nils-herrmann/clean_scirepeval huggingface dataset:
Curved double-layer strip folded dipole antenna for WBAN applications

This paper presents an optimal design of strip folded dipole antenna with a stacking technique to stabilize the resonance frequency. The designed double-layer strip folded dipole antenna worked well with the cylindrically curved surface on the human body. While being bent, the antenna will change the extended length in each layer within the stacking flexible copper-clad laminate. In this case, the properties of the antenna will be automatically adjusted. However, the extended length can occur at the contact point in each layer. So, we can choose between two points and four points by selecting different types of the designs. This reason, the performance of the extended length can be added by two and four when being compared to the previous multilayer design and the total layer of the new antenna was only two. The extended length of the antenna resulted in making antenna resonance frequency improvement become more stable. The contribution of proposed antenna was used only two layers of flexible copper-clad laminate that reduces a number of antenna's layers with a folded dipole antenna, which causes the designed less complicated, and the flexibility is advantageous for WBAN applications. The double-layer strip folded dipole antenna is designed for 2.45GHz WBAN application.
Incidence of alarplasty in primary and revision rhinoplasty in a private practice setting.

To evaluate the incidence of alarplasty in primary and revision rhinoplasty during a 3-year period.



We conducted a retrospective medical record review of patients undergoing primary and revision rhinoplasty between January 1, 2004, and December 31, 2006. Patients were divided into the following categories: primary or revision rhinoplasty, alarplasty, and ethnicity. The incidence of alarplasty was calculated and compared for primary vs revision rhinoplasty and for ethnicity. Statistically significant differences were confirmed using the chi(2) test.



A total of 168 patients underwent rhinoplasty with minimum follow-up of 6 months; 116 patients had primary rhinoplasty and 52 revision rhinoplasty. All alarplasties were bilateral. Of 50 alarplasties performed, 35 were in the primary group and 15 in the revision group. The incidence of alarplasty was 30.2% in the primary group and 28.8% in the revision group. Nonwhite patients had a higher overall incidence of alarplasty (63.0%) than did white patients (25.4%). Each ethnic category identified had a higher incidence of alarplasty than the white group.



Alarplasty is an important technique in rhinoplasty. The high incidence of alarplasty in revision rhinoplasty procedures suggests that alarplasty is underused by many surgeons currently performing rhinoplasty. One should consider alarplasty for all patients' noses, not just those of nonwhite ethnic rhinoplasty patients.
Compact THz LTCC Receiver Module for 300 GHz Wireless Communications

A compact, low-cost, fully-integrated package solution has been developed for 300 GHz short-range communication systems. Using low-temperature co-fired ceramic (LTCC) technology, an integrated reflector for the on-chip antenna and high-data-rate signal interconnections including a flip-chip and via transition are embedded in a package. A reduced-size silicon lens antenna is placed in a package cavity together with a flip-chip bonded receiver IC with an on-chip antenna. The overall size of the front-end receiver is only 10 × 10 × 4 mm3, including the 6 mm diameter silicon lens. This compact terahertz receiver, mounted on an evaluation board, demonstrated wireless links with data rates up to 27Gb/s.
A novel three-phase DC/DC converter for high-power applications

This paper proposes a new three-phase series resonant converter. The principle working of the converter is described analytically in detail for switching frequencies equal to the resonant frequency and for switching frequencies greater than the resonant frequency. Additionally, detailed simulations are performed to analyse the converter. Based on the analysis, design criteria for a 5 kW breadboard are derived. The 5 kW breadboard version of the converter has been built to validate the analytical investigations and simulations experimentally. Moreover, the breadboard is used to investigate the ZVS and ZCS possibilities of the topology and the influence of the deadtime and the switching frequency on the overall efficiency.
Design and construction of organic computing systems

The next generation of embedded computing systems will have to meet new challenges. The systems are expected to act mainly autonomously, to dynamically adapt to changing environments and to interact with one another if necessary. Such systems are called organic. Organic Computing systems are similar to autonomic computing systems. In addition Organic Computing systems often behave life-like and are inspired by nature/biological phenomena. Design and construction of such systems brings new challenges for the software engineering process. In this paper we present a framework for design, construction and analysis of organic computing systems. It can facilitate design and construction as well as it can be used to (semi-)formally define organic properties like self-configuration or self-adaptation. We illustrate the framework on a real-world case study from production automation.
Transport layer protocols and architectures for satellite networks

Designing efficient transmission mechanisms for advanced satellite networks is a demanding task, requiring the definition and the implementation of protocols and architectures well suited to this challenging environment. In particular, transport protocols performance over satellite networks is impaired by the characteristics of the satellite radio link, specifically by the long propagation delay and the possible presence of segment losses due to physical channel errors. The level of impact on performance depends upon the link design (type of constellation, link margin, coding and modulation) and operational conditions (link obstructions, terminal mobility, weather conditions, etc.). To address these critical aspects a number of possible solutions have been presented in the literature, ranging from limited modifications of standard protocols (e.g. TCP, transmission control protocol) to completely alternative protocol and network architectures. However, despite the great number of different proposals (or perhaps also because of it), the general framework appears quite fragmented and there is a compelling need of an integration of the research competences and efforts. This is actually the intent of the transport protocols research line within the European SatNEx (Satellite Network of Excellence) project. Stemming from the authors’ work on this project, this paper aims to provide the reader with an updated overview of all the possible approaches that can be pursued to overcome the limitations of current transport protocols and architectures, when applied to satellite communications. In the paper the possible solutions are classified in the following categories: optimization of TCP interactions with lower layers, TCP enhancements, performance enhancement proxies (PEP) and delay tolerant networks (DTN). Advantages and disadvantages of the different approaches, as well as their interactions, are investigated and discussed, taking into account performance improvement, complexity, and compliance to the standard semantics. From this analysis, it emerges that DTN architectures could integrate some of the most efficient solutions from the other categories, by inserting them in a new rigorous framework. These innovative architectures therefore may represent a promising solution for solving some of the important problems posed at the transport layer by satellite networks, at least in a medium-tolong-term perspective.
Discrete Delaunay: Boundary extraction from voxel objects

We present a discrete approach for boundary extraction from 3D image data. The proposed technique is based on the duality between the Voronoi graph computed accross the digital boundary and the Delaunay triangulation. The originality of the approach is that algorithms perform only integer arithmetic and the method does not suffer from standard round problems and numerical instabilities in the case of floating point computations. This method has been applied both on segmented anatomical structures and on manufactured objects presenting corners and edges. The experimental results show that the method allows to produce a polygonal boundary representation which is guaranteed to be a 2-manifold. This representation is successfully transformed into a triangular quality mesh which meets all topological and geometrical requirements of applications such as augmented reality or simulation.
"Oops, I Did It Again" - Security of One-Time Signatures Under Two-Message Attacks

One-time signatures (OTS) are called one-time, because the accompanying security reductions only guarantee security under single-message attacks. However, this does not imply that efficient attacks are possible under two-message attacks. Especially in the context of hash-based OTS (which are basic building blocks of recent standardization proposals) this leads to the question if accidental reuse of a one-time key pair leads to immediate loss of security or to graceful degradation. In this work we analyze the security of the most prominent hash-based OTS, Lamport’s scheme, its optimized variant, and WOTS, under different kinds of two-message attacks. Interestingly, it turns out that the schemes are still secure under two message attacks, asymptotically. However, this does not imply anything for typical parameters. Our results show that for Lamport’s scheme, security only slowly degrades in the relevant attack scenarios and typical parameters are still somewhat secure, even in case of a two-message attack. As we move on to optimized Lamport and its generalization WOTS, security degrades faster and faster, and typical parameters do not provide any reasonable level of security under two-message attacks.
Deep Neural Network Based Subspace Learning of Robotic Manipulator Workspace Mapping

The manipulator workspace mapping is an important problem in robotics and has attracted significant attention in the community. However, most of the pre-existing algorithms have expensive time complexity due to the reliance on sophisticated kinematic equations. To solve this problem, this paper introduces subspace learning (SL), a variant of subspace embedding, where a set of robot and scope parameters is mapped to the corresponding workspace by a deep neural network (DNN). Trained on a large dataset of around 6× 10 samples obtained from a MATLAB R © implementation of a classical method and sampling of designed uniform distributions, the experiments demonstrate that the embedding significantly reduces run-time from 5.23× 10 s of traditional discretization method to 0.224 s, with high accuracies (average F-measure is 0.9665 with batch gradient descent and resilient backpropagation).
Expressing Emotions with the Social Robot Probo

Probo is a huggable animal-like robot, designed to act as a social interface. It will be used as a platform to study human robot interaction (HRI) while employing human-like social cues and communication modalities. The robot has a fully actuated head, with 20 degrees of freedom, capable of showing facial expressions and making eyecontact. The basic facial expressions are represented as a vector in the 2-dimensional emotion space based on Russel’s circomplex model of affect (Posner et al.  2005). The recognition of the underlying emotions based on the robot’s facial expressions were tested in different user studies and compared with similar robotic projects. This paper describes the concepts of the robot Probo and the ability to express emotional states.
WearIA: Wearable device implicit authentication based on activity information

Privacy and authenticity of data pushed by or into wearable devices are of important concerns. Wearable devices equipped with various sensors can capture user's activity in fine-grained level. In this work, we investigate the possibility of using user's activity information to develop an implicit authentication approach for wearable devices. We design and implement a framework that does continuous and implicit authentication based on ambulatory activities performed by the user. The system is validated using data collected from 30 participants with wearable devices worn across various regions of the body. The evaluation results show that the proposed approach can achieve as high as 97% accuracy rate with less than 1% false positive rate to authenticate a user using a single wearable device. And the accuracy rate can go up to 99.6% when we use the fusion of multiple wearable devices.
Depression Scale Recognition from Audio, Visual and Text Analysis

Depression is a major mental health disorder that is rapidly affecting lives worldwide. Depression not only impacts emotional but also physical and psychological state of the person. Its symptoms include lack of interest in daily activities, feeling low, anxiety, frustration, loss of weight and even feeling of selfhatred. This report describes work done by us for Audio Visual Emotion Challenge (AVEC) 2017 during our second year BTech summer internship. With the increase in demand to detect depression automatically with the help of machine learning algorithms, we present our multimodal feature extraction and decision level fusion approach for the same. Features are extracted by processing on the provided Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) database. Gaussian Mixture Model (GMM) clustering and Fisher vector approach were applied on the visual data; statistical descriptors on gaze, pose; low level audio features and head pose and text features were also extracted. Classification is done on fused as well as independent features using Support Vector Machine (SVM) and neural networks. The results obtained were able to cross the provided baseline on validation data set by 17% on audio features and 24.5% on video features. AVEC 2017, SVM, Depression, Neural network, RMSE, MAE, fusion, speech processing.
The cortical organization of speech processing

Despite decades of research, the functional neuroanatomy of speech processing has been difficult to characterize. A major impediment to progress may have been the failure to consider task effects when mapping speech-related processing systems. We outline a dual-stream model of speech processing that remedies this situation. In this model, a ventral stream processes speech signals for comprehension, and a dorsal stream maps acoustic speech signals to frontal lobe articulatory networks. The model assumes that the ventral stream is largely bilaterally organized — although there are important computational differences between the left- and right-hemisphere systems — and that the dorsal stream is strongly left-hemisphere dominant.
Energy-Aware Dual-Path Geographic Routing to Bypass Routing Holes in Wireless Sensor Networks

Geographic routing has been considered as an attractive approach for resource-constrained wireless sensor networks (WSNs) since it exploits local location information instead of global topology information to route data. However, this routing approach often suffers from the routing hole (i.e., an area free of nodes in the direction closer to destination) in various environments such as buildings and obstacles during data delivery, resulting in route failure. Currently, existing geographic routing protocols tend to walk along only one side of the routing holes to recover the route, thus achieving suboptimal network performance such as longer delivery delay and lower delivery ratio. Furthermore, these protocols cannot guarantee that all packets are delivered in an energy-efficient manner once encountering routing holes. In this paper, we focus on addressing these issues and propose an energy-aware dual-path geographic routing (EDGR) protocol for better route recovery from routing holes. EDGR adaptively utilizes the location information, residual energy, and the characteristics of energy consumption to make routing decisions, and dynamically exploits two node-disjoint anchor lists, passing through two sides of the routing holes, to shift routing path for load balance. Moreover, we extend EDGR into three-dimensional (3D) sensor networks to provide energy-aware routing for routing hole detour. Simulation results demonstrate that EDGR exhibits higher energy efficiency, and has moderate performance improvements on network lifetime, packet delivery ratio, and delivery delay, compared to other geographic routing protocols in WSNs over a variety of communication scenarios passing through routing holes. The proposed EDGR is much applicable to resource-constrained WSNs with routing holes.
Autonomous Vehicles for Smart and Sustainable Cities: An In-Depth Exploration of Privacy and Cybersecurity Implications

Amidst rapid urban development, sustainable transportation solutions are required to meet the increasing demands for mobility whilst mitigating the potentially negative social, economic, and environmental impacts. This study analyses autonomous vehicles (AVs) as a potential transportation solution for smart and sustainable development. We identified privacy and cybersecurity risks of AVs as crucial to the development of smart and sustainable cities and examined the steps taken by governments around the world to address these risks. We highlight the literature that supports why AVs are essential for smart and sustainable development. We then identify the aspects of privacy and cybersecurity in AVs that are important for smart and sustainable development. Lastly, we review the efforts taken by federal governments in the US, the UK, China, Australia, Japan, Singapore, South Korea, Germany, France, and the EU, and by US state governments to address AV-related privacy and cybersecurity risks in-depth. Overall, the actions taken by governments to address privacy risks are mainly in the form of regulations or voluntary guidelines. To address cybersecurity risks, governments have mostly resorted to regulations that are not specific to AVs and are conducting research and fostering research collaborations with the private sector.
Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning

Detection and rejection of adversarial examples in security sensitive and safety-critical systems using deep CNNs is essential. In this paper, we propose an approach to augment CNNs with out-distribution learning in order to reduce misclassification rate by rejecting adversarial examples. We empirically show that our augmented CNNs can either reject or classify correctly most adversarial examples generated using well-known methods (> 95% for MNIST and > 75% for CIFAR-10 on average). Furthermore, we achieve this without requiring to train using any specific type of adversarial examples and without sacrificing the accuracy of models on clean samples significantly (< 4%).
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Point Clouds

The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking. Recent computer vision research has demonstrated that Mask R-CNN can be trained to segment specific categories of objects in RGB images when massive handlabeled datasets are available. As generating these datasets is time-consuming, we instead train with synthetic depth images. Many robots now use depth sensors, and recent results suggest training on synthetic depth data can generalize well to the real world. We present a method for automated dataset generation and rapidly generate a training dataset of 50k depth images and 320k object masks synthetically using simulated scenes of 3D CAD models. We train a variant of Mask R-CNN on the generated dataset to perform category-agnostic instance segmentation without hand-labeled data. We evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution images of challenging, denselycluttered bins containing objects with highly-varied geometry. SD Mask R-CNN outperforms point cloud clustering baselines by an absolute 15% in Average Precision and 20% in Average Recall, and achieves performance levels similar to a Mask RCNN trained on a massive, hand-labeled RGB dataset and fine-tuned on real images from the experimental setup. The network also generalizes well to a lower-resolution depth sensor. We deploy the model in an instance-specific grasping pipeline to demonstrate its usefulness in a robotics application. Code, the synthetic training dataset, and supplementary material are available at https://bit.ly/2letCuE.
Comparison of the Full Outline of UnResponsiveness and Glasgow Liege Scale/Glasgow Coma Scale in an intensive care unit population.

The Full Outline of UnResponsiveness (FOUR) has been proposed as an alternative for the Glasgow Coma Scale (GCS)/Glasgow Liège Scale (GLS) in the evaluation of consciousness in severely brain-damaged patients. We compared the FOUR and GLS/GCS in intensive care unit patients who were admitted in a comatose state.



FOUR and GLS evaluations were performed in randomized order in 176 acutely (<1 month) brain-damaged patients. GLS scores were transformed in GCS scores by removing the GLS brainstem component. Inter-rater agreement was assessed in 20% of the studied population (N = 35). A logistic regression analysis adjusted for age, and etiology was performed to assess the link between the studied scores and the outcome 3 months after injury (N = 136).



GLS/GCS verbal component was scored 1 in 146 patients, among these 131 were intubated. We found that the inter-rater reliability was good for the FOUR score, the GLS/GCS. FOUR, GLS/GCS total scores predicted functional outcome with and without adjustment for age and etiology. 71 patients were considered as being in a vegetative/unresponsive state based on the GLS/GCS. The FOUR score identified 8 of these 71 patients as being minimally conscious given that these patients showed visual pursuit.



The FOUR score is a valid tool with good inter-rater reliability that is comparable to the GLS/GCS in predicting outcome. It offers the advantage to be performable in intubated patients and to identify non-verbal signs of consciousness by assessing visual pursuit, and hence minimal signs of consciousness (11% in this study), not assessed by GLS/GCS scales.
Psychopathy and the Five-factor model of personality: a replication and extension.

It has recently been argued that psychopathy can be understood and represented using common dimensions of personality taken from the Five-factor model (FFM). In this research, we examined this possibility by using the Revised NEO Personality Inventory (NEO-PI-R; Costa & McCrae, 1992) to assess psychopathy in an undergraduate sample. Specifically, we matched individuals' NEO-PI-R profiles with an expert-generated psychopathy prototype to yield a psychopathy score. These scores were correlated with self-reports of drug use, delinquency, risky sex, aggression, and several laboratory tasks. FFM psychopathy was significantly related to all forms of deviance, although the effects tended to be small in size. Moreover, individuals who more closely resembled the prototypic FFM psychopath were more aggressive in a laboratory aggression task, less willing to delay gratification in a time discounting task, and demonstrated a preference for aggressive responses in a social information-processing paradigm.