Computer Science

An interesting research problem in supply chain industry is evaluating and determining provenance of physical goods - demonstrating authenticity of luxury goods. Yet, there have been a few innovative software solutions addressing product anti-counterfeiting and record provenance of today's goods that are produced and transported in complex and internationally-spanning supply chain networks. However, these supply chain systems have been implemented with centralized system architecture, relying on centralized authorities or any form of intermediaries, and leading to issues such as single-point processing, storage and failure, which could be susceptible to malicious modifications of product records or various potential attacks to system components by dishonest participant nodes traversing along the supply chain. Blockchain technology has evolved from being merely a decentralized, distributed and immutable ledger of cryptocurrency transactions to a programmable interactive environment for building decentralized and reliable applications addressing different use cases and existing problems in the world. In this research, the Decentralized NFC-Enabled Anti-Counterfeiting System (dNAS) is proposed and developed, decentralizing a legacy anti-counterfeiting system of supply chain industry using Blockchain technology, to facilitate trustworthy data provenance retrieval, verification and management, as well as strengthening capability of product anti-counterfeiting in supply chain industry. The proposed dNAS utilizes decentralized blockchain network on a consensus protocol compatible with the concept of enterprise consortium, programmable smart contracts and a distributed file storage system to develop a secure and immutable scientific data provenance tracking and management platform on which provenance records, providing compelling properties on data integrity, are validated automatically.

Much of the recent success of Artificial Intelligence (AI) has been spurred on by impressive achievements within a broader family of machine learning methods, commonly referred to as Deep Learning (DL). This paper provides insights on the diffusion and impact of DL in science. Through a Natural Language Processing (NLP) approach on the arXiv.org publication corpus, we delineate the emerging DL technology and identify a list of relevant search terms. These search terms allow us to retrieve DL-related publications from Web of Science across all sciences. Based on that sample, we document the DL diffusion process in the scientific system. We find i) an exponential growth in the adoption of DL as a research tool across all sciences and all over the world, ii) regional differentiation in DL application domains, and iii) a transition from interdisciplinary DL applications to disciplinary research within application domains. In a second step, we investigate how the adoption of DL methods affects scientific development. Therefore, we empirically assess how DL adoption relates to re-combinatorial novelty and scientific impact in the health sciences. We find that DL adoption is negatively correlated with re-combinatorial novelty, but positively correlated with expectation as well as variance of citation performance. Our findings suggest that DL does not (yet?) work as an autopilot to navigate complex knowledge landscapes and overthrow their structure. However, the 'DL principle' qualifies for its versatility as the nucleus of a general scientific method that advances science in a measurable way.

The design of products and services such as a Smart doorbell, demonstrating video analytics software/algorithm functionality, is expected to address a new kind of requirements such as designing a scalable solution while considering the trade-off between cost and accuracy; a flexible architecture to deploy new AI-based models or update existing models, as user requirements evolve; as well as seamlessly integrating different kinds of user interfaces and devices. To address these challenges, we propose a smart doorbell that orchestrates video analytics across Edge and Cloud resources. The proposal uses AWS as a base platform for implementation and leverages Commercially Available Off-The-Shelf(COTS) affordable devices such as Raspberry Pi in the form of an Edge device.

There is a growing need for data-driven research efforts on how the public perceives the ethical, moral, and legal issues of autonomous AI systems. The current debate on the responsibility gap posed by these systems is one such example. This work proposes a mixed AI ethics model that allows normative and descriptive research to complement each other, by aiding scholarly discussion with data gathered from the public. We discuss its implications on bridging the gap between optimistic and pessimistic views towards AI systems' deployment.

Artificial intelligence (AI) literacy is a rapidly growing research area and a critical addition to K-12 education. However, support for designing tools and curriculum to teach K-12 AI literacy is still limited. There is a need for additional interdisciplinary human-computer interaction and education research investigating (1) how general AI literacy is currently implemented in learning experiences and (2) what additional guidelines are required to teach AI literacy in specifically K-12 learning contexts. In this paper, we analyze a collection of K-12 AI and education literature to show how core competencies of AI literacy are applied successfully and organize them into an educator-friendly chart to enable educators to efficiently find appropriate resources for their classrooms. We also identify future opportunities and K-12 specific design guidelines, which we synthesized into a conceptual framework to support researchers, designers, and educators in creating K-12 AI learning experiences.

In the current years, due to the significant developments in technologies in almost every domain, the standard of living has been improved. Emergence of latest innovations, advanced machinery and equipment especially in the healthcare domain, have simplified the diagonalizing process to a wide extent.

Internet is one of the important inventions and a large number of persons are its users. These persons use this for different purposes. There are different social media platforms that are accessible to these users. Any user can make a post or spread the news through the online platforms. These platforms do not verify the users or their posts. So some of the users try to spread fake news through these platforms. These news can be propaganda against an individual, society, organization or political party. A human being is unable to detect all these fake news. So there is a need for machine learning classifiers that can detect these fake news automatically. Use of machine learning classifiers for detecting fake news is described in this systematic literature review.

One of the main topics in human resources management is the subject of informal organizations in the organization such that recognizing and managing such informal organizations play an important role in the organizations. Some managers are trying to recognize the relations between informal organizations and being a member of them by which they could assist the formal organization development. Methods of recognizing informal organizations are complicated and occasionally even impossible. This study aims to provide a method for recognizing such organizations using data mining techniques. This study classifies indices of human resources influencing the creation of informal organizations, including individual, social, and work characteristics of an organizations employees. Then, a questionnaire was designed and distributed among employees. A database was created from obtained data. Applied data mining techniques in this study are factor analysis, clustering by K-means, classification by decision trees, and finally association rule mining by GRI algorithm. At the end, a model is presented that is applicable for recognizing the similar characteristics between people for optimal recognition of informal organizations and usage of this information.

An infectious kind of disease affects a huge number of human beings. A lot of investigation being conducted throughout the world. There are many interactive hardware platform packages like IoT in healthcare including smart tracking, smart sensors, and clinical device integration available in the market. Emerging technology like IoT has a notable ability to hold patients secure and healthful and also enhance how physicians supply care. Healthcare IoT also can bolster affected person pride by permitting patients to spend more time interacting with their medical doctors due to the fact docs aren't as taken with the mundane and rote aspects of their career. The most considerable advantage to IoT in healthcare is that it supports doctors in undertaking extra significant clinical work in a profession that already is experiencing a worldwide professional hard work shortage. This paper investigates the basis exploration of the applicability of IoT in the healthcare System.

The constant growth of maritime traffic leads to the need of automatic anomaly detection, which has been attracting great research attention. Information provided by AIS (Automatic Identification System) data, together with recent outstanding progresses of deep learning, make vessel monitoring using neural networks (NNs) a very promising approach. This paper analyses a novel neural network we have recently introduced -- GeoTrackNet -- regarding operational contexts. Especially, we aim to evaluate (i) the relevance of the abnormal behaviours detected by GeoTrackNet with respect to expert interpretations, (ii) the extent to which GeoTrackNet may process AIS data streams in real time. We report experiments showing the high potential to meet operational levels of the model.