nils-herrmann/clean_scirepeval huggingface dataset:
A New Metrics for Countries' Fitness and Products' Complexity

Classical economic theories prescribe specialization of countries industrial production. Inspection of the country databases of exported products shows that this is not the case: successful countries are extremely diversified, in analogy with biosystems evolving in a competitive dynamical environment. The challenge is assessing quantitatively the non-monetary competitive advantage of diversification which represents the hidden potential for development and growth. Here we develop a new statistical approach based on coupled non-linear maps, whose fixed point defines a new metrics for the country Fitness and product Complexity. We show that a non-linear iteration is necessary to bound the complexity of products by the fitness of the less competitive countries exporting them. We show that, given the paradigm of economic complexity, the correct and simplest approach to measure the competitiveness of countries is the one presented in this work. Furthermore our metrics appears to be economically well-grounded.
A Distributed Newton Method for Large Scale Consensus Optimization

In this paper, we propose a distributed Newton method for consensus optimization. Our approach outperforms state-of-the-art methods, including ADMM. The key idea is to exploit the sparsity of the dual Hessian and recast the computation of the Newton step as one of efficiently solving symmetric diagonally dominant linear equations. We validate our algorithm both theoretically and empirically. On the theory side, we demonstrate that our algorithm exhibits superlinear convergence within a neighborhood of optimality. Empirically, we show the superiority of this new method on a variety of machine learning problems. The proposed approach is scalable to very large problems and has a low communication overhead.
Credit scoring using support vector machines with direct search for parameters selection

Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the model depends on its parameters’ setting. In this study, we use direct search method to optimize the SVM-based credit scoring model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment (DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the direct search method can find the effective model with high classification accuracy and good robustness and keep less dependency on the initial search space or point setting.
An Open Source Tracking Testbed and Evaluation Web Site

We have implemented a GUI-based tracking testbed system in C and Intel OpenCV, running within the Microsoft Windows environment. The motivation for the testbed is to run and log tracking experiments in near real-time. The testbed allows a tracking experiment to be repeated from the same starting state using different tracking algorithms and parameter settings, thereby facilitating comparison of algorithms. We have also developed a tracking evaluation web site to encourage third-party self-evaluation of state-of-the-art tracking algorithms. The site hosts source code for the tracking testbed, a set of ground-truth datasets, and a method for on-line evaluation of uploaded tracking results.
Machine Learning Based Detection of Clickbait Posts in Social Media

Clickbait (headlines) make use of misleading titles that hide critical information from or exaggerate the content on the landing target pages to entice clicks. As clickbaits often use eye-catching wording to attract viewers, target contents are often of low quality. Clickbaits are especially widespread on social media such as Twitter, adversely impacting user experience by causing immense dissatisfaction. Hence, it has become increasingly important to put forward a widely applicable approach to identify and detect clickbaits. In this paper, we make use of a dataset from the clickbait challenge 2017 (clickbait-challenge.com) comprising of over 21,000 headlines/titles, each of which is annotated by at least five judgments from crowdsourcing on how clickbait it is. We attempt to build an effective computational clickbait detection model on this dataset. We first considered a total of 331 features, filtered out many features to avoid overfitting and improve the running time of learning, and eventually selected the 60 most important features for our final model. Using these features, Random Forest Regression achieved the following results: MSE=0.035 MSE, Accuracy=0.82, and F1sore=0.61 on the clickbait class.
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are a remarkable demonstration of deep reinforcement learning’s capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy. However, many obstacles remain in the understanding of and usability of these promising approaches by the research community. Toward elucidating unresolved mysteries and facilitating future research, we propose ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. ELF OpenGo is the first open-source Go AI to convincingly demonstrate superhuman performance with a perfect (20:0) record against global top professionals. We apply ELF OpenGo to conduct extensive ablation studies, and to identify and analyze numerous interesting phenomena in both the model training and in the gameplay inference procedures. Our code, models, selfplay datasets, and auxiliary data are publicly available.
Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams

The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances flexibility of data stream method in exploiting unlabelled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves in-depth study because it characterizes a fixed network capacity which cannot adapt to rapidly changing environments. An automated construction of a denoising autoeconder, namely deep evolving denoising autoencoder (DEVDAN), is proposed in this paper. DEVDAN features an open structure both in the generative phase and in the discriminative phase where input features can be automatically added and discarded on the fly. A network significance (NS) method is formulated in this paper and is derived from the bias-variance concept. This method is capable of estimating the statistical contribution of the network structure and its hidden units which precursors an ideal state to add or prune input features. Furthermore, DEVDAN is free of the problemspecific threshold and works fully in the single-pass learning fashion. The efficacy of DEVDAN is numerically validated using nine non-stationary data stream problems simulated under the prequential test-then-train protocol where DEVDAN is capable of delivering improvement of classification accuracy to recently published online learning works while having flexibility in the automatic extraction of robust input features and in adapting to rapidly changing environments.
A Key Characteristic of Sex Differences in the Developing Brain: Greater Variability in Brain Structure of Boys than Girls.

In many domains, including cognition and personality, greater variability is observed in males than in females in humans. However, little is known about how variability differences between sexes are represented in the brain. The present study tested whether there is a sex difference in variance in brain structure using a cohort of 643 males and 591 females aged between 3 and 21 years. The broad age-range of the sample allowed us to test if variance differences in the brain differ across age. We observed significantly greater male than female variance for several key brain structures, including cerebral white matter and cortex, hippocampus, pallidum, putamen, and cerebellar cortex volumes. The differences were observed at both upper and lower extremities of the distributions and appeared stable across development. These findings move beyond mean levels by showing that sex differences were pronounced for variability, thereby providing a novel perspective on sex differences in the developing brain.
Characteristics of cross (bypass) coupling through higher/lower order modes and their applications in elliptic filter design

This paper presents a new set of results concerning the use of higher/lower order modes as a means to implement bypass or cross coupling for applications in elliptic filter design. It is shown that the signs of the coupling coefficients to produce a transmission zero (TZ) either below or above the passband are, in certain situations, reversed from the predictions of simpler existing models. In particular, the bypass coupling to higher/lower order modes must be significantly stronger than the coupling to the main resonance in order to generate TZs in the immediate vicinity of the passband. Planar (H-plane) singlets are used to illustrate the derived results. This study should provide very important guidelines in selecting the proper main and bypass couplings for sophisticated filtering structures. Example filters are designed, built, and measured to demonstrate the validity of the introduced theory.
High-dimensional signature compression for large-scale image classification

We address image classification on a large-scale, i.e. when a large number of images and classes are involved. First, we study classification accuracy as a function of the image signature dimensionality and the training set size. We show experimentally that the larger the training set, the higher the impact of the dimensionality on the accuracy. In other words, high-dimensional signatures are important to obtain state-of-the-art results on large datasets. Second, we tackle the problem of data compression on very large signatures (on the order of 105 dimensions) using two lossy compression strategies: a dimensionality reduction technique known as the hash kernel and an encoding technique based on product quantizers. We explain how the gain in storage can be traded against a loss in accuracy and/or an increase in CPU cost. We report results on two large databases — ImageNet and a dataset of lM Flickr images — showing that we can reduce the storage of our signatures by a factor 64 to 128 with little loss in accuracy. Integrating the decompression in the classifier learning yields an efficient and scalable training algorithm. On ILSVRC2010 we report a 74.3% accuracy at top-5, which corresponds to a 2.5% absolute improvement with respect to the state-of-the-art. On a subset of 10K classes of ImageNet we report a top-1 accuracy of 16.7%, a relative improvement of 160% with respect to the state-of-the-art.
THE EFFECTS OF A 6 WEEK PLYOMETRIC TRAINING PROGRAMME ON EXPLOSIVE STRENGTH AND AGILITY IN PROFESSIONAL BASKETBALL PLAYERS

Explosive strength of the lower extremities and agility are important parts of game performance in basketball. Although numerous studies have focused on the assessment of the training effect of plyometric training, studies focusing on elite players are missing.  The aim of the study was to find out what changes in explosive strength of the lower extremities take place after a 6 week plyometric training applied in training units during the pre-season in elite basketball players.  Elite basketball players (N = 12, age 24.36 ± 3.9 years, height 196.2 ± 9.6 cm, weight 92.9 ± 13.9 kg) performed a 6 week plyometric training (PT) programme predominantly focused on explosive strength of the lower body and upper body and was conducted in sixteen training units during pre-season. The changes in explosive strength were measured by the Counter Movement Jump Free Arms test and Two Step Run Up Jump test; agility was measured using the “T” Drill test and Hexagonal Obstacle test. The players participated in three measurements. The 1st (pretesting) was performed on the first day of pre-season, the 2nd (post-testing) was done two days after completing the PT programme and the 3rd (post-testing) six weeks after completing the PT programme. Friedman’s ANOVA for repeated measurements was used to determine the significance of differences between the measurement sessions (p < .05).  A significant effect of the training programme was observed only for the Hexagonal Obstacle test (p = .01). A post hoc analysis revealed a significant increase in test performance between the 1st and 3rd measurement (p < .01) and between the 2nd and 3rd measurement (p < .01).  The results of the study of elite basketball players did not positively support the assumption that plyometric exercises can be an effective tool for the improvement of explosive strength and agility. However, in some players the improvements corresponded to average improvements after training programmes presented in literature.
"Why does rain fall?": children prefer to learn from an informant who uses noncircular explanations.

These two studies explored 3- and 5-year-olds' evaluation of noncircular and circular explanations, and their use of such explanations to determine informant credibility. Although 5-year-olds demonstrated a selective preference for noncircular over circular explanations (Experiment 1: Long Explanations; Experiment 2: Short Explanations), 3-year-olds only demonstrated a preference for the noncircular when the explanations were shortened (Experiment 2). Children's evaluation of the explanations extended to their inferences about the informants' future credibility. Both age groups demonstrated a selective preference for learning novel explanations from an informant who had previously provided noncircular explanations-although only 5-year-olds also preferred to learn novel labels from her. The implications and scope of children's ability to monitor the quality of an informant's explanation are discussed.
GeeLytics: Geo-distributed edge analytics for large scale IoT systems based on dynamic topology

High data rate sensors such as video cameras, audio sensors, and motion sensors are becoming ubiquitous in the Internet of Things (IoT). In large scale IoT systems like smart cities, a large number of sensors are now widely deployed at different locations, generating a huge amount of stream data. Although the generated data provide us great potential to sense our live environments, it still remains a big challenge to efficiently extract real-time results from sensor data to make fast decisions. Existing stream processing platforms, such as Storm, Spark Streaming, and S4, are well designed to process stream data within a cluster in the Cloud, but they are not suitable for highly distributed IoT systems in which data are naturally geo-distributed and low latency analytics results are expected to be shared across users and applications. To tackle this problem, we design an edge analytics platform called GeeLytics, which can perform real-time stream processing both at the network edges and in the Cloud in a dynamic and transparent manner. In this position paper we discuss its use cases, motivation, and preliminary architecture design. As compared with the start of the art, GeeLytics is designed to support dynamic stream processing topologies by taking into account the system characteristics of heterogeneous edge/Cloud nodes and also the current system workload. This shall achieve low latency analytics results while minimizing the edge-to-Cloud bandwidth consumption. In addition, using docker application containers for packaging up deployable tasks and a distributed pub/sub mechanism for inter-task stream data routing, GeeLytics shall provide better resource isolation and system efficiency to support multi-tenancy.
Deep learning for 2D scan matching and loop closure

Although 2D LiDAR based Simultaneous Localization and Mapping (SLAM) is a relatively mature topic nowadays, the loop closure problem remains challenging due to the lack of distinctive features in 2D LiDAR range scans. Existing research can be roughly divided into correlation based approaches e.g. scan-to-submap matching and feature based methods e.g. bag-of-words (BoW). In this paper, we solve loop closure detection and relative pose transformation using 2D LiDAR within an end-to-end Deep Learning framework. The algorithm is verified with simulation data and on an Unmanned Aerial Vehicle (UAV) flying in indoor environment. The loop detection ConvNet alone achieves an accuracy of 98.2% in loop closure detection. With a verification step using the scan matching ConvNet, the false positive rate drops to around 0.001%. The proposed approach processes 6000 pairs of raw LiDAR scans per second on a Nvidia GTX1080 GPU.
A Review of 40 Years of Cognitive Architecture Research: Focus on Perception, Attention, Learning and Applications

In this paper we present a broad overview of the last 40 years of research on cognitive architectures. Although the number of existing architectures is nearing several hundred, most of the existing surveys do not reflect this growth and focus on a handful of well-established architectures. While their contributions are undeniable, they represent only a part of the research in the field. Thus, in this survey we wanted to shift the focus towards a more inclusive and high-level overview of the research in cognitive architectures. Our final set of 86 architectures includes 55 that are still actively developed, and borrow from a diverse set of disciplines, spanning areas from psychoanalysis to neuroscience. To keep the length of this paper within reasonable limits we discuss only the core cognitive abilities, such as perception, attention mechanisms, learning and memory structure. To assess the breadth of practical applications of cognitive architectures we gathered information on over 700 practical projects implemented using the cognitive architectures in our list. We use various visualization techniques to highlight overall trends in the development of the field. For instance, our data confirms that the hybrid approach to cognitive modeling already dominates the field and will likely continue to do so in the future. Our analysis of practical applications shows that most architectures are very narrowly focused on a particular application domain. Furthermore, there is an apparent gap between general research in robotics and computer vision and research in these areas within the cognitive architectures field. It is very clear that biologically inspired models do not have the same range and efficiency compared to the systems based on engineering principles and heuristics. Another observation is related to a general lack of collaboration, which is surprising to see in the inherently interdisciplinary area of cognitive architectures. Several factors hinder communication, among which are the closed nature of the individual projects (only one-third of the reviewed here architectures are open-source) and terminological differences.
Layout guidelines for web text and a web service to improve accessibility for dyslexics

In this paper, we offer set of guidelines and a web service that presents Web texts in a more more accessible way to people with dyslexia. The layout guidelines for developing this service are based on a user study with a group of twenty two dyslexic users. The data collected from our study combines qualitative data from interviews and questionnaires and quantitative data from tests carried out using eye tracking. We analyze and compare both kinds of data and present a set of layout guidelines for making the text Web more readable for dyslexic users. To the best of our knowledge, our methodology for defining dyslexic-friendly guidelines and our web service are novel.
Anytime Stereo Image Depth Estimation on Mobile Devices

Many real-world applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints. Current state-of-the-art algorithms can either generate accurate but slow mappings, or fast but inaccurate ones, and typically require far too many parameters for poweror memoryconstrained devices. Motivated by this shortcoming, we propose a novel approach for disparity prediction in the anytime setting. In contrast to prior work, our end-to-end learned approach can trade off computation and accuracy at inference time. The depth estimation is performed in stages, during which the model can be queried at any time to output its current best estimate. Our final model can process 1242×375 resolution images within a range of 10-35 FPS on an NVIDIA Jetson TX2 module with only marginal increases in error – using two orders of magnitude fewer parameters than the most competitive baseline. Our code is available as open source on https://github.com/mileyan/AnyNet.
Exploring Engaging Gamification Mechanics in Massive Online Open Courses

Massive open online courses (MOOCs) have developed rapidly and become tremendously popular because of their plentiful gamification designs, such as reputation points, rewards, and goal setting. Although previous studies have mentioned a broad range of gamification designs that might influence MOOC learner engagement, most gamified MOOCs fail to meet learning objectives because of a lack of research regarding suitable game design, as well as poor rationale for or design of gamification mechanics. This study aims to explore and identify engaging gamification mechanics for MOOC learners. We conducted a focus group interview with 25 MOOC frequent users to identify 40 gamification mechanics. This study then determined the relative engagingness of these gamification mechanics by administering an online survey to 5,020 MOOC learners. The results indicated that the 10 most engaging gamification mechanics accounted for more than 50% of the engagingness. The mechanics of the Where’s Wally game is extremely engaging for MOOC learners; however, they it is not been demonstrated in previous relevant studies. Finally, we discuss the top five engaging gamification mechanics and their implications.
On the simulation of large-scale architectures using multiple application abstraction levels

Simulation is a key tool for computer architecture research. In particular, cycle-accurate simulators are extremely important for microarchitecture exploration and detailed design decisions, but they are slow and, so, not suitable for simulating large-scale architectures, nor are they meant for this. Moreover, microarchitecture design decisions are irrelevant, or even misleading, for early processor design stages and high-level explorations. This allows one to raise the abstraction level of the simulated architecture, and also the application abstraction level, as it does not necessarily have to be represented as an instruction stream.
 In this paper we introduce a definition of different application abstraction levels, and how these are employed in TaskSim, a multi-core architecture simulator, to provide several architecture modeling abstractions, and simulate large-scale architectures with hundreds of cores. We compare the simulation speed of these abstraction levels to the ones in existing simulation tools, and also evaluate their utility and accuracy. Our simulations show that a very high-level abstraction, which may be even faster than native execution, is useful for scalability studies on parallel applications; and that just simulating explicit memory transfers, we achieve accurate simulations for architectures using non-coherent scratchpad memories, with just a 25x slowdown compared to native execution. Furthermore, we revisit trace memory simulation techniques, that are more abstract than instruction-by-instruction simulations and provide an 18x simulation speedup.