborne images for many years. In this paper, we focus on one of the most popular of such indices, the normalized difference vegetation index (NDVI), and we introduce a statistical framework to analyze it. As the degree of vegetation increases, the corresponding NDVI values begin to saturate and cannot represent highly vegetated regions reliably. By adopting the statistical viewpoint, we show how to obtain a linearized and more reliable measure. While the NDVI uses only red and near-infrared bands, we use the statistical framework to introduce new indices using the blue and green bands as well. We compare these indices with that obtained by linearizing the NDVI with extensive experimental results on real IKONOS multispectral images.
Conditional must not aliasing for static race detection

Race detection algorithms for multi-threaded programs using the common lock-based synchronization idiom must correlate locks with the memory locations they guard. The heart of a proof of race freedom is showing that if two locks are distinct, then the memory locations they guard are also distinct. This is an example of a general property we call conditional must not aliasing: Under the assumption that two objects are not aliased, prove that two other objects are not aliased. This paper introduces and gives an algorithm for conditional must not alias analysis and discusses experimental results for sound race detection of Java programs.
Evolving Memory Cell Structures for Sequence Learning

Long Short-Term Memory (LSTM) is one of the best recent supervised sequence learning methods. Using gradient descent, it trains memory cells represented as differentiable computational graph structures. Interestingly, LSTM’s cell structure seems somewhat arbitrary. In this paper we optimize its computational structure using a multiobjective evolutionary algorithm. The fitness function reflects the structure’s usefulness for learning various formal languages. The evolved cells help to understand crucial features that aid sequence learning.
Enhancing Music Recommender Systems with Personality Information and Emotional States: A Proposal

This position paper describes the initial research assumptions to improve music recommendations by including personality and emotional states. By including these psychological factors, we believe that the accuracy of the recommendation can be enhanced. We will give attention to how people use music to regulate their emotional states, and how this regulation is related to their personality. Furthermore, we will focus on how to acquire data from social media (i.e., microblogging sites such as Twitter) to predict the current emotional state of users. Finally, we will discuss how we plan to connect the correct emotionally laden music pieces to support the emotion regulation style of users.