Miracle-dz/UniFAR-FTUs huggingface dataset:
Antecedents of ISD team performance: Knowledge management activities

The topic of knowledge management is an important one in team-based studies. Many previous studies have shown that the proper team structure facilitates sharing and applying the expertise and knowledge which is scattered among members, allowing for team tasks to be executed more efficiently and effectively. Knowledge management involves four basic processes: storing/retrieving, transferring, applying, and creating knowledge. This study uses transactive memory system, knowledge sharing, knowledge application and creativity as knowledge management process activities in order to explore their impact on team performance. Based on data from a study of 64 ISD teams, all our hypotheses were found to be significant.
Indexing Method for Assessment of Air Quality

Rapid urbanization and globalization has led to severe degradation of existing air quality in the majority of Indian cities. In this context, the general public has been aware of their exposure to ambient air quality and the effects of such air pollutants on human health. Hence, the concept of Air quality indices (AQI) is often used by regulatory authorities in conveying the status of existing ambient air quality to the general public. The chapter presents the application of air quality indices for assessing the existing air quality standards in an Indian city, Dharamshala, a tourist location in Himachal Pradesh, for the period of 2016-2017. Two different methods of determining AQI have been used wherein one method is used as the revised Central Pollution Control Board (CPCB), India with different sub-indices for Indian conditions based on the United States Environmental Protection Agency (USEPA) methodology and another alternative method utilizes contribution from all criteria pollutants.
Comparison of consumers’ acceptance of online apparel mass customization across web and mobile channels

ABSTRACT The purpose of this study is to shed light on understanding consumer apparel mass customization experiences through an experiment focusing on comparing customers’ experiences across different online channels. Applying the Technology Acceptance Model, we systematically examined how favorable attitudes were influenced across web and mobile channels. Structural equation modeling, multiple group comparison, and MANOVA were conducted to assess construct validity, and test the proposed framework and hypotheses, respectively. An online survey experiment was designed to collect empirical data. A total of 388 college students from a major university in the United States participated in the study. Based on consumers’ real experience, research results showed that beliefs about ease of use, enjoyment, choice variety, and not usefulness or risk affect attitudes, and acceptance of OAMC via both web and mobile channels. Overall consumers favor web channels more than mobile channels and were more likely to get OAMC through websites over retail apps. Theoretical and practical implications were provided based on research findings.
A New Testing Device for RFID Performance Factors of Conveyor Belt System

Combining with RFID technology, Conveyor plays a very important role in modern industrial automation. However, RFID performance will be affected by many factors in conveyor application. In this paper, we build a new experimental device to test RFID performance factors of conveyor belt, the innovative aspects of our new experimental device are we adopt automated guided vehicle (AGV) instead of traditional real conveyor belt. It makes the test become more convenient and comprehensive. Meanwhile, we provide serial important and indispensable test cases based on our new experimental device, the methodology and test data of these test cases are discussed. Our new experimental device can be put into practical application without making complex changes on the existing production line.
Model of Information Retrieval in the Context of Organizations

An information retrieval model is presented as a result of a study of two different but integrated perspectives of Information Science. On the one hand, Information Science is considered from the point of view of Information Management, and on the other from the Information Science retrieval. The latter encompasses the point of view of some cognitivist authors who have created an information retrieval model and these have been exhaustively studied. This appraisal begins with the intention of finding realistic solutions to the problems in order that the information chain can flow unhindered and, thus, enable to achieve the objectives for which the information was generated.
Understanding Emergent Social Phenomena Methods, Tools, and Applications for Agent-Based Modeling

Agent-based modeling is a new branch of computer simulation, especially suited for the modeling of complex social systems. Its main tenet is to model the individual, together with its imperfections (e.g., limited cognitive and computational abilities), its idiosyncrasies, and unique interactions. Thus, the approach builds the model from ‘the bottom-up’, focusing mostly on micro rules and seeking the understanding of the emergence of macro behavior. In the first part of the dissertation I provide an overview of agent-based modeling and simulation. Chapter 1 gives an introduction to the historical background, main concepts and general approach of the technique, and points out the need for dedicated computational tools and methodologies. In Chapter 2, I present a framework that allows for finer distinctions among implementation strategies than the traditional ‘equation-based’ versus ‘agent-based’ debate. I illustrate the framework by a case study that draws special attention to the potential importance of bounded rationality, limited information access, and a heterogeneous agent population. The second part summarizes my contributions to the advancement of the computational tools of agent-based modeling. In Chapter 3, I introduce the Multi-Agent Modeling Language (MAML), the first programming language dedicated to agent-based modeling. In Chapter 4, I propose the use of design patterns, a software engineering concept, for the description of common agent-based implementation practices. I also introduce three new design patterns helping to develop robust and correct simulations. In the final, third part of the thesis I present two applications of the agent-based modeling approach. Chapter 5 introduces a model of endogenous city formation that is capable of replicating the rank-size regularity found in US survey data. Chapter 6 deals with social influence in a setting of repeated discrete choices, where individual choices depend on the previous decisions of one’s personal contacts. Investigating the effect of various influence networks, I conclude that as long as the underlying social network has the small-world property, the system behaves as if in the fully connected case.
Heuristics applying stochastic information as tools for thermoacoustic standing-wave engine optimization

In this article, two numerical methods for solving engineering problems defined as multicriteria optimization and inverse problem are presented. It deals with the optimization of the design of thermoacoustic engine in the frame of which both types of tasks are solved. The first proposed heuristic serves to find many p-optimal solutions simultaneously, which represents a compromise between usually mutually contradictory goals at work. Based on them, the full Pareto front is approximated. The inverse problem solution reproduces parameters for solutions located on a designated front but those that are not found in multicriteria optimization. In this article, it is proposed to use the RACO heuristics for determining p-optimal solutions and the Bayesian approach as a method for solving ill-conditioned inverse problems. Optimization of the construction of the thermoacoustic engine is aimed at verifying proposed methodology and present the possibility of using both methods in engineering problems. The problem discussed in this article has been formulated and the numerical methods used in the solution have been presented in details.
A Compact and Language-Sensitive Multilingual Translation Method

Multilingual neural machine translation (Multi-NMT) with one encoder-decoder model has made remarkable progress due to its simple deployment. However, this multilingual translation paradigm does not make full use of language commonality and parameter sharing between encoder and decoder. Furthermore, this kind of paradigm cannot outperform the individual models trained on bilingual corpus in most cases. In this paper, we propose a compact and language-sensitive method for multilingual translation. To maximize parameter sharing, we first present a universal representor to replace both encoder and decoder models. To make the representor sensitive for specific languages, we further introduce language-sensitive embedding, attention, and discriminator with the ability to enhance model performance. We verify our methods on various translation scenarios, including one-to-many, many-to-many and zero-shot. Extensive experiments demonstrate that our proposed methods remarkably outperform strong standard multilingual translation systems on WMT and IWSLT datasets. Moreover, we find that our model is especially helpful in low-resource and zero-shot translation scenarios.
Coreference Resolution with Entity Equalization

A key challenge in coreference resolution is to capture properties of entity clusters, and use those in the resolution process. Here we provide a simple and effective approach for achieving this, via an “Entity Equalization” mechanism. The Equalization approach represents each mention in a cluster via an approximation of the sum of all mentions in the cluster. We show how this can be done in a fully differentiable end-to-end manner, thus enabling high-order inferences in the resolution process. Our approach, which also employs BERT embeddings, results in new state-of-the-art results on the CoNLL-2012 coreference resolution task, improving average F1 by 3.6%.
Photonics Links -- From Theory to Automated Design

Author(s): Settaluri, Krishna Tej | Advisor(s): Stojanovic, Vladimir | Abstract: Recent advancements in silicon photonics show great promise in meeting the high bandwidth and low energy demands of emerging applications. However, a key gating factor in ensuring this necessity is met is the utilization of a link design method- ology which transcends the various levels in the hierarchy, ranging from the device and platform level up to the systems level. In this dissertation, a comprehensive methodology for link design will be introduced which takes a two-prong approach to tackling the issue of silicon photonic link efficiency. Namely, a fundamentals-based first principles approach to link optimization will be introduced and validated. In addition, physical design trade-offs connecting levels in the architectural hierarchy will also be studied and explored. This culminates in an intermediate goal of this dissertation, which is the first-ever design and verification of a full silicon photonic interconnect on a 3D integrated electronic-photonic platform. To proceed and further enable the rapid exploration of the link design architectural space, the analog macros for a majority of this dissertation were auto-generated using the Berkeley Analog Generator (BAG). With these key design tools and framework, performance bottlenecks and improvements for silicon photonic links will be analyzed and, from this analysis, the motivation for a new, single comparator-based PAM4 receiver architecture shall emerge. This architecture not only showcases the tight bond in dependency between high-level link specifications and low level device parameters, but also shows the im- portance of physical design constraints alongside fundamental theory in influencing end-to-end link performance.
Fault Diagnosis With Dual Cointegration Analysis of Common and Specific Nonstationary Fault Variations

Nonstationary variations widely exist in abnormal industrial processes, in which the mean values and variances of the fault nonstationary variables change with time. Thus, the stationary fault information may be buried by nonstationary fault variations resulting in high misclassification rate for fault diagnosis. Besides, the existing fault diagnosis methods do not consider underlying relations among different fault classes, which may lose important classification information. Here, it is recognized that different faults may not only share some common information but also have some specific characteristics. A fault diagnosis strategy with dual analysis of common and specific nonstationary fault variations is proposed here. The nonstationary variables and stationary variables are first separated using Augmented Dickey–Fuller (ADF) test. Then common and specific information is analyzed for fault diagnosis. Two models are developed, in which, the fault-common model is constructed by cointegration analysis (CA) to capture common nonstationary fault variations, and the fault-specific model is built to capture specific fault nonstationary variations of each fault class. With dual consideration of common and specific fault characteristics, the classification accuracy and fault diagnosis performance can be greatly improved. The performance of the proposed method is illustrated with both a well–known benchmark process and a real industrial process. Note to Practitioners—Process data analysis methods play an increasing important role in system maintenance and process monitoring in real industrial processes. The focus of this paper is to develop a fault diagnosis strategy with dual analysis of common and specific nonstationary fault variations. The proposed strategy can automatically describe the relationships between different fault classes using process data without complex mechanism knowledge. By exploring the relationship information between different faults, more accurate diagnosis models can be developed. Besides, the proposed strategy is a feasible technique for the nonstationary problem of complicated and varied industrial processes.
How can Customer Analytics Capabilities Influence Organizational Performance? A moderated mediation analysis

A theoretical model is proposed to test the relationship between Customer Analytics Capabilities and Market Orientation with Organizational Performance, encompassing Marketing Capabilities as a mediator mechanism moderated by Environmental Dynamism. Its contribution lies in the test of this mediation in different types of industries in Brazil, by using SmartPLS software for structural equation modeling (SEM) and IBM SPSS with PROCESS macro for more in-depth insights. The results confirm the moderated mediation but show different behaviors about the direct effect for Customer Analytics Capabilities and Market Orientation, which suggest future studies. The study supports a better understanding of some of the diverse capabilities types and proposes an adaptive new one, Customer Analytics Capabilities, which is the final insertion of the Analytics concept in Marketing and Strategy disciplines.
HTTP Integrity Header

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Enhancing Cybersecurity Awareness Training: A Comprehensive Phishing Exercise Approach

[Abstract] Email communications is a critical service in nearly every aspect of business and personal activities. A person’s email address is the online identity most relied upon for reliable and accountable communications. Banking, health and recreational activities conducted online in most cases depend on a person possessing and actively maintaining an email address. Unfortunately, email inherently possesses security shortcomings that enable malicious actors to commit fraud through phishing emails. Technology solutions do not entirely solve this issue, and the users themselves require training on how to identify and respond to suspected phishing emails. A structured and comprehensive phishing exercise training program can (1) reduce the probability of a cybersecurity compromise originating from fraudulent emails and (2) improve the overall cybersecurity posture.
We Need to Talk about Standard Splits

It is standard practice in speech & language technology to rank systems according to their performance on a test set held out for evaluation. However, few researchers apply statistical tests to determine whether differences in performance are likely to arise by chance, and few examine the stability of system ranking across multiple training-testing splits. We conduct replication and reproduction experiments with nine part-of-speech taggers published between 2000 and 2018, each of which claimed state-of-the-art performance on a widely-used “standard split”. While we replicate results on the standard split, we fail to reliably reproduce some rankings when we repeat this analysis with randomly generated training-testing splits. We argue that randomly generated splits should be used in system evaluation.
Scalable Methods and Algorithms for Very Large Graphs Based on Sampling

Analyzing real-life networks is a computationally intensive task due to the sheer size of networks. Direct analysis is even impossible when the network data is not entirely accessible. For instance, user networks in Twitter and Facebook are not available for third parties to explore their properties directly. Thus, sampling-based algorithms are indispensable. This dissertation addresses the confidence interval (CI) and bias problems in realworld network analysis. It uses estimations of the number of triangles (hereafter ∆) and clustering coefficient (hereafter C) as a case study. Metric ∆ in a graph is an important measurement for understanding the graph. It is also directly related to C in a graph, which is one of the most important indicators for social networks. The methods proposed in this dissertation can be utilized in other sampling problems. First, we proposed two new methods to estimate ∆ based on random edge sampling in both streaming and non-streaming models. These methods outperformed the stateof-the-art methods consistently and could be better by orders of magnitude when the graph is very large. More importantly, we proved the improvement ratio analytically and verified our result extensively in real-world networks. The analytical results were achieved by simplifying the variances of the estimators based on the assumption that the graph is very large. We believe that such big data assumption can lead to interesting results not only in triangle estimation but also in other sampling problems. Next, we studied the estimation of C in both streaming and non-streaming sampling models. Despite numerous algorithms proposed in this area, the bias and variance of the estimators remain an open problem. We quantified the bias using Taylor expansion and found that the bias can be determined by the structure of the sampled data. Based on the understanding of the bias, we gave new estimators that correct the bias. The results were derived analytically and verified in 56 real networks ranging in different sizes and structures. The experiments reveal that the bias ranges widely from data to data. The relative bias can be as high as 4% in non-streaming model and 2% in streaming model, or it can be negative. We also derived the variances of the estimators, and the estimators for the variances. Our simplified estimators can be used in practice to control the accuracy level of estimations.
Spatial Information and Representations of Word Meaning: Accessing Semantic Size during Reading

symbols that are inherently amodal (Pylyshyn, 2003). Within this framework, language functions as a system that connects external representations, such as words and sentences, to internal representations of meaning. Importantly, these internal representations are qualitatively distinct from representations in our sensorimotor system (e.g. Fodor, 1985). However, several fields within contemporary cognitive psychology have traditionally recognized the role of analogue perceptual representations. Baddely (1986) assigned a specialized system to visuospatial information within working memory, and long term episodic memory is considered to rely crucially on multimodal simulation of previous events (Paivio, 1986; Conway, 1990). Within the study of memory and learning, implicit learning (Jacoby, 1983) repetition priming (Kirsner et al., 1989) and transfer appropriate processing (Morris, Bransford & Franks, 1977) are considered to depend largely on the amount of overlap in perceptual circumstances of the learning and testing episode. In addition, mental imagery is considered to play an important role in the representation of concepts, as has been found in studies of categorization and property verification (Kosslyn, 1980). Within language research, spatial representations are considered crucial to the construction of mental models (Bower & Morrow, 1990; Bransford, Barclay & Franks, 1972; Glenberg, Meyer & Lindem,
Cross-Domain NER using Cross-Domain Language Modeling

Due to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both the source and target domains. A disadvantage of such methods is that they cannot train for domains without NER data. To address this issue, we consider using cross-domain LM as a bridge cross-domains for NER domain adaptation, performing cross-domain and cross-task knowledge transfer by designing a novel parameter generation network. Results show that our method can effectively extract domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while also giving state-of-the-art results among supervised domain adaptation methods.
Machiavellians, Unethical Workmates And Intention To Stay: An Empirical Exploration

Machiavellianism is usually studied as an individual characteristic affecting an individual’s ethical/unethical behavior in organizations, turning into an important influential factor in that matter. However, no studies have been conducted to date testing the influence of this personal trait on the individual’s intention to stay, which on the basis of the theoretical perspective of the Resource-Based View of the Firm, have important valuable implications for the organization. Furthermore, no empirical research has been conducted in relation to test the comfort that Machiavellians experiment when an unethical climate is perceived in the organization. This paper will study the effect that a Machiavellian personality has on the individual’s intention to stay and what happens if Machiavellians are within the organization in company of workmates who behave unethically in human interaction. Results obtained through the empirical analysis in a sample of Spanish banking employees are discussed and conclusions and implications both for academics and business professionals are presented.