nils-herrmann/clean_scirepeval huggingface dataset:
Prediction of Heart Disease using Classification Algorithms

— The heart disease accounts to be the leading cause of death worldwide. It is difficult for medical practitioners to predict the heart attack as it is a complex task that requires experience and knowledge. The health sector today contains hidden information that can be important in making decisions. Data mining algorithms such as J48, Naïve Bayes, REPTREE, CART, and Bayes Net are applied in this research for predicting heart attacks. The research result shows prediction accuracy of 99%. Data mining enable the health sector to predict patterns in the dataset.
A Performance Evaluation of Lightweight Approaches to Virtualization

The growing prevalence of the microservice paradigm has initiated a shift away from operating single image appliances that host many services, towards encapsulating each service within individual, smaller images. As a result thereof, the demand for low-overhead virtualization techniques is increasing. While containerization approaches already enjoy great popularity, unikernels are emerging as alternative approaches. With both approaches undergoing rapid improvements, the current landscape of lightweight approaches to virtualization presents a confusing scenery, impeding the task of picking an adequate technology for an intended purpose. While previous work has mostly dealt with comparing the performance of either approach with whole-system virtualization, this work provides an overarching performance evaluation covering containers, unikernels, wholesystem virtualization, native hardware, and combinations thereof. Representing common workloads in cloud-based applications, we evaluate application performance by the example of HTTP servers and a key-value store. Lightweight Virtualization; Performance; Unikernel; Container
Support Vector Machines Under Adversarial Label Noise

Battista Biggio battista.biggio@diee.unica.it Dept. of Electrical and Electronic Engineering University of Cagliari Piazza d’Armi, 09123, Cagliari, Italy and Blaine Nelson blaine.nelson@wsii.uni-tuebingen.de Dept. of Mathematics and Natural Sciences Eberhard-Karls-Universität Tübingen Sand 1, 72076, Tübingen, Germany and Pavel Laskov pavel.laskov@uni-tuebingen.de Dept. of Mathematics and Natural Sciences Eberhard-Karls-Universität Tübingen Sand 1, 72076, Tübingen, Germany
Building Simulation Modeling Environments Using Systems Theory and Software Architecture Principles

Use of simulations for design of systems requires assurance that the underlying modeling theories and simulation strategies are correctly realized. For example, not only it is crucial to employ modeling and simulation theories, but it is also critical to develop software architectures (environments) that guarantee correct implementation of simulation Model execution. In this paper, we will (i) examine key features of an object-oriented modeling and simulation (M&S) environment with respect to its software architecture traits and (ii) propose an approach capable of supporting essential functionalities advocated by systemtheoretic modeling and simulation concepts and methods. We introduce a software architecture for discrete event M&S environments. We illustrate the software architecture using a prototype simulation tracking environment.
Dynamic graph metrics: Tutorial, toolbox, and tale

The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
An Integrated Biometric-Based Security Framework Using Wavelet-Domain HMM in Wireless Body Area Networks (WBAN)

In this paper, we proposed an integrated biometric-based security framework for wireless body area networks, which takes advantage of biometric features shared by body sensors deployed at different positions of a person's body. The data communications among these sensors are secured via the proposed authentication and selective encryption schemes that only require low computational power and less resources (e.g., battery and bandwidth). Specifically, a wavelet-domain Hidden Markov Model (HMM) classification is utilized by considering the non-Gaussian statistics of ECG signals for accurate authentication. In addition, the biometric information such as ECG parameters is selected as the biometric key for the encryption in the framework. Our experimental results demonstrated that the proposed approach can achieve more accurate authentication performance without extra requirements of key distribution and strict time synchronization.
Gamify Employee Collaboration - A Critical Review of Gamification Elements in Social Software

Social software solutions in enterprises such as IBM Connections are said to have the potential to support communication and collaboration among employees. However, companies are faced to manage the adoption of such collaborative tools and therefore need to raise the employees’ acceptance and motivation. To solve these problems, developers started to implement Gamification elements in social software tools, which aim to increase users’ motivation. In this research-in-progress paper, we give first insights and critically examine the current market of leading social software solutions to find out which Gamification approaches are implementated in such collaborative tools. Our findings show, that most of the major social collaboration solutions do not offer Gamification features by default, but leave the integration to a various number of third party plug-in vendors. Furthermore we identify a trend in which Gamification solutions majorly focus on rewarding quantitative improvement of work activities, neglecting qualitative performance. Subsequently, current solutions do not match recent findings in research and ignore risks that can lower the employees’ motivation and work performance in the long run.
On the Capacity of Face Representation

Face recognition is a widely used technology with numerous large-scale applications, such as surveillance, social media and law enforcement. There has been tremendous progress in face recognition accuracy over the past few decades, much of which can be attributed to deep learning based approaches during the last five years. Indeed, automated face recognition systems are now believed to surpass human performance in some scenarios. Despite this progress, a crucial question still remains unanswered: given a face representation, how many identities can it resolve? In other words, what is the capacity of the face representation? A scientific basis for estimating the capacity of a given face representation will not only benefit the evaluation and comparison of different face representation methods, but will also establish an upper bound on the scalability of an automatic face recognition system. We cast the face capacity estimation problem under the information theoretic framework of capacity of a Gaussian noise channel. By explicitly accounting for two sources of representational noise: epistemic (model) uncertainty and aleatoric (data) variability, our approach is able to estimate the capacity of any given face representation. To demonstrate the efficacy of our approach, we estimate the capacity of a 128-dimensional deep neural network based face representation, FaceNet , and that of the classical Eigenfaces  representation of the same dimensionality. Our numerical experiments on unconstrained faces indicate that, (a) our capacity estimation model yields a capacity upper bound of 5.8×108 for FaceNet and 1×100 for Eigenface representation at a false acceptance rate (FAR) of 1%, (b) the capacity of the face representation reduces drastically as you lower the desired FAR (for FaceNet representation; the capacity at FAR of 0.1% and 0.001% is 2.4×106 and 7.0×102, respectively), and (c) the empirical performance of the FaceNet representation is significantly below the theoretical limit.
Determination of the PID Controller Parameters by Modified Genetic Algorithm for Improved Performance

This paper presents an efficient and fast tuning method based on a modified genetic algorithm (MGA) structure to find the optimal parameters of the proportional-integralderivative (PID) controller so that the desired system specifications are satisfied. To achieve more effective search, the optimization algorithm is based on the integration of classical genetic algorithm structure and a systematic neighbourhood structure. To demonstrate the effectiveness of presented method, the step responses of closed loop system were compared with that of the existing methods in the literature. Simulation results indicate that the performance of the PID controlled system can be significantly improved by the MGA-based method.
Who will buy smart glasses? Empirical results of two pre-market-entry studies on the role of personality in individual awareness and intended adoption of Google Glass wearables

Recent market studies reveal that augmented reality (AR) devices, such as smart glasses, will substantially influence the media landscape. Yet, little is known about the intended adoption of smart glasses, particularly: Who are the early adopters of such wearables? We contribute to the growing body of research that investigates the role of personality in predicting media usage by analyzing smart glasses, such as Google Glass or Microsoft Hololens. First, we integrate AR devices into the current evolution of media and technologies. Then, we draw on the Big Five Model of human personality and present the results from two studies that investigate the direct and moderating effects of human personality on the awareness and innovation adoption of smart glasses. Our results show that open and emotionally stable consumers tend to be more aware of Google Glass. Consumers who perceive the potential for high functional benefits and social conformity of smart glasses are more likely to adopt such wearables. The strength of these effects is moderated by consumers’ individual personality, particularly by their levels of openness to experience, extraversion and neuroticism. This article concludes with a discussion of theoretical and managerial implications for research on technology adoption, and with suggestions for avenues for future research.
Event2Mind: Commonsense Inference on Events, Intents, and Reactions

We investigate a new commonsense inference task: given an event described in a short free-form text (“X drinks coffee in the morning”), a system reasons about the likely intents (“X wants to stay awake”) and reactions (“X feels alert”) of the event’s participants. To support this study, we construct a new crowdsourced corpus of 25,000 event phrases covering a diverse range of everyday events and situations. We report baseline performance on this task, demonstrating that neural encoder-decoder models can successfully compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. In addition, we demonstrate how commonsense inference on people’s intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts.
On the Number of RF Chains and Phase Shifters, and Scheduling Design With Hybrid Analog–Digital Beamforming

This paper considers hybrid beamforming (HB) for downlink multiuser massive multiple-input multiple-output (MIMO) systems with frequency selective channels. The proposed HB design employs sets of digitally controlled phase (fixed phase) paired phase shifters (PSs) and switches. For this system, first we determine the required number of radio frequency (RF) chains and PSs such that the proposed HB achieves the same performance as that of the digital beamforming (DB) which utilizes N (number of transmitter antennas) RF chains. We show that the performance of the DB can be achieved with our HB just by utilizing rt RF chains and 2rt(N-rt + 1) PSs, where rt ≤ N is the rank of the combined digital precoder matrices of all subcarriers. Second, we provide a simple and novel approach to reduce the number of PSs with only a negligible performance degradation. Numerical results reveal that only 20-40 PSs per RF chain are sufficient for practically relevant parameter settings. Finally, for the scenario where the deployed number of RF chains (Na) is less than rt, we propose a simple user scheduling algorithm to select the best set of users in each subcarrier. Simulation results validate theoretical expressions, and demonstrate the superiority of the proposed HB design over the existing HB designs in both flat fading and frequency selective channels.
Move prediction in Gomoku using deep learning

The Gomoku board game is a longstanding challenge for artificial intelligence research. With the development of deep learning, move prediction can help to promote the intelligence of board game agents as proven in AlphaGo. Following this idea, we train deep convolutional neural networks by supervised learning to predict the moves made by expert Gomoku players from RenjuNet dataset. We put forward a number of deep neural networks with different architectures and different hyperparameters to solve this problem. With only the board state as the input, the proposed deep convolutional neural networks are able to recognize some special features of Gomoku and select the most likely next move. The final neural network achieves the accuracy of move prediction of about 42% on the RenjuNet dataset, which reaches the level of expert Gomoku players. In addition, it is promising to generate strong Gomoku agents of human-level with the move prediction as a guide.
Embroidered Multiband Body-Worn Antenna for GSM/PCS/WLAN Communications

A novel textile-based body-worn antenna covering the Global System for Mobile Communications/personal communications services/wireless local-area network frequency bands is presented. This antenna was made of densely embroidered metal-coated polymer fibers (e-fibers). These e-fibers are 15 μm thick and consist of high strength, flexible polymer cores with conductive silver coatings, providing mechanical flexibility and low loss at radio frequencies. When measured in free space, the textile antenna showed comparable performance to its copper counterpart, having ~ 2 dBi realized gain at all three bands. This textile antenna was simulated and measured on a full body phantom to determine the body's influence on antenna performance, including frequency detuning and pattern shadowing. The measured radiation pattern of the body-worn antenna matched well with simulation at various on-body locations for the three bands. Field measurements were also carried out by mounting the antenna onto the shoulder of a jacket, and using it to replace the one of a cell phone. We found that the communication quality using the body-worn textile antenna was equivalent to the best location of the original cell-phone antenna. Therefore, this textile-based antenna provided for a more reliable body-worn communication when mounted on the body's shoulder.
Graphene oxide: a nonspecific enhancer of cellular growth.

There have been multiple conflicting reports about the biocompatibility and antimicrobial activity of graphene oxide. To address this, we conducted a study to characterize the antimicrobial properties of graphene oxide (GO) and its biocompatibility with mammalian cells. When GO was added to a bacterial culture at 25 μg/mL, the results showed that bacteria grew faster and to a higher optical density than cultures without GO. Scanning electron microscopy indicated that bacteria formed dense biofilms in the presence of GO. This was shown by a large mass of aggregated cells and extracellular polymeric material. Bacterial growth on filters coated with 25 and 75 μg of GO grew 2 and 3 times better than on filters without GO. Closer analysis showed that bacteria were able to attach and proliferate preferentially in areas containing the highest GO levels. Graphene oxide films failed to produce growth inhibition zones around them, indicating a lack of antibacterial properties. Also, bacteria were able to grow on GO films to 9.5 × 10(9) cells from an initial inoculation of 1.0 × 10(6), indicating that it also lacks bacteriostatic activity. Thus, silver-coated GO films were able to produce clearing zones and cell death. Also, graphene oxide was shown to greatly enhance the attachment and proliferation of mammalian cells. This study conclusively demonstrates that graphene oxide does not have intrinsic antibacterial, bacteriostatic, and cytotoxic properties in both bacteria and mammalian cells. Furthermore, graphene oxide acts as a general enhancer of cellular growth by increasing cell attachment and proliferation.
DiskFiltration: Data Exfiltration from Speakerless Air-Gapped Computers via Covert Hard Drive Noise

Air-gapped computers are disconnected from the Internet physically and logically. This measure is taken in order to prevent the leakage of sensitive data from secured networks. It has been shown that malware can exfiltrate data from air-gapped computers by transmitting ultrasonic signals via the computer’s speakers. However, such acoustic communication relies on the availability of speakers on a computer.
Working relations of technology production and use

This paper explores the relevance of recent feminist reconstructions of objectivity for the development of alternative visions of technology production and use. I take as my starting place the working relations that make up the design and use of technical systems. Working relations are understood as networks or webs of connections that sustain the visible and invisible work required to construct coherent technologies and put them into use. I outline the boundaries that characterize current relations of development and use, and the boundary crossings required to transform them. Three contrasting premises for design-the view from nowhere, detached engagement, and located accountability — are taken to represent incommensurate alternatives for a politics of professional design. From the position of located accountability, I close by sketching aspects of what a feminist politics and associated practices of system development could be.
Latency-aware composition of Virtual Functions in 5G

The adoption of the virtualization paradigm in both computing and networking domains portends a landscape of heterogeneous service capabilities and resources pervasively distributed and interconnected and deeply integrated through the 5G network infrastructure. In this service ecosystem, dynamic service demand can be flexibly and elastically accomplished by composing heterogeneous services provisioned over a distributed and virtualized resource infrastructure. Indeed, with the term Virtual Functions we refer to virtual computing as well as network service capabilities (e.g., routers and middlebox functions provided as Virtual Network Functions). In order to cope with the increasingly resource intensive demand, these virtual functions will be deployed in distributed clusters of small-scale datacenters typically located in current exchanges at the network edge and will supplement those deployed in traditional large cloud datacenters. In this work we formulate the problem of composing, computing and networking Virtual Functions to select those nodes along the path that minimizes the overall latency (i.e. network and processing latency) in the above mentioned scenario. The optimization problem is formulated as a Resource Constrained Shortest Path problem on an auxiliary layered graph accordingly defined. The layered structure of the graph ensures that the order of VFs specified in the request is preserved. Additional constraints can be also taken into account in the graph construction phase. Finally, we provide a use case preliminary evaluation of the proposed model.
Transcortical selective amygdalohippocampectomy in temporal lobe epilepsy.

Numerous studies of the electrophysiology and neuropathology of temporal lobe epilepsy have demonstrated the mesial temporal structures to be the site of seizure origin in the majority of cases. This is the rationale for a transcortical selective approach, first introduced by Niemeyer, for removal of the hippocampus and amygdala. Series from a number of centers have demonstrated the efficacy of selective amygdalohippocampectomy compared to a more traditional resection. The technique described here and used at the Montreal Neurological Institute (MNI) utilizes a strictly endopial resection of the hippocampal formation and amygdala in addition to computer image guidance to perform the procedure. Ninety-five percent of patients at the MNI who underwent selective amygdalohippocampectomy realized a cessation of seizures, or greater than 90% reduction, with minimal risk of complications.
Learning to rank for recommender systems

Recommender system aim at providing a personalized list of items ranked according to the preferences of the user, as such ranking methods are at the core of many recommendation algorithms. The topic of this tutorial focuses on the cutting-edge algorithmic development in the area of recommender systems. This tutorial will provide an in depth picture of the progress of ranking models in the field, summarizing the strengths and weaknesses of existing methods, and discussing open issues that could be promising for future research in the community. A qualitative and quantitative comparison between different models will be provided while we will also highlight recent developments in the areas of Reinforcement Learning.
Building a Big Data Platform for Smart Cities: Experience and Lessons from Santander

The Internet of Things (IoT) is now shaping our cities to make them more connected, convenient, and intelligent. However, this change will highly rely on extracted values and insights from the big data generated by our cities via sensors, devices, and human activities. Many existing studies and projects have been done to make our cities smart, focusing more on how to deploy various sensors and devices and then collect data from them. However, this is just the first step towards smart cities and next step will be to make good use of the collected data and enable context-awareness and intelligence into all kinds of applications and services via a flexible big data platform. In this paper, we introduce the system architecture and the major design issues of a live City Data and Analytics Platform, namely CiDAP. More importantly, we share our experience and lessons learned from building this practical system for a large scale running smart city test bed, SmartSantander. Our work provides a valuable example to future Smart City platform designers so that they can foresee some practice issues and refer to our solution when building their own smart city data platforms.