nils-herrmann/clean_scirepeval huggingface dataset:
Implementing Multidimensional Data Warehouses into NoSQL

Not only SQL (NoSQL) databases are becoming increasingly popular and have some interesting strengths such as scalability and flexibility. In this paper, we investigate on the use of NoSQL systems for implementing OLAP (On-Line Analytical Processing) systems. More precisely, we are interested in instantiating OLAP systems (from the conceptual level to the logical level) and instantiating an aggregation lattice (optimization). We define a set of rules to map star schemas into two NoSQL models: columnoriented and document-oriented. The experimental part is carried out using the reference benchmark TPC. Our experiments show that our rules can effectively instantiate such systems (star schema and lattice). We also analyze differences between the two NoSQL systems considered. In our experiments, HBase (columnoriented) happens to be faster than MongoDB (document-oriented) in terms of loading time.
Traffic monitoring and accident detection at intersections

Among the most important research in Intelligent Transportation Systems (ITS) is the development of systems that automatically monitor traffic flow at intersections. Rather than being based on global flow analysis as is currently done, these automatic monitoring systems should be based on local analysis of the behavior of each vehicle at the intersection. The systems should be able to identify each vehicle and track its behavior, and to recognize situations or events that are likely to result from a chain of such behavior. The most difficult problem associated with vehicle tracking is the occlusion effect among vehicles. In order to solve this problem we have developed an algorithm, referred to as spatio-temporal Markov random field (MRF), for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the – image axes as well as the time axes. Vehicles, of course, are of various shapes and they move in random fashion, thereby leading to full or partial occlusion at intersections. Despite these complications, our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%–96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness.
Blind grasping: Stable robotic grasping using tactile feedback and hand kinematics

We propose a machine learning approach to the perception of a stable robotic grasp based on tactile feedback and hand kinematic data, which we call blind grasping. We first discuss a method for simulating tactile feedback using a soft finger contact model in GraspIt!, which is a robotic grasping simulator . Using this simulation technique, we compute tactile contacts of thousands of grasps with a robotic hand using the Columbia Grasp Database . The tactile contacts along with the hand kinematic data are then input to a Support Vector Machine (SVM) which is trained to estimate the stability of a given grasp based on this tactile feedback and also the robotic hand kinematics. Experimental results indicate that the tactile feedback along with the hand kinematic data carry meaningful information for the prediction of the stability of a blind robotic grasp.
Reading Touch Screen Storybooks with Mothers Negatively Affects 7-Year-Old Readers’ Comprehension but Enriches Emotional Engagement

Touch screen storybooks turn reading into an interactive multimedia experience, with hotspot-activated animations, sound effects, and games. Positive and negative effects of reading multimedia stories have been reported, but the underlying mechanisms which explain how children's learning is affected remain uncertain. The present study examined the effect of storybook format (touch screen and print) on story comprehension, and considered how level of touch screen interactivity (high and low) and shared reading behaviors (cognitive and emotional scaffolding, emotional engagement) might contribute to comprehension. Seven-year-olds (n = 22) were observed reading one touch screen storybook and one print storybook with their mothers. Story comprehension was inferior for the touch screen storybooks compared to the print formats. Touch screen interactivity level had no significant effect on comprehension but did affect shared reading behaviors. The mother-child dyads spent less time talking about the story in the highly interactive touch screen condition, despite longer shared reading sessions because of touch screen interactions. Positive emotional engagement was greater for children and mothers in the highly interactive touch screen condition, due to additional positive emotions expressed during touch screen interactions. Negative emotional engagement was greater for children when reading and talking about the story in the highly interactive condition, and some mothers demonstrated negative emotional engagement with the touch screen activities. The less interactive touch screen storybook had little effect on shared reading behaviors, but mothers controlling behaviors were more frequent. Storybook format had no effect on the frequency of mothers' cognitive scaffolding behaviors (comprehension questions, word help). Relationships between comprehension and shared reading behaviors were examined for each storybook, and although length of the shared reading session and controlling behaviors had significant effects on comprehension, the mechanisms driving comprehension were not fully explained by the data. The potential for touch screen storybooks to contribute to cognitive overload in 7-year-old developing readers is discussed, as is the complex relationship between cognitive and emotional scaffolding behaviors, emotional engagement, and comprehension. Sample characteristics and methodological limitations are also discussed to help inform future research.
Simulator sickness in virtual display gaming: a comparison of stereoscopic and non-stereoscopic situations

In this paper we compare simulator sickness symptoms produced by racing game in three different conditions. In the first experiment the participants played the Need for Speed car racing game with an ordinary 17" display and in the second and third experiments they used a head-worn virtual display for the game playing. The difference between experiments 2 and 3 was in the use of stereoscopy, as in the third experiment the car racing game was seen in stereoscopic three-dimensions. Our results indicated that there were no significant differences in sickness symptoms when we compared the ordinary display and the virtual display in non-stereoscopic mode. In stereoscopic condition the eye strain and disorientation symptoms were significantly elevated compared to the ordinary display. We conclude that using a virtual display as an accessory in a mobile device is a viable alternative, because the non-stereoscopic virtual display did not produce significantly more sickness symptoms compared to ordinary game playing.
A New Word Language Model Evaluation Metric for Character Based Languages

Perplexity is a widely used measure to evaluate word prediction power of a word-based language model. It can be computed independently and has shown good correlation with word error rate (WER) in speech recognition. However, for character based languages, character error rate (CER) is commonly used instead of WER as the measure for speech recognition, although language model is still word based. Due to the fact that different word segmentation strategies may result in different word vocabulary for the same text corpus, in many cases, wordbased perplexity is incompetent to evaluate the combined effect of word segmentation and language model training to predict final CER. In this paper, a new word-based language model evaluation measure is proposed to account for the effect of word segmentation and the goal of predicting CER. Experiments were conducted on Chinese speech recognition. Compared to the traditional word-based perplexity, the new measure is more robust to word segmentation and shows much more consistent correlation with CER in a large vocabulary continuous Chinese speech recognition task.
Comparative Analysis of Data Mining Techniques on Educational Dataset

Data mining is a relatively young and interdisciplinary field of computer science. It is a process that attempts to discover new patterns in large data sets. Different types of mining algorithms have been proposed by different researchers in recent years. A single algorithm may not be applied to all applications due to difficulty for suitable data types of the algorithm. Therefore the selection of a correct data mining algorithm depends on not only the goal of an application, but also on the compatibility of the data set. The aim of this paper is how to use suitable data mining algorithms on educational dataset. This paper focuses on comparative analysis of various data mining techniques and algorithms.
Language Model Pre-training for Hierarchical Document Representations

Hierarchical neural architectures are often used to capture long-distance dependencies and have been applied to many document-level tasks such as summarization, document segmentation, and sentiment analysis. However, effective usage of such a large context can be difficult to learn, especially in the case where there is limited labeled data available. Building on the recent success of language model pretraining methods for learning flat representations of text, we propose algorithms for pre-training hierarchical document representations from unlabeled data. Unlike prior work, which has focused on pre-training contextual token representations or context-independent sentence/paragraph representations, our hierarchical document representations include fixed-length sentence/paragraph representations which integrate contextual information from the entire documents. Experiments on document segmentation, document-level question answering, and extractive document summarization demonstrate the effectiveness of the proposed pre-training algorithms.
A Geographically Aware Poll-Based Distributed File Consistency Maintenance Method for P2P Systems

File consistency maintenance in P2P systems is a technique for maintaining consistency between files and their replicas. Most previous consistency maintenance methods depend on either message spreading or structure-based pushing. Message spreading generates high overhead due to a large amount of messages; structure-based pushing methods reduce this overhead. However, both approaches cannot guarantee that every replica node receives an update in churn, because replica nodes passively wait for updates. As opposed to push-based methods that are not effective in high-churn and low-resource P2P systems, polling is churn resilient and generates low overhead. However, it is faced with a number of challenges: 1) ensuring a limited inconsistency; 2) realizing polling in a distributed manner; 3) considering physical proximity in polling; and 4) leveraging polling to further reduce polling overhead. To handle these challenges, this paper introduces a poll-based distributed file consistency maintenance method called geographically aware wave (GeWave). GeWave further reduces update overhead, enhances the fidelity of file consistency, and takes proximity into account. Using adaptive polling in a dynamic structure, GeWave avoids redundant file updates and ensures that every node receives an update in a limited time period even in churn. Furthermore, it propagates updates between geographically close nodes in a distributed manner. Extensive experimental results from the PlanetLab real-world testbed demonstrate the efficiency and effectiveness of GeWave in comparison with other representative consistency maintenance schemes. It dramatically reduces the overhead and yields significant improvements on effectiveness, scalability, and churn resilience of previous file consistency maintenance methods.
An Analysis on the factors causing telecom churn: First Findings

The exponential growth in number of telecom providers and unceasing competition in the market has caused telecom operators to divert their focus more towards customer retention rather than customer acquisition. Hence it becomes extremely essential to identify the factors causing a customer to switch and take proactive actions in order to retain them. The paper aims to review existing literature to bring up a holistic view of various factors and their relationships that cause a customer to churn in the telecommunication industry. A total of 140 factors related to customer churn were compiled by reviewing several studies on customer churn behavior. The identified factors are then discussed in detail with their impact on customer’s switching behavior. The limitations of the existing studies and directions for future research have also been provided. The paper will aid practitioners and researchers by serving a strong base for their industrial practice and future research.
A Floor and Obstacle Height Map for 3D Navigation of a Humanoid Robot

With the development of biped robots, systems became able to navigate in a 3 dimensional world, walking up and down stairs, or climbing over small obstacles. We present a method for obtaining a labeled 2.5D grid map of the robot's surroundings. Each cell is marked either as floor or obstacle and contains a value telling the height of the floor or obstacle. Such height maps are useful for path planning and collision avoidance. The method uses a novel combination of a 3D occupancy grid for robust sensor data interpretation and a 2.5D height map for fine resolution floor values. We evaluate our approach using stereo vision on the humanoid robot QRIO and show the advantages over previous methods. Experimental results from navigation runs on an obstacle course demonstrate the ability of the method to generate detailed maps for autonomous navigation.
Modeling and yield estimation of SRAM sub-system for different capacities subjected to parametric variations

Process variations have become a major challenge with the advancement in CMOS technologies. The performance of memory sub-systems such as Static Random Access Memory (SRAMs) is heavily dependent on these variations. Also, the VLSI industry requires the SRAM bit cell to qualify in the order of less than 0.1ppb to achieve higher Yield (Y). This paper proposes an efficient qualitative statistical analysis and Yield estimation method of SRAM sub-system which considers deviations due to variations in process parameters in bit line differential and input offset of sense amplifier (SA) all together. The Yield of SRAM is predicted for different capacities of SRAM array by developing a statistical model of memory sub-system in 65nm bulk CMOS technology. For the sub-system with 64 bit cells, it is estimated that the probability of failure is 4.802 ∗ 10−13 in a read cycle of frequency 1GHz. Furthermore, the probability of failure for 8MB capacity is 5.035 ∗ 10−7 while for 2GB capacity it increases to 1.289 ∗ 10−5. It is also observed that as the load on one SA per column is doubled, the probability of failure of memory slice increases by 70%. The proposed technique estimates the Yield(Y) for SRAM array to be more than 99.9999.
Love, hate, arousal and engagement: exploring audience responses to performing arts

Understanding audience responses to art and performance is a challenge. New sensors are promising for measurement of implicit and explicit audience engagement. However, the meaning of biometric data, and its relationship to engagement, is unclear. We conceptually explore the audience engagement domain to uncover opportunities and challenges in the assessment and interpretation of audience engagement data. We developed a display that linked performance videos with audience biometric data and presented it to 7 performing arts experts, to explore the measurement, interpretation and application of biometric data. Experts were intrigued by the response data and reflective in interpreting it. We deepened our inquiry with an empirical study with 49 participants who watched a video of a dance performance. We related temporal galvanic skin response (GSR) data to two self-report scales, which provided insights on interpreting this measure. Our findings, which include strong correlations, support the interpretation of GSR as a valid representation of audience engagement.
Credit Card Fraud Detection with a Neural-Network

Using data from a credit card issuer, a neural network based fraud detection system was trained on a large sample of labelled credit card account transactions and tested on a holdout data set that consisted of all account activity over a subsequent two-month period of time. The neural network was trained on examples of fraud due to lost cards, stolen cards, application fraud, counterfeit fraud, mail-order fraud and NRI (non-received issue) fraud. The network detected significantly more fraud accounts (an order of magnitude more) with significantly fewer false positives (reduced by a factor of 20) over rulebased fraud detection procedures. We discuss the performance of the network on this data set in terms of detection accuracy and earliness of fraud detection. The system has been installed on an IBM 3090 at Mellon Bank and is currently in use for fraud detection on that bank’s cmlit card portfolio. Credit Card Fraud Problem Credit card fraud is a growing problem in the credit card industry. In the US alone, losses from all types of credit card fraud are projected to exceed $850 million, representing a 10% increase in fraud losses over 1991 111. Though small when compared to credit card losses due to charge-offs of seriously delinquent accounts (charge-offs accounted for $8.5 billion of losses in 1992). fraud represents an increasing percentage of total charge volume, indicating that it is growing faster than the credit card business itself. From 1988 through 1991, the size of the fraud problem grew from 8 basis points to over 20. Although credit card fraud takes many forms, there are several principal categories. Fraud due to lost cards and stolen cards generally accounts for a certain “base level” of fraud activity. The size of this base level can be affected by general economic conditions (e.g., times of high unemployment are correlated with increases in fraud losses due to lost 1060-3425/94 $3.00
Fast human segmentation using color and depth

Accurate segmentation of humans from live videos is an important problem to be solved in developing immersive video experience. We propose to extract the human segmentation information from color and depth cues in a video using multiple modeling techniques. The prior information from human skeleton data is also fused along with the depth and color models to obtain the final segmentation inside a graph-cut framework. The proposed method runs real time on live videos using single CPU and is shown to be quantitatively outperforming the methods that directly fuse color and depth data.
Automated reference resolution in legal texts

This paper investigates the task of reference resolution in the legal domain. This is a new interesting task in Legal Engineering research. The goal is to create a system which can automatically detect references and then extracts their referents. Previous work limits itself to detect and resolve references at the document targets. In this paper, we go a step further in trying to resolve references to sub-document targets. Referents extracted are the smallest fragments of texts in documents, rather than the entire documents that contain the referenced texts. Based on analyzing the characteristics of reference phenomena in legal texts, we propose a four-step framework to deal with the task: mention detection, contextual information extraction, antecedent candidate extraction, and antecedent determination. We also show how machine learning methods can be exploited in each step. The final system achieves 80.06 % in the F1 score for detecting references, 85.61 % accuracy for resolving them, and 67.02 % in the F1 score for the end-to-end setting task on the Japanese National Pension Law corpus.
LASH: Large-Scale Sequence Mining with Hierarchies

We propose LASH, a scalable, distributed algorithm for mining sequential patterns in the presence of hierarchies. LASH takes as input a collection of sequences, each composed of items from some application-specific vocabulary. In contrast to traditional approaches to sequence mining, the items in the vocabulary are arranged in a hierarchy: both input sequences and sequential patterns may consist of items from different levels of the hierarchy. Such hierarchies naturally occur in a number of applications including mining natural-language text, customer transactions, error logs, or event sequences. LASH is the first parallel algorithm for mining frequent sequences with hierarchies; it is designed to scale to very large datasets. At its heart, LASH partitions the data using a novel, hierarchy-aware variant of item-based partitioning and subsequently mines each partition independently and in parallel using a customized mining algorithm called pivot sequence miner. LASH is amenable to a MapReduce implementation; we propose effective and efficient algorithms for both the construction and the actual mining of partitions. Our experimental study on large real-world datasets suggest good scalability and run-time efficiency.
Conditional Generative Adversarial Nets

Generative Adversarial Nets  were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.
Publication bias in studies of an applied behavior-analytic intervention: an initial analysis.

Publication bias arises when studies with favorable results are more likely to be reported than are studies with null findings. If this bias occurs in studies with single-subject experimental designs(SSEDs) on applied behavior-analytic (ABA) interventions, it could lead to exaggerated estimates of intervention effects. Therefore, we conducted an initial test of bias by comparing effect sizes, measured by percentage of nonoverlapping data (PND), in published SSED studies (n=21) and unpublished dissertations (n=10) on 1 well-established intervention for children with autism, pivotal response treatment (PRT). Although published and unpublished studies had similar methodologies, the mean PND in published studies was 22% higher than in unpublished studies, 95% confidence interval (4%, 38%). Even when unpublished studies are included, PRT appeared to be effective (PNDM=62%). Nevertheless, the disparity between published and unpublished studies suggests a need for further assessment of publication bias in the ABA literature.
An Unsupervised Approach to Recognizing Discourse Relations

We presentan unsupervisedapproachto recognizingdiscourserelationsof CONTRAST, EXPLANATION-EVIDENCE, CONDITION andELABORATION that hold betweenarbitraryspansof texts. We show that discourserelation classifierstrained on examples that are automaticallyextractedfrom massi ve amountsof text can be usedto distinguishbetweensomeof theserelationswith accuraciesashigh as 93%, even whenthe relationsarenot explicitly markedby cuephrases.