nils-herrmann/clean_scirepeval huggingface dataset:
The Relative Effectiveness of Human Tutoring , Intelligent Tutoring Systems , and Other Tutoring Systems

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Monitoring endurance running performance using cardiac parasympathetic function

The aims of the present study were to (1) assess relationships between running performance and parasympathetic function both at rest and following exercise, and (2) examine changes in heart rate (HR)-derived indices throughout an 8-week period training program in runners. In 14 moderately trained runners (36 ± 7 years), resting vagal-related HR variability (HRV) indices were measured daily, while exercise HR and post-exercise HR recovery (HRR) and HRV indices were measured fortnightly. Maximal aerobic speed (MAS) and 10 km running performance were assessed before and after the training intervention. Correlations (r > 0.60, P < 0.01) were observed between changes in vagal-related indices and changes in MAS and 10 km running time. Exercise HR decreased progressively during the training period (P < 0.01). In the 11 subjects who lowered their 10 km running time >0.5% (responders), resting vagal-related indices showed a progressively increasing trend (time effect P = 0.03) and qualitative indications of possibly and likely higher values during week 7 [+7% (90% CI −3.7;17.0)] and week 9 [+10% (90% CI −1.5;23)] compared with pre-training values, respectively. Post-exercise HRV showed similar changes, despite less pronounced between-group differences. HRR showed a relatively early possible decrease at week 3 [−20% (90% CI −42;10)], with only slight reductions near the end of the program. The results illustrate the potential of resting, exercise and post-exercise HR measurements for both assessing and predicting the impact of aerobic training on endurance running performance.
New trends in dark silicon

This paper presents new trends in dark silicon reflecting, among others, the deployment of FinFETs in recent technology nodes and the impact of voltage/frquency scaling, which lead to new less-conservative predictions. The focus is on dark silicon from a thermal perspective: we show that it is not simply the chip's total power budget, e.g., the Thermal Design Power (TDP), that leads to the dark silicon problem, but instead it is the power density and related thermal effects. We therefore propose to use Thermal Safe Power (TSP) as a more efficient power budget. It is also shown that sophisticated spatio-temporal mapping decisions result in improved thermal profiles with reduced peak temperatures. Moreover, we discuss the implications of Near-Threshold Computing (NTC) and employment of Boosting techniques in dark silicon systems.
Extending the ISO/IEC 9126-1 quality model with non-technical factors for COTS components selection

The selection of Commercial Off-The-Shelf (COTS) components is currently a central activity in the development of information systems. Criteria for selecting COTS components include both technical and non-technical issues. Whilst many catalogues of technical quality factors exist, it is not the case for non-technical ones. In this paper, we propose an extension of the ISO/IEC 9126-1 catalogue with non-technical factors. The extension is designed to be integrated smoothly in the departing catalogue, therefore we keep the hierarchy-oriented structure and we distinguish the three abstraction levels of the ISO/IEC standard. We provide some evidence of the adequacy of the catalogue by reporting on its use in some recent COTS selection experiences carried out in the context of the ETAPATELECOM company (Ecuador).
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016

This paper presents the method that underlies our submission to the untrimmed video classification task of ActivityNet Challenge 2016. We follow the basic pipeline of temporal segment networks [ 16] and further raise the performance via a number of other techniques. Specifically, we use the latest deep model architecture, e.g., ResNet and Inception V3, and introduce new aggregation schemes (top-k and attention-weighted pooling). Additionally, we incorp rate the audio as a complementary channel, extracting relevant information via a CNN applied to the spectrograms. With these techniques, we derive an ensemble of deep models, which, together, attains a high classification accurac y (mAP93.23%) on the testing set and secured the first place in the challenge.
Extending asset management system functionality in bitcoin platform

Bitcoin is a digital payment system empowered by a distributed database called blockchain. The blockchain is an open ledger containing records of every transaction within Bitcoin system maintained by Bitcoin nodes all over the world. Because of its availability and robustness, the blockchain could be utilized as a record-keeping tool for information not related to any Bitcoin transactions. We propose a method of utilizing the blockchain of Bitcoin system to publish information by embedding an arbitrary size of data into Bitcoin transactions. By publishing information using the blockchain, the information also carries the characteristics of Bitcoin transaction: anonymous, decentralized, and permanent. The proposed protocol could be used to extend the functionality of asset management systems which are limited to a maximum of 80 bytes data. The proposed method also offers an efficiency of the transaction fee by 18 percent compared to Bitcoin Messaging protocol.
Specificity of sprint and agility training methods.

The purpose of this study was to determine if straight sprint training transferred to agility performance tests that involved various change-of-direction complexities and if agility training transferred to straight sprinting speed. Thirty-six males were tested on a 30-m straight sprint and 6 agility tests with 2-5 changes of direction at various angles. The subjects participated in 2 training sessions per week for 6 weeks using 20-40-m straight sprints (speed) or 20-40-m change-of-direction sprints (3-5 changes of 100 degrees) (agility). After the training period, the subjects were retested, and the speed training resulted in significant improvements (p < 0.05) in straight sprinting speed but limited gains in the agility tests. Generally, the more complex the agility task, the less the transfer from the speed training to the agility task. Conversely, the agility training resulted in significant improvements in the change-of-direction tests (p < 0.05) but no significant improvement (p > 0.05) in straight sprint performance. We concluded that straight speed and agility training methods are specific and produce limited transfer to the other. These findings have implications for the design of speed and agility training and testing protocols.
Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

This paper presents a real-time path planning algorithm that guarantees probabilistic feasibility for autonomous robots with uncertain dynamics operating amidst one or more dynamic obstacles with uncertain motion patterns. Planning safe trajectories under such conditions requires both accurate prediction and proper integration of future obstacle behavior within the planner. Given that available observation data is limited, the motion model must provide generalizable predictions that satisfy dynamic and environmental constraints, a limitation of existing approaches. This work presents a novel solution, named RR-GP, which builds a learned motion pattern model by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based reachability computation. Obstacle trajectory GP predictions are conditioned on dynamically feasible paths identified from the reachability analysis, yielding more accurate predictions of future behavior. RR-GP predictions are integrated with a robust path planner, using chance-constrained RRT, to identify probabilistically feasible paths. Theoretical guarantees of probabilistic feasibility are shown for linear systems under Gaussian uncertainty; approximations for nonlinear dynamics and/or non-Gaussian uncertainty are also presented. Simulations demonstrate that, with this planner, an autonomous vehicle can safely navigate a complex environment in realtime while significantly reducing the risk of collisions with dynamic obstacles. G. Aoude, B. Luders, Room 41-105 J. M. Joseph, Room 32-331 N. Roy, Room 33-315 J. P. How, Room 33-326 77 Massachusetts Avenue Tel.: +1-617-314-4375 E-mail: gaoude@alum.mit.edu, luders@mit.edu, jmjoseph@mit.edu, nickroy@mit.edu, jhow@mit.edu
Discovering competitive intelligence by mining changes in patent trends

Obtaining sufficient competitive intelligence is a critical factor in helping business managers gain and maintain competitive advantages. Patent data is an important source of competitive intelligence that enterprises can use to gain a strategic advantage. Under existing approaches, to detect changes in patent trends, business managers must rely on patent analysts to compare two patent analysis charts of different time periods. The discovery of change of trends currently still needs laborious human efforts and no efficient computer-based approaches are available for helping this task. In this paper, we propose a patent trend change mining (PTCM) approach that can identify changes in patent trends without the need for specialist knowledge. The proposed approach consists of steps including patent collection, patent indicator calculation, and change detection. In change detection phase the approach firstly excavate rules between two different time periods, comparing them to determine the trend changes. These trend changes are then classified into four categories of change, evaluated with change degree and ranked by their change degree as the output information to be referred by decision makers. We apply the PTCM approach to Taiwan’s semiconductor industry to discover changes in four types of patent trends: the R&D activities of a company, the R&D activities of the industry, company activities in the industry and industry activities generally. The proposed approach generates competitive intelligence to help managers develop appropriate business strategies.
A Review on Massive E-Learning (MOOC) Design, Delivery and Assessment

MOOCs or Massive Online Open Courses based on Open Educational Resources (OER) might be one of the most versatile ways to offer access to quality education, especially for those residing in far or disadvantaged areas. This article analyzes the state of the art on MOOCs, exploring open research questions and setting interesting topics and goals for further research. Finally, it proposes a framework that includes the use of software agents with the aim to improve and personalize management, delivery, efficiency and evaluation of massive online courses on an individual level basis.
Dynamic state estimation in distributed aircraft electric control systems via adaptive submodularity

We consider the problem of estimating the discrete state of an aircraft electric system under a distributed control architecture through active sensing. The main idea is to use a set of controllable switches to reconfigure the system in order to gather more information about the unknown state. By adaptively making a sequence of reconfiguration decisions with uncertain outcome, then correlating measurements and prior information to make the next decision, we aim to reduce the uncertainty. A greedy strategy is developed that maximizes the one-step expected uncertainty reduction. By exploiting recent results on adaptive submodularity, we give theoretical guarantees on the worst-case performance of the greedy strategy. We apply the proposed method in a fault detection scenario where the discrete state captures possible faults in various circuit components. In addition, simple abstraction rules are proposed to alleviate state space explosion and to scale up the strategy. Finally, the efficiency of the proposed method is demonstrated empirically on different circuits.
Hex-Layer: Layered All-Hex Mesh Generation on Thin Section Solids via Chordal Surface Transformation

This paper proposes chordal surface transform for representation and discretization of thin section solids, such as automobile bodies, plastic injection mold components and sheet metal parts. A multiple-layered all-hex mesh with a high aspect ratio is a typical requirement for mold flow simulation of thin section objects. The chordal surface transform reduces the problem of 3D hex meshing to 2D quad meshing on the chordal surface. The chordal surface is generated by cutting a tet mesh of the input CAD model at its mid plane. Radius function and curvature of the chordal surface are used to provide sizing function for quad meshing. Two-way mapping between the chordal surface and the boundary is used to sweep the quad elements from the chordal surface onto the boundary, resulting in a layered all-hex mesh. The algorithm has been tested on industrial models, whose chordal surface is 2-manifold. The graphical results of the chordal surface and the multiple-layered all-hex mesh are presented along with the quality measures. The results show geometrically adaptive high aspect ratio all-hex mesh, whose average scaled Jacobean, is close to 1.0.
Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction

Three diierent algorithms for obstacle detection are presented in this paper each based on diierent assumptions. The rst two algorithms are qualitative in that they return only yes/no answers regarding the presence of obstacles in the eld of view; no 3D reconstruction is performed. They have the advantage of fast determination of the existence of obstacles in a scene based on the solvability of a linear system. The rst algorithm uses information about the ground plane, while the second only assumes that the ground is planar. The third algorithm is quantitative in that it continuously estimates the ground plane and reconstructs partial 3D structures by determining the height above the ground plane of each point in the scene. Experimental results are presented for real and simulated data, and the performance of the three algorithms under diierent noise levels is compared in simulation. We conclude that in terms of the robustness of performance, the third algorithm is superior to the other two.
Rel-grams: A Probabilistic Model of Relations in Text

We introduce the Rel-grams language model, which is analogous to an n-grams model, but is computed over relations rather than over words. The model encodes the conditional probability of observing a relational tuple R, given that R′ was observed in a window of prior relational tuples. We build a database of Rel-grams co-occurence statistics from ReVerb extractions over 1.8M news wire documents and show that a graphical model based on these statistics is useful for automatically discovering event templates. We make this database freely available and hope it will prove a useful resource for a wide variety of NLP tasks.
Weakly Supervised Object Boundaries

State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, there is a need to relax the requirement to carefully annotate images to make both the training more affordable and to extend the amount of training data. In this paper we propose a technique to generate weakly supervised annotations and show that bounding box annotations alone suffice to reach high-quality object boundaries without using any object-specific boundary annotations. With the proposed weak supervision techniques we achieve the top performance on the object boundary detection task, outperforming by a large margin the current fully supervised state-of-theart methods.
Slicing: A New Approach to Privacy Preserving Data Publishing

Several anonymity techniques, such as generalization and bucketization, have been designed for privacy preserving micro data publishing. Recent work has shown that generalization loses considerable amount of information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called overlapping slicing, which partitions the data both horizontally and vertically. We show that overlapping slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of overlapping slicing is that it can handle high-dimensional data storage.
Applying reinforcement learning for web pages ranking algorithms

Ranking web pages for presenting the most relevant web pages to user’s queries is one of the main issues in any search engine. In this paper, two new ranking algorithms are offered, using Reinforcement Learning (RL) concepts. RL is a powerful technique of modern artificial intelligence that tunes agent’s parameters, interactively. In the first step, with formulation of ranking as an RL problem, a new connectivity-based ranking algorithm, called RL Rank, is proposed. In RL Rank, agent is considered as a surfer who travels between web pages by clicking randomly on a link in the current page. Each web page is considered as a
A neural network method for prediction of 2006 World Cup Football Game

A neural network method is adopted to predict the football game's winning rate of two teams according to their previous stage's official statistical data of 2006 World Cup Football Game. The adopted prediction model is based on multi-layer perceptron (MLP) with back propagation learning rule. The input data are transformed to the relative ratios between two teams of each game. New training samples are added to the training samples at the previous stages. By way of experimental results, the determined neural network architecture for MLP is 8 inputs, 11 hidden nodes, and 1 output (8-11-1). The learning rate and momentum coefficient are sequentially determined by experiments as well. Based on the adopted MLP prediction method, the prediction accuracy can achieve 76.9% if the draw games are excluded.
Distributed GraphLab: A Framework for Machine Learning in the Cloud

While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of parallel performance in the shared-memory setting. In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees. We develop graph based extensions to pipelined locking and data versioning to reduce network congestion and mitigate the effect of network latency. We also introduce fault tolerance to the GraphLab abstraction using the classic Chandy-Lamport snapshot algorithm and demonstrate how it can be easily implemented by exploiting the GraphLab abstraction itself. Finally, we evaluate our distributed implementation of the GraphLab abstraction on a large Amazon EC2 deployment and show 1-2 orders of magnitude performance gains over Hadoop-based implementations.
The comprehensive antibiotic resistance database.

The field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment.
Learning Race from Face: A Survey

Faces convey a wealth of social signals, including race, expression, identity, age and gender, all of which have attracted increasing attention from multi-disciplinary research, such as psychology, neuroscience, computer science, to name a few. Gleaned from recent advances in computer vision, computer graphics, and machine learning, computational intelligence based racial face analysis has been particularly popular due to its significant potential and broader impacts in extensive real-world applications, such as security and defense, surveillance, human computer interface (HCI), biometric-based identification, among others. These studies raise an important question: How implicit, non-declarative racial category can be conceptually modeled and quantitatively inferred from the face? Nevertheless, race classification is challenging due to its ambiguity and complexity depending on context and criteria. To address this challenge, recently, significant efforts have been reported toward race detection and categorization in the community. This survey provides a comprehensive and critical review of the state-of-the-art advances in face-race perception, principles, algorithms, and applications. We first discuss race perception problem formulation and motivation, while highlighting the conceptual potentials of racial face processing. Next, taxonomy of feature representational models, algorithms, performance and racial databases are presented with systematic discussions within the unified learning scenario. Finally, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potentially important cross-cutting themes and research directions for the issue of learning race from face.