nils-herrmann/clean_scirepeval huggingface dataset:
Low-Cost X/Ku/Ka-Band Dual-Polarized Array With Shared Aperture

This paper presents a novel tri-band (X/Ku/Ka-band) planar antenna array with dual polarizations and shared aperture. Compared with traditional dual-polarized arrays, the proposed array has advantages of low cost, low profile, and high integration. Three types of antennas resonating at different frequencies, including the perforated patch, stacked patch, and slim crosspatch, are innovatively interleaved in the same aperture. The crosspatch fed by proximity coupling is presented as Ku-band element for its advantages of compact size, high isolation, and pure polarization. The techniques such as series feed and reverse feed are utilized to implement the six feed networks in a compact size with reduced cross polarizations. Measured results agree well with the simulations, showing three operation bands at X-, Ku-, and Ka-bands with the bandwidths of 3.6%, 6.7%, and 5.3%, respectively. The antenna also exhibits excellent radiation performance with the cross-polarization discrimination over 25 dB at the three bands. To the best of the author’s knowledge, this is the first shared-aperture X/Ku/Ka-band dual-polarized antenna array reported, which is useful for potential synthetic aperture radar applications.
Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding

This paper describes a single-image super-resolution (SR) algorithm based on nonnegative neighbor embedding. It belongs to the family of single-image example-based SR algorithms, since it uses a dictionary of low resolution (LR) and high resolution (HR) trained patch pairs to infer the unknown HR details. Each LR feature vector in the input image is expressed as the weighted combination of its K nearest neighbors in the dictionary; the corresponding HR feature vector is reconstructed under the assumption that the local LR embedding is preserved. Three key aspects are introduced in order to build a low-complexity competitive algorithm: (i) a compact but efficient representation of the patches (feature representation) (ii) an accurate estimation of the patches by their nearest neighbors (weight computation) (iii) a compact and already built (therefore external) dictionary, which allows a one-step upscaling. The neighbor embedding SR algorithm so designed is shown to give good visual results, comparable to other state-of-the-art methods, while presenting an appreciable reduction of the computational time.
Design and simulation of a four-arm hemispherical helix antenna realized through a stacked printed circuit board structure

Electrically small antennas have received much research attention and antenna designs have been developed that approach the Chu lower bound. One such antenna design is the spherical helix antenna which can achieve Q values that are 1.5 times the Chu limit. The spherical helix antenna presents a complex geometry that is a design challenge to implement, especially for use in printed circuit board assemblies. A novel hemispherical antenna design is developed and simulated that utilizes stacks of two-layer printed circuit boards to create the 3D hemispherical structure. The advantages of this approach are: the realization of complex antenna geometries using cost efficient printed circuit board technology and the ability to change substrate material to provide dielectric loading of the antenna, while achieving comparable performance with wire based designs.
Sample Selection for Statistical Parsing

Corpus-based statistical parsing relies on using large quantities of annotated text as training examples. Building this kind of resource is expensive and labor-intensive. This work proposes to use sample selection to find helpful training examples and reduce human effort spent on annotating less informative ones. We consider several criteria for predicting whether unlabeled data might be a helpful training example. Experiments are performed across two syntactic learning tasks and within the single task of parsing across two learning models to compare the effect of different predictive criteria. We find that sample selection can significantly reduce the size of annotated training corpora and that uncertainty is a robust predictive criterion that can be easily applied to different learning models.
A real-time grading method of apples based on features extracted from defects

This paper presents a hierarchical grading method applied to Jonagold apples. Several images covering the whole surface of the fruits were acquired thanks to a prototype grading machine. These images were then segmented and the features of the defects were extracted. During a learning procedure, the objects were classified into clusters by k-mean clustering. The classification probabilities of the objects were summarised and on this basis the fruits were graded using quadratic discriminant analysis. The fruits were correctly graded with a rate of 73 %. The errors were found having origins in the segmentation of the defects or for a particular wound, in a confusion with the calyx end.
Sexual arousal patterns of bisexual men.

There has long been controversy about whether bisexual men are substantially sexually aroused by both sexes. We investigated genital and self-reported sexual arousal to male and female sexual stimuli in 30 heterosexual, 33 bisexual, and 38 homosexual men. In general, bisexual men did not have strong genital arousal to both male and female sexual stimuli. Rather, most bisexual men appeared homosexual with respect to genital arousal, although some appeared heterosexual. In contrast, their subjective sexual arousal did conform to a bisexual pattern. Male bisexuality appears primarily to represent a style of interpreting or reporting sexual arousal rather than a distinct pattern of genital sexual arousal.
Cardiac Computed Tomography Angiography for Left Atrial Appendage Closure.

Atrial fibrillation is prevalent and percutaneous left atrial appendage (LAA) closure is increasingly performed worldwide. This procedure is technically challenging and the success and procedural complexities depend on anatomy of the LAA and surrounding structures. These are readily depicted on cardiac computed tomography angiography (CCTA), which offers unique imaging planes. CCTA allows not only preplanning anatomic LAA assessment, but can also be used to evaluate for pre-existing LAA thrombus, and done postprocedure for surveillance for device-related thrombus, residual leak, and complications. In this article, we review the practical utility of CCTA for LAA closure.
Markerless tracking using planar structures in the scene

We describea markerlesscamera tracking systemfor augmentedreality thatoperatesin environmentswhich contain oneor more planes. This is a commonspecialcase, which we showsignificantlysimplifiestracking. The result is a practical, reliable, vision-basedtracker. Furthermore, thetrackedplaneimposesa natural referenceframe, sothat thealignmentof thereal andvirtual coordinatesystemsis rather simplerthan would be the casewith a general structure-and-motionsystem.Multiple planescan be tracked, and additional data such as 2D point tracks are easilyincorporated.
Modern electrical machine analysis and design techniques applied to hybrid vehicle drive machines

In this paper a review is made of the different analysis and design techniques that can be used in modern electrical machine design, and references them to the developing area of hybrid vehicle drives. These machines are characterized by high transient torque requirement, compactness and forced cooling. While rare-earth magnet machines are commonly used in these applications, there is an increasing interest in motors without magnets. An example is made of the published Toyota Prius permanent-magnet machine specification. An alternative induction motor design is addressed.
Improving Power-Delay Performance of Ultra-Low-Power Subthreshold SCL Circuits

This brief presents a technique for improving the power-delay performance of subthreshold source-coupled logic (SCL) circuits. Based on the proposed approach, a source-follower buffer stage is used at the output of each SCL stage. Analytical results confirmed by measurements in 0.18-mum CMOS technology show an improvement by a factor of as high as 2.4 in power-delay product (PDP). It is also shown that the proposed technique can be used for implementing subthreshold ultra-low power SCL logic gates with a better power and area efficiency, compared to the traditional SCL subthreshold circuits. An optimized approach is proposed to improve the power efficiency of ultra-low power STSCL library cells.
Mandibular Rim Trilogy with Botulinum Toxin Injection: Reduction, Projection, and Lift.

"Onabotulinum toxin A (Botox) revolution" has brought the fundamental change in the facial rejuvenation as well as the concept of microinjection. The aesthetic standard tends to be the "globalization"; however, Asians have different aesthetic cultures and unique facial features compared with Caucasians. A new rejuvenation concept is proposed during our practice; the Asian face should preserve the original facial identity during Botox treatments. The lower face is treated with botulinum toxin to achieve a harmonious facial profile. Twenty young females ranging in age from 30 to 45 years consented and received the three-pronged procedure from March 2014 to April 2015; photography at baseline and follow-up visit were taken and analyzed. After posttreatment for 2 months, significant improvement was observed compared with the baseline. And the reduced masseter prominence and prominent chin were obtained, showing a favorable facial contour and harmonious appearance during the follow-up. The novel three-pronged approach to lower facial rejuvenation was aimed at the Asian characteristic of hypertrophic masseter, chin retrusion, and the facial sagging during the aging process. Botox treatment was a quite effective and safe strategy to improve the appearance and contour of the lower face in Asian patients.
Cipher Type Detection

Manual analysis and decryption of enciphered documents is a tedious and error prone work. Often—even after spending large amounts of time on a particular cipher—no decipherment can be found. Automating the decryption of various types of ciphers makes it possible to sift through the large number of encrypted messages found in libraries and archives, and to focus human effort only on a small but potentially interesting subset of them. In this work, we train a classifier that is able to predict which encipherment method has been used to generate a given ciphertext. We are able to distinguish 50 different cipher types (specified by the American Cryptogram Association) with an accuracy of 58.5%. This is a 11.2% absolute improvement over the best previously published classifier.
Question/Answer Matching for CQA System via Combining Lexical and Sequential Information

Community-based Question Answering (CQA) has become popular in knowledge sharing sites since it allows users to get answers to complex, detailed, and personal questions directly from other users. Large archives of historical questions and associated answers have been accumulated. Retrieving relevant historical answers that best match a question is an essential component of a CQA service. Most state of the art approaches are based on bag-of-words models, which have been proven successful in a range of text matching tasks, but are insufficient for capturing the important word sequence information in short text matching. In this paper, a new architecture is proposed to more effectively model the complicated matching relations between questions and answers. It utilises a similarity matrix which contains both lexical and sequential information. Afterwards the information is put into a deep architecture to find potentially suitable answers. The experimental study shows its potential in improving matching accuracy of question and answer.
Increasing psychological well-being and resilience by psychotherapeutic methods.

A specific psychotherapeutic strategy for increasing psychological well-being and resilience, well-being therapy, has been developed and validated in a number of randomized controlled trials. The findings indicate that flourishing and resilience can be promoted by specific interventions leading to a positive evaluation of one's self, a sense of continued growth and development, the belief that life is purposeful and meaningful, the possession of quality relations with others, the capacity to manage effectively one's life, and a sense of self-determination. A decreased vulnerability to depression and anxiety has been demonstrated after well-being therapy in high-risk populations. There are important implications for the state/trait dichotomy in psychological well-being and for the concept of recovery in mood and anxiety disorders.
Traffic Light Recognition for Complex Scene With Fusion Detections

Traffic light recognition is one of the important tasks in the studies of intelligent transport system. In this paper, a robust traffic light recognition model based on vision information is introduced for on-vehicle camera applications. Our contribution mainly includes three aspects. First, in order to reduce computational redundancy, the aspect ratio, area, location, and context of traffic lights are utilized as prior information, which establishes a task model for traffic light recognition. Second, in order to improve the accuracy, we propose a series of improved methods based on an aggregate channel feature method, including modifying the channel feature for each types of traffic light and establishing a structure of fusion detectors. Third, we introduce a method of inter-frame information analysis, utilizing detection information of previous frame to modify original proposal regions, which makes the accuracy further improved. In the comparison of other traffic light detection algorithms, our model achieves competitive results on the complex scene VIVA data set. Furthermore, an analysis of small target luminous object detection tasks is given.
Estimation , Navigation and Control of MultiRotor Drones in an Urban Wind Field

The paper presents an on-board estimation, navigation and control architecture for multi-rotor drones ying in urban environment. It consists of adaptive algorithms to estimate vehicle's aerodynamic drag coe cients with respect to still air and the urban wind components along the ight trajectory, with guaranteed fast and reliable convergence to the true values; navigation algorithms to generate feasible trajectories between given way-points that take into account the estimated wind; and of control algorithms to track the generated trajectories as long as the vehicle retains su cient number of functioning rotors capable of compensating for the estimated wind. All components of this on-board system are computationally e ective and are intended for a real time implementation. The algorithms were tested in simulations.
Selective Symbolic Execution

Symbolic execution is a powerful technique for analyzing program behavior, finding bugs, and generating tests, but suffers from severely limited scalability: the largest programs that can be symbolically executed today are on the order of thousands of lines of code. To ensure feasibility of symbolic execution, even small programs must curtail their interactions with libraries, the operating syst em, and hardware devices. This paper introduces s lective symbolic execution, a technique for creating the illusion of fullsystem symbolic execution, while symbolically running onl y the code that is of interest to the developer. We describe a prototype that can symbolically execute arbitrary portion s of a full system, including applications, libraries, opera ting system, and device drivers. It seamlessly transitions back and forth between symbolic and concrete execution, while transparently converting system state from symbolic to con crete and back. Our technique makes symbolic execution practical for large software that runs in real environments , without requiring explicit modeling of these environments .
Taxonomy of malware detection techniques: A systematic literature review

Malware is an international software disease. Research shows that the effect of malware is becoming chronic. To protect against malware detectors are fundamental to the industry. The effectiveness of such detectors depends on the technology used. Therefore, it is paramount that the advantages and disadvantages of each type of technology are scrutinized analytically. This study's aim is to scrutinize existing publications on this subject and to follow the trend that has taken place in the advancement and development with reference to the amount of information and sources of such literature. Many of the malware programs are huge and complicated and it is not easy to comprehend the details. Dissemination of malware information among users of the Internet and also training them to correctly use anti-malware products are crucial to protecting users from the malware onslaught. This paper will provide an exhaustive bibliography of methods to assist in combating malware.
GenomeGraphs: integrated genomic data visualization with R

Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses. We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system. GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.
Evaluating the Effectiveness of Serious Games for Cultural Awareness: The Icura User Study

There is an increasing awareness about the potential of serious games for education and training in many disciplines. However, research still witnesses a lack of methodologies, guidelines and best practices on how to develop effective serious games and how to integrate them in the actual learning and training processes. This process of integration heavily depends on providing and spreading evidence of the effectiveness of serious games This paper reports a user study to evaluate the effectiveness of Icura, a serious game about Japanese culture and etiquette. The evaluation methodology extends the set of instruments used in previous studies by evaluating the effects of the game on raising awareness, by avoiding the selective attention bias and by assessing the medium-term retention. . With this research we aim to provide a handy toolkit for evaluating the effectiveness a serious games for cultural awareness and heritage.
Connecting Intelligent Things in Smart Hospitals Using NB-IoT

The widespread use of Internet of Things (IoT), especially smart wearables, will play an important role in improving the quality of medical care, bringing convenience for patients and improving the management level of hospitals. However, due to the limitation of communication protocols, there exists non unified architecture that can connect all intelligent things in smart hospitals, which is made possible by the emergence of the Narrowband IoT (NB-IoT). In light of this, we propose an architecture to connect intelligent things in smart hospitals based on NB-IoT, and introduce edge computing to deal with the requirement of latency in medical process. As a case study, we develop an infusion monitoring system to monitor the real-time drop rate and the volume of remaining drug during the intravenous infusion. Finally, we discuss the challenges and future directions for building a smart hospital by connecting intelligent things.
Scaling Agile across the Global Organization: An Early Stage Industrial SAFe Self-Assessment

The adoption, scaling and tailoring of agile methods depends on several factors, such as the size of the software development organization, business goals, and operative model. The Scaled Agile Framework (SAFe) was developed to support organizations in scaling agile practices across the enterprise.
 Large multi-national enterprises report that adoption of SAFe led to significant productivity and quality gains. However, whether these benefits translate to small to medium sized enterprises (SMEs) is unknown.
 As such, in this study we ask: To what extent can SAFe practices be implemented in a global SME? We administrated three surveys to members of the development organization of an Irish SME, to identify and evaluate the adoption rate of SAFe practices in the Global environment.
 We found teams and program level personnel are transitioning well towards SAFe. But the Portfolio level personnel appear not to have, as yet, adopted many SAFe practices.
 In an SME context it might not be necessary for Portfolio level members to fully adopt SAFe providing they are supportive of their teams. The SAFe self-assessment highlighted where training is required, to improve the vertical communication between teams, program level and upper management.
CycleGAN Face-off

Face-off is an interesting case of style transfer where the facial expressions and attributes of one person could be fully transformed to another face. We are interested in the unsupervised training process which only requires two sequences of unaligned video frames from each person and learns what shared attributes to extract automatically. In this project, we explored various improvements for adversarial training (i.e. CycleGAN[Zhu et al., 2017]) to deal with the common problem of model collapse, to capture details in facial expressions and head poses, and thus transfer facial expressions with higher consistency and stability.
IoT security: Review, blockchain solutions, and open challenges

With the advent of smart homes, smart cities, and smart everything, the Internet of Things (IoT) has emerged as an area of incredible impact, potential, and growth, with Cisco Inc. predicting to have 50 billion connected devices by 2020. However, most of these IoT devices are easy to hack and compromise. Typically, these IoT devices are limited in compute, storage, and network capacity, and therefore they are more vulnerable to attacks than other endpoint devices such as smartphones, tablets, or computers. In this paper, we present and survey major security issues for IoT. We review and categorize popular security issues with regard to the IoT layered architecture, in addition to protocols used for networking, communication, and management. We outline security requirements for IoT along with the existing attacks, threats, and state-of-the-art solutions. Furthermore, we tabulate and map IoT security problems against existing solutions found in the literature. More importantly, we discuss, how blockchain, which is the underlying technology for bitcoin, can be a key enabler to solve many IoT security problems. The paper also identifies open research problems and challenges for IoT security.