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The smart specialization strategy (S3) introduces a new “policy-prioritization logic” according to which regional strategies should target interventions in those research and innovation domains in which the region has a competitive advantage. In other words, the adoption of an S3 strategy has shifted the regional innovation strategies’ focus from privileging exclusively high-tech sectors to including also those lower tech domains that are recognized as a source of regional competitive advantage. As a consequence, this shift calls for a rethinking of university–industry relationships for innovation, which have traditionally privileged high-tech sectors. In this light, this article aims to analyze how the introduction of the S3 has changed the role of universities by comparing patterns of academic engagement with local entrepreneurs in both low-tech and high-tech domains. To this purpose, we conduct an empirical study in the Region of Campania (South of Italy) to analyze differences in terms of university–industry channels and motivations in different S3 technological domains. [ABSTRACT FROM AUTHOR]

How spontaneous brain neural activities emerge from the underlying anatomical architecture, characterized by structural connectivity (SC), has puzzled researchers for a long time. Over the past decades, much effort has been directed toward the graph modeling of SC, in which the brain SC is generally considered as relatively invariant. However, the graph representation of SC is unable to directly describe the connections between anatomically unconnected brain regions and fail to model the negative functional correlations. Here, we extend the static graph model to a spatiotemporal varying hypergraph Laplacian diffusion (STV-HGLD) model to describe the propagation of the spontaneous neural activity in human brain by incorporating the Laplacian of the hypergraph representation of the structural connectome (${h}$ SC) into the regular wave equation. Theoretical solution shows that the dynamic functional couplings between brain regions fluctuate in the form of an exponential wave regulated by the spatiotemporal varying Laplacian of ${h}$ SC. Empirical study suggests that the cortical wave might give rise to resonance with SC during the self-organizing interplay between excitation and inhibition among brain regions, which orchestrates the cortical waves propagating with harmonics emanating from the ${h}$ SC while being bound by the natural frequencies of SC. Besides, the average statistical dependencies between brain regions, normally defined as the functional connectivity (FC), arises just at the moment before the cortical wave reaches the steady state after the wave spreads across all the brain regions. Comprehensive tests on four extensively studied empirical brain connectome datasets with different resolutions confirm our theory and findings. [ABSTRACT FROM AUTHOR]

Contribution: In this study, we accumulated the knowledge and generated evidence on how and in what context CDIO framework has been used in software engineering (SE) education. The aggregated evidence will enable SE academics in making informed decisions while adopting CDIO for SE education and build upon it. Background: CDIO framework is relevant for SE as it focuses on enabling engineering graduates in conceiving, designing, implementing, and operating complex systems and products. We were not able to find any study that identifies and aggregates the evidence on the use of CDIO for SE education. Research Questions: This study attempts to answer the following research questions: 1) how CDIO has been used in SE education? and 2) what are the experiences of academics in applying the CDIO framework in SE education? Methodology: Using a mixed-method approach (systematic mapping study and interview study with experienced academics in SE), we established the state of the art and practice on the use of CDIO in SE education. Findings: Getting a commitment from the higher management, teachers, and students is a major challenge in the adoption of the CDIO initiative followed by a lack of competence, finance, and resources. Ownership, motivation, persistence, and training of teachers and students are required not only to adopt CDIO for SE but also to sustain it. [ABSTRACT FROM AUTHOR]

So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top- $k$ k spatial textual cluster retrieval ($k$ k -STC) query that returns the top- $k$ k clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance. [ABSTRACT FROM AUTHOR]

The purpose of this article is to identify and evaluate the limitations and emerging trends of Six Sigma from the perspectives of Six Sigma experts. The authors developed an online global survey and deployed the survey to 1250 Six Sigma experts of which 307 experts responded. The article finds integration of Six Sigma with Big Data to be the topmost among Asian, South American, and African experts, whereas European and North American experts felt Six Sigma in Small and Medium Sized Enterprises and Micro-enterprises would be very beneficial. The manufacturing sector experts nominated the topmost emerging trend as Six Sigma in Small and Medium Sized Enterprises and Micro-enterprises to be very challenging and will be rewarding if implemented properly. In the service sector, the topmost emerging trend was the integration of Six Sigma with Big Data. However, public sector experts felt variance reduction should not be the only goal of Six Sigma implementation. The that master black belts perceived Six Sigma in Small and Medium Sized Enterprises and Micro-enterprises would be advantageous, whereas Black and Green Belts perceived Integration of Six Sigma with Big Data to be topmost emerging trend. [ABSTRACT FROM AUTHOR]

New products are major sources of competitive advantage and success for most of the manufacturing enterprises. New product development (NPD) processes are getting increasingly complex due to today's globalized market and customers’ desire for technologically advanced products. This complexity comes along with added risks in the NPD process in terms of both number and severity, making it riskier and costly. Thus, the risk factors occurring in the NPD process need to be pre-emptively recognized at an early stage of the NPD process. This article provides a comprehensive study of the prevalent risk factors in the NPD process and proposes taxonomy of those risks based on an empirical research on Indian automotive industry. Exploratory and confirmatory factor analyses techniques are utilized to classify a large number of risks (obtained via systematic literature review and Delphi method) into seven underlying risk dimensions. The results of this article would help researchers and practitioners in developing a better understanding of the risk factors existing in their NPD projects and serve as a risk reference framework. [ABSTRACT FROM AUTHOR]

Systematically identification and building of digital capability in the Digital Age are of great significance to the obtainment of enterprises’ sustainable competitive advantages. This article first proposes typical application paradigms for the general framework of building digital capability of industrial enterprises in the Digital Age (FBDC) announced as international standard. Then data of over 4100 practical cases of building digital capabilities using FBDC by over 4000 Chinese industrial enterprises are collected. Based on the data, major directions, core implementations of digital capabilities building are systematically demonstrated through statistical analysis. Furthermore, we divide the investigated enterprises into different groups by sector, then organize in-depth research works to illustrate the major directions and typical practices of building digital capabilities in China's raw materials sector, equipment sector, and consumer goods sector. The results show that the major concerns of Chinese industrial enterprises building digital capabilities are centered on six aspects: R&D and innovation, production management and control, supply chain management, financial management and control, business management, and customer service. The results of sector-wise analysis also provide practical reference to industrial enterprises and other stakeholders to identify suitable digital capability based on information and communication technologies application in the Digital Age and efficiently implement the digital capability building process. [ABSTRACT FROM AUTHOR]

Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective optimization problem which varies over time. As changes in DMOP may exist some patterns that are predictable, to solve DMOP, a number of research efforts have been made to develop evolutionary search with prediction approaches to estimate the changes of the problem. A common practice of existing prediction approaches is to predict the change of Pareto-optimal solutions (POS) based on the historical solutions obtained in the decision space. However, the change of a DMOP may occur in both decision and objective spaces. Prediction only in the decision space thus may not be able to give the proper estimation of the problem change. Taking this cue, in this article, we propose an evolutionary search with multiview prediction for solving DMOP. In contrast to existing prediction methods, the proposed approach conducts prediction from the views of both decision and objective spaces. To estimate dynamic changes in DMOP, a kernelized autoencoding model is derived to perform the multiview prediction in a reproducing kernel Hilbert space (RKHS), which holds a closed-form solution. To examine the performance of the proposed method, comprehensive empirical studies on the commonly used DMOP benchmarks, as well as a real-world case study on the movie recommendation problem, are presented. The obtained experimental results verified the efficacy of the proposed method for solving both benchmark and real-world DMOPs. [ABSTRACT FROM AUTHOR]

Recently, just-in-time (JIT) defect prediction technique attracted a lot of attention. In JIT defect prediction, all branches and omitting changes outside the main branch should be considered which can significantly affect the performance of JIT defect prediction. However, there are many duplicate changes among all the branches, which are referred to as a pair of changes with identical implementation in different branches. Such changes can influence the calculation of developer experience metrics and are considered as the noisy data for JIT defect prediction. In this article, the impact of duplicate changes on JIT defect prediction is explored. An empirical study on a total of 105 828 changes from eight Apache open-source projects is given. We find that 13% of changes from different branches are duplicate among the studied projects. The duplicate changes have a great influence on the model metrics for JIT defect prediction. For 50% of the changes, removing duplicate changes decreases the experience metrics with an average of 6–55. In addition, the duplicate changes have a significant impact on the evaluation and interpretation of JIT defect prediction models. Removing duplicate changes among the studied projects can significantly improve the performance of JIT defect prediction models ranging from 1 to 125% concerning various performance measures (i.e., area under the curve, Matthews correlation coefficient, and F1). Given the impact of duplicate changes, we suggest that researchers should remove duplicate changes from the original historical changes of software repository when evaluating the performance of JIT defect prediction models in future work. [ABSTRACT FROM AUTHOR]

The original loss separation formula introduced by Bertotti has long been utilized mainly for low-frequency sinusoidal applications. In this article, the traditional method is improved to fit into the core loss calculation under higher frequency sinusoidal and nonsinusoidal excitations, verified by the measured data of nanocrystalline material that is often used in medium-frequency transformers (MFTs). For high-frequency sinusoidal waveform, it is proposed that the exponent of the excess loss needs to be reidentified, and it is necessary to consider the skin effect for hysteresis and eddy current loss calculation under broader frequency scope. When the core is supplied with nonsinusoidal excitation (square and rectangular waveform), a new empirical method is derived based on the concept of effective frequency and the loss separation results, with which the estimation accuracy is greatly guaranteed compared to empirical methods originated from the Steinmetz equation such as improved generalized Steinmetz equation (iGSE). Due to clear separation of loss components, the new method has the potential to precisely predict losses for higher frequency applications. [ABSTRACT FROM AUTHOR]

A museum hybrid space combines physical artifacts co-located with virtual and augmented reality displays. Although the technology exists to provide museums with hybrid space, there are no empirical studies on effectiveness of the museum hybrid space in terms of learning and enjoyment. This article takes an experimental approach and measures the enjoyment and learning (dependent variables) of participants in response to selected environments (independent variables) including a traditional environment (based on photos and labels), a video-enhanced environment (based on projected video clips), and a VR-enhanced environment (based on video game). The main outcome of this article is demonstrating that the use of VR technology and the resulting hybrid space (i.e., VR-enhanced environment) results in novel museum experiences that provide greater impacts on audience in terms of learning and enjoyment. [ABSTRACT FROM AUTHOR]

To improve the efficiency of the fault localization process, different automatic fault localization approaches have been proposed. Among these approaches, the spectrum-based fault localization (SBFL) approach has been widely used and studied due to its lightweight and high effectiveness. However, while the existence of coincidental correct (CC) test cases can influence the usefulness of SBFL in single-fault programs, their influence on multiple fault programs has not been thoroughly investigated. Therefore, in this article, we conduct a theoretical analysis and an empirical study to investigate the effect of CC test cases on multiple fault localization. The theoretical analysis is based on a suspiciousness calculation formula of SBFL, which divides CC test cases into three categories (specific, irrelevant, and unspecific) according to their association with a specific faulty statement. Following this analysis, we conduct an empirical study on two well-known open-source repositories (SIR and Defects4J), and the experimental results verify the correctness of our theoretical analysis. Specifically, reducing the number of specific CC test cases for a faulty statement can improve or maintain fault localization accuracy, while eliminating irrelevant CC test cases can have a negative effect. Finally, we design a CC test case identification solution based on the isolation-based multiple fault localization approach and demonstrate its effectiveness via a simulation experiment. [ABSTRACT FROM AUTHOR]

Python is an interpreted programming language that has been widely used in many fields. The successful execution of a Python program depends on both the correctness of Python program and the correctness of Python interpreter. As an infrastructure software, there are many bugs in the Python interpreter. Exploring the bugs in Python interpreters can help developers and maintainers of Python interpreters detect and fix bugs and help users of Python avoid risks. In this article, we conduct an empirical study on the bugs in two mainstream Python interpreters: CPython and PyPy. By analyzing 25 958 fixed bugs, 18 824 revisions, 2 116 test cases, and root causes of randomly sampled 510 bugs, we have summarized the following findings. 1)The distribution of bugs in the Python interpreter is so uneven that the vast majority of bugs are distributed in a few components and source files. 2)The scales of the testing programs that reveal bugs are small. 3)The fixing works seem to be not complicated since the number of modified source files and lines of code are limited; however, most bugs need a long time to be fixed; nearly 15% of the bugs need more than one year to fix. 4)The priorities of bugs are independent of their locations, but they significantly correlate with duration of bugs. 5)Semantic bugs are the most frequent root causes of bugs, and their proportion exceeds other types of root causes. These results could indicate some potential problems during the detecting and fixing of Python interpreter’s bugs, and provide some assistance to developers and maintainers of Python interpreters, users of Python, as well as researchers in related fields. [ABSTRACT FROM AUTHOR]

First-order mutants (FOMs) have been widely used in mutation-based fault localization (MBFL) approaches and have achieved promising results in single-fault localization scenarios (SFL-scenario). Higher-order mutants (HOMs) are proposed to simulate complex faults and can be applied in MBFL theoretically for multiple-fault localization scenarios (MFL-scenario). However, whether HOMs can improve MBFL’s performance is not investigated and the effectiveness is not thoroughly evaluated. In this empirical study, we investigate the impact of HOMs on the performance of MBFL in SFL-scenario and MFL-scenario. The experiments on two real-world benchmarks reveal that 1) 2-HOMs can help improve the MBFL performance in SFL-scenarios; 2) in MFL-scenarios, both 2-HOMs and 3-HOMs can achieve better performance than FOMs; and 3) huge computational cost cannot be ignored in the practice of HOMs. Therefore, effective methods to reduce the number of HOMs for future MBFL studies should be considered. [ABSTRACT FROM AUTHOR]

In this article, a novel approach is proposed for accurate moisture content (MC) estimation of cross-linked polyethylene (XLPE) insulation employing electric modulus. Additionally, the variation of real and imaginary part of electric modulus in the argand plane is fitted using Cole-Cole (C-C) model for understanding of distributed relaxation behavior of XLPE insulation. Based on the frequency variation of dielectric parameters, five moisture sensitive parameters are identified and using these parameters, five empirical relationships are proposed for quantitative assessment of MC in XLPE insulation. It is observed that parameters obtained from the complex modulus spectrum yields better accuracy in estimation of MC compared to the parameters obtained from traditional frequency domain spectroscopy (FDS). [ABSTRACT FROM AUTHOR]

Transition-edge sensors (TES) are outstanding calorimeters based on the steep superconductive transition of a metallic film. Among other photon detectors, they are renowned for the fine energy resolution, the photon-number resolving (PNR) capability and an extremely low dark count rate. Due to the broad detection spectrum, from gamma-ray to visible and submillimetre wavelengths, TESs are highly sought-after in a great variety of fields, such as X-ray detection and quantum technologies. Each of these fields demands a step forward in TESs performance with regards to the recovery time and energy resolution. Here we present a program, primarily capable of predicting the performance of TESs. Using established theoretical and empirical methods we developed a software that allows the users to choose active area, thickness, and material composition of a TES and to calculate its performance. Furthermore, the software can simulate TES properties at different working points. The aim of the software is to minimize the production cost and speed up the overall process for the creation of new devices with improved performance. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) technology is gaining popularity in a variety of fields, including education, games, movies, medicine, and engineering, to name a few. 360$^\circ$∘ VR video could provide an immersive experience and attract more researchers' and developers' attention. Some literature focused on the head movement when users watched 360$^\circ$∘ videos and released head tracking datasets. With the popularity of VR games, how the game contexts influence players' head movement and the effect of head movement on VR sickness is a topic worth studying. In this article, we collected a head movement dataset of 30 participants while playing five different VR games (Aircar, Beat Saber, Moss, Arizona Sunshine, and SUPERHOT), and the participants filled the Simulator Sickness Questionnaire (SSQ) after playing VR games. Finally, we analyzed the SSQ scores and the impact of VR games on VR sickness. To the best of our knowledge, this is the first available head movement trajectory dataset based on playing several types of VR games. [ABSTRACT FROM AUTHOR]

A very important function of an issue tracking system is to assign labels to issue reports, such as bug, feature, enhancement, etc., in order to categorize issues to facilitate various development activities. In practice, it is very common that an issue has multiple labels. However, current works are mainly based on single-label prediction, which are not suitable for just-in-time multi-labeling services, due to the low efficiency. Therefore, in this paper, we propose MULA, a just-in-time MUlti-LAbeling system, which learns and automatically assigns multiple labels to issue reports. We have built a dataset with 81,601 entries and 11 labels, as the first benchmark for this task, and implemented a GitHub app. To the best of our knowledge, this is the first work and tool for online multi-labeling GitHub issues based on their categories. We conduct a comprehensive empirical study, including comparisons with five commonly adopted labeling models that show the superiority of MULA, as well as an evaluation that shows high consistency between MULA’s suggestions and developers’ opinions. [ABSTRACT FROM AUTHOR]

One of the challenges in ocean surface current retrieval from synthetic aperture radar (SAR) data is the estimation and removal of the wave-induced Doppler centroid (DC). This article demonstrates empirically the relationship between the dc derived from spaceborne X-band InSAR data and the ocean surface wind and waves. In this study, we analyzed over 300 TanDEM-X image pairs. It is found that the general characteristics of the estimated dc follow the theoretically expected variation with incidence angle, wind speed, and wind direction. An empirical geophysical model function (GMF) is fit to the estimated dc and compared to existing models and previous experiments. Our GMF is in good agreement (within 0.2 m/s) with other models and data sets. It is found that the wind-induced Doppler velocity contributes to the total Doppler velocity with about 15% of the radial wind speed. This is much larger than the sum of the contributions from the Bragg waves (~0.2 m/s) and the wind-induced drift current (~3% of wind speed). This indicates a significant (dominant) contribution of the long wind waves to the SAR dc. Moreover, analysis of dual-polarized data shows that the backscatter polarization ratio ($PR=\sigma ^{0}_{VV}/\sigma ^{0}_{HH}$) and the dc polarization difference ($PD={|dc_{VV}|-|dc_{HH}|}$) are systematically larger than 1 and smaller than 0 Hz, respectively, and both increase in magnitude with incidence angle. The estimated PR and PD are compared to other theoretical and empirical models. The Bragg scattering theory-based (pure Bragg and composite surface) models overestimate both PR and PD, suggesting that other scattering mechanisms, e.g., wave breaking, are involved. In general, it is found that empirical models are more consistent with both backscatter and Doppler data than theory-based models. This motivates a further improvement of SAR dc GMFs. [ABSTRACT FROM AUTHOR]

We consider the fundamental problem of matching a template to a signal. We do so by M-estimation, which encompasses procedures that are robust to gross errors (i.e., outliers). Using standard results from empirical process theory, we derive the convergence rate and the asymptotic distribution of the M-estimator under relatively mild assumptions. We also discuss the optimality of the estimator, both in finite samples in the minimax sense and in the large-sample limit in terms of local minimaxity and relative efficiency. Although most of the paper is dedicated to the study of the basic shift model in the context of a random design, we consider many extensions towards the end of the paper, including more flexible templates, fixed designs, the agnostic setting, and more. [ABSTRACT FROM AUTHOR]

Dimensionality Reduction (DR) techniques can generate 2D projections and enable visual exploration of cluster structures of high-dimensional datasets. However, different DR techniques would yield various patterns, which significantly affect the performance of visual cluster analysis tasks. We present the results of a user study that investigates the influence of different DR techniques on visual cluster analysis. Our study focuses on the most concerned property types, namely the linearity and locality, and evaluates twelve representative DR techniques that cover the concerned properties. Four controlled experiments were conducted to evaluate how the DR techniques facilitate the tasks of 1) cluster identification, 2) membership identification, 3) distance comparison, and 4) density comparison, respectively. We also evaluated users' subjective preference of the DR techniques regarding the quality of projected clusters. The results show that: 1) Non-linear and Local techniques are preferred in cluster identification and membership identification; 2) Linear techniques perform better than non-linear techniques in density comparison; 3) UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-Distributed Stochastic Neighbor Embedding) perform the best in cluster identification and membership identification; 4) NMF (Nonnegative Matrix Factorization) has competitive performance in distance comparison; 5) t-SNLE (t-Distributed Stochastic Neighbor Linear Embedding) has competitive performance in density comparison. [ABSTRACT FROM AUTHOR]

Identifying security bug reports (SBRs) accurately from a bug repository can reduce a software product’s security risk. However, the class imbalance problem exists for SBR prediction since the number of SBRs is often limited, and this issue has not been thoroughly investigated in previous studies. In our study, we choose six real-world projects of different sizes with over 120 000 bug reports in total as our empirical subjects. We first analyze the impact of the class imbalance issue on SBR prediction and confirm its negative impact on prediction performance. Then we perform a comparative study of six state-of-the-art class rebalancing methods combined with five popular classification algorithms for SBR prediction. By comparing with the baseline method Farsec, using the class rebalancing methods can improve the performance in 78% of cases in the worst case. Moreover, the combination of the Rose and random forest classification algorithm can construct the model with the best performance, which increases the performance by 267% in the best case and 75% on average in terms of F1-score. Finally, we summarize eight main findings based on our empirical studies’ results, which can provide guidelines for choosing appropriate class rebalancing methods and classifiers for SBR prediction in practice. [ABSTRACT FROM AUTHOR]

Background: Although a number of empirical studies have investigated communication challenges during recent years, we still need to discover the most critical challenges that face communication when agile development is geographically distributed. We also need to discover how successful geographically distributed agile development (GDAD) organizations deal with these challenges. Literature review: Most previous studies reported that the critical challenges facing GDAD communication can be categorized into five themes: differences in cultures, different time zones, different spoken languages, different personal skills, and the efficiency and effectiveness of communication tools used. Research questions: 1. What are the challenges of communication between GDAD teams? 2. How can the impact of GDAD communication challenges be mitigated? Methodology: Data were collected by interviewing 12 members of a three-team organization using distributed agile development. These teams are distributed over three countries; the main team located in Australia, the developers’ team located in China, and the testers’ team located in India. A thematic analysis technique was used to identify communication challenges and practices used to mitigate the effect of these challenges. Results: Our findings reveal that the five challenges are still critical to GDAD. Moreover, we report a new critical challenge of communication in GDAD, the insufficient documentation provided by distributed teams and members. In addition, we recommend several practices to mitigate the impact of these challenges. Conclusions: Communication among distributed agile development teams still faces several critical challenges, and the solutions to these challenges provided in recent years have not been sufficient. This fact prompts the need for more research on how the impact of these challenges can be lessened. [ABSTRACT FROM AUTHOR]

Goals: This paper discusses the need for a predictable method to evaluate gains and gaps of collaborative technology-mediated workflows and introduces an evaluation framework to address this need. Methods: The Collaborative Space – Analysis Framework (CS-AF), introduced in this research, is a cross-disciplinary evaluation method designed to evaluate technology-mediated collaborative workflows. The 5-step CS-AF meta-process includes: (1) current-state workflow definition, (2) current-state (baseline) workflow assessment, (3) technology-mediated workflow development and deployment, (4) technology-mediated workflow assessment, (5) analysis and conclusions. For this research, a comprehensive, empirical study of hypertension exam workflow for telehealth was conducted using the CS-AF approach. Results: The CS-AF systemized approach reveals critical cross-disciplinary evaluation data concerning gains and gaps of collaborative workflows when technology-mediated enhancements are characterized and compared with a baseline workflow for the goal of continuous workflow improvement. Conclusion: The CS-AF is an effective meta-analysis process that can be adapted for use in multiple domains. [ABSTRACT FROM AUTHOR]

In the recent years, the use of Information and Communication Technologies (ICTs) to enhance the educational and developmental interventions of individuals with intellectual disabilities has increased drastically. Current literature about the efficacy of assistive technologies for people with intellectual disabilities includes numerous examples of co-design and research involving people with intellectual disabilities and their stakeholders. However, literature regarding the challenges and opportunities presented by this research is scarce. The discussion and reflection presented in this work is based on the authors' previous experience and ground-breaking literature in the field. After a review of relevant work, the the authors' research is described in detail, in order to reflect on the challenges encountered, and on opportunities for new researchers and open questions. The paper concludes with a reflection on the use of technologies to support people with intellectual disabilities, adopted strategies and limitations.

The research on the behavior of short video's creators is an important part of the research on short video communication mechanism, communication elements, short video public opinion evolution and its development situation prediction. Clarifying the motives that affect the behavior of short video's creators can achieve the accurate dissemination of short video content, which has a positive significance for the development of the short video industry and has practical value for the network content management department to guide the development of the industry. In the “video communication era”, as a communication carrier, short video has been widely used in e-commerce, government information dissemination and other fields to improve the traditional form of communication, and many good results have already been achieved. However, there are few studies on the origin characteristics of short videos, that is, the motivation of short video's creators' behavior. In this article, first, a motivation analysis model of short video's creators' behavior is constructed, a questionnaire is used to obtain 2,582 short video's creators' behavior, motivation and other characteristic elements, followed by factor analysis, correlation analysis, reliability analysis and other methods to test the authenticity and validity of the variables; second, the stepwise multiple linear regression method is adopted to analyze the dependence relationship between behavior and motivation; finally, through the data analysis results, the degree relationship between the behavior and motivation of the short video's creator is obtained. The results show that information communication, economic benefits, emotional control, and self-expression are the main motives that affect the behavior of short video's creators. At the same time, it is found that there is a phenomenon of professional development in short video's creator groups, and the internal scale of their creations shows the same direction. This research provides new contributions to the establishment of an accurate short video content communication model and the governance of future short video-induced network public opinion.

Social media has received considerable research attention in various academic disciplines but is still in its infancy in operations management. In particular, the existing literature does not provide sufficient empirical evidence on how social media technologies affect a company's supply chain relationships. Thus, this study conceptualized the role of social media in forming supply chain networks between a focal company, suppliers, and customers, based on social network theory. This study also empirically investigated the impact of social media technologies on supplier and customer relationship management using sample data from U.S. industrial organizations. Hypotheses were verified through the structural equation modeling analysis. Sample analysis has proven that social media has a significant positive impact on the operational efficiency of supplier and customer relationship management. In addition, the evidence from this study suggests that social media networks built into supply chain relationships function as a source of knowledge resources that can create a competitive advantage, especially for focal companies. This study also contributes to research methodology by introducing alternative mitigation methods for common method biases that often occur in survey-based data collection. Finally, we discussed the implications of the study findings for practice and research, along with limitations and future research directions.

Process monitoring, which is used to ensure the product quality in semiconductor manufacturing, develops a control chart and alerts engineers whenever a control limit is exceeded. However, a late alarm could generate defects or scraps, and thus, a prealarm is urgent to be developed. This study proposes an in-line predictive monitoring (ILPM) framework that uses process parameter monitoring (PPM) in the first phase and equipment parameter monitoring (EPM) in the second phase. PPM includes off-line training and in-line prediction, where we collect the first half of time-series data and predict the second half for in-line quality control. To maintain robustness and accuracy, the concept drift is used to update the ILPM model in real time; specifically, the EPM detects the loss of prediction by cumulative sum (CUSUM) control chart or detects the change of equipment parameters by Bayesian approach for developing retraining mechanism. An empirical study of a semiconductor manufacturer indicates that the proposed ILPM framework improved both the quality control and production capacity. Note to Practitioners—Although some in-line monitoring approaches have been proposed for specific conditions, little research has been done to develop a prealarm method; in particular, this study proposes predictive monitoring by using data from multiple sensors (or status variable identifications, SVIDs) to predict one sensor in one process step of semiconductor manufacturer. The prealarm benefits the early troubleshooting and equipment capacity. This study also applies concept drift and equipment parameter monitoring (EPM) to identify the prediction model misalignment or equipment misalignment. In practice, root cause identification of the prediction error is critical and benefits the model retraining and equipment maintenance. [ABSTRACT FROM AUTHOR]

Cluster analysis is an important technique in data analysis. However, there is no encompassing theory on scatterplots to evaluate clustering. Human visual perception is regarded as a gold standard to evaluate clustering. The cluster analysis based on human visual perception requires the participation of many probands, to obtain diverse data, and hence is a challenge to do. We contribute an empirical and data-driven study on human perception for visual clustering of large scatterplot data. First, we systematically construct and label a large, publicly available scatterplot dataset. Second, we carry out a qualitative analysis based on the dataset and summarize the influence of visual factors on clustering perception. Third, we use the labeled datasets to train a deep neural network for modeling human visual clustering perception. Our experiments show that the data-driven model successfully models the human visual perception, and outperforms conventional clustering algorithms in synthetic and real datasets. [ABSTRACT FROM AUTHOR]

Recommendation models have been widely embedded in various online services, while most of which are designed with the assumption that users' original behaviors are available in a central server. This may cause the privacy issue. As a response, we follow a recent work called federated collaborative filtering (FCF) for item recommendation with implicit feedback and propose a novel and generic federated recommendation (FedRec) framework for rating prediction with explicit feedback. Specifically, we federate some basic and advanced factorization-based recommendation models both in batch style and in stochastic style. More importantly, in order to protect the private information of which items each user has rated, as well as not to significantly increase the computational and communication cost, we design two simple but effective strategies, i.e., user averaging and hybrid filling, in which some (instead of all) unrated items are randomly sampled and assigned with some virtual ratings accordingly. Empirical studies on two public datasets show the effectiveness of our FedRec in terms of the closeness of a federated model and an unfederated one, and the usefulness of the two filling strategies. [ABSTRACT FROM AUTHOR]

The severe resource constraints that characterize markets at the bottom of the world's economic pyramid have forced firms to innovate both their products and processes. However, discourse in frugal innovation literature on how firms can design frugal processes in response to the resource constraints they face remains relatively sparse. Based on a systematic literature review, in this article we consolidate existing work on the process echelon of frugal innovation. We outline three ways frugal processes that have been conceptualized in the literature thus far, and distill their key characteristics. Based on an exploration of the supply chains of 87 small- and medium-sized enterprises, we show how firms configure these characteristics to overcome resource constraints at each stage of their supply chain. Ultimately, we propose three archetypes of a frugal supply chain that firms can strategically adopt. Firms with the primary goal of increasing consumer access to affordable products opt for a supply-chain structure that favors traditional economies of scale, and postpone any decentralization as further downstream as possible. In contrast, firms aiming to provide consumers with greener alternatives can adopt a hyperdecentralized structure throughout their supply-chain, which is made economically viable through low-cost approaches to distributed manufacturing. Firms interested in empowering local communities should consider hybrid structures that balance the high costs of investments in local capabilities. [ABSTRACT FROM AUTHOR]

The successful launch of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a new and advanced tool for sea ice thickness (SIT) estimations in the Arctic. However, the performance of ICESat-2 for SIT estimations still remains unknown. In the present study, SIT estimates derived from ICESat-2 are examined using three retrieval methods, namely, two buoyancy methods with the merged snow depth and empirical snow depth (BMA and BME, respectively) and one empirical estimation method (EEM), and these estimates are compared to near-simultaneous airborne measurements from the IceBird mission in April 2019. Overall, the ICESat-2 total freeboard registers quite well with that from the near-concurrent IceBird mission with a mean bias of 2.5 cm, which demonstrates the high reliability of ICESat-2 data for SIT estimation. However, the much more evident difference between SIT estimations than total freeboard from ICESat-2 and IceBird indicates that other parameters (e.g., snow depth and snow/ice densities) may bring increased uncertainties to the SIT estimation. Overall, BMA is the best method for SIT estimation and has the closest thickness distribution to that of IceBird data with a mean bias of 0.11 m, followed by the BME and EEM methods. The dominate error sources for SIT estimation using the buoyancy method are ice density and snow depth that require further investigation in future studies. [ABSTRACT FROM AUTHOR]

A growing number of efforts aim to understand what people see when using a visualization. These efforts provide scientific grounding to complement design intuitions, leading to more effective visualization practice. However, published visualization research currently reflects a limited set of available methods for understanding how people process visualized data. Alternative methods from vision science offer a rich suite of tools for understanding visualizations, but no curated collection of these methods exists in either perception or visualization research. We introduce a design space of experimental methods for empirically investigating the perceptual processes involved with viewing data visualizations to ultimately inform visualization design guidelines. This paper provides a shared lexicon for facilitating experimental visualization research. We discuss popular experimental paradigms, adjustment types, response types, and dependent measures used in vision science research, rooting each in visualization examples. We then discuss the advantages and limitations of each technique. Researchers can use this design space to create innovative studies and progress scientific understanding of design choices and evaluations in visualization. We highlight a history of collaborative success between visualization and vision science research and advocate for a deeper relationship between the two fields that can elaborate on and extend the methodological design space for understanding visualization and vision. [ABSTRACT FROM AUTHOR]

Ubiquitous, situated, and physical visualizations create entirely new possibilities for tasks contextualized in the real world, such as doctors inserting needles. During the development of situated visualizations, evaluating visualizations is a core requirement. However, performing such evaluations is intrinsically hard as the real scenarios are safety-critical or expensive to test. To overcome these issues, researchers and practitioners adapt classical approaches from ubiquitous computing and use surrogate empirical methods such as Augmented Reality (AR), Virtual Reality (VR) prototypes, or merely online demonstrations. This approach's primary assumption is that meaningful insights can also be gained from different, usually cheaper and less cumbersome empirical methods. Nevertheless, recent efforts in the Human-Computer Interaction (HCI) community have found evidence against this assumption, which would impede the use of surrogate empirical methods. Currently, these insights rely on a single investigation of four interactive objects. The goal of this work is to investigate if these prior findings also hold for situated visualizations. Therefore, we first created a scenario where situated visualizations support users in do-it-yourself (DIY) tasks such as crafting and assembly. We then set up five empirical study methods to evaluate the four tasks using an online survey, as well as VR, AR, laboratory, and in-situ studies. Using this study design, we conducted a new study with 60 participants. Our results show that the situated visualizations we investigated in this study are not prone to the same dependency on the empirical method, as found in previous work. Our study provides the first evidence that analyzing situated visualizations through different empirical (surrogate) methods might lead to comparable results. [ABSTRACT FROM AUTHOR]

This article presents the evaluation of using pulsed-radio frequency (RF) signals as multitone phase standards for modulated measurements at millimeter-wave (mm-Wave) bands, offering an alternative phase calibration choice for nonlinear vector network analyzers (NVNAs). Based on the NVNA measurements of two pulsed-RF signals generated by square-wave-driven mixers, a high correlation between magnitude and phase errors of unideal pulsed-RF signals is discovered, and an empirical method is developed to accurately estimate the phase errors according to magnitude-only measurements. To confirm its robustness and key preconditions for wide-range applications, theoretical support and explanation to the empirical method is revealed, in which the dominant waveform distortion of an applicable pulsed-RF signal can be decomposed into modulated perturbations with Sinc-like envelopes. The validity of the proposed empirical method, as well as its application for mm-Wave NVNA phase calibration, is verified by both experimental measurements and simulation results, where an estimation error (or phase calibration error) less than ±2° is demonstrated that is reachable in practice. [ABSTRACT FROM AUTHOR]

The widespread use of the LLC converter in dc–dc applications has increased the importance of small-signal modeling to control the converter below- to above-resonant frequency. The analysis of LLC converters is complicated, since they process the electrical energy through a high-frequency resonant tank that causes excessive nonlinearity. As a result of this complexity, small-signal modeling of the LLC converter is traditionally performed using empirical methods, iterative simulation approaches, or theory limited to the vicinity of the resonant frequency. Often, such approaches may lead to limited insight (just empirical trends) or low accuracy below and above resonance. This article proposes a new average small-signal modeling technique for the LLC converter, and it is performed in the time domain. The proposed technique is based on the analysis of the homopolarity cycle and accurately predicts not only the small-signal dynamic behavior of the LLC converter at resonance, but also below and above resonance. By using the homopolarity cycle, the theoretical analysis of the LLC converter is significantly simplified to a level that the small-signal dynamic behavior of the LLC converter is expressed by two second-order circuit models. Experimental and simulation results of a 650-W LLC converter are provided to verify the theoretical analyses and accuracy of the circuit models. The results have shown that the proposed small-signal circuit models can accurately predict the small-signal dynamic behaviors of the LLC converter from below- to above-resonant operations. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature review of learning analytics dashboards (LADs) research that reports empirical findings to assess the impact on learning and teaching. Several previous literature reviews identified self-regulated learning as a primary focus of LADs. However, there has been much less understanding how learning analytics are grounded in the literature on self-regulated learning and how self-regulated learning is supported. To address this limitation, this review analyzed the existing empirical studies on LADs based on the well-known model of self-regulated learning proposed by Winne and Hadwin. The results show that existing LADs are rarely grounded in learning theory, cannot be suggested to support metacognition, do not offer any information about effective learning tactics and strategies, and have significant limitations in how their evaluation is conducted and reported. Based on the findings of the study and through the synthesis of the literature, the paper proposes that future research and development should not make any a priori design decisions about representation of data and analytic results in learning analytics systems such as LADs. To formalize this proposal, the paper defines the model for user-centered learning analytics systems (MULAS). The MULAS consists of the four dimensions that are cyclically and recursively interconnected including: theory, design, feedback, and evaluation. [ABSTRACT FROM AUTHOR]

Asynchronous event-driven server architecture has been considered as a superior alternative to the thread-based counterpart due to reduced multithreading overhead. In this paper, we conduct empirical research on the efficiency of asynchronous Internet servers, showing that an asynchronous server may perform significantly worse than a thread-based one due to two design deficiencies. The first one is the widely adopted one-event-one-handler event processing model in current asynchronous Internet servers, which could generate frequent unnecessary context switches between event handlers, leading to significant CPU overhead of the server. The second one is a write-spin problem (i.e., repeatedly making unnecessary I/O system calls) in asynchronous servers due to some specific runtime workload and network conditions (e.g., large response size and non-trivial network latency). To address these two design deficiencies, we present a hybrid solution by exploiting the merits of different asynchronous architectures so that the server is able to adapt to dynamic runtime workload and network conditions in the cloud. Concretely, our hybrid solution applies a lightweight runtime request checking and seeks for the most efficient path to process each request from clients. Our results show that the hybrid solution can achieve from 10 to 90 percent higher throughput than all the other types of servers under the various realistic workload and network conditions in the cloud. [ABSTRACT FROM AUTHOR]

Mental fatigue is growingly considered to be associated with functional brain dysconnectivity. Although conventional wisdom suggests that rest break is an effective countermeasure, the underlying neural mechanisms and how they modulate fatigue-related brain dysconnectivity is largely unknown. Here, we introduce an empirical method to examine the reorganization of dynamic functional connectivity (FC) in a two-session experiment where one session including a mid-task break (Rest) compared to a successive task design in the other session (No-rest). Temporal brain networks were estimated from 20 participants and the spatiotemporal architecture was examined using our newly developed temporal efficiency analysis framework. We showed that taking a mid-task break leads to a restorative effect towards the end of experiment instead of immediate post-rest behaviour benefits. More importantly, we revealed a potential neural basis of our behaviour observation: the reduced spatiotemporal global integrity of temporal brain network in No-rest session was significantly improved with the break opportunity in the last task block of Rest session. Overall, we provided novel evidence to support beneficial effect of rest breaks in both behaviour performance and brain function. Moreover, these findings extended prior static FC studies of mental fatigue and highlight that altered dynamic FC may underlie cognitive fatigue. [ABSTRACT FROM AUTHOR]

The increasing penetration of electric vehicles and distributed generation into distribution networks are giving rise to higher, more volatile, and bi-directional energy flows through distribution transformers (DTs). To minimize future capital expenditure, improved DT design approaches are required. Therefore, a novel DT design approach based on a new paradigm of “designing for maximum utilization” is proposed. It incorporates manufacturer and customer-sourced empirical data to achieve a desired DT retirement age and utilization rate, and allows for transient operation beyond DT nameplate rating through accurately accounting for thermal inertia. The novelty of the proposed approach derives from not only a new notion of designing for maximum utilization, but also allowing for the fact that winding insulation may not be the most critical factor affecting a DT's lifetime. Using the proposed approach, DT capital expenditure and utilization constraints can be optimized at a planning stage. The algorithm is applied and validated. [ABSTRACT FROM AUTHOR]

Nitrogen (N) has been linked to different ecosystem processes, and retrieving this important foliar biochemical constituent from remote sensing observations is of widespread interest. Since N is not explicitly represented in physically based radiative transfer models, empirical methods have been used as an alternative. The spectral bands selected during the calibration of empirical methods have been interpreted in the context of light-N interactions and, consequently, ecosystem function. The low amount of leaves on shrubs and their sparse distribution in drylands create an environment, in which the canopy structure and the bright background soil play an important role in the canopy total radiation budget. In this paper, we examine the assumption that removing the impact of canopy structure and soil will result in improved N retrieval using the empirical methods. We report the inconsistencies in the selection of spectral bands among the empirical approaches. Moreover, these methods are sensitive to the scale of the study and band transformations. Using the generalized theory of canopy spectral invariants, we found that at the canopy scale, a combination of canopy structure and soil dominates the total canopy radiation. At the plot scale, soil contributes up to 95% of the total reflectance. Correction for these two confounding factors leads to no correlation between N and vegetation reflectance at both scales. We conclude that while cross-validated predictive models may be statistically achievable, caution should be taken when interpreting their results in the context of N-light interactions and ecosystem function. Our approach can be extended to all terrestrial ecosystems with multiple layers of canopy and understory. [ABSTRACT FROM AUTHOR]

Data consolidation is a challenging issue in data integration. The usefulness of data increases when it is linked and fused with other data from numerous (Web) sources. The promise of Big Data hinges upon addressing several big data integration challenges, such as record linkage at scale, real-time data fusion, and integrating Deep Web. Although much work has been conducted on these problems, there is limited work on creating a uniform, standard record from a group of records corresponding to the same real-world entity. We refer to this task as record normalization. Such a record representation, coined normalized record, is important for both front-end and back-end applications. In this paper, we formalize the record normalization problem, present in-depth analysis of normalization granularity levels (e.g., record, field, and value-component) and of normalization forms (e.g., typical versus complete). We propose a comprehensive framework for computing the normalized record. The proposed framework includes a suit of record normalization methods, from naive ones, which use only the information gathered from records themselves, to complex strategies, which globally mine a group of duplicate records before selecting a value for an attribute of a normalized record. We conducted extensive empirical studies with all the proposed methods. We indicate the weaknesses and strengths of each of them and recommend the ones to be used in practice. [ABSTRACT FROM AUTHOR]

Universal source coding at short blocklengths is considered for an i.i.d. exponential family of distributions. The Type Size code has previously been shown to be optimal up to the third-order rate for universal compression of all memoryless sources over finite alphabets. The Type Size code assigns sequences ordered based on their type class sizes to binary strings ordered lexicographically. To generalize this type class approach for parametric sources, a natural scheme is to define two sequences to be in the same type class if and only if they are equiprobable under any model in the parametric class. This natural approach, however, is shown to be suboptimal. A variation of the Type Size code is introduced, where type classes are defined based on neighborhoods of minimal sufficient statistics. The asymptotics of the overflow rate of this variation are derived and a converse result establishes its optimality up to the third-order term. [ABSTRACT FROM AUTHOR]

Current regulations leave a few television (TV) white spaces in populated urban areas where spectrum shortage is mostly experienced. As TV set feedback becomes essential in the next generation terrestrial TV standard, an opportunistic TV spectrum sharing based on TV receiver activity information and transmit power control is proposed to exploit the underutilized active TV channels. Based on investigation of the spatial–spectral–temporal characteristics of TV receiver activities, analytical models are developed to capture the spatio-temporal distributions of available spectrum and corresponding capacity. The influence of multiple factors, such as feedback delay, spectrum handover overhead, ranking order, and distribution of TV channel popularity are discussed and modeled. The proposed power control mechanism is verified through experiments at representative campus and residential environments. Empirical data-based simulations and geographic analyses are conducted to evaluate the developed models and further profile the spectrum opportunities within a cell, across New York city (NYC) and other 273 cities in the United States. In NYC, the proposed solution provides a $3.8$ – $11.7$ -fold increase of average spectrum availability, and $2.5$ – $6.6$ -fold increase of capacity from current regulations. By investigating the feasibility and prospects of this approach, this paper intends to motivate further discussions in policy, business, and privacy aspects to reach its significant potential. [ABSTRACT FROM AUTHOR]

An empirical model is proposed for predicting surface variations following dielectric spin coating as applied to existing underlying topography in redistribution layer fabrication. Test structures that represent a wide range of underlying feature widths, spacings, and heights are designed and fabricated. These are coated with multiple thicknesses of polyimide, and their surfaces are profiled and analyzed. An empirical model based on spatial filtering over underlying features is developed, with model parameters fit to coatings over a single feature for each set of process parameters. The model predicts post-coating thicknesses at the chip-scale. Comparisons between predictions and experimental results show an RMS error of 4.4% of the feature heights when averaged over each region profiled. Finally, fill patterns are designed and new test structures are simulated using these patterns. Simulation results suggest that global variations can be substantially reduced using such fill patterns, and these results are confirmed experimentally. [ABSTRACT FROM AUTHOR]

We present an approach to capture the 3D motion of a group of people engaged in a social interaction. The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions. The Panoptic Studio is a system organized around the thesis that social interactions should be measured through the integration of perceptual analyses over a large variety of view points. We present a modularized system designed around this principle, consisting of integrated structural, hardware, and software innovations. The system takes, as input, 480 synchronized video streams of multiple people engaged in social activities, and produces, as output, the labeled time-varying 3D structure of anatomical landmarks on individuals in the space. Our algorithm is designed to fuse the “weak” perceptual processes in the large number of views by progressively generating skeletal proposals from low-level appearance cues, and a framework for temporal refinement is also presented by associating body parts to reconstructed dense 3D trajectory stream. Our system and method are the first in reconstructing full body motion of more than five people engaged in social interactions without using markers. We also empirically demonstrate the impact of the number of views in achieving this goal. [ABSTRACT FROM AUTHOR]

Representation-based classification (RC) methods such as sparse RC (SRC) have attracted great interest in pattern recognition recently. Despite their empirical success, few theoretical results are reported to justify their effectiveness. In this paper, we establish the theoretical guarantees for a general unified framework termed as atomic representation-based classification (ARC), which includes most RC methods as special cases. We introduce a new condition called atomic classification condition (ACC), which reveals important geometric insights for the theory of ARC. We show that under such condition ARC is provably effective in correctly recognizing any new test sample, even corrupted with noise. Our theoretical analysis significantly broadens the range of conditions under which RC methods succeed for classification in the following two aspects: (1) prior theoretical advances of RC are mainly concerned with the single SRC method while our theory can apply to the general unified ARC framework, including SRC and many other RC methods; and (2) previous works are confined to the analysis of noiseless test data while we provide theoretical guarantees for ARC using both noiseless and noisy test data. Numerical results are provided to validate and complement our theoretical analysis of ARC and its important special cases for both noiseless and noisy test data. [ABSTRACT FROM AUTHOR]