nils-herrmann/clean_scirepeval huggingface dataset:
Sustainability guidelines for long-living software systems

Economically sustainable software systems must be able to cost-effectively evolve in response to changes in their environment, their usage profile, and business demands. However, in many software development projects, sustainability is treated as an afterthought, as developers are driven by time-to-market pressure and are often not educated to apply sustainability-improving techniques. While software engineering research and practice has suggested a large amount of such techniques, a holistic overview is missing and the effectiveness of individual techniques is often not sufficiently validated. On this behalf we created a catalog of “software sustainability guidelines” to support project managers, software architects, and developers during system design, development, operation, and maintenance. This paper describes how we derived these guidelines and how we applied selected techniques from them in two industrial case studies. We report several lessons learned about sustainable software development.
SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence

Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1-99.6%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.
A framework for robust discovery of entity synonyms

Entity synonyms are critical for many applications like information retrieval and named entity recognition in documents. The current trend is to automatically discover entity synonyms using statistical techniques on web data. Prior techniques suffer from several limitations like click log sparsity and inability to distinguish between entities of different concept classes. In this paper, we propose a general framework for robustly discovering entity synonym with two novel similarity functions that overcome the limitations of prior techniques. We develop efficient and scalable techniques leveraging the MapReduce framework to discover synonyms at large scale. To handle long entity names with extraneous tokens, we propose techniques to effectively map long entity names to short queries in query log. Our experiments on real data from different entity domains demonstrate the superior quality of our synonyms as well as the efficiency of our algorithms. The entity synonyms produced by our system is in production in Bing Shopping and Video search, with experiments showing the significance it brings in improving search experience.
Music-evoked nostalgia: affect, memory, and personality.

Participants listened to randomly selected excerpts of popular music and rated how nostalgic each song made them feel. Nostalgia was stronger to the extent that a song was autobiographically salient, arousing, familiar, and elicited a greater number of positive, negative, and mixed emotions. These effects were moderated by individual differences (nostalgia proneness, mood state, dimensions of the Affective Neurosciences Personality Scale, and factors of the Big Five Inventory). Nostalgia proneness predicted stronger nostalgic experiences, even after controlling for other individual difference measures. Nostalgia proneness was predicted by the Sadness dimension of the Affective Neurosciences Personality Scale and Neuroticism of the Big Five Inventory. Nostalgia was associated with both joy and sadness, whereas nonnostalgic and nonautobiographical experiences were associated with irritation.
Multilingual Sentiment and Subjectivity Analysis

Subjectivity and sentiment analysis focuses on the automatic identification of private states, such as opinions, emotions, sentiments, evaluations, beliefs, and speculations in natural language. While subjectivity classification labels text as either subjective or objective, sentiment classification adds an additional level of granularity, by further classifying subjective text as either positive, negative or neutral. To date, a large number of text processing applications have already used techniques for automatic sentiment and subjectivity analysis, including automatic expressive text-to-speech synthesis , tracking sentiment timelines in on-line forums and news [22, 2], and mining opinions from product reviews [11]. In many natural language processing tasks, subjectivity and sentiment classification have been used as a first phase filtering to generate more viable data. Research that benefitted from this additional layering ranges from question answering [48], to conversation summarization [7] and text semantic analysis [41, 8]. Much of the research work to date on sentiment and subjectivity analysis has been applied to English, but work on other languages is growing, including Japanese [19, 34, 35, 15], Chinese [12, 49], German [18], and Romanian [23, 4]. In addition, several participants in the Chinese and Japanese Opinion Extraction tasks of NTCIR-6 [17] performed subjectivity and sentiment analysis in languages other than English.1 As only 29.4% of Internet users speak English,2 the construction of resources and tools for subjectivity and sentiment analysis in languages other than English is a growing need. In this chapter, we review the main directions of research focusing on the development of resources and tools for multilingual subjectivity and sentiment analysis. Specifically, we identify and overview three main categories of methods: (1) those focusing on word and phrase level annotations, overviewed in Section 4; (2) methods targeting the labeling of sentences, described in Section 5; and finally (3) methods for document-level annotations, presented in Section 6. We address both multilingual and cross-lingual methods. For multilingual methods, we review work concerned with languages other than English, where the resources and tools have been specifically developed for a given target language. In this category, in Section 3 we also briefly overview the main directions of work on English data, highlighting the methods that can be easily
Reconstruction of Surfaces behind Occlusions in Range Images

Analysis and reconstruction of range images usually focuses on complex objects completely contained in the field of view; little attention has been devoted so far to the reconstruction of partially occluded simple-shaped wide areas like parts of a wall hidden behind furniture pieces in an indoor range image. The work in this paper is aimed at such reconstruction. First of all the range image is partitioned and surfaces are fitted to these partitions. A further step lo cates possibly occluded areas, while a final step determines which areas are actually occluded. The reconstruction of data occurs in this last step.
Robust Capped Norm Nonnegative Matrix Factorization: Capped Norm NMF

As an important matrix factorization model, Nonnegative Matrix Factorization (NMF) has been widely used in information retrieval and data mining research. Standard Nonnegative Matrix Factorization is known to use the Frobenius norm to calculate the residual, making it sensitive to noises and outliers. It is desirable to use robust NMF models for practical applications, in which usually there are many data outliers. It has been studied that the 2,1, or 1-norm can be used for robust NMF formulations to deal with data outliers. However, these alternatives still suffer from the extreme data outliers. In this paper, we present a novel robust capped norm orthogonal Nonnegative Matrix Factorization model, which utilizes the capped norm for the objective to handle these extreme outliers. Meanwhile, we derive a new efficient optimization algorithm to solve the proposed non-convex non-smooth objective. Extensive experiments on both synthetic and real datasets show our proposed new robust NMF method consistently outperforms related approaches.
A novel feature selection approach for biomedical data classification

This paper presents a novel feature selection approach to deal with issues of high dimensionality in biomedical data classification. Extensive research has been performed in the field of pattern recognition and machine learning. Dozens of feature selection methods have been developed in the literature, which can be classified into three main categories: filter, wrapper and hybrid approaches. Filter methods apply an independent test without involving any learning algorithm, while wrapper methods require a predetermined learning algorithm for feature subset evaluation. Filter and wrapper methods have their, respectively, drawbacks and are complementary to each other in that filter approaches have low computational cost with insufficient reliability in classification while wrapper methods tend to have superior classification accuracy but require great computational power. The approach proposed in this paper integrates filter and wrapper methods into a sequential search procedure with the aim to improve the classification performance of the features selected. The proposed approach is featured by (1) adding a pre-selection step to improve the effectiveness in searching the feature subsets with improved classification performances and (2) using Receiver Operating Characteristics (ROC) curves to characterize the performance of individual features and feature subsets in the classification. Compared with the conventional Sequential Forward Floating Search (SFFS), which has been considered as one of the best feature selection methods in the literature, experimental results demonstrate that (i) the proposed approach is able to select feature subsets with better classification performance than the SFFS method and (ii) the integrated feature pre-selection mechanism, by means of a new selection criterion and filter method, helps to solve the over-fitting problems and reduces the chances of getting a local optimal solution.
Dynamic torque control of a hydraulic quadruped robot

Legged robots have the potential to serve as versatile and useful autonomous robotic platforms for use in unstructured environments such as disaster sites. They need to be both capable of fast dynamic locomotion and precise movements. However, there is a lack of platforms with suitable mechanical properties and adequate controllers to advance the research in this direction. In this paper we are presenting results on the novel research platform HyQ, a torque controlled hydraulic quadruped robot. We identify the requirements for versatile robotic legged locomotion and show that HyQ is fulfilling most of these specifications. We show that HyQ is able to do both static and dynamic movements and is able to cope with the mechanical requirements of dynamic movements and locomotion, such as jumping and trotting. The required control, both on hydraulic level (force/torque control) and whole body level (rigid model based control) is discussed.
Analysis and Optimization of Convolutional Neural Network Architectures

Convolutional Neural Networks (CNNs) dominate various computer vision tasks since Alex Krizhevsky showed that they can be trained effectively and reduced the top-5 error from 26.2 % to 15.3 % on the ImageNet large scale visual recognition challenge. Many aspects of CNNs are examined in various publications, but literature about the analysis and construction of neural network architectures is rare. This work is one step to close this gap. A comprehensive overview over existing techniques for CNN analysis and topology construction is provided. A novel way to visualize classification errors with confusion matrices was developed. Based on this method, hierarchical classifiers are described and evaluated. Additionally, some results are confirmed and quantified for CIFAR-100. For example, the positive impact of smaller batch sizes, averaging ensembles, data augmentation and test-time transformations on the accuracy. Other results, such as the positive impact of learned color transformation on the test accuracy could not be confirmed. A model which has only one million learned parameters for an input size of 32× 32× 3 and 100 classes and which beats the state of the art on the benchmark dataset Asirra, GTSRB, HASYv2 and STL-10 was developed.
Iterative reconstruction methods in X-ray CT.

Iterative reconstruction (IR) methods have recently re-emerged in transmission x-ray computed tomography (CT). They were successfully used in the early years of CT, but given up when the amount of measured data increased because of the higher computational demands of IR compared to analytical methods. The availability of large computational capacities in normal workstations and the ongoing efforts towards lower doses in CT have changed the situation; IR has become a hot topic for all major vendors of clinical CT systems in the past 5 years. This review strives to provide information on IR methods and aims at interested physicists and physicians already active in the field of CT. We give an overview on the terminology used and an introduction to the most important algorithmic concepts including references for further reading. As a practical example, details on a model-based iterative reconstruction algorithm implemented on a modern graphics adapter (GPU) are presented, followed by application examples for several dedicated CT scanners in order to demonstrate the performance and potential of iterative reconstruction methods. Finally, some general thoughts regarding the advantages and disadvantages of IR methods as well as open points for research in this field are discussed.
A martingale framework for concept change detection in time-varying data streams

In a data streaming setting, data points are observed one by one. The concepts to be learned from the data points may change infinitely often as the data is streaming. In this paper, we extend the idea of testing exchangeability online (Vovk et al., 2003) to a martingale framework to detect concept changes in time-varying data streams. Two martingale tests are developed to detect concept changes using: (i) martingale values, a direct consequence of the Doob's Maximal Inequality, and (ii) the martingale difference, justified using the Hoeffding-Azuma Inequality. Under some assumptions, the second test theoretically has a lower probability than the first test of rejecting the null hypothesis, "no concept change in the data stream", when it is in fact correct. Experiments show that both martingale tests are effective in detecting concept changes in time-varying data streams simulated using two synthetic data sets and three benchmark data sets.
SentiTurkNet: a Turkish polarity lexicon for sentiment analysis

Sentiment analysis aims to extract the sentiment polarity of given segment of text. Polarity resources that indicate the sentiment polarity of words are commonly used in different approaches. While English is the richest language in regard to having such resources, the majority of other languages, including Turkish, lack polarity resources. In this work we present the first comprehensive Turkish polarity resource, SentiTurkNet, where three polarity scores are assigned to each synset in the Turkish WordNet, indicating its positivity, negativity, and objectivity (neutrality) levels. Our method is general and applicable to other languages. Evaluation results for Turkish show that the polarity scores obtained through this method are more accurate compared to those obtained through direct translation (mapping) from SentiWordNet.
Fingerprinting Web Users Through Font Metrics

We describe a web browser fingerprinting technique based on measuring the onscreen dimensions of font glyphs. Font rendering in web browsers is affected by many factors—browser version, what fonts are installed, and hinting and antialiasing settings, to name a few— that are sources of fingerprintable variation in end-user systems. We show that even the relatively crude tool of measuring glyph bounding boxes can yield a strong fingerprint, and is a threat to users’ privacy. Through a user experiment involving over 1,000 web browsers and an exhaustive survey of the allocated space of Unicode, we find that font metrics are more diverse than User-Agent strings, uniquely identifying 34% of participants, and putting others into smaller anonymity sets. Fingerprinting is easy and takes only milliseconds. We show that of the over 125,000 code points examined, it suffices to test only 43 in order to account for all the variation seen in our experiment. Font metrics, being orthogonal to many other fingerprinting techniques, can augment and sharpen those other techniques. We seek ways for privacy-oriented web browsers to reduce the effectiveness of font metric–based fingerprinting, without unduly harming usability. As part of the same user experiment of 1,000 web browsers, we find that whitelisting a set of standard font files has the potential to more than quadruple the size of anonymity sets on average, and reduce the fraction of users with a unique font fingerprint below 10%. We discuss other potential countermeasures.
Magic finger: always-available input through finger instrumentation

We present Magic Finger, a small device worn on the fingertip, which supports always-available input. Magic Finger inverts the typical relationship between the finger and an interactive surface: with Magic Finger, we instrument the user's finger itself, rather than the surface it is touching. Magic Finger senses touch through an optical mouse sensor, enabling any surface to act as a touch screen. Magic Finger also senses texture through a micro RGB camera, allowing contextual actions to be carried out based on the particular surface being touched. A technical evaluation shows that Magic Finger can accurately sense 22 textures with an accuracy of 98.9%. We explore the interaction design space enabled by Magic Finger, and implement a number of novel interaction techniques that leverage its unique capabilities.
Video Captioning With Attention-Based LSTM and Semantic Consistency

Recent progress in using long short-term memory (LSTM) for image captioning has motivated the exploration of their applications for video captioning. By taking a video as a sequence of features, an LSTM model is trained on video-sentence pairs and learns to associate a video to a sentence. However, most existing methods compress an entire video shot or frame into a static representation, without considering attention mechanism which allows for selecting salient features. Furthermore, existing approaches usually model the translating error, but ignore the correlations between sentence semantics and visual content. To tackle these issues, we propose a novel end-to-end framework named aLSTMs, an attention-based LSTM model with semantic consistency, to transfer videos to natural sentences. This framework integrates attention mechanism with LSTM to capture salient structures of video, and explores the correlation between multimodal representations (i.e., words and visual content) for generating sentences with rich semantic content. Specifically, we first propose an attention mechanism that uses the dynamic weighted sum of local two-dimensional convolutional neural network representations. Then, an LSTM decoder takes these visual features at time <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> and the word-embedding feature at time <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula><inline-formula><tex-math notation="LaTeX"> $-$</tex-math></inline-formula>1 to generate important words. Finally, we use multimodal embedding to map the visual and sentence features into a joint space to guarantee the semantic consistence of the sentence description and the video visual content. Experiments on the benchmark datasets demonstrate that our method using single feature can achieve competitive or even better results than the state-of-the-art baselines for video captioning in both BLEU and METEOR.
Robust material discrimination by a soft anthropomorphic finger with tactile and thermal sense

Thermal sense plays an important role in object recognition. The idea to identify materials by heat flow from the skin to the touched materials have been introduced to artificial thermal sensors and proved effective. Moreover, the addition of tactile sensors is found to improve the performance of material identification by obtaining further properties of objects such as roughness and stiffness. However, the application of these sensors to autonomous recognition by a robot is not a well investigated topic. Contact with the objects will vary in such cases, but no previous sensor has shown the ability of material recognition robust to change in contact conditions with the required size and softness. The limitation is partly due to the design of current thermal sensors where the skin and the sensing devices are separated. This paper introduces a soft anthropomorphic finger with both thermal and tactile sensing elements embedded in soft polyurethane material. Experimental results show the ability to discriminate materials robust to variety in contact condition by virtue of the deformable tissue and compensation by the tactile sensors. Recognition robust to change of temperature is also realized by an additional thermal sensor.
Activity Recognition Using One Triaxial Accelerometer: A Neuro-fuzzy Classifier with Feature Reduction

This paper presents a neuro-fuzzy classifer for activity recognition using one triaxial accelerometer and feature reduction approaches. We use a triaxial accelerometer to acquire subjects’ acceleration data and train the neurofuzzy classifier to distinguish different activities/movements. To construct the neuro-fuzzy classifier, a modified mapping-constrained agglomerative clustering algorithm is devised to reveal a compact data configuration from the acceleration data. In addition, we investigate two different feature reduction methods, a feature subset selection and linear discriminate analysis. These two methods are used to determine the significant feature subsets and retain the characteristics of the data distribution in the feature space for training the neuro-fuzzy classifier. Experimental results have successfully validated the effectiveness of the proposed classifier.
It is not for fun: An examination of social network site usage

Social networking sites (SNS) have become a significant component of people’s daily lives and have revolutionized the ways that business is conducted, from product development and marketing to operation and human resource management. However, there have been few systematic studies that ask why people use such systems. To try to determine why, we proposed a model based on uses and gratifications theory. Hypotheses were tested using PLS on data collected from 148 SNS users. We found that user utilitarian (rational and goal-oriented) gratifications of immediate access and coordination, hedonic (pleasure-oriented) gratifications of affection and leisure, and website social presence were positive predictors of SNS usage. While prior research focused on the hedonic use of SNS, we explored the predictive value of utilitarian factors in SNS. Based on these findings, we suggest a need to focus on the SNS functionalities to provide users with both utilitarian and hedonic gratifications, and suggest incorporating appropriate website features to help users evoke a sense of human contact in the SNS context.