nils-herrmann/clean_scirepeval huggingface dataset:
An opponent-process theory of motivation. I. Temporal dynamics of affect.

A new theory of motivation is described along with its applications to addiction and aversion. The theory assumes that many hedonic, affective, or emotional states are automatically opposed by central nervous system mechanisms which reduce the intensity of hedonic feelings, both pleasant and aversive. The opponent processes for most hedonic states are strengthened by use and are weakened by disuse. These simple assumptions lead to deductions of many known facts about acquired motivation. In addition, the theory suggests several new lines of research on motivation. It argues that the establishment of some types of acquired motivation does not depend on conditioning and is nonassociative in nature. The relationships between conditioning processes and postulated opponent processes are discussed. Finally, it is argued that the data on several types of acquired motivation, arising from either pleasurable or aversive stimulation, can be fruitfully reorganized and understood within the framework provided by the opponent-process model.
Discriminative Learning with Natural Annotations: Word Segmentation as a Case Study

Structural information in web text provides natural annotations for NLP problems such as word segmentation and parsing. In this paper we propose a discriminative learning algorithm to take advantage of the linguistic knowledge in large amounts of natural annotations on the Internet. It utilizes the Internet as an external corpus with massive (although slight and sparse) natural annotations, and enables a classifier to evolve on the large-scaled and real-time updated web text. With Chinese word segmentation as a case study, experiments show that the segmenter enhanced with the Chinese wikipedia achieves significant improvement on a series of testing sets from different domains, even with a single classifier and local features.
A Comparison of Questionnaires for Assessing Website Usability

Five questionnaires for assessing the usability of a website were compared in a study with 123 participants. The questionnaires studied were SUS, QUIS, CSUQ, a variant of Microsoft’s Product Reaction Cards, and one that we have used in our Usability Lab for several years. Each participant performed two tasks on each of two websites: finance.yahoo.com and kiplinger.com. All five questionnaires revealed that one site was significantly preferred over the other. The data were analyzed to determine what the results would have been at different sample sizes from 6 to 14. At a sample size of 6, only 30-40% of the samples would have identified that one of the sites was significantly preferred. Most of the data reach an apparent asymptote at a sample size of 12, where two of the questionnaires (SUS and CSUQ) yielded the same conclusion as the full dataset at least 90% of the time.
A state-of the-art survey of TOPSIS applications

0957-4174/$ see front matter 2012  A http://dx.doi.org/10.1016/j.eswa.2012.05.056 ⇑ Corresponding author. E-mail address: behzadian_ie@yahoo.com (M. Beh Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors’ nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.
CubeSat Deployable Antenna Using Bistable Composite Tape-Springs

In this letter, a new conductive composite tape-spring is proposed for CubeSat deployable antennas that is constructed using a glass fiber reinforced epoxy with an embedded copper alloy conductor. The tape-spring is bistable enabling the antenna to be elastically stable in both the deployed and stowed states. A dipole antenna is designed, simulated, and tested to prove the viability of the electrical properties of this material.
A 13,000 km Intercontinental Trip with Driverless Vehicles: The VIAC Experiment

In recent years the interest in autonomous vehicles has incrementally increased. After the DARPA Challenges new fields of application as agricultural, construction, mining, and also nautical are continuously opening up. In this paper a huge test is presented, the first of this kind in the history of vehicular robotics. A trip from Italy to China with four electric autonomous vehicles will be described focusing on all aspects of the challenge, from the managing issues to the most technical ones. A vehicle-following application (or virtual towing) is the system under test for a three consecutive months and 13,000 km long unique experience.
POP: Privacy-Preserving Outsourced Photo Sharing and Searching for Mobile Devices

Facing a large number of personal photos and limited resource of mobile devices, cloud plays an important role in photo storing, sharing and searching. Meanwhile, some recent reputation damage and stalk events caused by photo leakage increase people's concern about photo privacy. Though most would agree that photo search function and privacy are both valuable, few cloud system supports both of them simultaneously. The center of such an ideal system is privacy-preserving outsourced image similarity measurement, which is extremely challenging when the cloud is untrusted and a high extra overhead is disliked. In this work, we introduce a framework POP, which enables privacy-seeking mobile device users to outsource burdensome photo sharing and searching safely to untrusted servers. Unauthorized parties, including the server, learn nothing about photos or search queries. This is achieved by our carefully designed architecture and novel non-interactive privacy-preserving protocols for image similarity computation. Our framework is compatible with the state-of-the-art image search techniques, and it requires few changes to existing cloud systems. For efficiency and good user experience, our framework allows users to define personalized private content by a simple check-box configuration and then enjoy the sharing and searching services as usual. All privacy protection modules are transparent to users. The evaluation of our prototype implementation with 31,772 real-life images shows little extra communication and computation overhead caused by our system.
Soft Actuators for Small-Scale Robotics.

This review comprises a detailed survey of ongoing methodologies for soft actuators, highlighting approaches suitable for nanometer- to centimeter-scale robotic applications. Soft robots present a special design challenge in that their actuation and sensing mechanisms are often highly integrated with the robot body and overall functionality. When less than a centimeter, they belong to an even more special subcategory of robots or devices, in that they often lack on-board power, sensing, computation, and control. Soft, active materials are particularly well suited for this task, with a wide range of stimulants and a number of impressive examples, demonstrating large deformations, high motion complexities, and varied multifunctionality. Recent research includes both the development of new materials and composites, as well as novel implementations leveraging the unique properties of soft materials.
Original Article: A proposed model for data warehouse ETL processes

osting by E Abstract Extraction–transformation–loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, its cleansing, customization, reformatting, integration, and insertion into a data warehouse. Building the ETL process is potentially one of the biggest tasks of building a warehouse; it is complex, time consuming, and consume most of data warehouse project’s implementation efforts, costs, and resources. Building a data warehouse requires focusing closely on understanding three main areas: the source area, the destination area, and the mapping area (ETL processes). The source area has standard models such as entity relationship diagram, and the destination area has standard models such as star schema, but the mapping area has not a standard model till now. In spite of the importance of ETL processes, little research has been done in this area due to its complexity. There is a clear lack of a standard model that can be used to represent the ETL scenarios. In this paper we will try to navigate through the efforts done to conceptualize
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks

Frame interpolation attempts to synthesise intermediate frames given one or more consecutive video frames. In recent years, deep learning approaches, and in particular convolutional neural networks, have succeeded at tackling lowand high-level computer vision problems including frame interpolation. There are two main pursuits in this line of research, namely algorithm efficiency and reconstruction quality. In this paper, we present a multi-scale generative adversarial network for frame interpolation (FIGAN). To maximise the efficiency of our network, we propose a novel multi-scale residual estimation module where the predicted flow and synthesised frame are constructed in a coarse-tofine fashion. To improve the quality of synthesised intermediate video frames, our network is jointly supervised at different levels with a perceptual loss function that consists of an adversarial and two content losses. We evaluate the proposed approach using a collection of 60fps videos from YouTube-8m. Our results improve the state-of-the-art accuracy and efficiency, and a subjective visual quality comparable to the best performing interpolation method.
A system for voice conversion based on probabilistic classification and a harmonic plus noise model

Voice conversion is defined as modifying the speech signal of one speaker (source speaker) so that it sounds as if it had been pronounced by a different speaker (target speaker). This paper describes a system for efficient voice conversion. A novel mapping function is presented which associates the acoustic space of the source speaker with the acoustic space of the target speaker. The proposed system is based on the use of a Gaussian Mixture Model, GMM, to model the acoustic space of a speaker and a pitch synchronous harmonic plus noise representation of the speech signal for prosodic modifications. The mapping function is a continuous parametric function which takes into account the probab ilistic classification provided by the mixture model (GMM). Evaluation by objective tests showed that the proposed system was able to reduce the perceptual distance between the source and target speaker by 70%. Formal listening tests also showed that 97% of the converted speech was judged to be spoken from the target speaker while maintaining high speech qua lity.
A Broadband Dual-Polarized Base Station Antenna With Sturdy Construction

A broadband dual-polarized base station antenna with sturdy construction is presented in this letter. The antenna mainly contains four parts: main radiator, feeding baluns, bedframe, and reflector. First, two orthogonal dipoles are etched on a substrate as main radiator forming dual polarization. Two baluns are then introduced to excite the printed dipoles. Each balun has four bumps on the edges for electrical connection and fixation. The bedframe is designed to facilitate the installation, and the reflector is finally used to gain unidirectional radiation. Measured results show that the antenna has a 48% impedance bandwidth with reflection coefficient less than –15 dB and port isolation more than 22 dB. A four-element antenna array with 6° ± 2° electrical down tilt is also investigated for wideband base station application. The antenna and its array have the advantages of sturdy construction, high machining accuracy, ease of integration, and low cost. They can be used for broadband base station in the next-generation wireless communication system.
Mining Successful Answers in Stack Overflow

Recent research has shown that drivers of success in online question answering encompass presentation quality as well as temporal and social aspects. Yet, we argue that also the emotional style of a technical contribution influences its perceived quality. In this paper, we investigate how Stack Overflow users can increase the chance of getting their answer accepted. We focus on actionable factors that can be acted upon by users when writing an answer and making comments. We found evidence that factors related to information presentation, time and affect all have an impact on the success of answers.
Streaming trend detection in Twitter

Twitter is a popular microblogging and social networking service with over 100 million users. Users create short messages pertaining to a wide variety of topics. Certain topics are highlighted by Twitter as the most popular and are known as “trending topics.” In this paper, we will outline methodologies of detecting and identifying trending topics from streaming data. Data from Twitter’s streaming API will be collected and put into documents of equal duration. Data collection procedures will allow for analysis over multiple timespans, including those not currently associated with Twitter-identified trending topics. Term frequency-inverse document frequency analysis and relative normalized term frequency analysis are performed on the documents to identify the trending topics. Relative normalized term frequency analysis identifies unigrams, bigrams, and trigrams as trending topics, while term frequcny-inverse document frequency analysis identifies unigrams as trending topics.
Maximum entropy modeling of species geographic distributions

The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In mmals: a diction emaining outline eceiver dicating ts present ues horder to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical ma lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Pre (GARP). We made predictions on 10 random subsets of the occurrence records for both species, and then used the r localities for testing. Both algorithms provided reasonable estimates of the species’ range, far superior to the shaded maps available in field guides. All models were significantly better than random in both binomial tests of omission and r operating characteristic (ROC) analyses. The area under the ROC curve (AUC) was almost always higher for Maxent, in better discrimination of suitable versus unsuitable areas for the species. The Maxent modeling approach can be used in i form for many applications with presence-only datasets, and merits further research and development.
Calibration-Free Pulse Oximetry Based on Two Wavelengths in the Infrared — A Preliminary Study

The assessment of oxygen saturation in arterial blood by pulse oximetry (SpO₂) is based on the different light absorption spectra for oxygenated and deoxygenated hemoglobin and the analysis of photoplethysmographic (PPG) signals acquired at two wavelengths. Commercial pulse oximeters use two wavelengths in the red and infrared regions which have different pathlengths and the relationship between the PPG-derived parameters and oxygen saturation in arterial blood is determined by means of an empirical calibration. This calibration results in an inherent error, and pulse oximetry thus has an error of about 4%, which is too high for some clinical problems. We present calibration-free pulse oximetry for measurement of SpO₂, based on PPG pulses of two nearby wavelengths in the infrared. By neglecting the difference between the path-lengths of the two nearby wavelengths, SpO₂ can be derived from the PPG parameters with no need for calibration. In the current study we used three laser diodes of wavelengths 780, 785 and 808 nm, with narrow spectral line-width. SaO₂ was calculated by using each pair of PPG signals selected from the three wavelengths. In measurements on healthy subjects, SpO₂ values, obtained by the 780-808 nm wavelength pair were found to be in the normal range. The measurement of SpO₂ by two nearby wavelengths in the infrared with narrow line-width enables the assessment of SpO₂ without calibration.
WoTKit: a lightweight toolkit for the web of things

In this position paper, we discuss our experiences with a lightweight Web of Things (WoT) toolkit and use those experiences to explore what an effective WoT toolkit looks like. We argue that while the WoT community has experimented, like us, with a variety of toolkits, it hasn't yet found one that appeals sufficiently to a broad range of developers. This failure, we believe, is hindering the adoption of the WoT and the growth of the community. We conclude the paper with a set of open questions, which, although not exhaustive, are aimed at opening up a community discussion on the needs of developers and how best the community can meet those needs and so further the adoption of the WoT. In essence, we believe that the time may be right to begin to agree on some basic functionality and approaches to WoT toolkits.
Using S-parameter measurements to determine the threshold voltage, gain factor, and mobility degradation factor for microwave bulk-MOSFETs

In this paper, we present an extraction and characterization methodology which allows for the determination, from S-parameter measurements, of the threshold voltage, the gain factor, and the mobility degradation factor, neither requiring data regressions involving multiple devices nor DC measurements. This methodology takes into account the substrate effects occurring in MOSFETs built in bulk technology so that physically meaningful parameters can be obtained. Furthermore, an analysis of the substrate impedance is presented, showing that this parasitic component not only degrades the performance of a microwave MOSFET, but may also lead to determining unrealistic values for the model parameters when not considered during a high-frequency characterization process. Measurements were made on transistors of different lengths, the shortest being 80 nm, in the 10 MHz to 40 GHz frequency range.
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior

Single image super-resolution (SR) is an ill-posed problem, which tries to recover a high-resolution image from its low-resolution observation. To regularize the solution of the problem, previous methods have focused on designing good priors for natural images, such as sparse representation, or directly learning the priors from a large data set with models, such as deep neural networks. In this paper, we argue that domain expertise from the conventional sparse coding model can be combined with the key ingredients of deep learning to achieve further improved results. We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data. The network has a cascaded structure, which boosts the SR performance for both fixed and incremental scaling factors. The proposed training and testing schemes can be extended for robust handling of images with additional degradation, such as noise and blurring. A subjective assessment is conducted and analyzed in order to thoroughly evaluate various SR techniques. Our proposed model is tested on a wide range of images, and it significantly outperforms the existing state-of-the-art methods for various scaling factors both quantitatively and perceptually.
A unified framework of the automated lane centering/changing control for motion smoothness adaptation

This paper describes an automated lane centering/changing control algorithm that was developed at General Motors Research and Development. Over the past few decades, there have been numerous studies in the autonomous vehicle motion control. These studies typically focused on improving the control accuracy of the autonomous driving vehicles. In addition to the control accuracy, driver/passenger comfort is also an important performance measure of the system. As an extension of authors' prior study, this paper further considers vehicle motion control to provide driver/passenger comfort based on the adjustment of the lane change maneuvering time in various traffic situations. While defining the driver/passenger comfort level is a human factor study topic, this paper proposes a framework to integrate the motion smoothness into the existing lane centering/changing control problem. The proposed algorithm is capable of providing smooth and aggressive lane change maneuvers according to traffic situation and driver preference. Several simulation results as well as on-road vehicle test results confirm the effectiveness of the proposed algorithm.
A Series Slot Array Antenna for 45 $^{\circ}$-Inclined Linear Polarization With SIW Technology

The design method for a standing-wave series slot array antenna with 45°-inclined linear polarization in the Ka-band is presented. The proposed 16 × 8 planar slot array antenna consisted of arrays with alternating reactance slot pairs that could achieve impedance matching and uniform field ex- citation, simultaneously. Furthermore, the grating lobes were effectively suppressed with the help of these radiating units due to the one-half guided wavelength slot spacing. An equivalent circuit analysis was done to evaluate the input impedances of the radiating and feeding lines with the impedance recursive formulas as well as the mode voltages of the radiating slot elements. In addition, a wideband transition with a 30.13% bandwidth under the criteria of less than VSWR 1.5:1 was presented to provide the necessary power with the minimal reflection and to prevent the distortion of the radiation patterns. The proposed planar slot array antenna occupied an aperture area of 57.4× 51.6 mm. The measured bandwidth, gain, and efficiency were 990 MHz (2.88%), 24.3 dBi, and 53.7%, respectively. Furthermore, the side lobe levels (SLLs) were verified for each cutting plane with -13.57 dB and -13.17 dB, respectively. The proposed antenna structure was achieved with a substrate integrated waveguide (SIW) with the low costs and lightweight features.