nils-herrmann/clean_scirepeval huggingface dataset:
Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification

This paper presents a new version of Dropout called Split Dropout (sDropout) and rotational convolution techniques to improve CNNs’ performance on image classification. The widely used standard Dropout has advantage of preventing deep neural networks from overfitting by randomly dropping units during training. Our sDropout randomly splits the data into two subsets and keeps both rather than discards one subset. We also introduce two rotational convolution techniques, i.e. rotate-pooling convolution (RPC) and flip-rotate-pooling convolution (FRPC) to boost CNNs’ performance on the robustness for rotation transformation. These two techniques encode rotation invariance into the network without adding extra parameters. Experimental evaluations on ImageNet2012 classification task demonstrate that sDropout not only enhances the performance but also converges faster. Additionally, RPC and FRPC make CNNs more robust for rotation transformations. Overall, FRPC together with sDropout bring 1.18% (model of Zeiler and Fergus [24], 10-view, top-1) accuracy increase in ImageNet 2012 classification task compared to the original network.
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples

Adversarial machine learning in the context of image processing and related applications has received a large amount of attention. However, adversarial machine learning, especially adversarial deep learning, in the context of malware detection has received much less attention despite its apparent importance. In this paper, we present a framework for enhancing the robustness of Deep Neural Networks (DNNs) against adversarial malware samples, dubbed Hashing Transformation Deep Neural Networks (HashTran-DNN). The core idea is to use hash functions with a certain locality-preserving property to transform samples to enhance the robustness of DNNs in malware classification. The framework further uses a Denoising Auto-Encoder (DAE) regularizer to reconstruct the hash representations of samples, making the resulting DNN classifiers capable of attaining the locality information in the latent space. We experiment with two concrete instantiations of the HashTranDNN framework to classify Android malware. Experimental results show that four known attacks can render standard DNNs useless in classifying Android malware, that known defenses can at most defend three of the four attacks, and that HashTran-DNN can effectively defend against all of the four attacks.
The E-Marketing Mix : A Contribution of the E-Tailing Wars

In the context of the wars between the upstart Internet retailers and the existing bricks-and-mortar retailers, many e-marketing techniques were invented. This article develops a single unifying and theoretically based taxonomy for e-marketing techniques: the e-marketing mix. Drawing on the paradigms of exchange, relationships, and digital interactions in networks, 11 e-marketing functions are identified that form the elements of the e-marketing mix. Nine of the 11 e-marketing functions are considered basic, while 7 functions moderate the effects of others and are termed overlapping. The 11 e-marketing functions provide a categorization of the e-marketing techniques. Compared to the conventional marketing mix, the e-marketing mix has more overlapping elements and directly represents personalization, an aspect of segmentation, as a basic function. The existence of multiple elements that are basic and overlapping in the e-marketing mix indicates that integration across elements should be more commonplace compared to the traditional marketing mix.
Merging Comparable Data Sources for the Discrimination of Similar Languages : The DSL Corpus Collection

In this article, we present the first academic comparable corpus involving written French and French Sign Language. After explaining our initial motivation to build a parallel set of such data, especially in the context of our work on Sign Language modelling and our prospect of machine translation into Sign Language, we present the main problems posed when mixing language channels and modalities (oral, written, signed), discussing the translation-vs-interpretation narrative in particular. We describe the process followed to guarantee feature coverage and exploitable results despite a serious cost limitation, the data being collected from professional translations. We conclude with a few uses and prospects of the corpus.
Effect of testosterone on muscle mass and muscle protein synthesis.

We have studied the effect of a pharmacological dose of testosterone enanthate (3 mg.kg-1.wk-1 for 12 wk) on muscle mass and total-body potassium and on whole-body and muscle protein synthesis in normal male subjects. Muscle mass estimated by creatinine excretion increased in all nine subjects (20% mean increase, P less than 0.02); total body potassium mass estimated by 40K counting increased in all subjects (12% mean increase, P less than 0.0001). In four subjects, a primed continuous infusion protocol with L-[1-13C]leucine was used to determine whole-body leucine flux and oxidation. Whole-body protein synthesis was estimated from nonoxidative flux. Muscle protein synthesis rate was determined by measuring [13C]leucine incorporation into muscle samples obtained by needle biopsy. Testosterone increased muscle protein synthesis in all subjects (27% mean increase, P less than 0.05). Leucine oxidation decreased slightly (17% mean decrease, P less than 0.01), but whole-body protein synthesis did not change significantly. Muscle morphometry showed no significant increase in muscle fiber diameter. These studies suggest that testosterone increases muscle mass by increasing muscle protein synthesis.
An IoT Enabled Real-Time Communication and Location Tracking System for Vehicular Emergency

Internet of Things is an emerging technology having the ability to change the way we live. In IoT vision, each and every 'thing' has the ability of talking to each other that brings the idea of Internet of Everything in reality. Numerous IoT services can make our daily life easier, smarter, and even safer. Using IoT in designing some special services can make a lifesaver system. In this paper, we have presented an IoT enabled approach that can provide emergency communication and location tracking services in a remote car that meets an unfortunate accident or any other emergency situation. Immediately after an accident or an emergency, the system either starts automatically or may be triggered manually. Depending upon type of emergency (police and security, fire and rescue, medical, or civil) it initiates communication and shares critical information e.g. location information, a set of relevant images taken from prefixed angles etc. with appropriate server / authority. Provision of interactive real-time multimedia communication, real-time location tracking etc. have also been integrated to the proposed system to monitor the exact condition in real-time basis. The system prototype has been designed with Raspberry Pi 3 Model B and UMTS-HSDPA communication protocol.
Renewable Energy

The Energy Committee of the Royal Swedish Academy of Sciences has in a series of projects gathered information and knowledge on renewable energy from various sources, both within and outside the academic world. In this article, we synthesize and summarize some of the main points on renewable energy from the various Energy Committee projects and the Committee’s Energy 2050 symposium, regarding energy from water and wind, bioenergy, and solar energy. We further summarize the Energy Committee’s scenario estimates of future renewable energy contributions to the global energy system, and other presentations given at the Energy 2050 symposium. In general, international coordination and investment in energy research and development is crucial to enable future reliance on renewable energy sources with minimal fossil fuel use.
DETECTING INFORMAL SETTLEMENTS FROM IKONOS IMAGE DATA USING METHODS OF OBJECT ORIENTED IMAGE ANALYSIS – AN EXAMPLE FROM CAPE TOWN ( SOUTH AFRICA )

Detecting informal settlements might be one of the most challenging tasks within urban remote sensing. This phenomenon occurs mostly in developing countries. In order to carry out the urban planning and development tasks necessary to improve living conditions for the poorest world-wide, an adequate spatial data basis is needed (see Mason, O. S. & Fraser, C. S., 1998). This can only be obtained through the analysis of remote sensing data, which represents an additional challenge from a technical point of view. Formal settlements by definition are mapped sufficiently for most purposes. However, this does not hold for informal settlements. Due to their microstructure and instability of shape, the detection of these settlements is substantially more difficult. Hence, more sophisticated data and methods of image analysis are necessary, which ideally act as a spatial data basis for a further informal settlement management. While these methods are usually quite labour-intensive, one should nonetheless bear in mind cost-effectivity of the applied methods and tools. In the present article, it will be shown how eCognition can be used to detect and discriminate informal settlements from other land-use-forms by describing typical characteristics of colour, texture, shape and context. This software is completely objectoriented and uses a patented, multi-scale image segmentation approach. The generated segments act as image objects whose physical and contextual characteristics can be described by means of fuzzy logic. The article will show methods and strategies using eCognition to detect informal settlements from high resolution space-borne image data such as IKONOS. A final discussion of the results will be given.
Evaluating Fast Algorithms for Convolutional Neural Networks on FPGAs

In recent years, Convolutional Neural Networks (CNNs) have become widely adopted for computer vision tasks. FPGAs have been adequately explored as a promising hardware accelerator for CNNs due to its high performance, energy efficiency, and reconfigurability. However, prior FPGA solutions based on the conventional convolutional algorithm is often bounded by the computational capability of FPGAs (e.g., the number of DSPs). In this paper, we demonstrate that fast Winograd algorithm can dramatically reduce the arithmetic complexity, and improve the performance of CNNs on FPGAs. We first propose a novel architecture for implementing Winograd algorithm on FPGAs. Our design employs line buffer structure to effectively reuse the feature map data among different tiles. We also effectively pipeline the Winograd PE engine and initiate multiple PEs through parallelization. Meanwhile, there exists a complex design space to explore. We propose an analytical model to predict the resource usage and reason about the performance. Then, we use the model to guide a fast design space exploration. Experiments using the state-of-the-art CNNs demonstrate the best performance and energy efficiency on FPGAs. We achieve an average 1006.4 GOP/s for the convolutional layers and 854.6 GOP/s for the overall AlexNet and an average 3044.7 GOP/s for the convolutional layers and 2940.7 GOP/s for the overall VGG16 on Xilinx ZCU102 platform.
Gosig: Scalable Byzantine Consensus on Adversarial Wide Area Network for Blockchains

Existing Byzantine fault tolerance (BFT) protocols face significant challenges in the consortium blockchain scenario. On the one hand, we can make little assumptions about the reliability and security of the underlying Internet. On the other hand, the applications on consortium blockchains demand a system as scalable as the Bitcoin but providing much higher performance, as well as provable safety. We present a new BFT protocol, Gosig, that combines crypto-based secret leader selection and multi-round voting in the protocol layer with implementation layer optimizations such as gossip-based message propagation. In particular, Gosig guarantees safety even in a network fully controlled by adversaries, while providing provable liveness with easy-to-achieve network connectivity assumption. On a wide area testbed consisting of 140 Amazon EC2 servers spanning 14 cities on five continents, we show that Gosig can achieve over 4,000 transactions per second with less than 1 minute transaction confirmation time.
Quantitative Data Analysis in Finance

Quantitative tools have been widely adopted in order to extract the massive information from a variety of financial data. Mathematics, statistics and computers algorithms have never been so important to financial practitioners in history. Investment banks develop equilibrium models to evaluate financial instruments; mutual funds applied time series to identify the risks in their portfolio; and hedge funds hope to extract market signals and statistical arbitrage from noisy market data. The rise of quantitative finance in the last decade relies on the development of computer techniques that makes processing large datasets possible. As more data is available at a higher frequency, more researches in quantitative finance have switched to the microstructures of financial market. High frequency data is a typical example of big data that is characterized by the 3V’s: velocity, variety and volume. In addition, the signal to noise ratio in financial time series is usually very small. High frequency datasets are more likely to be exposed to extreme values, jumps and errors than the low frequency ones. Specific data processing techniques and quantitative models are elaborately designed to extract information from financial data efficiently. In this chapter, we present the quantitative data analysis approaches in finance. First, we review the development of quantitative finance in the past decade. Then we discuss the characteristics of high frequency data and the challenges it brings. The quantitative data analysis consists of two basic steps: (i) data cleaning and aggregating; (ii) data modeling. We review the mathematics tools and computing technologies behind the two steps. The valuable information extracted from raw data is represented by a group of statistics. The most widely used statistics in finance are expected return and volatility, which are the fundamentals of modern portfolio theory. We further introduce some simple portfolio optimization strategies as an example of the application of financial data analysis. Big data has already changed financial industry fundamentally; while quantitative tools for addressing massive financial data still have a long way to go. Adoptions of advanced statistics, information theory, machine learning and faster computing algorithm are inevitable in order to predict complicated financial markets. These topics are briefly discussed in the later part of this chapter. Xiang Shi, Ph.D. Stony Brook University, Stony Brook, NY 11794, USA e-mail:
Transient Stability Analysis of IEEE 9 Bus System Using Power World Simulator

The aim of this paper is to check the transient stability of IEEE 7 bus system in electric power system. Three generators are modelled in this system with different MW. Furthermore, modelling of all the system equipment is obtained by Power World Simulator Software. The upgrading and designing the electric power system transient stability analysis is necessary and also study of the stability of the system in short term disturbances is also content for study in itself. The transient stability analysis of IEEE 7 bus system in Power World Simulator determining the idea about the power system transient stability analysis is more important factor. The frequency and variation of power angle at bus and generator parameters are studied with 3-phase balanced fault condition. Also impact of disturbance on load in the system can also model.
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN

Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns. Long short-term memory (LSTM) and gated recurrent unit (GRU) were developed to address these problems, but the use of hyperbolic tangent and the sigmoid action functions results in gradient decay over layers. Consequently, construction of an efficiently trainable deep network is challenging. In addition, all the neurons in an RNN layer are entangled together and their behaviour is hard to interpret. To address these problems, a new type of RNN, referred to as independently recurrent neural network (IndRNN), is proposed in this paper, where neurons in the same layer are independent of each other and they are connected across layers. We have shown that an IndRNN can be easily regulated to prevent the gradient exploding and vanishing problems while allowing the network to learn long-term dependencies. Moreover, an IndRNN can work with non-saturated activation functions such as relu (rectified linear unit) and be still trained robustly. Multiple IndRNNs can be stacked to construct a network that is deeper than the existing RNNs. Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly. Better performances have been achieved on various tasks by using IndRNNs compared with the traditional RNN and LSTM.
Comparing Generative Adversarial Network Techniques for Image Creation and Modification

Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN techniques, both supervised and unsupervised. The effects on training stability of different objective functions are compared. We add an encoder to the network, making it possible to encode images to the latent space of the GAN. The generator, discriminator and encoder are parameterized by deep convolutional neural networks. For the discriminator network we experimented with using the novel Capsule Network, a state-of-the-art technique for detecting global features in images. Experiments are performed using a digit and face dataset, with various visualizations illustrating the results. The results show that using the encoder network it is possible to reconstruct images. With the conditional GAN we can alter visual attributes of generated or encoded images. The experiments with the Capsule Network as discriminator result in generated images of a lower quality, compared to a standard convolutional neural network.
SHAPE PRIORS FOR MONOCULAR OBJECT LOCALIZATION IN DYNAMIC SCENES

We tackle the problem of reconstructing moving vehicles in autonomous driving scenarios using only a monocular camera. Though the problem appears to be ill-posed, we demonstrate that prior knowledge about how 3D shapes of vehicles project to an image can be used to reason about the reverse process, i.e., how shapes (back-)project from 2D to 3D. We encode this knowledge in shape priors, which are learnt over a small dataset comprising of annotated RGB images of vehicles. Each shape prior comprises of a deformable wireframe model whose vertices are semantically unique keypoints of that vehicle. The first contribution is an approach for reconstructing vehicles from just a single (RGB) image. To obtain a 3D wireframe representing the shape, we first localize the vertices of the wireframe (keypoints) in 2D using a Convolutional Neural Network (CNN). We then formulate a shape-aware optimization problem that uses the learnt shape priors to lift the detected 2D keypoints to 3D, thereby recovering the 3D pose and shape of a query object from an image. The shape-aware adjustment robustly recovers shape (3D locations of the detected keypoints) while simultaneously filling in occluded keypoints. To tackle estimation errors incurred due to erroneously detected keypoints, we use an Iteratively Reweighted Least Squares (IRLS) scheme for robust optimization, and as a by-product characterize noise models for each predicted keypoint. We evaluate our approach on autonomous driving benchmarks, and present superior results to existing monocular, as well as stereo approaches. The second contribution is a real-time monocular object localization system that estimates the shape and pose of dynamic objects in real-time, using video frames captured from a moving monocular camera. Here again, by incorporating prior knowledge of the object category, we can obtain more detailed instance-level reconstructions. As opposed to earlier object model specifications, the proposed shapeprior model leads to the formulation of a Bundle Adjustment-like optimization problem for simultaneous shape and pose estimation. We demonstrate how these keypoints can be used to recover 3D object properties, while accounting for any 2D localization errors and self-occlusion. We show significant performance improvements compared to state-of-the-art monocular competitors for 2D keypoint detection, as well as 3D localization and reconstruction of dynamic objects.
Nano-emulsion formulation using spontaneous emulsification: solvent, oil and surfactant optimisation.

Nano-emulsions consist of fine oil-in-water dispersions, having droplets covering the size range of 100-600 nm. In the present work, nano-emulsions were prepared using the spontaneous emulsification mechanism which occurs when an organic phase and an aqueous phase are mixed. The organic phase is an homogeneous solution of oil, lipophilic surfactant and water-miscible solvent, the aqueous phase consists on hydrophilic surfactant and water. An experimental study of nano-emulsion process optimisation based on the required size distribution was performed in relation with the type of oil, surfactant and the water-miscible solvent. The results showed that the composition of the initial organic phase was of great importance for the spontaneous emulsification process, and so, for the physico-chemical properties of the obtained emulsions. First, oil viscosity and HLB surfactants were changed, alpha-tocopherol, the most viscous oil, gave the smallest droplets size (171 +/- 2 nm), HLB required for the resulting oil-in-water emulsion was superior to 8. Second, the effect of water-solvent miscibility on the emulsification process was studied by decreasing acetone proportion in the organic phase. The solvent-acetone proportion leading to a fine nano-emulsion was fixed at 15/85% (v/v) with EtAc-acetone and 30/70% (v/v) with MEK-acetone mixture. To strength the choice of solvents, physical characteristics were compared, in particular, the auto-inflammation temperature and the flash point. This phase of emulsion optimisation represents an important step in the process of polymeric nanocapsules preparation using nanoprecipitation or interfacial polycondensation combined with spontaneous emulsification technique.