Miracle-dz/UniFAR-FTUs huggingface dataset:
Organizational Change in the Artificial Intelligence Age - The Case of Marketing and Sales

Artificial intelligence (AI), robotics, machine learning, and automation are revolutionizing the field of marketing and sales. Although the field of sales and marketing has been impacted by advanced technologies continuously, the impact of AI on sales and marketing field is expected to be transformational and revolutionary. The main threats pose by AI are job losses and upheaval of the society as a result (Siau, 2018; Siau and Wang, 2018). A report published in February 2016 by Citibank in partnership with the University of Oxford predicted that 47% of US jobs are at risk of automation. In UK, it is 35%. In China, it is 77%. Across OECD, it is an average of 57%. In the past, technology advancement has consistently generated more new jobs than it destroys. Many are wondering if it will be the same for the AI revolution. In this research, we will focus on organizational changes affected by AI. Jobs in marketing and sales are predicted to be easily replaceable by AI (Siau and Yang, 2017) and AI will result in major organizational changes in marketing companies and sales units. This research will examine the organizational changes in the field of marketing and sales. We are interested in the process of organizational changes, and the impact of such changes on marketing and sales personnel, and on organizational structures and processes. Two popular organizational change theories, Lewin’s change model (Burnes, 2004) and Transtheoretical Model (Prochaska and DiClemente, 1984), will form the conceptual foundation of this research. Multiple case studies will be carried out in various marketing and sales units in different organizations. Qualitative research is used as it enables us to have an in-depth understanding of the organizational changes and allows the researchers to better interact with the interviewees. This research will also scrutinize existing theoretical and conceptual foundations related to organizational change and their applicability in the AI age. The subjects in this study will include marketers and sales personnel as well as their managers and executives. Purposeful and convenience sampling will be used. This research will enable us to study the organizational changes resulting from AI with the focus on marketing and sales areas. This research is of value to both academics and practitioners as it aims to provide a detailed analysis and documentation of the organizational changes in marketing and sales units, and the evolving marketing and sales functionalities as AI technology advances.
Is Blockchain Hashing an Effective Method for Electronic Governance?

Governments across the world are testing different uses of the blockchain for the delivery of their public services. Blockchain hashing - or the insertion of data in the blockchain - is one of the potential applications of the blockchain in this space. With this method, users can apply special scripts to add their data to blockchain transactions, ensuring both immutability and publicity. Blockchain hashing also secures the integrity of the original data stored on central governmental databases. The paper starts by analysing possible scenarios of hashing on the blockchain and assesses in which cases it may work and in which it is less likely to add value to a public administration. Second, the paper also compares this method with traditional digital signatures using PKI (Public Key Infrastructure) and discusses standardisation in each domain. Third, it also addresses issues related to concepts such as distributed ledger technology and permissioned blockchains. Finally, it raises the question of whether blockchain hashing is an effective solution for electronic governance, and concludes that its value is controversial, even if it is improved by PKI and other security measures. In this regard, we claim that governments need first to identify pain points in governance, and then consider the trade-offs of the blockchain as a potential solution versus other alternatives.
pioNER: Datasets and Baselines for Armenian Named Entity Recognition

In this work, we tackle the problem of Armenian named entity recognition, providing silver- and gold-standard datasets as well as establishing baseline results on popular models. We present a 163000-token named entity corpus automatically generated and annotated from Wikipedia, and another 53400-token corpus of news sentences with manual annotation of people, organization and location named entities. The corpora were used to train and evaluate several popular named entity recognition models. Alongside the datasets, we release 50-, 100-, 200-, 300-dimensional GloVe word embeddings trained on a collection of Armenian texts from Wikipedia, news, blogs, and encyclopedia.
Recent Advances in Endothelial Colony Forming Cells Toward Their Use in Clinical Translation

The term “Endothelial progenitor cell” (EPC) has been used to describe multiple cell populations that express endothelial surface makers and promote vascularisation. However, the only population that has all the characteristics of a real “EPC” is the Endothelial Colony Forming Cells (ECFC). ECFC possess clonal proliferative potential, display endothelial and not myeloid cell surface markers, and exhibit pronounced postnatal vascularisation ability in vivo. ECFC have been used to investigate endothelial molecular dysfunction in several diseases, as they give access to endothelial cells from patients in a non-invasive way. ECFC also represent a promising tool for revascularization of damaged tissue. Here we review the translational applications of ECFC research. We discuss studies which have used ECFC to investigate molecular endothelial abnormalities in several diseases and review the evidence supporting the use of ECFC for autologous cell therapy, gene therapy and tissue regeneration. Finally, we discuss ways to improve the therapeutic efficacy of ECFC in clinical applications, as well as the challenges that must be overcome to use ECFC in clinical trials for regenerative approaches.
IoT and semantic web technologies for event detection in natural disasters

Natural disasters cannot be predicted well in advance, but it is still possible to decrease the loss of life and mitigate the damages, exploiting some peculiarities that distinguish them. Smart collection, integration, and analysis of data produced by distributed sensors and services are key elements for understanding the context and supporting decision making process for disaster prevention and management. In this paper, we demonstrate how Internet of Things and Semantic Web technologies can be effectively used for abnormal event detection in the contest of an earthquake. In our proposal, a prototype system, which retrieves the data streams from IoT sensors and web services, is presented. In order to contextualize and give a meaning to the data, semantic web technologies are applied for data annotation. We evaluate our system performances by measuring the response time and other parameters that are important in a disaster detection scenario.
An Overview of Crowd-based Markets and Racial Discrimination

Crowd-based markets are gaining in prominence as providers of goods and services (Airbnb, Uber), sources of employment (TaskRabbit, Mechanical Turk), and sources of capital (Kickstarter, IndieGogo). Although these markets have an open and egalitarian premise, multiple studies have found evidence of racial discrimination. This paper provides an overview of the existing research in the racial discrimination of crowd-based markets and examines their measurement choices, recommendations to reduce discrimination, and the ethical values embedded in the anti-discrimination suggestions. Based on the survey of the current literature, this analysis suggests new research avenues and highlights automation as the most promising new avenue to reduce racial discrimination in the crowd.
What Drives In-App Purchase Intention in Video Games? An Examination of Patience and the Enjoyment of Routine Tasks

Developers of free-to-play games often depend on people’s in-app purchases. In this article, I evaluate the potential influence of patience and the enjoyment of routine tasks on in-app purchase intention. After collecting 100 completed online questionnaires about the mobile video game Clash of Clans, and applying a structural equation modeling approach, I confirmed that patience is a negative predictor of in-app purchase intention in video games. In contrast, I could not confirm a corresponding influence of the enjoyment of routine tasks on in-app purchase intention. A potential explanation for this insignificance is provided and the other finding’s practical implications as well as their limitations are discussed.
The effects of Students' Motivation and Student-Student Dialogue, Student-Instructor Dialogue on Students' Perceived E-Learning Outcomes

Several attributes of students, the primary participants of e-learning systems, have been major subjects of intense research over the past decade (Bitzer and Janson 2014). Structural equation modeling is applied to examine the effects of intrinsic motivation, extrinsic motivation, student-student dialogue, and studentinstructor dialogue on e-learners’ perceived learning outcomes in the context of university online courses. A total of 372 valid, unduplicated responses from students who have completed at least one online course at a university in the Midwest were used to examine the structural model. The results indicated that intrinsic and extrinsic motivation exhibit higher positive correlations with student-student (SS) dialog and self-regulated learning strategy. Moreover, self-regulated learning strategies, SS dialog, and SI dialog positively affect the e-learners’ perceived learning outcomes. However, extrinsic student motivation shows a positive correlation with SS dialog, but it had no significant relationship with SI dialog.
Opioid Deaths: Trends, Biomarkers, and Potential Drug Interactions Revealed by Decision Tree Analyses

Opioid abuse is now the primary cause of accidental deaths in the United States. Studies over several decades established the cyclical nature of abused drugs of choice, with a current resurgence of heroin abuse and, more recently, fentanyl’s emergence as a major precipitant of drug-related deaths. To better understand abuse trends and to explore the potential lethality of specific drug–drug interactions, we conducted statistical analyses of forensic toxicological data from the Wayne County Medical Examiner’s Office from 2012–2016. We observed clear changes in opioid abuse over this period, including the rapid emergence of fentanyl and its analogs as highly significant causes of lethality starting in 2014. We then used Chi-square Automatic Interaction Detector (CHAID)-based decision tree analyses to obtain insights regarding specific drugs, drug combinations, and biomarkers in blood most predictive of cause of death or circumstances surrounding death. The presence of the non-opioid drug acetaminophen was highly predictive of drug-related deaths, likely reflecting the abuse of various combined acetaminophen-opioid formulations. The short-lived cocaine adulterant levamisole was highly predictive of a short post-cocaine survival time preceding sudden non-drug-related death. The combination of the opioid methadone and the antidepressant citalopram was uniformly linked to drug death, suggesting a potential drug–drug interaction at the level of a pathophysiological effect on the heart and/or drug metabolism. The presence of fentanyl plus the benzodiazepine midazolam was diagnostic for in-hospital deaths following serious medical illness and interventions that included these drugs. These data highlight the power of decision tree analyses not only in the determination of cause of death, but also as a key surveillance tool to inform drug abuse treatment and public health policies for combating the opioid crisis.
Data Motif-based Proxy Benchmarks for Big Data and AI Workloads

For the architecture community, reasonable simulation time is a strong requirement in addition to performance data accuracy. However, emerging big data and AI workloads are too huge at binary size level and prohibitively expensive to run on cycle-accurate simulators. The concept of data motif, which is identified as a class of units of computation performed on initial or intermediate data, is the first step towards building proxy benchmark to mimic the real-world big data and AI workloads. However, there is no practical way to construct a proxy benchmark based on the data motifs to help simulation based research.In this paper, we embark on a study to bridge the gap between data motif and a practical proxy benchmark. We propose a data motif-based proxy benchmark generating methodology by means of machine learning method, which combine data motifs with different weights to mimic the big data and AI workloads. Furthermore, we implement various data motifs using light-weight stacks and apply the methodology to five real-world workloads to construct a suite of proxy benchmarks, considering the data types, patterns, and distributions. The evaluation results show that our proxy benchmarks shorten the execution time by 100s times on real systems while maintaining the average system and micro-architecture performance data accuracy above 90%, even changing the input data sets or cluster configurations. Moreover, the generated proxy benchmarks reflect consistent performance trends across different architectures. To facilitate the community, we will release the proxy benchmarks on the project homepage http://prof.ict. ac.cn/BigDataBench.
Alternating Linear Bandits for Online Matrix-Factorization Recommendation

We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. At each time step, the user (selected by the environment) consumes an item (selected by the agent) and provides a rating of the selected item. In this paper, we propose a novel algorithm for online matrix factorization recommendation that combines linear bandits and alternating least squares. In this formulation, the bandit feedback is equal to the difference between the ratings of the best and selected items. We evaluate the performance of the proposed algorithm over time using both cumulative regret and average cumulative NDCG. Simulation results over three synthetic datasets as well as three real-world datasets for online collaborative filtering indicate the superior performance of the proposed algorithm over two state-of-the-art online algorithms.
Characterization of Diversity and Probiotic Efficiency of the Autochthonous Lactic Acid Bacteria in the Fermentation of Selected Raw Fruit and Vegetable Juices

The diversity of indigenous lactic acid bacteria (LAB) in fermented broccoli, cherry, ginger, white radish, and white-fleshed pitaya juices was analyzed using culture-independent and -dependent approaches. The major properties of selected probiotic strains, including dynamic variations in pH, viable cell counts, antibiotic resistance, bacterial adhesion to hydrophobic compounds, and survivability during simulated gastrointestinal transit, were investigated using broccoli as the fermentation substrate. In broccoli and ginger juices, the genus Lactobacillus occupied the dominant position (abundances of 79.0 and 30.3%, respectively); in cherry and radish juices, Weissella occupied the dominant position (abundances of 78.3 and 83.2%, respectively); and in pitaya juice, Streptococcus and Lactococcus occupied the dominant positions (52.2 and 37.0%, respectively). Leuconostoc mesenteroides, Weissella cibaria/soli/confusa, Enterococcus gallinarum/durans/hirae, Pediococcus pentosaceus, Bacillus coagulans, and Lactococcus garvieae/lactis subspecies were identified by partial 16S rRNA gene sequencing. In general, the selected autochthonous LAB isolates displayed no significant differences in comparison with commercial strains with regard to growth rates or acidification in fermented broccoli juice. Among all the isolates, L. mesenteroides B4-25 exhibited the highest antibiotic resistance profile (equal to that of L. plantarum CICC20265), and suitable adhesion properties (adhesion of 13.4 ± 5.2% ∼ 36.4 ± 3.2% and 21.6 ± 1.4% ∼ 69.6 ± 2.3% to ethyl acetate and xylene, respectively). Furthermore, P. pentosaceus Ca-4 and L. mesenteroides B-25 featured the highest survival rates (22.4 ± 2.6 and 21.2 ± 1.4%, respectively), after simulated gastrointestinal transit. These results indicated a high level of diversity among the autochthonous bacterial community in fermented fruit and vegetable juices, and demonstrated the potential of these candidate probiotics for applications in fermentation.
Developing and testing a smartphone health application for older people to improve their mental health

There is an increasing number of smartphone health applications available to smartphone users. Mental health applications are becoming an increasingly influential part of healthcare. While the adoption of smartphones has emerged as a vital tool for health-related behavioural interventions, making mental health support more accessible, and reducing barriers to help seeking, little is known about the potential benefits that smartphone health applications can provide in the mental health care of older people. There are hardly any contributions that focus on smartphone mental health applications for older people. This paper asks what are the key features needed for smartphone health application designed to improve mental health of older people? To answer this question, a comprehensive literature review of studies conducted in information systems and mental health disciplines has been undertaken and a theoretical model is proposed. This study contributes to the existing knowledge base through the development of a new theoretical model and the introduction of the features of a mobile health application that may have a positive impact on older peoples’ mental health. Key words: smartphone health applications, mental health, older peoples’ mental health.
The Business Value of Engaging in Counter-Breach Initiatives

Facing an increasing information security threat, companies react by engaging in various counter-breach initiatives. The business value of such initiatives is under-studied. This paper utilizes an organizational legitimacy view, overlaid by behavioral theory of the firm (BTOF), to examine whether counter-breach initiatives are rewarded by the market, and whether the magnitude of such rewards depends on the institutional environment. Based on a dataset of 3,212 observations from 2005 to 2015, and focusing on counter-breach initiatives that are revealed in public announcements and/or emphasized in annual reports, the results unfold an overall significant positive effect of counter-breach initiatives on Tobin’s
A minimax near-optimal algorithm for adaptive rejection sampling

Rejection Sampling is a fundamental Monte-Carlo method. It is used to sample from distributions admitting a probability density function which can be evaluated exactly at any given point, albeit at a high computational cost. However, without proper tuning, this technique implies a high rejection rate. Several methods have been explored to cope with this problem, based on the principle of adaptively estimating the density by a simpler function, using the information of the previous samples. Most of them either rely on strong assumptions on the form of the density, or do not offer any theoretical performance guarantee. We give the first theoretical lower bound for the problem of adaptive rejection sampling and introduce a new algorithm which guarantees a near-optimal rejection rate in a minimax sense.
Risk-Sensitive Reinforcement Learning: A Constrained Optimization Viewpoint

The classic objective in a reinforcement learning (RL) problem is to find a policy that minimizes, in expectation, a long-run objective such as the infinite-horizon discounted or long-run average cost. In many practical applications, optimizing the expected value alone is not sufficient, and it may be necessary to include a risk measure in the optimization process, either as the objective or as a constraint. Various risk measures have been proposed in the literature, e.g., mean-variance tradeoff, exponential utility, the percentile performance, value at risk, conditional value at risk, prospect theory and its later enhancement, cumulative prospect theory. In this article, we focus on the combination of risk criteria and reinforcement learning in a constrained optimization framework, i.e., a setting where the goal to find a policy that optimizes the usual objective of infinite-horizon discounted/average cost, while ensuring that an explicit risk constraint is satisfied. We introduce the risk-constrained RL framework, cover popular risk measures based on variance, conditional value-at-risk and cumulative prospect theory, and present a template for a risk-sensitive RL algorithm. We survey some of our recent work on this topic, covering problems encompassing discounted cost, average cost, and stochastic shortest path settings, together with the aforementioned risk measures in a constrained framework. This non-exhaustive survey is aimed at giving a flavor of the challenges involved in solving a risk-sensitive RL problem, and outlining some potential future research directions.
A Perspective on Training Methods Aimed at Building Local Capacity for the Assessment and Implementation of Environmental Flows in Rivers

This paper describes the development and delivery of a global training programme for environmental flows in rivers. The programme was developed in South Africa, and formalised with WWF. It has been delivered at various levels of detail, to specialist teams of scientists and managers (as learning-by-doing), and to large numbers of post-graduate students, in more than 20 countries worldwide. The intention has been to build local capacity and initiate E flows implementation. The general format of the training is described, and a number of examples and case studies are used to demonstrate the successes and pitfalls of the process. The examples concentrate on the need for long-term commitment and persistence in the face of multiple impediments, chief among them the need to change mind-sets of water policy makers, managers, scientists, and all levels of stakeholders, who traditionally view rivers as resources to be used to the maximum extent, rather than as valuable assets to be protected. They also illustrate examples of misunderstanding and resistance to implementing E flows. Lessons from training and implementation over 25 years include: • The need for local champions, with a long-term commitment • The need for understanding and support from all levels of stakeholders • The need (at least initially) to simplify the process as much as possible, so as to foster understanding and support, and to demonstrate successes within a time frame that maintains that support.
Cyber Risk Quantification and Mitigation Framework for Healthcare using Machine Learning

Compromise of confidentiality, integrity and availability (C-I-A) of patient data may lead to tangible as well as intangible losses to a healthcare organization including loss of reputation, compensation, restoring and improving security. Prior work has already mentioned the effect of immediate environment in fostering criminal intent. However, significant literature is absent with regards to socio-economic factors. Our work will help in complete risk management using the CRQ-CRM framework. The first module (CRQ) includes classification of attack-type using attack patterns and socio-economic factors. Our second module (CRM) takes the prediction accuracy, as an input and computes the expected loss and the consequent impactprobability matrix. Using the matrix, we could prescribe further course of action to improve on the prediction model. Our work will aid managers towards effective risk management and further open new avenues of research which includes state-level factors.