nils-herrmann/clean_scirepeval huggingface dataset:
STUDYING THE IMPACT OF ORGANIZATIONAL CULTURE ON KNOWLEDGE MANAGEMENT EFFECTIVENESS WITH THE INTERMEDIATE ROLE OF KNOWLEDGE SHARING PROCESS AMONG MELI BANK STAFF OF MELI BANK IN ILAM PROVINCE

General aim of this research is studying the amount of impact of organizational culture on Knowledgemanagement effectiveness with the intermediate role of knowledge sharing process among staff of Meli bank in Ilam province.Statistical population islimited and includes 420 subjects of Meli bank staffs in Ilam province. Sample volume was determined 200 participants with using kokaran formula and sample method was classification. This sample volume is proportional with each category (the number of Meli bank staff in any town) and base on it some questioner were distributed among them. The questioner which was used for three variables of organizational culture, knowledge management effectiveness and knowledge sharing process was Moon and Lee’s standard questioner (2014) which has high validity and its reliability was obtained base on Cronach alpha and it was 0.948. It shows that reliability of questioner is pleasant. Inorder to consider normality of data distribution of variables Kolmogorov-Smirnov test was used. The used statistical method for data analysis is modeling structural equations by LISRAL software. The result of research shows that variable of organizational culture has direct and positive impact on knowledge sharing process and knowledge management effectiveness. Also knowledge sharing process has a significant and positive impact on knowledgemanagement effectiveness and finally, organizational culture has significant and positive impact on knowledgemanagement effectiveness with intermediate role of knowledge sharing, and all of hypotheses were accepted with confidence level of %95. Therefor it can be concluded that existence of organizational culture is a motivation for affecting knowledge sharing on knowledgemanagement effectiveness among staff of Meli bank in Ilam province.
Pattern classification of foot strike type using body worn accelerometers

The automatic classification of foot strike patterns into the three basic categories forefoot, midfoot and rearfoot striking plays an important role for applications like shoe fitting with instant feedback. This paper presents methods for this classification based on body worn accelerometers that allow giving the required direct feedback to the user. For our study, we collected data from 40 runners who had a standard accelerometer in a custom-built sensor pod attached to the laces of their running shoes. The acceleration in three axes was recorded continuously while the runners conducted their runs. Data for repeated runs at two different speed levels were collected in order to have sufficient sensor data for classification. The data was analyzed using features computed for individual steps of the runners to distinguish the three foot strike pattern classes. The labels for the strike pattern classes were established using high-speed video that was concurrently collected. We could show that the classification of the strike types based on the measured accelerations and the extracted features was up to 95.3% accurate. The established classification system can be used to support runners, for example by giving running shoe recommendations that ideally match the prevailing strike type of the runner.
Merkle-Damgård Revisited: How to Construct a Hash Function

The most common way of constructing a hash function (e.g., SHA-1) is to iterate a compression function on the input message. The compression function is usually designed from scratch or made out of a blockcipher. In this paper, we introduce a new security notion for hash-functions, stronger than collisionresistance. Under this notion, the arbitrary length hash function H must behave as a random oracle when the fixed-length building block is viewed as a random oracle or an ideal block-cipher. The key property is that if a particular construction meets this definition, then any cryptosystem proven secure assuming H is a random oracle remains secure if one plugs in this construction (still assuming that the underlying fixedlength primitive is ideal). In this paper, we show that the current design principle behind hash functions such as SHA-1 and MD5 — the (strengthened) Merkle-Damg̊ard transformation — does not satisfy this security notion. We provide several constructions that provably satisfy this notion; those new constructions introduce minimal changes to the plain Merkle-Damg̊ard construction and are easily implementable in practice. University of Luxembourg,  coron@clipper.ens.fr New York University,  dodis@cs.nyu.edu Gemplus Card International,  cecile.malinaud@normalesup.org New York University,  puniya@cs.nyu.edu
NAACL HLT 2018 Subword and Character LEvel Models in NLP Proceedings of the Second Workshop

Neural machine translation has achieved impressive results in the last few years, but its success has been limited to settings with large amounts of parallel data. One way to improve NMT for lower-resource settings is to initialize a word-based NMT model with pretrained word embeddings. However, rare words still suffer from lower quality word embeddings when trained with standard word-level objectives. We introduce word embeddings that utilize morphological resources, and compare to purely unsupervised alternatives. We work with Arabic, a morphologically rich language with available linguistic resources, and perform Ar-to-En MT experiments on a small corpus of TED subtitles. We find that word embeddings utilizing subword information consistently outperform standard word embeddings on a word similarity task and as initialization of the source word embeddings in a low-resource NMT system.
A brief survey on deep belief networks and introducing a new object oriented MATLAB toolbox (DeeBNet)

Nowadays, this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many ability like feature extraction and classification that are used in many applications like image processing, speech processing and etc. This paper introduces a new object oriented MATLAB toolbox with most of abilities needed for the implementation of DBNs. According to the results of the experiments conducted on MNIST (image), ISOLET (speech), and 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. Also on all datasets, the obtained classification errors are comparable to those of state of the art classifiers. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), using GPU, etc. The toolbox is a user-friendly open source software and is freely available on the website http://ceit.aut.ac.ir/~keyvanrad/DeeBNet%20Toolbox.html .
Learning Mixtures of Gaussians in High Dimensions

Efficiently learning mixture of Gaussians is a fundamental problem in statistics and learning theory. Given samples coming from a random one out of k Gaussian distributions in Rn, the learning problem asks to estimate the means and the covariance matrices of these Gaussians. This learning problem arises in many areas ranging from the natural sciences to the social sciences, and has also found many ma- chine learning applications. Unfortunately, learning mixture of Gaussians is an information theoretically hard problem: in order to learn the parameters up to a reasonable accuracy, the number of samples required is exponential in the number of Gaussian components in the worst case. In this work, we show that provided we are in high enough dimensions, the class of Gaussian mixtures is learnable in its most general form under a smoothed analysis framework, where the parameters are randomly perturbed from an adversarial starting point. In particular, given samples from a mixture of Gaussians with randomly perturbed parameters, when n ≥ Ω(k2), we give an algorithm that learns the parameters with polynomial running time and using polynomial number of samples.
 The central algorithmic ideas consist of new ways to de- compose the moment tensor of the Gaussian mixture by exploiting its structural properties. The symmetries of this tensor are derived from the combinatorial structure of higher order moments of Gaussian distributions (sometimes referred to as Isserlis' theorem or Wick's theorem). We also develop new tools for bounding smallest singular values of structured random matrices, which could be useful in other smoothed analysis settings.
Learning feed-forward one-shot learners

One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on deep learning, which are very effective in other learning scenarios, are ill-suited for one-shot learning as they need large amounts of training data. In this paper, we propose a method to learn the parameters of a deep model in one shot. We construct the learner as a second deep network, called a learnet, which predicts the parameters of a pupil network from a single exemplar. In this manner we obtain an efficient feed-forward one-shot learner, trained end-to-end by minimizing a one-shot classification objective in a learning to learn formulation. In order to make the construction feasible, we propose a number of factorizations of the parameters of the pupil network. We demonstrate encouraging results by learning characters from single exemplars in Omniglot, and by tracking visual objects from a single initial exemplar in the Visual Object Tracking benchmark.
Enabling ubiquitous sensing with RFID

Radio frequency identification has attracted considerable press attention in recent years, and for good reasons: RFID not only replaces traditional barcode technology, it also provides additional features and removes boundaries that limited the use of previous alternatives. Printed bar codes are typically read by a laser-based optical scanner that requires a direct line-of-sight to detect and extract information. With RFID, however, a scanner can read the encoded information even when the tag is concealed for either aesthetic or security reasons. In the future, RFID tags will likely be used as environmental sensors on an unprecedented scale.
Evaluating features and classifiers for road weather condition analysis

Weather-dependent road conditions are a major factor in many automobile incidents; computer vision algorithms for automatic classification of road conditions can thus be of great benefit. This paper presents a system for classification of road conditions using still-frames taken from an uncalibrated dashboard camera. The problem is challenging due to variability in camera placement, road layout, weather and illumination conditions. The system uses a prior distribution of road pixel locations learned from training data then fuses normalized luminance and texture features probabilistically to categorize the segmented road surface. We attain an accuracy of 80% for binary classification (bare vs. snow/ice-covered) and 68% for 3 classes (dry vs. wet vs. snow/ice-covered) on a challenging dataset, suggesting that a useful system may be viable.
A printed 16 ports massive MIMO antenna system with directive port beams

The design of a massive multiple-input-multiple-output (mMIMO) antenna system based on patch antennas is presented in this paper. The array consists of 16 ports, each port consists of a 2×2 patch antenna array with different phase excitation at each element to tilt the beam toward different directions to provide lower correlation coefficient values. A fixed progressive phase feed network is designed to provide the beam tilts. The proposed antenna system is designed using a 3-layer FR4 substrate with total size of 33.33×33.33×0.16 cm3.
An intuitive daylighting performance analysis and optimization approach

The effective integration of daylighting considerations into the design process requires many issues to be addressed simultaneously, such as daily and seasonal variations, illumination and thermal comfort. To address the need for early integration into the design process, a new approach called Lightsolve, has been developed. Its key objectives are to support the design process using a goal-oriented approach based on iterative design improvement suggestions; to provide climate-based annual metrics in a visual and synthesized format; and to relate quantitative and qualitative performance criteria using daylighting analysis data in various forms. This methodology includes the development of a time-segmentation process to represent weather and time in a condensed form, the adaptation of daylight metrics that encompass temporal and spatial considerations, and the creation of an interactive analysis interface to explore design options and design iterations. This system relies on optimization techniques to generate these suggestions. Lightsolve allows the designer to explore other design alternatives that may better fulfill his objectives and to learn about appropriate strategies to resolve daylight or sunlight penetration issues. It offers architects and building engineers support for daylighting design that can be employed interactively within the existing design process.
Mining frequent closed graphs on evolving data streams

Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this paper we present a framework for studying graph pattern mining on time-varying streams. Three new methods for mining frequent closed subgraphs are presented. All methods work on coresets of closed subgraphs, compressed representations of graph sets, and maintain these sets in a batch-incremental manner, but use different approaches to address potential concept drift. An evaluation study on datasets comprising up to four million graphs explores the strength and limitations of the proposed methods. To the best of our knowledge this is the first work on mining frequent closed subgraphs in non-stationary data streams.
Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting

In this paper, we propose a novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH), to deal with multi-structure data even in the presence of severe outliers. The proposed method formulates geometric model fitting as a mode seeking problem on a hypergraph in which vertices represent model hypotheses and hyperedges denote data points. MSH intuitively detects model instances by a simple and effective mode seeking algorithm. In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a "weight-aware sampling" technique. The proposed method not only alleviates sensitivity to the data distribution, but also is scalable to large scale problems. Experimental results further demonstrate that the proposed method has significant superiority over the state-of-the-art fitting methods on both synthetic data and real images.
An exploration of axiomatic approaches to information retrieval

Existing retrieval models generally do not offer any guarantee for optimal retrieval performance. Indeed, it is even difficult, if not impossible, to predict a model's empirical performance analytically. This limitation is at least partly caused by the way existing retrieval models are developed where relevance is only coarsely modeled at the level of documents and queries as opposed to a finer granularity level of terms. In this paper, we present a new axiomatic approach to developing retrieval models based on direct modeling of relevance with formalized retrieval constraints defined at the level of terms. The basic idea of this axiomatic approach is to search in a space of candidate retrieval functions for one that can satisfy a set of reasonable retrieval constraints. To constrain the search space, we propose to define a retrieval function inductively and decompose a retrieval function into three component functions. Inspired by the analysis of the existing retrieval functions with the inductive definition, we derive several new retrieval functions using the axiomatic retrieval framework. Experiment results show that the derived new retrieval functions are more robust and less sensitive to parameter settings than the existing retrieval functions with comparable optimal performance.
Incremental Local Outlier Detection for Data Streams

Outlier detection has recently become an important problem in many industrial and financial applications. This problem is further complicated by the fact that in many cases, outliers have to be detected from data streams that arrive at an enormous pace. In this paper, an incremental LOF (local outlier factor) algorithm, appropriate for detecting outliers in data streams, is proposed. The proposed incremental LOF algorithm provides equivalent detection performance as the iterated static LOF algorithm (applied after insertion of each data record), while requiring significantly less computational time. In addition, the incremental LOF algorithm also dynamically updates the profiles of data points. This is a very important property, since data profiles may change over time. The paper provides theoretical evidence that insertion of a new data point as well as deletion of an old data point influence only limited number of their closest neighbors and thus the number of updates per such insertion/deletion does not depend on the total number of points TV in the data set. Our experiments performed on several simulated and real life data sets have demonstrated that the proposed incremental LOF algorithm is computationally efficient, while at the same time very successful in detecting outliers and changes of distributional behavior in various data stream applications
Recommending citations: translating papers into references

When we write or prepare to write a research paper, we always have appropriate references in mind. However, there are most likely references we have missed and should have been read and cited. As such a good citation recommendation system would not only improve our paper but, overall, the efficiency and quality of literature search.
 Usually, a citation's context contains explicit words explaining the citation. Using this, we propose a method that "translates" research papers into references. By considering the citations and their contexts from existing papers as parallel data written in two different "languages", we adopt the translation model to create a relationship between these two "vocabularies".
 Experiments on both CiteSeer and CiteULike dataset show that our approach outperforms other baseline methods and increase the precision, recall and f-measure by at least 5% to 10%, respectively. In addition, our approach runs much faster in the both training and recommending stage, which proves the effectiveness and the scalability of our work.
Improving Storage System Reliability with Proactive Error Prediction

This paper proposes the use of machine learning techniques to make storage systems more reliable in the face of sector errors. Sector errors are partial drive failures, where individual sectors on a drive become unavailable, and occur at a high rate in both hard disk drives and solid state drives. The data in the affected sectors can only be recovered through redundancy in the system (e.g. another drive in the same RAID) and is lost if the error is encountered while the system operates in degraded mode, e.g. during RAID reconstruction. In this paper, we explore a range of different machine learning techniques and show that sector errors can be predicted ahead of time with high accuracy. Prediction is robust, even when only little training data or only training data for a different drive model is available. We also discuss a number of possible use cases for improving storage system reliability through the use of sector error predictors. We evaluate one such use case in detail: We show that the mean time to detecting errors (and hence the window of vulnerability to data loss) can be greatly reduced by adapting the speed of a scrubber based on error predictions.
Software strategies for portable computer energy management

Limiting the energy consumption of computers, especially portables, is becoming increasingly important. Thus, new energy-saving computer components and architectures have been and continue to be developed. Many architectural features have both high performance and low power modes, with the mode selection under software control. The problem is to minimize energy consumption while not significantly impacting the effective performance. We group the software control issues as follows: transition, load-change, and adaptation. The transition problem is deciding when to switch to low-power, reduced-functionality modes. The loadchange problem is determining how to modify the load on a component so that it can make further use of its low-power modes. The adaptation problem is how to create software that allows components to be used in novel, power-saving ways. We survey implemented and proposed solutions to software energy management issues created by existing and suggested hardware innovations.
B2B E-commerce: Frameworks for e-readiness assessment

This paper looks into the development of e- readiness model for B2B e-commerce of Small and Medium Enterprises (SMEs). Drawing on existing research on e-readiness models of ICT (information, Communications Technology) specifically B2B e-commerce technologies, this paper formulates a conceptual framework of B2B e-commerce assessment constructs together with the specific research approach towards the development of a more robust e-readiness model. The research presents a conceptual model and framework that highlight the key factors in B2B e-commerce implementation, index score formulations, focus group evaluations and the recommendation approaches to improve B2B e-commerce readiness.
A block-based noise level estimation from X-ray images in SVD domain

Accurate and fast estimation of noise levels from medical images has numerous applications in medical image processing, including image enhancement, image segmentation and feature extraction. In this paper, a block-based noise level estimation algorithm in SVD domain is proposed. The proposed algorithm employs the non-overlapping block image segmentation to estimate homogenous image regions. Each homogenous block is used to obtain an independent noise level estimates in SVD domain. For any particular image, the overall noise level estimate is ascertained by averaging over the set of noise level estimates associated with the homogenous image blocks. In this paper, the optimal size of image segmentation blocks is evaluated systematically over a large dataset of x-ray images. The experimental results show that the proposed method offers numerous advantages over some alternative SVD domain method.
Twitter Geolocation and Regional Classification via Sparse Coding

Experimental Methodology ! Three types of embedding schemes (binary bagof-words, word-counts, word-sequence) ! For a fair comparison, we follow the identical experimental methodology as Eisenstein et al. (2010) ! Twitter’s location service enables users to add location information to their Tweets ! Location information of message is useful (e.g., consumer marketing) Ø However, < 3% of messages are geotagged† ! Machine learning can geolocate based on message content Ø Best results are from supervised modelbased approaches ! We focus on unsupervised data-driven approach, namely sparse coding, to take advantage of abundance of unlabeled messages to geolocate Twitter