nils-herrmann/clean_scirepeval huggingface dataset:
Miniaturized and Reconfigurable CPW Square-Ring Slot Antenna Loaded With Ferroelectric BST Thin Film Varactors

A novel miniaturized and reconfigurable coplanar waveguide (CPW) square-ring slot antenna is presented in this paper. The miniaturization is achieved via a hybrid approach including ferroelectric varactor loadings, high dielectric constant materials, and tuning stub for impedance matching. For the first time, nine shunt ferroelectric (FE) BST (Ba<sub>(1-x)</sub>Sr<sub>x</sub>TiO<sub>3</sub>) thin film varactors are integrated with the CPW antenna structure achieving both antenna miniaturization and reconfiguration at the same time. The size of the miniaturized antenna is reduced to 0.067λ<sub>0</sub> 0.067λ<sub>0</sub> without ground, and 0.1λ<sub>0</sub> 0.1λ<sub>0</sub> with ground. The resonant frequency of the miniaturized antenna can be reconfigured from 5.3 GHz to 5.8 GHz by applying a DC voltage. Measured E-plane and H-plane co-polarized patterns are presented.
R-CNN minus R

Deep convolutional neural networks (CNNs) have had a major impact in most areas of image understanding, including object category detection. In object detection, methods such as R-CNN have obtained excellent results by integrating CNNs with region proposal generation algorithms such as selective search. In this paper, we investigate the role of proposal generation in CNN-based detectors in order to determine whether it is a necessary modelling component, carrying essential geometric information not contained in the CNN, or whether it is merely a way of accelerating detection. We do so by designing and evaluating a detector that uses a trivial region generation scheme, constant for each image. Combined with SPP, this results in an excellent and fast detector that does not require to process an image with algorithms other than the CNN itself. We also streamline and simplify the training of CNN-based detectors by integrating several learning steps in a single algorithm, as well as by proposing a number of improvements that accelerate detection.
Trichostasis Spinulosa Confirmed by Standard Skin Surface Biopsy

Trichostasis spinulosa (TS) is a common but rarely diagnosed disease. For diagnosis, it's sufficient to see a bundle of vellus hair located in a keratinous sheath microscopically. In order to obtain these vellus hair settled in comedone-like openings, Standard skin surface biopsy (SSSB), a non-invasive method was chosen. It's aimed to remind the differential diagnosis of TS in treatment-resistant open comedone-like lesions and discuss the SSSB method in diagnosis. A 25-year-old female patient was admitted with a complaint of the black spots located on bilateral cheeks and nose for 12 years. In SSSB, multiple vellus hair bundles in funnel-shaped structures were observed under the microscope, and a diagnosis of 'TS' was made. After six weeks of treatment with tretinoin 0.025% and 4% erythromycin jel topically, the appearance of black macules was significantly reduced. Treatment had to be terminated due to her pregnancy, and the lesions recurred within 1 month. It's believed that TS should be considered in the differential diagnosis of treatment-resistant open comedone-like lesions, and SSSB might be an inexpensive and effective alternative method for the diagnosis of TS.
Solar flare M-class prediction using artificial intelligence techniques

Currently, astronomical data have increased in terms of volume and complexity. To bring out the information in order to analyze and predict, the artificial intelligence techniques are required. This paper aims to apply artificial intelligence techniques to predict M-class solar flare. Artificial neural network, support vector machine and naïve bayes techniques are compared to define the best prediction performance accuracy technique. The dataset have been collected from daily data for 16 years, from 1998 to 2013. The attributes consist of solar flares data and sunspot number. The sunspots are a cooler spot on the surface of the sun, which have relation with solar flares. The Java-based machine learning WEKA is used for analysis and predicts solar flares. The best forecasted performance accuracy is achieved based on the artificial neural network method.
Clustering and Classification of Software Component for Efficient Component Retrieval and Building Component Reuse Libraries

A Software Repository is a collection of library files and function codes. Programmers and Engineers design develop and build software libraries in a continuous process. Selecting suitable function code from one among many in the repository is quite challenging and cumbersome as we need to analyze semantic issues in function codes or components. Clustering and Mining Software Components for efficient reuse is the current topic of interest among researchers in Software Reuse Engineering and Information Retrieval. A relatively less research work is contributed in this field and has a good scope in the future. In this paper, the main idea is to cluster the software components and form a subset of libraries from the available repository. These clusters thus help in choosing the required component with high cohesion and low coupling quickly and efficiently. We define a similarity function and use the same for the process of clustering the software components and for estimating the cost of new project. The approach carried out is a feature vector based approach.
HyViDE: a framework for virtual data center network embedding

Cloud computing technologies have matured enough that the service providers are compelled to migrate their services to virtualized infrastructure in cloud data centers. However, moving the computation and network to shared physical infrastructure poses a multitude of questions, both for service providers and for data center owners. In this work, we propose HyViDE - a framework for optimal placement of multiple virtual data center networks on a physical data center network. HyViDE preselects a subset of virtual data center network requests and uses a hybrid strategy for embedding them on the physical data center. Coordinated static and dynamic embedding algorithms are used in this hybrid framework to minimize the rejection of requests and fulfill QoS demands of the embedded networks. HyViDE can employ suitable static and dynamic strategies to meet the objectives of data center owners and customers. Experimental evaluation of our algorithms on HyViDE shows that, the acceptance rate is high with faster servicing of requests.
A major impediment to B2C success is...the concept 'B2C'

It's been over a decade now. We've forgotten how slow the adoption of consumer Internet commerce has been compared to other Internet growth metrics. And we're surprised when security scares like spyware and phishing result in lurches in consumer use.This paper re-visits an old theme, and finds that consumer marketing is still characterised by aggression and dominance, not sensitivity to customer needs. This conclusion is based on an examination of terms and privacy policy statements, which shows that businesses are confronting the people who buy from them with fixed, unyielding interfaces. Instead of generating trust, marketers prefer to wield power.These hard-headed approaches can work in a number of circumstances. Compelling content is one, but not everyone sells sex, gambling services, short-shelf-life news, and even shorter-shelf-life fashion goods. And, after decades of mass-media-conditioned consumer psychology research and experimentation, it's far from clear that advertising can convert everyone into salivating consumers who 'just have to have' products and services brand-linked to every new trend, especially if what you sell is groceries or handyman supplies.The thesis of this paper is that the one-dimensional, aggressive concept of B2C has long passed its use-by date. Trading is two-way -- consumers' attention, money and loyalty, in return for marketers' products and services, and vice versa.So B2C is conceptually wrong, and needs to be replaced by some buzzphrase that better conveys 'B-with-C' rather than 'to-C' and 'at-C'. Implementations of 'customised' services through 'portals' have to mature beyond data-mining-based manipulation to support two-sided relationships, and customer-managed profiles.It's all been said before, but now it's time to listen.
Optimation of Xen scheduler for multitasking

The rise of cloud computing brings virtualization technology continues to heat up. Based on Xen's I/O virtualization subsystem, under the virtual machine environment which has multi-type tasks, the existing schedulers can't achieve response with I/O-bound tasks in time. This paper presents ECredit scheduler combined complexity evaluation of I/O task in Xen's I/O virtualization subsystem. It prioritizes the I/O-bound task realizing fair scheduling. The experiments show that the optimized scheduling algorithm can reduce the response time of I/O-bound task and improve the performance of the virtual system.
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning

In the field of reinforcement learning there has been recent progress towards safety and high-confidence bounds on policy performance. However, to our knowledge, no practical methods exist for determining high-confidence policy performance bounds in the inverse reinforcement learning setting— where the true reward function is unknown and only samples of expert behavior are given. We propose a sampling method based on Bayesian inverse reinforcement learning that uses demonstrations to determine practical high-confidence upper bounds on the α-worst-case difference in expected return between any evaluation policy and the optimal policy under the expert’s unknown reward function. We evaluate our proposed bound on both a standard grid navigation task and a simulated driving task and achieve tighter and more accurate bounds than a feature count-based baseline. We also give examples of how our proposed bound can be utilized to perform riskaware policy selection and risk-aware policy improvement. Because our proposed bound requires several orders of magnitude fewer demonstrations than existing high-confidence bounds, it is the first practical method that allows agents that learn from demonstration to express confidence in the quality of their learned policy.
Binary artificial algae algorithm for multidimensional knapsack problems

The multidimensional knapsack problem (MKP) is a well-known NP-hard optimization problem. Various meta-heuristic methods are dedicated to solve this problem in literature. Recently a new meta-heuristic algorithm, called artificial algae algorithm (AAA), was presented, which has been successfully applied to solve various continuous optimization problems. However, due to its continuous nature, AAA cannot settle the discrete problem straightforwardly such as MKP. In view of this, this paper proposes a binary artificial algae algorithm (BAAA) to efficiently solve MKP. This algorithm is composed of discrete process, repair operators and elite local search. In discrete process, two logistic functions with different coefficients of curve are studied to achieve good discrete process results. Repair operators are performed to make the solution feasible and increase the efficiency. Finally, elite local search is introduced to improve the quality of solutions. To demonstrate the efficiency of our proposed algorithm, simulations and evaluations are carried out with total of 94 benchmark problems and compared with other bio-inspired state-of-the-art algorithms in the recent years including MBPSO, BPSOTVAC, CBPSOTVAC, GADS, bAFSA, and IbAFSA. The results show the superiority of BAAA to many compared existing algorithms.
Real-time clickstream data analytics and visualization

Nowadays web is becoming a main channel for reaching customers and prospects; Clickstream data generated by websites has become another important enterprise data source. As simple as it sounds for recording every click a customer made, so that we can use clickstream data for modelling user behavior, gaining valuable customer insights. Clickstream analysis commonly refers to analyzing click data and website optimization. Such analysis is typically done to extract insights into website visitor behavior especially social-media or e-commerce websites. Also nowadays online-learning became a trend in education system. We can see many online learning portals which are providing live training on various technologies. To identify potential customers or to identify recommendations for existing customers. Clickstream analysis can be used to figure out which geographies and time zones is most of traffic coming from, and which devices, Browsers (such as its name, versions), time spent, Operating Systems, are used to access the websites, which common paths users take before they do something in site. Analysis of clickstream data in real time(streaming) has more value than batch mode(stored). We analyze and visualize online-learning portal's clickstream data on the fly for business intelligence purpose. We constructed a full data pipeline using tools such as Apache Kafka, Apache Spark for streaming and elastic search, Kibana to query and visualize clickstream data respectively.
Beanstalk: A unity game addressing balance principles, socio-emotional learning and scientific inquiry

Beanstalk is an educational game for children ages 6-10 teaching balance-fulcrum principles while folding in scientific inquiry and socio-emotional learning. This paper explores the incorporation of these additional dimensions using intrinsic motivation and a framing narrative. Four versions of the game are detailed, along with preliminary player data in a 2×2 pilot test with 64 children shaping the modifications of Beanstalk for much broader testing.
Deep Learning for 3D Shape Classification based on Volumetric Density and Surface Approximation Clues

This paper proposes a novel approach for the classification of 3D shapes exploiting surface and volumetric clues inside a deep learning framework. The proposed algorithm uses three different data representations. The first is a set of depth maps obtained by rendering the 3D object. The second is a novel volumetric representation obtained by counting the number of filled voxels along each direction. Finally NURBS surfaces are fitted over the 3D object and surface curvature parameters are selected as the third representation. All the three data representations are fed to a multi-branch Convolutional Neural Network. Each branch processes a different data source and produces a feature vector by using convolutional layers of progressively reduced resolution. The extracted feature vectors are fed to a linear classifier that combines the outputs in order to get the final predictions. Experimental results on the ModelNet dataset show that the proposed approach is able to obtain a state-of-the-art performance.
Combining a Modified Vector Field Histogram Algorithm and Real-time Image Processing for Unknown Environment Navigation

Q is an unmanned ground vehicle designed to compete in the Autonomous and Navigation Challenges of the AUVSI Intelligent Ground Vehicle Competition (IGVC). Built on a base platform of a modified PerMobil Trax off-road wheel chair frame, and running off a Dell Inspiron D820 laptop with an Intel t7400 Core 2 Duo Processor, Q gathers information from a SICK laser range finder (LRF), video cameras, differential GPS, and digital compass to localize its behavior and map out its navigational path. This behavior is handled by intelligent closed loop speed control and robust sensor data processing algorithms. In the Autonomous challenge, data taken from two IEEE 1394 cameras and the LRF are integrated and plotted on a custom-defined occupancy grid and converted into a histogram which is analyzed for openings between obstacles. The image processing algorithm consists of a series of steps involving plane extraction, normalizing of the image histogram for an effective dynamic thresholding, texture and morphological analysis and particle filtering to allow optimum operation at varying ambient conditions. In the Navigation Challenge, a modified Vector Field Histogram (VFH) algorithm is combined with an auto-regressive path planning model for obstacle avoidance and better localization. Also, Q features the Joint Architecture for Unmanned Systems (JAUS)  The paper will focus on explaining the various algorithms that make up Q’s intelligence and the different ways and modes of their implementation.
A Performance Study of Big Data on Small Nodes

The continuous increase in volume, variety and velocity of Big Data exposes datacenter resource scaling to an energy utilization problem. Traditionally, datacenters employ x8664 (big) server nodes with power usage of tens to hundreds of Watts. But lately, low-power (small) systems originally developed for mobile devices have seen significant improvements in performance. These improvements could lead to the adoption of such small systems in servers, as announced by major industry players. In this context, we systematically conduct a performance study of Big Data execution on small nodes in comparison with traditional big nodes, and present insights that would be useful for future development. We run Hadoop MapReduce, MySQL and in-memory Shark workloads on clusters of ARM big.LITTLE boards and Intel Xeon server systems. We evaluate execution time, energy usage and total cost of running the workloads on selfhosted ARM and Xeon nodes. Our study shows that there is no one size fits all rule for judging the efficiency of executing Big Data workloads on small and big nodes. But small memory size, low memory and I/O bandwidths, and software immaturity concur in canceling the lower-power advantage of ARM servers. We show that I/O-intensive MapReduce workloads are more energy-efficient to run on Xeon nodes. In contrast, database query processing is always more energy-efficient on ARM servers, at the cost of slightly lower throughput. With minor software modifications, CPU-intensive MapReduce workloads are almost four times cheaper to execute on ARM servers.
Music Influences Ratings of the Affect of Visual Stimuli

This review provides an overview of recent studies that have examined how music influences the judgment of emotional stimuli, including affective pictures and film clips. The relevant findings are incorporated within a broader theory of music and emotion, and suggestions for future research are offered. Music is important in our daily lives, and one of its primary uses by listeners is the active regulation of one's mood. Despite this widespread use as a regulator of mood and its general pervasiveness in our society, the number of studies investigating the issue of whether, and how, music affects mood and emotional behaviour is limited however. Experiments investigating the effects of music have generally focused on how the emotional valence of background music impacts how affective pictures and/or film clips are evaluated. These studies have demonstrated strong effects of music on the emotional judgment of such stimuli. Most studies have reported concurrent background music to enhance the emotional valence when music and pictures are emotionally congruent. On the other hand, when music and pictures are emotionally incongruent, the ratings of the affect of the pictures will inor decrease depending on the emotional valence of the background music. These results appear to be consistent in studies investigating the effects of (background) music.
Word embedding with hellinger PCA to detect the sentiment of bengali text

The sentiment of a sentence or a comment can be detected more accurately by applying Word Embeddings. This article presents the idea of word co-occurrence matrix and Skip-Gram to determine the actual contexts of the words, Hellinger PCA to determine the most similar words and generate a sliding window of most probable context words around each word. It is shown that, by applying Word Embeddings to classify the sentiment of a comment achieves higher accuracy with larger corpus. For our corpus of 2500 comments, the accuracy achieved is 70%, which is rapidly increasing with the size of the corpus.
The importance of educational theories for facilitating learning when using technology in medical education.

There is an increasing use of technology for teaching and learning in medical education but often the use of educational theory to inform the design is not made explicit. The educational theories, both normative and descriptive, used by medical educators determine how the technology is intended to facilitate learning and may explain why some interventions with technology may be less effective compared with others.



The aim of this study is to highlight the importance of medical educators making explicit the educational theories that inform their design of interventions using technology.



The use of illustrative examples of the main educational theories to demonstrate the importance of theories informing the design of interventions using technology.



Highlights the use of educational theories for theory-based and realistic evaluations of the use of technology in medical education.



An explicit description of the educational theories used to inform the design of an intervention with technology can provide potentially useful insights into why some interventions with technology are more effective than others. An explicit description is also an important aspect of the scholarship of using technology in medical education.
The occurrence of two bill morphs of prions Pachyptila vittata on Gough Island

Pachyptila are abundant seabirds in the Southern Ocean that have been used to infer environmental change, but this relies on an understanding of their morphological diversity. Species limits among prions are largely defined by the size and structure of their bills. The broad-billed prion P. vittata, which breeds at temperate islands in the central South Atlantic Ocean and around New Zealand, is the largest species with adult bill widths averaging 21–22 mm. We report local differences in bill sizes on Gough Island, with typical adults breeding in some areas and narrower-billed birds in other areas (average bill width 18–19 mm, although at one site there was a second mode at bill widths of 16–17 mm). The narrow-billed birds have slightly shorter bills, heads and wings (averaging 1–2 % smaller than typical adult broad-billed prions), but the difference in bill width is much more marked (15 %). The small-billed birds differ from typical broad-billed prions in having blue colouration in the upper mandible and are similar in size to MacGillivray’s prion P. [salvini/vittata] macgillivrayi from Amsterdam and St Paul islands in the temperate Indian Ocean. The occurrence of two prion morphs on Gough Island raises intriguing questions about their ecology and systematics. Small-billed birds breed 3 months later than large-billed birds, suggesting that they are a separate species, not an example of bill polymorphism.