nils-herrmann/clean_scirepeval huggingface dataset:
HYBRID ENCRYPTION FOR CLOUD DATABASE SECURITY

In cloud computing environment the new data management model is in use now a days that enables data integration and access on a large scale cloud computing as a service termed as Database-as-a-service (DAAS). Through which service provider offers customer management functionalities as well as the expensive hardware. Data privacy is the major security determinant in DAAS because data will be shared with a third party; an un-trusted server is dangerous and unsafe for the user. This paper shows a concern on the security element in cloud environment. It suggests a technique to enhance the security of cloud database. This technique provides the flexible multilevel and hybrid security. It uses RSA, Triple DES and Random Number generator algorithms as an encrypting tool.
The Essential Guide to Realizing 5G-Connected UAVs with Massive MIMO

What will it take for drones—and the whole associated ecosystem—to take off? Arguably, infallible command and control (C&C) channels for safe and autonomous flying, and high-throughput links for multi-purpose live video streaming. And indeed, meeting these aspirations may entail a full cellular support, provided through 5G-and-beyond hardware and software upgrades by both mobile operators and manufacturers of these unmanned aerial vehicles (UAVs). In this article, we vouch for massive MIMO as the key building block to realize 5G-connected UAVs. Through the sheer evidence of 3GPPcompliant simulations, we demonstrate how massive MIMO can be enhanced by complementary network-based and UAVbased solutions, resulting in consistent UAV C&C support, large UAV uplink data rates, and harmonious coexistence with legacy ground users.
Despeckle Filtering for Multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) Texture Analysis of Ultrasound Images of the Intima-Media Complex

The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). Typically, the IMT grows with age and this is used as a sign of increased risk of CVD. Beyond thickness, there is also clinical interest in identifying how the composition and texture of the intima-media complex (IMC) changed and how these textural changes grow into atherosclerotic plaques that can cause stroke. Clearly though texture analysis of ultrasound images can be greatly affected by speckle noise, our goal here is to develop effective despeckle noise methods that can recover image texture associated with increased rates of atherosclerosis disease. In this study, we perform a comparative evaluation of several despeckle filtering methods, on 100 ultrasound images of the CCA, based on the extracted multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) texture features and visual image quality assessment by two clinical experts. Texture features were extracted from the automatically segmented IMC for three different age groups. The despeckle filters hybrid median and the homogeneous mask area filter showed the best performance by improving the class separation between the three age groups and also yielded significantly improved image quality.
A universal weak method

The weak methods occur pervasively in AI systems and may form die basic methods for all intelligent systems. The purpose of this paper is to characterize die weak methods and to explain how and why they arise in intelligent systems. We propose an organization, called a universal weak method that provides functionality of all the weak methods. A universal weak method is an organizational scheme for knowledge that produces the appropriate search behavior given the available task-domain knowledge. We present a problem solving architecture, called SOAR, in which we realize a universal weak method. We then demonstrate the universal weak method with a variety of weak methods on a set of tasks. This research was sponsored by die Defense Advanced Research Projects Agency (DOD), ARPA Order No: 3597, monitored by die Air Force Avionics Laboratory Under Contract F33515-78-C-155L The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of die Defense Advanced Research Projects Agency or the US Government.
CNN Cascades for Segmenting Whole Slide Images of the Kidney

Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based image analysis systems. In this work, the focus is on the segmentation of the glomeruli constituting a highly relevant structure in renal histopathology, which has not been investigated before in combination with CNNs. We propose two different CNN cascades for segmentation applications with sparse objects. These approaches are applied to the problem of glomerulus segmentation and compared with conventional fully-convolutional networks. Overall, with the best performing cascade approach, single CNNs are outperformed and a pixel-level Dice similarity coefficient of 0.90 is obtained. Combined with qualitative and further object-level analyses the obtained results are assessed as excellent also compared to recent approaches. In conclusion, we can state that especially one of the proposed cascade networks proved to be a highly powerful tool for segmenting the renal glomeruli providing best segmentation accuracies and also keeping the computing time at a low level.
Boredom proneness: its relationship to positive and negative affect.

170 undergraduate students completed the Boredom Proneness Scale by Farmer and Sundberg and the Multiple Affect Adjective Checklist by Zuckerman and Lubin. Significant negative relationships were found between boredom proneness and negative affect scores (i.e., Depression, Hostility, Anxiety). Significant positive correlations also obtained between boredom proneness and positive affect (i.e., Positive Affect, Sensation Seeking). The correlations between boredom proneness "subscales" and positive and negative affect were congruent with those obtained using total boredom proneness scores. Implications for counseling are discussed.
Optimizing Liver Allocation System Incorporating Disease Evolution

We propose an e¢ cient liver allocation system for allocating donated organs to patients waiting for transplantation, the only viable treatment for End-Stage Liver Disease. We optimize two metrics which are used to measure the e¢ ciency: total quality adjusted life years and the number of organs wasted due to patients rejecting some organ o¤ers. Our model incorporates the possibility that the patients may turn down the organ o¤ers. Given the scarcity of available organs relative to the number patients waiting for transplantation, we model the system as a multiclass uid model of overloaded queues. The uid model we advance captures the disease evolution over time by allowing the patients to switch between classes over time, e.g. patients waiting for transplantation may get sicker/better, or may die. We characterize the optimal solution to the uid model using the duality framework for optimal control problems developed by Rockafellar (1970a). The optimal solution for assigning livers to patients is an intuitive dynamic index policy, where the indices depend on patients acceptance probabilities of the organ o¤er, immediate rewards, and the shadow prices calculated from the dual dynamical system. Finally, we perform a detailed simulation study to demonstrate the e¤ectiveness of the proposed policy using data from the United Network for Organ Sharing System (UNOS).
STCT: Sequentially Training Convolutional Networks for Visual Tracking

Due to the limited amount of training samples, finetuning pre-trained deep models online is prone to overfitting. In this paper, we propose a sequential training method for convolutional neural networks (CNNs) to effectively transfer pre-trained deep features for online applications. We regard a CNN as an ensemble with each channel of the output feature map as an individual base learner. Each base learner is trained using different loss criterions to reduce correlation and avoid over-training. To achieve the best ensemble online, all the base learners are sequentially sampled into the ensemble via important sampling. To further improve the robustness of each base learner, we propose to train the convolutional layers with random binary masks, which serves as a regularization to enforce each base learner to focus on different input features. The proposed online training method is applied to visual tracking problem by transferring deep features trained on massive annotated visual data and is shown to significantly improve tracking performance. Extensive experiments are conducted on two challenging benchmark data set and demonstrate that our tracking algorithm can outperform state-of-the-art methods with a considerable margin.
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN

The recently proposed distributional approach to reinforcement learning (DiRL) is centered on learning the distribution of the reward-to-go, often referred to as the value distribution. In this work, we show that the distributional Bellman equation, which drives DiRL methods, is equivalent to a generative adversarial network (GAN) model. In this formulation, DiRL can be seen as learning a deep generative model of the value distribution, driven by the discrepancy between the distribution of the current value, and the distribution of the sum of current reward and next value. We use this insight to propose a GAN-based approach to DiRL, which leverages the strengths of GANs in learning distributions of highdimensional data. In particular, we show that our GAN approach can be used for DiRL with multivariate rewards, an important setting which cannot be tackled with prior methods. The multivariate setting also allows us to unify learning the distribution of values and state transitions, and we exploit this idea to devise a novel exploration method that is driven by the discrepancy in estimating both values and states.
Indoor Coverage Path Planning: Survey, Implementation, Analysis

Coverage Path Planning (CPP) describes the process of generating robot trajectories that fully cover an area or volume. Applications are, amongst many others, mobile cleaning robots, lawn mowing robots or harvesting machines in agriculture. Many approaches and facets of this problem have been discussed in literature but despite the availability of several surveys on the topic there is little work on quantitative assessment and comparison of different coverage path planning algorithms. This paper analyzes six popular off-line coverage path planning methods, applicable to previously recorded maps, in the setting of indoor coverage path planning on room-sized units. The implemented algorithms are thoroughly compared on a large dataset of over 550 rooms with and without furniture.
Eating disorders in athletes: overview of prevalence, risk factors and recommendations for prevention and treatment.

The prevalence of disordered eating and eating disorders vary from 0-19% in male athletes and 6-45% in female athletes. The objective of this paper is to present an overview of eating disorders in adolescent and adult athletes including: (1) prevalence data; (2) suggested sport- and gender-specific risk factors and (3) importance of early detection, management and prevention of eating disorders. Additionally, this paper presents suggestions for future research which includes: (1) the need for knowledge regarding possible gender-specific risk factors and sport- and gender-specific prevention programmes for eating disorders in sports; (2) suggestions for long-term follow-up for female and male athletes with eating disorders and (3) exploration of a possible male athlete triad.
An intuitive framework for real-time freeform modeling

We present a freeform modeling framework for unstructured triangle meshes which is based on constraint shape optimization. The goal is to simplify the user interaction even for quite complex freeform or multiresolution modifications. The user first sets various boundary constraints to define a custom tailored (abstract) basis function which is adjusted to a given design task. The actual modification is then controlled by moving one single 9-dof manipulator object. The technique can handle arbitrary support regions and piecewise boundary conditions with smoothness ranging continuously from C0 to C2. To more naturally adapt the modification to the shape of the support region, the deformed surface can be tuned to bend with anisotropic stiffness. We are able to achieve real-time response in an interactive design session even for complex meshes by precomputing a set of scalar-valued basis functions that correspond to the degrees of freedom of the manipulator by which the user controls the modification.
Image Restoration by Iterative Denoising and Backward Projections

Inverse problems appear in many applications, such as image deblurring and inpainting. The common approach to address them is to design a specific algorithm for each problem. The Plug-and-Play (P&P) framework, which has been recently introduced, allows solving general inverse problems by leveraging the impressive capabilities of existing denoising algorithms. While this fresh strategy has found many applications, a burdensome parameter tuning is often required in order to obtain high-quality results. In this paper, we propose an alternative method for solving inverse problems using off-the-shelf denoisers, which requires less parameter tuning. First, we transform a typical cost function, composed of fidelity and prior terms, into a closely related, novel optimization problem. Then, we propose an efficient minimization scheme with a P&P property, i.e., the prior term is handled solely by a denoising operation. Finally, we present an automatic tuning mechanism to set the method’s parameters. We provide a theoretical analysis of the method and empirically demonstrate its competitiveness with task-specific techniques and the P&P approach for image inpainting and deblurring.
"Bricks and Clicks": The Impact of Product Returns on the Strategies of Multichannel Retailers

T Internet has increased the flexibility of retailers, allowing them to operate an online arm in addition to their physical stores. The online channel offers potential benefits in selling to customer segments that value the convenience of online shopping, but it also raises new challenges. These include the higher likelihood of costly product returns when customers’ ability to “touch and feel” products is important in determining fit. We study competing retailers that can operate dual channels (“bricks and clicks”) and examine how pricing strategies and physical store assistance levels change as a result of the additional Internet outlet. A central result we obtain is that when differentiation among competing retailers is not too high, having an online channel can actually increase investment in store assistance levels (e.g., greater shelf display, more-qualified sales staff, floor samples) and decrease profits. Consequently, when the decision to open an Internet channel is endogenized, there can exist an asymmetric equilibrium where only one retailer elects to operate an online arm but earns lower profits than its bricks-only rival. We also characterize equilibria where firms open an online channel, even though consumers only use it for research and learning purposes but buy in stores. A number of extensions are discussed, including retail settings where firms carry multiple product categories, shipping and handling costs, and the role of store assistance in impacting consumer perceived benefits.
Low-Cost Standard Public Key Cryptography Services for Wireless IoT Systems

Internet of Things (IoT) is an integral part of application domains such as smart-home and digital healthcare. Various standard public key cryptography techniques (e.g., key exchange, public key encryption, signature) are available to provide fundamental security services for IoTs. However, despite their pervasiveness and well-proven security, they also have been shown to be highly energy costly for embedded devices. Hence, it is a critical task to improve the energy efficiency of standard cryptographic services, while preserving their desirable properties simultaneously.
 In this paper, we exploit synergies among various cryptographic primitives with algorithmic optimizations to substantially reduce the energy consumption of standard cryptographic techniques on embedded devices. Our contributions are: (i) We harness special precomputation techniques, which have not been considered for some important cryptographic standards to boost the performance of key exchange, integrated encryption, and hybrid constructions. (ii) We provide self-certification for these techniques to push their performance to the edge. (iii) We implemented our techniques and their counterparts on 8-bit AVR ATmega 2560 and evaluated their performance. We used microECC library and made the implementations on NIST-recommended secp192 curve, due to its standardization. Our experiments confirmed significant improvements on the battery life (up to 7x) while preserving the desirable properties of standard techniques. Moreover, to the best of our knowledge, we provide the first open-source framework including such set of optimizations on low-end devices.
HL7 middleware framework for healthcare information system

In this paper, a new middleware framework developed for the Health Information System (HIS) rightsizing project of National Taiwan University Hospital (NTUH) is proposed. The framework is basically a Service-Oriented Architecture (SOA). Challenges as formatting complex medical information as well as integrating heterogeneous systems in our hospital are addressed by introducing HL7 and Web services standard into our framework. Finally, the performance of a operating HIS based on our framework is analyzed and presented to evaluate the efficiency of our design. HL7, Healthcare information system, web services, Service-oriented Architecture
A novel Si-Tunnel FET based SRAM design for ultra low-power 0.3V VDD applications

Steep sub-threshold transistors are promising candidates to replace the traditional MOSFETs for sub-threshold leakage reduction. In this paper, we explore the use of Inter-Band Tunnel Field Effect Transistors (TFETs) in SRAMs at ultra low supply voltages. The uni-directional current conducting TFETs limit the viability of 6T SRAM cells. To overcome this limitation, 7T SRAM designs were proposed earlier at the cost of extra silicon area. In this paper, we propose a novel 6T SRAM design using Si-TFETs for reliable operation with low leakage at ultra low voltages. We also demonstrate that a functional 6T TFET SRAM design with comparable stability margins and faster performances at low voltages can be realized using proposed design when compared with the 7T TFET SRAM cell. We achieve a leakage reduction improvement of 700X and 1600X over traditional CMOS SRAM designs at VDD of 0.3V and 0.5V respectively which makes it suitable for use at ultra-low power applications.
One-year clinical study of NeuroRegen scaffold implantation following scar resection in complete chronic spinal cord injury patients

The objective of this clinical study was to assess the safety and feasibility of the collagen scaffold, NeuroRegen scaffold, one year after scar tissue resection and implantation. Scar tissue is a physical and chemical barrier that prevents neural regeneration. However, identification of scar tissue is still a major challenge. In this study, the nerve electrophysiology method was used to distinguish scar tissue from normal neural tissue, and then different lengths of scars ranging from 0.5–4.5 cm were surgically resected in five complete chronic spinal cord injury (SCI) patients. The NeuroRegen scaffold along with autologous bone marrow mononuclear cells (BMMCs), which have been proven to promote neural regeneration and SCI recovery in animal models, were transplanted into the gap in the spinal cord following scar tissue resection. No obvious adverse effects related to scar resection or NeuroRegen scaffold transplantation were observed immediately after surgery or at the 12-month follow-up. In addition, patients showed partially autonomic nervous function improvement, and the recovery of somatosensory evoked potentials (SSEP) from the lower limbs was also detected. The results indicate that scar resection and NeuroRegen scaffold transplantation could be a promising clinical approach to treating SCI.
Weakly-Supervised Hierarchical Text Classification

Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models are gaining increasing popularity for text classification due to their expressive power and minimum requirement for feature engineering. However, applying deep neural networks for hierarchical text classification remains challenging, because they heavily rely on a large amount of training data and meanwhile cannot easily determine appropriate levels of documents in the hierarchical setting. In this paper, we propose a weakly-supervised neural method for hierarchical text classification. Our method does not require a large amount of training data but requires only easy-to-provide weak supervision signals such as a few class-related documents or keywords. Our method effectively leverages such weak supervision signals to generate pseudo documents for model pre-training, and then performs self-training on real unlabeled data to iteratively refine the model. During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism. Experiments on three datasets from different domains demonstrate the efficacy of our method compared with a comprehensive set of baselines .
The dynamic anatomy and patterning of skin.

The skin is often viewed as a static barrier that protects the body from the outside world. Emphasis on studying the skin's architecture and biomechanics in the context of restoring skin movement and function is often ignored. It is fundamentally important that if skin is to be modelled or developed, we do not only focus on the biology of skin but also aim to understand its mechanical properties and structure in living dynamic tissue. In this review, we describe the architecture of skin and patterning seen in skin as viewed from a surgical perspective and highlight aspects of the microanatomy that have never fully been realized and provide evidence or concepts that support the importance of studying living skin's dynamic behaviour. We highlight how the structure of the skin has evolved to allow the body dynamic form and function, and how injury, disease or ageing results in a dramatic changes to the microarchitecture and changes physical characteristics of skin. Therefore, appreciating the dynamic microanatomy of skin from the deep fascia through to the skin surface is vitally important from a dermatological and surgical perspective. This focus provides an alternative perspective and approach to addressing skin pathologies and skin ageing.