A Survey on Differential Privacy for Unstructured Data Content

Huge amount of unstructured data including image, video, audio, and text are ubiquitously generated and shared, it is a challenge to protect sensitive personal information in them, such as human faces, voiceprints, and authorships. Differential privacy is the standard privacy protection technology that provides rigorous privacy guarantees for various data. This survey summarizes and analyzes differential privacy solutions to protect unstructured data content before they are shared with untrusted parties. These differential privacy methods obfuscate unstructured data after they are represented with vectors, and then reconstruct them with obfuscated vectors. We summarize specific privacy models and mechanisms together with possible challenges in them. We also conclude their privacy guarantees against AI attacks and utility losses. Finally, we discuss several possible directions for future research.

Classification of Astrophysics Journal Articles with Machine Learning to Identify Data for NED

The NASA/IPAC Extragalactic Database (NED) is a comprehensive online service that combines fundamental multi-wavelength information for known objects beyond the Milky Way and provides value-added, derived quantities and tools to search and access the data. The contents and relationships between measurements in the database are continuously augmented and revised to stay current with astrophysics literature and new sky surveys. The conventional process of distilling and extracting data from the literature involves human experts to review the journal articles and determine if an article is of extragalactic nature, and if so, what types of data it contains. This is both labor intensive and unsustainable, especially given the ever-increasing number of publications each year. We present here a machine learning (ML) approach developed and integrated into the NED production pipeline to help automate the classification of journal article topics and their data content for inclusion into NED. We show that this ML application can successfully reproduce the classifications of a human expert to an accuracy of over 90% in a fraction of the time it takes a human, allowing us to focus human expertise on tasks that are more difficult to automate.

Rural Livelihood Activities

Rural households are found to depend on diverse portfolio of activities and income sources. This study sought to explore the diverse livelihood activities of rural households in Awra Amba Community. Both qualitative and quantitative methodologies were used to collect data. Content analysis method was used to analyze the data. The results of the study revealed that almost all members (94%) of Awra Amba community are depending on non-farm activities because of the scarcity of land. As a result, they are dominantly weavers (86%) and the rest are traders. The results of the study indicated that weaving is their major source of livelihood and a backbone for their survival.

Big data analytics opportunities for applications in process engineering

Big data is an expression for massive data sets consisting of both structured and unstructured data that are particularly difficult to store, analyze and visualize. Big data analytics has the potential to help companies or organizations improve operations as well as disclose hidden patterns and secret correlations to make faster and intelligent decisions. This article provides useful information on this emerging and promising field for companies, industries, and researchers to gain a richer and deeper insight into advancements. Initially, an overview of big data content, key characteristics, and related topics are presented. The paper also highlights a systematic review of available big data techniques and analytics. The available big data analytics tools and platforms are categorized. Besides, this article discusses recent applications of big data in chemical industries to increase understanding and encourage its implementation in their engineering processes as much as possible. Finally, by emphasizing the adoption of big data analytics in various areas of process engineering, the aim is to provide a practical vision of big data.

Local actors in the co-management of mole national park and the impacts associated with it

Effective management of the National Parks largely depends on a participatory approach. Hitherto, fringe communities of Mole National Park were sidelined in its management. In recent times, the participation of communities in the management of forest resources in the Mole National Park is encouraged. This study examines how actors such as chiefs, land priests, clan heads, diviners, women leaders and youth groups support conservation using resource and habitat taboos, totemic system, traditional fire belt, sacred tree species and traditional awareness creation as strategies and their impacts thereof. The study employed a concurrent triangulation mixed methods approach in data collection, analysis, and presentation. Besides questionnaire administration as a quantitative method of data collection, the study made use of Key Informant Interviews, and Focus Group Discussions as qualitative methods of data collection. Apart from the use of descriptive statistics as a component of SPSS for the analysis of quantitative data, content analysis was used for the analysis of qualitative data. The study revealed that the fringe communities endorse the chiefs and the land priests (kasawule wura) as most effective actors in the management of forest flora and fauna and the totemic system as the most effective management strategy. The study concluded that, there exists local management actors, and strategies in resource management, and fringe communities and the park are impacted positively because of community participation in park management. It is recommended that, benefit-sharing schemes should be considered and developed by park management and fringe communities since this can engender commitment to participation.

Perceptions of Personal Health Records in Rural Primary Health Clinics in Canada – Patient Perspectives

Personal health records are increasingly being deployed in healthcare settings. In this study we explored patients’ perceptions of personal health records in a rural community in Canada where a primary health network is being deployed. A focus group was held and data were thematically analysed. All patients used technology on a regular basis. Themes included communication and information sharing, issues with access to prior health records, data content and data control and features and functions for continuity of care. Participants expressed desire to be owners of their own record, but described instances where they might be too ill to do so. Participants were hopeful that the functions of a personal health record might help to overcome frustrations with current fragmented information and open to using technologies as part of their care process. Personal health records are promising technologies to overcome fragmented care in rural communities.

Where to hike? Mobile application for guiding tourists in the Bükkalja Region, Hungary

Hiking is one of the most popular outdoor sports activities in Hungary. Despite not having many mountainous areas, a wide network of hiking trails crosses the country’s landscapes. As online tourist maps and thematic mobile applications become more and more popular among hikers, the role of paper-based, analogue tourist maps decreases. However, no thematic application has been issued that contains detailed surveyed (or crowdsourced) data on attractions or the natural circumstances (coverage, difficulty) for a certain area in Hungary yet. Nature tourism in the Bükkalja Region, Hungary is mostly based on geological-geomorphological features that are completed with cultural facilities. The length of the hiking trail system is more than 370 km in the examined 354 km2 large sample area. We have developed an OS mobile application that offers guidance for tourists based on four basic pillars: the physical condition of the trails, the attractions along a trail, dangerous trail segments and hiking trail marking quality. These pillars are visualized with an OpenLayers-based online map. The result is a multi-purpose smartphone application. Its main aim is to offer a planning platform for tourists by examining the difficulty of the trails and designating the attractions to visit. There is information on the most important attractions of the area: cultural and geoscientific sites are also presented. We also encourage users to report changes to the map data content via the crowdsourcing menu. These comments and remarks are continuously checked for validity and the database is modified with the use of them.

A NFT verification method for distributed AI system

This paper proposes a method invention, namely an efficient NFT data inspection method with minimum granularity and probability comparison. The invention establishes a fast comparison method of AI model and data, that is, the direct comparison of small files priority and the maximum-minimum interval comparison. The invention takes the substantial identity inside the NFT data and the processing method of NFT data coincidence into account, so that the data content outside the token of the NFT publicly shared by the AI distributed system can also be unique on the Internet. Therefore, it can avoid the problem of incremental packaging and repeated packaging, and can successfully balance the efficiency and security of the comparison process. portions given in this document

THE RELEVANCE OF NATIONHOOD KNOWLEDGE IN MALAYSIAN STUDIES COURSES: A CONVEYANCE FOR NATIONAL UNITY AND AN INTEGRATION OF UNIVERSITY STUDENTS’ IDENTITY

This paper debates the relevance of knowledge of nationhood as a conveyance for national unity identity and integration of university students’ self-identity in addressing the issues and challenges of the Covid-19 post-pandemic era. In dissecting the significance of university student’s understanding of the Malaysian Studies course, the thrust of this discussion perspective works on the two-pronged objectives of (1) identifying the relevance of national unity identity issues of the younger generation, and (2) examining the challenges of applying National Principles as self-identity integration of university students. The synthesis of the discussion was also embroidered on the nurturing of the national language in society and the issue of compliance with the country’s constitution and laws. The qualitative approach utilises secondary data content analysis methods, namely the results of research in journals, reports, books and online news sources. The results of the study show that the course education of the nature of Malaysian Studies is a strong medium that can help understand the knowledge of nationhood and foster the retention of self-identity among students. The element of integration is also argued to place the relevance of university students to know the origin of a national identity and to avoid being influenced by external negative cultures. Naturally, each individual learns norms, values and habits through socialisation agents such as family, education, government and peers. Therefore, in the context of the relevance of learning and teaching elements that contain aspects of Malaysian Studies, understanding the knowledge of nationhood is a critical agenda for the future of the country where the formation of national identity must be inculcated in every student to strengthen national unity.

Secure and Sustainable Predictive Framework for IoT-Based Multimedia Services Using Machine Learning

In modern years, the Internet of Things (IoT) has gained tremendous growth and development in various sectors because of its scalability, self-configuring, and heterogeneous factors. It performs a vital role in improving multimedia communication and reducing production costs. The multimedia data consist of various types and formats (text, audio, videos, etc.), which are forwarded in the form of blocks of bits in the network layer of TCP/IP. Due to limited resources available to IoT-built devices, most of the Multimedia Internet of Things (MIoT)-based applications are delay constraints, especially for big data content. Similarly, multimedia-based applications are more vulnerable to security burdens and lower the trust of data processing. In this paper, we present a secure and sustainable prediction framework for MIoT data transmission using machine learning, which aims to offer intelligent behavior of the system with information protection. Firstly, the network edges exploit a regression analysis for a real-time multimedia routing scheme and achieve precise delivery towards the media servers. Secondly, an efficient and low-processing asymmetric process is proposed to provide secure data transmission between the IoT devices, edges, and data servers. Extensive experiments are performed over the OMNET++ network simulator, and its significance is achieved by an average for energy consumption by 71%, throughput by 30.5%, latency by 22%, bandwidth by 34.5%, packets overheads by 38.5%, computation time by 12.5%, and packet drop ratio by 35% in the comparison of existing schemes.