nils-herrmann/clean_scirepeval huggingface dataset:
Dual Refinement Network for Single-Shot Object Detection

Object detection methods fall into two categories, i.e., two-stage and single-stage detectors. The former is characterized by high detection accuracy while the latter usually has considerable inference speed. Hence, it is imperative to fuse their metrics for a better accuracy vs. speed trade-off. To this end, we propose a dual refinement network (DRN) to boost the performance of the single-stage detector. Inheriting from the advantages of two-stage approaches (i.e., two-step regression and accurate features for detection), anchor refinement and feature offset refinement are conducted in anchor-offset detection, where the detection head is comprised of deformable convolutions. Moreover, to leverage contextual information for describing objects, we design a multi-deformable head, in which multiple detection paths with different receptive field sizes devote themselves to detecting objects. Extensive experiments on PASCAL VOC and ImageNet VID datasets are conducted, and we achieve the state-of-the-art results and a better accuracy vs. speed trade-off, i.e., 81.4% mAP vs. 42.3 FPS on VOC2007 test set. Codes will be publicly available.
Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation

Understanding the visual relationship between two objects involves identifying the subject, the object, and a predicate relating them. We leverage the strong correlations between the predicate and the hsubj; obji pair (both semantically and spatially) to predict predicates conditioned on the subjects and the objects. Modeling the three entities jointly more accurately reflects their relationships compared to modeling them independently, but it complicates learning since the semantic space of visual relationships is huge and training data is limited, especially for longtail relationships that have few instances. To overcome this, we use knowledge of linguistic statistics to regularize visual model learning. We obtain linguistic knowledge by mining from both training annotations (internal knowledge) and publicly available text, e.g., Wikipedia (external knowledge), computing the conditional probability distribution of a predicate given a (subj, obj) pair. As we train the visual model, we distill this knowledge into the deep model to achieve better generalization. Our experimental results on the Visual Relationship Detection (VRD) and Visual Genome datasets suggest that with this linguistic knowledge distillation, our model outperforms the stateof- the-art methods significantly, especially when predicting unseen relationships (e.g., recall improved from 8.45% to 19.17% on VRD zero-shot testing set).
Blind Format String Attacks

Although Format String Attacks(FSAs) are known for many years there is still a number of applications that have been found to be vulnerable to such attacks in the recent years.According to the CVE database, the number of FSA vulnerabilities is stable over the last 5 years, even as FSA vulnerabilities are assumingly easy to detect. Thus we can assume, that this type of bugs will still be present in future. Current compiler-based or system-based protection mechanisms are helping to restrict the exploitation this kind of vulnerabilities, but are insu cient to circumvent an attack in all cases. Currently FSAs are mainly used to leak information such as pointer addresses to circumvent protection mechanisms like Address Space Layout Randomization (ASLR). So current attacks are also interested in the output of the format string. In this paper we present a novel method for attacking format string vulnerabilities in a blind manner. Our method does not require any memory leakage or output to the attacker. In addition, we show a way to exploit format string vulnerabilities on the heap, where we can not bene t from direct destination control, i. e. we can not place arbitrary addresses onto the stack, as is possible in stack-based format string.
Enhancing citation recommendation with various evidences

With the tremendous amount of citations available in digital library, how to suggest citations automatically, to meet the information needs of researchers has become an important problem. In this paper, we propose a model which treats citation recommendation as a special retrieval task to address this challenge. First, users provide a target paper with some metadata to our system. Second, the system retrieves a relevant candidate citation set. Then the candidate citations are reranked by well-chosen citation evidence, such as publication time preference, self-citation preference, co-citation preference and publication reputation preference. Especially, various measures are introduced to integrate the evidence. We experimented with the proposed model on an established bibliographic corpus-ACL Anthology Network, the results show that the model is valuable in practice, and citation recommendation can be significantly improved using proposed evidences.
Interactive whiteboards: Interactive or just whiteboards?

Over the last decade, interactive whiteboards have become popular teaching and learning tools, especially in primary school classrooms. Research studies from recent literature report on high levels of student motivation, teacher enthusiasm and wholeschool support associated with these technological tools. Much research to date has reported on the potential of interactive whiteboards (IWBs) to improve the quality of teaching and learning processes by enhancing levels of interaction, communication and collaboration. Whether these claims have been substantiated has not yet been fully investigated. This paper reports on the findings from a collaborative research project between university lecturers, school-based primary school teachers and principals, preservice teachers and district education consultants who worked together on a small, school-based research project. The researchers involved in this project had three main purposes: (1) to investigate different ways that IWBs are used in primary schools; (2) to share ideas and expertise on the use of IWBs; and (3) to document teachers’ current practice with IWBs. Field notes from professional development meetings and classroom observations were analysed using a grounded theory methodology. Findings from this research are reported in this paper and compared to other recent research. The paper concludes with recommendations for practice and future research.
Selective Combination of Visual and Thermal Imaging for Resilient Localization in Adverse Conditions: Day and Night, Smoke and Fire

Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and lowvisibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g. operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localisation. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e. data most likely to induce large localisation errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using 5 different variations of data sources and data combination strategies in the localisation method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without pre-selection. We show that the proposed framework allows for camera-based localisation resilient to a large range of low-visibility conditions.
A survey of deep-learning frameworks

Deep learning is a model of machine learning loosely based on our brain. Artificial neural network has been around since the 1950s, but recent advances in hardware like graphical processing units (GPU), software like cuDNN, TensorFlow, Torch, Caffe, Theano, Deeplearning4j, etc. and new training methods have made training artificial neural networks fast and easy. In this paper, we are comparing some of the deep learning frameworks on the basis of parameters like modeling capability, interfaces available, platforms supported, parallelizing techniques supported, availability of pre-trained models, community support and documentation quality.
Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies.

Most of our social interactions involve perception of emotional information from the faces of other people. Furthermore, such emotional processes are thought to be aberrant in a range of clinical disorders, including psychosis and depression. However, the exact neurofunctional maps underlying emotional facial processing are not well defined.



Two independent researchers conducted separate comprehensive PubMed (1990 to May 2008) searches to find all functional magnetic resonance imaging (fMRI) studies using a variant of the emotional faces paradigm in healthy participants. The search terms were: "fMRI AND happy faces," "fMRI AND sad faces," "fMRI AND fearful faces," "fMRI AND angry faces," "fMRI AND disgusted faces" and "fMRI AND neutral faces." We extracted spatial coordinates and inserted them in an electronic database. We performed activation likelihood estimation analysis for voxel-based meta-analyses.



Of the originally identified studies, 105 met our inclusion criteria. The overall database consisted of 1785 brain coordinates that yielded an overall sample of 1600 healthy participants. Quantitative voxel-based meta-analysis of brain activation provided neurofunctional maps for 1) main effect of human faces; 2) main effect of emotional valence; and 3) modulatory effect of age, sex, explicit versus implicit processing and magnetic field strength. Processing of emotional faces was associated with increased activation in a number of visual, limbic, temporoparietal and prefrontal areas; the putamen; and the cerebellum. Happy, fearful and sad faces specifically activated the amygdala, whereas angry or disgusted faces had no effect on this brain region. Furthermore, amygdala sensitivity was greater for fearful than for happy or sad faces. Insular activation was selectively reported during processing of disgusted and angry faces. However, insular sensitivity was greater for disgusted than for angry faces. Conversely, neural response in the visual cortex and cerebellum was observable across all emotional conditions.



Although the activation likelihood estimation approach is currently one of the most powerful and reliable meta-analytical methods in neuroimaging research, it is insensitive to effect sizes.



Our study has detailed neurofunctional maps to use as normative references in future fMRI studies of emotional facial processing in psychiatric populations. We found selective differences between neural networks underlying the basic emotions in limbic and insular brain regions.
A Bibliometric Model for Identifying Emerging Research Topics

Detecting emerging research topics is essential, not only for research agencies but also for individual researchers. Previous studies have created various bibliographic indicators for the identification of emerging research topics. However, as indicated by Rotolo et al. (2015), the most serious problems are the lack of an acknowledged definition of emergence and incomplete elaboration of the linkages between the definitions that are used and the indicators that are created. With these issues in mind, this study first adjusts the definition of an emerging technology that Rotolo et al. (2015) have proposed in order to accommodate the analysis. Next, a set of criteria for the identification of emerging topics is proposed according to the adjusted definition and attributes of emergence. By the use of two sets of parameter values, several emerging research topics are identified. Finally, evaluation tests are conducted by demonstration of the proposed approach and comparison with previous studies. The strength of the present methodology lies in the fact that it is fully transparent, straightforward, and flexible.
Issues in the credit risk modeling of retail markets

We survey the most recent BIS proposals for the credit risk measurement of retail credits in capital regulations. We also describe the recent trend away from relationship lending toward transactional lending in the small business loan arena. These trends create the opportunity to adopt more analytical, data-based approaches to credit risk measurement. We survey proprietary credit scoring models (such as Fair Isaac), as well as options-theoretic structural models (such as KMV and Moody’s RiskCalc), and reduced-form models (such as Credit Risk Plus). These models allow lenders and regulators to develop techniques that rely on portfolio aggregation to measure retail credit risk exposure.
A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks

Stability problems of continuous-time recurrent neural networks have been extensively studied, and many papers have been published in the literature. The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks, and related models. Since time delay is inevitable in practice, stability results of recurrent neural networks with different classes of time delays are reviewed in detail. For the case of delay-dependent stability, the results on how to deal with the constant/variable delay in recurrent neural networks are summarized. The relationship among stability results in different forms, such as algebraic inequality forms, M-matrix forms, linear matrix inequality forms, and Lyapunov diagonal stability forms, is discussed and compared. Some necessary and sufficient stability conditions for recurrent neural networks without time delays are also discussed. Concluding remarks and future directions of stability analysis of recurrent neural networks are given.
Domains and image schemas*

Despite diering theoretical views within cognitive semantics there appears to be a consensus on certain fundamental theoretical constructs: (i) the basic semantic unit is a mental concept; (ii) concepts cannot be understood independent of the domain in which they are embedded; (iii) conceptual structures represent a construal of experience, that is, an active mental operation; and (iv) concept categories involve prototypes and are organized by (at least) taxonomic relations. Although the basic constructs of ``concept'', ``domain'', ``construal'', and ``category structure'' go by dierent names, they are essentially the same among researchers in cognitive linguistics. We examine a ®fth theoretical construct, that of ``image schemas'' (recurring basic conceptual structures), and argue that image schemas are a subtype of domain. We begin with the theory of domains proposed by Langacker, which is similar to Fillmore's theory of frame semantics. Langacker distinguishes two types of domains, locational and con®gurational; we argue that it is concepts in domains that are locational or con®gurational, not the domains themselves. We then analyze image schemas and show how they function like domains, in which are found both locational and con®gurational concepts.
Aerodynamic Admittance Function of Tall Buildings

The aerodynamic admittance function (AAF) has been widely invoked to relate wind pressures on building surfaces to the oncoming wind velocity. In current practice, strip and quasi-steady theories are generally employed in formulating wind effects in the along-wind direction. These theories permit the representation of the wind pressures on building surfaces in terms of the oncoming wind velocity field. Synthesis of the wind velocity field leads to a generalized wind load that employs the AAF. This paper reviews the development of the current AAF in use. It is followed by a new definition of the AAF, which is based on the base bending moment. It is shown that the new AAF is numerically equivalent to the currently used AAF for buildings with linear mode shape and it can be derived experimentally via high frequency base balance. New AAFs for square and rectangular building models were obtained and compared with theoretically derived expressions. Some discrepancies between experimentally and theoretically derived AAFs in the high frequency range were noted.
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed “DeepLab” system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.
A conceptual framework for cloud data governance-driven decision making

Data governance has become a significant approach that drives decision making in public organisations. Thus, the loss of data governance is a concern to decision makers, acting as a barrier to achieving their business plans in many countries and also influencing both operational and strategic decisions. The adoption of cloud computing is a recent trend in public sector organisations, that are looking to move their data into the cloud environment. The literature shows that data governance is one of the main concerns of decision makers who are considering adopting cloud computing; it also shows that data governance in general and for cloud computing in particular is still being researched and requires more attention from researchers. However, in the absence of a cloud data governance framework, this paper seeks to develop a conceptual framework for cloud data governance-driven decision making in the public sector.
Gesture Recognition for Indonesian Sign Language Systems (ISLS) Using Multimodal Sensor Leap Motion and Myo Armband Controllers Based-on Naïve Bayes Classifier

Indonesian Sign Language System (ISLS) has been used widely by Indonesian for translating the sign language of disabled people to many applications, including education or entertainment. ISLS consists of static and dynamic gestures in representing words or sentences. However, individual variations in performing sign language have been a big challenge especially for developing automatic translation. The accuracy of recognizing the signs will decrease linearly with the increase of variations of gestures. This research is targeted to solve these issues by implementing the multimodal methods: leap motion and Myo armband controllers (EMG electrodes). By combining these two data and implementing Naïve Bayes classifier, we hypothesized that the accuracy of gesture recognition system for ISLS then can be increased significantly. The data streams captured from hand-poses were based on time-domain series method which will warrant the generated data synchronized accurately. The selected features for leap motion data would be based on fingers positions, angles, and elevations, while for the Myo armband would be based on electrical signal generated by eight channels of EMG electrodes relevant to the activities of linked finger’s and forearm muscles. This study will investigate the accuracy of gesture recognition by using either single modal or multimodal for translating Indonesian sign language. For multimodal strategy, both features datasets were merged into a single dataset which was then used for generating a model for each hand gesture. The result showed that there was a significant improvement on its accuracy, from 91% for single modal using leap motion to 98% for multi-modal (combined with Myo armband). The confusion matrix of multimodal method also showed better performance than the single-modality. Finally, we concluded that the implementation of multi-modal controllers for ISLS’s gesture recognition showed better accuracy and performance compared of single modality of using only leap motion controller.
Evolutionary Psychology and the Emotions

Evolutionary psychology is an approach to the psychological sciences in which principles and results drawn from evolutionary biology, cognitive science, anthropology, and neuroscience are integrated with the rest of psychology in order to map human nature. By human nature, evolutionary psychologists mean the evolved, reliably developing, species-typical computational and neural architecture of the human mind and brain. According to this view, the functional components that comprise this architecture were designed by natural selection to solve adaptive problems faced by our hunter-gatherer ancestors, and to regulate behavior so that these adaptive problems were successfully addressed (for discussion, see Cosmides & Tooby, 1987, Tooby & Cosmides, 1992). Evolutionary psychology is not a specific subfield of psychology, such as the study of vision, reasoning, or social behavior. It is a way of thinking about psychology that can be applied to any topic within it including the emotions.