nils-herrmann/clean_scirepeval huggingface dataset:
An optimal sampling method for web accessibility quantitative metric

When evaluating the accessibility of a website, we usually resort to sampling methods to reduce the cost of evaluation. In this kind of approaches, a small subset of pages in a website are chosen for evaluating the accessibility value of the whole website. Good sampling quality means the selected subset can represent well the accessibility level of the whole website, i.e. minimizing the accessibility evaluation difference between the whole site and the sampled subset. As existing studies show the accuracy of sampling methods depends heavily on the metric, we propose in this paper a specific sampling method OPS-WAQM that is optimized for Web Accessibility Quantitative Metric (WAQM). OPS-WAQM minimizes the sampling error by choosing the optimal sample numbers in different page depth layers. A greedy algorithm is proposed to approximately solve the optimization problem in an efficient way. We use a dataset of 20 websites, 365780 web pages to validate our method. Experimental results show that our sampling method is effective for web accessibility evaluation.
Lossless data compression

This thesis makes several contributions to the field of data compression. Lossless data compression algorithms shorten the description of input objects, such as sequences of text, in a way that allows perfect recovery of the original object. Such algorithms exploit the fact that input objects are not uniformly distributed: by allocating shorter descriptions to more probable objects and longer descriptions to less probable objects, the expected length of the compressed output can be made shorter than the object’s original description. Compression algorithms can be designed to match almost any given probability distribution over input objects. This thesis employs probabilistic modelling, Bayesian inference, and arithmetic coding to derive compression algorithms for a variety of applications, making the underlying probability distributions explicit throughout. A general compression toolbox is described, consisting of practical algorithms for compressing data distributed by various fundamental probability distributions, and mechanisms for combining these algorithms in a principled way. Building on the compression toolbox, new mathematical theory is introduced for compressing objects with an underlying combinatorial structure, such as permutations, combinations, and multisets. An example application is given that compresses unordered collections of strings, even if the strings in the collection are individually incompressible. For text compression, a novel unifying construction is developed for a family of contextsensitive compression algorithms. Special cases of this family include the PPM algorithm and the Sequence Memoizer, an unbounded depth hierarchical Pitman–Yor process model. It is shown how these algorithms are related, what their probabilistic models are, and how they produce fundamentally similar results. The work concludes with experimental results, example applications, and a brief discussion on cost-sensitive compression and adversarial sequences.
Input-to-Output Gate to Improve RNN Language Models

This paper proposes a reinforcing method that refines the output layers of existing Recurrent Neural Network (RNN) language models. We refer to our proposed method as Input-to-Output Gate (IOG)1. IOG has an extremely simple structure, and thus, can be easily combined with any RNN language models. Our experiments on the Penn Treebank and WikiText-2 datasets demonstrate that IOG consistently boosts the performance of several different types of current topline RNN language models.
Frame Semantics

FRAME SEMANTICS is a research program in empirical semantics which emphasizes the continuities between language and experience, and provides a framework for presenting the results of that research. A FRAME is any system of concepts related in such a way that to understand any one concept it is necessary to understand the entire system; introducing any one concept results in all of them becoming available. In Frame Semantics, a word represents a category of experience; part of the research endeavor is the uncovering of reasons a speech community has for creating the category represented by the word and including that reason in the description of the meaning of the word.
Learning Bayesian networks: approaches and issues

Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to the difficulty domain experts have in specifying them, techniques that learn Bayesian networks from data have become indispensable. Recently, however, there have been many important new developments in this field. This work takes a broad look at the literature on learning Bayesian networks—in particular their structure—from data. Specific topics are not focused on in detail, but it is hoped that all the major fields in the area are covered. This article is not intended to be a tutorial—for this, there are many books on the topic, which will be presented. However, an effort has been made to locate all the relevant publications, so that this paper can be used as a ready reference to find the works on particular sub-topics.
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification

Person re-identification is to match pedestrian images from disjoint camera views detected by pedestrian detectors. Challenges are presented in the form of complex variations of lightings, poses, viewpoints, blurring effects, image resolutions, camera settings, occlusions and background clutter across camera views. In addition, misalignment introduced by the pedestrian detector will affect most existing person re-identification methods that use manually cropped pedestrian images and assume perfect detection. In this paper, we propose a novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter. All the key components are jointly optimized to maximize the strength of each component when cooperating with others. In contrast to existing works that use handcrafted features, our method automatically learns features optimal for the re-identification task from data. The learned filter pairs encode photometric transforms. Its deep architecture makes it possible to model a mixture of complex photometric and geometric transforms. We build the largest benchmark re-id dataset with 13, 164 images of 1, 360 pedestrians. Unlike existing datasets, which only provide manually cropped pedestrian images, our dataset provides automatically detected bounding boxes for evaluation close to practical applications. Our neural network significantly outperforms state-of-the-art methods on this dataset.
Implicit and explicit prejudice and interracial interaction.

The present research examined how implicit racial associations and explicit racial attitudes of Whites relate to behaviors and impressions in interracial interactions. Specifically, the authors examined how response latency and self-report measures predicted bias and perceptions of bias in verbal and nonverbal behavior exhibited by Whites while they interacted with a Black partner. As predicted, Whites' self-reported racial attitudes significantly predicted bias in their verbal behavior to Black relative to White confederates. Furthermore, these explicit attitudes predicted how much friendlier Whites felt that they behaved toward White than Black partners. In contrast, the response latency measure significantly predicted Whites' nonverbal friendliness and the extent to which the confederates and observers perceived bias in the participants' friendliness.
Multimodal One-Shot Learning of Speech and Images

Imagine a robot is shown new concepts visually together with spoken tags, e.g. “milk”, “eggs”, “butter”. After seeing one paired audiovisual example per class, it is shown a new set of unseen instances of these objects, and asked to pick the “milk”. Without receiving any hard labels, could it learn to match the new continuous speech input to the correct visual instance? Although unimodal one-shot learning has been studied, where one labelled example in a single modality is given per class, this example motivates multimodal oneshot learning. Our main contribution is to formally define this task, and to propose several baseline and advanced models. We use a dataset of paired spoken and visual digits to specifically investigate recent advances in Siamese convolutional neural networks. Our best Siamese model achieves twice the accuracy of a nearest neighbour model using pixel-distance over images and dynamic time warping over speech in 11-way cross-modal matching.
A Survey on Optical Character Recognition System

Optical Character Recognition (OCR) is a piece of software that converts printed text and images into digitized form such that it can be manipulated by machine. Unlike human brain which has the capability to very easily recognize the text/ characters from an image, machines are not intelligent enough to perceive the information available in image. Therefore, a large number of research efforts have been put forward that attempts to transform a document image to format understandable for machine. OCR is a complex problem because of the variety of languages, fonts and styles in which text can be written, and the complex rules of languages etc. Hence, techniques from different disciplines of computer science (i.e. image processing, pattern classification and natural language processing etc. are employed to address different challenges. This paper introduces the reader to the problem. It enlightens the reader with the historical perspectives, applications, challenges and techniques of OCR.
Municipal solid waste management challenges in developing countries--Kenyan case study.

This paper provides an overview of the state of municipal solid waste management (MSWM) by local authorities in Kenya as a case study of a low-income developing country. Approaches of possible solutions that can be undertaken to improve municipal solid waste (MSW) services are discussed. Poor economic growth (1.1% in 1993) has resulted in an increase in the poverty level which presently stands at 56%. Migration from the rural areas to the urban areas has resulted in unplanned settlements in suburban areas accommodating about 60% of the urban population on only 5% urban land area. Political interference also hampers smooth running of local authorities. Vulnerability of pollution of surface and groundwater is high because local authorities rarely considered environmental impact in siting MSW disposal sites. Illegal dumping of MSW on the river banks or on the roadside poses environmental and economic threats on nearby properties. Poor servicing of MSW collection vehicles, poor state of infrastructure and the lack of adequate funding militate against optimization of MSW disposal service. The rural economy needs to be improved if rural-urban migration is to be managed. Involvement of stakeholders is important to achieve any meaningful and sustainable MSWM. The role of the informal sector through community-based organizations (CBOs), Non-Governmental Organizations (NGOs) and the private sector in offering solutions towards improvement of MSWM also is explored.
Geared Rotationally Identical and Invariant Convolutional Neural Network Systems

Theorems and techniques to form different types of transformationally invariant processing and to produce the same output quantitatively based on either transformationally invariant operators or symmetric operations have recently been introduced by the authors. In this study, we further propose to compose a geared rotationally identical CNN system (GRI-CNN) with a small angle increment by connecting networks of participated processes at the first flatten layer. Using an ordinary CNN structure as a base, requirements for constructing a GRI-CNN include the use of either symmetric input vector or kernels with an angle increment that can form a complete cycle as a "gearwheel". Four basic GRI-CNN structures were studied. Each of them can produce quantitatively identical output results when a rotation angle of the input vector is evenly divisible by the increment angle of the gear. Our study showed when a rotated input vector does not match to a gear angle, the GRI-CNN can also produce a highly consistent result. With an ultrafine increment angle (e.g., 1 or 0.1), a virtually isotropic CNN system can be constructed.
Dinosaur tracks from the Early Cretaceous ( Albian ) of Parede ( Cascais , Portugal ) : new contributions for the sauropod palaeobiology of the Iberian Peninsula

A recently discovered Early Cretaceous (early late Albian) dinosaur tracksite at Parede beach (Cascais, Portugal) reveals evidence of dinoturbation and at least two sauropod trackways. One of these trackways can be classified as narrow-gauge, which represents unique evidence in the Albian of the Iberian Peninsula and provides for the improvement of knowledge of this kind of trackway and its probable trackmaker, in an age when the sauropod record is scarce. These dinosaur tracks are preserved on the upper surface of a marly limestone bed that belongs to the Galé Formation (Água Doce Member, middle to lower upper Albian). The study of thin-sections of the beds C22/24 and C26 in the Parede section has revealed a microfacies composed of foraminifers, radiolarians, ostracods, corals, bivalves, gastropods, and echinoids in a mainly wackestone texture with biomicritic matrix. These assemblages match with the lithofacies, marine molluscs, echinids, and ichnofossils sampled from the section and indicate a shallow marine, inner shelf palaeoenvironment with a shallowing-upward trend. The biofacies and the sequence analysis are compatible with the early late Albian age attributed to the tracksite. These tracks and the moderate dinoturbation index indicate sauropod activity in this palaeoenvironment. Titanosaurs can be dismissed as possible trackmakers on the basis of the narrow-gauge trackway, and probably by the kidney-shaped manus morphology and the pes-dominated configuration of the trackway. Narrow-gauge sauropod trackways have been positively associated with coastal palaeoenvironments, and the Parede tracksite supports this interpretation. In addition, this tracksite adds new data about the presence of sauropod pes-dominated trackways in cohesive substrates. As the Portuguese Cretaceous sauropod osteological remains are very scarce, the Parede tracksite yields new and relevant evidence of these dinosaurs. Furthermore, the Parede tracksite is the youngest evidence of sauropods in the Portuguese record and some of the rare evidence of sauropods in Europe during the Albian. This discovery enhances the palaeobiological data for the Early Cretaceous Sauropoda of the Iberian Peninsula, where the osteological remains of these dinosaurs are relatively scarce in this region of southwestern Europe. Therefore, this occurrence is also of overall interest due to its impact on Cretaceous Sauropoda palaeobiogeography.
Incremental control and guidance of hybrid aircraft applied to the Cyclone tailsitter UAV

Hybrid unmanned aircraft, that combine hover capability with a wing for fast and efficient forward flight, have attracted a lot of attention in recent years. Many different designs are proposed, but one of the most promising is the tailsitter concept. However, tailsitters are difficult to control across the entire flight envelope, which often includes stalled flight. Additionally, their wing surface makes them susceptible to wind gusts. In this paper, we propose incremental nonlinear dynamic inversion control for the attitude and position control. The result is a single, continuous controller, that is able to track the acceleration of the vehicle across the flight envelope. The proposed controller is implemented on the Cyclone hybrid UAV. Multiple outdoor experiments are performed, showing that unmodeled forces and moments are effectively compensated by the incremental control structure, and that accelerations can be tracked across the flight envelope. Finally, we provide a comprehensive procedure for the implementation of the controller on other types of hybrid UAVs.
Probabilistic Neural Architecture Search

In neural architecture search (NAS), the space of neural network architectures is automatically explored to maximize predictive accuracy for a given task. Despite the success of recent approaches, most existing methods cannot be directly applied to large scale problems because of their prohibitive computational complexity or high memory usage. In this work, we propose a Probabilistic approach to neural ARchitecture SEarCh (PARSEC) that drastically reduces memory requirements while maintaining state-of-the-art computational complexity, making it possible to directly search over more complex architectures and larger datasets. Our approach only requires as much memory as is needed to train a single architecture from our search space. This is due to a memory-efficient sampling procedure wherein we learn a probability distribution over high-performing neural network architectures. Importantly, this framework enables us to transfer the distribution of architectures learnt on smaller problems to larger ones, further reducing the computational cost. We showcase the advantages of our approach in applications to CIFAR-10 and ImageNet, where our approach outperforms methods with double its computational cost and matches the performance of methods with costs that are three orders of magnitude larger.
Comparisons of CFD Simulations and In-service Data for the Self Propelled Performance of an Autonomous Underwater Vehicle

A blade element momentum theory propeller model is coupled with a commercial RANS solver. This allows the fully appended self propulsion of the autonomous underwater vehicle Autosub 3 to be considered. The quasi-steady propeller model has been developed to allow for circumferential and radial variations in axial and tangential inflow. The non-uniform inflow is due to control surface deflections and the bow-down pitch of the vehicle in cruise condition. The influence of propeller blade Reynolds number is included through the use of appropriate sectional lift and drag coefficients. Simulations have been performed over the vehicles operational speed range (Re = 6.8× 10 to 13.5× 10). A workstation is used for the calculations with mesh sizes up to 2x10 elements. Grid uncertainty is calculated to be 3.07% for the wake fraction. The initial comparisons with in service data show that the coupled RANS-BEMT simulation under predicts the drag of the vehicle and consequently the required propeller rpm. However, when an appropriate correction is made for the effect on resistance of various protruding sensors the predicted propulsor rpm matches well with that of in-service rpm measurements for vessel speeds (1m/s 2m/s). The developed analysis captures the important influence of the propeller blade and hull Reynolds number on overall system efficiency.
Learning Distributed Representations from Reviews for Collaborative Filtering

Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regularizing the derived product representations. Motivated by the success of this approach, we introduce two different models of reviews and study their effect on collaborative filtering performance. While the previous state-of-the-art approach is based on a latent Dirichlet allocation (LDA) model of reviews, the models we explore are neural network based: a bag-of-words product-of-experts model and a recurrent neural network. We demonstrate that the increased flexibility offered by the product-of-experts model allowed it to achieve state-of-the-art performance on the Amazon review dataset, outperforming the LDA-based approach. However, interestingly, the greater modeling power offered by the recurrent neural network appears to undermine the model's ability to act as a regularizer of the product representations.
Behavioral phenotypes of inbred mouse strains: implications and recommendations for molecular studies

Choosing the best genetic strains of mice for developing a new knockout or transgenic mouse requires extensive knowledge of the endogenous traits of inbred strains. Background genes from the parental strains may interact with the mutated gene, in a manner which could severely compromise the interpretation of the mutant phenotype. The present overview summarizes the literature on a wide variety of behavioral traits for the 129, C57BL/6, DBA/2, and many other inbred strains of mice. Strain distributions are described for open field activity, learning and memory tasks, aggression, sexual and parental behaviors, acoustic startle and prepulse inhibition, and the behavioral actions of ethanol, nicotine, cocaine, opiates, antipsychotics, and anxiolytics. Using the referenced information, molecular geneticists can choose optimal parental strains of mice, and perhaps develop new embryonic stem cell progenitors, for new knockouts and transgenics to investigate gene function, and to serve as animal models in the development of novel therapeutics for human genetic diseases.
MPMA: Mixture Probabilistic Matrix Approximation for Collaborative Filtering

Matrix approximation (MA) is one of the most popular techniques for collaborative filtering (CF). Most existing MA methods train user/item latent factors based on a user-item rating matrix and then use the global latent factors to model all users/items. However, globally optimized latent factors may not reflect the unique interests shared among only subsets of users/items, without which unique interests of users may not be accurately modelled. As a result, existing MA methods, which cannot capture the uniqueness of different user/item, cannot provide optimal recommendation. In this paper, a mixture probabilistic matrix approximation (MPMA) method is proposed, which unifies globally optimized user/item feature vectors (on the entire rating matrix) and locally optimized user/item feature vectors (on subsets of user/item ratings) to improve recommendation accuracy. More specifically, in MPMA, a method is developed to find both globally and locally optimized user/item feature vectors. Then, a Gaussian mixture model is adopted to combine global predictions and local predictions to produce accurate rating predictions. Experimental study using MovieLens and Netflix datasets demonstrates that MPMA outperforms five state-of-the-art MA based CF methods in recommendation accuracy with good scalability.
Low-Latency Video Semantic Segmentation

Recent years have seen remarkable progress in semantic segmentation. Yet, it remains a challenging task to apply segmentation techniques to video-based applications. Specifically, the high throughput of video streams, the sheer cost of running fully convolutional networks, together with the low-latency requirements in many real-world applications, e.g. autonomous driving, present a significant challenge to the design of the video segmentation framework. To tackle this combined challenge, we develop a framework for video semantic segmentation, which incorporates two novel components: (1) a feature propagation module that adaptively fuses features over time via spatially variant convolution, thus reducing the cost of per-frame computation: and (2) an adaptive scheduler that dynamically allocate computation based on accuracy prediction. Both components work together to ensure low latency while maintaining high segmentation quality. On both Cityscapes and CamVid, the proposed framework obtained competitive performance compared to the state of the art, while substantially reducing the latency, from 360 ms to 119 ms.