nils-herrmann/clean_scirepeval huggingface dataset:
THE DUALITY RETHINKING TECHNOLOGY OF TECHNOLOGY : THE CONCEPT OF IN ORGANIZATIONS

This paper develops a new theoretical model with which to examine the interaction between technology and organizations. Early research studies assumed technology to be an objective, external force that would have deterministic impacts on organizational properties such as structure. Later researchers focused on the human aspect of technology, seeing it as the outcome of strategic choice and social action. This paper suggests that either view is incomplete, and proposes a reconceptualization of technology that takes both perspectives into account. A theoretical model-the structurational model of technology--is built on the basis of this new conceptualization, and its workings explored through discussion of a field study of information technology. The paper suggests that the reformulation of the technology concept and the structurational model of technology allow a deeper and more dialectical understanding of the interaction between technology and organizations. This understanding provides insight into the limits and opportunities of human choice, technology development and use, and organizational design. Implications for future research of the new concept of technology and structurational model of technology are discussed.
Prototype of automatic accident detection and management in vehicular environment using VANET and IoT

The increase in the population of cities and the number of vehicles leads to increase in congestion on roads and accidents. The lack of quick medical service on road accidents is the major cause of death. In such situation, an automatic accident detection can help to save the loss of life. In this paper, a prototype is designed for an automatic accident detection using Vehicular Adhoc Network (VANET) and Internet of Things (IoT). The application is able to detect accident and severity of the emergency level with the help of mechanical and medical sensors deployed in the vehicle. In case of emergency, the message is sent to a hospital using VANET, where our central server is able to find out the location of the accident and nearest medical center. It sends a message for an ambulance after detecting basic information. In order to clear the path on the way to accident's location, the client application on the ambulance generates alert messages.
A comparison of Deep Learning methods for environmental sound detection

Environmental sound detection is a challenging application of machine learning because of the noisy nature of the signal, and the small amount of (labeled) data that is typically available. This work thus presents a comparison of several state-of-the-art Deep Learning models on the IEEE challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge task and data, classifying sounds into one of fifteen common indoor and outdoor acoustic scenes, such as bus, cafe, car, city center, forest path, library, train, etc. In total, 13 hours of stereo audio recordings are available, making this one of the largest datasets available.
Enhancing Social Inclusion of Children With Externalizing Problems Through Classroom Seating Arrangements: A Randomized Controlled Trial

Inclusive education has brought new challenges for teachers, including the search for a suitable place in the classroom for children with externalizing problems. In the current study, we examined whether a careful rearrangement of the classroom seats could promote social acceptance and more prosocial behaviors for children with externalizing problems, and limit the potential negative consequences for classmates sitting next to them. The sample of this randomized controlled trial consisted of 64 classrooms with 221 fourth- to sixth-grade children selected by their teachers because of elevated levels of externalizing behavior. Results showed that over time children with externalizing behavior were better liked by their seatmates and showed fewer externalizing problems according to the teacher. This was particularly the case when students sat next to a well-liked and prosocial buddy, or when they were initially disliked. Classmates who sat next to a child with externalizing problems did not become more aggressive or less prosocial over time. Yet their social status did decrease slightly over time as a result of the rearrangement. We discuss implications and future directions for research on classroom seating arrangements to support children with externalizing problems.
Intermanual apparent tactile motion on handheld tablets

Handheld and wearable devices frequently engage users with simple haptic feedback, such as alerting, shaking, and pulsating. Here we explored intermanual apparent tactile motion-illusory movement between two hands-as a means to enrich such feedback. A series of psychophysical experiments determined the control space for generating smooth and consistent motion across the hands while users held the device. Experiment 1 calibrated the system and showed that vibrotactile detection thresholds decreased with increasing frequency, with similar trends for both hands. Experiment 2 measured effects of vibrotactile parameters on perceived motion. Both duration and temporal separation of stimuli, but not frequency and amplitude, affected subjective motion ratings. In Experiment 3, subjective ratings showed that stimuli with gradual onsets produced a stronger percept of motion than those with abrupt onsets. Finally, Experiment 4 determined a multimodal factor to match moving visual cues across the screen to moving tactile motion across hands. Our results showed compression of visual duration by the tactile system by a factor of approximately 1/3 at two test frequencies. The results of this research are useful for media designers and developers to generate reliable motion across the hands and integrate haptic motion with visual media.
Attributes as Semantic Units between Natural Language and Visual Recognition

Impressive progress has been made in the fields of computer vision and natural language processing. However, it remains a challenge to find the best point of interaction for these very different modalities. In this chapter we discuss how attributes allow us to exchange information between the two modalities and in this way lead to an interaction on a semantic level. Specifically we discuss how attributes allow using knowledge mined from language resources for recognizing novel visual categories, how we can generate sentence description about images and video, how we can ground natural language in visual content, and finally, how we can answer natural language questions about images.
A Context-Aware Usability Model for Mobile Health Applications

Mobile healthcare is a fast growing area of research that capitalizes on mobile technologies and wearables to provide realtime and continuous monitoring and analysis of vital signs of users. Yet, most of the current applications are developed for general population without taking into consideration the context and needs of different user groups. Designing and developing mobile health applications and diaries according to the user context can significantly improve the quality of user interaction and encourage the application use. In this paper, we propose a user context model and a set of usability attributes for developing mobile applications in healthcare. The proposed model and the selected attributes are integrated into a mobile application development framework to provide user-centered and context-aware guidelines. To validate our framework, a mobile diary was implemented for patients undergoing Peritoneal Dialysis (PD) and tested with real users.
Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams

In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular timesampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.
An RNN-Based Multimodal Sentiment Analysis : Focusing on Facial Expressions and Multimodal Dynamic Representations

Multimodal sentiment analysis is rapidly gaining popularity due to its potential importance in elds like big data management and human-agent interaction systems. In this paper we present a multimodal opinion classication system fully constructed on deep learning technology. It is RNN-based, thus taking into account the time-dependency of the data. e features used are chosen in a way to allow the system to learn its own internal representation of the data. is was done with the goal to obtain a more accurate system that can generalize beer when given a sucient amount of data, leveraging the deep learning ability to learn features. It was trained and tested on the MOSI dataset which makes it an uerance-level sentiment analysis system. e results presented here are, to the best of our knowledge, the rst results obtained on MOSI for such a task (highest accuracy of 84.30%). Additionally, as a validation experiment, we compare and show that our RNN-based system outperforms a CNN-based one (presented in previous work) on the linguistic modality of the MOUD dataset.
Semantic Parsing with Syntax- and Table-Aware SQL Generation

We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query wordby-word, however, a large portion of the generated results is incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest questionSQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0% to 74.4%.
Simple and strong : Twisted silver painted nylon artificial muscle actuated by Joule heating

Highly oriented nylon and polyethylene fibres shrink in length when heated and expand in diameter. By twisting and then coiling monofilaments of these materials to form helical springs, the anisotropic thermal expansion has recently been shown to enable tensile actuation of up to 49% upon heating. Joule heating, by passing a current through a conductive coating on the surface of the filament, is a convenient method of controlling actuation. In previously reported work this has been done using highly flexible carbon nanotube sheets or commercially available silver coated fibres. In this work silver paint is used as the Joule heating element at the surface of the muscle. Up to 29% linear actuation is observed with energy and power densities reaching 840 kJ m (528 J kg) and 1.1 kW kg (operating at 0.1 Hz, 4% strain, 1.4 kg load). This simple coating method is readily accessible and can be applied to any polymer filament. Effective use of this technique relies on uniform coating to avoid temperature gradients.
A reconfigurable three- and single-phase AC/DC non-isolated bi-directional converter for multiple worldwide voltages

AC/DC converters for worldwide deployment or multiple applications are often required to operate with single-or three-phase AC connections at a variety of voltages. Electric vehicle charging is an excellent example: a typical AC/DC converter optimized for a 400 V three-phase European connection will only produce about 35% of rated power when operating on a 240 V single-phase connection in North America. This paper proposes a reconfigurable AC-DC converter with integral DC-DC stage that allows the single-phase AC power to roughly double compared to a standard configuration, up to 2/3 of the DC/DC stage rating. The system is constructed with two conventional back-to-back three-phase two-level bridges. When operating in single-phase mode, rather than leaving the DC side under-utilized, our proposed design repurposes one of the DC phase legs and the unused AC phase to create two parallel single-phase connections. This roughly doubles the AC output current and hence doubles the single phase AC power.
Broadband Balanced Frequency Doublers With Fundamental Rejection Enhancement Using a Novel Compensated Marchand Balun

<?Pub Dtl?>In this paper, a novel compensation technique is proposed to improve the imbalance of a Marchand balun due to the unequal odd- and even-mode phase velocities of the coupled lines. Using this method, the fundamental rejection of the balanced doubler with the Marchand balun can be effectively enhanced. Two single-balanced doublers using the improved Marchand balun are designed, fabricated, and measured to verify the concept in CMOS processes. One doubler for 15–36-GHz possesses <formula formulatype="inline"><tex Notation="TeX">$-$</tex></formula>10-dB conversion gain with the 3-dB bandwidth of 82.4% and the fundamental rejection of 33 dB. The other doubler for 95–150 GHz achieves <formula formulatype="inline"><tex Notation="TeX">$-$</tex></formula>7.9-dB conversion gain with the 3-dB bandwidth of 45% and the fundamental rejection of 30 dB. With the proposed compensation technique, these frequency doublers feature wide bandwidths and high fundamental rejections.
Impact of research on practice in the field of inspections, reviews and walkthroughs: learning from successful industrial uses

Software inspections, reviews, and walkthroughs have become a standard process component in many software development domains. Maturity level 3 of the CMM-I requires establishment of peer reviews [12] and substantial sustained improvements in quality and productivity have been reported as a result of using reviews ([16], [21], [22], ). The NSF Impact project identifies the degree to which these industrial success cases have been instigated and improved by research in software engineering.
 This research identifies that there is widespread adoption of inspections, reviews or walkthroughs but that companies do not generally exploit their full potential. However there exist sustained industrial success cases with respect to the wide-spread and measurably successful application of them. It also identifies research in software engineering that can be credibly documented as having influenced the industrial success cases. Credible documentation may exist in the form of publications or documented reports by witnesses. Due to the semi-formal nature of inspections, reviews, and walkthroughs, a specific focus is given to empirical research results as motivators for adoption. Through the examination of one detailed case study, it is shown that software engineering research has had a significant impact on practice and that the impact can be traced in this case from research to that practice. The case study chosen provides evidence of both success and failure regarding sustained application in practice.
 Thus the analysis of historic impact chains of research reveals a clear impact of software engineering research on sustained industrial success for inspections, reviews and walkthroughs. More importantly, in impact chains where the empirical results have not been established, we conclude that success has not been achieved or has not been sustained.
 The paper closes with (1) lessons learned for creating the sustained use and impact of semi-formal software engineering processes, (2) a request for researchers and practitioners to further consider how their work can improve the effectiveness of research and practice, and (3) a request to contribute additional success cases and impact factors to the authors database for future enhancements of this paper.
Design of Simple Multiband Patch Antenna for Mobile Communication Applications Using New E-Shape Fractal

In this study, a probe fed E-shape fractal patch antenna (EFPA) for heptads' band LTE/WWAN (GSM850/900/1800/1900/UMTS/LTE2300/2500) operation is proposed. Various iterations of this fractal antenna are compared, and an optimized design is presented. Exploitation of E-shape fractal notion in antenna design makes the patch antenna flexible in terms of generating resonances and bandwidth as an iteration order of the fractal is increased. Several properties of the antenna such as impedance bandwidth, radiation patterns, radiation efficiency, electric current distributions on the patch, and gain have been demonstrated numerically and empirically in detail. Experimental results have authorized the design procedure and confirm the gratification of the requirements for multistandard mobile terminal applications.
Comparative Study of MOSFET and IGBT for High Repetitive Pulsed Power Modulators

This paper presents an investigation of semiconductor switches for high voltage solid-state pulsed power modulators from the viewpoint of pulse rising time, efficiency, and reliability. A comparative study of insulated gate bipolar transistor (IGBT) and metal oxide semiconductor field effect transistor (MOSFET) devices is provided, focusing particularly on the characteristics of each device for pulsed power applications. Two kinds of stacks for 10-kV pulses are developed with a high voltage capacitor charger in order to experimentally compare the characteristics of each device. Although the structure of each stack is similar, the number of power cells for the 10-kV pulse is different due to the device voltage rating. The power cell voltage for IGBT and MOSFET-based stacks is about 840 V and 560 V, respectively. Therefore, two types of transformer, with six secondary windings for the IGBT stack and nine secondary windings for the MOSFET stack, are also designed for storage capacitor charging. Moreover, the ease of varying the pulse repetition rate and the pulse width shows the advantages of the developed modulators in facilitating the measurement of device characteristics in different pulse output conditions. The experiments are performed with noninductive resistor loads, and the results of the comparison tests are discussed. In addition, the results prove that the developed modulator can be effectively used for high repetitive pulsed power applications.
Windows driver memory analysis: A reverse engineering methodology

In a digital forensics examination, the capture and analysis of volatile data provides significant information on the state of the computer at the time of seizure. Memory analysis is a premier method of discovering volatile digital forensic information. While much work has been done in extracting forensic artifacts from Windows kernel structures, less focus has been paid to extracting information from Windows drivers. There are two reasons for this: (1) source code for one version of the Windows kernel (but not associated drivers) is available for educational use and (2) drivers are generally called asynchronously and contain no exported functions. Therefore, finding the handful of driver functions of interest out of the thousands of candidates makes reverse code engineering problematic at best. Developing a methodology to minimize the effort of analyzing these drivers, finding the functions of interest, and extracting the data structures of interest is highly desirable. This paper provides two contributions. First, it describes a general methodology for reverse code engineering of Windows drivers memory structures. Second it applies the methodology to tcpip.sys, a Windows driver that controls network connectivity. The result is the extraction from tcpip.sys of the data structures needed to determine current network connections and listeners from the 32 and 64 bit versions of Windows Vista and Windows 7.
A Soft Intelligent Risk Evaluation Model for Credit Scoring Classification

Risk management is one of the most important branches of business and finance. Classification models are the most popular and widely used analytical group of data mining approaches that can greatly help financial decision makers and managers to tackle credit risk problems. However, the literature clearly indicates that, despite proposing numerous classification models, credit scoring is often a difficult task. On the other hand, there is no universal credit-scoring model in the literature that can be accurately and explanatorily used in all circumstances. Therefore, the research for improving the efficiency of credit-scoring models has never stopped. In this paper, a hybrid soft intelligent classification model is proposed for credit-scoring problems. In the proposed model, the unique advantages of the soft computing techniques are used in order to modify the performance of the traditional artificial neural networks in credit scoring. Empirical results of Australian credit card data classifications indicate that the proposed hybrid model outperforms its components, and also other classification models presented for credit scoring. Therefore, the proposed model can be considered as an appropriate alternative tool for binary decision making in business and finance, especially in high uncertainty conditions.
Facing prejudice: implicit prejudice and the perception of facial threat.

We propose that social attitudes, and in particular implicit prejudice, bias people's perceptions of the facial emotion displayed by others. To test this hypothesis, we employed a facial emotion change-detection task in which European American participants detected the offset (Study 1) or onset (Study 2) of facial anger in both Black and White targets. Higher implicit (but not explicit) prejudice was associated with a greater readiness to perceive anger in Black faces, but neither explicit nor implicit prejudice predicted anger perceptions regarding similar White faces. This pattern indicates that European Americans high in implicit racial prejudice are biased to perceive threatening affect in Black but not White faces, suggesting that the deleterious effects of stereotypes may take hold extremely early in social interaction.
Radar Cross-Section Analysis of Backscattering RFID Tags

In this letter, we present a graphical method based on Green's analysis of scattering antennas to optimize the load impedances for achieving maximum modulated radar cross-section (RCS) of backscattering radio-frequency identification (RFID) tags. A planar tag antenna specifically designed for working in the ultra-high-frequency band is used to experimentally validate the method. The measured RCS of the loaded tag antenna shows acceptable agreement with the theory and simulated results. This method is especially valuable to the design of semipassive RFID tags when the communication range is solely determined by the backscattering modulation efficiency of tags.
Optimality of Frequency Flat Precoding in Frequency Selective Millimeter Wave Channels

Millimeter wave (mmWave) MIMO communication is a key feature of next generation wireless systems. The selection of precoders and combiners for wideband mmWave channels has involved frequency selective designs based on channel state information. In this letter, we show that under some assumptions, the dominant subspaces of the frequency domain channel matrices are equivalent. This means that semi-unitary frequency flat precoding and combining are sufficient to achieve the maximum spectral efficiency when there is not too much scattering in the channel. It also motivates the use of techniques such as compressive subspace estimation as an alternative to estimating the full channel.