nils-herrmann/clean_scirepeval huggingface dataset:
Momentum and Credit Rating

This paper establishes a strong link between momentum and firm credit rating. Momentum profitability is statistically significant and economically large among low-grade firms, but it is nonexistent among high-grade firms. The momentum payoffs documented in the literature are generated by low rated firms that account for less than four percent of the overall market capitalization of rated firms. For loser (winner) stocks in the low rating category, profit margins, sales growth, operating cash flows, and interest coverage decrease (increase) over the formation and holdings periods, while illiquidity and volatility increase (decrease). This operating performance of the winner and loser stocks is not anticipated by the market as evidenced by the earnings surprises and analyst forecast revisions. As the market observes the deteriorating (improving) conditions there is a pressure to sell (buy) losers (winners). Indeed, we show that institutional investors buy winners and sell losers during one year into the holding period. Such buying and selling activity accompanied with high illiquidity precipitates the gains among high credit-risk winners and losses among high credit-risk losers.
Exploring the Possibility of Using Humanoid Robots as Instructional Tools for Teaching a Second Language in Primary School

ISSN 1436-4522.  The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@massey.ac.nz. ISSN 1436-4522 (online) and 1176-3647 (print).  The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. Journal of Educational Technology & Society
No More Pesky Learning Rates

The performance of stochastic gradient descent (SGD) depends critically on how learning rates are tuned and decreased over time. We propose a method to automatically adjust multiple learning rates so as to minimize the expected error at any one time. The method relies on local gradient variations across samples. In our approach, learning rates can increase as well as decrease, making it suitable for non-stationary problems. Using a number of convex and non-convex learning tasks, we show that the resulting algorithm matches the performance of SGD or other adaptive approaches with their best settings obtained through systematic search, and effectively removes the need for learning rate tuning.
On the selection of tags for tag clouds

We examine the creation of a tag cloud for exploring and understanding a set of objects (e.g., web pages, documents). In the first part of our work, we present a formal system model for reasoning about tag clouds. We then present metrics that capture the structural properties of a tag cloud, and we briefly present a set of tag selection algorithms that are used in current sites (e.g., del.icio.us, Flickr, Technorati) or that have been described in recent work. In order to evaluate the results of these algorithms, we devise a novel synthetic user model. This user model is specifically tailored for tag cloud evaluation and assumes an "ideal" user. We evaluate the algorithms under this user model, as well as the model itself, using two datasets: CourseRank (a Stanford social tool containing information about courses) and del.icio.us (a social bookmarking site). The results yield insights as to when and why certain selection schemes work best.
Three-Dimensional Face Recognition

An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods. The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.
Spatial patterns underlying population differences in the background EEG

A method is described which can be used to extract common spatial patterns underlying the EEGs from two human populations. These spatial patterns account, in the least-squares sense, maximally for the variance in the EEGs from one population and minimally for the variance in the other population and therefore would seem to be optimal for quantitatively discriminating between the individual EEGs in the two populations. By using this method, it is suggested that the problems associated with the more common approach to discriminating EEGs, significance probability mapping, can be avoided. The method is tested using EEGs from a population of normal subjects and using the EEGs from a population of patients with neurologic disorders. The results in most cases are excellent and the misclassification which occurs in some cases is attributed to the nonhomogeneity of the patient population particularly. The advantages of the method for feature selection, for automatically classifying the clinical EEG, and with respect to the reference-free nature of the selected features are discussed.
Translating Thoughts Into Actions by Finding Patterns in Brainwaves

Principal Components Analysis (PCA) is often used to project high-dimensional signals to lower dimensional subspaces defined by basis vectors that maximize the variance of the projected signals. Data containing variations of relatively short duration and small magnitude, such as those seen in EEG signals, may not be captured by PCA when applied to time series of long duration. Here, PCA is applied independently to short segments of data and the basis vectors themselves are used as features for classification. In addition, timeembedding the EEG by augmenting each sample with previous samples prior to PCA results in a representation that captures EEG variations in space and time. The resulting features are classified into categories corresponding to which mental task a subject is performing in a brain-computer interface (BCI) paradigm. Approximately 80% of test samples are correctly classified as one of five mental tasks. In addition, an on-line artifact removal method is demonstrated and an inexpensive hardware and software system for BCI research is described.
GhostVLAD for set-based face recognition

The objective of this paper is to learn a compact representation of image sets for template-based face recognition. We make the following contributions: first, we propose a network architecture which aggregates and embeds the face descriptors produced by deep convolutional neural networks into a compact fixed-length representation. This compact representation requires minimal memory storage and enables efficient similarity computation. Second, we propose a novel GhostVLAD layer that includes ghost clusters, that do not contribute to the aggregation. We show that a quality weighting on the input faces emerges automatically such that informative images contribute more than those with low quality, and that the ghost clusters enhance the network’s ability to deal with poor quality images. Third, we explore how input feature dimension, number of clusters and different training techniques affect the recognition performance. Given this analysis, we train a network that far exceeds the state-of-the-art on the IJB-B face recognition dataset. This is currently one of the most challenging public benchmarks, and we surpass the state-of-the-art on both the identification and verification protocols.
Third-wave livestreaming: teens' long form selfie

Mobile livestreaming is now well into its third wave. From early systems such as Bambuser and Qik, to more popular apps Meerkat and Periscope, to today's integrated social streaming features in Facebook and Instagram, both technology and usage have changed dramatically. In this latest phase of livestreaming, cameras turn inward to focus on the streamer, instead of outwards on the surroundings. Teens are increasingly using these platforms to entertain friends, meet new people, and connect with others on shared interests. We studied teens' livestreaming behaviors and motivations on these new platforms through a survey completed by 2,247 American livestreamers and interviews with 20 teens, highlighting changing practices, teens' differences from the broader population, and implications for designing new livestreaming services.
Sensor fault and patient anomaly detection and classification in medical wireless sensor networks

Wireless Sensor Networks are vulnerable to a plethora of different fault types and external attacks after their deployment. We focus on sensor networks used in healthcare applications for vital sign collection from remotely monitored patients. These types of personal area networks must be robust and resilient to sensor failures as their capabilities encompass highly critical systems. Our objective is to propose an anomaly detection algorithm for medical wireless sensor networks. Our proposed approach firstly classifies instances of sensed patient attributes as normal and abnormal. Once we detect an abnormal instance, we use regression prediction to discern between a faulty sensor reading and a patient entering into a critical state. Our experimental results on real patient datasets show that our proposed approach is able to quickly detect patient anomalies and sensor faults with high detection accuracy while maintaining a low false alarm ratio.
Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices

This paper uses time-series analysis to study the relationship between Bitcoin prices and fundamental economic variables, technological factors and measurements of collective mood derived from Twitter feeds. Sentiment analysis has been performed on a daily basis through the utilization of a state-of-theart machine learning algorithm, namely Support Vector Machines (SVMs). A series of short-run regressions shows that the Twitter sentiment ratio is positively correlated with Bitcoin prices. The short-run analysis also reveals that the number of Wikipedia search queries (showing the degree of public interest in Bitcoins) and the hash rate (measuring the mining difficulty) have a positive effect on the price of Bitcoins. On the contrary, the value of Bitcoins is negatively affected by the exchange rate between the USD and the euro (which represents the general level of prices). A vector errorcorrection model is used to investigate the existence of long-term relationships between cointegrated variables. This kind of long-run analysis reveals that the Bitcoin price is positively associated with the number of Bitcoins in circulation (representing the total stock of money supply) and negatively associated with the Standard and Poor’s 500 stock market index (which indicates the general state of the global economy).
The success of hashing technique is indeed beyond fast image search

This paper proposes a generic formulation that significantly expedites the training and deployment of image classification models, particularly under the scenarios of many image categories and high feature dimensions. As the core idea, our method represents both the images and learned classifiers using binary hash codes, which are simultaneously learned from the training data. Classifying an image thereby reduces to retrieving its nearest class codes in the Hamming space. Specifically, we formulate multiclass image classification as an optimization problem over binary variables. The optimization alternatingly proceeds over the binary classifiers and image hash codes. Profiting from the special property of binary codes, we show that the sub-problems can be efficiently solved through either a binary quadratic program (BQP) or a linear program. In particular, for attacking the BQP problem, we propose a novel bit-flipping procedure which enjoys high efficacy and a local optimality guarantee. Our formulation supports a large family of empirical loss functions and is, in specific, instantiated by exponential and linear losses. Comprehensive evaluations are conducted on several representative image benchmarks. The experiments consistently exhibit reduced computational and memory complexities of model training and deployment, without sacrificing classification accuracy.
Short Text Classification Using Deep Representation: A Case Study of Spanish Tweets in Coset Shared Task

Topic identification as a specific case of text classification is one of the primary steps toward knowledge extraction from the raw textual data. In such tasks, words are dealt with as a set of features. Due to high dimensionality and sparseness of feature vector result from traditional feature selection methods, most of the proposed text classification methods for this purpose lack performance and accuracy. In dealing with tweets which are limited in the number of words the aforementioned problems are reflected more than ever. In order to alleviate such issues, we have proposed a new topic identification method for Spanish tweets based on the deep representation of Spanish words. In the proposed method, words are represented as multi-dimensional vectors, in other words, words are replaced with their equivalent vectors which are calculated based on some transformation of raw text data. Average aggregation technique is used to transform the word vectors into tweet representation. Our model is trained based on deep vectorized representation of the tweets and an ensemble of different classifiers is used for Spanish tweet classification. The best result obtained by a fully connected multi-layer neural network with three hidden layers. The experimental results demonstrate the feasibility and scalability of the proposed method.
G-SQL: Fast Query Processing via Graph Exploration

A lot of real-life data are of graph nature. However, it is not until recently that business begins to exploit data’s connectedness for business insights. On the other hand, RDBMSs are a mature technology for data management, but they are not for graph processing. Take graph traversal, a common graph operation for example, it heavily relies on a graph primitive that accesses a given node’s neighborhood. We need to join tables following foreign keys to access the nodes in the neighborhood if an RDBMS is used to manage graph data. Graph exploration is a fundamental building block of many graph algorithms. But this simple operation is costly due to a large volume of I/O caused by the massive amount of table joins. In this paper, we present G-SQL, our effort toward the integration of a RDBMS and a native in-memory graph processing engine. G-SQL leverages the fast graph exploration capability provided by the graph engine to answer multi-way join queries. Meanwhile, it uses RDBMSs to provide mature data management functionalities, such as reliable data storage and additional data access methods. Specifically, G-SQL is a SQL dialect augmented with graph exploration functionalities and it dispatches query tasks to the in-memory graph engine and its underlying RDMBS. The G-SQL runtime coordinates the two query processors via a unified cost model to ensure the entire query is processed efficiently. Experimental results show that our approach greatly expands capabilities of RDBMs and delivers exceptional performance for SQL-graph hybrid queries.
Loda: Lightweight on-line detector of anomalies

In supervised learning it has been shown that a collection of weak classifiers can result in a strong classifier with error rates similar to those of more sophisticated methods. In unsupervised learning, namely in anomaly detection such a paradigm has not yet been demonstrated despite the fact that many methods have been devised as counterparts to supervised binary classifiers. This work partially fills the gap by showing that an ensemble of very weak detectors can lead to a strong anomaly detector with a performance equal to or better than state of the art methods. The simplicity of the proposed ensemble system (to be called Loda) is particularly useful in domains where a large number of samples need to be processed in real-time or in domains where the data stream is subject to concept drift and the detector needs to be updated on-line. Besides being fast and accurate, Loda is also able to operate and update itself on data with missing variables. Loda is thus practical in domains with sensor outages. Moreover, Loda can identify features in which the scrutinized sample deviates from the majority. This capability is useful when the goal is to find out what has caused the anomaly. It should be noted that none of these favorable properties increase Loda’s low time and space complexity. We compare Loda to several state of the art anomaly detectors in two settings: batch training and on-line training on data streams. The results on 36 datasets from UCI repository illustrate the strengths of the proposed system, but also provide more insight into the more general questions regarding batch-vs-on-line anomaly detection.
A New Approach to Secure Logging

The need for secure logging is well-understood by the security professionals, including both researchers and practitioners. The ability to efficiently verify all (or some) log entries is important to any application employing secure logging techniques. In this article, we begin by examining the state of the art in secure logging and identify some problems inherent to systems based on trusted third-party servers. We then propose a different approach to secure logging based upon recently developed Forward-Secure Sequential Aggregate (FssAgg) authentication techniques. Our approach offers both space-efficiency and provable security. We illustrate two concrete schemes—one private-verifiable and one public-verifiable—that offer practical secure logging without any reliance on online trusted third parties or secure hardware. We also investigate the concept of immutability in the context of forward-secure sequential aggregate authentication to provide finer grained verification. Finally we evaluate proposed schemes and report on our experience with implementing them within a secure logging system.
Optical Character Recognition using Neural Network

Optical character recognition is one of the most challenging problems in pattern recognition. It is a complex challenge because there are lots of variations in a handwritten character and different person have different handwriting. This paper implements neural network based character recognition system using multi layered feed forward neural network. Different training algorithms have implemented and the performance of these algorithms is compared. Error-back propagation algorithm is used to train the multi layered feed forward neural network. Gradient descent training and Levenberg-Marquardt algorithm based training is also used in error back propagation algorithm Keywords— Optical character recognition, LevenbergMarquardt algorithm, gradient descent algorithm
Anchoring 2D gesture annotations in augmented reality

Augmented reality enhanced collaboration systems often allow users to draw 2D gesture annotations onto video feeds to help collaborators to complete physical tasks. This works well for static cameras, but for movable cameras, perspective effects cause problems when trying to render 2D annotations from a new viewpoint in 3D. In this paper, we present a new approach towards solving this problem by using gesture enhanced annotations. By first classifying which type of gesture the user drew, we show that it is possible to render annotations in 3D in a way that conforms more to the original intention of the user than with traditional methods. We first determined a generic vocabulary of important 2D gestures for remote collaboration by running an Amazon Mechanical Turk study with 88 participants. Next, we designed a novel system to automatically handle the top two 2D gesture annotations - arrows and circles. Arrows are handled by identifying their anchor points and using surface normals for better perspective rendering. For circles, we designed a novel energy function to help infer the object of interest using both 2D image cues and 3D geometric cues. Results indicate that our approach outperforms previous methods in terms of better conveying the original drawing's meaning from different viewpoints.
Sequential Neural Models with Stochastic Layers

:  SRNNs achieve state-of-the-art for speech. • Music data requires simpler SRNN architecture. Achieved 2% better than original Piano ELBO using small SRNN. • Replication results for Muse, Nottingham and JSB Chorales are within 18% of ELBO for original results. Replication achieved 8% improvement for Piano data. • Decreasing z increases ELBO. Negligible effect on ELBO by changing a, d and s. • Small dimensional SRNN achieves similar ELBO to replication. Improved performance for Piano and JSB Chorales. • Combining datasets for training and validation achieved 2% improvement of ELBO on Piano dataset compared to the paper’s results.
Inferring user demographics and social strategies in mobile social networks

Demographics are widely used in marketing to characterize different types of customers. However, in practice, demographic information such as age, gender, and location is usually unavailable due to privacy and other reasons. In this paper, we aim to harness the power of big data to automatically infer users' demographics based on their daily mobile communication patterns. Our study is based on a real-world large mobile network of more than 7,000,000 users and over 1,000,000,000 communication records (CALL and SMS). We discover several interesting social strategies that mobile users frequently use to maintain their social connections. First, young people are very active in broadening their social circles, while seniors tend to keep close but more stable connections. Second, female users put more attention on cross-generation interactions than male users, though interactions between male and female users are frequent. Third, a persistent same-gender triadic pattern over one's lifetime is discovered for the first time, while more complex opposite-gender triadic patterns are only exhibited among young people.
 We further study to what extent users' demographics can be inferred from their mobile communications. As a special case, we formalize a problem of double dependent-variable prediction-inferring user gender and age simultaneously. We propose the WhoAmI method, a Double Dependent-Variable Factor Graph Model, to address this problem by considering not only the effects of features on gender/age, but also the interrelation between gender and age. Our experiments show that the proposed WhoAmI method significantly improves the prediction accuracy by up to 10% compared with several alternative methods.