nils-herrmann/clean_scirepeval huggingface dataset:
Stane: synthesized surfaces for tactile input

Stane is a hand-held interaction device controlled by tactile input: scratching or rubbing textured surfaces and tapping. The system has a range of sensors, including contact microphones, capacitive sensing and inertial sensing, and provides audio and vibrotactile feedback. The surface textures vary around the device, providing perceivably different textures to the user. We demonstrate that the vibration signals generated by stroking and scratching these surfaces can be reliably classified, and can be used as a very cheaply manufacturable way to control different aspects of interaction. The system is demonstrated as a control for a music player.
Smart Massive MIMO: An Infrastructure toward 5th Generation Smart Cities Network

, Ali Jaber 3 , Vahid Meghdadi 2 Cances  e r r Pie , Jean 1 oud b Ahmad Ab  1,2,3,Department Of Components Circuits Signals And High Frequency Systems Xlim labs, University Of Limoges Limoges, France 4Department of Statistic, Lebanese University,  On the Optimizing of Wireless Networks and toward improving the future 5th Generation mobile Network Infrastructure, we propose a novel infrastructure that can be the next Smart City Network. Our proposed Infrastructure takes into consideration most future demands and challenges, includes Capacity, Reliability, Scalability, and Flexibility. To deal with this issues we propose a wireless network infrastructure that is based on latest technologies of Massive MIMO systems. We further extend our infrastructure with many smart features, to be capable of coping with Cloud Computing, Smartphones, IoT and other intelligence-based services. The proposed infrastructure uses Network Functions Virtualization (NFV), Software-Defined Networking (SDN), Virtual Antenna Arrays (VAA) and Joint Beamforming to afford flexibility. We further propose a Terminalcentric rather than a Cell-centric based Infrastructure, which optimize interference aware environment and lead to higher capacity and reliability. The new infrastructure includes multi-purpose nodes that run a Network Operating System (NOS). This node will afford a scalable and flexible cost effective and semi-distributed network resources. Other propositions that meet Power-Effective, Cost-Effective, and Scenery aware design are discussed.
Multi-view 3D Object Detection Network for Autonomous Driving

This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes. We encode the sparse 3D point cloud with a compact multi-view representation. The network is composed of two subnetworks: one for 3D object proposal generation and another for multi-view feature fusion. The proposal network generates 3D candidate boxes efficiently from the birds eye view representation of 3D point cloud. We design a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths. Experiments on the challenging KITTI benchmark show that our approach outperforms the state-of-the-art by around 25% and 30% AP on the tasks of 3D localization and 3D detection. In addition, for 2D detection, our approach obtains 14.9% higher AP than the state-of-the-art on the hard data among the LIDAR-based methods.
[Internet- and computer game addiction: phenomenology, comorbidity, etiology, diagnostics and therapeutic implications for the addictives and their relatives].

Excessive and addictive internet use and computer game playing is reported as an increasing problem in outpatient care. The aim of this paper is to give an overview about the current scientific discussion of the overuse and addiction of internet and computer game playing.



Pubmed was used for a systematic literature research considering original papers and review articles dealing with Internet/computer game addiction.



Recent epidemiological data from Germany suggest that 1.5-3.5 % of adolescent computer and internet users show signs of an overuse or addictive use of computer and video games. Moreover there is evidence that the disorder is associated with higher rates of depression, anxiety, as well as lower achievements e. g. at school. Although the nosological assignment still remains unclear there is some evidence from neurobiological data that the disorder can be conceptualized as behavioral addiction. As treatment strategy CBT-techniques have been proposed, but there is still a lack of controlled clinical trials concerning their efficacy.



Since the addicted persons often show little motivation for a behavioural change we consider it a promising approach to treat and train their relatives with the aim of increasing the motivation for a behavioural change of the addicted person.
Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals

Gaussian Process (GP) regression models typically assume that residuals are Gaussian and have the same variance for all observations. However, applications with input-dependent noise (heteroscedastic residuals) frequently arise in practice, as do applications in which the residuals do not have a Gaussian distribution. In this paper, we propose a GP Regression model with a latent variable that serves as an additional unobserved covariate for the regression. This model (which we call GPLC) allows for heteroscedasticity since it allows the function to have a changing partial derivative with respect to this unobserved covariate. With a suitable covariance function, our GPLC model can handle (a) Gaussian residuals with input-dependent variance, or (b) nonGaussian residuals with input-dependent variance, or (c) Gaussian residuals with constant variance. We compare our model, using synthetic datasets, with a model proposed by Goldberg, Williams and Bishop (1998), which we refer to as GPLV, which only deals with case (a), as well as a standard GP model which can handle only case (c). Markov Chain Monte Carlo methods are developed for both modelsl. Experiments show that when the data is heteroscedastic, both GPLC and GPLV give better results (smaller mean squared error and negative log-probability density) than standard GP regression. In addition, when the residual are Gaussian, our GPLC model is generally nearly as good as GPLV, while when the residuals are non-Gaussian, our GPLC model is better than GPLV.
Building Information Modeling and real world knowledge: A methodological approach to accurate semantic documentation for the built environment

Building Information Modeling is considered by the scientific literature as an emerging trend in the architectural documentation scenario, as it is basically a digital representation of physical and functional features of facilities, serving as a shared knowledge resource during their whole life cycle. BIM is actually a process (not a software, as someone indicated), in which different players act sharing data through digital models in a coordinated, consistent and always up to date workflow, in order to reach reliability and higher quality all over the construction process. This way BIM tools were originally meant to ease the design of new architectures, generated by parametric geometries connected through hierarchical relationships of “smart objects” (components self-aware of their identity and conscious of their interactions with each other). However, this approach can also be successfully applied to what already exists: TLS (Terrestrial Laser Scanning) or digital photogrammetry are supposed to be the first abstraction step in a methodology proposal intended as a scientific strategy in which BIM, relying on its own semantic splitting attitude and its topological structure, is explicitly used in representation of existing buildings belonging to the Cultural Heritage. Presenting some progresses in the development of a specific free Autodesk Revit plug-in, nicknamed GreenSpider after its capability to layout points in the digital domain as if they were nodes of an ideal cobweb, this paper examines how point clouds collected during high definition surveys can be processed with accuracy in a BIM environment, highlighting critical aspects and advantages deriving from the application of parametric techniques to the real world domain representation.
Perturbative Neural Networks

Convolutional neural networks are witnessing wide adoption in computer vision systems with numerous applications across a range of visual recognition tasks. Much of this progress is fueled through advances in convolutional neural network architectures and learning algorithms even as the basic premise of a convolutional layer has remained unchanged. In this paper, we seek to revisit the convolutional layer that has been the workhorse of state-of-the-art visual recognition models. We introduce a very simple, yet effective, module called a perturbation layer as an alternative to a convolutional layer. The perturbation layer does away with convolution in the traditional sense and instead computes its response as a weighted linear combination of non-linearly activated additive noise perturbed inputs. We demonstrate both analytically and empirically that this perturbation layer can be an effective replacement for a standard convolutional layer. Empirically, deep neural networks with perturbation layers, called Perturbative Neural Networks (PNNs), in lieu of convolutional layers perform comparably with standard CNNs on a range of visual datasets (MNIST, CIFAR-10, PASCAL VOC, and ImageNet) with fewer parameters.
Security Viewpoint in a Reference Architecture Model for Cyber-Physical Production Systems

Cyber-physical Production Systems (CPPS) are one of the technical driving forces behind the transformation of industrial production towards "digital factory of the future" in the context of Industry 4.0. Security is a major concern for such systems as they become more intelligent, interconnected, and coupled with physical devices. For various security activities from security analysis to designing security controls and architecture, a systematic and structured view and presentation of security-related information is required. Based on the draft standard of Reference Architecture Model for Industry 4.0 (RAMI 4.0), we propose a practical approach to establish a security viewpoint in the CPPS reference architecture model. We investigate the feasibility of using an architecture modeling tool to implement the concept and leverage existing work on models of layered architecture. We demonstrate the applicability for security analysis in two example case studies.
Optimizing Soft Real-Time Scheduling Performance for Virtual Machines with SRT-Xen

Multimedia applications are an important part of today's Internet. However, currently most virtualization solutions, including Xen, lack adequate support for soft real-time tasks. Soft real-time applications, e.g. media workloads, are impeded by components of virtualization, such as the increase of scheduling latency. This paper focuses on improving scheduling scheme to support soft real-time workloads in virtualization systems. In this paper, we present an enhanced scheduler SRT-Xen. SRT-Xen can promote the soft real-time domain's performance compared with Xen's existing scheduling. It focuses on not only bringing a new realtime-friendly scheduling framework with corresponding strategies but also improving the management of the virtual CPUs' queuing in order to implement a fair scheduling mechanism for both real-time and non-real-time tasks. Finally, we use PESQ (Perceptual Evaluation of Speech Quality) and other benchmarks to evaluate and compare SRT-Xen with some other works. The results show that SRT-Xen supports soft real-time domains well without penalizing non-real-time ones.
Positive emotions in early life and longevity: findings from the nun study.

Handwritten autobiographies from 180 Catholic nuns, composed when participants were a mean age of 22 years, were scored for emotional content and related to survival during ages 75 to 95. A strong inverse association was found between positive emotional content in these writings and risk of mortality in late life (p < .001). As the quartile ranking of positive emotion in early life increased, there was a stepwise decrease in risk of mortality resulting in a 2.5-fold difference between the lowest and highest quartiles. Positive emotional content in early-life autobiographies was strongly associated with longevity 6 decades later. Underlying mechanisms of balanced emotional states are discussed.
Readings in information visualization - using vision to think

The use of computer-supported, interactive, visual representations of data to amplify cognition. (6) Cognition is the acquisition or use of knowledge. This definition has the virtue of focusing as much on the purpose of visualization as the means. Hamming (1973) saud, “the purpose of computation is insight, not numbers.” Likewise for visualization, “the purpose of visualization is insight not pictures.” The main goals of this insight are discovery, decision  making, and explanation.
Regularization for Unsupervised Deep Neural Nets

Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep belief networks (DBNs), are powerful tools for feature selection and pattern recognition tasks. We demonstrate that overfitting occurs in such models just as in deep feedforward neural networks, and discuss possible regularization methods to reduce overfitting. We also propose a “partial” approach to improve the efficiency of Dropout/DropConnect in this scenario, and discuss the theoretical justification of these methods from model convergence and likelihood bounds. Finally, we compare the performance of these methods based on their likelihood and classification error rates for various pattern recognition data sets.
Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its energy demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a system's ability to satisfy demand by improving its predictions. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a wind or solar energy harvesting prediction, and quantify its accuracy. We evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a sensor testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts in both wind- and solar-powered sensor systems increases each system's ability to satisfy its demands compared with existing prediction strategies.
Ensemble Classifiers for Network Intrusion Detection System

Two of the major challenges in designing anomaly intrusion detection are to maximize detection accuracy and to minimize false alarm rate. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each adopts different learning paradigms. The techniques deployed in this ensemble model are; Linear Genetic Programming (LGP), Adaptive Neural Fuzzy Inference System (ANFIS) and Random Forest (RF). The strengths from the individual models were evaluated and ensemble rule was formulated. Prior to classification, a 2-tier feature selection process was performed to expedite the detection process. Empirical results show an improvement in detection accuracy for all classes of network traffic; Normal, Probe, DoS, U2R and R2L. Random Forest, which is an ensemble learning technique that generates many classification trees and aggregates the individual result was also able to address imbalance dataset problem that many of machine learning techniques fail to sufficiently address it.
SPOT the Drug! An Unsupervised Pattern Matching Method to Extract Drug Names from Very Large Clinical Corpora

Although structured electronic health records are becoming more prevalent, much information about patient health is still recorded only in unstructured text. “Understanding” these texts has been a focus of natural language processing (NLP) research for many years, with some remarkable successes, yet there is more work to be done. Knowing the drugs patients take is not only critical for understanding patient health (e.g., for drug-drug interactions or drug-enzyme interaction), but also for secondary uses, such as research on treatment effectiveness. Several drug dictionaries have been curated, such as RxNorm, FDA's Orange Book, or NCI, with a focus on prescription drugs. Developing these dictionaries is a challenge, but even more challenging is keeping these dictionaries up-to-date in the face of a rapidly advancing field-it is critical to identify grapefruit as a “drug” for a patient who takes the prescription medicine Lipitor, due to their known adverse interaction. To discover other, new adverse drug interactions, a large number of patient histories often need to be examined, necessitating not only accurate but also fast algorithms to identify pharmacological substances. In this paper we propose a new algorithm, SPOT, which identifies drug names that can be used as new dictionary entries from a large corpus, where a “drug” is defined as a substance intended for use in the diagnosis, cure, mitigation, treatment, or prevention of disease. Measured against a manually annotated reference corpus, we present precision and recall values for SPOT. SPOT is language and syntax independent, can be run efficiently to keep dictionaries up-to-date and to also suggest words and phrases which may be misspellings or uncatalogued synonyms of a known drug. We show how SPOT's lack of reliance on NLP tools makes it robust in analyzing clinical medical text. SPOT is a generalized bootstrapping algorithm, seeded with a known dictionary and automatically extracting the context within which each drug is mentioned. We define three features of such context: support, confidence and prevalence. Finally, we present the performance tradeoffs depending on the thresholds chosen for these features.
Balanced Hashing and Efficient GPU Sparse General Matrix-Matrix Multiplication

General sparse matrix-matrix multiplication (SpGEMM) is a core component of many algorithms. A number of recent works have used high throughput graphics processing units (GPUs) to accelerate SpGEMM. However, exploiting the power of GPUs for SpGEMM requires addressing a number of challenges, including highly imbalanced workloads and large numbers of inefficient random global memory accesses. This paper presents a SpGEMM algorithm which uses several novel techniques to overcome these problems. We first propose two low cost methods to achieve perfect load balancing during the most expensive step in SpGEMM. Next, we show how to eliminate nearly all random global memory accesses using shared memory based hash tables. To optimize the performance of the hash tables, we propose a lightweight method to estimate the number of nonzeros in the output matrix. We compared our algorithm to the CUSP, CUSPARSE and the state-of-the-art BHSPARSE GPU SpGEMM algorithms, and show that it performs 5.6x, 2.4x and 1.5x better on average, and up to 11.8x, 9.5x and 2.5x better in the best case, respectively. Furthermore, we show that our algorithm performs especially well on highly imbalanced and unstructured matrices.
Surface-Wave Launchers for Beam Steering and Application to Planar Leaky-Wave Antennas

A linear array of surface-wave launchers (SWLs) is presented for surface-wave (SW) beam steering on a grounded dielectric slab (GDS). Specifically, a two-element array of directive Yagi-Uda like SWL antennas (operating between 21-24 GHz) is investigated. By varying the relative phase difference between SWL elements, the excited SW field distribution can be steered. In addition, a six-element linear array of SWLs with nonuniform weighting is also presented. If metallic gratings are placed on top of the GDS, the bound SW mode can be transformed into a radiated leaky-wave (LW) mode and directive beam patterns can be generated in the far field. A specific elliptical grating configuration is also investigated demonstrating single frequency cylindrical-sector LW beam steering. These slotted SWL arrays can be useful for novel SW beam scanning designs, millimeter wave power distribution systems and new LW antennas.
Language, cognition, and short-term memory in individuals with Down syndrome.

The developmentally emerging phenotype of language and cognition in individuals with Down syndrome is summarized on the basis of the project's prior work. Identified are a) the emerging divergence of expressive and receptive language, b) the emerging divergence of lexical and syntactic knowledge in each process, and c) the emerging divergence within cognitive skills of auditory short-term memory and visuospatial short-term memory from other visuospatial skills. Expressive syntax and auditory short-term memory are identified as areas of particular difficulty. Evidence for the continued acquisition of language skills in adolescence is presented. The role of the two components of working memory, auditory and visual, in language development is investigated in studies of narrative and longitudinal change in language skills. Predictors of individual differences during six years of language development are evaluated through hierarchical linear modelling. Chronological age, visuospatial short-term memory, and auditory-short term memory are identified as key predictors of performance at study entry, but not individual change over time, for expressive syntax. The same predictors account for variation in comprehension skill at study outset; and change over the six years can be predicted by chronological age and the change in visuospatial short-term memory skills.
Design considerations and technology assessment of phased-array antenna systems with RF MEMS for automotive radar applications

Planar array antennas are attractive for use in future automotive radar systems due to their flexibility in design and control of radar beams. The complexity and cost of a radar front-end phased array can be decreased by applying a beam-steering/switching concept, which reduces the number of parallel RF and baseband signal paths. RF-microelectromechanical systems (MEMS) subsystems are employed because of their excellent RF properties and potential low-cost manufacturability. We present design considerations for prototypical automotive applications of RF-MEMS-based automotive radar front-ends using phased-array antennas based on phase shifters or a Rotman lens. The single RF-MEMS switch is optimized with respect to its RF and thermomechanical behavior taking into account automotive requirements. The respective RF-MEMS subsystems, i.e., phase shifters and single-pole-multiple-throw switching networks are presented in conjunction with packaging and mounting approaches. We evaluate two different wafer-level packaging technologies using glass-frit sealing or polymer sealing. Finally, functional packaged devices are demonstrated: a glass-frit-sealed and flip-chip-mounted RF-MEMS switch and a benzocyclobutene-packaged single-pole-quadruple-throw switch network.