nils-herrmann/clean_scirepeval huggingface dataset:
Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN). The proposed approach employs several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. These features are useful for image classification and target detection. Furthermore, in order to address the common issue of imbalance between high dimensionality and limited availability of training samples for the classification of HSI, a few strategies such as L2 regularization and dropout are investigated to avoid overfitting in class data modeling. More importantly, we propose a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery. Finally, in order to further improve the performance, a virtual sample enhanced method is proposed. The proposed approaches are carried out on three widely used hyperspectral data sets: Indian Pines, University of Pavia, and Kennedy Space Center. The obtained results reveal that the proposed models with sparse constraints provide competitive results to state-of-the-art methods. In addition, the proposed deep FE opens a new window for further research.
An Analysis of Power Consumption in a Smartphone

Mobile consumer-electronics devices, especially phones, are powered from batteries which are limited in size and therefore capacity. This implies that managing energy well is paramount in such devices. Good energy management requires a good understanding of where and how the energy is used. To this end we present a detailed analysis of the power consumption of a recent mobile phone, the Openmoko Neo Freerunner. We measure not only overall system power, but the exact breakdown of power consumption by the device’s main hardware components. We present this power breakdown for micro-benchmarks as well as for a number of realistic usage scenarios. These results are validated by overall power measurements of two other devices: the HTC Dream and Google Nexus One. We develop a power model of the Freerunner device and analyse the energy usage and battery lifetime under a number of usage patterns. We discuss the significance of the power drawn by various components, and identify the most promising areas to focus on for further improvements of power management. We also analyse the energy impact of dynamic voltage and frequency scaling of the device’s application processor.
Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media

In this paper, we describe the Lithium Natural Language Processing (NLP) system a resource-constrained, highthroughput and language-agnostic system for information extraction from noisy user generated text on social media. Lithium NLP extracts a rich set of information including entities, topics, hashtags and sentiment from text. We discuss several real world applications of the system currently incorporated in Lithium products. We also compare our system with existing commercial and academic NLP systems in terms of performance, information extracted and languages supported. We show that Lithium NLP is at par with and in some cases, outperforms stateof-the-art commercial NLP systems.
Cortical representation of the sensory dimension of pain.

It is well accepted that pain is a multidimensional experience, but little is known of how the brain represents these dimensions. We used positron emission tomography (PET) to indirectly measure pain-evoked cerebral activity before and after hypnotic suggestions were given to modulate the perceived intensity of a painful stimulus. These techniques were similar to those of a previous study in which we gave suggestions to modulate the perceived unpleasantness of a noxious stimulus. Ten volunteers were scanned while tonic warm and noxious heat stimuli were presented to the hand during four experimental conditions: alert control, hypnosis control, hypnotic suggestions for increased-pain intensity and hypnotic suggestions for decreased-pain intensity. As shown in previous brain imaging studies, noxious thermal stimuli presented during the alert and hypnosis-control conditions reliably activated contralateral structures, including primary somatosensory cortex (S1), secondary somatosensory cortex (S2), anterior cingulate cortex, and insular cortex. Hypnotic modulation of the intensity of the pain sensation led to significant changes in pain-evoked activity within S1 in contrast to our previous study in which specific modulation of pain unpleasantness (affect), independent of pain intensity, produced specific changes within the ACC. This double dissociation of cortical modulation indicates a relative specialization of the sensory and the classical limbic cortical areas in the processing of the sensory and affective dimensions of pain.
Ranking-based clustering of heterogeneous information networks with star network schema

A heterogeneous information network is an information network
 composed of multiple types of objects. Clustering on such a network may lead to better understanding of both hidden structures of the network and the individual role played by every object in each cluster. However, although clustering on homogeneous networks has been studied over decades, clustering on heterogeneous networks has not been addressed until recently.
 A recent study proposed a new algorithm, RankClus, for clustering on bi-typed heterogeneous networks. However, a real-world network may consist of more than two types, and the interactions among multi-typed objects play a key role at disclosing the rich semantics that a network carries. In this paper, we study clustering of multi-typed heterogeneous networks with a star network schema and propose a novel algorithm, NetClus, that utilizes links across multityped objects to generate high-quality net-clusters. An iterative enhancement method is developed that leads to effective ranking-based clustering in such heterogeneous networks. Our experiments on DBLP data show that NetClus generates more accurate clustering results than the baseline topic model algorithm PLSA and the recently proposed algorithm, RankClus. Further, NetClus generates informative clusters, presenting good ranking and cluster membership information for each attribute object in each net-cluster.
Multi-core real-time scheduling for generalized parallel task models

Multi-core processors offer a significant performance increase over single-core processors. They have the potential to enable computation-intensive real-time applications with stringent timing constraints that cannot be met on traditional single-core processors. However, most results in traditional multiprocessor real-time scheduling are limited to sequential programming models and ignore intra-task parallelism. In this paper, we address the problem of scheduling periodic parallel tasks with implicit deadlines on multi-core processors. We first consider a synchronous task model where each task consists of segments, each segment having an arbitrary number of parallel threads that synchronize at the end of the segment. We propose a new task decomposition method that decomposes each parallel task into a set of sequential tasks. We prove that our task decomposition achieves a resource augmentation bound of 4 and 5 when the decomposed tasks are scheduled using global EDF and partitioned deadline monotonic scheduling, respectively. Finally, we extend our analysis to a directed acyclic graph (DAG) task model where each node in the DAG has a unit execution requirement. We show how these tasks can be converted into synchronous tasks such that the same decomposition can be applied and the same augmentation bounds hold. Simulations based on synthetic workload demonstrate that the derived resource augmentation bounds are safe and sufficient.
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties

Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics soware. Encoded in each SMILES string is structural information that can be used to predict complex chemical properties. In this work, we develop SMILES2vec, a deep RNN that automatically learns features from SMILES to predict chemical properties, without the need for additional explicit feature engineering. Using Bayesian optimization methods to tune the network architecture, we show that an optimized SMILES2vec model can serve as a general-purpose neural network for predicting distinct chemical properties including toxicity, activity, solubility and solvation energy, while also outperforming contemporary MLP neural networks that uses engineered features. Furthermore, we demonstrate proof-of-concept of interpretability by developing an explanation mask that localizes on the most important characters used in making a prediction. When tested on the solubility dataset, it identied specic parts of a chemical that is consistent with established rst-principles knowledge with an accuracy of 88%. Our work demonstrates that neural networks can learn technically accurate chemical concept and provide state-of-the-art accuracy, making interpretable deep neural networks a useful tool of relevance to the chemical industry.
Time Warping of Audio Signals

This paper describes a technique to obtain a time dilation or contraction of an audio signal. Different Computer Graphics applications can take advantage of this technique. In real-time networked VR applications, such as teleconference or games, audio might be transmited independently from the rest of the data, These different signals arrive asynchronously and need to be somehow resynchronized on the fly. In animation, it can help to automatically fit and merge pre-recorded sound samples to special timed events. It also makes it easier to accomplish special effects like lip-sync for dubbing or changing the voice of an animated character. Our technique tries to eliminate distortions by the replication of the original signal frequencies. Malvar wavelets are used to avoid clicking between segment transitions.
Finding the right social media site for questions

Social media has become a part of our daily life and we use it for many reasons. One of its uses is to get our questions answered. Given a multitude of social media sites, however, one immediate challenge is to pick the most relevant site for a question. This is a challenging problem because (1) questions are usually short, and (2) social media sites evolve. In this work, we propose to utilize topic specialization to find the most relevant social media site for a given question. In particular, semantic knowledge is considered for topic specialization as it can not only make a question more specific, but also dynamically represent the content of social sites, which relates a given question to a social media site. Thus, we propose to rank social media sites based on combined search engine query results. Our algorithm yields compelling results for providing a meaningful and consistent site recommendation. This work helps further understand the innate characteristics of major social media platforms for the design of social Q&A systems.
NMT or SMT: Case Study of a Narrow-domain English-Latvian Post-editing Project

The recent technological shift in machine translation from statistical machine translation (SMT) to neural machine translation (NMT) raises the question of the strengths and weaknesses of NMT. In this paper, we present an analysis of NMT and SMT systems’ outputs from narrow domain English-Latvian MT systems that were trained on a rather small amount of data. We analyze post-edits produced by professional translators and manually annotated errors in these outputs. Analysis of post-edits allowed us to conclude that both approaches are comparably successful, allowing for an increase in translators’ productivity, with the NMT system showing slightly worse results. Through the analysis of annotated errors, we found that NMT translations are more fluent than SMT translations. However, errors related to accuracy, especially, mistranslation and omission errors, occur more often in NMT outputs. The word form errors, that characterize the morphological richness of Latvian, are frequent for both systems, but slightly fewer in NMT outputs.
Navigation Assistance for the Visually Impaired Using RGB-D Sensor With Range Expansion

Navigation assistance for visually impaired (NAVI) refers to systems that are able to assist or guide people with vision loss, ranging from partially sighted to totally blind, by means of sound commands. In this paper, a new system for NAVI is presented based on visual and range information. Instead of using several sensors, we choose one device, a consumer RGB-D camera, and take advantage of both range and visual information. In particular, the main contribution is the combination of depth information with image intensities, resulting in the robust expansion of the range-based floor segmentation. On one hand, depth information, which is reliable but limited to a short range, is enhanced with the long-range visual information. On the other hand, the difficult and prone-to-error image processing is eased and improved with depth information. The proposed system detects and classifies the main structural elements of the scene providing the user with obstacle-free paths in order to navigate safely across unknown scenarios. The proposed system has been tested on a wide variety of scenarios and data sets, giving successful results and showing that the system is robust and works in challenging indoor environments.
Dynamic lightweight text compression

We address the problem of adaptive compression of natural language text, considering the case where the receiver is much less powerful than the sender, as in mobile applications. Our techniques achieve compression ratios around 32% and require very little effort from the receiver. Furthermore, the receiver is not only lighter, but it can also search the compressed text with less work than that necessary to decompress it. This is a novelty in two senses: it breaks the usual compressor/decompressor symmetry typical of adaptive schemes, and it contradicts the long-standing assumption that only semistatic codes could be searched more efficiently than the uncompressed text. Our novel compression methods are preferable in several aspects over the existing adaptive and semistatic compressors for natural language texts.
Supply Chain Risk Management: Systematic literature review and a conceptual framework for capturing interdependencies between risks

The purpose of this research is to conduct a comprehensive and systematic review of the literature in the field of `Supply Chain Risk Management' and identify important research gaps for potential research. Furthermore, a conceptual risk management framework is also proposed that encompasses holistic view of the field. `Systematic Literature Review' method is used to examine quality articles published over a time period of almost 15 years (2000 - June, 2014). The findings of the study are validated through text mining software. Systematic literature review has identified the progress of research based on various descriptive and thematic typologies. The review and text mining analysis have also provided an insight into major research gaps. Based on the identified gaps, a framework is developed that can help researchers model interdependencies between risk factors.
Inferring relations in knowledge graphs with tensor decompositions

Multi-relational data, like knowledge graphs, are generated from multiple data sources by extracting entities and their relationships. We often want to include inferred, implicit or likely relationships that are not explicitly stated, which can be viewed as link-prediction in a graph. Tensor decomposition models have been shown to produce state-of-the-art results in link-prediction tasks. We describe a simple but novel extension to an existing tensor decomposition model to predict missing links using similarity among tensor slices, as opposed to an existing tensor decomposition models which assumes each slice to contribute equally in predicting links. Our extended model performs better than the original tensor decomposition and the non-negative tensor decomposition variant of it in an evaluation on several datasets.
Recognising Affect in Text using Pointwise-Mutual Information

This dissertation describes experiments conducted to evaluate an algorithm that attempts to automatically recognise emotions (affect) in written language. Examples from several areas of research that can inform affect recognition experiments are reviewed, including sentiment analysis, subjectivity analysis, and the psychology of emotion. An affect annotation exercise was carried out in order to build a suitable set of test data for the experiment. An algorithm to classify according to the emotional content of sentences was derived from an existing technique for sentiment analysis. When compared against the manual annotations, the algorithm achieved an accuracy of 32.78%. Several factors indicate that the method is making slightly informed choices, and could be useful as part of a holistic approach to recognising the affect represented in text. iii Acknowledgements
Unrealistic optimism in smokers: implications for smoking myth endorsement and self-protective motivation.

Although some optimists may be accurate in their positive beliefs about the future, others may be unrealistic-their optimism is misplaced. Research shows that some smokers exhibit unrealistic optimism by underestimating their relative chances of experiencing disease. An important question is whether such unrealistic optimism is associated with risk-related attitudes and behavior. We addressed this question by investigating if one's perceived risk of developing lung cancer, over and above one's objective risk, predicted acceptance of myths and other beliefs about smoking. Hierarchical regressions showed that those individuals who were unrealistically optimistic (i.e., whose perceived risk was less than their objective risk) were more likely to endorse beliefs that there is no risk of lung cancer if one only smokes for a few years and that getting lung cancer depends on one's genes. Unrealistic optimists were also more likely to believe that a greater number of lung cancer patients are cured, but they were less likely to identify smoking cessation/avoidance as a way to reduce cancer risk. Most importantly, unrealistic optimists were less likely to plan on quitting smoking. Taken together, these data suggest that in the smoking arena, unrealistic optimism is a potentially costly cognitive strategy.
A 12-bit digitally controlled oscillator with low power consumption

In this paper, a low power and low jitter 12-bit CMOS digitally controlled oscillator (DCO) design is presented. The CMOS DCO is designed based on a ring oscillator implemented with Schmitt trigger based inverters. Simulations of the proposed DCO using 32 nm CMOS predictive transistor model (PTM) achieves controllable frequency range of 570 MHz~850 MHz with a wide linearity. Monte Carlo simulation demonstrates that the time-period jitter due to random power supply fluctuation is under 75 ps and the power consumption is 2.3 mW at 800 MHz with 0.9 V power supply.
A First Look at Problems in the Cloud

Cloud computing provides a revolutionary model for the deployment of enterprise applications and Web services alike. In this new model, cloud users save on the cost of purchasing and managing base infrastructure, while the cloud providers save on the cost of maintaining underutilized CPU, memory, and network resources. In migrating to this new model, users face a variety of issues. Commercial clouds provide several support models to aide users in resolving the reported issues This paper arises from our quest to understand how to design IaaS support models for more efficient user troubleshooting. Using a data driven approach, we start our exploration into this issue with an investigation into the problems encountered by users and the methods utilized by the cloud support’s staff to resolve these problems. We examine message threads appearing in the forum of a large IaaS provider over a 3 year period. We argue that the lessons derived from this study point to a set of principles that future IaaS offerings can implement to provide users with a more efficient support model. This data driven approach enables us to propose a set of principles that are pertinent to the experiences of users and that we believe could vastly improve the SLA observed by the users.
A Exploratory Study of @-Mention in GitHub's Pull-Requests

Pull-request mechanism is an outstanding social development method in Git Hub. @-mention is a social media tool that deeply integrated with pull-request mechanism. Recently, many research results show that social media tools can promote the collaborative software development, but few work focuses on the impacts of @-mention. In this paper, we conduct an exploratory study of @-mention in pull-request based software development, including its current situation and benefits. We obtain some interesting findings which indicate that @-mention is beneficial to the processing of pull-request. Our work also proposes some possible research directions and problems of the @-mention. It helps the developers and researchers notice the significance of @-mention in the pull-request based software development.
On Clustering Graph Streams

In this paper, we will examine the problem of clustering massive graph streams. Graph clustering poses significant challenges because of the complex structures which may be present in the underlying data. The massive size of the underlying graph makes explicit structural enumeration very difficult. Consequently, most techniques for clustering multi-dimensional data are difficult to generalize to the case of massive graphs. Recently, methods have been proposed for clustering graph data, though these methods are designed for static data, and are not applicable to the case of graph streams. Furthermore, these techniques are especially not effective for the case of massive graphs, since a huge number of distinct edges may need to be tracked simultaneously. This results in storage and computational challenges during the clustering process. In order to deal with the natural problems arising from the use of massive disk-resident graphs, we will propose a technique for creating hash-compressed micro-clusters from graph streams. The compressed micro-clusters are designed by using a hash-based compression of the edges onto a smaller domain space. We will provide theoretical results which show that the hash-based compression continues to maintain bounded accuracy in terms of distance computations. We will provide experimental results which illustrate the accuracy and efficiency of the underlying method.