nils-herrmann/clean_scirepeval huggingface dataset:
Automotive Radars: A review of signal processing techniques

Automotive radars, along with other sensors such as lidar, (which stands for "light detection and ranging"), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter-wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird's-eye view to the existing research community.
Analysis of the Cyber-Security of industry 4.0 technologies based on RAMI 4.0 and identification of requirements

The exhaustive digitalization of the economy, and to be more specific, of industrial production systems results in a new quality of information transparency. This is the basis for added values in terms of effectiveness, quality, and individuality. However, these added values also result in an increased exposure to Cyber-Security threats, due to the increased digitalization, information transparency and standardization. In this work, the procedural model for a Cyber-Security analysis based on reference architecture model Industry 4.0 (RAMI 4.0) and the VDI/VDE guideline 2182 is exemplary shown for the use case of a Cloud-based monitoring of the production. The derived procedure supports the identification of protection demands and allows a risk-based selection of suitable countermeasures.
Senescence in Health and Disease

Many cellular stresses activate senescence, a persistent hyporeplicative state characterized in part by expression of the p16INK4a cell-cycle inhibitor. Senescent cell production occurs throughout life and plays beneficial roles in a variety of physiological and pathological processes including embryogenesis, wound healing, host immunity, and tumor suppression. Meanwhile, the steady accumulation of senescent cells with age also has adverse consequences. These non-proliferating cells occupy key cellular niches and elaborate pro-inflammatory cytokines, contributing to aging-related diseases and morbidity. This model suggests that the abundance of senescent cells in vivo predicts "molecular," as opposed to chronologic, age and that senescent cell clearance may mitigate aging-associated pathology.
Polyphenols as dietary supplements: A double-edged sword

Keith r Martin Nutrition Program, Health Lifestyles research Center, College of Nursing and Health innovation, Arizona State University, 6950 east williams Field road Mesa, AZ 85212, USA Tel +1 480 727-1925 Fax +1 480 727-1064 email  Increased consumption of fruits and vegetables is associated with a lower risk of chronic disease such as cardiovascular disease, some forms of cancer, and neurodegeneration. Pro-oxidant-induced oxidative stress contributes to the pathogenesis of numerous chronic diseases and, as such, dietary antioxidants can quench and/or retard such processes. Dietary polyphenols, ie, phenolic acids and flavonoids, are a primary source of antioxidants for humans and are derived from plants including fruits, vegetables, spices, and herbs. Based on compelling evidence regarding the health effects of polyphenol-rich foods, new dietary supplements and polyphenol-rich foods are being developed for public use. Consumption of such products can increase dietary polyphenol intake and subsequently plasma concentrations beyond expected levels associated with dietary consumption and potentially confer additional health benefits. Furthermore, bioavailability can be modified to further increase absorption and ultimately plasma concentrations of polyphenols. However, the upper limit for plasma concentrations of polyphenols before the elaboration of adverse effects is unknown for many polyphenols. Moreover, a considerable amount of evidence is accumulating which supports the hypothesis that high-dose polyphenols can mechanistically cause adverse effects through pro-oxidative action. Thus, polyphenol-rich dietary supplements can potentially confer additional benefits but high-doses may elicit toxicity thereby establishing a double-edge sword in supplement use.
Capsule Networks Against Medical Imaging Data Challenges

A key component to the success of deep learning is the availability of massive amounts of training data. Building and annotating large datasets for solving medical image classification problems is today a bottleneck for many applications. Recently, capsule networks were proposed to deal with shortcomings of Convolutional Neural Networks (ConvNets). In this work, we compare the behavior of capsule networks against ConvNets under typical datasets constraints of medical image analysis, namely, small amounts of annotated data and class-imbalance. We evaluate our experiments on MNIST, Fashion-MNIST and medical (histological and retina images) publicly available datasets. Our results suggest that capsule networks can be trained with less amount of data for the same or better performance and are more robust to an imbalanced class distribution, which makes our approach very promising for the medical imaging community.
Automatic Drum Transcription for Polyphonic Recordings Using Soft Attention Mechanisms and Convolutional Neural Networks

Automatic drum transcription is the process of generating symbolic notation for percussion instruments within audio recordings. To date, recurrent neural network (RNN) systems have achieved the highest evaluation accuracies for both drum solo and polyphonic recordings, however the accuracies within a polyphonic context still remain relatively low. To improve accuracy for polyphonic recordings, we present two approaches to the ADT problem: First, to capture the dynamism of features in multiple time-step hidden layers, we propose the use of soft attention mechanisms (SA) and an alternative RNN configuration containing additional peripheral connections (PC). Second, to capture these same trends at the input level, we propose the use of a convolutional neural network (CNN), which uses a larger set of time-step features. In addition, we propose the use of a bidirectional recurrent neural network (BRNN) in the peak-picking stage. The proposed systems are evaluated along with two state-of-the-art ADT systems in five evaluation scenarios, including a newly-proposed evaluation methodology designed to assess the generalisability of ADT systems. The results indicate that all of the newly proposed systems achieve higher accuracies than the stateof-the-art RNN systems for polyphonic recordings and that the additional BRNN peak-picking stage offers slight improvement in certain contexts.
Multichannel LSTM-CRF for Named Entity Recognition in Chinese Social Media

Named Entity Recognition (NER) is a tough task in Chinese social media due to a large portion of informal writings. Existing research uses only limited in-domain annotated data and achieves low performance. In this paper, we utilize both limited in-domain data and enough out-of-domain data using a domain adaptation method. We propose a multichannel LSTM-CRF model that employs different channels to capture general patterns, in-domain patterns and out-of-domain patterns in Chinese social media. The extensive experiments show that our model yields 9.8% improvement over previous state-of-the-art methods. We further find that a shared embedding layer is important and randomly initialized embeddings are better than the pretrained ones.
Overview of deep learning in medical imaging.

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.
Infrastructure Enabled Autonomy: A Distributed Intelligence Architecture for Autonomous Vehicles

Multiple studies have illustrated the potential for dramatic societal, environmental and economic benefits from significant penetration of autonomous driving. However, all the current approaches to autonomous driving require the automotive manufacturers to shoulder the primary responsibility and liability associated with replacing human perception and decision making with automation, potentially slowing the penetration of autonomous vehicles, and consequently slowing the realization of the societal benefits of autonomous vehicles. We propose here a new approach to autonomous driving that will re-balance the responsibility and liabilities associated with autonomous driving between traditional automotive manufacturers, private infrastructure players, and third-party players. Our proposed distributed intelligence architecture leverages the significant advancements in connectivity and edge computing in the recent decades to partition the driving functions between the vehicle, edge computers on the road side, and specialized third-party computers that reside in the vehicle. Infrastructure becomes a critical enabler for autonomy. With this Infrastructure Enabled Autonomy (IEA) concept, the traditional automotive manufacturers will only need to shoulder responsibility and liability comparable to what they already do today, and the infrastructure and third-party players will share the added responsibility and liabilities associated with autonomous functionalities. We propose a Bayesian Network Model based framework for assessing the risk benefits of such a distributed intelligence architecture. An additional benefit of the proposed architecture is that it enables “autonomy as a service” while still allowing for private ownership of automobiles.
Fusing LIDAR and images for pedestrian detection using convolutional neural networks

In this paper, we explore various aspects of fusing LIDAR and color imagery for pedestrian detection in the context of convolutional neural networks (CNNs), which have recently become state-of-art for many vision problems. We incorporate LIDAR by up-sampling the point cloud to a dense depth map and then extracting three features representing different aspects of the 3D scene. We then use those features as extra image channels. Specifically, we leverage recent work on HHA  (horizontal disparity, height above ground, and angle) representations, adapting the code to work on up-sampled LIDAR rather than Microsoft Kinect depth maps. We show, for the first time, that such a representation is applicable to up-sampled LIDAR data, despite its sparsity. Since CNNs learn a deep hierarchy of feature representations, we then explore the question: At what level of representation should we fuse this additional information with the original RGB image channels? We use the KITTI pedestrian detection dataset for our exploration. We first replicate the finding that region-CNNs (R-CNNs)  can outperform the original proposal mechanism using only RGB images, but only if fine-tuning is employed. Then, we show that: 1) using HHA features and RGB images performs better than RGB-only, even without any fine-tuning using large RGB web data, 2) fusing RGB and HHA achieves the strongest results if done late, but, under a parameter or computational budget, is best done at the early to middle layers of the hierarchical representation, which tend to represent midlevel features rather than low (e.g. edges) or high (e.g. object class decision) level features, 3) some of the less successful methods have the most parameters, indicating that increased classification accuracy is not simply a function of increased capacity in the neural network.
The Timing of Resource Development and Sustainable Competitive Advantage

We deepen and extend resource-level theorizing about sustainable competitive advantage by developing a formal model of resource development in competitive markets. Our model incorporates three important barriers to imitation: time compression diseconomies, causal ambiguity and the magnitude of ...xed investments. Time compression diseconomies are derived from a micro-model of resource development with diminishing returns to effort. We characterize two dimensions of sustainability: whether a resource is imitable and how long imitation takes. We identify conditions under which competitive advantage does not lead to superior performance and show that an imitator can sometimes bene...t from increases in causal ambiguity. Despite recent criticisms, we rea¢rm the usefulness of a resource-level of analysis, especially when the focus is on resources developed through internal projects with identi...able stopping times.
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence

Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.
Mosaik - Smart Grid Simulation API - Toward a Semantic based Standard for Interchanging Smart Grid Simulations

Simulation is an important method to test and evaluate new control mechanisms for future Smart Grids. To generate sound simulation results one has to use validated and established simulation models. In this paper we present the first two out of six layers of our approach to a modular simulation framework, called mosaik. It will allows to specify, compose and simulate Smart Grid scenarios based on the reuse of existing, technologically heterogeneous simulation models. The layers presented here include a syntactic layer defining an interface for Smart Grid simulators and a semantic layer implemented using a domain-specific language that allows to describe the structure and semantics of the simulator that is interfaced.
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network

Intrusion detection for computer network systems becomes one of the most critical tasks for network administrators today. It has an important role for organizations, governments and our society due to its valuable resources on computer networks. Traditional misuse detection strategies are unable to detect new and unknown intrusion. Besides , anomaly detection in network security is aim to distinguish between illegal or malicious events and normal behavior of network systems. Anomaly detection can be considered as a classification problem where it builds models of normal network behavior, which it uses to detect new patterns that significantly deviate from the model. Most of the current research on anomaly detection is based on the learning of normally and anomaly behaviors. They do not take into account the previous, recent events to detect the new incoming one. In this paper, we propose a real time collective anomaly detection model based on neural network learning and feature operating. Normally a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) is trained only on normal data and it is capable of predicting several time steps ahead of an input. In our approach, a LSTM RNN is trained with normal time series data before performing a live prediction for each time step. Instead of considering each time step separately, the observation of prediction errors from a certain number of time steps is now proposed as a new idea for detecting collective anomalies. The prediction errors from a number of the latest time steps above a threshold will indicate a collective anomaly. The model is built on a time series version of the KDD 1999 dataset. The experiments demonstrate that it is possible to offer reliable and efficient for collective anomaly detection.
CS 224 N Project : Natural Language Inference for Quora Dataset

Attention-based Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) cells were applied to identify duplicate question-pairs in the Quora dataset. The implementation of this architecture as well as other neural architectures is described in this project. The optimal architecture was then chosen based on the F1 score. Hyper-parameter tuning was then performed to maximize the accuracy of the model. The final test score obtained was 0.82 with an F1 score of 0.76.
Semantically Equivalent Adversarial Rules for Debugging NLP models

Complex machine learning models for NLP are often brittle, making different predictions for input instances that are extremely similar semantically. To automatically detect this behavior for individual instances, we present semantically equivalent adversaries (SEAs) – semantic-preserving perturbations that induce changes in the model’s predictions. We generalize these adversaries into semantically equivalent adversarial rules (SEARs) – simple, universal replacement rules that induce adversaries on many instances. We demonstrate the usefulness and flexibility of SEAs and SEARs by detecting bugs in black-box state-of-the-art models for three domains: machine comprehension, visual questionanswering, and sentiment analysis. Via user studies, we demonstrate that we generate high-quality local adversaries for more instances than humans, and that SEARs induce four times as many mistakes as the bugs discovered by human experts. SEARs are also actionable: retraining models using data augmentation significantly reduces bugs, while maintaining accuracy.
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

Current Zero-Shot Learning (ZSL) approaches are restricted to recognition of a single dominant unseen object category in a test image. We hypothesize that this setting is ill-suited for real-world applications where unseen objects appear only as a part of a complex scene, warranting both the ‘recognition’ and ‘localization’ of an unseen category. To address this limitation, we introduce a new ‘Zero-Shot Detection’ (ZSD) problem setting, which aims at simultaneously recognizing and locating object instances belonging to novel categories without any training examples. We also propose a new experimental protocol for ZSD based on the highly challenging ILSVRC dataset, adhering to practical issues, e.g., the rarity of unseen objects. To the best of our knowledge, this is the first end-to-end deep network for ZSD that jointly models the interplay between visual and semantic domain information. To overcome the noise in the automatically derived semantic descriptions, we utilize the concept of meta-classes to design an original loss function that achieves synergy between max-margin class separation and semantic space clustering. Furthermore, we present a baseline approach extended from recognition to detection setting. Our extensive experiments show significant performance boost over the baseline on the imperative yet difficult ZSD problem.
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures, which bring network functions and contents to the network edge, are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks, including definition, architecture, and advantages. Next, a comprehensive survey of issues on computing, caching, and communication techniques at the network edge is presented. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks, such as cloud technology, SDN/NFV, and smart devices are discussed. Finally, open research challenges and future directions are presented as well.
Double ridged horn antenna designs for wideband applications

In this paper, different double ridged horn antenna (DRHA) designs are investigated for wideband applications. A classic design of 1–18 GHz DRHA with exponential ridges is modelled and the antenna pattern deficiencies are detected at frequencies above 12 GHz. The antenna pattern is optimized by modification of the antenna structure. However, the impedance matching is affected and the VSWR is increased at the frequencies below 2 GHz. The matching problem can be resolved by adding lossy materials to the back cavity of antenna. We have shown reduction of the antenna efficiency by 15% over the whole frequency range, except at the lower frequencies.