nils-herrmann/clean_scirepeval huggingface dataset:
EOMM: An Engagement Optimized Matchmaking Framework

Matchmaking connects multiple players to participate in online player-versus-player games. Current matchmaking systems depend on a single core strategy: create fair games at all times. These systems pair similarly skilled players on the assumption that a fair game is best player experience. We will demonstrate, however, that this intuitive assumption sometimes fails and that matchmaking based on fairness is not optimal for engagement. In this paper, we propose an Engagement Optimized Matchmaking (EOMM) framework that maximizes overall player engagement. We prove that equal-skill based matchmaking is a special case of EOMM on a highly simplified assumption that rarely holds in reality. Our simulation on real data from a popular game made by Electronic Arts,Inc. (EA) supports our theoretical results, showing significant improvement in enhancing player engagement compared to existing matchmaking methods.
Analysis and Experimental Kinematics of a Skid-Steering Wheeled Robot Based on a Laser Scanner Sensor

Skid-steering mobile robots are widely used because of their simple mechanism and robustness. However, due to the complex wheel-ground interactions and the kinematic constraints, it is a challenge to understand the kinematics and dynamics of such a robotic platform. In this paper, we develop an analysis and experimental kinematic scheme for a skid-steering wheeled vehicle based-on a laser scanner sensor. The kinematics model is established based on the boundedness of the instantaneous centers of rotation (ICR) of treads on the 2D motion plane. The kinematic parameters (the ICR coefficient , the path curvature variable and robot speed ), including the effect of vehicle dynamics, are introduced to describe the kinematics model. Then, an exact but costly dynamic model is used and the simulation of this model's stationary response for the vehicle shows a qualitative relationship for the specified parameters and . Moreover, the parameters of the kinematic model are determined based-on a laser scanner localization experimental analysis method with a skid-steering robotic platform, Pioneer P3-AT. The relationship between the ICR coefficient and two physical factors is studied, i.e., the radius of the path curvature and the robot speed . An empirical function-based relationship between the ICR coefficient of the robot and the path parameters is derived. To validate the obtained results, it is empirically demonstrated that the proposed kinematics model significantly improves the dead-reckoning performance of this skid-steering robot.
Fast texture synthesis using tree-structured vector quantization

Texture synthesis is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an algorithm that is both efficient and capable of generating high quality results. In this paper, we present an efficient algorithm for realistic texture synthesis. The algorithm is easy to use and requires only a sample texture as input. It generates textures with perceived quality equal to or better than those produced by previous techniques, but runs two orders of magnitude faster. This permits us to apply texture synthesis to problems where it has traditionally been considered impractical. In particular, we have applied it to constrained synthesis for image editing and temporal texture generation. Our algorithm is derived from Markov Random Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using tree-structured vector quantization.
Program interference in MLC NAND flash memory: Characterization, modeling, and mitigation

As NAND flash memory continues to scale down to smaller process technology nodes, its reliability and endurance are degrading. One important source of reduced reliability is the phenomenon of program interference: when a flash cell is programmed to a value, the programming operation affects the threshold voltage of not only that cell, but also the other cells surrounding it. This interference potentially causes a surrounding cell to move to a logical state (i.e., a threshold voltage range) that is different from its original state, leading to an error when the cell is read. Understanding, characterizing, and modeling of program interference, i.e., how much the threshold voltage of a cell shifts when another cell is programmed, can enable the design of mechanisms that can effectively and efficiently predict and/or tolerate such errors. In this paper, we provide the first experimental characterization of and a realistic model for program interference in modern MLC NAND flash memory. To this end, we utilize the read-retry mechanism present in some state-of-the-art 2Y-nm (i.e., 20-24nm) flash chips to measure the changes in threshold voltage distributions of cells when a particular cell is programmed. Our results show that the amount of program interference received by a cell depends on 1) the location of the programmed cells, 2) the order in which cells are programmed, and 3) the data values of the cell that is being programmed as well as the cells surrounding it. Based on our experimental characterization, we develop a new model that predicts the amount of program interference as a function of threshold voltage values and changes in neighboring cells. We devise and evaluate one application of this model that adjusts the read reference voltage to the predicted threshold voltage distribution with the goal of minimizing erroneous reads. Our analysis shows that this new technique can reduce the raw flash bit error rate by 64% and thereby improve flash lifetime by 30%. We hope that the understanding and models developed in this paper lead to other error tolerance mechanisms for future flash memories.
The Web as a Graph: Measurements, Models, and Methods

The pages and hyperlinks of the World Wide Web may be viewed as nodes and edges in a directed graph This graph is a fascinating object of study it has several hundred million nodes today over a billion links and appears to grow exponentially with time There are many rea sons mathematical sociological and commercial for studying the evolution of this graph In this paper we begin by describing two algo rithms that operate on the Web graph addressing problems from Web search and automatic community discovery We then report a number of measurements and properties of this graph that manifested themselves as we ran these algorithms on the Web Finally we observe that tradi tional random graph models do not explain these observations and we propose a new family of random graph models These models point to a rich new sub eld of the study of random graphs and raise questions about the analysis of graph algorithms on the Web
NFLB dropout: Improve generalization ability by dropping out the best -A biologically inspired adaptive dropout method for unsupervised learning

Generalization ability is widely acknowledged as one of the most important criteria to evaluate the quality of unsupervised models. The objective of our research is to find a better dropout method to improve the generalization ability of convolutional deep belief network (CDBN), an unsupervised learning model for vision tasks. In this paper, the phenomenon of low feature diversity during the training process is investigated. The attention mechanism of human visual system is more focused on rare events and depresses well-known facts. Inspired by this mechanism, No Feature Left Behind Dropout (NFLB Dropout), an adaptive dropout method is firstly proposed to automatically adjust the dropout rate feature-wisely. In the proposed method, the algorithm drops well-trained features and keeps poorly-trained ones with a high probability during training iterations. In addition, we apply two approximations of the quality of features, which are inspired by theory of saliency and optimization. Compared with the model trained by standard dropout, experiment results show that our NFLB Dropout method improves not only the accuracy but the convergence speed as well.
Location diversity in anonymity networks

Anonymity networks have long relied on diversity of node location for protection against attacks---typically an adversary who can observe a larger fraction of the network can launch a more effective attack. We investigate the diversity of two deployed anonymity networks, Mixmaster and Tor, with respect to an adversary who controls a single Internet administrative domain.
 Specifically, we implement a variant of a recently proposed technique that passively estimates the set of administrative domains (also known as autonomous systems, or ASes) between two arbitrary end-hosts without having access to either end of the path. Using this technique, we analyze the AS-level paths that are likely to be used in these anonymity networks. We find several cases in each network where multiple nodes are in the same administrative domain. Further, many paths between nodes, and between nodes and popular endpoints, traverse the same domain.
Concept Based Query Expansion

Query expansion methods have been studied for a long time - with debatable success in many instances. In this paper we present a probabilistic query expansion model based on a similarity thesaurus which was constructed automatically. A similarity thesaurus reflects domain knowledge about the particular collection from which it is constructed. We address the two important issues with query expansion: the selection and the weighting of additional search terms. In contrast to earlier methods, our queries are expanded by adding those terms that are most similar to the concept of the query, rather than selecting terms that are similar to the query terms. Our experiments show that this kind of query expansion results in a notable improvement in the retrieval effectiveness when measured using both recall-precision and usefulness.
Identifying Learning Styles in Learning Management Systems by Using Indications from Students' Behaviour

Making students aware of their learning styles and presenting them with learning material that incorporates their individual learning styles has potential to make learning easier for students and increase their learning progress. This paper proposes an automatic approach for identifying learning styles with respect to the Felder-Silverman learning style model by inferring their learning styles from their behaviour during they are learning in an online course. The approach was developed for learning management systems, which are commonly used in e-learning. In order to evaluate the proposed approach, a study with 127 students was performed, comparing the results of the automatic approach with those of a learning style questionnaire. The evaluation yielded good results and demonstrated that the proposed approach is suitable for identifying learning styles. By using the proposed approach, studentspsila learning styles can be identified automatically and be used for supporting students by considering their individual learning styles.
Explaining and Harnessing Adversarial Examples

Several machine learning models, including neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks’ vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset.
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention

Traditional topic models are insufficient for topic extraction in social media. The existing methods only consider text information or simultaneously model the posts and the static characteristics of social media. They ignore that one discusses diverse topics when dynamically interacting with different people. Moreover, people who talk about the same topic have different effects on the topic. In this paper, we propose an Interaction-Aware Topic Model (IATM) for microblog conversations by integrating network embedding and user attention. A conversation network linking users based on reposting and replying relationship is constructed to mine the dynamic user behaviours. We model dynamic interactions and user attention so as to learn interaction-aware edge embeddings with social context. Then they are incorporated into neural variational inference for generating the more consistent topics. The experiments on three real-world datasets show that our proposed model is effective.
Adversarial Autoencoders

In this paper we propose a new method for regularizing autoencoders by imposing an arbitrary prior on the latent representation of the autoencoder. Our method, named “adversarial autoencoder”, uses the recently proposed generative adversarial networks (GAN) in order to match the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior. Matching the aggregated posterior to the prior ensures that there are no “holes” in the prior, and generating from any part of prior space results in meaningful samples. As a result, the decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution. We show how adversarial autoencoders can be used to disentangle style and content of images and achieve competitive generative performance on MNIST, Street View House Numbers and Toronto Face datasets.
Self-monitoring: appraisal and reappraisal.

Theory and research on self-monitoring have accumulated into a sizable literature on the impact of variation in the extent to which people cultivate public appearances in diverse domains of social functioning. Yet self-monitoring and its measure, the Self-Monitoring Scale, are surrounded by controversy generated by conflicting answers to the critical question, Is self-monitoring a unitary phenomenon? A primary source of answers to this question has been largely neglected--the Self-Monitoring Scale's relations with external criteria. We propose a quantitative method to examine the self-monitoring literature and thereby address major issues of the controversy. Application of this method reveals that, with important exceptions, a wide range of external criteria tap a dimension directly measured by the Self-Monitoring Scale. We discuss what this appraisal reveals about with self-monitoring is and is not.
Feature Engineering and Classifier Ensemble for KDD Cup 2010

KDD Cup 2010 is an educational data mining competition. Participants are asked to learn a model from students’ past behavior and then predict their future performance. At National Taiwan University, we organized a course for KDD Cup 2010. Most student sub-teams expanded features by various binarization and discretization techniques. The resulting sparse feature sets were trained by logistic regression (using LIBLINEAR). One subteam considered condensed features using simple statistical techniques and applied random forest (through Weka) for training. Initial development was conducted on an internal split of training data for training and validation. We identified some useful feature combinations to improve the performance. For the final submission, we combined results of student subteams by regularized linear regression. Our team is the first prize winner of both tracks (all teams and student teams) of KDD Cup 2010.
Environmental Scanning-The Impact of the Stakeholder Concept

This paper discusses the advantages of the use of the stakeholder framework as a basis for focusing an organization's environmental scanning effort. Arising from the discussion, a contingency model for environmental scanning is developed to relate the focus and method used for environmental scanning to the dynamism of the environment and the power of the stakeholder relative to the organization. Steps for implementing the environmental scanning system are then discussed. Increasing environmental turbulence in the remotely useful from a managerial or theo1950's led practicing managers and theoretical perspective... Scanning systems reticians to the realization that organiface two problems: ( 1) how to register zations could no longer be regarded as needed information, and (2) how to act closed systems--organizations had to be upon the information," (Pfeffer & Salancik, regarded as open systems. The open 1978). system concept of organizations necessarily led to the inclusion of environmental King overcame these problems to some considerations in the planning process. In extent by analyzing the type of inforso doing, strategic planning came into mation needed for strategic planning in being (Ansoff & Thanheiser, 1978). The order to derive some sort of focus for the mere fact that environmental considerscanner (King & Cleland, 1978). Thus, he ations had to be included in the planning identified six environmental information process necessitated the development of a sub-systems--the image, the customer, the process whereby information about the potential customer, the competitive inforenvironment could be collected, analyzed, mation, the regulatory information, and and acted upon. The concept of environthe critical intelligence information submental scanning thus came into being. systems. However the focus and the method to be used in the environmental But concepts are paper tigers--it is pracscanning process were still ill-defined. tice that counts. Agui lar ( 1967) recognized Subsequently, Fahey developed a typology four modes of environmental scanning-of scanning processes based on the impetus undirected viewing, conditioned viewing, for scanning (Fahey, King, Vadake, 1981). informal search, and formal search. This Thus crisis initiated scanning gave rise to did not help the manager to determine irregular scanning. Scanning initiated by what to scan, nor how to scan it. And the need for problem solving was to be some eleven years later, Pfeffer remarked done on a periodic basis, whi le scanning for that "... the allusion to the environment is opportunity finding and problem avoidance frequently pro forma and seldom follows up was to be done continuously. Clearly at the open systems perspective with anything this juncture there is still no method of
Haraka - Efficient Short-Input Hashing for Post-Quantum Applications

Recently, many efficient cryptographic hash function design strategies have been explored, not least because of the SHA-3 competition. These designs are, almost exclusively, geared towards high performance on long inputs. However, various applications exist where the performance on short (fixed length) inputs matters more. Such hash functions are the bottleneck in hash-based signature schemes like SPHINCS or XMSS, which is currently under standardization. Secure functions specifically designed for such applications are scarce. We attend to this gap by proposing two short-input hash functions (or rather simply compression functions). By utilizing AES instructions on modern CPUs, our proposals are the fastest on such platforms, reaching throughputs below one cycle per hashed byte even for short inputs, while still having a very low latency of less than 60 cycles. Under the hood, this results comes with several innovations. First, we study whether the number of rounds for our hash functions can be reduced, if only second-preimage resistance (and not collision resistance) is required.  only a little. Second, since their inception, AES-like designs allow for supportive security arguments by means of counting and bounding the number of active S-boxes. However, this ignores powerful attack vectors using truncated differentials, including the powerful rebound attacks. We develop a general tool-based method to include arguments against attack vectors using truncated differentials.
Airwriting: a wearable handwriting recognition system

We present a wearable input system which enables interaction through 3D handwriting recognition. Users can write text in the air as if they were using an imaginary blackboard. The handwriting gestures are captured wirelessly by motion sensors applying accelerometers and gyroscopes which are attached to the back of the hand. We propose a two-stage approach for spotting and recognition of handwriting gestures. The spotting stage uses a support vector machine to identify those data segments which contain handwriting. The recognition stage uses hidden Markov models (HMMs) to generate a text representation from the motion sensor data. Individual characters are modeled by HMMs and concatenated to word models. Our system can continuously recognize arbitrary sentences, based on a freely definable vocabulary. A statistical language model is used to enhance recognition performance and to restrict the search space. We show that continuous gesture recognition with inertial sensors is feasible for gesture vocabularies that are several orders of magnitude larger than traditional vocabularies for known systems. In a first experiment, we evaluate the spotting algorithm on a realistic data set including everyday activities. In a second experiment, we report the results from a nine-user experiment on handwritten sentence recognition. Finally, we evaluate the end-to-end system on a small but realistic data set.
SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones

Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage and processing capability, it is a challenge to transfer the huge amount of crowdsourced photos. To address this problem, we propose a framework, called SmartPhoto, to quantify the quality (utility) of crowdsourced photos based on the accessible geographical and geometrical information (called metadata) including the smartphone's orientation, position and all related parameters of the built-in camera. From the metadata, we can infer where and how the photo is taken, and then only transmit the most useful photos. Three optimization problems regarding the tradeoffs between photo utility and resource constraints, namely the Max-Utility problem, the online Max-Utility problem and the Min-Selection problem, are studied. Efficient algorithms are proposed and their performance bounds are theoretically proved. We have implemented SmartPhoto in a testbed using Android based smartphones, and proposed techniques to improve the accuracy of the collected metadata by reducing sensor reading errors and solving object occlusion issues. Results based on real implementations and extensive simulations demonstrate the effectiveness of the proposed algorithms.
Summarizing Newspaper Comments

This work investigates summarizing the conversations that occur in the comments section of the UK newspaper the Guardian. In the comment summarization task comments are clustered and ranked within the cluster. The top comments from each cluster are used to give an overview of that cluster. It was found that topic model clustering gave the most agreement when evaluated against a human gold standard. This approach is compared to cosine distance clustering and k-means clustering. PageRank was found to be the prefered ranking system when compared with TF-IDF, Mutual Information gain and Maximal Marginal Relevance and evaluated against sets of comments summarized by a journalist for the Guardian letters page.