nils-herrmann/clean_scirepeval huggingface dataset:
5G Backhaul Challenges and Emerging Research Directions: A Survey

5G is the next cellular generation and is expected to quench the growing thirst for taxing data rates and to enable the Internet of Things. Focused research and standardization work have been addressing the corresponding challenges from the radio perspective while employing advanced features, such as network densification, massive multiple-input-multiple-output antennae, coordinated multi-point processing, inter-cell interference mitigation techniques, carrier aggregation, and new spectrum exploration. Nevertheless, a new bottleneck has emerged: the backhaul. The ultra-dense and heavy traffic cells should be connected to the core network through the backhaul, often with extreme requirements in terms of capacity, latency, availability, energy, and cost efficiency. This pioneering survey explains the 5G backhaul paradigm, presents a critical analysis of legacy, cutting-edge solutions, and new trends in backhauling, and proposes a novel consolidated 5G backhaul framework. A new joint radio access and backhaul perspective is proposed for the evaluation of backhaul technologies which reinforces the belief that no single solution can solve the holistic 5G backhaul problem. This paper also reveals hidden advantages and shortcomings of backhaul solutions, which are not evident when backhaul technologies are inspected as an independent part of the 5G network. This survey is key in identifying essential catalysts that are believed to jointly pave the way to solving the beyond-2020 backhauling challenge. Lessons learned, unsolved challenges, and a new consolidated 5G backhaul vision are thus presented.
A 2.45-GHz high-efficiency loop-shaped PIFA rectenna for portable devices and wireless sensors

An efficient compact rectenna based on a novel asymmetric loop-shaped planar inverted-F antenna (PIFA) is proposed for portable devices and wireless sensors. The antenna element, or the loop-shaped PIFA, has a compact uniplanar structure and exhibits higher radiation efficiency. The rectifier used is a half-bridge boost converter, which can double the output dc voltage. In between the rectifier and loop-shaped PIFA is a matching circuit with harmonic suppression capability, further enhancing the conversion efficiency. For the 2.45-GHz prototype design, the peak conversion efficiency of 61.4% is achieved as the incident power density is 7.4 mW/m2 and the load resistance is 510 Ω.
Validation of heart rate extraction through an iPhone accelerometer

Ubiquitous medical technology may provide advanced utility for evaluating the status of the patient beyond the clinical environment. The iPhone provides the capacity to measure the heart rate, as the iPhone consists of a 3-axis accelerometer that is sufficiently sensitive to perceive tiny body movements caused by heart pumping. In this preliminary study, an iPhone was tested and evaluated as the reliable heart rate extractor to use for medical purpose by comparing with reference electrocardiogram. By comparing the extracted heart rate from acquired acceleration data with the extracted one from ECG reference signal, iPhone functioning as the reliable heart rate extractor has demonstrated sufficient accuracy and consistency.
Diagnosing Performance Variations in HPC Applications Using Machine Learning

With the growing complexity and scale of high performance computing (HPC) systems, application performance variation has become a significant challenge in efficient and resilient system management. Application performance variation can be caused by resource contention as well as softwareand firmware-related problems, and can lead to premature job termination, reduced performance, and wasted compute platform resources. To effectively alleviate this problem, system administrators must detect and identify the anomalies that are responsible for performance variation and take preventive actions. However, diagnosing anomalies is often a difficult task given the vast amount of noisy and high-dimensional data being collected via a variety of system monitoring infrastructures. In this paper, we present a novel framework that uses machine learning to automatically diagnose previously encountered performance anomalies in HPC systems. Our framework leverages resource usage and performance counter data collected during application runs. We first convert the collected time series data into statistical features that retain application characteristics to significantly reduce the computational overhead of our technique. We then use machine learning algorithms to learn anomaly characteristics from this historical data and to identify the types of anomalies observed while running applications. We evaluate our framework both on an HPC cluster and on a public cloud, and demonstrate that our approach outperforms current state-of-the-art techniques in detecting anomalies, reaching an F-score over 0.97.
Low-cost medium-range optical underwater modem: short paper

Enhancing underwater instruments with networking capabilities is opening up exciting new opportunities for remote ocean exploration. Underwater communication is essential in enabling networking, and in this work, we focus specifically on those applications that require a communication range of 5 to 10 meters. We will show that optical wireless technology is an excellent candidate when cost-efficient design is paramount and data rate requirements are modest. Our optical modem uses inexpensive components, and compensates by employing sophisticated detection algorithms. Our design is validated experimentally, establishing it as a viable cost-effective communication technology for low range underwater applications.
CNN-Webshell: Malicious Web Shell Detection with Convolutional Neural Network

Malicious web shell detection is one of the most important methods for protecting the network security. Most state of the art methods are based on malicious keywords matching, where the keywords are usually defined by the domain experts. So its effect depends on the domain experts and it is hard to detect new type of malicious web shells. This paper proposed a new malicious web shell detection approach based on 'word2vec' representation and convolutional neural network (CNN). Firstly, each word, separated from the HTTP requests, is represented as a vector by using the 'word2vec' tool. Next, a web request can be represented as a size-fixed matrix. Finally, a CNN-based model is designed to classify the malicious web shells and the normal ones. Experimental results showed that this approach achieves the best performance, comparing with several other classification methods. To the best of our knowledge, this is the first time that CNN has been applied to malicious web shell detection.
Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification

Music tag words that describe music audio by text have different levels of abstraction. Taking this issue into account, we propose a music classification approach that aggregates multilevel and multi-scale features using pre-trained feature extractors. In particular, the feature extractors are trained in sample-level deep convolutional neural networks using raw waveforms. We show that this approach achieves state-of-the-art results on several music classification datasets.
The Cloud Adoption Toolkit: Addressing the Challenges of Cloud Adoption in Enterprise

Cloud computing promises a radical shift in the provisioning of computing resource within enterprise.  Toolkit that provides a framework to support decision-makers in identifying their concerns, and matching these concerns to appropriate tools/techniques that can be used to address them. The paper adopts a position paper methodology such that case study evidence is provided, where available, to support claims. We conclude that the Cloud Adoption Toolkit, whilst still under development, shows signs that it is a useful tool for decision-makers as it helps address the feasibility challenges of cloud adoption in enterprise.
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations

We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. canonical correlations (also known as principal or canonical angles), which can be thought of as the angles between two d-dimensional subspaces, have recently attracted attention for image set matching. Canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the two main classical methods: parametric distribution-based and nonparametric sample-based matching of sets. Here, this is first demonstrated experimentally for reasonably sized data sets using existing methods exploiting canonical correlations. Motivated by their proven effectiveness, a novel discriminative learning method over sets is proposed for set classification. Specifically, inspired by classical linear discriminant analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of within-class sets and minimizes the canonical correlations of between-class sets. Image sets transformed by the discriminant function are then compared by the canonical correlations. Classical orthogonal subspace method (OSM) is also investigated for the similar purpose and compared with the proposed method. The proposed method is evaluated on various object recognition problems using face image sets with arbitrary motion captured under different illuminations and image sets of 500 general objects taken at different views. The method is also applied to object category recognition using ETH-80 database. The proposed method is shown to outperform the state-of-the-art methods in terms of accuracy and efficiency
A survey of document image classification: problem statement, classifier architecture and performance evaluation

Document image classification is an important step in Office Automation, Digital Libraries, and other document image analysis applications. There is great diversity in document image classifiers: they differ in the problems they solve, in the use of training data to construct class models, and in the choice of document features and classification algorithms. We survey this diverse literature using three components: the problem statement, the classifier architecture, and performance evaluation. This brings to light important issues in designing a document classifier, including the definition of document classes, the choice of document features and feature representation, and the choice of classification algorithm and learning mechanism. We emphasize techniques that classify single-page typeset document images without using OCR results. Developing a general, adaptable, high-performance classifier is challenging due to the great variety of documents, the diverse criteria used to define document classes, and the ambiguity that arises due to ill-defined or fuzzy document classes.
Efficacy of Virtual Reality-Based Therapy on Balance in Children with Down Syndrome

Virtual reality-based therapy is one of the most innovative and promising recent developments in rehabilitation technology. Virtual reality is the use of interactive simulations created with computer hardware and software to present users with opportunities to engage in environments that appear to be and feel similar to real world objects and events. Wii-Fit is considered as one of the virtual reality-based therapy. Children with Down syndrome have lower scores on balance and agility tasks than do children with other mental impairments. The purpose of this study was to examine the effect of Wii-Fit on balance in children with Down syndrome. Balance was measured by Bruininks-Oseretsky Test of Motor Proficiency for 30 children with Down syndrome. The subjects were randomly divided into two groups of equal size (control and study). They ranged in age from 10 to 13 years old and they were selected from both genders. The control group received the traditional physical therapy program. A program of three Wii-Fit games was conducted for the study group in addition to the traditional physical therapy program. The program for both groups continued for six weeks. The results revealed high significant improvement of balance in the study group (p= 0.000) when compared with that of the control group indicating that Wii-Fit games as a virtual reality-based therapy could improve balance for children with Down syndrome.
1 Modeling , Control , and Flight Testing of a Small Ducted Fan Aircraft

Small ducted fan autonomous vehicles have potential for several applications, especially for missions in urban environments. This paper discusses the use of dynamic inversion with neural network adaptation to provide an adaptive controller for the GTSpy, a small ducted fan autonomous vehicle based on the Micro Autonomous Systems’ Helispy. This approach allows utilization of the entire low speed flight envelope with a relatively poorly understood vehicle. A simulator model is constructed from a force and moment analysis of the vehicle, allowing for a validation of the controller in preparation for flight testing. Data from flight testing of the system is provided.
A printed LPDA with UWB capability

This work deals with the design of a wideband microstrip log periodic array operating between 4 and 18 GHz (thus working in C,X and Ku bands). A few studies, since now, have been proposed but they are significantly less performing and usually quite complicated. Our solution is remarkably simple and shows both SWR and gain better than likely structures proposed in the literature. The same antenna can also be used as an UWB antenna. The design has been developed using CST MICROWAVE STUDIO 2009, a general purpose and specialist tool for the 3D electromagnetic simulation of microwave high frequency components.
The Elusive Nature of Executive Functions: A Review of our Current Understanding

Executive functions include abilities of goal formation, planning, carrying out goal-directed plans, and effective performance. This article aims at reviewing some of the current knowledge surrounding executive functioning and presenting the contrasting views regarding this concept. The neural substrates of the executive system are examined as well as the evolution of executive functioning, from development to decline. There is clear evidence of the vulnerability of executive functions to the effects of age over lifespan. The first executive function to emerge in children is the ability to inhibit overlearned behavior and the last to appear is verbal fluency. Inhibition of irrelevant information seems to decline earlier than set shifting and verbal fluency during senescence. The sequential progression and decline of these functions has been paralleled with the anatomical changes of the frontal lobe and its connections with other brain areas. Generalization of the results presented here are limited due to methodological differences across studies. Analysis of these differences is presented and suggestions for future research are offered.
Fast decorrelated neural network ensembles with random weights

Negative correlation learning (NCL) aims to produce ensembles with sound generalization capability through controlling the disagreement among base learners’ outputs. Such a learning scheme is usually implemented by using feed-forward neural networks with error back-propagation algorithms (BPNNs). However, it suffers from slow convergence, local minima problem and model uncertainties caused by the initial weights and the setting of learning parameters. To achieve a better solution, this paper employs the random vector functional link (RVFL) networks as base components, and incorporates with the NCL strategy for building neural network ensembles. The basis functions of the base models are generated randomly and the parameters of the RVFL networks can be determined by solving a linear equation system. An analytical solution is derived for these parameters, where a cost function defined for NCL and the well-known least squares method are used. To examine the merits of our proposed algorithm, a comparative study is carried out with nine benchmark datasets. Results indicate that our approach outperforms other ensembling techniques on the testing datasets in terms of both effectiveness and efficiency.
Design of Microstrip-to-Microstrip Via Transition in Multilayered LTCC for Frequencies up to 67 GHz

A wide-band microstrip-to-microstrip via transition proposed for connecting an integrated circuit chip and an antenna array on the opposite sides of a multilayered low-temperature co-fired ceramic substrate is investigated in this paper. To facilitate the design, it is decomposed into external and internal segments, which consist of two microstrip-to-via transitions and a multilayered through-hole via with four ground vias, respectively. The equivalent impedance of the internal segment is carefully calculated from the lump-circuit model extracted by Ansoft Q3D, and verified by high-frequency structure simulation (HFSS). The S-parameters of external segments are simulated by HFSS for impedance matching, thereby obtaining the appropriate physical parameters. The physical mechanisms that result in the insertion loss are explored in detail, and the effects of via diameter are also investigated for the reduction of insertion loss. By combining the designs of the external and internal segments, the simulated S-parameters demonstrated that the overall return loss level was above 20 dB within 57-67 GHz spectrum and insertion loss was better than 0.48 dB from d.c. up to 67 GHz. Coherent results between simulation and measurement were also obtained with a back-to-back transition structure.
Deep FisherNet for Object Classification

Despite the great success of convolutional neural networks (CNN) for the image classification task on datasets like Cifar and ImageNet, CNN’s representation power is still somewhat limited in dealing with object images that have large variation in size and clutter, where Fisher Vector (FV) has shown to be an effective encoding strategy. FV encodes an image by aggregating local descriptors with a universal generative Gaussian Mixture Model (GMM). FV however has limited learning capability and its parameters are mostly fixed after constructing the codebook. To combine together the best of the two worlds, we propose in this paper a neural network structure with FV layer being part of an end-to-end trainable system that is differentiable; we name our network FisherNet that is learnable using backpropagation. Our proposed FisherNet combines convolutional neural network training and Fisher Vector encoding in a single end-to-end structure. We observe a clear advantage of FisherNet over plain CNN and standard FV in terms of both classification accuracy and computational efficiency on the challenging PASCAL VOC object classification task.
An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown

Dynamic control theory has long been used in solving optimal asset allocation problems, and a number of trading decision systems based on reinforcement learning methods have been applied in asset allocation and portfolio rebalancing. In this paper, we extend the existing work in recurrent reinforcement learning (RRL) and build an optimal variable weight portfolio allocation under a coherent downside risk measure, the expected maximum drawdown, E(MDD). In particular, we propose a recurrent reinforcement learning method, with a coherent risk adjusted performance objective function, the Calmar ratio, to obtain both buy and sell signals and asset allocation weights. Using a portfolio consisting of the most frequently traded exchange-traded funds, we show that the expected maximum drawdown risk based objective function yields superior return performance compared to previously proposed RRL objective functions (i.e. the Sharpe ratio and the Sterling ratio), and that variable weight RRL long/short portfolios outperform equal weight RRL long/short portfolios under different transaction cost scenarios. We further propose an adaptive E(MDD) risk based RRL portfolio rebalancing decision system with a transaction cost and market condition stop-loss retraining mechanism, and we show that the ∗Corresponding author: Steve Y. Yang, Postal address: School of Business, Stevens Institute of Technology, 1 Castle Point on Hudson, Hoboken, NJ 07030 USA.   Email addresses: salmahdi@stevens.edu (Saud Almahdi), steve.yang@stevens.edu (Steve Y. Yang)  to Expert Systems with Applications June 15, 2017
A formal approach towards measuring trust in distributed systems

Emerging digital environments and infrastructures, such as distributed security services and distributed computing services, have generated new options of communication, information sharing, and resource utilization in past years. However, when distributed services are used, the question arises of to what extent we can trust service providers to not violate security requirements, whether in isolation or jointly. Answering this question is crucial for designing trustworthy distributed systems and selecting trustworthy service providers. This paper presents a novel trust measurement method for distributed systems, and makes use of propositional logic and probability theory. The results of the qualitative part include the specification of a formal trust language and the representation of its terms by means of propositional logic formulas. Based on these formulas, the quantitative part returns trust metrics for the determination of trustworthiness with which given distributed systems are assumed to fulfill a particular security requirement.