nils-herrmann/clean_scirepeval huggingface dataset:
Scratch: A Way to Logo and Python

There is concern amongst teachers about how to support all pupils in making the transition from popular graphical languages like Scratch to text-based languages like Python. In a new subject, not taught widely before at both primary and secondary education in England, there is inevitably a lack of tuned-in pedagogical expertise. In this paper, the authors address the transition process by exploring established pedagogy in Computer Science, and other subjects including Mathematics, Science and Languages, and by sharing and testing their findings with pupils and teachers in the classroom. Teaching the fundamentals of programming is well served by applying sequential solutions in both graphical and text-based languages. This practitioner action research paper focuses on scaffolding support for pupils when making the transition from graphical to text-based languages. In an approach which uses graphical languages in conjunction with, not in place of, text-based programming languages, the authors discuss ways to tackle the difficulties presented to pupils by text-based languages, and propose a tested strategy for teachers to enable pupils to undertake the transition successfully.
Pleasures of the brain

How does the brain cause positive affective reactions to sensory pleasure? An answer to pleasure causation requires knowing not only which brain systems are activated by pleasant stimuli, but also which systems actually cause their positive affective properties. This paper focuses on brain causation of behavioral positive affective reactions to pleasant sensations, such as sweet tastes. Its goal is to understand how brain systems generate 'liking,' the core process that underlies sensory pleasure and causes positive affective reactions. Evidence suggests activity in a subcortical network involving portions of the nucleus accumbens shell, ventral pallidum, and brainstem causes 'liking' and positive affective reactions to sweet tastes. Lesions of ventral pallidum also impair normal sensory pleasure. Recent findings regarding this subcortical network's causation of core 'liking' reactions help clarify how the essence of a pleasure gloss gets added to mere sensation. The same subcortical 'liking' network, via connection to brain systems involved in explicit cognitive representations, may also in turn cause conscious experiences of sensory pleasure.
Automatic Feature Extraction for Classifying Audio Data

Today, many private households as well as broadcasting or film companies own large collections of digital music plays. These are time series that differ from, e.g., weather reports or stocks market data. The task is normally that of classification, not prediction of the next value or recognizing a shape or motif. New methods for extracting features that allow to classify audio data have been developed. However, the development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires tailoring the feature set anew. This paper presents a unifying framework for feature extraction from value series. Operators of this framework can be combined to feature extraction methods automatically, using a genetic programming approach. The construction of features is guided by the performance of the learning classifier which uses the features. Our approach to automatic feature extraction requires a balance between the completeness of the methods on one side and the tractability of searching for appropriate methods on the other side. In this paper, some theoretical considerations illustrate the trade-off. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments: classification of genres and classification according to user preferences.
A Scenario-Based Analysis of Mobile Payment Acceptance

In recent years, a lot of mobile payment service providers entered the market and often left it without any success. They have often disregarded the high complexity of the ecosystem and of the particularities of mobile communication techniques. The contribution of this paper is to understand the determinants of customer acceptance of mobile payment especially the influence of payment scenarios. For that purpose, the Technology Acceptance Model (TAM) was extended by new constructs measuring the m-payment particularities like expressiveness and the applicability in different payment scenarios.
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron

We present an extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing the desired prosody. We show that conditioning Tacotron on this learned embedding space results in synthesized audio that matches the prosody of the reference signal with fine time detail even when the reference and synthesis speakers are different. Additionally, we show that a reference prosody embedding can be used to synthesize text that is different from that of the reference utterance. We define several quantitative and subjective metrics for evaluating prosody transfer, and report results with accompanying audio samples from single-speaker and 44-speaker Tacotron models on a prosody transfer task.
Characterizations of Parallelism in Applications and Their Use In Scheduling

As multiprocessors with large numbers of processors become more prevalent, we face the task of developing scheduling algorithms for the multiprogrammed use of such machines. The scheduling decisions must take into account the number of processors available, the overall system load, and the ability of each application awaiting activation to make use of a given number of processors.
The parallelism within an application can be characterized at a number of different levels of detail. At the highest level, it might be characterized by a single parameter (such as the proportion of the application that is sequential, or the average number of processors the application would use if an unlimited number of processors were available). At the lowest level, representing all the parallelism in the application requires the full data dependency graph (which is more information than is practically manageable).
In this paper, we examine the quality of processor allocation decisions under multiprogramming that can be made with several different high-level characterizations of application parallelism. We demonstrate that decisions based on parallelism characterizations with two to four parameters are superior to those based on single-parameter characterizations (such as fraction sequential or average parallelism). The results are based predominantly on simulation, with some guidance from a simple analytic model.
Low-Rank Similarity Metric Learning in High Dimensions

Metric learning has become a widespreadly used tool in machine learning. To reduce expensive costs brought in by increasing dimensionality, low-rank metric learning arises as it can be more economical in storage and computation. However, existing low-rank metric learning algorithms usually adopt nonconvex objectives, and are hence sensitive to the choice of a heuristic low-rank basis. In this paper, we propose a novel low-rank metric learning algorithm to yield bilinear similarity functions. This algorithm scales linearly with input dimensionality in both space and time, therefore applicable to highdimensional data domains. A convex objective free of heuristics is formulated by leveraging trace norm regularization to promote low-rankness. Crucially, we prove that all globally optimal metric solutions must retain a certain low-rank structure, which enables our algorithm to decompose the high-dimensional learning task into two steps: an SVD-based projection and a metric learning problem with reduced dimensionality. The latter step can be tackled efficiently through employing a linearized Alternating Direction Method of Multipliers. The efficacy of the proposed algorithm is demonstrated through experiments performed on four benchmark datasets with tens of thousands of dimensions.
Online slip parameter estimation for tracked vehicle odometry on loose slope

In case of volcanic eruption, a robotic volcano exploration for observing restricted areas is expected to judge the evacuation call for inhabitants. An unmanned ground vehicle (UGV) is one possibility to apply to such exploration missions. When a UGV traverses on volcanic fields, a slippage between the vehicle and the terrain occurs. This is because the volcanic environment is covered with loose soil and rocks, and there are many slopes. The slippage causes several problems for UGVs, particularly localization and terrainability. Therefore, in this research, we propose a slip estimation method based on a slip model to apply to slip-compensated odometry for tracked vehicles. First, we propose a slip model for tracked vehicles based on the force acting on a robot on a slope. The proposed slip model has two parameters: a pitch angle dependence and a constant component, and these parameters were identified by indoor slope-traveling experiments. Next, we propose a slip parameter estimation method using a particle filter technique with a velocity measurement sensor, and report on the effectiveness of our method by slope-traveling experiments. The experimental result shows that the accuracy of our position estimation method based on the slip-compensated odometry is improved in comparison with conventional methods by using the slip parameters.
Test-driven learning in early programming courses

Coercing new programmers to adopt disciplined development practices such as thorough unit testing is a challenging endeavor. Test-driven development (TDD) has been proposed as a solution to improve both software design and testing. Test-driven learning (TDL) has been proposed as a pedagogical approach for teaching TDD without imposing significant additional instruction time.
 This research evaluates the effects of students using a test-first (TDD) versus test-last approach in early programming courses, and considers the use of TDL on a limited basis in CS1 and CS2. Software testing, programmer productivity, programmer performance, and programmer opinions are compared between test-first and test-last programming groups. Results from this research indicate that a test-first approach can increase student testing and programmer performance, but that early programmers are very reluctant to adopt a test-first approach, even after having positive experiences using TDD. Further, this research demonstrates that TDL can be applied in CS1/2, but suggests that a more pervasive implementation of TDL may be necessary to motivate and establish disciplined testing practice among early programmers.
Continuous-Discrete Extended Kalman Filter on Matrix Lie Groups Using Concentrated Gaussian Distributions

In this paper we generalize the continuous-discrete extended Kalman filter (CD-EKF) to the case where the state and the observations evolve on connected unimodular matrix Lie groups. We propose a new assumed density filter called continuous-discrete extended Kalman filter on Lie groups (CD-LG-EKF). It is built upon a geometrically meaningful modeling of the concentrated Gaussian distribution on Lie groups. Such a distribution is parametrized by a mean and a covariance matrix defined on the Lie group and in its associated Lie algebra respectively. Our formalism yields tractable equations for both non-linear continuous time propagation and discrete update of the distribution parameters under the assumption that the posterior distribution of the state is a concentrated Gaussian. As a side effect, we contribute to the derivation of the first and second order differential of the matrix Lie group logarithm using left connection. We also show that the CD-LG-EKF reduces to the usual CD-EKF if the state and the observations evolve on Euclidean spaces. Our approach leads to a systematic methodology for the design of filters, which is illustrated by the application to a camera pose filtering problem with observations on Lie group. In this application, the CD-LG-EKF significantly outperforms two constrained non-linear filters (one based on a linearization technique and the other on the unscented transform) applied on the embedding space of the Lie group.
Adaptively Weighted Multi-task Deep Network for Person Attribute Classification

Multi-task learning aims to boost the performance of multiple prediction tasks by appropriately sharing relevant information among them. However, it always suffers from the negative transfer problem. And due to the diverse learning difficulties and convergence rates of different tasks, jointly optimizing multiple tasks is very challenging. To solve these problems, we present a weighted multi-task deep convolutional neural network for person attribute analysis. A novel validation loss trend algorithm is, for the first time proposed to dynamically and adaptively update the weight for learning each task in the training process. Extensive experiments on CelebA, Market-1501 attribute and Duke attribute datasets clearly show that state-of-the-art performance is obtained; and this validates the effectiveness of our proposed framework.
Integrating Classification and Association Rule Mining

Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target of discovery is not pre-determined, while for classification rule mining there is one and only one predetermined target. In this paper, we propose to integrate these two mining techniques. The integration is done by focusing on mining a special subset of association rules, called class association rules (CARs). An eff icient algorithm is also given for building a classifier based on the set of discovered CARs. Experimental results show that the classifier built this way is, in general, more accurate than that produced by the state-of-the-art classification system C4.5. In addition, this integration helps to solve a number of problems that exist in the current classification systems.
Filling holes in triangular meshes by curve unfolding

We propose a novel approach to automatically fill holes in triangulated models. Each hole is filled using a minimum energy surface that is obtained in three steps. First, we unfold the hole boundary onto a plane using energy minimization. Second, we triangulate the unfolded hole using a constrained Delaunay triangulation. Third, we embed the triangular mesh as a minimum energy surface in ℝ3. The running time of the method depends primarily on the size of the hole boundary and not on the size of the model, thereby making the method applicable to large models. Our experiments demonstrate the applicability of the algorithm to the problem of filling holes bounded by highly curved boundaries in large models.
A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion

Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a novel hierarchical recurrent encoder-decoder architecture that makes possible to account for sequences of previous queries of arbitrary lengths. As a result, our suggestions are sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that our model outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our architecture is general enough to be used in a variety of other applications.
Deanonymisation of Clients in Bitcoin P2P Network

Bitcoin is a digital currency which relies on a distributed set of miners to mint coins and on a peer-to-peer network to broadcast transactions. The identities of Bitcoin users are hidden behind pseudonyms (public keys) which are recommended to be changed frequently in order to increase transaction unlinkability.
 We present an efficient method to deanonymize Bitcoin users, which allows to link user pseudonyms to the IP addresses where the transactions are generated. Our techniques work for the most common and the most challenging scenario when users are behind NATs or firewalls of their ISPs. They allow to link transactions of a user behind a NAT and to distinguish connections and transactions of different users behind the same NAT. We also show that a natural countermeasure of using Tor or other anonymity services can be cut-off by abusing anti-DoS countermeasures of the Bitcoin network. Our attacks require only a few machines and have been experimentally verified. The estimated success rate is between 11% and 60% depending on how stealthy an attacker wants to be. We propose several countermeasures to mitigate these new attacks.
Thinking , Fast and Slow , by D .

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DocUNet: Document Image Unwarping via a Stacked U-Net

Capturing document images is a common way for digitizing and recording physical documents due to the ubiquitousness of mobile cameras. To make text recognition easier, it is often desirable to digitally flatten a document image when the physical document sheet is folded or curved. In this paper, we develop the first learning-based method to achieve this goal. We propose a stacked U-Net  with intermediate supervision to directly predict the forward mapping from a distorted image to its rectified version. Because large-scale real-world data with ground truth deformation is difficult to obtain, we create a synthetic dataset with approximately 100 thousand images by warping non-distorted document images. The network is trained on this dataset with various data augmentations to improve its generalization ability. We further create a comprehensive benchmark1 that covers various real-world conditions. We evaluate the proposed model quantitatively and qualitatively on the proposed benchmark, and compare it with previous non-learning-based methods.
High-frequency dielectric measurements

The demands on dielectric material measurements have increased over the years as electrical components have been miniaturized and device frequency bands have increased. Well-characterized dielectric measurements on thin materials are needed for circuit design, minimization of crosstalk, and characterization of signal-propagation speed. Bulk material applications have also increased. For accurate dielectric measurements, each measurement band and material geometry requires specific fixtures. Engineers and researchers must carefully match their material system and uncertainty requirements to the best available measurement system. Broadband measurements require transmission-line methods, and accurate measurements on low-loss materials are performed in resonators. The development of the most accurate methods for each application requires accurate fixture selection in terms of field geometry, accurate field models, and precise measurement apparatus.
Automatic Number Plate Recognition Using Artificial Neural Network

Automatic Number Plate Recognition (ANPR) became a very important tool in our daily life because of the unlimited increase of cars and transportation systems, which make it impossible to be fully managed and monitored by humans. Examples are so many, like traffic monitoring, tracking stolen cars, managing parking toll, red-light violation enforcement, border and customs checkpoints. Yet, it’s a very challenging problem, due to the diversity of plate formats, different scales, rotations and non-uniform illumination conditions during image acquisition. The objective of this paper is to provide a novel algorithm for license plate recognition in complex scenes, particularly for the all-day traffic surveillance environment. This is achieved using mathematical morphology and artificial neural network (ANN). A preprocessing step is applied to improve the performance of license plate localization and character segmentation in case of severe imaging conditions. The first and second stages utilize edge detection and mathematical morphology followed by connected component analysis. ANN is employed in the last stage to construct a classifier to categorize the input numbers of the license plate. The algorithm has been applied on 102 car images with different backgrounds, license plate angles, distances, lightening conditions, and colors. The average accuracy of the license plate localization is 97.06%, 95.10% for license plate segmentation, and 94.12% for character recognition. The experimental results show the outstanding detection performance of the proposed method comparing with traditional algorithms.
Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences

The availability of user check-in data in large volume from the rapid growing location-based social networks (LBSNs) enables a number of important location-aware services. Point-of-interest (POI) recommendation is one of such services, which is to recommend POIs that users have not visited before. It has been observed that: (i) users tend to visit nearby places, and (ii) users tend to visit different places in different time slots, and in the same time slot, users tend to periodically visit the same places. For example, users usually visit a restaurant during lunch hours, and visit a pub at night. In this paper, we focus on the problem of time-aware POI recommendation, which aims at recommending a list of POIs for a user to visit at a given time. To exploit both geographical and temporal influences in time aware POI recommendation, we propose the Geographical-Temporal influences Aware Graph (GTAG) to model check-in records, geographical influence and temporal influence. For effective and efficient recommendation based on GTAG, we develop a preference propagation algorithm named Breadth first Preference Propagation (BPP). The algorithm follows a relaxed breath-first search strategy, and returns recommendation results within at most 6 propagation steps. Our experimental results on two real-world datasets show that the proposed graph-based approach outperforms state-of-the-art POI recommendation methods substantially.