nils-herrmann/clean_scirepeval huggingface dataset:
Emotional persistence in online chatting communities

How do users behave in online chatrooms, where they instantaneously read and write posts? We analyzed about 2.5 million posts covering various topics in Internet relay channels, and found that user activity patterns follow known power-law and stretched exponential distributions, indicating that online chat activity is not different from other forms of communication. Analysing the emotional expressions (positive, negative, neutral) of users, we revealed a remarkable persistence both for individual users and channels. I.e. despite their anonymity, users tend to follow social norms in repeated interactions in online chats, which results in a specific emotional "tone" of the channels. We provide an agent-based model of emotional interaction, which recovers qualitatively both the activity patterns in chatrooms and the emotional persistence of users and channels. While our assumptions about agent's emotional expressions are rooted in psychology, the model allows to test different hypothesis regarding their emotional impact in online communication.
Discrete Extended Kalman Filter on Lie groups

In this paper, we generalize the Discrete Extended Kalman Filter (D-EKF) to the case where the state and the observations evolve on Lie group manifolds. We propose a new filter called Discrete Extended Kalman Filter on Lie Groups (D-LG-EKF). It assumes that the posterior distribution of the state is a concentrated Gaussian distribution on Lie groups. Our formalism yields closed-form equations for both nonlinear discrete propagation and update of the distribution parameters based on the likelihood. We also show that the D-LG-EKF reduces to the traditional D-EKF if the state evolves on an Euclidean space. Our approach leads to a systematic methodology for the design of filters, which is illustrated by the application to a camera pose estimation problem. Results show that the D-LG-EKF outperforms both a constrained D-EKF and a D-EKF applied on the Lie algebra of the Lie group.
Incremental Sequence Learning

Deep learning research over the past years has shown that by increasing the scope or difficulty of the learning problem over time, increasingly complex learning problems can be addressed. We study incremental learning in the context of sequence learning, using generative RNNs in the form of multi-layer recurrent Mixture Density Networks. While the potential of incremental or curriculum learning to enhance learning is known, indiscriminate application of the principle does not necessarily lead to improvement, and it is essential therefore to know which forms of incremental or curriculum learning have a positive effect. This research contributes to that aim by comparing three instantiations of incremental or curriculum learning. We introduce Incremental Sequence Learning, a simple incremental approach to sequence learning. Incremental Sequence Learning starts out by using only the first few steps of each sequence as training data. Each time a performance criterion has been reached, the length of the parts of the sequences used for training is increased. To evaluate Incremental Sequence Learning and comparison methods, we introduce and make available a novel sequence learning task and data set: predicting and classifying MNIST pen stroke sequences, where the familiar handwritten digit images have been transformed to pen stroke sequences representing the skeletons of the digits. We find that Incremental Sequence Learning greatly speeds up sequence learning and reaches the best test performance level of regular sequence learning 20 times faster, reduces the test error by 74%, and in general performs more robustly; it displays lower variance and achieves sustained progress after all three comparison methods have stopped improving. The two other instantiations of curriculum learning do not result in any noticeable improvement. A trained sequence prediction model is also used in transfer learning to the task of sequence classification, where it is found that transfer learning realizes improved classification performance compared to methods that learn to classify from scratch.
Tendon Arrangement and Muscle Force Requirements for Humanlike Force Capabilities in a Robotic Finger

Human motion can provide a rich source of examples for use in robot grasping and manipulation. Adapting human examples to a robot manipulator is a difficult problem, however, in part due to differences between human and robot hands. Even hands that are anthropomorphic in external design may differ dramatically from the human hand in ability to grasp and manipulate objects due to internal design differences. For example, force transmission mechanisms in robot fingers are generally symmetric about flexion / extension axes, but in human fingers they are focused toward flexion. This paper describes how a tendon driven robot finger can be optimized for force transmission capability equivalent to the human index finger. We show that two distinct tendon arrangements that are similar to those that have been used in robot hands can achieve the same range of forces as the human finger with minimal additional cost in total muscle force requirements.
The Conjugate Gradient Method and Trust Regions in Large Scale Optimization Author ( s ) :

Algorithms based on trust regions have been shown to be robust methods for unconstrained optimization problems. All existing methods, either based on the dogleg strategy or Hebden-More iterations, require solution of system of linear equations. In large scale optimization this may be prohibitively expensive. It is shown in this paper that an approximate solution of the trust region problem may be found by the preconditioned conjugate gradient method. This may be regarded as a generalized dogleg technique where we asymptotically take the inexact quasi-Newton step. We also show that we have the same convergence properties as existing methods based on the dogleg strategy using an approximate Hessian.
NimbRo Rescue: Solving Disaster-Response Tasks through Mobile Manipulation Robot Momaro

Robots that solve complex tasks in environments too dangerous for humans to enter are desperately needed, e.g., for search and rescue applications. We describe our mobile manipulation robot Momaro, with which we participated successfully in the DARPA Robotics Challenge. It features a unique locomotion design with four legs ending in steerable wheels, which allows it both to drive omnidirectionally and to step over obstacles or climb. Furthermore, we present advanced communication and teleoperation approaches, which include immersive 3D visualization, and 6D tracking of operator head and arm motions. The proposed system is evaluated in the DARPA Robotics Challenge, the DLR SpaceBot Cup Qualification and lab experiments. We also discuss the lessons learned from the competitions.
Text normalization of code mix and sentiment analysis

The field of getting insights from various text forms such as feedback, opinions, blogs and classifying them based on their polarity as positive or negative is known as sentiment analysis. But from last few years we find huge amount of code - mix (mixture of two languages) text available on social media. This text is available in Romanized English format in Indian social media, which is the transliteration of one language into another, which demands normalization to get further insights into the text. In this paper, we have presented various methods to normalize the text and judged the polarity of the statement as positive or negative using various sentiment resources.
Deep transfer learning for error decoding from non-invasive EEG

We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the use of transfer learning for error decoding with deep convolutional neural networks (deep ConvNets). In comparison with a regularized linear discriminant analysis (rLDA) classifier, ConvNets were significantly better in both intra- and inter-subject decoding, achieving an average accuracy of 84.1 % within subject and 81.7 % on unknown subjects (flanker task). Neither method was, however, able to generalize reliably between paradigms. Visualization of features the ConvNets learned from the data showed plausible patterns of brain activity, revealing both similarities and differences between the different kinds of errors. Our findings indicate that deep learning techniques are useful to infer information about the correctness of action in BCI applications, particularly for the transfer of pre-trained classifiers to new recording sessions or subjects.
Sampling the disparity space image

A central issue in stereo algorithm design is the choice of matching cost. Many algorithms simply use squared or absolute intensity differences based on integer disparity steps. In this paper, we address potential problems with such approaches. We begin with a careful analysis of the properties of the continuous disparity space image (DSI) and propose several new matching cost variants based on symmetrically matching interpolated image signals. Using stereo images with ground truth, we empirically evaluate the performance of the different cost variants and show that proper sampling can yield improved matching performance.
Towards Detecting Rumours in Social Media

The spread of false rumours during emergencies can jeopardise the well-being of citizens as they are monitoring the stream of news from social media to stay abreast of the latest updates. In this paper, we describe the methodology we have developed within the PHEME project for the collection and sampling of conversational threads, as well as the tool we have developed to facilitate the annotation of these threads so as to identify rumourous ones. We describe the annotation task conducted on threads collected during the 2014 Ferguson unrest and we present and analyse our findings. Our results show that we can collect effectively social media rumours and identify multiple rumours associated with a range of stories that would have been hard to identify by relying on existing techniques that need manual input of rumour-specific keywords.
IMAGE RETRIEVAL : PAST , PRESENT , AND

This paper provides a comprehensive survey of the technical achievements in the research area of Image Retrieval , especially Content-Based Image Retrieval, an area so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multi-dimensional indexing, and system design, three of the fundamental bases of Content-Based Image Retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identiied, and future promising research directions are suggested.
DirectFix: Looking for Simple Program Repairs

Recent advances in program repair techniques have raised the possibility of patching bugs automatically. For an automatically generated patch to be accepted by developers, it should not only resolve the bug but also satisfy certain human-related factors including readability and comprehensibility. In this paper, we focus on the simplicity of patches (the size of changes). We present a novel semantics-based repair method that generates the simplest patch such that the program structure of the buggy program is maximally preserved. To take into account the simplicity of repairs in an efficient way (i.e., without explicitly enumerating each repair candidate for each fault location), our method fuses fault localization and repair generation into one step. We do so by leveraging partial MaxSAT constraint solving and component-based program synthesis. We compare our prototype implementation, DirectFix, with the state-of-the-art semantics-based repair tool SemFix, that performs fault localization before repair generation. In our experiments with SIR programs and GNU Coreutils, DirectFix generates repairs that are simpler than those generated by SemFix. Since both DirectFix and SemFix are test-driven repair tools, they can introduce regressions for other tests which do not drive the repair. We found that DirectFix causes substantially less regression errors than SemFix.
DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval

This paper concerns a deep learning approach to relevance ranking in information retrieval (IR). Existing deep IR models such as DSSM and CDSSM directly apply neural networks to generate ranking scores, without explicit understandings of the relevance. According to the human judgement process, a relevance label is generated by the following three steps: 1) relevant locations are detected; 2) local relevances are determined; 3) local relevances are aggregated to output the relevance label. In this paper we propose a new deep learning architecture, namely DeepRank, to simulate the above human judgment process. Firstly, a detection strategy is designed to extract the relevant contexts. Then, a measure network is applied to determine the local relevances by utilizing a convolutional neural network (CNN) or two-dimensional gated recurrent units (2D-GRU). Finally, an aggregation network with sequential integration and term gating mechanism is used to produce a global relevance score. DeepRank well captures important IR characteristics, including exact/semantic matching signals, proximity heuristics, query term importance, and diverse relevance requirement. Experiments on both benchmark LETOR dataset and a large scale clickthrough data show that DeepRank can significantly outperform learning to ranking methods, and existing deep learning methods.
A semantic similarity metric combining features and intrinsic information content

In many research fields such as Psychology, Linguistics, Cognitive Science and Artificial Intelligence, computing semantic similarity between words is an important issue. In this paper a new semantic similarity metric, that exploits some notions of the feature based theory of similarity and translates it into the information theoretic domain, which leverages the notion of Information Content (IC), is presented. In particular, the proposed metric exploits the notion of intrinsic IC which quantifies IC values by scrutinizing how concepts are arranged in an ontological structure. In order to evaluate this metric, an on line experiment asking the community of researchers to rank a list of 65 word pairs has been conducted. The experiment’s web setup allowed to collect 101 similarity ratings and to differentiate native and non-native English speakers. Such a large and diverse dataset enables to confidently evaluate similarity metrics by correlating them with human assessments. Experimental evaluations using WordNet indicate that the proposed metric, coupled with the notion of intrinsic IC, yields results above the state of the art. Moreover, the intrinsic IC formulation also improves the accuracy of other IC-based metrics. In order to investigate the generality of both the intrinsic IC formulation and proposed similarity metric a further evaluation using the MeSH biomedical ontology has been performed. Even in this case significant results were obtained. The proposed metric and several others have been implemented in the Java WordNet Similarity Library.
Linked Movie Data Base

The Linked Movie Database (LinkedMDB) project provides a demonstration of the first open linked dataset connecting several major existing (and highly popular) movie web resources. The database exposed by LinkedMDB contains millions of RDF triples with hundreds of thousands of RDF links to existing web data sources that are part of the growing Linking Open Data cloud, as well as to popular movierelated web pages such as IMDb. LinkedMDB uses a novel way of creating and maintaining large quantities of high quality links by employing state-of-the-art approximate join techniques for finding links, and providing additional RDF metadata about the quality of the links and the techniques used for deriving them.
A review on consensus algorithm of blockchain

Blockchain is the basic technology of bitcoin. With the value appreciation and stable operation of bitcoin, blockchain is attracting more and more attention in many areas. Blockchain has the characteristics of decentralization, stability, security, and non-modifiability. It has the potential to change the network architecture. The consensus algorithm plays a crucial role in maintaining the safety and efficiency of blockchain. Using a right algorithm may bring a significant increase to the performance of blockchain application. In this paper, we reviewed the basic principles and characteristics of the consensus algorithms and analyzed the performance and application scenarios of different consensus mechanisms. We also gave a technical guidance of selecting a suitable consensus algorithm and summarized the limitations and future development of blockchain technology.
Sentiment Summarization: Evaluating and Learning User Preferences

We present the results of a large-scale, end-to-end human evaluation of various sentiment summarization models. The evaluation shows that users have a strong preference for summarizers that model sentiment over non-sentiment baselines, but have no broad overall preference between any of the sentiment-based models. However, an analysis of the human judgments suggests that there are identifiable situations where one summarizer is generally preferred over the others. We exploit this fact to build a new summarizer by training a ranking SVM model over the set of human preference judgments that were collected during the evaluation, which results in a 30% relative reduction in error over the previous best summarizer.
Learn-as-you-go: New Ways of Cloud-Based Micro-learning for the Mobile Web

Micro-learning refers to short-term learning activities on small learning units. In our contemporary mobile/web society, microlearning pertains to small pieces of knowledge based on web resources. Micro-learning falls into the group of informal learning processes. The existing web and mobile services have great potential to support informal learning processes, especially micro-learning. However, several specific aspects need to be considered. In this paper, we propose a micro-learning model based on three technical aspects: (i) ubiquitous learning resource acquisition; (ii) cloud-based data management; and (iii) tag-based regulation of learning processes and content. A micro-learning prototype consisting of an Android application and a web browser add-on is evaluated in the use case of bilingual vocabulary learning. The initial prototype evaluation study shows promising results in enhanced flexibility in personal learning content creation and increased efficiency in filling knowledge gaps.
EmotionCheck: A Wearable Device to Regulate Anxiety through False Heart Rate Feedback

Previous studies indicate that the way we perceive our bodily signals, such as our heart rate, can influence how we feel. Inspired by these studies, we built EmotionCheck, which is a wearable device that can change users' perception of their heart rate through subtle vibrations on the wrist. The results of an experiment with 67 participants show that the EmotionCheck device can help users regulate their anxiety through false feedback of a slow heart rate.
Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation

We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by the complex and variable background. To alleviate this, researchers proposed a coarse-to-fine approach , which used prediction from the first (coarse) stage to indicate a smaller input region for the second (fine) stage. Despite its effectiveness, this algorithm dealt with two stages individually, which lacked optimizing a global energy function, and limited its ability to incorporate multi-stage visual cues. Missing contextual information led to unsatisfying convergence in iterations, and that the fine stage sometimes produced even lower segmentation accuracy than the coarse stage. This paper presents a Recurrent Saliency Transformation Network. The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration. This brings us two-fold benefits. In training, it allows joint optimization over the deep networks dealing with different input scales. In testing, it propagates multi-stage visual information throughout iterations to improve segmentation accuracy. Experiments in the NIH pancreas segmentation dataset demonstrate the state-of-the-art accuracy, which outperforms the previous best by an average of over 2%. Much higher accuracies are also reported on several small organs in a larger dataset collected by ourselves. In addition, our approach enjoys better convergence properties, making it more efficient and reliable in practice.
Online Planning Algorithms for POMDPs

Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP is often intractable except for small problems due to their complexity. Here, we focus on online approaches that alleviate the computational complexity by computing good local policies at each decision step during the execution. Online algorithms generally consist of a lookahead search to find the best action to execute at each time step in an environment. Our objectives here are to survey the various existing online POMDP methods, analyze their properties and discuss their advantages and disadvantages; and to thoroughly evaluate these online approaches in different environments under various metrics (return, error bound reduction, lower bound improvement). Our experimental results indicate that state-of-the-art online heuristic search methods can handle large POMDP domains efficiently.
ROBUST MULTIMODAL HAND-AND HEAD GESTURE RECOGNITION FOR CONTROLLING AUTOMOTIVE INFOTAINMENT SYSTEMS

The use of gestures in automotive environments provides an intuitive addition to existing interaction styles for seamlessly controlling various infotainment applications like radio-tuner, cd-player and telephone. In this work, we describe a robust, context-specific approach for a video-based analysis of dynamic handand head gestures. The system, implemented in a BMW limousine, evaluates a continuous stream of infrared pictures using a combination of adapted preprocessing methods and a hierarchical, mainly rule based classification scheme. Currently, 17 different hand gestures and six different head gestures can be recognized in realtime on standard hardware. As a key-feature of the system, the active gesture vocabulary can be reduced with regard to the current operating context yielding more robust performance.
HAPPY PEOPLE BECOME HAPPIER THROUGH KINDNESS: A COUNTING KINDNESSES INTERVENTION.

We examined the relationship between the character strength of kindness and subjective happiness (Study 1), and the effects of a counting kindnesses intervention on subjective happiness (Study 2). In Study 1, participants were 175 Japanese undergraduate students and in Study 2, participants were 119 Japanese women (71 in the intervention group and 48 in the control group). Results showed that: (a) Happy people scored higher on their motivation to perform, and their recognition and enactment of kind behaviors. (b) Happy people have more happy memories in daily life in terms of both quantity and quality. (c) Subjective happiness was increased simply by counting one's own acts of kindness for one week. (d) Happy people became more kind and grateful through the counting kindnesses intervention. Discussion centers on the importance of kindness in producing subjective happiness.