nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Meander-Line Polarizer Modeling Procedure and Broadband Equivalent Circuit

A new modeling approach for the design of planar multilayered meander-line polarizers is presented. For the first time a multielement equivalent circuit is adopted to characterize the meander-line unit cell. This equivalent circuit significantly improves the bandwidth performance with respect to the state-of-the-art. In addition to this, a polynomial interpolation matrix approach is employed to take into account the dependence on the meander-line geometrical parameters. This leads to an accuracy comparable to that of a full-wave analysis. At the same time, the computational cost is minimized so as to make this model suitable for real-time tuning and fast optimizations. A four-layer polarizer is designed to validate the presented modeling procedure. Comparison with full-wave simulations confirms its high accuracy over a wide frequency range.
An open financial services architecture based on the use of intelligent mobile devices

The scope of this paper is to explore, analyze and develop a universal architecture that supports mobile payments and mobile banking, taking into consideration the third and the emerging fourth generation communication technologies. Interaction and cooperation between payment and banking systems, integration of existing technologies and exploitation of intelligent procedures provide the prospect to develop an open financial services architecture (OFSA), which satisfies requirements of all involved entities. A unified scenario is designed and a prototype is implemented to demonstrate the feasibility of the proposed architecture.
A Distributed Deep Representation Learning Model for Big Image Data Classification

This paper describes an effective and efficient image classification framework nominated distributed deep representation learning model (DDRL). The aim is to strike the balance between the computational intensive deep learning approaches (tuned parameters) which are intended for distributed computing, and the approaches that focused on the designed parameters but often limited by sequential computing and cannot scale up. In the evaluation of our approach, it is shown that DDRL is able to achieve state-of-art classification accuracy efficiently on both medium and large datasets. The result implies that our approach is more efficient than the conventional deep learning approaches, and can be applied to big data that is too complex for parameter designing focused approaches. More specifically, DDRL contains two main components, i.e., feature extraction and selection. A hierarchical distributed deep representation learning algorithm is designed to extract image statistics and a nonlinear mapping algorithm is used to map the inherent statistics into abstract features. Both algorithms are carefully designed to avoid millions of parameters tuning. This leads to a more compact solution for image classification of big data. We note that the proposed approach is designed to be friendly with parallel computing. It is generic and easy to be deployed to different distributed computing resources. In the experiments, the large∗Corresponding author.  13981763623;  Email address: ledong@uestc.edu.cn (Le Dong)  2016 scale image datasets are classified with a DDRM implementation on Hadoop MapReduce, which shows high scalability and resilience.
Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence

Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications. Perspective-n-Point (PnP) solvers are routinely used for camera pose estimation, provided that a good quality set of 2D-3D feature correspondences are known beforehand. However, finding optimal correspondences between 2D key-points and a 3D point-set is non-trivial, especially when only geometric (position) information is known. Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefore unlikely to find the optimal solution without a good pose initialisation, or introduce restrictive assumptions. Since a large proportion of outliers are common for this problem, we instead propose a globally-optimal inlier set cardinality maximisation approach which jointly estimates optimal camera pose and optimal correspondences. Our approach employs branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose prior. The geometry of SE(3) is used to find novel upper and lower bounds for the number of inliers and local optimisation is integrated to accelerate convergence. The evaluation empirically suppons the optimality proof and shows that the method performs much more robustly than existing approaches, including on a large-scale outdoor data-set.
Affordance detection for task-specific grasping using deep learning

In this paper we utilize the notion of affordances to model relations between task, object and a grasp to address the problem of task-specific robotic grasping. We use convolutional neural networks for encoding and detecting object affordances, class and orientation, which we utilize to formulate grasp constraints. Our approach applies to previously unseen objects from a fixed set of classes and facilitates reasoning about which tasks an object affords and how to grasp it for that task. We evaluate affordance detection on full-view and partial-view synthetic data and compute task-specific grasps for objects that belong to ten different classes and afford five different tasks. We demonstrate the feasibility of our approach by employing an optimization-based grasp planner to compute task-specific grasps.
Malicious Behavior Detection using Windows Audit Logs

As antivirus and network intrusion detection systems have increasingly proven insufficient to detect advanced threats, large security operations centers have moved to deploy endpoint-based sensors that provide deeper visibility into low-level events across their enterprises. Unfortunately, for many organizations in government and industry, the installation, maintenance, and resource requirements of these newer solutions pose barriers to adoption and are perceived as risks to organizations' missions. To mitigate this problem we investigated the utility of agentless detection of malicious endpoint behavior, using only the standard built-in Windows audit logging facility as our signal. We found that Windows audit logs, while emitting manageable sized data streams on the endpoints, provide enough information to allow robust detection of malicious behavior. Audit logs provide an effective, low-cost alternative to deploying additional expensive agent-based breach detection systems in many government and industrial settings, and can be used to detect, in our tests, 83% percent of malware samples with a 0.1% false positive rate. They can also supplement already existing host signature-based antivirus solutions, like Kaspersky, Symantec, and McAfee, detecting, in our testing environment, 78% of malware missed by those antivirus systems.
Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views

This paper presents an end-to-end convolutional neural network (CNN) for 2D-3D exemplar detection. We demonstrate that the ability to adapt the features of natural images to better align with those of CAD rendered views is critical to the success of our technique. We show that the adaptation can be learned by compositing rendered views of textured object models on natural images. Our approach can be naturally incorporated into a CNN detection pipeline and extends the accuracy and speed benefits from recent advances in deep learning to 2D-3D exemplar detection. We applied our method to two tasks: instance detection, where we evaluated on the IKEA dataset [36], and object category detection, where we out-perform Aubry et al.  for "chair" detection on a subset of the Pascal VOC dataset.
Evaluation of Musical Features for Emotion Classification

Because music conveys and evokes feelings, a wealth of research has been performed on music emotion recognition. Previous research has shown that musical mood is linked to features based on rhythm, timbre, spectrum and lyrics. For example, sad music correlates with slow tempo, while happy music is generally faster. However, only limited success has been obtained in learning automatic classifiers of emotion in music. In this paper, we collect a ground truth data set of 2904 songs that have been tagged with one of the four words “happy”, “sad”, “angry” and “relaxed”, on the Last.FM web site. An excerpt of the audio is then retrieved from 7Digital.com, and various sets of audio features are extracted using standard algorithms. Two classifiers are trained using support vector machines with the polynomial and radial basis function kernels, and these are tested with 10-fold cross validation. Our results show that spectral features outperform those based on rhythm, dynamics, and, to a lesser extent, harmony. We also find that the polynomial kernel gives better results than the radial basis function, and that the fusion of different feature sets does not always lead to improved classification.
IN DEEP GENERATIVE MODELS

Deep generative models provide a powerful and flexible means to learn complex distributions over data by incorporating neural networks into latent-variable models. Variational approaches to training such models introduce a probabilistic encoder that casts data, typically unsupervised, into an entangled representation space. While unsupervised learning is often desirable, sometimes even necessary, when we lack prior knowledge about what to represent, being able to incorporate domain knowledge in characterising certain aspects of variation in the data can often help learn better disentangled representations. Here, we introduce a new formulation of semi-supervised learning in variational autoencoders that allows precisely this. It permits flexible specification of probabilistic encoders as directed graphical models via a stochastic computation graph, containing both continuous and discrete latent variables, with conditional distributions parametrised by neural networks. We demonstrate how the provision of dependency structures, along with a few labelled examples indicating plausible values for some components of the latent space, can help quickly learn disentangled representations. We then evaluate its ability to do so, both qualitatively by exploring its generative capacity, and quantitatively by using the disentangled representation to perform classification, on a variety of models and datasets.
LinkNet: Exploiting encoder representations for efficient semantic segmentation

Pixel-wise semantic segmentation for visual scene understanding not only needs to be accurate, but also efficient in order to find any use in real-time application. Existing algorithms even though are accurate but they do not focus on utilizing the parameters of neural network efficiently. As a result they are huge in terms of parameters and number of operations; hence slow too. In this paper, we propose a novel deep neural network architecture which allows it to learn without any significant increase in number of parameters. Our network uses only 11.5 million parameters and 21.2 GFLOPs for processing an image of resolution 3 × 640 × 360. It gives state-of-the-art performance on CamVid and comparable results on Cityscapes dataset. We also compare our networks processing time on NVIDIA GPU and embedded system device with existing state-of-the-art architectures for different image resolutions.
Normalizing tweets with edit scripts and recurrent neural embeddings

Tweets often contain a large proportion of abbreviations, alternative spellings, novel words and other non-canonical language. These features are problematic for standard language analysis tools and it can be desirable to convert them to canonical form. We propose a novel text normalization model based on learning edit operations from labeled data while incorporating features induced from unlabeled data via character-level neural text embeddings. The text embeddings are generated using an Simple Recurrent Network. We find that enriching the feature set with text embeddings substantially lowers word error rates on an English tweet normalization dataset. Our model improves on stateof-the-art with little training data and without any lexical resources.
Mining Process Models from Workflow Logs

Modern enterprises increasingly use the work ow paradigm to prescribe how business processes should be performed. Processes are typically modeled as annotated activity graphs. We present an approach for a system that constructs process models from logs of past, unstructured executions of the given process. The graph so produced conforms to the dependencies and past executions present in the log. By providing models that capture the previous executions of the process, this technique allows easier introduction of a work ow system and evaluation and evolution of existing process models. We also present results from applying the algorithm to synthetic data sets as well as process logs obtained from an IBM Flowmark installation.
Modeling Musical Context with Word2vec

We present a semantic vector space model for capturing complex polyphonic musical context. A word2vec model based on a skip-gram representation with negative sampling was used to model slices of music from a dataset of Beethoven’s piano sonatas. A visualization of the reduced vector space using t-distributed stochastic neighbor embedding shows that the resulting embedded vector space captures tonal relationships, even without any explicit information about the musical contents of the slices. Secondly, an excerpt of the Moonlight Sonata from Beethoven was altered by replacing slices based on context similarity. The resulting music shows that the selected slice based on similar word2vec context also has a relatively short tonal distance from the original slice.
LegUp: An open-source high-level synthesis tool for FPGA-based processor/accelerator systems

It is generally accepted that a custom hardware implementation of a set of computations will provide superior speed and energy efficiency relative to a software implementation. However, the cost and difficulty of hardware design is often prohibitive, and consequently, a software approach is used for most applications. In this article, we introduce a new high-level synthesis tool called LegUp that allows software techniques to be used for hardware design. LegUp accepts a standard C program as input and automatically compiles the program to a hybrid architecture containing an FPGA-based MIPS soft processor and custom hardware accelerators that communicate through a standard bus interface. In the hybrid processor/accelerator architecture, program segments that are unsuitable for hardware implementation can execute in software on the processor. LegUp can synthesize most of the C language to hardware, including fixed-sized multidimensional arrays, structs, global variables, and pointer arithmetic. Results show that the tool produces hardware solutions of comparable quality to a commercial high-level synthesis tool. We also give results demonstrating the ability of the tool to explore the hardware/software codesign space by varying the amount of a program that runs in software versus hardware. LegUp, along with a set of benchmark C programs, is open source and freely downloadable, providing a powerful platform that can be leveraged for new research on a wide range of high-level synthesis topics.
Bayesian regression and Bitcoin

In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency. Bayesian regression refers to utilizing empirical data as proxy to perform Bayesian inference. We utilize Bayesian regression for the so-called “latent source model”. The Bayesian regression for “latent source model” was introduced and discussed by Chen, Nikolov and Shah  and Bresler, Chen and Shah  for the purpose of binary classification. They established theoretical as well as empirical efficacy of the method for the setting of binary classification. In this paper, instead we utilize it for predicting real-valued quantity, the price of Bitcoin. Based on this price prediction method, we devise a simple strategy for trading Bitcoin. The strategy is able to nearly double the investment in less than 60 day period when run against real data trace.
Named Entity Recognition Using Machine Learning Approaches

The term Named-entity recognition is a subtask of information extraction that seeks to locate and classify the named entities from the given text. This paper surveys about the different approaches and techniques that are used for Named Entity Recognition, which identifies the named entities from the given data. It explains the different machine learning models that are used in the Named Entity Recognition. The performances of various Named Entity Recognition systems that are developed using machine learning models are reviewed. It also enumerates the applications of Named Entity Recognition in various fields and the challenges faced by them.
Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer

In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10−8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10−14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10−10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10−8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10−8). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene. Genetic variants associated with susceptibility to pancreatic cancer have been identified using genome wide association studies (GWAS). Here, the authors combine data from over 9000 patients and perform a meta-analysis to identify five novel loci linked to pancreatic cancer.
The Sybil Attack

Large-scale peer-to-peer systems face security threats from faulty or hostile remote computing elements. To resist these threats, many such systems employ redundancy. However, if a single faulty entity can present multiple identities, it can control a substantial fraction of the system, thereby undermining this redundancy. One approach to preventing these “Sybil attacks” is to have a trusted agency certify identities. This paper shows that, without a logically centralized authority, Sybil attacks are always possible except under extreme and unrealistic assumptions of resource parity and coordination among entities.
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation

Recommender systems objectives can be broadly characterized as modeling user preferences over shortor long-term time horizon. A large body of previous research studied long-term recommendation through dimensionality reduction techniques applied to the historical user-item interactions. A recently introduced session-based recommendation setting highlighted the importance of modeling short-term user preferences. In this task, Recurrent Neural Networks (RNN) have shown to be successful at capturing the nuances of user’s interactions within a short time window. In this paper, we evaluate RNN-based models on both short-term and long-term recommendation tasks. Our experimental results suggest that RNNs are capable of predicting immediate as well as distant user interactions. We also find the best performing configuration to be a stacked RNN with layer normalization and tied item embeddings.
Knowledge Base Completion via Coupled Path Ranking

Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. The path ranking algorithm (PRA) is one of the most promising approaches to this task. Previous work on PRA usually follows a single-task learning paradigm, building a prediction model for each relation independently with its own training data. It ignores meaningful associations among certain relations, and might not get enough training data for less frequent relations. This paper proposes a novel multi-task learning framework for PRA, referred to as coupled PRA (CPRA). It first devises an agglomerative clustering strategy to automatically discover relations that are highly correlated to each other, and then employs a multi-task learning strategy to effectively couple the prediction of such relations. As such, CPRA takes into account relation association and enables implicit data sharing among them. We empirically evaluate CPRA on benchmark data created from Freebase. Experimental results show that CPRA can effectively identify coherent clusters in which relations are highly correlated. By further coupling such relations, CPRA significantly outperforms PRA, in terms of both predictive accuracy and model interpretability.
Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs

Conversational participants tend to immediately and unconsciously adapt to each other’s language styles: a speaker will even adjust the number of articles and other function words in their next utterance in response to the number in their partner’s immediately preceding utterance. This striking level of coordination is thought to have arisen as a way to achieve social goals, such as gaining approval or emphasizing difference in status. But has the adaptation mechanism become so deeply embedded in the language-generation process as to become a reflex? We argue that fictional dialogs offer a way to study this question, since authors create the conversations but don’t receive the social benefits (rather, the imagined characters do). Indeed, we find significant coordination across many families of function words in our large movie-script corpus. We also report suggestive preliminary findings on the effects of gender and other features; e.g., surprisingly, for articles, on average, characters adapt more to females than to males.
Midge: Generating Image Descriptions From Computer Vision Detections

This paper introduces a novel generation system that composes humanlike descriptions of images from computer vision detections. By leveraging syntactically informed word co-occurrence statistics, the generator filters and constrains the noisy detections output from a vision system to generate syntactic trees that detail what the computer vision system sees. Results show that the generation system outperforms state-of-the-art systems, automatically generating some of the most natural image descriptions to date.