nils-herrmann/clean_scirepeval huggingface dataset:
What is user engagement? A conceptual framework for defining user engagement with technology

The purpose of this article is to critically deconstruct the term engagement as it applies to peoples' experiences with technology. Through an extensive, critical multidisciplinary literature review and exploratory study of users of Web searching, online shopping, Webcasting, and gaming applications, we conceptually and operationally defined engagement. Building on past research, we conducted semistructured interviews with the users of four applications to explore their perception of being engaged with the technology. Results indicate that engagement is a process comprised of four distinct stages: point of engagement, period of sustained engagement, disengagement, and reengagement. Furthermore, the process is characterized by attributes of engagement that pertain to the user, the system, and user-system interaction. We also found evidence of the factors that contribute to nonengagement. Emerging from this research is a definition of engagement—a term not defined consistently in past work—as a quality of user experience characterized by attributes of challenge, positive affect, endurability, aesthetic and sensory appeal, attention, feedback, variety/novelty, interactivity, and perceived user control. This exploratory work provides the foundation for future work to test the conceptual model in various application areas, and to develop methods to measure engaging user experiences.  In the past few decades, human-computer interaction studies have emphasized the need to move beyond usability to understand and design for more engaging experiences (Hassenzahl & Tractinsky, 2006; Jacques, Preece, & Carey, 1995; Laurel, 1993). A Web interface that is boring, a multimedia presentation that does not captivate users' attention, or an online forum that fails to engender a sense of community are quickly dismissed with a simple mouse click. Failing to engage users equates with no sale on an electronic commerce site and no transmission of information from a Web site; people go elsewhere to perform their tasks and communicate with colleagues and friends. Successful technologies are not just usable; they engage users. Despite the 1 This is the authors’ version of an article published in Journal of the American Society for Information Science & Technology, http://www.asis.org/jasist.html. This version has been updated to reflect changes in the final published version. Complete citation is as follows: O’Brien, H.L. & Toms, E.G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology.
Estimation of Primary Quantization Matrix in Double Compressed JPEG Images

In this report, we present a method for estimation of primary quantization matrix from a double compressed JPEG image. We first identify characteristic features that occur in DCT histograms of individual coefficients due to double compression. Then, we present 3 different approaches that estimate the original quantization matrix from double compressed images. Finally, most successful of them Neural Network classifier is discussed and its performance and reliability is evaluated in a series of experiments on various databases of double compressed images. It is also explained in this paper, how double compression detection techniques and primary quantization matrix estimators can be used in steganalysis of JPEG files and in digital forensic analysis for detection of digital forgeries.
A NOVEL ARCHITECTURE FOR HIGH SPEED 1-BIT FULL ADDER AT 45nm TECHNOLOGY

Adder’s area unit basic building block of Arithmetic VLSI circuits found in processors and microcontroller within Arithmetic and Logic units. Improvement of adder is so vital so as to extend the performance of those units wherever adders realize application. Full adders, till now, are designed wide selection of structures for improvement of varied parameters like power consumption, performance of speed and physical size. The paper here describes a comparative analysis of 10T and 28T full adder with the aim of accelerating power potency and reducing physical size at 45nm technology. The design carried out by using Tanner e EDA tool at 45nm technology.
Design and Modeling of A Crowdsource-Enabled System for Urban Parcel Relay and Delivery

This paper proposes a crowdsource-enabled system for urban parcel relay and delivery. We consider cyclists and pedestrians as crowdsources who are close to customers and interested in relaying parcels with a truck carrier and undertaking jobs for the last-leg parcel delivery and the first-leg parcel pickup. The crowdsources express their interests in doing so by submitting bids to the truck carrier. The truck carrier then selects bids and coordinates crowdsources’ last-leg delivery (first-leg pickup) with its truck operations. The truck carrier’s problem is formulated as a mixed integer non-linear program which simultaneously i) selects crowdsources to complete the last-leg delivery (first-leg pickup) between customers and selected points for crowdsource-truck relay; and ii) determines the relay points and truck routes and schedule. To solve the truck carrier problem, we first decompose the problem into a winner determination problem and a simultaneous pickup and delivery problem with soft time windows, and propose a Tabu Search based algorithm to iteratively solve the two subproblems. Numerical results show that this solution approach is able to yield close-to-optimum solutions with much less time than using offthe-shelf solvers. By adopting this new system, truck vehicle miles traveled (VMT) and total cost can be reduced compared to pure-truck delivery. The advantage of the system over pure-truck delivery is sensitive to factors such as penalty for servicing outside customers’ desired time windows, truck unit operating cost, time value of crowdsources, and the crowdsource mode.
Water quality prediction method based on LSTM neural network

Water quality prediction has more practical significance not only for the management of water resources but also for the prevention of water pollution. It's a time series prediction problem which the traditional neural network isn't suitable. A new water quality prediction method based on long and short term memory neural network (LSTM NN) for water quality prediction is proposed in this paper. Firstly, a prediction model based on LSTM NN is established. Secondly, as the training data, the data set of water quality indicators in Taihu Lake which measured monthly from 2000 to 2006 years is used for training model. Thirdly, to improve the predictive accuracy of the model, a series of simulations and parameters selection are carried out. Finally, the proposed method is compared with two methods: one is based on back propagation neural network, the other is based on online sequential extreme learning machine. The results show that the method is more accurate and more generalized.
Deep Learning Approach for Secondary Structure Protein Prediction based on First Level Features Extraction using a Latent CNN Structure

In Bioinformatics, Protein Secondary Structure Prediction (PSSP) has been considered as one of the main challenging tasks in this field. Today, secondary structure protein prediction approaches have been categorized into three groups (Neighbor-based, model-based, and meta predicator-based model). The main purpose of the model-based approaches is to detect the protein sequence-structure by utilizing machine learning techniques to train and learn a predictive model for that. In this model, different supervised learning approaches have been proposed such as neural networks, hidden Markov chain, and support vector machines have been proposed. In this paper, our proposed approach which is a Latent Deep Learning approach relies on detecting the first level features based on using Stacked Sparse Autoencoder. This approach allows us to detect new features out of the set of training data using the sparse autoencoder which will have used later as convolved filters in the Convolutional Neural Network (CNN) structure. The experimental results show that the highest accuracy of the prediction is 86.719% in the testing set of our approach when the backpropagation framework has been used to pre-trained techniques by relying on the unsupervised fashion where the whole network can be fine-tuned in a supervised learning fashion. Secondary structure protein prediction; secondary structure; fine-tuning; Stacked Sparse; Deep Learning; CNN
A 77-GHz FMCW MIMO Radar Based on an SiGe Single-Chip Transceiver

This paper describes a novel frequency-modulated continuous-wave radar concept, where methods like nonuniform sparse antenna arrays and multiple-input multiple-output techniques are used to improve the angular resolution of the proposed system. To demonstrate the practical feasibility using standard production techniques, a prototype sensor using a novel four-channel single-chip radar transceiver in combination with differential patch antenna arrays was realized on off-the-shelf RF substrate. Furthermore, to demonstrate its practical applicability, the assembled system was tested in real world measurement scenarios in conjunction with the presented efficient signal processing algorithms.
Media use, face-to-face communication, media multitasking, and social well-being among 8- to 12-year-old girls.

An online survey of 3,461 North American girls ages 8-12 conducted in the summer of 2010 through Discovery Girls magazine examined the relationships between social well-being and young girls' media use--including video, video games, music listening, reading/homework, e-mailing/posting on social media sites, texting/instant messaging, and talking on phones/video chatting--and face-to-face communication. This study introduced both a more granular measure of media multitasking and a new comparative measure of media use versus time spent in face-to-face communication. Regression analyses indicated that negative social well-being was positively associated with levels of uses of media that are centrally about interpersonal interaction (e.g., phone, online communication) as well as uses of media that are not (e.g., video, music, and reading). Video use was particularly strongly associated with negative social well-being indicators. Media multitasking was also associated with negative social indicators. Conversely, face-to-face communication was strongly associated with positive social well-being. Cell phone ownership and having a television or computer in one's room had little direct association with children's socioemotional well-being. We hypothesize possible causes for these relationships, call for research designs to address causality, and outline possible implications of such findings for the social well-being of younger adolescents.
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima

We establish theoretical results concerning all local optima of various regularized M-estimators, where both loss and penalty functions are allowed to be nonconvex. Our results show that as long as the loss function satisfies restricted strong convexity and the penalty function satisfies suitable regularity conditions, any local optimum of the composite objective function lies within statistical precision of the true parameter vector. Our theory covers a broad class of nonconvex objective functions, including corrected versions of the Lasso for errors-in-variables linear models; regression in generalized linear models using nonconvex regularizers such as SCAD and MCP; and graph and inverse covariance matrix estimation. On the optimization side, we show that a simple adaptation of composite gradient descent may be used to compute a global optimum up to the statistical precision εstat in log(1/εstat) iterations, which is the fastest possible rate of any first-order method. We provide a variety of simulations to illustrate the sharpness of our theoretical predictions. Disciplines Statistics and Probability This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/statistics_papers/222 Regularized M -estimators with nonconvexity: Statistical and algorithmic theory for local optima Po-Ling Loh Department of Statistics University of California, Berkeley Berkeley, CA 94720 ploh@berkeley.edu Martin J. Wainwright Departments of Statistics and EECS University of California, Berkeley Berkeley, CA 94720 wainwrig@stat.berkeley.edu
An Online Identity and Smart Contract Management System

In today's online environment, people attend various kinds of activities, exhibit different digital presence, build personal digital reputations, issuing and receiving feedbacks from online communities being involved with. These diverse information sources once aggregated can provide a valuablefuture reference for personal online digital identity and credits check. The primary objective of this paper is to propose a systematic framework for aggregating online identity and reputation information, to provide a holistic approach to personal online behavioral ratings. Major contributions include: An identity aggregation mechanism based on social dependency network is proposed, a smart contract management framework referring to personal online ratings based on the aggregated digital identity, an experiment implementation based on blockchain technology, with illustrative examples and theoretical evaluations to the proposed approach.
An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems

The affine rank minimization problem, which consists of finding a matrix of minimum rank subject to linear equality constraints, has been proposed in many areas of engineering and science. A specific rank minimization problem is the matrix completion problem, in which we wish to recover a (low-rank) data matrix from incomplete samples of its entries. A recent convex relaxation of the rank minimization problem minimizes the nuclear norm instead of the rank of the matrix. Another possible model for the rank minimization problem is the nuclear norm regularized linear least squares problem. This regularized problem is a special case of an unconstrained nonsmooth convex optimization problem, in which the objective function is the sum of a convex smooth function with Lipschitz continuous gradient and a convex function on a set of matrices. In this paper, we propose an accelerated proximal gradient algorithm, which terminates in O(1/ √ 2) iterations with an 2-optimal solution, to solve this unconstrained nonsmooth convex optimization problem, and in particular, the nuclear norm regularized linear least squares problem. We report numerical results for solving large-scale randomly generated matrix completion problems. The numerical results suggest that our algorithm is efficient and robust in solving large-scale random matrix completion problems. In particular, we are able to solve random matrix completion problems with matrix dimensions up to 105 each in less than 10 minutes on a modest PC.
Correction of Inverted Nipple with Modified Star Flap Technique

The authors describe the application of the modified star flap technique for correction of inverted nipples. This technique allows easy identification and dissection of the lactiferous ducts under a clear direct view without trans-nipple incision. The secure deep dermal buried sutures around the nipple base in three directions maintain the newly everted nipple. The blood supply to the nipple flap is reliable through the dermal and subdermal plexus from the remaining non-incised site of the nipple and areola region.
A model for smart agriculture using IoT

Climate changes and rainfall has been erratic over the past decade. Due to this in recent era, climate-smart methods called as smart agriculture is adopted by many Indian farmers. Smart agriculture is an automated and directed information technology implemented with the IOT (Internet of Things). IOT is developing rapidly and widely applied in all wireless environments. In this paper, sensor technology and wireless networks integration of IOT technology has been studied and reviewed based on the actual situation of agricultural system. A combined approach with internet and wireless communications, Remote Monitoring System (RMS) is proposed. Major objective is to collect real time data of agriculture production environment that provides easy access for agricultural facilities such as alerts through Short Massaging Service (SMS) and advices on weather pattern, crops etc.
Current Perspectives on Biotechnological Cannabinoid Production in Plants.

The plant Cannabis sativa contains a number of psychoactive chemical compounds, the cannabinoids, which possess a significant pharmaceutical potential. Recently, the usage of Cannabis for medicinal purposes was legalized in many countries. Thus, the study on the influence of different cannabinoids in combination with other Cannabis-derived compounds with respect to the treatment of various diseases becomes increasingly important. Besides the production of distinct cannabinoids in a heterologous host, like tobacco or yeast, transgenic Cannabis plants would be a suitable alternative to modify and therefore optimize the cannabinoid profile. This perspective highlights the current efforts on Cannabis cell culture systems, in vitro propagation, and transformation of the plant and reveals the resulting opportunities concerning biotechnological production of cannabinoids. Furthermore, alternative platform organisms for the heterologous production of cannabinoids, like tobacco, are considered and evaluated.
A 0.018% THD+N, 88-dB PSRR PWM Class-D Amplifier for Direct Battery Hookup

A low-distortion third-order class-D amplifier that is fully integrated into a 0.18-μ m CMOS process was designed for direct battery hookup in a mobile application. A class-D amplifier for direct battery hookup must have a sufficiently high power supply rejection ratio (PSRR) in preparation for noise, such as when a global system for mobile communications (GSM) bursts ripples through the system power line. This amplifier has a high PSRR of 88 dB for 217-Hz power supply ripples, using a third-order loop filter. System performance and stability are improved by applying the design technique of input-feedforward delta-sigma (ΔΣ) modulators to the pulse-width modulation (PWM) class-D amplifier. A filterless method that can remove the external LC filter is employed, which offers great advantages in terms of PCB space and system cost. This amplifier achieves a power efficiency of 85.5% while delivering an output power of 750 mW into an 8-Ω load from a 3.7-V supply voltage. Maximum achieved output power at 1% total harmonic distortion plus noise (THD+N) from a 4.9-V supply voltage into an 8-Ω load is 1.15 W. This class-D amplifier is designed to have a broad operational range of 2.7-4.9 V for the direct use of mobile phone battery power. It has a total area of 1.01 mm2 and achieves a THD+N of 0.018%.
Limitations of the approximation capabilities of neural networks with one hidden layer

Let s/> 1 be an integer and W be the class of all functions having integrable partial derivatives on [0, 1] s. We are interested in the minimum number of neurons in a neural network with a single hidden layer required in order to provide a mean approximation order of a preassigned e > 0 to each function in W. We prove that this number cannot be O(e -s log(I/e)) if a spline-like localization is required. This cannot be improved even if one allows different neurons to evaluate different activation functions, even depending upon the target function. Nevertheless, for any 6 > 0, a network with O(c -s-6) neurons can be constructed to provide this order of approximation, with localization. Analogous results are also valid for other L p n o r m s .
Humans: Still Vital After All These Years of Automation

The authors discuss empirical studies of human-automation interaction and their implications for automation design.



Automation is prevalent in safety-critical systems and increasingly in everyday life. Many studies of human performance in automated systems have been conducted over the past 30 years.



Developments in three areas are examined: levels and stages of automation, reliance on and compliance with automation, and adaptive automation.



Automation applied to information analysis or decision-making functions leads to differential system performance benefits and costs that must be considered in choosing appropriate levels and stages of automation. Human user dependence on automated alerts and advisories reflects two components of operator trust, reliance and compliance, which are in turn determined by the threshold designers use to balance automation misses and false alarms. Finally, adaptive automation can provide additional benefits in balancing workload and maintaining the user's situation awareness, although more research is required to identify when adaptation should be user controlled or system driven.



The past three decades of empirical research on humans and automation has provided a strong science base that can be used to guide the design of automated systems.



This research can be applied to most current and future automated systems.
Barriers to Physicians’ Adoption of Healthcare Information Technology: An Empirical Study on Multiple Hospitals

Prior research on technology usage had largely overlooked the issue of user resistance or barriers to technology acceptance. Prior research on the Electronic Medical Records had largely focused on technical issues but rarely on managerial issues. Such oversight prevented a better understanding of users’ resistance to new technologies and the antecedents of technology rejection. Incorporating the enablers and the inhibitors of technology usage intention, this study explores physicians’ reactions towards the electronic medical record. The main focus is on the barriers, perceived threat and perceived inequity. 115 physicians from 6 hospitals participated in the questionnaire survey. Structural Equation Modeling was employed to verify the measurement scale and research hypotheses. According to the results, perceived threat shows a direct and negative effect on perceived usefulness and behavioral intentions, as well as an indirect effect on behavioral intentions via perceived usefulness. Perceived inequity reveals a direct and positive effect on perceived threat, and it also shows a direct and negative effect on perceived usefulness. Besides, perceived inequity reveals an indirect effect on behavioral intentions via perceived usefulness with perceived threat as the inhibitor. The research finding presents a better insight into physicians’ rejection and the antecedents of such outcome. For the healthcare industry understanding the factors contributing to physicians’ technology acceptance is important as to ensure a smooth implementation of any new technology. The results of this study can also provide change managers reference to a smooth IT introduction into an organization. In addition, our proposed measurement scale can be applied as a diagnostic tool for them to better understand the status quo within their organizations and users’ reactions to technology acceptance. By doing so, barriers to physicians’ acceptance can be identified earlier and more effectively before leading to technology rejection.