nils-herrmann/clean_scirepeval huggingface dataset:
CariGAN: Caricature Generation through Weakly Paired Adversarial Learning

Caricature generation is an interesting yet challenging task. The primary goal is to generate a plausible caricature with reasonable exaggerations given a face image. Conventional caricature generation approaches mainly use low-level geometric transformations such as image warping to generate exaggerated images, which lack richness and diversity in terms of content and style. The recent progress in generative adversarial networks (GANs) makes it possible to learn an image-to-image transformation from data so as to generate diverse output images. However, directly applying GAN-based models to this task leads to unsatisfactory results due to the large variance in the caricature distribution. Moreover, some models require pixel-wisely paired training data which largely limits their usage scenarios. In this paper, we model caricature generation as a weakly paired image-to-image translation task, and propose CariGAN to address these issues. Specifically, to enforce reasonable exaggeration and facial deformation, facial landmarks are adopted as an additional condition to constrain the generated image. Furthermore, an image fusion mechanism is designed to encourage our model to focus on the key facial parts so that more vivid details in these regions can be generated. Finally, a diversity loss is proposed to encourage the model to produce diverse results to help alleviate the “mode collapse” problem of the conventional GAN-based models. Extensive experiments on a large-scale “WebCaricature” dataset show that the proposed CariGAN can generate more plausible caricatures with larger diversity compared with the state-of-the-art models.
Drone Indoor Self-Localization

Drones are becoming more and more popular for their possible use in different scenarios, not only outdoor, but even indoor. In this context, indoor self-localization may become crucial and, clearly, GPS is not an option. In this poster we discuss a possible strategy for drone indoor self-localization based on printed markers, drone cameras and software that is generally used for augmented reality purposes. Achieved results from a preliminary experimental campaign demonstrate the effectiveness of the approach with localization errors in the order of 20cm.
Efficient Bayesian network modeling of systems

The Bayesian network (BN) is a convenient tool for probabilistic modeling of system performance, particularly when it is of interest to update the reliability of the system or its components in light of observed information. In this paper, BN structures for modeling the performance of systems that are defined in terms of their minimum link or cut sets are investigated. Standard BN structures that define the system node as a child of its constituent components or its minimum link/cut sets lead to converging structures, which are computationally disadvantageous and could severely hamper application of the BN to real systems. A systematic approach to defining an alternative formulation is developed that creates chain-like BN structures that are orders of magnitude more efficient, particularly in terms of computational memory demand. The formulation uses an integer optimization algorithm to identify the most efficient BN structure. Example applications demonstrate the proposed methodology and quantify the gained computational advantage.
Effects of e-CRM on customer – bank relationship quality and outcomes : The case of Thailand

Available online 15 October 2011 This research examines and measures the outcomes of electronic customer relationship management (e-CRM) system implementation in the Thai banking industry from customers' perspectives. Because most e-CRM implementations cannot be directly seen or recognised by customers, a literature review and interviews with experts in the Thai banking industry were used to develop a new construct called ‘customer-based service attributes’ to measure e-CRM outcomes from customers' perspectives. A full-scale field survey of 684 customers of Thai commercial banks was then conducted. A service attribute model and a model that combined relationship quality and outcome were constructed, and their validity and reliability was confirmed. Analysis of the results by using structural equation modelling (SEM) illustrated that e-CRM implementation has a statistically significant positive relationship with customer-based service attributes and with the quality and outcome of customer–bank relationships as well as an indirect effect on relationship quality and outcome through customer-based service attributes.
Modular elliptic curves and Fermat ’ s Last Theorem

When Andrew John Wiles was 10 years old, he read Eric Temple Bell’s The Last Problem and was so impressed by it that he decided that he would be the first person to prove Fermat’s Last Theorem. This theorem states that there are no nonzero integers a, b, c, n with n > 2 such that an + bn = cn. This object of this paper is to prove that all semistable elliptic curves over the set of rational numbers are modular. Fermat’s Last Theorem follows as a corollary by virtue of work by Frey, Serre and Ribet.
Triply Supervised Decoder Networks for Joint Detection and Segmentation

Joint object detection and semantic segmentation can be applied to many fields, such as self-driving cars and unmanned surface vessels. An initial and important progress towards this goal has been achieved by simply sharing the deep convolutional features for the two tasks. However, this simple scheme is unable to make full use of the fact that detection and segmentation are mutually beneficial. To overcome this drawback, we propose a framework called TripleNet where triple supervisions including detection-oriented supervision, class-aware segmentation supervision, and class-agnostic segmentation supervision are imposed on each layer of the decoder network. Classagnostic segmentation supervision provides an objectness prior knowledge for both semantic segmentation and object detection. Besides the three types of supervisions, two light-weight modules (i.e., inner-connected module and attention skip-layer fusion) are also incorporated into each layer of the decoder. In the proposed framework, detection and segmentation can sufficiently boost each other. Moreover, class-agnostic and class-aware segmentation on each decoder layer are not performed at the test stage. Therefore, no extra computational costs are introduced at the test stage. Experimental results on the VOC2007 and VOC2012 datasets demonstrate that the proposed TripleNet is able to improve both the detection and segmentation accuracies without adding extra computational costs.
Do English and Mandarin speakers think about time differently?

Time is a fundamental domain of experience. In this paper we ask whether aspects of language and culture affect how people think about this domain. Specifically, we consider whether English and Mandarin speakers think about time differently. We review all of the available evidence both for and against this hypothesis, and report new data that further support and refine it. The results demonstrate that English and Mandarin speakers do think about time differently. As predicted by patterns in language, Mandarin speakers are more likely than English speakers to think about time vertically (with earlier time-points above and later time-points below).
Experimental testbed for 5G cognitive radio access in 4G LTE cellular systems

Cognitive radio technology addresses the limited availability of wireless spectrum and inefficiency of spectrum usage. Cognitive Radio (CR) devices sense their environment, detect spatially unused spectrum and opportunistically access available spectrum without creating harmful interference to the incumbents. In cellular systems with licensed spectrum, the efficient utilization of the spectrum as well as the protection of primary users is equally important, which imposes opportunities and challenges for the application of CR. This paper introduces an experimental framework for 5G cognitive radio access in current 4G LTE cellular systems. It can be used to study CR concepts in different scenarios, such as 4G to 5G system migrations, machine-type communications, device-to-device communications, and load balancing. Using our framework, selected measurement results are presented that compare Long Term Evolution (LTE) Orthogonal Frequency Division Multiplex (OFDM) with a candidate 5G waveform called Generalized Frequency Division Multiplexing (GFDM) and quantify the benefits of GFDM in CR scenarios.
Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

Continuously monitoring schizophrenia patients’ psychiatric symptoms is crucial for in-time intervention and treatment adjustment. The Brief Psychiatric Rating Scale (BPRS) is a survey administered by clinicians to evaluate symptom severity in schizophrenia. The CrossCheck symptom prediction system is capable of tracking schizophrenia symptoms based on BPRS using passive sensing from mobile phones. We present results from an ongoing randomized control trial, where passive sensing data, self-reports, and clinician administered 7-item BPRS surveys are collected from 36 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-12 months. We show that our system can predict a symptom scale score based on a 7-item BPRS within ±1.45 error on average using automatically tracked behavioral features from phones (e.g., mobility, conversation, activity, smartphone usage, the ambient acoustic environment) and user supplied self-reports. Importantly, we show our system is also capable of predicting an individual BPRS score within ±1.59 error purely based on passive sensing from phones without any self-reported information from outpatients. Finally, we discuss how well our predictive system reflects symptoms experienced by patients by reviewing a number of case studies.
''Black'' Cultural Capital, Status Positioning, and Schooling Conflicts for Low-Income African American Youth

Previous literature has failed to empirically demonstrate the conceptual distinction that social scientists make between “dominant” and “non-dominant” cultural capital. This article provides evidence of the coexistence of these two forms of capital within the social and academic lives of poor ethnic minority students. Using indepth interviews with 44 low-income African American youth, I illustrate how these students negotiate their perceptions of the differential values placed by educators on these two forms of capital. Often, scholars research the effects of (dominant) cultural capital in social reproduction across various social classes, but not the influence of (non-dominant) cultural capital on status relations within socially marginalized communities. By taking into account the interplay between these two forms of capital in the lives of low-income minority students, researchers might develop a more complete and nuanced understanding of how culture ultimately affects the prospects of mobility for lower status social groups.
High Speed Speculative Multipliers Based on Speculative Carry-Save Tree

This paper proposes a novel approach to build integer multiplication circuits based on speculation, a technique which performs a faster-but occasionally wrong-operation resorting to a multi-cycle error correction circuit only in the rare case of error. The proposed speculative multiplier uses a novel speculative carry-save reduction tree using three steps: partial products recoding, partial products partitioning, speculative compression. The speculative tree uses speculative (m:2) counters, with m > 3, that are faster than a conventional tree using full-adders and half-adders. A technique to automatically choose the suitable speculative counters, taking into accounts both error probability and delay, is also presented in the paper. The speculative tree is completed with a fast speculative carry-propagate adder and an error correction circuit. We have synthesized speculative multipliers for several operand lengths using the UMC 65 nm library. Comparisons with conventional multipliers show that speculation is effective when high speed is required. Speculative multipliers allow reaching a higher speed compared with conventional counterparts and are also quite effective in terms of power dissipation, when a high speed operation is required.
Trans-gram, Fast Cross-lingual Word-embeddings

We introduce Trans-gram, a simple and computationally-efficient method to simultaneously learn and align wordembeddings for a variety of languages, using only monolingual data and a smaller set of sentence-aligned data. We use our new method to compute aligned wordembeddings for twenty-one languages using English as a pivot language. We show that some linguistic features are aligned across languages for which we do not have aligned data, even though those properties do not exist in the pivot language. We also achieve state of the art results on standard cross-lingual text classification and word translation tasks.
A framework to implement Stakeholder participation in environmental projects.

Participation has emerged as an appropriate approach for enhancing natural resources management. However, despite long experimentation with participation, there are still possibilities for improvement in designing a process of stakeholder involvement by addressing stakeholder heterogeneity and the complexity of decision-making processes. This paper provides a state-of-the-art overview of methods. It proposes a comprehensive framework to implement stakeholder participation in environmental projects, from stakeholder identification to evaluation. For each process within this framework, techniques are reviewed and practical tools proposed. The aim of this paper is to establish methods to determine who should participate, when and how. The application of this framework to one river restoration case study in Switzerland will illustrate its strengths and weaknesses.
Heterogeneous Sensor Data Fusion By Deep Multimodal Encoding

Heterogeneous sensor data fusion is a challenging field that has gathered significant interest in recent years. Two of these challenges are learning from data with missing values, and finding shared representations for multimodal data to improve inference and prediction. In this paper, we propose a multimodal data fusion framework, the deep multimodal encoder (DME), based on deep learning techniques for sensor data compression, missing data imputation, and new modality prediction under multimodal scenarios. While traditional methods capture only the intramodal correlations, DME is able to mine both the intramodal correlations in the initial layers and the enhanced intermodal correlations in the deeper layers. In this way, the statistical structure of sensor data may be better exploited for data compression. By incorporating our new objective function, DME shows remarkable ability for missing data imputation tasks in sensor data. The shared multimodal representation learned by DME may be used directly for predicting new modalities. In experiments with a real-world dataset collected from a 40-node agriculture sensor network which contains three modalities, DME can achieve a root mean square error (RMSE) of missing data imputation which is only 20% of the traditional methods like K-nearest neighbors and sparse principal component analysis and the performance is robust to different missing rates. It can also reconstruct temperature modality from humidity and illuminance with an RMSE of $7\; {}^{\circ }$C, directly from a highly compressed (2.1%) shared representation that was learned from incomplete (80% missing) data.
Correlation of Digital Evidences in Forensic Investigation through Semantic Technologies

Due to the pervasiveness of digital technologies in all aspects of human lives, it is increasingly unlikely that a digital device is involved as goal, medium or simply ’witness’ of a criminal event. Forensic investigations include recovery, analysis and presentation of information stored in digital devices and related to computer crimes. These activities often involve the adoption of a wide range of imaging and analysis tools and the application of different techniques on different devices, with the consequence that the reconstruction and presentation activities result complicated. This work presents a method, based on Semantic Web technologies, that helps digital investigators to correlate and present information acquired from forensic data, with the aim to get a more valuable reconstruction of events or actions in order to reach case conclusions.
Fine-Grained Image Classification via Combining Vision and Language

Fine-grained image classification is a challenging task due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category. Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy. Despite achieving promising results, these methods mainly have two limitations: (1) not all the parts which obtained through the part detection models are beneficial and indispensable for classification, and (2) fine-grained image classification requires more detailed visual descriptions which could not be provided by the part locations or attribute annotations. For addressing the above two limitations, this paper proposes the two-stream model combing vision and language (CVL) for learning latent semantic representations. The vision stream learns deep representations from the original visual information via deep convolutional neural network. The language stream utilizes the natural language descriptions which could point out the discriminative parts or characteristics for each image, and provides a flexible and compact way of encoding the salient visual aspects for distinguishing sub-categories. Since the two streams are complementary, combing the two streams can further achieves better classification accuracy. Comparing with 12 state-of-the-art methods on the widely used CUB-200-2011 dataset for fine-grained image classification, the experimental results demonstrate our CVL approach achieves the best performance.
Security Threats and Challenges in Cloud Computing

Cloud Computing has emerged as a new paradigm of computing that builds on the foundations of Distributed Computing, Grid Computing, and Virtualization. Cloud computing is Internet-accessible business model with flexible resource allocation on demand, and computing on a pay-per-use as utilities. Cloud computing has grown to provide a promising business concept for computing infrastructure, where concerns are beginning to grow about how safe an environment is. Security is one of the major issues in the cloud-computing environment. In this paper we investigate some prime security attacks and possible solutions for clouds: XML Signature Wrapping attacks, Browser Security, and Vendor Lock-in.
A framework for evaluating Internet-of-Things platforms: Application provider viewpoint

Widespread adoption of Internet of Things (IoT) applications assumes a sustainable ecosystem of firms creating, distributing, and using these applications. Such an ecosystem often forms around a platform that implements commonly used functionality and offers it to the members of the ecosystem for creating applications and services. In IoT, such a platform may be materialized in a form of a cloud platform that supports the management of connected devices, and the development and use of the applications based on them. A number of cloud platforms targeting IoT applications are available in the market; to prosper, they need to assist the providers of the IoT applications in designing and developing the applications, as well as in deploying and operating the application software. In this paper, a framework for evaluating the IoT platforms from the perspective of how widely they cover the potential needs of the application providers is introduced. Based on the framework, the maturity of the available IoT platforms is evaluated, by using the publicly available information about the platforms' features and supporting services. As the results of this initial analysis suggest, none of the platforms today offers the comprehensive support. This can be seen as a factor contributing to a slow adaption of the IoT platforms, and therefore to the slower-than-expected takeoff of the IoT ecosystem in general.
Emotion Detection from Text : Survey

This paper discusses some relevant work of emotion detection from text which is a main field in affecting computing and artificial intelligence field. Artificial intelligence is not only the ability for a machine to think or interact with end user smartly but also to act humanly or rationally so emotion detection from text plays a key role in human-computer interaction. It has attracted the attention of many researchers due to the great revolution of emotional data available on social and web applications of computers and much more in mobile devices. This survey mainly collects history of unsupervised emotion detection from text.
Microbiome effects on immunity, health and disease in the lung

Chronic respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF), are among the leading causes of mortality and morbidity worldwide. In the past decade, the interest in the role of microbiome in maintaining lung health and in respiratory diseases has grown exponentially. The advent of sophisticated multiomics techniques has enabled the identification and characterisation of microbiota and their roles in respiratory health and disease. Furthermore, associations between the microbiome of the lung and gut, as well as the immune cells and mediators that may link these two mucosal sites, appear to be important in the pathogenesis of lung conditions. Here we review the recent evidence of the role of normal gastrointestinal and respiratory microbiome in health and how dysbiosis affects chronic pulmonary diseases. The potential implications of host and environmental factors such as age, gender, diet and use of antibiotics on the composition and overall functionality of microbiome are also discussed. We summarise how microbiota may mediate the dynamic process of immune development and/or regulation focusing on recent data from both clinical human studies and translational animal studies. This furthers the understanding of the pathogenesis of chronic pulmonary diseases and may yield novel avenues for the utilisation of microbiota as potential therapeutic interventions.
Comparing scalability of message queue system: ZeroMQ vs RabbitMQ

Modern web apps handle huge and increasing numbers of users and operations. A rise of event-driven architecture approach and message queue systems provide a new alternative to face this scenario evidencing the lack of quantitative measurements comparing performance and scalability between specific message queue products. This article proposes a prototype architecture applied in ZeroMQ and RabbitMQ, used for measure the impact of (1) the number of messages over performance, and (2) the numbers of consuming nodes over scalability. The results show that for both criteria, the degradation threshold of ZeroMQ is higher than RabbitMQ, thus more scalable and faster.