nils-herrmann/clean_scirepeval huggingface dataset:
Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models

This paper proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing unconstrained offline handwritten texts. The structural part of the optical models has been modeled with Markov chains, and a Multilayer Perceptron is used to estimate the emission probabilities. This paper also presents new techniques to remove slope and slant from handwritten text and to normalize the size of text images with supervised learning methods. Slope correction and size normalization are achieved by classifying local extrema of text contours with Multilayer Perceptrons. Slant is also removed in a nonuniform way by using Artificial Neural Networks. Experiments have been conducted on offline handwritten text lines from the IAM database, and the recognition rates achieved, in comparison to the ones reported in the literature, are among the best for the same task.
Wood Species Recognition Using GLCM and Correlation

The proposed system identifies the species of the wood using the textural features present in its barks. Each species of a wood has its own unique patterns in its bark, which enabled the proposed system to identify it accurately. Automatic wood recognition system has not yet been well established mainly due to lack of research in this area and the difficulty in obtaining the wood database. In our work, a wood recognition system has been designed based on pre-processing techniques, feature extraction and by correlating the features of those wood species for their classification. Texture classification is a problem that has been studied and tested using different methods due to its valuable usage in various pattern recognition problems, such as wood recognition, rock classification. The most popular technique used for the textural classification is Gray-level Co-occurrence Matrices (GLCM). The features from the enhanced images are thus extracted using the GLCM is correlated, which determines the classification between the various wood species. The result thus obtained shows a high rate of recognition accuracy proving that the techniques used in suitable to be implemented for commercial purposes.
The Tower of Hanoi - Myths and Maths

New updated! The latest book from a very famous author finally comes out. Book of the tower of hanoi myths and maths, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.
End-user debugging strategies: A sensemaking perspective

Despite decades of research into how professional programmers debug, only recently has work emerged about how end-user programmers attempt to debug programs. Without this knowledge, we cannot build tools to adequately support their needs. This article reports the results of a detailed qualitative empirical study of end-user programmers' sensemaking about a spreadsheet's correctness. Using our study's data, we derived a sensemaking model for end-user debugging and categorized participants' activities and verbalizations according to this model, allowing us to investigate how participants went about debugging. Among the results are identification of the prevalence of information foraging during end-user debugging, two successful strategies for traversing the sensemaking model, potential ties to gender differences in the literature, sensemaking sequences leading to debugging progress, and sequences tied with troublesome points in the debugging process. The results also reveal new implications for the design of spreadsheet tools to support end-user programmers' sensemaking during debugging.
Scilla: a Smart Contract Intermediate-Level LAnguage

This paper outlines key design principles of Scilla—an intermediatelevel language for verified smart contracts. Scilla provides a clean separation between the communication aspect of smart contracts on a blockchain, allowing for the rich interaction patterns, and a programming component, which enjoys principled semantics and is amenable to formal verification. Scilla is not meant to be a high-level programming language, and we are going to use it as a translation target for high-level languages, such as Solidity, for performing program analysis and verification, before further compilation to an executable bytecode. We describe the automata-based model of Scilla, present its programming component and show how contract definitions in terms of automata streamline the process of mechanised verification of their safety and temporal properties.
Gameplaying for maths learning: cooperative or not?

This study investigated the effects of gameplaying on fifth-graders’ maths performance and attitudes. One hundred twenty five fifth graders were recruited and assigned to a cooperative Teams-Games-Tournament (TGT), interpersonal competitive or no gameplaying condition. A state standardsbased maths exam and an inventory on attitudes towards maths were used for the pretest and posttest. The students’ gender, socio-economic status and prior maths ability were examined as the moderating variables and covariate. Multivariate analysis of covariance (MANCOVA) indicated that gameplaying was more effective than drills in promoting maths performance, and cooperative gameplaying was most effective for promoting positive maths attitudes regardless of students’ individual differences.  The problem of low achievement in American mathematics education has been discussed in numerous policy reports (Mathematical Sciences Education Board, 2004). Educational researchers (eg Ferrini-Mundy & Schram, 1996) and administrators (eg Brodinsky, 1985), for years, have appealed for mathematics-education reform and proposed various solutions to foster mathematics learning. Amongst these propositions were computer games as powerful mathematical learning tools with great motivational appeal and multiple representations of learning materials (Betz, 1995; Malone, 1981; Moreno, 2002; Quinn, 1994). Researchers reported (eg Ahl, 1981; Bahr & Rieth, 1989; Inkpen, 1994) that a variety of computer games have been used in classrooms to support learning of basic arithmetic and problem-solving skills. Other researchers (Amory, Naicker, Vincent & Adams, 1999; Papert, 1980) contend that computer games need to be carefully aligned with sound learning strategies and 250 British Journal of Educational Technology Vol 38 No 2 2007  conditions to be beneficial. Consistent with this proposition, the incorporation of computer games within a cooperative learning setting becomes an attractive possibility. Cooperative learning in mathematics has been well discussed by Davidson (1990): group learning helps to remove students’ frustration; it is not only a source for additional help but also offers a support network. Empirical research (Jacobs, 1996; Reid, 1992; Whicker, Bol & Nunnery, 1997) verifies the importance of cooperative learning in mathematics education. Hence, the potential benefit of combining computer games with cooperative learning in mathematics warrants a field investigation. Specific research on the cooperative use of computer games is limited. Empirical study of this technique is especially sparse. Therefore, the purpose of this research was to explore whether computer games and cooperative learning could be used together to enrich K-12 mathematics education. Employing a pretest–posttest experimental design, the study examined the effects of cooperative gameplaying on fifth-grade students’ maths performance and maths attitudes when compared to the interpersonal competitive gameplaying and control groups.
A Compact Frequency-Reconfigurable Narrowband Microstrip Slot Antenna

A frequency-reconfigurable microstrip slot antenna is proposed. The antenna is capable of frequency switching at six different frequency bands between 2.2 and 4.75 GHz. Five RF p-i-n diode switches are positioned in the slot to achieve frequency reconfigurability. The feed line and the slot are bended to reduce 33% of the original size of the antenna. The biasing circuit is integrated into the ground plane to minimize the parasitic effects toward the performance of the antenna. Simulated and measured results are used to demonstrate the performance of the antenna. The simulated and measured return losses, together with the radiation patterns, are presented and compared.
Single document extractive text summarization using Genetic Algorithms

This paper presents an extraction based single document text summarization technique using Genetic Algorithms. A given document is represented as a weighted Directed Acyclic Graph. A fitness function is defined to mathematically express the quality of a summary in terms of some desired properties of a summary, such as, topic relation, cohesion and readability. Genetic Algorithm is designed to maximize this fitness function, and get the corresponding summary by extracting the most important sentences. Results are compared with a couple of other existing text summarization methods keeping the DUC2002 data as benchmark, and using the precision-recall evaluation technique. The initial results obtained seem promising and encouraging for future work in this area.
ExprGAN: Facial Expression Editing With Controllable Expression Intensity

Facial expression editing is a challenging task as it needs a high-level semantic understanding of the input face image. In conventional methods, either paired training data is required or the synthetic face’s resolution is low. Moreover, only the categories of facial expression can be changed. To address these limitations, we propose an Expression Generative Adversarial Network (ExprGAN) for photo-realistic facial expression editing with controllable expression intensity. An expression controller module is specially designed to learn an expressive and compact expression code in addition to the encoder-decoder network. This novel architecture enables the expression intensity to be continuously adjusted from low to high. We further show that our ExprGAN can be applied for other tasks, such as expression transfer, image retrieval, and data augmentation for training improved face expression recognition models. To tackle the small size of the training database, an effective incremental learning scheme is proposed. Quantitative and qualitative evaluations on the widely used Oulu-CASIA dataset demonstrate the effectiveness of ExprGAN.
The State of the Art in Visualizing Group Structures in Graphs

Graph visualizations encode relationships between objects. Abstracting the objects into group structures provides an overview of the data. Groups can be disjoint or overlapping, and might be organized hierarchically. However, the underlying graph still needs to be represented for analyzing the data in more depth. This work surveys research in visualizing group structures as part of graph diagrams. A particular focus is the explicit visual encoding of groups, rather than only using graph layout to implicitly indicate groups. We introduce a taxonomy of visualization techniques structuring the field into four main categories: visual node attributes vary properties of the node representation to encode the grouping, juxtaposed approaches use two separate visualizations, superimposed techniques work with two aligned visual layers, and embedded visualizations tightly integrate group and graph representation. We discuss results from evaluations of those techniques as well as main areas of application. Finally, we report future challenges based on interviews we conducted with leading researchers of the field.
Recommender systems in e-commerce

Recommender systems are changing from novelties used by a few E-commerce sites, to serious business tools that are re-shaping the world of E-commerce. Many of the largest commerce Web sites are already using recommender systems to help their customers find products to purchase. A recommender system learns from a customer and recommends products that she will find most valuable from among the available products. In this paper we present an explanation of how recommender systems help E-commerce sites increase sales, and analyze six sites that use recommender systems including several sites that use more than one recommender system. Based on the examples, we create a taxonomy of recommender systems, including the interfaces they present to customers, the technologies used to create the recommendations, and the inputs they need from customers. We conclude with ideas for new applications of recommender systems to E-commerce.
Community detection and stochastic block models: recent developments

The stochastic block model (SBM) is a random graph model with planted clusters. It is widely employed as a canonical model to study clustering and community detection, and provides generally a fertile ground to study the statistical and computational tradeoffs that arise in network and data sciences. This note surveys the recent developments that establish the fundamental limits for community detection in the SBM, both with respect to information-theoretic and computational thresholds, and for various recovery requirements such as exact, partial and weak recovery (a.k.a., detection). The main results discussed are the phase transitions for exact recovery at the Chernoff-Hellinger threshold, the phase transition for weak recovery at the Kesten-Stigum threshold, the optimal distortion-SNR tradeoff for partial recovery, the learning of the SBM parameters and the gap between information-theoretic and computational thresholds. The note also covers some of the algorithms developed in the quest of achieving the limits, in particular two-round algorithms via graph-splitting, semi-definite programming, linearized belief propagation, classical and nonbacktracking spectral methods. A few open problems are also discussed.
Mapping brain asymmetry

Brain asymmetry has been observed in animals and humans in terms of structure, function and behaviour. This lateralization is thought to reflect evolutionary, hereditary, developmental, experiential and pathological factors. Here, we review the diverse literature describing brain asymmetries, focusing primarily on anatomical differences between the hemispheres and the methods that have been used to detect them. Brain-mapping approaches, in particular, can identify and visualize patterns of asymmetry in whole populations, including subtle alterations that occur in disease, with age and during development. These and other tools show great promise for assessing factors that modulate cognitive specialization in the brain, including the ontogeny, phylogeny and genetic determinants of brain asymmetry.
DIRA: Automatic Detection, Identification and Repair of Control-Hijacking Attacks

Buffer overflow attacks are known to be the most common type of attacks that allow attackers to hijack a remote system by sending a specially crafted packet to a vulnerable network application running on it. A comprehensive defense strategy against such attacks should include (1) an attack detection component that determines the fact that a program is compromised and prevents the attack from further propagation, (2) an attack identification component that identifies attack packets so that one can block such packets in the future, and (3) an attack repair component that restores the compromised application’s state to that before the attack and allows it to continue running normally. Over the last decade, a significant amount of research has been vested in the systems that can detect buffer overflow attacks either statically at compile time or dynamically at run time. However, not much effort is spent on automated attack packet identification or attack repair. In this paper we present a unified solution to the three problems mentioned above. We implemented this solution as a GCC compiler extension called DIRA that transforms a program’s source code so that the resulting program can automatically detect any buffer overflow attack against it, repair the memory damage left by the attack, and identify the actual attack packet(s). We used DIRA to compile several network applications with known vulnerabilities and tested DIRA’s effectiveness by attacking the transformed programs with publicly available exploit code. The DIRA-compiled programs were always able to detect the attacks, identify the attack packets and most often repair themselves to continue normal execution. The average run-time performance overhead for attack detection and attack repair/identification is 4% and 25% respectively.
Active Bias: Training a More Accurate Neural Network by Emphasizing High Variance Samples

Self-paced learning and hard example mining re-weight training instances to improve learning accuracy. This paper presents two improved alternatives based on lightweight estimates of sample uncertainty in stochastic gradient descent (SGD): the variance in predicted probability of the correct class across iterations of minibatch SGD, and the proximity of the correct class probability to the decision threshold. Extensive experimental results on six datasets show that our methods reliably improve accuracy in various network architectures, including additional gains on top of other popular training techniques, such as residual learning, momentum, ADAM, batch normalization, dropout, and distillation.
ADVIO: An Authentic Dataset for Visual-Inertial Odometry

The lack of realistic and open benchmarking datasets for pedestrian visual-inertial odometry has made it hard to pinpoint differences in published methods. Existing datasets either lack a full six degree-of-freedom ground-truth or are limited to small spaces with optical tracking systems. We take advantage of advances in pure inertial navigation, and develop a set of versatile and challenging real-world computer vision benchmark sets for visual-inertial odometry. For this purpose, we have built a test rig equipped with an iPhone, a Google Pixel Android phone, and a Google Tango device. We provide a wide range of raw sensor data that is accessible on almost any modern-day smartphone together with a high-quality ground-truth track. We also compare resulting visual-inertial tracks from Google Tango, ARCore, and Apple ARKit with two recent methods published in academic forums. The data sets cover both indoor and outdoor cases, with stairs, escalators, elevators, office environments, a shopping mall, and metro station.
When crowdsourcing meets mobile sensing: a social network perspective

Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social networks, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing. A prototype of social-network-inspired mobile multimedia and sensing application is presented for illustrative purposes. Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.
An Empirical Approach to Conceptual Case Frame Acquisition

Conceptual natural language processing systems usually rely on case frame instantiation to recognize events and role objects in text. But generating a good set of case frames for a domain is timeconsuming, tedious, and prone to errors of omission. We have developed a corpus-based algorithm for acquiring conceptual case frames empirically from unannotated text. Our algorithm builds on previous research on corpus-based methods for acquiring extraction patterns and semantic lexicons. Given extraction patterns and a semantic lexicon for a domain, our algorithm learns semantic preferences for each extraction pattern and merges the syntactically compatible patterns to produce multi-slot case frames with selectional restrictions. The case frames generate more cohesive output and produce fewer false hits than the original extraction patterns. Our system requires only preclassified training texts and a few hours of manual review to filter the dictionaries, demonstrating that conceptual case frames can be acquired from unannotated text without special training resources.
Fast algorithms for L∞ problems in multiview geometry

Many problems in multi-view geometry, when posed as minimization of the maximum reprojection error across observations, can be solved optimally in polynomial time. We show that these problems are instances of a convex-concave generalized fractional program. We survey the major solution methods for solving problems of this form and present them in a unified framework centered around a single parametric optimization problem. We propose two new algorithms and show that the algorithm proposed by Olsson et al.  is a special case of a classical algorithm for generalized fractional programming. The performance of all the algorithms is compared on a variety of datasets, and the algorithm proposed by Gugat  stands out as a clear winner. An open source MATLAB toolbox that implements all the algorithms presented here is made available.
Recurrent neural network and LSTM models for lexical utterance classification

Utterance classification is a critical pre-processing step for many speech understanding and dialog systems. In multi-user settings, one needs to first identify if an utterance is even directed at the system, followed by another level of classification to determine the intent of the user’s input. In this work, we propose RNN and LSTM models for both these tasks. We show how both models outperform baselines based on ngram-based language models (LMs), feedforward neural network LMs, and boosting classifiers. To deal with the high rate of singleton and out-of-vocabulary words in the data, we also investigate a word input encoding based on character ngrams, and show how this representation beats the standard one-hot vector word encoding. Overall, these proposed approaches achieve over 30% relative reduction in equal error rate compared to boosting classifier baseline on an ATIS utterance intent classification task, and over 3.9% absolute reduction in equal error rate compared to a the maximum entropy LM baseline of 27.0% on an addressee detection task. We find that RNNs work best when utterances are short, while LSTMs are best when utterances are longer.
An outdoor high-accuracy local positioning system for an autonomous robotic golf greens mower

This paper presents a high-accuracy local positioning system (LPS) for an autonomous robotic greens mower. The LPS uses a sensor tower mounted on top of the robot and four active beacons surrounding a target area. The proposed LPS concurrently determines robot location using a lateration technique and calculates orientation using angle measurements. To perform localization, the sensor tower emits an ultrasonic pulse that is received by the beacons. The time of arrival is measured by each beacon and transmitted back to the sensor tower. To determine the robot's orientation, the sensor tower has a circular receiver array that detects infrared signals emitted by each beacon. Using the direction and strength of the received infrared signals, the relative angles to each beacon are obtained and the robot orientation can be determined. Experimental data show that the LPS achieves a position accuracy of 3.1 cm RMS, and an orientation accuracy of 0.23° RMS. Several prototype robotic mowers utilizing the proposed LPS have been deployed for field testing, and the mowing results are comparable to an experienced professional human worker.