nils-herrmann/clean_scirepeval huggingface dataset:
Estimating the Effect of Web-Based Homework

VanLehn’s recent meta-analysis suggests that the AI aspects of computer tutors are adding significant value beyond simple adaptive approaches. For example, the beneficial effects of human tutors and various computer-based interventions compared to regular classroom instruction have estimated values that range between 0.8 std and .31 std. At the upper end of effects are human tutors followed closely by computer tutors (0.74). At the lower end is simple computer-based practice with feedback systems (0.31 std). In this research we are concerned with estimating the effects of web-based homework (WBH) involving practice and feedback. An argument is made that this could serve as a more appropriate condition for comparing the benefits of additional AIED tutoring features. WBH gives students feedback on correctness only as they go. It does not offer hints, feedback messages on common wrong answers, or mastery learning in the problem selection algorithm (used in what VanLehn calls the outer loop). A second underappreciated aspect of WBH is that teachers can use the data to more efficiently review homework. Universities across the world are employing these WBH systems but there are no known comparisons of this in K12. In this work we randomly assigned 63 thirteen and fourteen year olds to either a traditional homework condition (TH) involving practice without feedback or a WBH condition that added correctness feedback and ability to try again. All students used ASSISTments to do their homework but we ablated all of the intelligent tutoring aspects of hints, feedback messages and mastery learning as appropriate to the two practice conditions. We found that students learned reliably more in the web-based homework condition and with a large effect size of 0.56. Given the small sample size and confidence interval for the effect, more studies are needed to better estimate the effect size of WBH. An argument is made that effects associated with the practice with feedback condition should serve as a more accurate baseline for comparing the benefits of additional AIED tutoring features. Future work will systematically compare conditions that add back in hints, feedback messages and mastery learning so that we can measure the value added by these components.
Web-Based Educational Information System for Enhanced Learning, EISEL: Student Assessment

During the last decade, Information Technology (IT) has been the primary force driving the transformation of roles in the education industry. More specifically, the World Wide Web (WWW) and associated technologies provided a new playground with new rules and tools to conduct instruction and create novel approaches to learning. We have seen the application of IT in education in the form of CD-ROMs. With the evolution of the WWW we saw education marketed as long distance learning, web based learner centered environments, internet based learning environments, and self instructed learning. With all the different models used on the web, few have studied their acceptance and their effectiveness on learning. Many educational institutions today have embarked in the development of web based courses. However, they face enormous difficulty in achieving successful strategies including the delivery, effectiveness, and acceptance of the courses. This is mainly due to the fact that the problem of developing a successful web based course involves multiple inter-related dimensions ranging from technology related issues to pedagogical considerations.
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network

Retweeting is the key mechanism for information diffusion in Twitter. It emerged as a simple yet powerful way of disseminating useful information. Even though a lot of information is shared via its social network structure in Twitter, little is known yet about how and why certain information spreads more widely than others. In this paper, we examine a number of features that might affect retweetability of tweets. We gathered content and contextual features from 74M tweets and used this data set to identify factors that are significantly associated with retweet rate. We also built a predictive retweet model. We found that, amongst content features, URLs and hashtags have strong relationships with retweetability. Amongst contextual features, the number of followers and followees as well as the age of the account seem to affect retweetability, while, interestingly, the number of past tweets does not predict retweetability of a user’s tweet. We believe that this research would inform the design of sensemaking tools for Twitter streams as well as other general social media collections. Twitter; retweet; tweet; follower; social network; social media; factor analysis
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network

We propose a highly efficient and faster Single Image Super-Resolution (SISR) model with Deep Convolutional neural networks (Deep CNN). Deep CNN have recently shown that they have a significant reconstruction performance on single-image super-resolution. The current trend is using deeper CNN layers to improve performance. However, deep models demand larger computation resources and are not suitable for network edge devices like mobile, tablet and IoT devices. Our model achieves state-of-the-art reconstruction performance with at least 10 times lower calculation cost by Deep CNN with Residual Net, Skip Connection and Network in Network (DCSCN). A combination of Deep CNNs and Skip connection layers are used as a feature extractor for image features on both local and global areas. Parallelized 1x1 CNNs, like the one called Network in Network, are also used for image reconstruction. That structure reduces the dimensions of the previous layer’s output for faster computation with less information loss, and make it possible to process original images directly. Also we optimize the number of layers and filters of each CNN to significantly reduce the calculation cost. Thus, the proposed algorithm not only achieves stateof-the-art performance but also achieves faster and more efficient computation. Code is available at https://github.com/jiny2001/dcscn-super-resolution.
Fudan-Huawei at MediaEval 2015: Detecting Violent Scenes and Affective Impact in Movies with Deep Learning

Techniques for violent scene detection and affective impact prediction in videos can be deployed in many applications. In MediaEval 2015, we explore deep learning methods to tackle this challenging problem. Our system consists of several deep learning features. First, we train a Convolutional Neural Network (CNN) model with a subset of ImageNet classes selected particularly for violence detection. Second, we adopt a specially designed two-stream CNN framework  to extract features on both static frames and motion optical flows. Third, Long Short Term Memory (LSTM) models are applied on top of the two-stream CNN features, which can capture the longer-term temporal dynamics. In addition, several conventional motion and audio features are also extracted as complementary information to the deep learning features. By fusing all the advanced features, we achieve a mean average precision of 0.296 in the violence detection subtask, and an accuracy of 0.418 and 0.488 for arousal and valence respectively in the induced affect detection subtask. 1.  1 gives an overview of our system. In this short paper, we briefly describe each of the key components. For more information about the task definitions, interested readers may refer to .
Using laser range data for 3D SLAM in outdoor environments

Traditional simultaneous localization and mapping (SLAM) algorithms have been used to great effect in flat, indoor environments such as corridors and offices. We demonstrate that with a few augmentations, existing 2D SLAM technology can be extended to perform full 3D SLAM in less benign, outdoor, undulating environments. In particular, we use data acquired with a 3D laser range finder. We use a simple segmentation algorithm to separate the data stream into distinct point clouds, each referenced to a vehicle position. The SLAM technique we then adopt inherits much from 2D delayed state (or scan-matching) SLAM in that the state vector is an ever growing stack of past vehicle positions and inter-scan registrations are used to form measurements between them. The registration algorithm used is a novel combination of previous techniques carefully balancing the need for maximally wide convergence basins, robustness and speed. In addition, we introduce a novel post-registration classification technique to detect matches which have converged to incorrect local minima
Artificial intelligence meets natural stupidity

As a field, artificial intelligence has always been on the border of respectability, and therefore on the border of crackpottery. Many critics <Dreyfus, 1972>, <Lighthill, 1973> have urged that we are over the border. We have been very defensive toward this charge, drawing ourselves up with dignity when it is made and folding the cloak of Science about us. On the other hand, in private, we have been justifiably proud of our willingness to explore weird ideas, because pursuing them is the only way to make progress.
TaintDroid: an information flow tracking system for real-time privacy monitoring on smartphones

Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their privacy-sensitive data. We address these shortcomings with TaintDroid, an efficient, systemwide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides real-time analysis by leveraging Android's virtualized execution environment. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of misappropriation of users' location and device identification information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications.
Recommending collaboration with social networks: a comparative evaluation

Studies of information seeking and workplace collaboration often find that social relationships are a strong factor in determining who collaborates with whom. Social networks provide one means of visualizing existing and potential interaction in organizational settings. Groupware designers are using social networks to make systems more sensitive to social situations and guide users toward effective collaborations. Yet, the implications of embedding social networks in systems have not been systematically studied. This paper details an evaluation of two different social networks used in a system to recommend individuals for possible collaboration. The system matches people looking for expertise with individuals likely to have expertise. The effectiveness of social networks for matching individuals is evaluated and compared. One finding is that social networks embedded into systems do not match individuals' perceptions of their personal social network. This finding and others raise issues for the use of social networks in groupware. Based on the evaluation results, several design considerations are discussed.
Modeling and Analysis of the Toyota Hybrid System

Toyota Hybrid System is the innovative powertrain used in the current best-selling hybrid vehicle on the market—the Prius. It uses a split-type hybrid configuration which contains both a parallel and a serial power path to achieve the benefits of both. The main purpose of this paper is to develop a dynamic model to investigate the unique design of THS, which will be used to analyze the control strategy, and explore the potential of further improvement. A Simulink model is developed and a control algorithm is derived. Simulations confirm our model captures the fundamental behavior of THS reasonably well.
Causal Effect Identification by Adjustment under Confounding and Selection Biases

Controlling for selection and confounding biases are two of the most challenging problems in the empirical sciences as well as in artificial intelligence tasks. Covariate adjustment (or, Backdoor Adjustment) is the most pervasive technique used for controlling confounding bias, but the same is oblivious to issues of sampling selection. In this paper, we introduce a generalized version of covariate adjustment that simultaneously controls for both confounding and selection biases. We first derive a sufficient and necessary condition for recovering causal effects using covariate adjustment from an observational distribution collected under preferential selection. We then relax this setting to consider cases when additional, unbiased measurements over a set of covariates are available for use (e.g., the age and gender distribution obtained from census data). Finally, we present a complete algorithm with polynomial delay to find all sets of admissible covariates for adjustment when confounding and selection biases are simultaneously present and unbiased data is available.
Evaluating Appearance Models for Recognition, Reacquisition, and Tracking

Traditionally, appearance models for recognition, reacquisition and tracking problems have been evaluated independently using metrics applied to a complete system. It is shown that appearance models for these three problems can be evaluated using a cumulative matching curve on a standardized dataset, and that this one curve can be converted to a synthetic reacquisition or disambiguation rate for tracking. A challenging new dataset for viewpoint invariant pedestrian recognition (VIPeR) is provided as an example. This dataset contains 632 pedestrian image pairs from arbitrary viewpoints. Several baseline methods are tested on this dataset and the results are presented as a benchmark for future appearance models and matchin methods.
Difference Expansion Based Reversible Data Hiding Using Two Embedding Directions

Current difference-expansion (DE) embedding techniques perform one layer embedding in a difference image. They do not turn to the next difference image for another layer embedding unless the current difference image has no expandable differences left. The obvious disadvantage of these techniques is that image quality may have been severely degraded even before the later layer embedding begins because the previous layer embedding has used up all expandable differences, including those with large magnitude. Based on integer Haar wavelet transform, we propose a new DE embedding algorithm, which utilizes the horizontal as well as vertical difference images for data hiding. We introduce a dynamical expandable difference search and selection mechanism. This mechanism gives even chances to small differences in two difference images and effectively avoids the situation that the largest differences in the first difference image are used up while there is almost no chance to embed in small differences of the second difference image. We also present an improved histogram-based difference selection and shifting scheme, which refines our algorithm and makes it resilient to different types of images. Compared with current algorithms, the proposed algorithm often has better embedding capacity versus image quality performance. The advantage of our algorithm is more obvious near the embedding rate of 0.5 bpp.
Do Risk Perceptions Influence Physician's Resistance to Use Electronic Medical Records? An Exploratory Research in German Hospitals

IT in health care can lower the cost of health care delivery, improve the quality of care for patients and reduce medical errors. Given these strong advantages, it is interesting that technology diffusion for process support in hospitals is somewhat slow. The major process of a hospital -delivering patient careis still supported by traditional paper files in the vast majority of German hospitals. In this paper, we ask what the barriers towards implementing and using an electronic medical record (EMR) -the electronic patient filemight be. Technology resistance theories indicate that perceived risks are a major inhibitor towards systems acceptance. In the absence of thorough empirical studies, we start our investigation by conducting exploratory research into the risks hospital-based physicians associate with using an EMR. A list of possible risks was derived from the literature and 10 physicians were interviewed to gather their assessment. Our findings show that, indeed, physicians associate several risks with adopting EMRs, thereby suggesting these risks will need to be mitigated to enable proper user acceptance.
Unified segmentation

A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.
Adapted magnetic wheel unit for compact robots inspecting complex shaped pipe structures

This paper describes a novel magnetic wheel unit integrating a mechanism that can be used for lifting and stabilizing the unit. The mechanism consists of 2 active lever arms mounted on each side of the wheel and rotating coaxially with the wheel. This mechanism allows slightly lifting the magnetic wheel at any desired position on the wheel circumference and consequently decreasing the magnetic force at this specific location. The same mechanism can also be used to stabilize the wheel, when external forces are unfavorable. This paper also describes the potential of this concept for in-pipe inspection technologies. Indeed it can be used to increase the mobility of magnetic wheels robots which are currently not able to negotiate complex obstacles. At the same time, it allows building smaller robots, since the self stabilizer system allows reducing the amount of required magnetic wheels to only two units.
Turning analysis and its implementation of link-based dolphin-like robots

This paper addresses the turning issues of a multilink dolphin-like robot with a pair of mechanical flippers. The propulsion and maneuvering of the robot depend on the dorsoventral oscillations of the tail and the flapping movements of the artificial flippers. Taking into account both mechanical structure and functional characteristics of the dolphin-like robot, we present a practical approach to perform planar turning maneuvers as well as its dynamic analysis. The characteristic parameters affecting turning performance primarily include deflection angle, turning radius, and turning rate, which are discussed with a comparison between the simulation results and the experiments on circular motions. Preliminary tests in a robotics context partly demonstrate the effectiveness of the proposed turning scheme.
Symbolic olfactory display

This thesis explores the problems and possibilities of computer-controlled scent output. I begin with a thorough literature review of how we smell and how scents are categorized. I look at applications of aroma through the ages, with particular emphasis on the role of scent in information display in a variety of media. I then present and discuss several projects I have built to explore the use of computer-controlled olfactory display, and some pilot studies of issues related to such display. I quantify human physical limitations on olfactory input, and conclude that olfactory display must rely on differences between smells, and not differences in intensity of the same smell. I propose a theoretical framework for scent in human-computer interactions, and develop concepts of olfactory icons and ‘smicons’. I further conclude that scent is better suited for display slowly changing, continuous information than discrete events. I conclude with my predictions for the prospects of symbolic, computer-controlled, olfactory display.  Michael J. Hawley Assistant Professor of Media Arts & Sciences Funding provided by the sponsors of the Counter Intelligence Special Interest Group.
General Control Considerations for Input-Series Connected DC/DC Converters

This paper discusses the general control problems of dc/dc converters connected in series at the input. As the input voltage is shared by a number of dc/dc converters, the resulting converter relieves the voltage stresses of individual devices and hence is suitable for high input-voltage applications. At the output side, parallel connection provides current sharing and is suitable for high output-current applications. Moreover, series connection at the output side is also possible, resulting in output voltage sharing. Theoretically, from a power balance consideration, one can show that fulfillment of input-voltage sharing implies fulfillment of output-current or of output-voltage sharing, and vice versa. However, the presence of right-half-plane poles can cause instability when the sharing is implemented at the output side. As a consequence, control should be directed to input-voltage sharing in order to ensure a stable sharing of the input voltage and of the output current (parallel connection at output) or output voltage (series connection at output). In this paper, general problems in input-series connected converter systems are addressed. Minimal control structures are then derived and some practical design considerations are discussed in detail. Illustrative examples are given for addressing these general control considerations. Finally, experimental prototypes are built to validate these considerations.
FPGA-Based Test-Bench for Resonant Inverter Load Characterization

Resonant converters often require accurate load characterization in order to ensure appropriate and safe control. Besides, for systems with a highly variable load, as the induction heating systems, a real-time load estimation is mandatory. This paper presents the development of an FPGA-based test-bench aimed to extract the electrical equivalent of the induction heating loads. The proposed test-bench comprises a resonant power converter, sigma-delta ADCs, and an embedded system implemented in an FPGA. The characterization algorithm is based on the discrete-time Fourier series computed directly from the ΔΣ ADC bit-streams, and the FPGA implementation has been partitioned into hardware and software platforms to optimize the performance and resources utilization. Analytical and simulation results are verified through experimental measurements with the proposed test-bench. As a result, the proposed platform can be used as a load identification tool either for stand-alone or PC-hosted operation.
Phoneme-specific speech separation

Speech separation or enhancement algorithms seldom exploit information about phoneme identities. In this study, we propose a novel phoneme-specific speech separation method. Rather than training a single global model to enhance all the frames, we train a separate model for each phoneme to process its corresponding frames. A robust ASR system is employed to identify the phoneme identity of each frame. This way, the information from ASR systems and language models can directly influence speech separation by selecting a phoneme-specific model to use at the test stage. In addition, phoneme-specific models have fewer variations to model and do not exhibit the data imbalance problem. The improved enhancement results can in turn help recognition. Experiments on the corpus of the second CHiME speech separation and recognition challenge (task-2) demonstrate the effectiveness of this method in terms of objective measures of speech intelligibility and quality, as well as recognition performance.