nils-herrmann/clean_scirepeval huggingface dataset:
Design method of unequal Wilkinson power divider using LC-ladder circuits for multi-way power dividers

This paper describes unequal Wilkinson power dividers using LC-ladder circuits for size reduction of multi-way Wilkinson power dividers. The proposed unequal Wilkinson power dividers consist of a couple of LC-ladder circuits and a series RL circuit between two output ports. For the verification of this design method, the circuit layout of a 5-way power divider utilizing unequal Wilkinson power dividers is decided by using an electromagnetic simulator, and the 5-way divider is fabricated at a center frequency of 300MHz. The experimental results of fabricated 5-way power divider indicate low reflection and high isolation and very flat power division with in-phase signals.
Advanced CAR parking system using Arduino

This paper explains the architecture and design of Arduino based car parking system. Authorization of driver or user is the basic rule used to park a vehicle in a parking place. Authorization card will be given to each user, which carries the vehicle number or other details. If the user is authorized and space is available in the parking, then the parking gate will open and the user is allowed to park the vehicle in parking place else the user is not allowed even the user is authorized person. If car is allowed to park, then mobile notification will be send to user about parking. It solves the parking issue in urban areas, also provides security to a vehicle and an unauthorized user is not allowed to enter into a parking place. It helps to park vehicle in multifloored parking also as it will display which floor has free space.
The effects of sleep extension on the athletic performance of collegiate basketball players.

To investigate the effects of sleep extension over multiple weeks on specific measures of athletic performance as well as reaction time, mood, and daytime sleepiness.



Stanford Sleep Disorders Clinic and Research Laboratory and Maples Pavilion, Stanford University, Stanford, CA.



Eleven healthy students on the Stanford University men's varsity basketball team (mean age 19.4 ± 1.4 years).



Subjects maintained their habitual sleep-wake schedule for a 2-4 week baseline followed by a 5-7 week sleep extension period. Subjects obtained as much nocturnal sleep as possible during sleep extension with a minimum goal of 10 h in bed each night. Measures of athletic performance specific to basketball were recorded after every practice including a timed sprint and shooting accuracy. Reaction time, levels of daytime sleepiness, and mood were monitored via the Psychomotor Vigilance Task (PVT), Epworth Sleepiness Scale (ESS), and Profile of Mood States (POMS), respectively.



Total objective nightly sleep time increased during sleep extension compared to baseline by 110.9 ± 79.7 min (P < 0.001). Subjects demonstrated a faster timed sprint following sleep extension (16.2 ± 0.61 sec at baseline vs. 15.5 ± 0.54 sec at end of sleep extension, P < 0.001). Shooting accuracy improved, with free throw percentage increasing by 9% and 3-point field goal percentage increasing by 9.2% (P < 0.001). Mean PVT reaction time and Epworth Sleepiness Scale scores decreased following sleep extension (P < 0.01). POMS scores improved with increased vigor and decreased fatigue subscales (P < 0.001). Subjects also reported improved overall ratings of physical and mental well-being during practices and games.



Improvements in specific measures of basketball performance after sleep extension indicate that optimal sleep is likely beneficial in reaching peak athletic performance.
AM Monitoring Driver Drowsiness and Stress in a Driving Simulator

Driver drowsiness, compounded by the high workloads and stress of the ever-increasing complexity of car and traffic environments, is a major cause of severe accidents. The objective of the project described in this paper is to develop reproducible and flexible methods for studying the relationships between physiological driver states and human-factor issues in a driving environment. For reasons of safety and reproducibility, a laboratory-based driving simulator is being used for the project experiments. Initial experiments were conducted with a cohort of about 60 healthy male subjects aged 22 to 28 under carefully controlled conditions. Performance was measured before, during, and after a 120 km stretch of stimulus-deprived, foggy highway that was intended to induce fatigue and stress. Across all trials 69% of the subjects experienced sleep events lasting several seconds, and 7 potentially fatal crashes occurred. Lane tracking behavior degraded by a factor of 2 to 3 prior to each crash. Much of the extensive data acquired by these experiments remains to be analyzed using both standard statistical techniques and highdimensional clustering algorithms. ALISA image-processing software is being applied to video images of the driver eyes and face to detect the onset of sleep and other critical situations.
A History and Survey of Network Firewalls

Firewalls are network devices which enforce an organization’s security policy. Since their development, various methods have been used to implement firewalls. These methods filter network traffic at one or more of the seven layers of the ISO network model, most commonly at the application, transport, and network, and data-link levels. In addition, researchers have developed some newer methods, such as protocol normalization and distributed firewalls, which have not yet been widely adopted. Firewalls involve more than the technology to implement them. Specifying a set of filtering rules, known as a policy, is typically complicated and error-prone. High-level languages have been developed to simplify the task of correctly defining a firewall’s policy. Once a policy has been specified, the firewall needs to be tested to determine if it actually implements the policy correctly. Little work exists in the area of firewall theory; however, this article summarizes what exists. Because some data must be able to pass in and out of a firewall, in order for the protected network to be useful, not all attacks can be stopped by firewalls. Some emerging technologies, such as Virtual Private Networks (VPN) and peer-to-peer networking pose new challenges for firewalls.
Vocal quality factors: analysis, synthesis, and perception.

The purpose of this study was to examine several factors of vocal quality that might be affected by changes in vocal fold vibratory patterns. Four voice types were examined: modal, vocal fry, falsetto, and breathy. Three categories of analysis techniques were developed to extract source-related features from speech and electroglottographic (EGG) signals. Four factors were found to be important for characterizing the glottal excitations for the four voice types: the glottal pulse width, the glottal pulse skewness, the abruptness of glottal closure, and the turbulent noise component. The significance of these factors for voice synthesis was studied and a new voice source model that accounted for certain physiological aspects of vocal fold motion was developed and tested using speech synthesis. Perceptual listening tests were conducted to evaluate the auditory effects of the source model parameters upon synthesized speech. The effects of the spectral slope of the source excitation, the shape of the glottal excitation pulse, and the characteristics of the turbulent noise source were considered. Applications for these research results include synthesis of natural sounding speech, synthesis and modeling of vocal disorders, and the development of speaker independent (or adaptive) speech recognition systems.
On segmentation of eCommerce queries

In this paper, we present QSEGMENT, a real-life query segmentation system for eCommerce queries. QSEGMENT uses frequency data from the query log which we call buyers' data and also frequency data from product titles what we call sellers' data. We exploit the taxonomical structure of the marketplace to build domain specific frequency models. Using such an approach, QSEGMENT performs better than previously described baselines for query segmentation. Also, we perform a large scale evaluation by using an unsupervised IR metric which we refer to as user-intent-score. We discuss the overall architecture of QSEGMENT as well as various use cases and interesting observations around segmenting eCommerce queries.
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments

The Computational Grid provides a promising platform for the eÆcient execution of parameter sweep applications over very large p arameter spaces. Sche duling such applications is challenging because target resour ces are heter ogene ous,because their load and availability varies dynamically, and because independent tasks may share common data les. In this p ap er, we pr op ose an adaptive scheduling algorithm for parameter sweep applications on the Grid. We modify standard heuristics for task/host assignment in perfectly predictable environments (Max-min, Min-min, Su erage), and we propose an extension of Su erage c alledXSu erage. Using simulation, we demonstrate that XSu erage can take advantage of le sharing to achieve better p erformance than the other heuristics. We also study the impact of inac cur ate p erformanc e prediction on sche duling. Our study shows that: (i) di er entheuristics behave differently when predictions are inac cur ate; (ii) incr eased adaptivity leads to b etter p erformanc e.
The value of visualization

The field of visualization is getting mature. Many problems have been solved, and new directions are sought for. In order to make good choices, an understanding of the purpose and meaning of visualization is needed. Especially, it would be nice if we could assess what a good visualization is. In this paper an attempt is made to determine the value of visualization. A technological viewpoint is adopted, where the value of visualization is measured based on effectiveness and efficiency. An economic model of visualization is presented, and benefits and costs are established. Next, consequences (brand limitations of visualization are discussed (including the use of alternative methods, high initial costs, subjective/less, and the role of interaction), as well as examples of the use of the model for the judgement of existing classes of methods and understanding why they are or are not used in practice. Furthermore, two alternative views on visualization are presented and discussed: viewing visualization as an art or as a scientific discipline. Implications and future directions are identified.
A general framework for analyzing sustainability of social-ecological systems.

A major problem worldwide is the potential loss of fisheries, forests, and water resources. Understanding of the processes that lead to improvements in or deterioration of natural resources is limited, because scientific disciplines use different concepts and languages to describe and explain complex social-ecological systems (SESs). Without a common framework to organize findings, isolated knowledge does not cumulate. Until recently, accepted theory has assumed that resource users will never self-organize to maintain their resources and that governments must impose solutions. Research in multiple disciplines, however, has found that some government policies accelerate resource destruction, whereas some resource users have invested their time and energy to achieve sustainability. A general framework is used to identify 10 subsystem variables that affect the likelihood of self-organization in efforts to achieve a sustainable SES.
Voice conversion with smoothed GMM and MAP adaptation

In most state-of-the-art voice conversion systems, speech quality of converted utterances is still unsatisfactory. In this paper, STRAIGHT analysis-synthesis framework is used to improve the quality. A smoothed GMM and MAP adaptation is proposed for spectrum conversion to avoid the overly smooth phenomenon in the traditional GMM method. Since frames are processed independently, the GMM based transformation function may generate discontinuous features. Therefore, a time domain low pass filter is applied on the transformation function during the conversion phase. The results of listening evaluations show that the quality of the speech converted by the proposed method is significantly better than that by the traditional GMM method. Meanwhile, speaker identifiability of the converted voice reaches 75%, even when the difference between the source speaker and the target speaker is not very large.
A framework for Cloud-based Smart Home

Smart Home is one of emerging application domains of The Internet of things which following the computer and Internet. Although home automation technologies have been commercially available already, they are basically designed for signal-family smart homes with a high cost, and along with the constant growth of digital appliances in smart home, we merge smart home into smart-home-oriented Cloud to release the stress on the smart home system which mostly installs application software on their local computers. In this paper, we present a framework for Cloud-based smart home for enabling home automation, household mobility and interconnection which easy extensible and fit for future demands. Through subscribing services of the Cloud, smart home consumers can easily enjoy smart home services without purchasing computers which owns strong power and huge storage. We focus on the overall Smart Home framework, the features and architecture of the components of Smart Home, the interaction and cooperation between them in detail.
Pattern Matching in Trees

ABSTgACT. Tree pattern matching is an interesting special problem which occurs as a crucial step m a number of programmmg tasks, for instance, design of interpreters for nonprocedural programming languages, automatic implementations of abstract data types, code optimization m compilers, symbohc computation, context searching in structure editors, and automatic theorem provmg. As with the sorting problem, the variations in requirements and resources for each application seem to preclude a uniform, umversal solution to the tree-pattern-matching problem. Instead, a collection of well-analyzed techmques, from which specific applications may be selected and adapted, should be sought. Five new techniques for tree pattern matching are presented, analyzed for time and space complexity, and compared with previously known methods. Particularly important are applications where the same patterns are matched against many subjects and where a subject may be modified incrementally Therefore, methods which spend some tune preprocessmg patterns in order to improve the actual matching time are included
Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks

This paper introduces quantum neural networks (QNNs), a class of feedforward neural networks (FFNNs) inherently capable of estimating the structure of a feature space in the form of fuzzy sets. The hidden units of these networks develop quantized representations of the sample information provided by the training data set in various graded levels of certainty. Unlike other approaches attempting to merge fuzzy logic and neural networks, QNNs can be used in pattern classification problems without any restricting assumptions such as the availability of a priori knowledge or desired membership profile, convexity of classes, a limited number of classes, etc. Experimental results presented here show that QNNs are capable of recognizing structures in data, a property that conventional FFNNs with sigmoidal hidden units lack.
Bayesian Active Model Selection with an Application to Automated Audiometry

We introduce a novel information-theoretic approach for active model selection and demonstrate its effectiveness in a real-world application. Although our method can work with arbitrary models, we focus on actively learning the appropriate structure for Gaussian process (GP) models with arbitrary observation likelihoods. We then apply this framework to rapid screening for noise-induced hearing loss (NIHL), a widespread and preventible disability, if diagnosed early. We construct a GP model for pure-tone audiometric responses of patients with NIHL. Using this and a previously published model for healthy responses, the proposed method is shown to be capable of diagnosing the presence or absence of NIHL with drastically fewer samples than existing approaches. Further, the method is extremely fast and enables the diagnosis to be performed in real time.
Using Blockchain and smart contracts for secure data provenance management

Blockchain technology has evolved from being an immutable ledger of transactions for cryptocurrencies to a programmable interactive environment for building distributed reliable applications. Although, blockchain technology has been used to address various challenges, to our knowledge none of the previous work focused on using blockchain to develop a secure and immutable scientific data provenance management framework that automatically verifies the provenance records. In this work, we leverage blockchain as a platform to facilitate trustworthy data provenance collection, verification and management. The developed system utilizes smart contracts and open provenance model (OPM) to record immutable data trails. We show that our proposed framework can efficiently and securely capture and validate provenance data, and prevent any malicious modification to the captured data as long as majority of the participants are honest.
Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention

On microblogging services, people usually use hashtags to mark microblogs, which have a specific theme or content, making them easier for users to find. Hence, how to automatically recommend hashtags for microblogs has received much attention in recent years. Previous deep neural network-based hashtag recommendation approaches converted the task into a multiclass classification problem. However, most of these methods only took the microblog itself into consideration. Motivated by the intuition that the history of users should impact the recommendation procedure, in this work, we extend end-to-end memory networks to perform this task. We incorporate the histories of users into the external memory and introduce a hierarchical attention mechanism to select more appropriate histories. To train and evaluate the proposed method, we also construct a dataset based on microblogs collected from Twitter. Experimental results demonstrate that the proposed methods can significantly outperform state-of-the-art methods. By incorporating the hierarchical attention mechanism, the relative improvement in the proposed method over the state-of-the-art method is around 67.9% in the F1-score.
Structural Minimax Probability Machine

Minimax probability machine (MPM) is an interesting discriminative classifier based on generative prior knowledge. It can directly estimate the probabilistic accuracy bound by minimizing the maximum probability of misclassification. The structural information of data is an effective way to represent prior knowledge, and has been found to be vital for designing classifiers in real-world problems. However, MPM only considers the prior probability distribution of each class with a given mean and covariance matrix, which does not efficiently exploit the structural information of data. In this paper, we use two finite mixture models to capture the structural information of the data from binary classification. For each subdistribution in a finite mixture model, only its mean and covariance matrix are assumed to be known. Based on the finite mixture models, we propose a structural MPM (SMPM). SMPM can be solved effectively by a sequence of the second-order cone programming problems. Moreover, we extend a linear model of SMPM to a nonlinear model by exploiting kernelization techniques. We also show that the SMPM can be interpreted as a large margin classifier and can be transformed to support vector machine and maxi–min margin machine under certain special conditions. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of SMPM.
Post-Adoption Variations in Usage and Value of E-Business by Organizations: Cross-Country Evidence from the Retail Industry

G in the innovation diffusion literature and the resource-based theory, this paper develops an integrative research model for assessing the diffusion and consequence of e-business at the firm level. Unlike the typical focus on adoption as found in the literature, we focus on postadoption stages, that is, actual usage and value creation. The model thus moves beyond dichotomous “adoption versus nonadoption” and accounts for the “missing link”—actual usage—as a critical stage of value creation. The model links technological, organizational, and environmental factors to e-business use and value, based on which a series of hypotheses are developed. The theoretical model is tested by using structural equation modeling on a dataset of 624 firms across 10 countries in the retail industry. To probe deeper into whether e-business use and value are influenced by economic environments, two subsamples from developed and developing countries are compared. The study finds that technology competence, firm size, financial commitment, competitive pressure, and regulatory support are important antecedents of e-business use. In addition, the study finds that, while both front-end and back-end capabilities contribute to e-business value, back-end integration has a much stronger impact. While front-end functionalities are becoming commodities, e-businesses are more differentiated by back-end integration. This is consistent with the resource-based theory because back-end integration possesses the value-creating characteristics of resources (e.g., firm specific, difficult to imitate), which are strengthened by the Internet-enabled connectivity. Our study also adds an international dimension to the innovation diffusion literature, showing that careful attention must be paid to the economic and regulatory factors that may affect technology diffusion across different countries.
Beauty and the Beast: Diverting Modern Web Browsers to Build Unique Browser Fingerprints

Worldwide, the number of people and the time spent browsing the web keeps increasing. Accordingly, the technologies to enrich the user experience are evolving at an amazing pace. Many of these evolutions provide for a more interactive web (e.g., boom of JavaScript libraries, weekly innovations in HTML5), a more available web (e.g., explosion of mobile devices), a more secure web (e.g., Flash is disappearing, NPAPI plugins are being deprecated), and a more private web (e.g., increased legislation against cookies, huge success of extensions such as Ghostery and AdBlock). Nevertheless, modern browser technologies, which provide the beauty and power of the web, also provide a darker side, a rich ecosystem of exploitable data that can be used to build unique browser fingerprints. Our work explores the validity of browser fingerprinting in today's environment. Over the past year, we have collected 118,934 fingerprints composed of 17 attributes gathered thanks to the most recent web technologies. We show that innovations in HTML5 provide access to highly discriminating attributes, notably with the use of the Canvas API which relies on multiple layers of the user's system. In addition, we show that browser fingerprinting is as effective on mobile devices as it is on desktops and laptops, albeit for radically different reasons due to their more constrained hardware and software environments. We also evaluate how browser fingerprinting could stop being a threat to user privacy if some technological evolutions continue (e.g., disappearance of plugins) or are embraced by browser vendors (e.g., standard HTTP headers).