Miracle-dz/UniFAR-FTUs huggingface dataset:
Emerging Technology Opportunity Identification Based on Community Detection and Burst Detection: A Case study of Intelligent Robots

: Due to a new round of technological revolution and industrial transformation driven by artificial intelligence, emerging technologies have triggered a new round of unprecedented developments. Understanding the technology development trend and identifying potential technology opportunities has become an essential proposition for academia and industry. Based on patent document data, we apply text analysis to extract technical terms, introduce the community detection model to technology topics, then construct a framework for identifying opportunities for emerging technologies from three perspectives: technological R&D trends, competitive environment, and technology layout. The intelligent robot technology is illustrated as an empirical study to clarify the proposed framework. We conclude three main findings: First, intelligent robot technology has undergone three evolution stages and formed 16 key topics, among which IoT robot, sensor fibre resistance, and pneumatic muscle have become hotspots in recent years; Second, China has an absolute advantage in the scale of intelligent robot technology, but it has a noticeable strength gap with other countries and regions in core technologies; Third, compared to the other leading countries in intelligent robots field, the layout of China's overseas technology market is relatively limited, especially in remote control, robot voice and intelligent detection. The proposed opportunity identification framework can clarify emerging technologies' overall context and provide a helpful reference for emerging technologies' market layout and technology layout.
Exploring Big Data Analytics Adoption using Affordance Theory

: This research explores big data analytics adoption in organisations using affordance theory. Big data analytics are a set of tools and techniques that help companies to get useful business insights from the data. Adoption of big data analytics is a challenging task. Affordance theory has been used to study usage and effect of information technology. In this work, we have modiﬁed the affordance theory framework to study adoption of big data analytics. The framework takes into account characteristics of the technology, the goal and characteristics of the organisation. Organisation achieve different outcomes based on their goals and characteristics. We have used case study method to verify efﬁcacy of the adopted framework. The results clearly show that the framework is effective in studying the adoption of big data analytics.
Hybrid Prototypical Networks Augmented by a Non-linear Classifier

Text classification is one of the most prolific domains in machine learning. Present in a raw format all around us in our daily life Starting from human to human communication mainly by the social networks apps, arriving at the human-machine interaction especially with chatbots, text is a rich source of information. However, despite the remarkable performances that deep learning achieves in this field, the cost in therm of the amount of data needed to train this model still considerably high, adding to that the need of retraining this model to learn every new task. Nevertheless, a new sub-field of machine learning has emerged, named meta-learning it targets the overcoming of those limitations, widely used for image-related tasks, it can also bring solutions to tasks associated with text. Starting from this perspective we proposed a hybrid architecture based on wellknown prototypical networks consisting of adapting this model to text classification and augmenting it with a non-linear classifier.
Using Academic Genealogy for Recommending Supervisors

Selecting an academic supervisor is a complicated task. Masters and Ph.D. candidates usually select the most prestigious universities in a given region, investigate the graduate programs in a research area of interest, and analyze the professors’ profiles. This choice is a manual task that requires extensive human effort, and usually, the result is not good enough. In this paper we propose a Recommender System that enables one to choose an academic supervisor based on his/her academic genealogy. We used metadata of different theses and dissertations and applied the nearest centroid model to perform the recommendation. The obtained results showed the high precision of the recommendations, which supports the hypothesis that the proposed system is a useful tool for graduate students.
Study Concept Drift in 150-year English Literature

The meaning of a concept or a word changes over time. Such concept drift reﬂects the change of the social consensus as well. Studying concept drift over time is valuable for researchers who are interested in language or culture evolution. Recent word embedding technologies inspire us to automatically detect concept drift in large-scale corpora. However, comparing embeddings generated from diﬀerent corpora is a complex task. In this paper, we propose to use a simple approach for detecting concept drift based on the change in word contexts from diﬀerent time periods and apply it to subsequent time periods so that the detailed drift could be detected and visualised. We dive into certain words to track how the meaning of a word changes gradually over a long time span with relevant historical events which demonstrates the eﬀect of our method.
Smart Cities V2I Cloud based Infrastructure using Road Side Units

Vehicles and city infrastructure can be interconnected through Road Side Units (RSUs) and On Board Units (OBUs) that utilize Radio-frequency identification (RFID) technology to send and receive information about various road conditions in real time. The objective of this work is to create and test a mesh network through Internet of Things (IoT) devices to emulate and test the RSUs capabilities. A vehicle-to-infrastructure (V2I) network is established through parent & child approach that rely on previously established infrastructure. The purpose for this design is to extend the reach of the system while limiting the amount of endpoint nodes needed. The mesh network is meant to target areas affected with road congestion. Using this information, the vehicles and users will be aware of traffic conditions on their routes in real time. Connected vehicles will be able to adjust their routes to experience more efficient commutes. The mesh network is capable of taking information from vehicles and transmitting it through the network until being uploaded to the cloud. In particular, the number of vehicles passing through an endpoint RSU within a certain time frame is collected and sent through the network, along with the location of the endpoint RSU. The parent node receives this information through a relay RSUs and uploads it to a cloud service where the data is collected and then analyzed through a data mining software. The software applies the k-means clustering algorithm to classify the traffic conditions of the road at a particular time. Results shows the capability of the algorithm to detect and classify the different
The Data Quality Index: Improving Data Quality in Irish Healthcare Records

: This paper describes the Data Quality Index (DQI), a new data quality governance method to improve data quality in both paper and electronic healthcare records. This is an important use case as digital transformation is a slow process in healthcare and hybrid systems exist in many countries such as Ireland. First a baseline study of the nature and extent of data quality issues in Irish healthcare records was conducted. The DQI model and tools were then developed, based on established data quality and data governance principles. Evaluation of the model and tools showed a significant improvement in data quality was achieved in a healthcare setting. This initial evaluation of the model was against paper healthcare records, but the model can also be used as part of an electronic healthcare record system.
Towards a Methodological Approach for the Definition of a Blockchain Network for Industry 4.0

The Industrial Internet of Things is expected to attract significant investments for Industry 4.0. In this new environment, blockchain has immediate potential in industrial applications, providing immutable, traceable and auditable communication. Blockchain gained prominence in the academy, being developed and evaluated in several application areas. However, no study has presented methodologies to definition blockchain networks in the industrial environment. To fill this gap, we present a methodology that presents paths to follow and important aspects to be analyzed for the definition and deployment of a blockchain network architecture. This methodology can help in the appropriate choice of platforms and parameters of blockchain networks, resulting in a reduction of costs for the factory and safety in meeting deadlines for industrial processes.
Information Flow Secure CAmkES

Component Architecture for microkernel-based Embedded Sy stems (CAmkES) is a framework used to build embedded systems software on the top of seL4. seL4, a general purpose microkernel, uses the underlying Discretionary Access Control (DAC) capability model to ens ure confidentiality and integrity of the systems built on it. These systems are not information flow secure as D AC model only considers direct read/write accesses and does not consider the indirect accesses. In ind irect access, an unauthorized subject can get access to an object through another subject which has the direct acc ess to that object. In this paper, we model and implement information flow secure CAmkES (IFS-CAmkES) whic h ensures complete mediation by RWFM monitor which is based upon Readers Writers Flow Model (RWFM ), a Mandatory Access Control (MAC) model. IFS-CAmkES can be considered as CAmkES enriched with MAC based security. Prototypes of some real life examples have been implemented on IFS-CAmkES. We a lso compare the performance of CAmkES and IFS-CAmkES based systems.
Measuring Interdisciplinary Interactions Using Citation Analysis and Semantic Analysis

Interdisciplinary interactions and integrations have become a major feature of the current development of science and technology. How to measure the strength of interdisciplinary interactions between two disciplines is a crucial issue. In our study, we propose a novel measurement framework based on both citation analytics and semantic analytics, which integrates three indicators - direct citation, bibliographic coupling and research content. Especially, LDA model is incorporated with a word embedding model to create a semantic solution that effectively constructing discipline-keyword vectors based on bibliometric data. At last, entropy method is applied with these three indicators to assess the interdisciplinary interactions strength. The interactions between information science & library science and other six subjects are analyzed as the case study to demonstrate the reliability of the methodology, with subsequent empirical validations.
Success Factors of Business Intelligence and Performance Dashboards to Improve Performance in Higher Education

The need for more effective communication becomes more important as the size of an organisation increases. This underlines the importance of using tools like Business Intelligence (BI) and dashboards to monitor and improve their output, as well as to improve accuracy and efficiency of the data that is available. However, there is a lack of understanding of applying analytics and strategic insight into analytics in Higher Education (HE), compared to other sectors such as business, government, and healthcare. In addition, the use of BI and dashboards in HE has been studied by a small number of papers, which is particularly limited in investigating the factors to ensure successful application within this context or understanding the metrics that determine this success. This highlights the importance of understanding successful adoption of such technologies to improve performance and decision-making processes, particularly within HE institutions. In this paper, we concentrate on investigating successful adoption of business intelligence and department-related level of tactical dashboards to support performance measurement and decision-making processes in HE. As the research area is complex and multidimensional, the triangulation method has been applied to support a rich set of data and a mixture of a qualitative approach to gather insights into potential factors, and a quantitative approach to confirm these factors. By adapting the concept of Balanced scorecard to measure the success factors, we conjecture that it would enhance successful adoption within this sector.
Evaluating a Session-based Recommender System using Prod2vec in a Commercial Application

: Recommender systems are a central component of many online stores and product websites. An essential functionality of them is to show users new products that they do not yet know they want to buy. Since the users of the website are often unknown to the system, a product recommendation must be made using the current activities within a browser session. In this paper we address this issue in a deep learning-based product-to-product recommendation problem for a commercial website with millions of user interactions. Our proposed approach is based on a prod2vec method for product embeddings, thus recommending those products that often occur together with the target product. Following the idea of word2vec methods from the NLP domain, we train an artiﬁcial neural network on user activity data extracted from historical browser sessions. As part of several real A/B tests on the website, we prove that our approach delivers successful product recommendations and outperforms the current system in use. In addition, the results show that the performance can be signiﬁcantly improved by an appropriate selection of the training data and the time range of historical user interactions.
O’Reach: Even Faster Reachability in Large Graphs

One of the most fundamental problems in computer science is the reachability problem: Given a directed graph and two vertices s and t, can sreach t via a path? We revisit existing techniques and combine them with new approaches to support a large portion of reachability queries in constant time using a linear-sized reachability index. Our new algorithm O’Reach can be easily combined with previously developed solutions for the problem or run standalone. In a detailed experimental study, we compare a variety of algorithms with respect to their index-building and query times as well as their memory footprint on a diverse set of instances. Our experiments indicate that the query performance often depends strongly not only on the type of graph but also on the result, i.e., reachable or unreachable. Furthermore, we show that previous algorithms are significantly sped up when combined with our new approach in almost all scenarios. Surprisingly, due to cache effects, a higher investment in space doesn’t necessarily pay off: Reachability queries can often be answered even faster than single memory accesses in a precomputed full reachability matrix.
On-Chip Mass Spectrometry-Based Immunoassay as a Tool for the Detection of Molecular Species from Prostate-Specific Antigen in Female Serum

ABSTRACT In females, low levels of prostate-specific antigen have been detected in hormonally dependent tissues as well as in serum and body fluids using immunometric assays. Enzymatically inactive free prostate-specific antigen was found to be the predominant form in sera of women with breast cancer, in contrast to sera from healthy women, but its molecular nature is not understood. In comparison to conventional immunoassays, mass spectrometry-based immunoassays are advantageous in terms of providing analytical data on both concentration and structural information regarding the analyte. This study was aimed at exploiting on-chip mass spectrometry-based immunoassay for profiling the molecular species of prostate-specific antigen in sera of women with breast cancer and healthy females to resolve presumed cancer-associated free prostate-specific antigen heterogeneity. Serum was analyzed using an immobilized anti-free prostate-specific antigen antibody array. Immunoreactive species were detected using surface-enhanced laser desorption–ionization time-of-flight mass spectrometry. The results obtained indicated similarities in profiles of the examined female sera regarding glycosylated full-length molecular species at 28 kDa, while noticeable differences were observed between the groups regarding immunoreactive species, reflecting modification of free prostate-specific antigen by proteolytic cleavage. The free prostate-specific antigen profile in breast cancer was specific for species at 12, 16, 21–23 kDa, and at 29 kDa, which might differ in glycosylation status. The results on low-abundance free prostate-specific antigen in female sera as yet unexploited biomarker direct further efforts to innovative use of conventional proteomic methods in diagnostic bioanalytical challenges.
Training Appalachian Kentucky Youth Cancer Advocates

Appalachian middle and high school students represent potential cancer advocates that could make lasting changes in the cancer disparities in Appalachian Kentucky. This mixed-methods study examined students’ attitudes and self-efficacy about cancer advocacy activities in their communities before and after participation in a cancer advocacy training program and an advocacy event. The results reveal no substantial change in student attitudes, suggesting that they may have been overconfident in their advocacy abilities before the training and advocacy event. Nevertheless, students are enthusiastic about using their voices to influence cancer legislation. We suggest that continued training is essential to ensuring students’ participation in present and future cancer advocacy activities. Objectives Kentucky has the highest cancer incidence and mortality rates in the United States, with the Appalachian region experiencing the highest of those rates. Cancer advocacy, which is defined as providing support to cancer patients and their communities, represents a means of decreasing the cancer cases in Appalachian Kentucky. This exploratory study examined the effects of advocacy training and experiential learning on Appalachian high school students’ cancer advocacy attitudes and self-efficacy. Methods The design of this study was a mixed-methods, one-group repeated measure with a group of participants from the Appalachian Career Training in Oncology (ACTION) Program (N = 9). The study assessed advocacy attitudes and self-efficacy before and after participants were provided advocacy training and participated in an advocacy event. Results Participating students’ attitudes and self-efficacy did not substantially change following the training and their participation in an advocacy event. Through their comments after the event, however, students seem eager to use their voices to influence the actions of state legislators. At the same time, they worry about the apathy of their community members to their cancer advocacy message. Conclusions Youth represent potentially powerful agents of advocacy that could help address the cancer burden in Kentucky. Participants in this study likely overestimated their advocacy abilities before learning more about advocacy and participating in the process. As such, additional trainings are likely necessary to increase students’ self-efficacy, encourage them to share their stories, and help them overcome perceived barriers.
Negative Healthcare Experiences, Healthcare-Seeking Behavior, and Mental Health in Tampa Bay Area Transgender and Gender-Nonconforming Individuals

This article describes the prevalence of negative healthcare experiences, depression, anxiety, and avoidance of care among transgender and gender nonconforming individuals in the Tampa Bay, Florida area, with a focus on denial of care and conversion therapy. The results show a high proportion of participants who underwent these adverse experiences, with potential associations between negative interactions and fear or avoidance of health care. Overall, these conclusions highlight a disparity within this region, and a need for continued research to investigate these patterns on a larger scale. Objective This study describes the prevalence of negative healthcare experiences, depression, and anxiety in the Tampa Bay transgender and gender non-conforming population. Methods Participants completed a survey encompassing healthcare interactions and validated mental health questionnaires. Results In total, 61% of participants experienced conversion therapy and 54% have been denied care. Depression and anxiety are prevalent, with higher levels statistically associated with denial of care; 60.5% and 43% fear seeking mental health or medical care, respectively. Conclusions Self-reported negative experiences are prevalent in this population, with a potential association with avoidance of care. Further research is necessary to clarify these potential relationships.
Erythromycin against Mycobacterium avium Complex Strains in J774 Cells and in Alveolar Macrophages from Human Immunodeficiency Virus Type 1-Infected Individuals

The intracellular activities of clarithromycin and erythromycin, alone and in combination with other antimicrobial agents, were tested against Mycobacterium avium complex (MAC) strains inside mouse J774 cells and inside alveolar macrophages obtained from human immunodeficiency type 1-infected individuals. Clarithromycin alone had greater intracellular activity than erythromycin alone, and drug combinations that included clarithromycin were usually more active than combinations that included erythromycin.
Effect of Body Image Satisfaction on Outcomes Among Women Undergoing Different Types of Breast Surgeries

body image satisfaction has important effect on psychological status for women with breast surgery. The present study aimed to evaluate impact of body image satisfaction on outcomes among women with different types of breast surgeries. Research design, one shot case research design was utilized to conduct data of this study on a convenience sample of available patients (30), female adult patients undergoing breast surgery. Setting: the study was conducted at plastic surgical department in Assiut University Hospitals. Tools: data were collected through: three tools; the structured interview questionnaire, patient assessment sheet and body image satisfaction questionnaire. Results: There are statistically significant differences for the study group as regards psychological state assessment pre / post 4 weeks. There was statistically significant difference as regarded pre and posttest total score for body image satisfaction about breast surgery. Conclusion: There is a significant differences improvement throughout implementation of body image satisfaction on outcomes for female with different types of breast surgeries. Recommendation, the study recommended that, pamphlets and simple illustration booklet should be available for patients illiterate to with simple explain how to safely live after breast surgery.
Effectiveness of Fine-Moving Value in Developing Theoretical Model of Organizational Performance: A Perspective of the Theory of Planned Behavior

This study was conducted to fill the research gaps on inconsistent findings about entrepreneurial culture in improving organizational performance by including effectively smooth motion values as a factor that can improve organizational performance. This study was also undertaken to build a conceptual model of organizational performance from an entrepreneurial culture through an effectively smooth motion values synthesis. Four hypotheses were developed and tested within the samples framework of 110 coffee processing industries in Jember, Indonesia. The findings of this study indicate the important role of the effectively smooth motion values which has been proven to be able to mediate entrepreneurial culture in improving organizational performance.