Search

How unequal are the gains from trade? This paper develops a structural framework to quantify the consequences of international trade on welfare of consumers across the income distribution, allowing for nonhomothetic demand and endogenous quality choices by firms. Using random coefficients demand estimation techniques, I infer demand and supply parameters, as well as household-specific price indexes for more than 3,000 distinct industries and find the gains from trade to be moderately unequal except in wealthier and small economies. Further, not accounting for endogenous vertical differentiation would overstate the impact of trade on cost-of-living inequality by close to 50%.

The majority of projects—even ultimately successful ones—run into significant problems during their development. While organizations have a variety of mechanisms at their disposal to correct projects that are experiencing difficulties, one of the most radical is replacing the project manager. Replacing a project manager “mid-stream” involves a major change to an ongoing project with the potential benefits of onboarding an individual with a different perspective or set of managerial and/or technical skills. Using agency theory as our critical evaluative lens and a qualitative data collection methodology, we interviewed 19 key informants who had experience as part of project manager replacement efforts. This article reports of the dynamics of replacing project managers, identifying the critical decision criteria and mechanisms involved in such decisions. We found that three themes emerged with regard to project manager replacement decision making: 1) replacement is a common correction practice for troubled projects; 2) replacement is viewed by decision makers and team members alike as a message for change; and 3) in reestablishing processes and trust in governance, project size is an important moderator when deciding on a course of action. We finally propose a process model, based on our analysis, which identifies the critical antecedents, effects, and consequences of project manager replacement.

As a huge variety of information floods pouring over us each day, identifying the authenticity of massive events becomes a necessary task to maintain the stability of Mobile Social Networks (MSNs). With this regard, this paper proposes a diffusion model that characterizes the simultaneous diffusion of both truth and rumor in realistic MSNs, and makes the first attempt to figure out their respective sources. The problem of interest can be stated as: Given an outcome of cascade of both truth and rumor in MSNs, i.e., a set of nodes that might be the ignorant, the spreader of truth or rumor, or simply the silent receiver, how can we infer both truth sources and rumor sources Different from previous sources detection works considering single type of nodes, the interplay between truth diffusions and rumor diffusions makes the conventional methods not work. To answer this question, we aim to maximize the similarity index, i.e., the number of nodes possessing the same states between the resulting network triggered by our estimated sources with the proposed diffusion model and the given observation network.i much harder to find two kinds of sets at the same time, including truth sources and rumor sources

Digital transformation provides an unprecedented opportunity for the exploration, integration, and utilization of scientific and technological resources (STR). In the wave of digital transformation, countries around the world have set up their own STR sharing platforms, which can match a variety of STR services and needs in a lower cost and more convenient way, and create interaction between the suppliers and demanders. These sharing platforms provide possibilities to realize the sharing economy in the field of scientific and technological innovation. However, it is also necessary to design the corresponding mechanisms to improve the sharing rate and sharing value. Therefore, in this article, we construct a new framework of operation mechanism for China's STR allocation system and design its three submechanisms. Then, a system dynamics model of the operation mechanism is designed, and the experiments are carried out. The simulation results show that the mechanism proposed in this article can effectively improve the sharing rate and sharing value. Finally, some policy suggestions are put forward, which will help the government to improve management mode and optimize resource allocation.

The architecture, engineering, and construction (AEC) industry is undergoing a substantial change, and the technological advancements are shaping the future of this industry. Over years, numerous tools have been developed based on building information modeling (BIM) and the Internet of Things (IoT) to manage a variety of tasks and activities within different construction and operational phases in the AEC industry. Recently, with the advent of novel internet-enabled technologies in providing the ability of real-time connectivity, researchers have started to develop virtual design and construction (VDC) and digital twin approaches to remotely monitor, control, and optimize different work progresses and activities in real-time at any work level in the AEC industry. This paper presents a reference model to illustrates the main advantages and applications of VDC and digital twin, and to demonstrate their trends and anticipated cost savings in the AEC industry at a global level. These technological advancements along with augmented reality (AR) and virtual reality (VR) can offer the unique benefit of real-time monitoring of the current status as well as predicting the future of any physical structure at any level of work. The global demand and usage of these technological advancements will lead to great cost savings in the AEC industry which would be US$950 million in design and construction and US$400 million in operation and maintenance phases in the non-residential AEC industry in 2025. This paper also discusses the future challenges of VDC and digital twin in the industry. As a main line of future research, these technologies can simultaneously be utilized with artificial intelligence to enable human-centered decision making to automate and optimize work progresses. Such integrations of VDC, digital twin, artificial intelligence, and human-centered approaches will shape the future of the AEC industry and provide numerous research opportunities in this domain.

While social capital is commonly held to support collective action, according to Putnam (2000) and Bourdieu (1986) social disparities may result in the formation of narrow social bonds that serve to exacerbate existing social cleavages and may serve as an impediment to effective collective action. Motivated by this insight, we examine the relationship between social disparities and social distancing during the initial, pre-vaccine period of the Covid pandemic in the US. Using a panel of weekly, county-level observations, we find that income, educational and racial disparities are associated with a statistically significant decrease in the social distancing. This result is robust to controls for a wide variety of socioeconomic variables as well as to the contemporaneous infection rate and a commonly used measures of social capital. Our results are consistent with Putnam and Bourdieu’s claims regarding the role of social disparities in the formation of social capital and suggest gains to the development of measures that capture different forms of social capital.

The well-being and mental health of principals is being threatened by changing working conditions such as a broader variety of roles and tasks. In this article, we argue that social capital might buffer against declining (mental) health. The purpose of this study was to investigate the potential influence of social capital, including both internal (bonding) and external (bridging and linking) social capital, on principals’ well-being. A longitudinal study was set up across three waves of data. Questionnaires were collected from 2084 Australian principals and 829 Irish principals, across six and two timepoints, respectively. The hypotheses were tested using a longitudinal path model approach using maximum likelihood estimation in lavaan for R. In line with our hypotheses, the results indicated that principals who reported higher levels of either internal or external social capital also reported higher levels of well-being. More specifically, support from colleagues outside the school and supervisor support (external social capital) and collaboration and trust in management (internal social capital) predicted well-being positively across time. The results of this study highlight the importance of having social support from colleagues and supervisors and possibilities for collaboration to maintain well-being as a school principal.

Enforcing admired machine learning approaches to huge data enhanced novel issues for researchers. Conventional libraries could not suitably fulfil the requirement of complex model with wide variety of data and system parameters. Therefore new methodologies are required performing the computation on more than one machine over distributed environment. Some distributed frameworks on huge data such as MapReduce and TensorFlow have been deployed to solve various machine learning problems in heterogeneous distributed environment. The objective of this paper is providing a wide variety of useful information about platforms, approaches, problems, datasets, and optimization approaches in distributed systems. So researchers have been used the beneficial information to develop new approaches for efficient machine learning. This paper also covers the various formats of data like structured, semi structured and unstructured big data. A brief review of previous works is also represented in text and tabular format to provide motivation to the researchers for developing new paradigm of distributed computing environment.

The Internet of Things has a vision of the internet permeating every aspect of our daily lives, with internet-enabled items playing a prominent role. The IoT makes it possible for things to be sensed or controlled remotely, using existing network infrastructure, resulting in increased efficiency, accuracy, and economic gain in addition to decreased human involvement. There are a wide variety of possible uses for this technology, including Solar cities, Smart villages, Microgrids, and Solar street lighting. As This time period was the most successful in history for renewable energy, with the growth rate being much higher than before. The method mentioned here talks about displaying the energy consumption of solar power on the internet as a renewable energy. The surveillance of this location is performed using a raspberry pi utilising Flask framework. Daily use of renewable energy is tracked via Smart Monitoring This makes it easier for the user to understand how much energy they are using. Renewable energy use and power problems are both affected by analysis.

This paper proposes two new measures of illiquidity for real estate markets, utilising concepts from asset pricing. Segregating real estate through a regional lens, we provide an in-depth analysis of real estate returns and illiquidity for the US and UK. Our results provide statistically significant and economically meaningful evidence that real estate illiquidity predicts real estate returns out-of-sample over and above a variety of control variables.

The incidence of flooding events and the threat they pose in urban areas is a growing global issue, but it is particularly acute in developing countries like India, where the hazard is typically underrecognized and understudied. With current estimates indicating that future scenarios would become more severe, it is necessary to adopt integrated flood risk management solutions that involve flood modelling. This study is part of a bigger effort to quantify and simulate urban flood dangers. It emphasizes cutting-edge research and concepts in the area of urban flood risk management. It reviews the literature on current flood modelling approaches and provides a comprehensive overview of the different approaches, including their advantages and disadvantages. Indeed, flood modelling research has been extensive throughout the years, leading in the development of a diverse array of models, databases, and methodologies for simulating flood hydrodynamics. However, the actual potential of these developments in terms of flood risk management has yet to be shown. There is currently no perfect model or generic approach that can adequately capture all aspects of flood hydrodynamics over a broad variety of study locations. As a consequence, extensive calibration of state-of-the-art flood models is crucial for resolving current challenges in flood modelling. Additionally, the prospect of developing unique flood models based on fundamental mathematical formulations that are simple to parameterize by using global, open source, and publicly accessible data should pique academics' attention.

Customer privacy is increasingly important to marketers. High-profile breaches of databases containing sensitive customer information, and the growing need to build the infrastructure required to support analysis of big data, present nontrivial obstacles to researchers seeking individual-level customer data from firms. In this paper, we show that recent developments in machine learning may enable firms to transfer a generative model, instead of data, thus potentially obviating the process of anonymizing and sampling customer data for release, for use in a variety of analytic use cases. We demonstrate the efficacy of a specific deep learning model, Generative Adversarial Networks (GANs), in preserving desired characteristics of original data. In real-world settings, we find that GANs can double the accuracy as compared to the best benchmark methods. We also demonstrate that GANs can be used to solve marketing problems of price markups for optimal profits and customer targeting, and that a single GAN can tackle multiple marketing problems. Furthermore, GANs have volume and velocity advantages, as the size of informational transfer grows according to model complexity, and it can readily handle real-time data streams.

We test whether living through conflict in childhood changes political beliefs and engagement. We combine data on the location and intensity of conflicts since 1945 with nationally representative data on political attitudes and behaviors from 17 sub-Saharan African countries. Exposure from ages 0 to 14 has a very small standardized impact on later attitudes and behaviors. This finding is robust to migration and holds across a variety of definitions, specifications, and sources of data. Our results suggest that at the population level in Africa, conflict does not alter political beliefs, though the most exposed sub-populations may experience large, lasting effects.

In the current day, information and communication technologies are tightly linked to provide ultimate business solutions. Cloud computing is one of these topics that has recently been hotly contested and carefully explored, but still has a lot of room for further research and growth. Technology-assisted learning is becoming more common, with most educational institutions across the globe using learning management systems, content management systems, virtual networks, and virtual machines to enhance student learning. In this day and age, educational institutions are even using private clouds to improve the student experience. Despite the fact that many educational institutions, colleges, and universities have effectively implemented and incorporated instructional technology, many of them still fall short in a variety of areas. The purpose of this research is to investigate the influence of cloud computing technology on educational institutions and corporate management.

When it comes to utilizing or harnessing human capital for any organization, it becomes very important to know the management science behind HRM. Intellectual capital, technology, globalization, profitability via expansion and change are all major business problems that companies are presently experiencing. These problems need companies to collaborate to create new skills, and human resource management is in charge of doing so. It assists companies in addressing competitive difficulties by acting as a leader. HR professionals have lost their stereotype of monitoring policies and enforcing rules. HR professionals are now held accountable for their employees’ engagement and dedication to the company, as well as motivating people to participate and take ownership. It’s no longer a relic of the past. It has evolved from a little reactive image to a much larger canvas. The new management of human resources is triggered by bringing the organization’s management together with the workers, resulting in a merger of the person’s and the organization’s reservoirs of knowledge, culminating in the joint victory of both. Global competitiveness may be accomplished not only by using updated technology and facilities but also by encouraging workers to adhere to international standards in an efficient and effective manner. Human resources specialists in every firm on the global stage have the goal to influence the business world; HR is currently in a paradigm change, and its role as a support function, relied on to assist others and to recruit and dismiss from businesses, is quickly fading. Human resources are taking a proactive role in addressing globalization’s issues. It brings about change and enhances people’s abilities to learn and cooperate, as well as handle variety, ambiguity, and complexity. The study and comparison of the perceptions of corporate workers, management faculty, and management students in an effort to understand the role of HRM in developing national talent for global success.

VRML (Virtual Reality Modeling Language) is a nonproprietary language used for scientific simulation and visualization. VRML alone do not have the capability of holding numerical values but with the help of slider design, the numeric values can be dynamically varied. The VRML Graphic User Interface (GUI) presented in this paper demonstrates dynamic simulation of Seed Grader Machine Parameters and its effect on final output. This paper describes how VRML capabilities can be utilized for simulating Seed Grader Machine Parameters in virtual Environment. The concept can be applied for variety of applications involving similar requirements.

With the increasing computing power, it is easier to develop machine learning and signal processing technologies. This led to massive significance in EEG-based Brain-Machine Interfaces that can be applied to a variety of sensing devices that can profit from the hands-free and individual conversion aspect of this field. Though, almost all of the investigation has been at the stage of proof-of-concept rather than at a more practical stage. As a result, there have been few innovations in techniques that enable people with seriously impaired nerve system (humans with Multiple Sclerosis, Amyotrophic Lateral Sclerosis, etc.) to navigate spatially in a simulated 2D or 3D environment. Newer technologies and growing trends in the computerization and artificial intelligence present a challenge to the ever-growing large community to introduce this new technology. This article will address the practicalities of the differences in signal processing and the significance of these innovations to the EEG- BMI in concurrent.

In the present digital world, Information and Communication Technology along with Internet of Things are considered as backbone for the smart city evolution. IoT is concentrated more nowadays, due to the convergence of images on Information Technology and human behaviour known as Operation Technology. It is in the progressing stage with notion on cyber-physical world, where things are facilitated to satisfy feasible needs. Similarly, Internet of Medical Things is gaining attention due to the current COVID-19 situation. It has gifted numerous sensors and devices with the increased accuracy, productivity and reliability to the healthcare industry. Increasing the sensor devices in smart city, results in the handling of large amount of data. Therefore, big data is focused due to their characteristics such as volume, variety, velocity and veracity. Extraction of hidden correlations and insights from data is performed through big data analytics or big data value chain. Such concepts are integrated with Artificial Intelligence and Machine Learning technologies which are more important in collecting the real-time data collection and helps in gaining knowledge on how the smart cities and their applications in health sector evolves to adapt the conditions. The main advantage on Big Data Analytics is that it helps in commonality along with heterogeneity principles. Even though cities are digitalized, urban health is still limited to IoT devices which fetch sensor data, eliminating the inclusion of human anatomy and biological data. Integration of these two data forms will help in attaining the target of Big Data and leads to the contextualized, sustainable and resilient smart cities which renders more living factors. This chapter will focus on role of Big Data in smart health applications which plays a vital role in safeguarding the human lives.

Monitoring economic conditions in real time, or nowcasting, and Big Data analytics share some challenges, sometimes called the three “Vs”. Indeed, nowcasting is characterized by the use of a large number of time series (Volume), the complexity of the data covering various sectors of the economy, with different frequencies and precision and asynchronous release dates (Variety), and the need to incorporate new information continuously and in a timely manner (Velocity). In this paper, we explore three alternative routes to nowcasting with Bayesian Vector Autoregressive (BVAR) models and find that they can effectively handle the three Vs by producing, in real time, accurate probabilistic predictions of US economic activity and a meaningful narrative by means of scenario analysis.

Inequitable access to healthy food has been associated with a variety of adverse health outcomes. In the United States, prior studies have suggested that low-income and racial minorities are more l...

Genomic research emerges from collaborative work within and across different scientific disciplines. A diverse range of visualisation techniques has been employed to aid this research, yet relatively little is known as to how these techniques facilitate collaboration. We conducted a case study of collaborative research within a biomedical institute to learn more about the role visualisation plays in genomic mapping. Interviews were conducted with molecular biologists (N = 5) and bioinformaticians (N = 6). We found that genomic research comprises a variety of distinct disciplines engaged in complex analytic tasks that each resist simplification, and their complexity influences how visualisations were used. Visualisation use was impacted by group-specific interactions and temporal work patterns. Visualisations were also crucial to the scientific workflow, used for both question formation and confirmation of hypotheses, and acted as an anchor for the communication of ideas and discussion. In the latter case, two approaches were taken: providing collaborators with either interactive or static imagery representing a viewpoint. The use of generic software for simplified visualisations, and quick production and curation was also noted. We discuss these findings with reference to group-specific interactions and present recommendations for improving collaborative practices through visual analytics.

Given the high uncertainty in R&D work, a key concern of R&D managers is to understand how to fail faster by managing time and cost efficiently among their R&D projects. This study investigates how using a variety of Six Sigma tools can differentially affect R&D project time and cost performance. Our data are collected from 35 R&D projects in an industrial R&D center, and a total of 30 different Six Sigma tools were found to be used in varying frequencies. We analyze this problem in two phases. First, by analyzing the frequencies of tool use in the 35 projects through factor analysis, we find the application of Six Sigma tools can be guided by two distinct patterns of tool use: design-oriented tools (DT) and non-design-oriented tools (non-DT). DT include quality function deployment and failure modes and effects analysis (FMEA) for defining and understanding of the focal problems, while non-DT include Pareto chart and design of experiment for generating and testing solutions for the defined problems. Second, based on the two categories of tool use, we further analyze the effects of the two patterns of tool use on project duration and project cost. Results show that using a variety of DT is associated with faster project completion and non-DT lower project cost. In addition, the use of Six Sigma tools (both DT and non-DT) is associated with shorter durations and higher costs for complex projects but lower costs and longer durations for less complex projects. That is, different modes of trade-ff between time and cost performance can occur when project complexity is considered as a moderating factor. Overall, our findings demonstrate new insights into how using a variety of problem-solving tools affects R&D project cost and duration simultaneously.

Abstract. Gatekeeper training (GKT) is one of the most widely used suicide prevention strategies. It involves training people who are not necessarily clinicians to be able to identify people experiencing suicidality and refer them to appropriate services. While there is a dearth of research that supports the causal link between GKT and reduced suicide rates, this is likely the result of a variety of factors including training design, definitions of “gatekeepers,” differing populations in which the gatekeeper (GK) operates, and other variables that may influence suicide rates. Despite this, research suggests that GKT improves people's knowledge, skills, and confidence in helping individuals who experience suicidal ideation and enhances positive beliefs about the efficacy of suicide prevention. However, there is no consensus on GK competencies to allow differences in effectiveness between various training programs to be measured, that is, knowledge, skills and abilities, attitudes, and self-efficacy attributes expected of a person resulting from the training. This paper discusses challenges in developing GK competencies. It uses developments in suicide prevention competencies for clinicians, vocational education, and training sector competencies, as well as empirical work in GKT, to propose minimum GK competencies that may be examined for further research and evaluation of programs.

Dealing with an uncertain and dynamic environment when facing multiple and fast-paced challenges forces professionals to adopt agile practices in different environments, resulting in the use of hybrid project management models. However, identifying the right practice to adopt can be challenging, given the variety of project types and environmental factors. In this article, a recommendation method that would allow for the identification of patterns of project management practices for different environments—using an agility indicator—is proposed. The proposed method is tested with a dataset of 856 projects. A cluster analysis is applied to divide the projects into three groups according to environmental characteristics, called scenarios: waterfall, agile, and hybrid. Then, we apply the association rule technique for each group separately, identifying specific patterns of practice for each group. Through a comparative analysis, we verify the consistency between the recommendations of the proposed method for each scenario and the literature on project management. The results indicate the feasibility of the proposed method, thus opening up new research opportunities for hybrid models that can be customized for different projects. This article can help project management professionals apply the agile method beyond its use in software development and improve the process of combining project management practices. We also suggest directions for new research to advance the knowledge of useful decision support tools for hybrid model customization.

In a real-world setting, object instances from new classes can be continuously encountered by object detectors. When existing object detectors are applied to such scenarios, their performance on old classes deteriorates significantly. A few efforts have been reported to address this limitation, all of which apply variants of knowledge distillation to avoid catastrophic forgetting. We note that although distillation helps to retain previous learning, it obstructs fast adaptability to new tasks, which is a critical requirement for incremental learning. In this pursuit, we propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared. This ensures a seamless information transfer via a meta-learned gradient preconditioning that minimizes forgetting and maximizes knowledge transfer. In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection. We evaluate our approach on a variety of incremental learning settings defined on PASCAL-VOC and MS COCO datasets, where our approach performs favourably well against state-of-the-art methods., Comment: Published in IEEE Transactions on Pattern Analysis & Machine Intelligence, Nov 2021. Code is available in https://github.com/JosephKJ/iOD

Trajectory or path planning is a fundamental issue in a wide variety of applications. In this article, we show that it is possible to solve path planning on a maze for multiple start point and endpoint highly efficiently with a novel configuration of multilayer networks that use only weighted pooling operations, for which no network training is needed. These networks create solutions, which are identical to those from classical algorithms such as breadth-first search (BFS), Dijkstra's algorithm, or TD(0). Different from competing approaches, very large mazes containing almost one billion nodes with dense obstacle configuration and several thousand importance-weighted path endpoints can this way be solved quickly in a single pass on parallel hardware.

In the post-Bitcoin era, many cryptocurrencies with a variety of goals and purposes have emerged in the digital arena. This article aims to map cryptocurrency protocols across three main defining dimensions, which are governance decentralization, security, and scalability. We theorize about the organizational and technological features that impact these three dimensions. Such features encompass roles permissiveness, validation network size, resource expenditure, and number of transactions per second. We map the different cryptocurrency constellations based on their consensus mechanisms, discussing the organizational and technological features of the various protocols applications and how they experience and play with the tradeoffs among governance decentralization, security, and scalability.

Problem definition: In high-product-variety businesses like fashion, mass production (MP) systems create environmental waste in the form of overproduction on a colossal scale. Mass customization (MC) has been proposed—without solid evidence—as a solution. In this paper, we analyze whether MC can indeed offer a win-win solution that helps both the bottom line and the environment. We also study the impact of three real policy options: promoting MC, charging a disposal fee for overproduction, and recycling. Academic/practical relevance: There is increasing interest in mass-customizing fashion goods, not only because consumers value customization, but also because MC is perceived to be environmentally friendly. Our paper puts this advocacy for MC to the test. We contribute to the literature, which has been largely silent on the issue, by uncovering when MC offers a win-win and relating such market outcomes to policy ideas. Methodology: We develop an analytical model of an MP firm adopting MC (going hybrid). The firm’s profit-maximizing variety, price, and inventory decisions then form the basis of our understanding the environmental impact of adopting MC and assessing various policy options. Results: Adopting MC can be a win-win, but it can also increase overproduction and hurt the environment. Our policy analyses reveal two kinds of insights. The first kind is about whether a policy expands win-win outcomes—encouraging sustainable adoption of MC. Among the policy ideas we explore, only promoting MC so as to increase consumers’ tolerance for waiting for mass-customized products can do that unambiguously. The second kind of insight is about whether a policy reduces the hybrid firm’s environmental impact. Only a disposal fee and costly recycling programs can do that unambiguously. Managerial implications: For MC adoption to be a win-win, policy makers must (1) work on convincing consumers to wait for bespoke fashion; (2) target MP firms with low cost of variety (high product-mix flexibility) with disposal fees or costly recycling programs; and (3) encourage those with relatively higher cost of variety to develop/acquire technology that would make recycling profitable. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1088 .

The ability to accurately predict human encounters can inspire a variety of promising applications, ranging from epidemiology to data forwarding in opportunistic networks. This work aims at designing a low cost, highly accurate human encounter prediction model based on Wi-Fi datasets. By leveraging the temporal dependency of human mobility, we propose the distributed human encounter prediction (DHEP) model, which uses the Wi-Fi access history and inferred contact information of only the person of interest to estimate future encounters of that person. We implement the proposed DHEP model using a recurrent neural network and a feed-forward neural network. An embedding model that learns the low-dimensional representation of a person's location is proposed to reduce the number of training parameters. The experimental results on two large Wi-Fi datasets show the proposed RNN-based DHEP model outperforms existing models, and achieves 87% to 91% accuracy based on University at Buffalo (UB) traces. We also compare DH P with the centralized human encounter prediction (CHEP) model, which gathers the contact history of all people for predicting future encounters. Despite a slightly lower performance than CHEP, DHEP has a low overhead and can protect data privacy.

Aspect-based Opinion Mining is a form of fine-grained Sentiment Analysis and it models the semasiological relationship between aspect terms and context words in a sentence. The presence of a variety of context words has a significant impact on a sentence's sentiment polarity. As a result, while designing a model, it is necessary to consider the interaction of aspects and context words. Although existing approaches have taken into account an aspect’s position in a sentence, much of the research works have not explored the use of Sentiment Lexicons with the Deep Learning algorithms. In this paper, we propose a framework for an Attention-based position-aware Bidirectional Long Short-Term Memory network for Aspect-based Opinion Mining that incorporates a Sentiment Intensity Lexicon. The aspect word’s pre-trained vector is adjusted to be closer to semantically and sentimentally similar nearest neighbors and further away from sentimentally dissimilar neighbors. The proposed framework calculates aspect weights by concatenating the external knowledge in the form of lexicon sentiment intensity scores with word embeddings and position information. The framework experiments on the SemEval 2014 dataset. The results of the experiments illustrate that injecting external knowledge into the Bidirectional Long Short-Term Memory network can improve classification accuracy significantly.

The incompleteness of knowledge graphs triggers considerable research interest in relation prediction. As the key to predicting relations among entities, many efforts have been devoted to learning the embeddings of entities and relations by incorporating a variety of neighbors' information which includes not only the information from direct outgoing and incoming neighbors but also the ones from the indirect neighbors on the multihop paths. However, previous models usually consider entity paths of limited length or ignore sequential information of the paths. Either simplification will make the model lack a global understanding of knowledge graphs and may result in the loss of important and indispensable information. In this article, we propose a novel global graph attention embedding network (GGAE) for relation prediction by combining global information from both direct neighbors and multihop neighbors. Concretely, given a knowledge graph, we first introduce the path construction algorithms to obtain meaningful paths, then design path modeling methods to capture the potential long-distance sequential information in the multihop paths, final propose an entity graph attention and a relation graph attention mechanisms to obtain entity embeddings and relation embeddings. Moreover, an entity graph attention mechanism is proposed to calculate the entity embeddings by aggregating direct incoming and outgoing neighbors from: 1) an original knowledge graph with the original entity and relation embeddings and 2) a new knowledge graph constructed by the paths whose embeddings are updated by path modeling methods. for each relation, we construct a new graph with related entities and present a relation graph attention to learn the features. Therefore, our model can encapsulate the information from different distance neighbors, and enable the embeddings of entities and relations to better capture all-sided semantic information. The experimental results on benchmark datasets verify the superiority of our model over the state-of-the-art ones.

Grounded Theory (GT), a sociological research method designed to study social phenomena, is increasingly being used to investigate the human and social aspects of software engineering (SE). However, being written by and for sociologists, GT is often challenging for a majority of SE researchers to understand and apply. Additionally, SE researchers attempting ad hoc adaptations of traditional GT guidelines for modern socio-technical (ST) contexts often struggle in the absence of clear and relevant guidelines to do so, resulting in poor quality studies. To overcome these research community challenges and leverage modern research opportunities, this paper presents Socio-Technical Grounded Theory (STGT) designed to ease application and achieve quality outcomes. It defines what exactly is meant by an ST research context and presents the STGT guidelines that expand GT's philosophical foundations, provide increased clarity and flexibility in its methodological steps and procedures, define possible scope and contexts of application, encourage frequent reporting of a variety of interim, preliminary, and mature outcomes, and introduce nuanced evaluation guidelines for different outcomes. It is hoped that the SE research community and related ST disciplines such as computer science, data science, artificial intelligence, information systems, human computer/robot/AI interaction, human-centered emerging technologies (and increasingly other disciplines being transformed by rapid digitalisation and AI-based augmentation), will benefit from applying STGT to conduct quality research studies and systematically produce rich findings and mature theories with confidence., Comment: Accepted to IEEE Transactions on Software Engineering (Aug 2021); This author preprint is 26 pages, the final IEEE version is 25 pages due to reformatting, content is same. More details: https://rashina.com/

Combinatorial Testing (CT) is an effective testing technique for detecting failures which are triggered by the interactions of various factors that influence the behaviour of a system. Although many studies in CT have designed elaborate test suites (called covering arrays) to systemically check each possible factor interaction, they provide weak support to locate the concrete failure-inducing interactions, i.e., the Minimal Failure-causing Schemas (MFS).To this end, a variety of MFS identification approaches have been proposed. However, as this study reveals, these approaches suffer from various issues such as cannot identify multiple overlapping MFSs, cannot handle MFSs with high degrees, cannot be applied to systems with large number of parameters, etc. These issues are essentially caused by the exponential computing complexity of checking every interaction in the test cases. Therefore, they can only focus on a subset of all the possible interactions, and thereby resulting in many interactions unnoticed. Ignoring these unnoticed interactions could potentially cause failures that have never been systematically checked. Hence, it is beneficial for MFS identification approaches to identify these interactions. In order to account for these unnoticed interactions in CT, this study introduces the notion of pending schema, based on which a theoretical framework of CT schemas is established. In particular, we formally define the determinability of a schema in CT with respect to given information; as such, the yet-to-be determined schemas are exactly the pending schemas. The relationships between the different schemas (faulty, healthy, and pending) and test cases are also theoretically analyzed. Based on which, we further propose three formulas, along with three corresponding algorithms, for the identification of the pending schemas in failing test cases, and formally prove their correctness. As a result, we reduce the complexity of obtaining pending schemas with respect to the number of factors that may have influences on the software.

The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to data analysis applications across a variety of research topics in computer science. This paper illustrates that, despite its success, there remain many challenges in KNN classification, including K computation, nearest neighbor selection, nearest neighbor search and classification rules. Having established these issues, recent approaches to their resolution are examined in more detail, thereby providing a potential roadmap for ongoing KNN-related research, as well as some new classification rules regarding how to tackle the issue of training sample imbalance. To evaluate the new approaches, some experiments were conducted with 15 datasets.

A recommendation system saves the user the trouble of searching for information and analyzes their profile to recommend the most suitable content. A variety of techniques, including content-based, collaborative, and knowledge-based techniques, have been proposed for performing recommendations. Recommendation systems are employed to suggest content, such as books, music, and videos; furthermore, they are often used in e-commerce. In particular, the South Korean film industry recommends movies using a collaborative filtering method based on genres, which is commonly used in film recommendation systems. However, this method can be ineffective when users initially encounter film recommendation services or have specific preferences with respect to movies, such as preferences regarding actors or directors. This motivated us to propose an actor-based recommendation system using the content-based filtering that considers actors' filmography information and the genres of 509 South Korean movies. The effectiveness and performance of the suggested system are evaluated in comparison with those of a traditional genre-oriented recommendation system.

This research explores the justification and implications of incorporating consumption variety into mobile-based food recommendation systems. Our study makes use of data from a popular mobile fitness app, in which we are able to observe large volumes of daily food logs of thousands of users. We first confirm that consumption variety is associated with potential health benefits, such as lower overall calories consumed, higher vegetable consumption, and lower snack consumption. In light of these suggestive health benefits from consumption variety, we seek out to design a novel multi-criteria food recommendation system (FOODVAR) that can accommodate for variety in recommended foods. We then assess the impact of including this additional variety criterion in recommendation system performance, where we show that the incorporation of variety improves the algorithm's evaluation metrics.

Machine learning and its subfield deep learning techniques provide opportunities for the development of operator mental state monitoring, especially for cognitive workload recognition using electroencephalogram (EEG) signals. Although a variety of machine learning methods have been proposed for recognizing cognitive workload via EEG recently, there does not yet exist a review that covers in-depth the application of machine learning methods. To alleviate this gap, in this paper, we survey cognitive workload and machine learning literature to identify the approaches and highlight the primary advances. To be specific, we first introduce the concepts of cognitive workload and machine learning. Then, we discuss the steps of classical machine learning for cognitive workload recognition from the following aspects, i.e., EEG data preprocessing, feature extraction and selection, classification method, and evaluation methods. Further, we review the commonly used deep learning methods for this domain. Finally, we expound on the open problem and future outlooks.

The purpose of action prediction is to recognize an action before it is completed to reduce recognition latency. Because action prediction has lower latency than action recognition, it can be applied to a variety of surveillance scenarios and responds faster. However, action prediction is more difficult because it cannot obtain the complete action execution. In this paper, we study the action prediction which is based on skeleton data and propose a new network called adaptive graph convolutional network with adversarial learning (AGCN-AL) for it. The AGCN-AL uses adversarial learning to make the features of the partial sequences as similar as possible to the features of the full sequences to learn the potential global information in the partial sequences. Besides, partial sequences with different numbers of frames contain different amounts of information. We introduce temporal-dependent loss functions to prevent the network from paying too much attention to partial sequences whose observation ratios are small, and ignoring partial sequences whose observation ratios are large. Moreover, the AGCN-AL is combined with the local AGCN into a two-stream network to enhance the prediction, proving that the local information and the potential global information in partial sequences are complementary. We evaluate the proposed approach on two datasets and show excellent performance.

Drawing on the stewardship and dominion theories, this paper empirically investigates the association between local religious diversity within which firms are headquartered and corporate environmental initiatives. Using a large U.S. sample, we document that firms headquartered in high religiously diverse counties are more involved in corporate environmental initiatives. This positive association remains robust after using a variety of robustness checks. The findings support the hypothesis that local religious diversity is significantly associated with firms’ decisions concerning the environment. This paper contributes to the literature by examining the extent to which the characteristics of society shape firms’ environmental initiatives.

Problem definition: The Singapore government has recently proposed the concept of “Locker Alliance” (LA), an interoperable network of public lockers in residential areas and hot spots in community, to improve the efficiency of last mile parcel delivery operations. This is to complement the existing infrastructure, composed mainly of proprietary lockers and collection points in commercial areas set up by large delivery companies. How do we determine the density and coverage of the LA network to promote adoption of locker pickup in Singapore? What will be the impact on the delivery profile in the central business district, far from the residential areas? Academic/practical relevance: We discuss the operational challenges associated with the problem of public locker installation in a city, following a new smart nation initiative in Singapore. We used data analytics to address the following questions: What are the chances that a customer will choose to pick up the parcel from a locker, over home or office deliveries, based on walking distance (to lockers) and a variety of other features? Without knowing the transit routes of the customers, how do we design the LA network to ensure that the lockers will be well utilized? Methodology: We use a set of locker usage data from a commercial courier company to calibrate a locker choice model to determine the impact of walking distance on locker pickup intentions. We use the current (observed) parcel delivery profile to develop a facility location model for the LA network. We use this model to extrapolate and approximate the true adoption and new delivery profiles when the LA network is built. Results: Contrary to conventional wisdom, our model does not always place lockers near areas with peak parcel volume (in preexisting data) because the LA lockers provide another option for customers to pick up from lockers near residential areas. Furthermore, the model suggests that a coverage of 250 meters is appropriate for the LA network in Singapore. Managerial implications: Commercial parcel locker installation has traditionally focused on hot spots in the transit routes of the citizens in the city. The LA network is the first attempt in Singapore to allow public lockers in residential areas. This paper develops an analytical method to determine network density and coverage based on a locker choice model and argues how useful insights can be gleaned from the model, despite not having the full transit route information of all citizens in the city.

The increasing complexity of Intelligent Transportation Systems (ITS), that comprise a wide variety of applications and services, has imposed a necessity for high-performance Modern Hardware Devices (MHDs). The performance challenge has become more noticeable with the integration of Machine Learning (ML) techniques deployed in large-scale settings. ML has effectively supported the field of ITS by providing efficient and optimized solutions to problems that were otherwise tackled using traditional statistical and analytical approaches. Addressing the hardware deployment needs of ITS in the era of ML is a challenging problem that involves temporal, spatial, environmental, and economical factors. This survey reviews the recent literature of ML-driven ITS, in which MHDs were utilized, with a focus on performance indicators. A taxonomy is then synthesized, giving a complete representation of what the current capabilities of the surveyed ITS rely on in terms of ML techniques and technological infrastructure. To alleviate the difficulties faced in the non-trivial task of selecting suitable ML techniques and MHDs for an ITS with a specific complexity level, a performance evaluation framework is proposed. The presented survey sets the basis for developing suitable hardware, facilitating the integration of ML within ITS, and bridging the gap between research and real-world deployments.

Firms offer a variety of products to meet different customer needs. In many horizontally differentiated markets, prices are stable, and firms make infrequent adjustments to their product lines. Although prior research focused on product line design, we investigate how firms should allocate their marketing effort when their product lines are fixed. We propose a simple model to analyze product line marketing. Our model exhibits a flagship product effect in which the firm’s optimal marketing effort is concentrated, provided that the ratio between consumer tastes dispersion and the convexity of the cost of marketing effort is below a threshold. The flagship product is selected according to a marketing effort allocation index that measures the trade-off between a product’s markup and its potential market share. This result is robust with or without competition and whether prices are exogenous or endogenous. Firms often experience shocks to their marketing cost because of technological improvement or externalities. If a monopolist’s cost of marketing effort declines, she should place more emphasis on a low-utility, high-markup product. Conversely, if the cost increases, the monopolist may find it beneficial to focus her marketing effort on a high-utility, low-markup product. When multiproduct firms compete against each other, we show that if the opponent’s cost of marketing effort decreases, there can be a spillover effect, in which the firm benefits from the opponent’s cost reduction, thereby leading to a win-win situation. This paper was accepted by Dmitri Kuksov, marketing.

Innovation steps in complex products often go hand in hand with the selection of suppliers. The suppliers are involved in the development of innovative elements of products, supplementing the capabilities of companies. Strategic relationships are established with these suppliers, where the companies need them to be flexible and willing to adapt to changing demands. Flexibility is usually evaluated against a variety of indicators. However, a high score on flexibility does not guarantee cooperative suppliers. Companies also require power to get a supplier to be flexible. They need suppliers who are malleable to their changing needs that are an inevitable part of design processes. The empirical study at a leading European engine company reported in this paper shows that the relative power between companies and suppliers influences the responsiveness of suppliers to requests. This paper analyses malleability as a combination of flexibility and relative power. It unpacks power and flexibility into sets of measurable indicators and proposes a tool which assesses both the relative power between buyer and supplier and the supplier’s flexibility, to provide a malleability index, which indicates to what extent a supplier is suitable to work with.

Plain English Summary Human capital is important for entrepreneurship. In particular, a varied skill set enables entrepreneurs to tackle the various tasks of starting a new firm. However, no one is born with such a skill set; it develops over time. In this study, we explore the origins of a varied skill set and its development. We find that skill variety in adulthood has its roots in a varied set of interests among teenagers, such as having many hobbies or finding different school subjects important. This growth in skill variety is driven by an entrepreneurial personality. For prospective entrepreneurs, our research suggests that investing in a varied skill set pays off. The implication for research is to look at the developmental process of how people become entrepreneurs. The most important conclusion for policymakers and educators is that educational support programs should center around encouraging especially adolescents and young adults to engage in varied activities and teach varied skills instead of focusing on a narrow curriculum. Given that recent research on entrepreneurial behavior and success has established skill variety as a central human capital factor, researchers, educators, and policymakers have turned their interest to a deeper understanding of the formation of skill variety. Based on human capital theory and the competence growth approach in developmental psychology (highlighting long-term, age-appropriate, and cumulative skill-growth processes), we hypothesize that a broad, early variety orientation in adolescence is a developmental precursor of such entrepreneurial human capital in adulthood. This was confirmed in an analysis of prospective longitudinal data via structural equation modeling and serial mediation tests. We also find that an entrepreneurial constellation of personality traits, but not entrepreneurial parents, predicts early variety orientation, skill variety, and entrepreneurial intentions. By shedding new light on the long-term formation of entrepreneurial human capital, the results suggest that establishing and benefiting from an early variety orientation is not only an important developmental mechanism in entrepreneurial careers but gives those with an entrepreneurial personality an early head start in their vocational entrepreneurial development. Implications for future research and practice are discussed.

An emerging body of studies demonstrates that practicing retrieval of studied information, by comparison with restudying or no treatment, can facilitate subsequent learning and retrieval of new information, a phenomenon termed the forward testing effect (FTE) or test-potentiated new learning. Several theoretical explanations have been proposed to account for the FTE. A release-from-PI theory proposes that interpolated testing induces context changes and enhances event segregation, which in turn protect new learning from proactive interference (PI). A strategy-change view hypothesizes that prior tests teach learners to adopt more effective/elaborative learning and retrieval strategies in subsequent study and test phases. Finally, a reset-of-encoding account proposes that interim testing on studied information reduces memory load, resets the subsequent encoding process, and enhances encoding of new information. The current study recruited a large sample (over 1,000 participants) and employed a multilist learning task and mediation analyses to test these theories. The results suggest that prior list intrusions (an index of PI) significantly mediated the FTE, supporting the release-from-PI theory. In addition, interim testing enhanced strategic processing of temporal information during new learning (reflected by increased clustering), and temporal clustering significantly mediated the FTE, supporting a role for strategy-change in the FTE. Lastly, a variety of indices were constructed to represent the benefit of reset-of-encoding, but none of them provided evidence supporting the reset-of-encoding view. The results shed new light on the complex mechanisms underlying the forward benefits of testing. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The road network serves as one of the most fundamental infrastructure systems for the society but is at risk from different types of disruptions. Due to the variety and unpredictability of disruptions, the road network has stochastic behavior, which refers to the state change of the network. In this paper, we propose an approach based on percolation theory to study the stochastic behavior of road networks impacted by disruptions. Two performance metrics, network connectivity and network efficiency, are defined to quantify the behavior of road networks. Instead of relying on detailed network structure, the proposed percolation theory-based approach uses only basic topology information, such as the distribution of node degrees and link capacities, which makes it suitable for large-scale networks. It is found that the efficiency of a disruption-impacted road network is the product of global connectivity, local connectivity, and connectivity strength. Validation by a real-world case shows that this new approach provides detailed and accurate evaluation of the behavior of disruption-impacted road networks.

Start-up accelerators have attracted increasing attention from start-ups in recent years. However, little is known about how they interact with start-ups and investors, how they prioritize a wide variety of their services, and how macro-level factors affect their performance. We use a game-theoretic approach to study mutual effects between an accelerator, a venture capitalist (VC), and start-ups. In the model, the accelerator selects start-ups and invests in them. During its program, it provides intensive mentorship for the selected start-ups and screens them to determine whether or not they are successful. Finally, it introduces successful start-ups to the VC to obtain financing for their ideas. Our results indicate that 1) the most vital role of an accelerator is its screening services. 2) Given limited resources of accelerators, prioritizing their services is the key to their efficacy. Screening, mentorship, and seed investment services possess the first, second, and third priorities, respectively. 3) Although accelerators are anywhere beneficial, macro-level heterogeneity of entrepreneurial ecosystems is associated to their efficacy. Their efficacy is higher (lower) in less (more) developed regions. 4) Institutional reforms targeting mentoring or entrepreneurial communities are unexpectedly detrimental to the efficacy of accelerators, while they may enhance start-ups’ equilibrium willingness to participate.

Field-programmable gate arrays (FPGAs) are remarkably versatile. FPGAs are used in a wide variety of applications and industries where use of application-specific integrated circuits (ASICs) is less economically feasible. Despite the area, cost, and power challenges designers face when integrating FPGAs into devices, they provide significant security and performance benefits. Many of these benefits can be realized in client compute hardware such as laptops, tablets, and smartphones.

As cultural institutions are digitizing their artifacts and interlinking their collections, new opportunities emerge to engage with cultural heritage. However, it is the often comprehensive and complex nature of collections that can make it difficult to grasp their distribution and extent across a variety of dimensions. After a brief introduction to the research area of collection visualizations, this paper presents a design study visualizing an aggregated collection from diverse cultural institutions in Germany. We detail our iterative design process leading to prototypical implementations of four stylistically and functionally coordinated visualizations, each one focusing on different facets of the collection.

The advent of in-vehicle networking systems as well as state-of-the-art sensors and communication technologies have facilitated the collection of large volume and almost real-time data on vehicles and drivers, thus opening up future possibilities. Processing and analyzing this data provides unprecedented opportunities to offer remarkable insights and solutions for driving behavior analysis (DBA). Characterizing driving behavior plays a key role in a variety of research areas such as traffic safety, the development of automated vehicles, energy and fuel management, risk assessment, and driver identification and profiling. Advances in DBA-based driver inattention or drunk driver detection can help reduce fatal car crashes, and understanding the driving style (e.g. eco-friendly or aggressive) of drivers can contribute to fuel management and risk assessment of the drivers. These facts have led to a growing interest in addressing DBA challenges. This paper aims to present the state-of-the-art methodologies for DBA and provide a clear roadmap about the main current and future trends in DBA. To this end, we propose categorizing the current research on driving behavior based on the types of data employed for the analysis, the ultimate goals of the analysis, and the techniques based on which the driving data are modeled. We provide an overview of different data resources and available datasets for DBA. Moreover, we discuss the application of DBA along with the key research challenges in this field and potential future directions.