nils-herrmann/clean_scirepeval huggingface dataset:
Boosting and Naive Bayesian learning

Although so-called " naive " Bayesian classification makes the unrealistic assumption that the values of the attributes of an example are independent given the class of the example, this learning method is remarkably successful in practice, and no uniformly better learning method is known. Boosting is a general method of combining multiple classifiers due to Yoav Freund and Rob Schapire. This paper shows that boosting applied to naive Bayesian classifiers yields combination classifiers that are representationally equivalent to standard feedforward multilayer perceptrons. (An ancillary result is that naive Bayesian classification is a nonparametric, nonlinear generalization of logistic regression.) As a training algorithm, boosted naive Bayesian learning is quite different from backpropagation, and has definite advantages. Boosting requires only linear time and constant space, and hidden nodes are learned incrementally, starting with the most important. On the real-world datasets on which the method has been tried so far, generalization performance is as good as or better than the best published result using any other learning method. Unlike all other standard learning algorithms, naive Bayesian learning, with and without boosting, can be done in logarithmic time with a linear number of parallel computing units. Accordingly, these learning methods are highly plausible computationally as models of animal learning. Other arguments suggest that they are plausible behaviorally also.
From mice to men: what can we learn about personality from animal research?

The author explores the viability of a comparative approach to personality research. A review of the diverse animal-personality literature suggests that (a) most research uses trait constructs, focuses on variation within (vs. across) species, and uses either behavioral codings or trait ratings; (b) ratings are generally reliable and show some validity (7 parameters that could influence reliability and 4 challenges to validation are discussed); and (c) some dimensions emerge across species, but summaries are hindered by a lack of standard descriptors. Arguments for and against cross-species comparisons are discussed, and research guidelines are suggested. Finally, a research agenda guided by evolutionary and ecological principles is proposed. It is concluded that animal studies provide unique opportunities to examine biological, genetic, and environmental bases of personality and to study personality change, personality-health links, and personality perception.
Grasp quality measures: review and performance

The correct grasp of objects is a key aspect for the right fulfillment of a given task. Obtaining a good grasp requires algorithms to automatically determine proper contact points on the object as well as proper hand configurations, especially when dexterous manipulation is desired, and the quantification of a good grasp requires the definition of suitable grasp quality measures. This article reviews the quality measures proposed in the literature to evaluate grasp quality. The quality measures are classified into two groups according to the main aspect they evaluate: location of contact points on the object and hand configuration. The approaches that combine different measures from the two previous groups to obtain a global quality measure are also reviewed, as well as some measures related to human hand studies and grasp performance. Several examples are presented to illustrate and compare the performance of the reviewed measures.
Wideband coplanar waveguide-to-rectangular waveguide transition using fin-line taper

This letter introduces a new wideband coplanar waveguide-to-rectangular waveguide transition. The transition uses a uniplanar circuit in line with the waveguide, which eases the design and fabrication. The design does not require airbridges. Simulations and measurements of X-band (8.2-12.4 GHz) transitions based on both a low- and high-permittivity material (/spl epsiv//sub r/= 2.33 and 10.8) show that the transition works fine over the full frequency band. For /spl epsiv//sub r/= 2.33 the measured return and insertion loss of a back-to-back transition are more than 16 dB and less than 0.4dB, respectively. The corresponding values for /spl epsiv//sub r/= 10.8 are more than 10 dB and less than 1.0 dB, respectively, over 90% of the frequency band. The measured insertion loss values indicate losses of less than 0.14 dB and 0.36 dB at the center frequency for a single transition on a substrate with /spl epsiv//sub r/= 2.33 and 10.8, respectively.
Facial expression recognition based on transfer learning from deep convolutional networks

It is well-known that deep models could extract robust and abstract features. We propose a efficient facial expression recognition model based on transfer features from deep convolutional networks (ConvNets). We train the deep ConvNets through the task of 1580-class face identification on the MSRA-CFW database and transfer high-level features from the trained deep model to recognize expression. To train and test the facial expression recognition model on a large scope, we built a facial expression database of seven basic emotion states and 2062 imbalanced samples depending on four facial expression databases (CK+, JAFFE, KDEF, Pain expressions form PICS). Compared with 50.65% recognition rate based on Gabor features with the seven-class SVM and 78.84% recognition rate based on distance features with the seven-class SVM, we achieve average 80.49% recognition rate with the seven-class SVM classifier on the self-built facial expression database. Considering occluded face in reality, we test our model in the occluded condition and demonstrate the model could keep its ability of classification in the small occlusion case. To increase the ability further, we improve the facial expression recognition model. The modified model merges high-level features transferred from two trained deep ConvNets of the same structure and the different training sets. The modified model obviously improves its ability of classification in the occluded condition and achieves average 81.50% accuracy on the self-built facial expression database.
Measurement of pitch by subharmonic summation.

In order to account for the phenomenon of virtual pitch, various theories assume implicitly or explicitly that each spectral component introduces a series of subharmonics. The spectral-compression method for pitch determination can be viewed as a direct implementation of this principle. The widespread application of this principle in pitch determination is, however, impeded by numerical problems with respect to accuracy and computational efficiency. A modified algorithm is described that solves these problems. Its performance is tested for normal speech and "telephone" speech, i.e., speech high-pass filtered at 300 Hz. The algorithm out-performs the harmonic-sieve method for pitch determination, while its computational requirements are about the same. The algorithm is described in terms of nonlinear system theory, i.c., subharmonic summation. It is argued that the favorable performance of the subharmonic-summation algorithm stems from its corresponding more closely with current pitch-perception theories than does the harmonic sieve.
Monza : Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning

Car detection and identification is an important task in the area of traffic control and management. Typically, to tackle this task, large datasets and domain-specific features are used to best fit the data. In our project, we implement, train, and test several state-of-the-art classifiers trained on domain-general datasets for the task of identifying the make and models of cars from various angles and different settings, with the added constraint of limited data and time. We experiment with different levels of transfer learning for fitting these models over to our domain. We report and compare these results to that of baseline models, and discuss the advantages of this approach.
Analysis of face recognition under varying facial expression: a survey

Automatic Face Recognition is one of the most emphasizing dilemmas in diverse of potential relevance like in different surveillance systems, security systems, authentication or verification of individual like criminals etc. Adjoining of dynamic expression in face causes a broad range of discrepancies in recognition systems. Facial Expression not only exposes the sensation or passion of any person but can also be used to judge his/her mental views and psychosomatic aspects. This paper is based on a complete survey of face recognition conducted under varying facial expressions. In order to analyze different techniques, motion-based, model-based and muscles-based approaches have been used in order to handle the facial expression and recognition catastrophe. The analysis has been completed by evaluating various existing algorithms while comparing their results in general. It also expands the scope for other researchers for answering the question of effectively dealing with such problems.
Noncespaces: Using randomization to defeat cross-site scripting attacks

Cross-site scripting (XSS) vulnerabilities are among the most common and serious web application vulnerabilities. It is challenging to eliminate XSS vulnerabilities because it is difficult for web applications to sanitize all user input appropriately. We present Noncespaces, a technique that enables web clients to distinguish between trusted and untrusted content to prevent exploitation of XSS vulnerabilities. Using Noncespaces, a web application randomizes the the (X)HTML tags and attributes in each document before delivering it to the client. As long as the attacker is unable to guess the random mapping, the client can distinguish between trusted content created by the web application and untrusted content provided by an attacker. To implement Noncespaces with minimal changes to web applications, we leverage a popular web application architecture to automatically apply Noncespaces to static content processed through a popular PHP template engine. We design a policy language for Noncespaces, implement a training mode to assist policy development, and conduct extensive security testing of a generated policy for two large web applications to show the effectiveness of our technique. a 2012 Elsevier Ltd. All rights reserved.
What Do We Know about Agile Software Development?

Agile software development has had a huge impact on how software is developed worldwide. We can view agile methods such as Extreme Programming (XP) and Scrum as a reaction to plan-based or traditional methods, which emphasize a "rationalized, engineering-based approach, incorporating extensive planning, codified processes, and rigorous reuse. In contrast, agile methods address the challenge of an unpredictable world, emphasizing the value competent people and their relationships bring to software development. To clarify the effectiveness of agile methods, we reviewed the agile development literature and conducted a systematic study of what we know empirically about its benefits and limitations.
3-D ultrasound imaging using a forward-looking CMUT ring array for intravascular/intracardiac applications

Forward-viewing ring arrays can enable new applications in intravascular and intracardiac ultrasound. This work presents compelling, full-synthetic, phased-array volumetric images from a forward-viewing capacitive micromachined ultrasonic transducer (CMUT) ring array wire bonded to a custom integrated circuit front end. The CMUT ring array has a diameter of 2 mm and 64 elements each 100 /spl mu/m /spl times/ 100 /spl mu/m in size. In conventional mode, echo signals received from a plane reflector at 5 mm had 70% fractional bandwidth around a center frequency of 8.3 MHz. In collapse mode, 69% fractional bandwidth is measured around 19 MHz. Measured signal-to-noise ratio (SNR) of the echo averaged 16 times was 29 dB for conventional operation and 35 dB for collapse mode. B-scans were generated of a target consisting of steel wires 0.3 mm in diameter to determine resolution performance. The 6 dB axial and lateral resolutions for the B-scan of the wire target are 189 /spl mu/m and 0.112 radians for 8 MHz, and 78 /spl mu/m and 0.051 radians for 19 MHz. A reduced firing set suitable for real-time, intravascular applications was generated and shown to produce acceptable images. Rendered three-dimensional (3-D) images of a Palmaz-Schatz stent also are shown, demonstrating that the imaging quality is sufficient for practical applications.
Probabilistic part-of-speech tagging using decision trees

In this paper, a new probabilistic tagging method is presented which avoids problems that Markov Model based taggers face, when they have to estimate transition probabilities from sparse data. In this tagging method, transition probabilities are estimated using a decision tree. Based on this method, a part-of-speech tagger (called TreeTagger) has been implemented which achieves 96.36 % accuracy on Penn-Treebank data which is better than that of a trigram tagger (96.06 %) on the same data.
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation

We present a simple and effective approach to incorporating syntactic structure into neural attention-based encoderdecoder models for machine translation. We rely on graph-convolutional networks (GCNs), a recent class of neural networks developed for modeling graph-structured data. Our GCNs use predicted syntactic dependency trees of source sentences to produce representations of words (i.e. hidden states of the encoder) that are sensitive to their syntactic neighborhoods. GCNs take word representations as input and produce word representations as output, so they can easily be incorporated as layers into standard encoders (e.g., on top of bidirectional RNNs or convolutional neural networks). We evaluate their effectiveness with English-German and English-Czech translation experiments for different types of encoders and observe substantial improvements over their syntax-agnostic versions in all the considered setups.
Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection

Despite significant progress in the development of human action detection datasets and algorithms, no current dataset is representative of real-world aerial view scenarios. We present Okutama-Action, a new video dataset for aerial view concurrent human action detection. It consists of 43 minute-long fully-annotated sequences with 12 action classes. Okutama-Action features many challenges missing in current datasets, including dynamic transition of actions, significant changes in scale and aspect ratio, abrupt camera movement, as well as multi-labeled actors. As a result, our dataset is more challenging than existing ones, and will help push the field forward to enable real-world applications.
A Soft Jellyfish Robot Driven by a Dielectric Elastomer Actuator

Although dielectric elastomers have been extensively studied recently, to date there has been little research into application of dielectric elastomer actuators to undersea robots. This letter focuses on development of a jellyfish robot using a dielectric elastomer actuator, which exhibits muscle-like properties including large deformation and high energy density. We carry out experiments to test the actuator's deformation and force. Theoretical simulations are conducted to analyze the performance of the actuator, which are qualitatively consistent with the experiments. The preliminary studies show that this jellyfish robot based on dielectric elastomer technology can move effectively in water. The robot also exhibits fast response and high capacity of payload (compared to its self-weight).
Intense-personal celebrity worship and body image: evidence of a link among female adolescents.

The aim of the present study was to examine the relationship between celebrity worship and body image within the theoretical perspective of intense para-social relationships with celebrities.



Correlation and multiple regression analyses were used to examine the relationships between celebrity worship and body image.



Three samples, 229 (102 males and 127 females) adolescents, 183 (88 males and 95 females) full-time university undergraduate students, and 289 (126 males and 163 females) adults were administered an amended version of the Celebrity Attitude Scale, the Attention to Body Shape Scale, and the Body Shape Questionnaire-Revised.



Significant relationships were found between attitudes toward celebrities and body image only among female adolescents. Multiple regression analyses suggested that Intense-personal celebrity worship accounted for unique variance in scores in body image.



Findings suggest that in female adolescents, there is an interaction between Intense-personal celebrity worship and body image between the ages of 14 and 16 years, and some tentative evidence has been found to suggest that this relationship disappears at the onset of adulthood, 17 to 20 years. Results are consistent with those authors who stress the importance of the formation of para-social relationships with media figures, and suggest that para-social relationships with celebrities perceived as having a good body shape may lead to a poor body image in female adolescents.
Implications of the fit between organizational structure and ERP: A structural contingency theory perspective

Despite the tremendous popularity and great potential, the field of Enterprise Resource Planning (ERP) adoption and implementation is littered with remarkable failures. Though many contributing factors have been cited in the literature, we argue that the integrated nature of ERP systems, which generally requires an organization to adopt standardized business processes reflected in the design of the software, is a key factor contributing to these failures. We submit that the integration and standardization imposed by most ERP systems may not be suitable for all types of organizations and thus the ‘‘fit’’ between the characteristics of the adopting organization and the standardized business process designs embedded in the adopted ERP system affects the likelihood of implementation success or failure. In this paper, we use the structural contingency theory to identify a set of dimensions of organizational structure and ERP system characteristics that can be used to gauge the degree of fit, thus providing some insights into successful ERP implementations. Propositions are developed based on analyses regarding the success of ERP implementations in different types of organizations. These propositions also provide directions for future research that might lead to prescriptive guidelines for managers of organizations contemplating implementing ERP systems.
Combining monoSLAM with object recognition for scene augmentation using a wearable camera

In wearable visual computing, maintaining a time-evolving representation of the 3D environment along with the pose of the camera provides the geometrical foundation on which person-centred processing can be built. In this paper, an established method for the recognition of feature clusters is used on live imagery to identify and locate planar objects around the wearer. Objects’ locations are incorporated as additional 3D measurements into a monocular simultaneous localization and mapping process, which routinely uses 2D image measurements to acquire and maintain a map of the surroundings, irrespective of whether objects are present or not. Augmenting the 3D maps with automatically recognized objects enables useful annotations of the surroundings to be presented to the wearer. After demonstrating the geometrical integrity of the method, experiments show its use in two augmented reality applications.
QARAB: A: Question Answering System to Support the Arabic Language

We describe the design and implementation of a question answering (QA) system called QARAB. It is a system that takes natural language questions expressed in the Arabic language and attempts to provide short answers. The system’s primary source of knowledge is a collection of Arabic newspaper text extracted from Al-Raya, a newspaper published in Qatar. During the last few years the information retrieval community has attacked this problem for English using standard IR techniques with only mediocre success. We are tackling this problem for Arabic using traditional Information Retrieval (IR) techniques coupled with a sophisticated Natural Language Processing (NLP) approach. To identify the answer, we adopt a keyword matching strategy along with matching simple structures extracted from both the question and the candidate documents selected by the IR system. To achieve this goal, we use an existing tagger to identify proper names and other crucial lexical items and build lexical entries for them on the fly. We also carry out an analysis of Arabic question forms and attempt a better understanding of what kinds of answers users find satisfactory. The paucity of studies of real users has limited results in earlier research.
Automated Problem List Generation from Electronic Medical Records in IBM Watson

Identifying a patient’s important medical problems requires broad and deep medical expertise, as well as significant time to gather all the relevant facts from the patient’s medical record and assess the clinical importance of the facts in reaching the final conclusion. A patient’s medical problem list is by far the most critical information that a physician uses in treatment and care of a patient. In spite of its critical role, its curation, manual or automated, has been an unmet need in clinical practice. We developed a machine learning technique in IBM Watson to automatically generate a patient’s medical problem list. The machine learning model uses lexical and medical features extracted from a patient’s record using NLP techniques. We show that the automated method achieves 70% recall and 67% precision based on the gold standard that medical experts created on a set of deidentified patient records from a major hospital system in the US. To the best of our knowledge this is the first successful machine learning/NLP method of extracting an open-ended patient’s medical problems from an Electronic Medical Record (EMR). This paper also contributes a methodology for assessing accuracy of a medical problem list generation technique.
A genetic algorithm-based multi-objective optimization of an artificial neural network classifier for breast cancer diagnosis

The conventional technique for diagnosing the breast cancer disease relies on human experiences to identify the presence of certain pattern from the database. It is time-consuming and incurs unnecessary burden to radiologists. This work proposes a genetic algorithm-based multi-objective optimization of an Artificial Neural Network classifier, namely GA-MOO-NN, for the automatic breast cancer diagnosis. It performs a simultaneous search for the significant feature subsets and the optimum architecture of the network. The combination of ANN’s parameters with feature selection to be optimized by Genetic Algorithm is novel. The Pareto-optimality with new ranking approach is applied for simultaneous minimizations of two competing objectives: the number of network‘s connections and squared error percentage of the validation data. Result shows that the algorithm with the proposed combination of objectives has achieved the best and average, 98.85 and 98.10 % accuracy of classification, respectively, on breast cancer dataset which outperform most systems of other works found in the literature.