nils-herrmann/clean_scirepeval huggingface dataset:
Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input and output . MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on ImageNet  classification, COCO object detection , VOC image segmentation . We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameters.
Object / Defect Removal via Single-image Super-resolution on NLM-priority-based Inpainting and Sparse Coding

Object removal is a topic highly involved in a wide range of image reconstruction applications such as restoration of corrupted or defected images, scene reconstruction, and film post-production. In recent years, there have been many efforts in the industry and academia to develop better algorithms for this subject. This paper discusses some of the recent work and various techniques currently adopted in this field and presents our algorithmic design that enhance the existing pixel-filling framework, and our post-inpaint refinement steps. This paper will further layout the implementation details and experimental results of our algorithm, on a mixture of images from both the standard image processing study papers and our own photo library. Results from our proposed methods will be evaluated and compared to the previous works in academia and other state-of-the-art approaches, with elaboration on the advantages and disadvantages. This paper will conclude with discussing some of the challenges encountered during the design and experiment phases and proposing potential steps to take in the future.
A Simple and Practical Approach to Unit Testing: The JML and JUnit Way

Writing unit test code is labor-intensive, hence it is often not done as an integral part of programming. However, unit testing is a practical approach to increasing the correctness and quality of software; for example, the Extreme Programming approach relies on frequent unit testing. In this paper we present a new approach that makes writing unit tests easier. It uses a formal specification language’s runtime assertion checker to decide whether methods are working correctly, thus automating the writing of unit test oracles. These oracles can be easily combined with hand-written test data. Instead of writing testing code, the programmer writes formal specifications (e.g., preand postconditions). This makes the programmer’s task easier, because specifications are more concise and abstract than the equivalent test code, and hence more readable and maintainable. Furthermore, by using specifications in testing, specification errors are quickly discovered, so the specifications are more likely to provide useful documentation and inputs to other tools. We have implemented this idea using the Java Modeling Language (JML) and the JUnit testing framework, but the approach could be easily implemented with other combinations of formal specification languages and unit test tools.
Dependency Parsing with Dilated Iterated Graph CNNs

Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale. Recent advances in GPU hardware have enabled neural networks to achieve significant gains over the previous best models, these models still fail to leverage GPUs’ capability for massive parallelism due to their requirement of sequential processing of the sentence. In response, we propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for graphbased dependency parsing, a graph convolutional architecture that allows for efficient end-to-end GPU parsing. In experiments on the English Penn TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best neural network parsers.
A three-level approach to the semantics of space

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. A three-level approach to the semantics of space Michel Aurnague, Laure Vieu
The Mond Paradigm

I review briefly different aspects of the MOND paradigm, with emphasis on phenomenology, epitomized here by many MOND laws of galactic motion–analogous to Kepler's laws of planetary motion. I then comment on the possible roots of MOND in cosmology, possibly the deepest and most far reaching aspect of MOND. This is followed by a succinct account of existing underlying theories. I also reflect on the implications of MOND's successes for the dark matter (DM) paradigm: MOND predictions imply that baryons alone accurately determine the full field of each and every individual galactic object. This conflicts with the expectations in the DM paradigm because of the haphazard formation and evolution of galactic objects and the very different influences that baryons and DM are subject to during the evolution, as evidenced, e.g., by the very small baryon-to-DM fraction in galaxies (compared with the cosmic value). All this should disabuse DM advocates of the thought that DM will someday be able to reproduce MOND: it is inconceivable that the modicum of baryons left over in galaxies can be made to determine everything if a much heavier DM component is present.
Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model

In this paper we introduce a novel single-document summarization method based on a hidden semi-Markov model. This model can naturally model single-document summarization as the optimization problem of selecting the best sequence from among the sentences in the input document under the given objective function and knapsack constraint. This advantage makes it possible for sentence selection to take the coherence of the summary into account. In addition our model can also incorporate sentence compression into the summarization process. To demonstrate the effectiveness of our method, we conduct an experimental evaluation with a large-scale corpus consisting of 12,748 pairs of a document and its reference. The results show that our method significantly outperforms the competitive baselines in terms of ROUGE evaluation, and the linguistic quality of summaries is also improved. Our method successfully mimicked the reference summaries, about 20 percent of the summaries generated by our method were completely identical to their references. Moreover, we show that large-scale training samples are quite effective for training a summarizer.
A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations

We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model’s ability to selectively focus on the relevant parts of an input sequence.
Artistic Style Transfer

We have shown that it is possible to achieve artistic style transfer within a purely image processing paradigm. This is in contrast to previous work that utilized deep neural networks to learn the difference between “style” and “content” in a painting. We leverage the work by Kwatra et. al. on texture synthesis to accomplish “style synthesis” from our given style images, building off the work of Elad and Milanfar. We have also introduced a novel “style fusion” concept that guides the algorithm to follow broader structures of style at a higher level while giving it the freedom to make its own artistic decisions at a smaller scale. Our results are comparable to the neural network approach, while improving speed and maintaining robustness to different styles and contents.
INITIAL DEGRADATION OF INDUSTRIAL SILICON SOLAR CELLS IN SOLAR PANELS

In the lifetime of a solar panel, efficiency is degrading continually because panel components are ageing during outdoor exposure (OE). This degradation is m ainly due to humidity, temperature, system bias eff ects and solar irradiation. The solar cell itself may be suffering different degradation mechanisms like light, tempe rature and potential induced degradation (LID, TID and PID ). The foc us of this paper is the initial degradation of sola r cells within the first hours of operation, which is generally associ ated with LID. In this work, differences in degrada tion mechanism for multi and mono cells are investigated for cells and panels based on p-type crystalline silicon. The qu ality of silicon material is essential as shown in a material compar ison. Low and high base resistivities are investiga ted and different silicon purities ranging from standard feedstock ma teri l of different qualities to material based on upgraded metallurgical (UMG) silicon are compared. Interesti ngly no clear difference between the LID of industr ial multiand mono-crystalline cells was found. However, UMG cell s show a higher degradation rate partly due to a me chanism identified as temperature induced degradation (TID) that occurs in parallel to LID.
Phantom Types and Subtyping

We investigate a technique from the literature, called the phantom-types technique, that uses parametric polymorphism, type constraints, and unification of polymorphic types to model a subtyping hierarchy. Hindley-Milner type systems, such as the one found in Standard ML, can be used to enforce the subtyping relation, at least for first-order values. We show that this technique can be used to encode any finite subtyping hierarchy (including hierarchies arising from multiple interface inheritance). We formally demonstrate the suitability of the phantom-types technique for capturing first-order subtyping by exhibiting a type-preserving translation from a simple calculus with bounded polymorphism to a calculus embodying the type system of SML.
A CONSTRAINED GROWTH METHOD FOR PROCEDURAL FLOOR PLAN GENERATION

Modern games often feature highly detailed urban environments. However, buildings are typically represented only by their façade, because of the excessive costs it would entail to manually model all interiors. Although automated procedural techniques for building interiors exist, their application is to date limited. One of the reasons for this is because designers have too little control over the resulting room topology. Also, generated floor plans do not always adhere to the necessary consistency constraints, such as reachability and connectivity. In this paper, we propose a novel and flexible technique for generating procedural floor plans subject to user-defined constraints. We demonstrate its versatility, by showing generated floor plans for different classes of buildings, some of which consist of several connected floors. It is concluded that this method results in plausible floor plans, over which a designer has control by defining functional constraints. Furthermore, the method is efficient and easy to implement and integrate into the larger context of procedural modeling of urban environments.
Wired Equivalent Privacy (WEP) versus Wi-Fi Protected Access (WPA)

Wireless technology has been gaining rapid popularity for some years. Adaptation of a standard depends on the ease of use and level of security it provides. In this case, contrast between wireless usage and security standards show that the security is not keeping up with the growth paste of end user’s usage. Current wireless technologies in use allow hackers to monitor and even change the integrity of transmitted data. Lack of rigid security standards has caused companies to invest millions on securing their wireless networks. There are three major types of security standards in wireless. In our previous paper which was presented in ICFCC2009 Conference in Kuala Lumpur and published by IEEE Computer Society , we explained the structure of WEP as a first wireless security standard and discussed all its versions, problems and improvements. Now, we try to explain all of WPA versions and problems with the best solutions and finally make a comparison between WEP and WPA. Then we are in the next phase which is to explain the structure of last standard (WPA2) and we hope that we will publish a complete comparison among wireless security techniques in the near future and recommend a new proposal as a new protocol.
Redesigning biodiversity conservation projects for climate change: examples from the field

Few conservation projects consider climate impacts or have a process for developing adaptation strategies. To advance climate adaptation for biodiversity conservation, we tested a step-by-step approach to developing adaptation strategies with 20 projects from diverse geographies. Project teams assessed likely climate impacts using historical climate data, future climate predictions, expert input, and scientific literature. They then developed adaptation strategies that considered ecosystems and species of concern, project goals, climate impacts, and indicators of progress. Project teams identified 176 likely climate impacts and developed adaptation strategies to address 42 of these impacts. The most common impacts were to habitat quantity or quality, and to hydrologic regimes. Nearly half of expected impacts were temperature-mediated. Twelve projects indicated that the project focus, either focal ecosystems and species or project boundaries, need to change as a result of considering climate impacts. More than half of the adaptation strategies were resistance strategies aimed at preserving the status quo. The rest aimed to make ecosystems and species more resilient in the face of expected changes. All projects altered strategies in some way, either by adding new actions, or by adjusting existing actions. Habitat restoration and enactment of policies and regulations were the most frequently prescribed, though every adaptation strategy required a unique combination of actions. While the effectiveness of these adaptation strategies remains to be evaluated, the application of consistent guidance has yielded important early lessons about how, when, and how often conservation projects may need to be modified to adapt to climate change.
Facial feature point detection: A comprehensive survey

This paper presents a comprehensive survey of facial feature point detection with the assistance of abundant manually labeled images. Facial feature point detection favors many applications such as face recognition, animation, tracking, hallucination, expression analysis and 3D face modeling. Existing methods can be categorized into the following four groups: constrained local model (CLM)-based, active appearance model (AAM)-based, regression-based, and other methods. CLM-based methods consist of a shape model and a number of local experts, each of which is utilized to detect a facial feature point. AAM-based methods fit a shape model to an image by minimizing texture synthesis errors. Regression-based methods directly learn a mapping function from facial image appearance to facial feature points. Besides the above three major categories of methods, there are also minor categories of methods which we classify into other methods: graphN. Wang VIPS Lab, School of Electronic Engineering, Xidian University, 710071, Xi’an, P. R. China E-mail: nannanwang.xidian@gamil.com X. Gao VIPS Lab, School of Electronic Engineering, Xidian University, 710071, Xi’an, P. R. China E-mail: xbgao@mail.xidian.edu.cn D. Tao Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering & Information Technology, University of Technology, Sydney, 235 Jones Street, Ultimo, NSW 2007, Australia E-mail: Dacheng.Tao@uts.edu.au X. Li School of Computer Science and Information Systems, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, U.K. E-mail: xuelong@dcs.bbk.ac.uk ical model-based methods, joint face alignment methods, independent facial feature point detectors, and deep learning-based methods. Though significant progress has been made, facial feature point detection is limited in its success by wild and real-world conditions: variations across poses, expressions, illuminations, and occlusions. A comparative illustration and analysis of representative methods provide us a holistic understanding and deep insight into facial feature point detection, which also motivates us to explore promising future directions.
A Recurrent Neural Network that Learns to Count

Parallel distributed processing (PDP) architectures demonstrate a potentially radical alternative to the traditional theories of language processing that are based on serial computational models. However, learning complex structural relationships in temporal data presents a serious challenge to PDP systems. For example, automata theory dictates that processing strings from a context-free language (CFL) requires a stack or counter memory device. While some PDP models have been hand-crafted to emulate such a device, it is not clear how a neural network might develop such a device when learning a CFL. This research employs standard backpropagation training techniques for a recurrent neural network (RNN) in the task of learning to predict the next character in a simple deterministic CFL (DCFL). We show that an RNN can learn to recognize the structure of a simple DCFL. We use dynamical systems theory to identify how network states re ̄ ect that structure by building counters in phase space. The work is an empirical investigation which is complementary to theoretical analyses of network capabilities, yet original in its speci ® c con® guration of dynamics involved. The application of dynamical systems theory helps us relate the simulation results to theoretical results, and the learning task enables us to highlight some issues for understanding dynamical systems that process language with counters.
Classification algorithm in Data mining : An Overview

Mining is a technique used in various domains to give meaning to the available data Classification is a data mining (machine learning) technique used to predict group membership for data instances. In this paper, we present the basic classification techniques. Several major kinds of classification method including decision tree, Bayesian networks, k-nearest neighbour classifier, Neural Network, Support vector machine. The goal of this paper is to provide a review of different classification techniques in data mining.  Data mining, classification, Supper vector machine (SVM), K-nearest neighbour (KNN), Decision Tree.
Improving Language Modeling using Densely Connected Recurrent Neural Networks

In this paper, we introduce the novel concept of densely connected layers into recurrent neural networks. We evaluate our proposed architecture on the Penn Treebank language modeling task. We show that we can obtain similar perplexity scores with six times fewer parameters compared to a standard stacked 2layer LSTM model trained with dropout (Zaremba et al., 2014). In contrast with the current usage of skip connections, we show that densely connecting only a few stacked layers with skip connections already yields significant perplexity reductions.
Human Communication in Collaborative Augmented Reality Systems

The main goal of this chapter is to give characteristics, evaluation methodologies, and research examples of collaborative augmented reality (AR) systems from a perspective of human-to-human communication. The chapter introduces classifications of conventional and 3D collaborative systems as well as typical characteristics and application examples of collaborative AR systems. Next, it discusses design considerations of collaborative AR systems from a perspective of human communication and then discusses evaluation methodologies of human communication behaviors. The next section discusses a variety of collaborative AR systems with regard to display devices used. Finally, the chapter gives conclusion with future directions. This will be a good starting point to learn existing collaborative AR systems, their advantages and limitations. This chapter will also contribute to the selection of appropriate hardware configurations and software designs of a collaborative AR system for given conditions.
Blockchain and IoT Integration: A Systematic Survey

The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes IoT vulnerable to privacy and security threats. With its "security by design," Blockchain (BC) can help in addressing major security requirements in IoT. BC capabilities like immutability, transparency, auditability, data encryption and operational resilience can help solve most architectural shortcomings of IoT. This article presents a comprehensive survey on BC and IoT integration. The objective of this paper is to analyze the current research trends on the usage of BC-related approaches and technologies in an IoT context. This paper presents the following novelties, with respect to related work: (i) it covers different application domains, organizing the available literature according to this categorization, (ii) it introduces two usage patterns, i.e., device manipulation and data management (open marketplace solution), and (iii) it reports on the development level of some of the presented solutions. We also analyze the main challenges faced by the research community in the smooth integration of BC and IoT, and point out the main open issues and future research directions. Last but not least, we also present a survey about novel uses of BC in the machine economy.
Contrastive-center loss for deep neural networks

The deep convolutional neural network(CNN) has significantly raised the performance of image classification and face recognition. Softmax is usually used as supervision, but it only penalizes the classification loss. In this paper, we propose a novel auxiliary supervision signal called contrastive-center loss, which can further enhance the discriminative power of the features, for it learns a class center for each class. The proposed contrastive-center loss simultaneously considers intra-class compactness and inter-class separability, by penalizing the contrastive values between: (1)the distances of training samples to their corresponding class centers, and (2)the sum of the distances of training samples to their non-corresponding class centers. Experiments on different datasets demonstrate the effectiveness of contrastive-center loss.