nils-herrmann/clean_scirepeval huggingface dataset:
A 2-Approximation Algorithm for the Undirected Feedback Vertex Set Problem

A feedback vertex set of a graph is a subset of vertices that contains at least one vertex from every cycle in the graph. The problem considered is that of finding a minimum feedback vertex set given a weighted and undirected graph. We present a simple and efficient approximation algorithm with performance ratio of at most 2, improving previous best bounds for either weighted or unweighted cases of the problem. Any further improvement on this bound, matching the best constant factor known for the vertex cover problem, is deemed challenging. The approximation principle, underlying the algorithm, is based on a generalized form of the classical local ratio theorem, originally developed for approximation of the vertex cover problem, and a more flexible style of its application.
A framework for cooperative communication game mechanics from grounded theory

A rich element of cooperative games are mechanics that communicate. Unlike automated awareness cues and synchronous verbal communication, cooperative communication mechanics enable players to share information and direct action by engaging with game systems. These include both explicitly communicative mechanics, such as built-in pings that direct teammates' attention to specific locations, and emergent communicative mechanics, where players develop their own conventions about the meaning of in-game activities, like jumping to get attention. We use a grounded theory approach with 40 digital games to identify and classify the types of cooperative communication mechanics game designers might use to enable cooperative play. We provide details on the classification scheme and offer a discussion on the implications of cooperative communication mechanics.
The roles of brand community and community engagement in building brand trust on social media

Brand communities and social media often overlap. Social media is an ideal environment for building brand communities. However, there is limited research about the benefits and consequences of brand communities established on social media platforms. This study addresses this issue by developing a model depicting how consumers’ relationship with the elements of a brand community based on social media (i.e. brand, product, company, and other consumers) influence brand trust. The findings include that three of the four relationships positively influence brand trust. However, customer-other customers’ relationships negatively influence brand trust, which is counter intuitive and interesting. The prominent role of engagement in a brand community is also investigated in the model. Community engagement amplifies the strength of the relationships consumers make with the elements of brand community and it has a moderating effect in translating the effects of such relationships on brand trust. Finally, theoretical and managerial implications are discussed.
Monte Carlo and quasi-Monte Carlo methods

Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N~^), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Carlo quadrature is attained using quasi-random (also called low-discrepancy) sequences, which are a deterministic alternative to random or pseudo-random sequences. The points in a quasi-random sequence are correlated to provide greater uniformity. The resulting quadrature method, called quasi-Monte Carlo, has a convergence rate of approximately O((log N^N'). For quasi-Monte Carlo, both theoretical error estimates and practical limitations are presented. Although the emphasis in this article is on integration, Monte Carlo simulation of rarefied gas dynamics is also discussed. In the limit of small mean free path (that is, the fluid dynamic limit), Monte Carlo loses its effectiveness because the collisional distance is much less than the fluid dynamic length scale. Computational examples are presented throughout the text to illustrate the theory. A number of open problems are described.
Unsupervised Topic Modeling for Short Texts Using Distributed Representations of Words

We present an unsupervised topic model for short texts that performs soft clustering over distributed representations of words. We model the low-dimensional semantic vector space represented by the dense distributed representations of words using Gaussian mixture models (GMMs) whose components capture the notion of latent topics. While conventional topic modeling schemes such as probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation (LDA) need aggregation of short messages to avoid data sparsity in short documents, our framework works on large amounts of raw short texts (billions of words). In contrast with other topic modeling frameworks that use word cooccurrence statistics, our framework uses a vector space model that overcomes the issue of sparse word co-occurrence patterns. We demonstrate that our framework outperforms LDA on short texts through both subjective and objective evaluation. We also show the utility of our framework in learning topics and classifying short texts on Twitter data for English, Spanish, French, Portuguese and Russian.
Balance control of a unicycle robot

This paper presents the design and implementation of balance control for a unicycle robot. The robot consists of a wheel, a body frame, and a reaction wheel. The wheel enables the robot to move forward or backward to obtain longitudinal stability, while the reaction wheel is used for obtaining lateral stability. Control of this system is a challenging task because of inherent nonlinearity, instability, and underactuation. A detailed dynamic model of the system is derived for control design. By retaining the predominant nonlinear terms and neglecting the high-order coupling terms, the system model is simplified to two decoupled systems. Sliding mode control and feedback linearization are then used to design the stabilizing controllers for the simplified models. The robot is constructed and the designed control schemes are realized through a digital signal processor. The effectiveness of the control schemes is demonstrated through experimental studies.
A location-based news article recommendation with explicit localized semantic analysis

The interest of users in handheld devices is strongly related to their location. Therefore, the user location is important, as a user context, for news article recommendation in a mobile environment. This paper proposes a novel news article recommendation that reflects the geographical context of the user. For this purpose, we propose the Explicit Localized Semantic Analysis (ELSA), an ESA-based topical representation of documents. Every location has its own geographical topics, which can be captured from the geo-tagged documents related to the location. Thus, not only news articles but locations are also represented as topic vectors. The main advantage of ELSA is that it stresses only the topics that are relevant to a given location, whereas all topics are equally important in ESA. As a result, geographical topics have different importance according to the user location in ELSA, even if they come from the same article. Another advantage of ELSA is that it allows a simple comparison of the user location and news articles, because it projects both locations and articles onto an identical space composed of Wikipedia topics. In the evaluation of ELSA with the New York Times corpus, it outperformed two simple baselines of Bag-Of-Words and LDA as well as two ESA-based methods. Rt10 of ELSA was improved up to 46.25% over other methods, and its NDCG@k was always higher than those of the others regardless of k.
DEVELOPMENT OF WIRELESS SENSOR NETWORK USING BLUETOOTH LOW ENERGY ( BLE ) FOR CONSTRUCTION NOISE MONITORING

In this paper the development of a Wireless Sensor Network (WSN) for construction noise identification and sound locating is investigated using the novel application of Bluetooth Low Energy (BLE). Three WSNs using different system-on-chip (SoC) devices and networking protocols have been prototyped using a Raspberry Pi as the gateway in the network. The functionality of the system has been demonstrated with data logging experiments and comparisons has been made between the different WSN systems developed to identify the relative advantages of BLE. Experiments using the WSN for vehicle noise identification and sound location have further demonstrated the potential of the system. This paper demonstrates the versatility of a BLE WSNs and the low power consumption that is achievable with BLE devices for noise detection applications.
Math Anxiety , Working Memory , and Math Achievement in Early Elementary School

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features

Manually crafted combinatorial features have been the "secret sauce" behind many successful models. For web-scale applications, however, the variety and volume of features make these manually crafted features expensive to create, maintain, and deploy. This paper proposes the Deep Crossing model which is a deep neural network that automatically combines features to produce superior models. The input of Deep Crossing is a set of individual features that can be either dense or sparse. The important crossing features are discovered implicitly by the networks, which are comprised of an embedding and stacking layer, as well as a cascade of Residual Units. Deep Crossing is implemented with a modeling tool called the Computational Network Tool Kit (CNTK), powered by a multi-GPU platform. It was able to build, from scratch, two web-scale models for a major paid search engine, and achieve superior results with only a sub-set of the features used in the production models. This demonstrates the potential of using Deep Crossing as a general modeling paradigm to improve existing products, as well as to speed up the development of new models with a fraction of the investment in feature engineering and acquisition of deep domain knowledge.
Reducing MOSFET 1/f noise and power consumption by switched biasing

Switched biasing is proposed as a technique for reducing the 1/f noise in MOSFET's. Conventional techniques, such as chopping or correlated double sampling, reduce the effect of 1/f noise in electronic circuits, whereas the switched biasing technique reduces the 1/f noise itself. Whereas noise reduction techniques generally lead to more power consumption, switched biasing can reduce the power consumption. It exploits an intriguing physical effect: cycling a MOS transistor from strong inversion to accumulation reduces its intrinsic 1/f noise. As the 1/f noise is reduced at its physical roots, high frequency circuits, in which 1/f noise is being upconverted, can also benefit. This is demonstrated by applying switched biasing in a 0.8 /spl mu/m CMOS sawtooth oscillator. By periodically switching off the bias currents, during time intervals that they are not contributing to the circuit operation, a reduction of the 1/f noise induced phase noise by more than 8 dB is achieved, while the power consumption is also reduced by 30%.
High Frequency Trading and Price Discovery

We examine the role of high-frequency traders (HFT) in price discovery and price efficiency. Overall HFT facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors on average days and the highest volatility days. This is done through their marketable orders. In contrast, HFT liquidity-supplying non-marketable orders are adversely selected in terms of the permanent and transitory components as these trades are in the direction opposite to permanent price changes and in the same direction as transitory pricing errors. HFT predicts price changes in the overall market over short horizons measured in seconds. HFT is correlated with public information, such as macro news announcements, marketwide price movements, and limit order book imbalances. (for internet appendix click: http://goo.gl/vyOEB)
Synthesizing benchmarks for predictive modeling

Predictive modeling using machine learning is an effective method for building compiler heuristics, but there is a shortage of benchmarks. Typical machine learning experiments outside of the compilation field train over thousands or millions of examples. In machine learning for compilers, however, there are typically only a few dozen common benchmarks available. This limits the quality of learned models, as they have very sparse training data for what are often high-dimensional feature spaces. What is needed is a way to generate an unbounded number of training programs that finely cover the feature space. At the same time the generated programs must be similar to the types of programs that human developers actually write, otherwise the learning will target the wrong parts of the feature space. We mine open source repositories for program fragments and apply deep learning techniques to automatically construct models for how humans write programs. We sample these models to generate an unbounded number of runnable training programs. The quality of the programs is such that even human developers struggle to distinguish our generated programs from hand-written code. We use our generator for OpenCL programs, CLgen, to automatically synthesize thousands of programs and show that learning over these improves the performance of a state of the art predictive model by 1.27x. In addition, the fine covering of the feature space automatically exposes weaknesses in the feature design which are invisible with the sparse training examples from existing benchmark suites. Correcting these weaknesses further increases performance by 4.30x.
Statistical exponential families: A digest with flash cards

This document describes concisely the ubiquitous class of exponential family distributions met in statistics. The first part recalls definitions and summarizes main properties and duality with Bregman divergences (all proofs are skipped). The second part lists decompositions and related formula of common exponential family distributions. We recall the Fisher-Rao-Riemannian geometries and the dual affine connection information geometries of statistical manifolds. It is intended to maintain and update this document and catalog by adding new distribution items. See the jMEF library, a Java package for processing mixture of exponential families. Available for download at http://www.lix.polytechnique.fr/~nielsen/MEF/ École Polytechnique (France) and Sony Computer Science Laboratories Inc. (Japan). École Polytechnique (France).
Biometric Authentication using Brain Responses to Visual Stimuli

This paper studies the suitability of brain activity, namely electroencephalogram signals, as raw material for conducting biometric authentication of individuals. Brain responses were extracted with visual stimulation, leading to biological brain responses known as Visual Evoked Potentials. We evaluated a novel method, using only 8 occipital electrodes and the energy of differential EEG signals, to extract information about the subjects for further use as their biometric features. To classify the features obtained from each individual, we used a one-class classifier per subject and we tested four types of classifiers: K-Nearest Neighbor, Support Vector Data Description and two other classifiers resulting from the combination of the two ones previously mentioned. After testing these four classifiers with features of 70 subjects, the results showed that visual evoked potentials are suitable for an accurate biometric authentication.
Efficient superpixel based segmentation for food image analysis

In this paper, we propose a segmentation method based on normalized cut and superpixels. The method relies on color and texture cues for fast computation and efficient use of memory. The method is used for food image segmentation as part of a mobile food record system we have developed for dietary assessment and management. The accurate estimate of nutrients relies on correctly labelled food items and sufficiently well-segmented regions. Our method achieves competitive results using the Berkeley Segmentation Dataset and outperforms some of the most popular techniques in a food image dataset.
A Relational View of Information Seeking and Learning in Social Networks

Research in organizational learning has demonstrated processes and occasionally performance implications of acquisition of declarative (know-what) and procedural (know-how) knowledge. However, considerably less attention has been paid to learned characteristics of relationships that affect the decision to seek information from other people. Based on a review of the social network, information processing, and organizational learning literatures, along with the results of a previous qualitative study, we propose a formal model of information seeking in which the probability of seeking information from another person is a function of (1) knowing what that person knows; (2) valuing what that person knows; (3) being able to gain timely access to that person’s thinking; and (4) perceiving that seeking information from that person would not be too costly. We also hypothesize that the knowing, access, and cost variables mediate the relationship between physical proximity and information seeking. The model is tested using two separate research sites to provide replication. The results indicate strong support for the model and the mediation hypothesis (with the exception of the cost variable). Implications are drawn for the study of both transactive memory and organizational learning, as well as for management practice.
ITIL Adoption Model based on TAM

although Information technology infrastructure library (ITIL) is the most popular framework for IT service management all around the world, problems for organizations in the process of adoption would eliminate its benefits. In fact the ability of organization to adopt this framework is the basis for IT service improvement. Few studies have tried to find out success factor that influence ITIL adoption but no study was found investigating an adoption model for this framework. In this study, first seven success factors were identified through qualitative meta-Synthesis relying on previous studies; then, a research model based on the technology acceptance model (TAM) for representing the influence of these critical success factors on ITIL adoption is proposed. The success factor used are: (1) top management support, (2) communication and cooperation, (3) training and competence of involved stakeholder in ITIL project, (4) change management and organizational culture, (5) project management and governance, (6) ITIL process implementation and applied technology, (7) monitoring and evaluation.  Technology Infrastructure Library; ITIL; ITSM; ITIL adoption; TAM.
Types of Information Technology Capabilities and Their Role in Competitive Advantage: An Empirical Study

During the past two decades, both business managers and academic researchers have shown considerable interest in understanding how information technologies (IT) help to create competitive advantage for a firm. While recently the idea of competitive differentiation through IT has been challenged, this study contrasts the traditional thinking about competitive advantage with the resource-based view. Specifically, it is argued that by demarcating specific types of capabilities, we can contribute to better understanding of the sources of IT-based competitive advantage. Conceptually, we distinguish here between value, competitive, and dynamic capabilities as three distinct types of capabilities. Within each type, we identify specific capa254 GANESH D. BHATT AND VARUN GROVER bilities, such as quality of the IT infrastructure, IT business experience, relationship infrastructure, and intensity of organizational learning, and present a model that describes relationships between these capabilities and competitive advantage. We then empirically test the model using data collected via a national mail survey from chief IT executives from 202 manufacturing firms. While the quality of the IT infrastructure is hypothesized as a value capability and expectedly did not have any significant effect on competitive advantage, the quality of IT business expertise and the relationship infrastructure (competitive capabilities) did. The results of the study also indicate that the intensity of organizational learning (dynamic capability) was significantly related to all of the capabilities. These results point to the importance of delineating capabilities such as relationship infrastructure that can facilitate differentiation in the marketplace, and dynamic capabilities such as organizational learning as an important antecedent to IT capability building.
Securely Outsourcing Attribute-Based Encryption with Checkability

Attribute-Based Encryption (ABE) is a promising cryptographic primitive which significantly enhances the versatility of access control mechanisms. Due to the high expressiveness of ABE policies, the computational complexities of ABE key-issuing and decryption are getting prohibitively high. Despite that the existing Outsourced ABE solutions are able to offload some intensive computing tasks to a third party, the verifiability of results returned from the third party has yet to be addressed. Aiming at tackling the challenge above, we propose a new Secure Outsourced ABE system, which supports both secure outsourced key-issuing and decryption. Our new method offloads all access policy and attribute related operations in the key-issuing process or decryption to a Key Generation Service Provider (KGSP) and a Decryption Service Provider (DSP), respectively, leaving only a constant number of simple operations for the attribute authority and eligible users to perform locally. In addition, for the first time, we propose an outsourced ABE construction which provides checkability of the outsourced computation results in an efficient way. Extensive security and performance analysis show that the proposed schemes are proven secure and practical.