nils-herrmann/clean_scirepeval huggingface dataset:
CRNN: A Joint Neural Network for Redundancy Detection

This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character- aware convolutional neural network (Char-CNN) with character-aware recurrent neural network (Char-RNN) to form a convolutional recurrent neural network (CRNN). Our model benefits from Char-CNN in that only salient features are selected and fed into the integrated Char-RNN. Char-RNN effectively learns long sequence semantics via sophisticated update mechanism. We compare our framework against the state-of-the- art text classification algorithms on four popular benchmarking corpus. For instance, our model achieves competing precision rate, recall ratio, and F1 score on the Google-news data-set. For twenty- news-groups data stream, our algorithm obtains the optimum on precision rate, recall ratio, and F1 score. For Brown Corpus, our framework obtains the best F1 score and almost equivalent precision rate and recall ratio over the top competitor. For the question classification collection, CRNN produces the optimal recall rate and F1 score and comparable precision rate. We also analyse three different RNN hidden recurrent cells' impact on performance and their runtime efficiency. We observe that MGU achieves the optimal runtime and comparable performance against GRU and LSTM. For TFIDF based algorithms, we experiment with word2vec, GloVe, and sent2vec embeddings and report their performance differences.
7 Probabilistic Entity-Relationship Models , PRMs , and Plate Models

In this chapter, we introduce a graphical language for relational data called the probabilistic entity-relationship (PER) model. The model is an extension of the entity-relationship model, a common model for the abstract representation of database structure. We concentrate on the directed version of this model—the directed acyclic probabilistic entity-relationship (DAPER) model. The DAPER model is closely related to the plate model and the probabilistic relational model (PRM), existing models for relational data. The DAPER model is more expressive than either existing model, and also helps to demonstrate their similarity. In addition to describing the new language, we discuss important facets of modeling relational data, including the use of restricted relationships, self relationships, and probabilistic relationships. Many examples are provided.
Realistic Adversarial Examples in 3D Meshes

Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications and achieved increasing success. However, recent studies show that such machine learning models appear to be vulnerable against adversarial examples. So far adversarial examples have been heavily explored for 2D images, while few works have conducted to understand vulnerabilities of 3D objects which exist in real world, where 3D objects are projected to 2D domains by photo taking for different learning (recognition) tasks. In this paper we consider adversarial behaviors in practical scenarios by manipulating the shape and texture of a given 3D mesh representation of an object. Our goal is to project the optimized “adversarial meshes" to 2D with a photorealistic renderer, and still able to mislead different machine learning models. Extensive experiments show that by generating unnoticeable 3D adversarial perturbation on shape or texture for a 3D mesh, the corresponding projected 2D instance can either lead classifiers to misclassify the victim object as an arbitrary malicious target, or hide any target object within the scene from object detectors. We conduct human studies to show that our optimized adversarial 3D perturbation is highly unnoticeable for human vision systems. In addition to the subtle perturbation for a given 3D mesh, we also propose to synthesize a realistic 3D mesh and put in a scene mimicking similar rendering conditions and therefore attack different machine learning models. In-depth analysis of transferability among various 3D renderers and vulnerable regions of meshes are provided to help better understand adversarial behaviors in real-world.
A Crowdsourcing Approach to Collecting Tutorial Videos - Toward Personalized Learning-at-Scale

We investigated the feasibility of crowdsourcing full- fledged tutorial videos from ordinary people on the Web on how to solve math problems related to logarithms. This kind of approach (a form of learnersourcing [9, 11]) to efficiently collecting tutorial videos and other learning resources could be useful for realizing personalized learning-at-scale, whereby students receive specific learning resources -- drawn from a large and diverse set -- that are tailored to their individual and time-varying needs. Results of our study, in which we collected 399 videos from 66 unique "teachers" on Mechanical Turk, suggest that (1) approximately 100 videos -- over 80% of which are mathematically fully correct -- can be crowdsourced per week for $5/video; (2) the average learning gains (posttest minus pretest score) associated with watching the videos was stat. sig. higher than for a control video (0.105 versus 0.045); and (3) the average learning gains (0.1416) from watching the best tested crowdsourced videos was comparable to the learning gains (0.1506) from watching a popular Khan Academy video on logarithms.
Sex affects immunity.

Sex based differences in immune responses, affecting both the innate and adaptive immune responses, contribute to differences in the pathogenesis of infectious diseases in males and females, the response to viral vaccines and the prevalence of autoimmune diseases. Indeed, females have a lower burden of bacterial, viral and parasitic infections, most evident during their reproductive years. Conversely, females have a higher prevalence of a number of autoimmune diseases, including Sjogren's syndrome, systemic lupus erythematosus (SLE), scleroderma, rheumatoid arthritis (RA) and multiple sclerosis (MS). These observations suggest that gonadal hormones may have a role in this sex differential. The fundamental differences in the immune systems of males and females are attributed not only to differences in sex hormones, but are related to X chromosome gene contributions and the effects of environmental factors. A comprehensive understanding of the role that sex plays in the immune response is required for therapeutic intervention strategies against infections and the development of appropriate and effective therapies for autoimmune diseases for both males and females. This review will focus on the differences between male and female immune responses in terms of innate and adaptive immunity, and the effects of sex hormones in SLE, MS and RA.
Pectus carinatum.

Pectus carinatum or keel chest is a spectrum of progressive inborn anomalies of the anterior chest wall, named after the keel (carina) of ancient Roman ships. It defines a wide spectrum of inborn protrusion anomalies of the sternum and/or the adjacent costal cartilages. Pectus carinatum is often associated with various conditions, notably Marfan disease, homocystinuria, prune belly, Morquio syndrome, osteogenesis imperfecta, Noonan syndrome, and mitral valve prolapse. Treatment of pectus carinatum by nonsurgical methods such as exercise and casting has not been worthwhile, whereas surgical management is simple and successful.
Twelve tips for facilitating team-based learning

Team-based learning (TBL) has become a more commonly recognized and implemented pedagogical approach in curricula of numerous disciplines. The desire to place more autonomy on the student and spend less in-class time delivering content has resulted in complete or partial adoption of this style of learning in many educational settings.



Provide faculty with tools that foster a well facilitated and interactive TBL learning environment.



We examined the published literature in the area of facilitation - specifically in TBL environments, and explored learning theories associated with team learning and our own experiences to create these facilitation tips.



We created 12 tips for TBL facilitation designed to assist faculty to achieve an effective and engaging TBL learning environment.



Applying these twelve tips while facilitating a TBL classroom session will help to ensure maximal participation and optimal learning in a safe yet stimulating environment.
INTEGRATING CONVENTIONAL ERP SYSTEM WITH CLOUD SERVICES : FROM THE PERSPECTIVE OF CLOUD SERVICE TYPE

It is proved that cloud computing has many benefits, and cloud services with flexible licensing model and cost saving features bring opportunities to enterprise's IT operations. Conventional ERP system, an important component of enterprises, is impacted by on-demand cloud services also. Integrating conventional ERP system with cloud services becomes a trend because it brings new processing capabilities without introducing significant changes to existing system. But previous studies about integrating ERP and cloud usually focused on SaaS (Software as a Service). PaaS (Platform as a Service) and IaaS (Infrastructure as a Service) are not discussed much. People sometimes even confuse cloud service with SaaS. Therefore, the objective of this paper is to present a framework that can describe respective implications of integrating ERP with three types of cloud services: SaaS, PaaS and IaaS. Qualitative methods such as systematic literature review and interviews are adopted to execute the research, so data collected through different sources can complement each other in order to support presenting the framework of integrating conventional ERP system and cloud services. The integration at SaaS level is for achieving immediate business value and productivity enhancement. At PaaS level the objective of integration is to enhance software development life cycle management. And the main integrating intent at IaaS level is to enable scalability and reliability of hardware resources without changing existing IT infrastructure. Furthermore, challenges and opportunities for integrating ERP with different cloud services are studied and motivation is derived from analyzing. Finally the key points are arranged in the presented framework of integration between conventional ERP systems and cloud services.
A hybrid solution for privacy preserving medical data sharing in the cloud environment

Storing and sharing of medical data in the cloud environment, where computing resources including storage is provided by a third party service provider, raise serious concern of individual privacy for the adoption of cloud computing technologies. Existing privacy protection researches can be classified into three categories, i.e., privacy by policy, privacy by statistics, and privacy by cryptography. However, the privacy concerns and data utilization requirements on different parts of the medical data may be quite different. The solution for medical dataset sharing in the cloud should support multiple data accessing paradigms with different privacy strengths. The statistics or cryptography technology a multiple privacy demands, which blocks their application in the real-world cloud. This paper proposes a practical solution for privacy preserving medical record sharing for cloud computing. Based on the classification of the attributes of medical records, we use vertical partition of medical dataset to achieve the consideration of different parts of medical data with different privacy concerns. It mainly includes four components, i.e., (1) vertical data partition for medical data publishing, (2) data merging for mecial dataset accessing, (3) integrity checking, and (4) hybrid search across plaintext and ciphertext, where the statistical analysis and cryptography are innovatively combined together to provide multiple paradigms of balance between medical data utilization and privacy protection. A prototype system for the large scale medical data access and sharing is implemented. Extensive experiments show the effectiveness of our proposed solution. K eywords: privacy protection, cloud storage, integrity check, medical data sharing.
Flash storage disaggregation

PCIe-based Flash is commonly deployed to provide datacenter applications with high IO rates. However, its capacity and bandwidth are often underutilized as it is difficult to design servers with the right balance of CPU, memory and Flash resources over time and for multiple applications. This work examines Flash disaggregation as a way to deal with Flash overprovisioning. We tune remote access to Flash over commodity networks and analyze its impact on workloads sampled from real datacenter applications. We show that, while remote Flash access introduces a 20% throughput drop at the application level, disaggregation allows us to make up for these overheads through resource-efficient scale-out. Hence, we show that Flash disaggregation allows scaling CPU and Flash resources independently in a cost effective manner. We use our analysis to draw conclusions about data and control plane issues in remote storage.
Delay Minimization for Relay-Based Cooperative Data Exchange With Network Coding

We study the Relay-based Cooperative Data Exchange (RCDE) problem, where initially each client has access to a subset of a set of n original packets, referred to as their side information, and wants to retrieve all other original packets via co-operation. Unlike traditional Cooperative Data Exchange (CDE), in our proposed relay-based model, clients can only cooperate via a relay. The data exchange is completed over two phases, namely Uploading Phase and Downloading Phase. In the Uploading Phase, the clients will encode the original packets and transmit the coded packets to the relay. In the Downloading Phase, the relay will reencode the received packets and multicast the reencoded packets, each to a subgroup of clients. The coded packets in the two phases are carefully selected so that each client can retrieve all original packets with minimum total transmission delay, based on its initial side information and on the coded packets it receives from the relay. In addition, we assume that the bandwidths between the relay and different clients are different, and that the upload/download bandwidths between the relay and the same client are also different. We establish a coding scheme that has the minimum total delay and show that it can be found in polynomial time, for sufficiently large underlying fields. We also design a heuristic algorithm that has a low complexity with binary field size. Our simulations show that the performance of the binary solution is very close to that of the optimal solution. All coding schemes considered in this work are scalar.
Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects

by Dimitrios Tzionas for the degree of Doctor rerum naturalium Hand motion capture with an RGB-D sensor gained recently a lot of research attention, however, even most recent approaches focus on the case of a single isolated hand. We focus instead on hands that interact with other hands or with a rigid or articulated object. Our framework successfully captures motion in such scenarios by combining a generative model with discriminatively trained salient points, collision detection and physics simulation to achieve a low tracking error with physically plausible poses. All components are unified in a single objective function that can be optimized with standard optimization techniques. We initially assume a-priori knowledge of the object’s shape and skeleton. In case of unknown object shape there are existing 3d reconstruction methods that capitalize on distinctive geometric or texture features. These methods though fail for textureless and highly symmetric objects like household articles, mechanical parts or toys. We show that extracting 3d hand motion for in-hand scanning e↵ectively facilitates the reconstruction of such objects and we fuse the rich additional information of hands into a 3d reconstruction pipeline. Finally, although shape reconstruction is enough for rigid objects, there is a lack of tools that build rigged models of articulated objects that deform realistically using RGB-D data. We propose a method that creates a fully rigged model consisting of a watertight mesh, embedded skeleton and skinning weights by employing a combination of deformable mesh tracking, motion segmentation based on spectral clustering and skeletonization based on mean curvature flow. To my family Maria, Konstantinos, Glyka. In the loving memory of giagià 'Olga & pappo‘c Giànnhc. ( πste ô yuqò πsper ô qe–r ‚stin· ka» gÄr ô qe»r ÓrganÏn ‚stin Êrgànwn, ka» Â no‹c e⁄doc e d¿n ka» ô a“sjhsic e⁄doc a sjht¿n.
A Novel Broadband Band-pass Filter Based on Spoof Surface Plasmon Polaritons

In this paper, we present a novel broadband bandpass filter based on spoof surface plasmon polaritons (SSPPs) in the microwave frequency band. The proposed bandpass filter includes three parts: (1) coplanar waveguide (CPW); (2) matching transition; and (3) coupled structure that is an asymmetric coupled filter constructed by five grooved strips. The proposed bandpass filter realizes excellent low loss performance from 7 to 10 GHz, in which its insertion loss is around 1.5 dB in the same frequency band. Meanwhile, this filter has a good band stop characteristic from 3 to 7 GHz. A simple but accurate transmission line model was proposed to evaluate the proposed broadband SSPPs filter. The measured data, simulated results and the results obtained from the transmission line model have shown a very good agreement. The proposed planar broadband filter plays an important role for filtering surface plasmon polaritons (SPPs) waves in plasmonic circuits and systems.
Modeling and Rendering Architecture from Photographs: A Hybrid Geometry- and Image-Based Approach

We present an approach for creating realistic synthetic views of existing architectural scenes from a sparse set of still photographs. Our approach, which combines both geometrybased and image-based modeling and rendering techniques, has two components. The rst component is an easy-to-use photogrammetric modeling system which facilitates the recovery of a basic geometric model of the photographed scene. The modeling system is e ective and robust because it exploits the constraints that are characteristic of architectural scenes. The second component is a model-based stereo algorithm, which recovers how the real scene deviates from the basic model. By making use of the model, our stereo approach can robustly recover accurate depth from image pairs with large baselines. Consequently, our approach can model large architectural environments with far fewer photographs than current imagebased modeling approaches. As an intermediate result, we present view-dependent texture mapping, a method of better simulating geometric detail on basic models. Our approach can recover models for use in either geometry-based or image-based rendering systems. We present results that demonstrate our approach's abilty to create realistic renderings of architectural scenes from viewpoints far from the original photographs.
An assessment of systems and software engineering scholars and institutions (2000-2004)

An ongoing, annual survey of publications in systems and software engineering identifies the top 15 scholars and institutions in the field over a 5-year period. Each ranking is based on the weighted scores of the number of papers published in TSE, TOSEM, JSS, SPE, EMSE, IST, and Software of the corresponding period. This report summarizes the results for 2003–2007 and 2004–2008. The top-ranked institution is Korea Advanced Institute of Science and Technology, Korea for 2003–2007, and Simula Research Laboratory, Norway for 2004–2008, while Magne Jørgensen is the top-ranked scholar for both periods.
Mediated Population Protocols: Leader Election and Applications

Mediated population protocols are an extension of population protocols in which communication links, as well as agents, have internal states. We study the leader election problem and some applications in constant-state mediated population protocols. Depending on the power of the adversarial scheduler, our algorithms are either stabilizing or allow the agents to explicitly reach a terminal state. We show how to elect a unique leader if the graph of the possible interactions between agents is complete (as in the traditional population protocol model) or a tree. Moreover, we prove that a leader can be elected in a complete bipartite graph if and only if the two sides have coprime size. We then describe how to take advantage the presence of a leader to solve the tasks of token circulation and construction of a shortest-path spanning tree of the network. Finally, we prove that with a leader we can transform any stabilizing protocol into a terminating one that solves the
Hybrid differential evolution based on fuzzy C-means clustering

In this paper, we propose a hybrid Differential Evolution (DE) algorithm based on the fuzzy C-means clustering algorithm, referred to as FCDE. The fuzzy C-means clustering algorithm is incorporated with DE to utilize the information of the population efficiently, and hence it can generate good solutions and enhance the performance of the original DE. In addition, the population-based algorithmgenerator is adopted to efficiently update the population with the clustering offspring. In order to test the performance of our approach, 13 high-dimensional benchmark functions of diverse complexities are employed. The results show that our approach is effective and efficient. Compared with other state-of-the-art DE approaches, our approach performs better, or at least comparably, in terms of the quality of the final solutions and the reduction of the number of fitness function evaluations (NFFEs).
SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization

The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. spot includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging) and combinations of different metamodeling approaches. This article exemplifies how spot can be used for automatic and interactive tuning.