nils-herrmann/clean_scirepeval huggingface dataset:
CloudProtect: Managing Data Privacy in Cloud Applications

This paper describes the CloudProtect middleware that empowers users to encrypt sensitive data stored within various cloud applications. However, most web applications require data in plaintext for implementing the various functionalities and in general, do not support encrypted data management. Therefore, CloudProtect strives to carry out the data transformations (encryption/decryption) in a manner that is transparent to the application, i.e., preserves all functionalities of the application, including those that require data to be in plaintext. Additionally, CloudProtect allows users flexibility in trading off performance for security in order to let them optimally balance their privacy needs and usage-experience.
Securing user identity and transactions symbiotically: IoT meets blockchain

Swarms of embedded devices provide new challenges for privacy and security. We propose Permissioned Blockchains as an effective way to secure and manage these systems of systems. A long view of blockchain technology yields several requirements absent in extant blockchain implementations. Our approach to Permissioned Blockchains meets the fundamental requirements for longevity, agility, and incremental adoption. Distributed Identity Management is an inherent feature of our Permissioned Blockchain and provides for resilient user and device identity and attribute management.
End-to-End Learning of Video Super-Resolution with Motion Compensation

Learning approaches have shown great success in the task of super-resolving an image given a low resolution input. Video superresolution aims for exploiting additionally the information from multiple images. Typically, the images are related via optical flow and consecutive image warping. In this paper, we provide an end-to-end video superresolution network that, in contrast to previous works, includes the estimation of optical flow in the overall network architecture. We analyze the usage of optical flow for video super-resolution and find that common off-the-shelf image warping does not allow video super-resolution to benefit much from optical flow. We rather propose an operation for motion compensation that performs warping from low to high resolution directly. We show that with this network configuration, video superresolution can benefit from optical flow and we obtain state-of-the-art results on the popular test sets. We also show that the processing of whole images rather than independent patches is responsible for a large increase in accuracy.
Bioinspired Visual Motion Estimation

Visual motion estimation is a computationally intensive, but important task for sighted animals. Replicating the robustness and efficiency of biological visual motion estimation in artificial systems would significantly enhance the capabilities of future robotic agents. Twenty five years ago, in this very journal, Carver Mead outlined his argument for replicating biological processing in silicon circuits. His vision served as the foundation for the field of neuromorphic engineering, which has experienced a rapid growth in interest over recent years as the ideas and technologies mature. Replicating biological visual sensing was one of the first tasks attempted in the neuromorphic field. In this paper, we focus specifically on the task of visual motion estimation. We describe the task itself, present the progression of works from the early first attempts through to the modern day state-of-the-art, and provide an outlook for future directions in the field.
The effects of deep pressure on self-stimulating behaviors in a child with autism and other disabilities.

The American journal 0/ Occupational Therapl' ThiS case report describes the effects of deeppressure tactile stimulation in reducing self-stimulating behaviors in a child with multiple disabilities including autism. These behaviors include hitting the hands together, one hand on top of the other, so that the palm of one hand hits the dorsum of the other, or hitting a surface with one or both hands. Such behaviors not only made classroom efforts to have her use her hands for selfcare functions such as holding an adapted spoon difficult or impossible, but also called attention to her disabling condition. These behaviors also were disruptive and noisy.
Object recognition speed improvement using BITMAP-HoG

Commonly, HoG/SVM classifier uses rectangular images for HoG feature descriptor extraction and training. This means significant additional work has to be done to process irrelevant pixels belonging to the background surrounding the object of interest. While some objects may indeed be square or rectangular, most of objects are not easily representable by simple geometric shapes. In Bitmap-HoG approach we propose in this paper, the irregular shape of object is represented by a bitmap to avoid processing of extra background pixels. Bitmap, derived from the training dataset, encodes those portions of an image to be used to train a classifier. Experimental results show that not only the proposed algorithm decreases the workload associated with HoG/SVM classifiers by 75% compared to the state-of-the-art, but also it shows an average increase about 5% in recall and a decrease about 2% in precision in comparison with standard HoG.
Projected augmentation - augmented reality using rotatable video projectors

In this paper, we propose a new way of augmenting our environment with information without making the user carry any devices. We propose the use of video projection to display the augmentation on the objects directly. We use a projector that can be rotated and in other ways controlled remotely by a computer, to follow objects carrying a marker. The main contribution of this paper is a system that keeps the augmentation displayed in the correct place while the object or the projector moves. We describe the hardware and software design of our system, the way certain functions such as following the marker or keeping it in focus are implemented and how to calibrate the multitude of parameters of all the subsystems.
Literal and Metaphorical Sense Identification through Concrete and Abstract Context

Metaphor is ubiquitous in text, even in highly technical text. Correct inference about textual entailment requires computers to distinguish the literal and metaphorical senses of a word. Past work has treated this problem as a classical word sense disambiguation task. In this paper, we take a new approach, based on research in cognitive linguistics that views metaphor as a method for transferring knowledge from a familiar, well-understood, or concrete domain to an unfamiliar, less understood, or more abstract domain. This view leads to the hypothesis that metaphorical word usage is correlated with the degree of abstractness of the word’s context. We introduce an algorithm that uses this hypothesis to classify a word sense in a given context as either literal (denotative) or metaphorical (connotative). We evaluate this algorithm with a set of adjectivenoun phrases (e.g., in dark comedy , the adjective dark is used metaphorically; in dark hair, it is used literally) and with the TroFi (Trope Finder) Example Base of literal and nonliteral usage for fifty verbs. We achieve state-of-theart performance on both datasets.
Crossbar RRAM Arrays: Selector Device Requirements During Read Operation

Passive crossbar resistive random access memory (RRAM) arrays require select devices with nonlinear I-V characteristics to address the sneak-path problem. Here, we present a systematical analysis to evaluate the performance requirements of select devices during the read operation of RRAM arrays for the proposed one-selector-one-resistor (1S1R) configuration with serially connected selector/storage element. We found high selector current density is critical and the selector nonlinearity (ON/OFF) requirement can be relaxed at present. Different read schemes were analyzed to achieve high read margin and low power consumption. Design optimizations of the sense resistance and the storage elements are also discussed.
Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis.

Global and regional prevalence estimates for blindness and vision impairment are important for the development of public health policies. We aimed to provide global estimates, trends, and projections of global blindness and vision impairment.



We did a systematic review and meta-analysis of population-based datasets relevant to global vision impairment and blindness that were published between 1980 and 2015. We fitted hierarchical models to estimate the prevalence (by age, country, and sex), in 2015, of mild visual impairment (presenting visual acuity worse than 6/12 to 6/18 inclusive), moderate to severe visual impairment (presenting visual acuity worse than 6/18 to 3/60 inclusive), blindness (presenting visual acuity worse than 3/60), and functional presbyopia (defined as presenting near vision worse than N6 or N8 at 40 cm when best-corrected distance visual acuity was better than 6/12).



Globally, of the 7·33 billion people alive in 2015, an estimated 36·0 million (80% uncertainty interval [UI] 12·9-65·4) were blind (crude prevalence 0·48%; 80% UI 0·17-0·87; 56% female), 216·6 million (80% UI 98·5-359·1) people had moderate to severe visual impairment (2·95%, 80% UI 1·34-4·89; 55% female), and 188·5 million (80% UI 64·5-350·2) had mild visual impairment (2·57%, 80% UI 0·88-4·77; 54% female). Functional presbyopia affected an estimated 1094·7 million (80% UI 581·1-1686·5) people aged 35 years and older, with 666·7 million (80% UI 364·9-997·6) being aged 50 years or older. The estimated number of blind people increased by 17·6%, from 30·6 million (80% UI 9·9-57·3) in 1990 to 36·0 million (80% UI 12·9-65·4) in 2015. This change was attributable to three factors, namely an increase because of population growth (38·4%), population ageing after accounting for population growth (34·6%), and reduction in age-specific prevalence (-36·7%). The number of people with moderate and severe visual impairment also increased, from 159·9 million (80% UI 68·3-270·0) in 1990 to 216·6 million (80% UI 98·5-359·1) in 2015.



There is an ongoing reduction in the age-standardised prevalence of blindness and visual impairment, yet the growth and ageing of the world's population is causing a substantial increase in number of people affected. These observations, plus a very large contribution from uncorrected presbyopia, highlight the need to scale up vision impairment alleviation efforts at all levels.
A low noise, high power supply rejection low dropout regulator for wireless system-on-chip applications

This paper presents a novel two-stage low dropout regulator (LDO) that minimizes output noise via a pre-regulator stage and achieves high power supply rejection via a simple subtractor circuit in the power driver stage. The LDO is fabricated with a standard 0.35mum CMOS process and occupies 0.26 mm2 and 0.39mm2 for single and dual output respectively. Measurement showed PSR is 60dB at 10kHz and integrated noise is 21.2uVrms ranging from 1kHz to 100kHz
Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Network

Non-orthogonal multiple access (NOMA) is a promising technique for the fifth generation mobile communication due to its high spectral efficiency. By applying superposition coding and successive interference cancellation techniques at the receiver, multiple users can be multiplexed on the same subchannel in NOMA systems. Previous works focus on subchannel assignment and power allocation to achieve the maximization of sum rate; however, the energy-efficient resource allocation problem has not been well studied for NOMA systems. In this paper, we aim to optimize subchannel assignment and power allocation to maximize the energy efficiency for the downlink NOMA network. Assuming perfect knowledge of the channel state information at base station, we propose a low-complexity suboptimal algorithm, which includes energy-efficient subchannel assignment and power proportional factors determination for subchannel multiplexed users. We also propose a novel power allocation across subchannels to further maximize energy efficiency. Since both optimization problems are non-convex, difference of convex programming is used to transform and approximate the original non-convex problems to convex optimization problems. Solutions to the resulting optimization problems can be obtained by solving the convex sub-problems iteratively. Simulation results show that the NOMA system equipped with the proposed algorithms yields much better sum rate and energy efficiency performance than the conventional orthogonal frequency division multiple access scheme.
Real-time volume graphics

The tremendous evolution of programmable graphics hardware has made high-quality real-time volume graphics a reality. In addition to the traditional application of rendering volume data in scientific visualization, the interest in applying these techniques for real-time rendering of atmospheric phenomena and participating media such as fire, smoke, and clouds is growing rapidly. This course covers both applications in scientific visualization, e.g., medical volume data, and real-time rendering, such as advanced effects and illumination in computer games, in detail. Course participants will learn techniques for harnessing the power of consumer graphics hardware and high-level shading languages for real-time rendering of volumetric data and effects. Beginning with basic texture-based approaches including hardware ray casting, the algorithms are improved and expanded incrementally, covering local and global illumination, scattering, pre-integration, implicit surfaces and non-polygonal isosurfaces, transfer function design, volume animation and deformation, dealing with large volumes, high-quality volume clipping, rendering segmented volumes, higher-order filtering, and non-photorealistic volume rendering. Course participants are provided with documented source code covering details usually omitted in publications.
Self-supervised weed detection in vegetable crops using ground based hyperspectral imaging

A critical step in treating or eradicating weed infestations amongst vegetable crops is the ability to accurately and reliably discriminate weeds from crops. In recent times, high spatial resolution hyperspectral imaging data from ground based platforms have shown particular promise in this application. Using spectral vegetation signatures to discriminate between crop and weed species has been demonstrated on several occasions in the literature over the past 15 years. A number of authors demonstrated successful per-pixel classification with accuracies of over 80%. However, the vast majority of the related literature uses supervised methods, where training datasets have been manually compiled. In practice, static training data can be particularly susceptible to temporal variability due to physiological or environmental change. A self-supervised training method that leverages prior knowledge about seeding patterns in vegetable fields has recently been introduced in the context of RGB imaging, allowing the classifier to continually update weed appearance models as conditions change. This paper combines and extends these methods to provide a self-supervised framework for hyperspectral crop/weed discrimination with prior knowledge of seeding patterns using an autonomous mobile ground vehicle. Experimental results in corn crop rows demonstrate the system's performance and limitations.
Movement Segmentation and Recognition for Imitation Learning

In human movement learning, it is most common to teach constituent elements of complex movements in isolation, before chaining them into complex movements. Segmentation and recognition of observed movement could thus proceed out of this existing knowledge, which is directly compatible with movement generation. In this paper, we address exactly this scenario. We assume that a library of movement primitives has already been taught, and we wish to identify elements of the library in a complex motor act, where the individual elements have been smoothed together, and, occasionally, there might be a movement segment that is not in our library yet. We employ a flexible machine learning representation of movement primitives based on learnable nonlinear attractor system. For the purpose of movement segmentation and recognition, it is possible to reformulate this representation as a controlled linear dynamical system.  This process allows a straightforward sequential segmentation of observed movement into known and new primitives, which are suitable for robot imitation learning. We illustrate our approach with synthetic examples and data collected from human movement. Appearing in Proceedings of the 15 International Conference on Artificial Intelligence and Statistics (AISTATS) 2012, La Palma, Canary Islands. Volume XX of JMLR: W&CP XX.
Search-evasion path planning for submarines using the Artificial Bee Colony algorithm

Submarine search-evasion path planning aims to acquire an evading route for a submarine so as to avoid the detection of hostile anti-submarine searchers such as helicopters, aircraft and surface ships. In this paper, we propose a numerical optimization model of search-evasion path planning for invading submarines. We use the Artificial Bee Colony (ABC) algorithm, which has been confirmed to be competitive compared to many other nature-inspired algorithms, to solve this numerical optimization problem. In this work, several search-evasion cases in the two-dimensional plane have been carefully studied, in which the anti-submarine vehicles are equipped with sensors with circular footprints that allow them to detect invading submarines within certain radii. An invading submarine is assumed to be able to acquire the real-time locations of all the anti-submarine searchers in the combat field. Our simulation results show the efficacy of our proposed dynamic route optimization model for the submarine search-evasion path planning mission.
Improved Music Genre Classification with Convolutional Neural Networks

In recent years, deep neural networks have been shown to be effective in many classification tasks, including music genre classification. In this paper, we proposed two ways to improve music genre classification with convolutional neural networks: 1) combining maxand averagepooling to provide more statistical information to higher level neural networks; 2) using shortcut connections to skip one or more layers, a method inspired by residual learning method. The input of the CNN is simply the short time Fourier transforms of the audio signal. The output of the CNN is fed into another deep neural network to do classification. By comparing two different network topologies, our preliminary experimental results on the GTZAN data set show that the above two methods can effectively improve the classification accuracy, especially the second one.
Policy Learning for Domain Selection in an Extensible Multi-domain Spoken Dialogue System

This paper proposes a Markov Decision Process and reinforcement learning based approach for domain selection in a multidomain Spoken Dialogue System built on a distributed architecture. In the proposed framework, the domain selection problem is treated as sequential planning instead of classification, such that confirmation and clarification interaction mechanisms are supported. In addition, it is shown that by using a model parameter tying trick, the extensibility of the system can be preserved, where dialogue components in new domains can be easily plugged in, without re-training the domain selection policy. The experimental results based on human subjects suggest that the proposed model marginally outperforms a non-trivial baseline.
Analysis of Customer Churn in Mobile Industry using Data Mining

Customer churn prediction in Mobile industry is a latest research topic in recent years. A huge amount of data is generated in Mobile industry every minute. Data mining techniques have also developed in various ways. Customer churn is considered as one of the major issues in Mobile industry. The research signifies that it is more expensive to gain a new customer than to retain an existing one. The knowledge extracted from Mobile industry helps to understand the reasons of customer churn and Telecom providers use the data to retain existing customers. This paper surveys the commonly used data mining techniques to identify customer churn patterns. Classification and association models are the two most commonly used models for data mining in Customer Relationship Management. This study focuses on data mining techniques for reducing customer churn and also likely to reduce error ratio. The recent literature in the area of predictive data mining techniques in customer churn behavior is reviewed and a discussion on the future research directions is offered.  Customer churn, Customer retention, Customer relationship management (CRM), Data mining, C4.5, Naive Bayes classifier.
Lightweight road manager: smartphone-based automatic determination of road damage status by deep neural network

Citizens in various locations can report local infrastructure issues to the government by posting reports on certain websites, such as Chiba Report and FixMyStreet. Recently, these systems have begun operating worldwide. In these systems, a large volume of information is collected on infrastructure problems that are identified by citizens (e.g., broken paving slabs, fly tipping, graffiti, potholes). This information is expected to be utilized for infrastructure maintenance. However, local problems (especially road defection) identified by citizens are sometimes not deemed an urgent matter for road managers. This is because it is difficult for an average person to determine road damage status. Furthermore, non-critical reports may be a burden for local government because each report requires visual confirmation.
 We therefore propose a smartphone application based on a deep neural network that can determine road damage status using only photographs of the road. This application is based on a deep neural network model trained by citizen reports and road manager inspection results, which are gathered daily on a government server. The application updates the model parameters each time it launches and thereby becomes increasingly more intelligent and effective. The proposed system enables average citizens to easily determine road damage status using only a smartphone application. In addition, because not only expert road managers but also local government officials without expert knowledge can inspect the road, the proposed system can be useful for local governments that lack expert road managers.
A Test of the Technology Acceptance Model: The Case of Cellular Telephone Adoption

This study examines patterns of cellular phone adoption and usage in an urban setting. One hundred and seventy-six cellular telephone users were surveyed abou their patterns of usage, demographic and socioeconomic characteristics, perceptions about the technology, and their motivations to use cellular services. The results of this study confirm that users' perceptions are significantly associated with their motivation to use cellular phones. Specifically, perceived ease of use was found to have significant effects on users' extrinsic and intrinsic motivations; apprehensiveness about cellular technology had a negative effect on intrinsic motivations. Implications of these findings for practice and research are examined.