nils-herrmann/clean_scirepeval huggingface dataset:
Monitoring and controlling of smart equipments using Android compatible devices towards IoT applications and services in manufacturing industry

The ever increasing requirements for information being accessible at any time, from any place, regardless the type of remote device or planned operation, together with the need of complete control of a specific scenario or device has paved the way towards the next technological revolution: Internet of Things (IoT) and led to several major research projects. Within this paperwork the authors' vision regarding the architecture of an IoT network and an experimental testing bench for one of the first steps leading towards implementing the IoT vision is briefly introduced. The first part presents an overview of the Internet of Things. In the second part, authors' concept regarding an architecture for IoT and the vision towards implementation it into manufacturing environments are presented. The third part illustrates the implementation and testing of the chosen solution for connectivity between a smart equipment and Android compatible devices. In the last part, conclusions are highlighted and the roadmap regarding to concept implementation is defined.
Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery

We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, the proposed mechanism is modular, architecture-independent and efficient both in terms of parameters and computation required. Experiments show that networks augmented with our approach systematically improve their classification accuracy and become more robust to clutter. As a result, Wide Residual Networks augmented with our proposal surpasses the state of the art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford dogs, and UEC Food-100.
A 77 GHz FM/CW radar frontend with a low-profile, low-loss printed antenna

Design and results of a 77 GHz FM/CW radar sensor based on a simple waveguide circuitry and a novel type of printed, low-profile, and low-loss antenna are presented. A Gunn VCO and a finline mixer act as transmitter and receiver, connected by two E-plane couplers. The folded reflector type antenna consists of a printed slot array and another planar substrate which, at the same time, provide twisting of the polarization and focussing of the incident wave. In this way, a folded low-profile, low-loss antenna can be realized. The performance of the radar is described, together with first results on a scanning of the antenna beam.
Alternative metrics in scientometrics: a meta-analysis of research into three altmetrics

Alternative metrics are currently one of the most popular research topics in scientometric research. This paper provides an overview of research into three of the most important altmetrics: microblogging (Twitter), online reference managers (Mendeley and CiteULike) and blogging. The literature is discussed in relation to the possible use of altmetrics in research evaluation. Since the research was particularly interested in the correlation between altmetrics counts and citation counts, this overview focuses particularly on this correlation. For each altmetric, a meta-analysis is calculated for its correlation with traditional citation counts. As the results of the meta-analyses show, the correlation with traditional citations for micro-blogging counts is negligible (pooled r = 0.003), for blog counts it is small (pooled r = 0.12) and for bookmark counts from online reference managers, medium to large (CiteULike pooled r = 0.23; Mendeley pooled r = 0.51).
Impact factors: use and abuse.

The impact factor is only one of three standardized measures created by the Institute of Scientific Information (ISI), which can be used to measure the way a journal receives citations to its articles over time. The build-up of citations tends to follow a curve like that of Figure 1. Citations to articles published in a given year rise sharply to a peak between two and six years after publication. From this peak citations decline exponentially. The citation curve of any journal can be described by the relative size of the curve (in terms of area under the line), the extent to which the peak of the curve is close to the origin and the rate of decline of the curve. These characteristics form the basis of the ISI indicators impact factor, immediacy index and cited half-life . The impact factor is a measure of the relative size of the citation curve in years 2 and 3. It is calculated by dividing the number of current citations a journal receives to articles published in the two previous years by the number of articles published in those same years. So, for example, the 1999 impact factor is the citations in 1999 to articles published in 1997 and 1998 divided by the number of articles published in 1997 and 1998. The number that results can be thought of as the average number of citations the average article receives per annum in the two years after the publication year. The immediacy index gives a measure of the skewness of the curve, that is, the extent to which the peak of the curve lies near the origin of the graph. It is calculated by dividing the citations a journal receives in the current year by the number of articles it publishes in that year, i.e., the 1999 immediacy index is the average number of citations in 1999 to articles published in 1999. The number that results can be thought of as the initial gradient of the citation curve, a measure of how quickly items in that journal get cited upon publication. The cited half-life is a measure of the rate of decline of the citation curve. It is the number of years that the number of current citations takes to decline to 50% of its initial value; the cited half-life is 6 years in the example given in . It is a measure of how long articles in a journal continue to be cited after publication.
On-the-fly detection of instability problems in floating-point program execution

The machine representation of floating point values has limited precision such that errors may be introduced during execution. These errors may get propagated and magnified by the following operations, leading to instability problems, e.g., control flow path may be undesirably altered and faulty output may be emitted. In this paper, we develop an on-the-fly efficient monitoring technique that can predict if an execution is stable. The technique does not explicitly compute errors as doing so incurs high overhead. Instead, it detects possible places where an error becomes substantially inflated regarding the corresponding value, and then tags the value with one bit to denote that it has an inflated error. It then tracks inflation bit propagation, taking care of operations that may cut off such propagation. It reports instability if any inflation bit reaches a critical execution point, such as a predicate, where the inflated error may induce substantial execution difference, such as different execution paths. Our experiment shows that with appropriate thresholds, the technique can correctly detect that over 99.999996% of the inputs of all the programs we studied are stable while a traditional technique relying solely on inflation detection mistakenly classifies majority of the inputs as unstable for some of the programs. Compared to the state of the art technique that is based on high precision computation and causes several hundred times slowdown, our technique only causes 7.91 times slowdown on average and can report all the true unstable executions with the appropriate thresholds.
A flexible and scalable SLAM system with full 3D motion estimation

For many applications in Urban Search and Rescue (USAR) scenarios robots need to learn a map of unknown environments. We present a system for fast online learning of occupancy grid maps requiring low computational resources. It combines a robust scan matching approach using a LIDAR system with a 3D attitude estimation system based on inertial sensing. By using a fast approximation of map gradients and a multi-resolution grid, reliable localization and mapping capabilities in a variety of challenging environments are realized. Multiple datasets showing the applicability in an embedded hand-held mapping system are provided. We show that the system is sufficiently accurate as to not require explicit loop closing techniques in the considered scenarios. The software is available as an open source package for ROS.
Cortical networks for working memory and executive functions sustain the conscious resting state in man

The cortical anatomy of the conscious resting state (REST) was investigated using a meta-analysis of nine positron emission tomography (PET) activation protocols that dealt with different cognitive tasks but shared REST as a common control state. During REST, subjects were in darkness and silence, and were instructed to relax, refrain from moving, and avoid systematic thoughts. Each protocol contrasted REST to a different cognitive task consisting either of language, mental imagery, mental calculation, reasoning, finger movement, or spatial working memory, using either auditory, visual or no stimulus delivery, and requiring either vocal, motor or no output. A total of 63 subjects and 370 spatially normalized PET scans were entered in the meta-analysis. Conjunction analysis revealed a network of brain areas jointly activated during conscious REST as compared to the nine cognitive tasks, including the bilateral angular gyrus, the left anterior precuneus and posterior cingulate cortex, the left medial frontal and anterior cingulate cortex, the left superior and medial frontal sulcus, and the left inferior frontal cortex. These results suggest that brain activity during conscious REST is sustained by a large scale network of heteromodal associative parietal and frontal cortical areas, that can be further hierarchically organized in an episodic working memory parieto-frontal network, driven in part by emotions, working under the supervision of an executive left prefrontal network.
Grounding words in perception and action: computational insights

We use words to communicate about things and kinds of things, their properties, relations and actions. Researchers are now creating robotic and simulated systems that ground language in machine perception and action, mirroring human abilities. A new kind of computational model is emerging from this work that bridges the symbolic realm of language with the physical realm of real-world referents. It explains aspects of context-dependent shifts of word meaning that cannot easily be explained by purely symbolic models. An exciting implication for cognitive modeling is the use of grounded systems to 'step into the shoes' of humans by directly processing first-person-perspective sensory data, providing a new methodology for testing various hypotheses of situated communication and learning.
Design of a Wideband Planar Printed Quasi-Yagi Antenna Using Stepped Connection Structure

Although conventional Yagi antenna has the advantage of unidirectional radiation patterns, it is not suitable for wideband applications due to its drawback of narrow bandwidth. In this communication, a compact wideband planar printed quasi-Yagi antenna is presented. The proposed quasi-Yagi antenna consists of a microstrip line to slotline transition structure, a driver dipole, and a parasitic strip element. The driver dipole is connected to the slotline through a coplanar stripline (CPS). The proposed antenna uses a stepped connection structure between the CPS and the slotline to improve the impedance matching. Two apertures are symmetrically etched in the ground plane to improve the unidirectional radiation characteristics. Simulation and experimental results show that the unidirectional radiation patterns of the proposed antenna are good. A 92.2% measured bandwidth with from 3.8 to 10.3 GHz is achieved. A moderate gain, which is better than 4 dBi, is also obtained.
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions

The large number of potential applications from bridging web data with knowledge bases have led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledge base. Potential applications include information extraction, information retrieval, and knowledge base population. However, this task is challenging due to name variations and entity ambiguity. In this survey, we present a thorough overview and analysis of the main approaches to entity linking, and discuss various applications, the evaluation of entity linking systems, and future directions.
Flexible dual-band LCP antenna for RFID applications

A dual-band antenna is developed on a flexible Liquid Crystal Polymer (LCP) substrate for simultaneous operation at 2.45 and 5.8 GHz in high frequency Radio Frequency IDentification (RFID) systems. The response of the low profile double T-shaped slot antenna is preserved when the antenna is placed on platforms such as wood and cardboard, and when bent to conform to a cylindrical plastic box. Furthermore, experiments show that the antenna is still operational when placed at a distance of around 5cm from a metallic surface.
Ensemble deep learning for speech recognition

Deep learning systems have dramatically improved the accuracy of speech recognition, and various deep architectures and learning methods have been developed with distinct strengths and weaknesses in recent years. How can ensemble learning be applied to these varying deep learning systems to achieve greater recognition accuracy is the focus of this paper. We develop and report linear and log-linear stacking methods for ensemble learning with applications specifically to speechclass posterior probabilities as computed by the convolutional, recurrent, and fully-connected deep neural networks. Convex optimization problems are formulated and solved, with analytical formulas derived for training the ensemble-learning parameters. Experimental results demonstrate a significant increase in phone recognition accuracy after stacking the deep learning subsystems that use different mechanisms for computing high-level, hierarchical features from the raw acoustic signals in speech.
Breaking Cryptographic Implementations Using Deep Learning Techniques

Template attack is the most common and powerful profiled side channel attack. It relies on a realistic assumption regarding the noise of the device under attack: the probability density function of the data is a multivariate Gaussian distribution. To relax this assumption, a recent line of research has investigated new profiling approaches mainly by applying machine learning techniques. The obtained results are commensurate, and in some particular cases better, compared to template attack. In this work, we propose to continue this recent line of research by applying more sophisticated profiling techniques based on deep learning. Our experimental results confirm the overwhelming advantages of the resulting new attacks when targeting both unprotected and protected cryptographic implementations.
Can training in a real-time strategy video game attenuate cognitive decline in older adults?

Declines in various cognitive abilities, particularly executive control functions, are observed in older adults. An important goal of cognitive training is to slow or reverse these age-related declines. However, opinion is divided in the literature regarding whether cognitive training can engender transfer to a variety of cognitive skills in older adults. In the current study, the authors trained older adults in a real-time strategy video game for 23.5 hr in an effort to improve their executive functions. A battery of cognitive tasks, including tasks of executive control and visuospatial skills, were assessed before, during, and after video-game training. The trainees improved significantly in the measures of game performance. They also improved significantly more than the control participants in executive control functions, such as task switching, working memory, visual short-term memory, and reasoning. Individual differences in changes in game performance were correlated with improvements in task switching. The study has implications for the enhancement of executive control processes of older adults.
A randomized controlled trial of compassion cultivation training : Effects on mindfulness , affect , and emotion regulation

Compassion is a positive orientation towards suffering that may be enhanced through compassion training and is thought to influence psychological functioning. However, the effects of compassion training on mindfulness, affect, and emotion regulation are not known. We conducted a randomized controlled trial in which 100 adults from the community were randomly assigned to either a 9-week compassion cultivation training (CCT) or a waitlist (WL) control condition. Participants completed self-report inventories that measured mindfulness, positive and negative affect, and emotion regulation. Compared to WL, CCT resulted in increased mindfulness and happiness, as well as decreased worry and emotional suppression. Within CCT, the amount of formal meditation practiced was related to reductions in worry and emotional suppression. These findings suggest that compassion cultivation training effects cognitive and emotion factors that support psychological flexible and adaptive functioning.
Internal and External Motivation to Respond Without Prejudice

Empirical evidence is presented from 7 samples regarding the factor structure; reliability; and convergent, discriminant, and predictive validity of separate measures of internal and external motivation to respond without prejudice. The scales reliably measure largely independent constructs and have good convergent and discriminant validity. Examination of the qualitatively distinct affective reactions to violations of ownand other-based standards as a function of the source of motivation to respond without prejudice provides evidence for the predictive validity of the scales. The final study demonstrated that reported stereotype endorsement varies as a function of motivation and whether reports are made in private or publicly Results are discussed in terms of their support for the internal-external distinction and the significance of this distinction for identifying factors that may either promote or thwart prejudice reduction.
Narrowband Internet of Things: Implementations and Applications

Recently, narrowband Internet of Things (NB-IoT), one of the most promising low power wide area (LPWA) technologies, has attracted much attention from both academia and industry. It has great potential to meet the huge demand for machine-type communications in the era of IoT. To facilitate research on and application of NB-IoT, in this paper, we design a system that includes NB devices, an IoT cloud platform, an application server, and a user app. The core component of the system is to build a development board that integrates an NB-IoT communication module and a subscriber identification module, a micro-controller unit and power management modules. We also provide a firmware design for NB device wake-up, data sensing, computing and communication, and the IoT cloud configuration for data storage and analysis. We further introduce a framework on how to apply the proposed system to specific applications. The proposed system provides an easy approach to academic research as well as commercial applications.
Use of lean and six sigma methodology to improve operating room efficiency in a high-volume tertiary-care academic medical center.

Operating rooms (ORs) are resource-intense and costly hospital units. Maximizing OR efficiency is essential to maintaining an economically viable institution. OR efficiency projects often focus on a limited number of ORs or cases. Efforts across an entire OR suite have not been reported. Lean and Six Sigma methodologies were developed in the manufacturing industry to increase efficiency by eliminating non-value-added steps. We applied Lean and Six Sigma methodologies across an entire surgical suite to improve efficiency.



A multidisciplinary surgical process improvement team constructed a value stream map of the entire surgical process from the decision for surgery to discharge. Each process step was analyzed in 3 domains, ie, personnel, information processed, and time. Multidisciplinary teams addressed 5 work streams to increase value at each step: minimizing volume variation; streamlining the preoperative process; reducing nonoperative time; eliminating redundant information; and promoting employee engagement. Process improvements were implemented sequentially in surgical specialties. Key performance metrics were collected before and after implementation.



Across 3 surgical specialties, process redesign resulted in substantial improvements in on-time starts and reduction in number of cases past 5 pm. Substantial gains were achieved in nonoperative time, staff overtime, and ORs saved. These changes resulted in substantial increases in margin/OR/day.



Use of Lean and Six Sigma methodologies increased OR efficiency and financial performance across an entire operating suite. Process mapping, leadership support, staff engagement, and sharing performance metrics are keys to enhancing OR efficiency. The performance gains were substantial, sustainable, positive financially, and transferrable to other specialties.
Single Sample Face Recognition via Learning Deep Supervised Autoencoders

This paper targets learning robust image representation for single training sample per person face recognition. Motivated by the success of deep learning in image representation, we propose a supervised autoencoder, which is a new type of building block for deep architectures. There are two features distinct our supervised autoencoder from standard autoencoder. First, we enforce the faces with variants to be mapped with the canonical face of the person, for example, frontal face with neutral expression and normal illumination; Second, we enforce features corresponding to the same person to be similar. As a result, our supervised autoencoder extracts the features which are robust to variances in illumination, expression, occlusion, and pose, and facilitates the face recognition. We stack such supervised autoencoders to get the deep architecture and use it for extracting features in image representation. Experimental results on the AR, Extended Yale B, CMU-PIE, and Multi-PIE data sets demonstrate that by coupling with the commonly used sparse representation-based classification, our stacked supervised autoencoders-based face representation significantly outperforms the commonly used image representations in single sample per person face recognition, and it achieves higher recognition accuracy compared with other deep learning models, including the deep Lambertian network, in spite of much less training data and without any domain information. Moreover, supervised autoencoder can also be used for face verification, which further demonstrates its effectiveness for face representation.