Search results for: Privacy Preserving Data Publication (PPDP)

Authors: Samia Alblwi, Amani Ayad

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Mutation testing is the art of generating syntactic variations of a base program and checking whether a candidate test suite can identify all the mutants that are not semantically equivalent to the base: this technique is widely used by researchers to select quality test suites. One of the main obstacles to the widespread use of mutation testing is cost: even small pro-grams (a few dozen lines of code) can give rise to a large number of mutants (up to hundreds): this has created an incentive to seek to reduce the number of mutants while preserving their collective effectiveness. Two criteria have been used to reduce the size of mutant sets: equiva-lence, which aims to partition the set of mutants into equivalence classes modulo semantic equivalence, and selecting one representative per class; subsumption, which aims to define a partial ordering among mutants that ranks mutants by effectiveness and seeks to select maximal elements in this ordering. In this paper we analyze these two policies using analytical and em-pirical criteria.

Keywords: mutation testing, mutant sets, mutant equivalence, mutant subsumption, mutant set minimization

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Authors: Ji Eun Shin, Dong Hoon Lim

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Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases.

Keywords: big data, combiner, K-means clustering, RHadoop

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Authors: Kristina F. Wolff

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Background: In the Federal Republic of Germany, violence against women is deeply embedded in society. Germany is, as of March 2020, the most populous member state of the European Union with 83.2 million inhabitants and, although more than half of its inhabitants are women, gender equality was not certified in the Basic Law until 1957. Women have only been allowed to enter paid employment without their husband's consent since 1977 and have marital rape prosecuted only since 1997. While the lack of equality between men and women is named in the preamble of the Istanbul Convention as the cause of gender-specific, structural, traditional violence against women, Germany continues to sink on the latest Gender Equality Index. According to Police Crime Statistics (PCS), women are significantly more often victims of lethal violence, emanating from men than vice versa. The PCS, which, since 2015, also collects gender-specific data on violent crimes, is kept by the Federal Criminal Police Office, but without taking into account the relevant criteria for targeted prevention, such as the history of violence of the perpetrator/killer, weapon, motivation, etc.. Institutions such as EIGE or the World Health Organization have been asking Germany for years in vain for comparable data on violence against women in order to gain an overview or to develop cross-border synergies. The PCS are the only official data collection on violence against women. All players involved are depend on this data set, which is published only in November of the following year and is thus already completely outdated at the time of publication. In order to combat German femicides causally, purposefully and efficiently, evidence-based data was urgently needed. Methodology: Beginning in January 2019, a database was set up that now tracks more than 600 German femicides, broken down by more than 100 crime-related individual criteria, which in turn go far beyond the official PCS. These data are evaluated on the one hand by daily media research, and on the other hand by case-specific inquiries at the respective public prosecutor's offices and courts nationwide. This quantitative long-term study covers domestic violence as well as a variety of different types of gender-specific, lethal violence, including, for example, femicides committed by German citizens abroad. Additionallyalcohol/ narcotic and/or drug abuse, infanticides and the gender aspect in the judiciary are also considered. Results: Since November 2020, evidence-based data from a scientific survey have been available for the first time in Germany, supplementing the rudimentary picture of reality provided by PCS with a number of relevant parameters. The most important goal of the study is to identify "red flags" that enable general preventive awareness, that serve increasingly precise hazard assessment in acute hazard situations, and from which concrete instructions for action can be identified. Already at a very early stage of the study it could be proven that in more than half of all femicides with a sexual perpetrator/victim constellation there was an age difference of five years or more. Summary: Without reliable data and an understanding of the nature and extent, cause and effect, it is impossible to sustainably curb violence against girls and women, which increasingly often culminates in femicide. In Germany, valid data from a scientific survey has been available for the first time since November 2020, supplementing the rudimentary reality picture of the official and, to date, sole crime statistics with several relevant parameters. The basic research provides insights into geo-concentration, monthly peaks and the modus operandi of male violent excesses. A significant increase of child homicides in the course of femicides and/or child homicides as an instrument of violence against the mother could be proven as well as a danger of affected persons due to an age difference of five years and more. In view of the steadily increasing wave of violence against women, these study results are an eminent contribution to the preventive containment of German femicides.

Keywords: femicide, violence against women, gender specific data, rule Of law, Istanbul convention, gender equality, gender based violence

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Authors: Sarthak Mohapatra, Ajith Babu, Shyam Prasad Ghosh

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The community of gamers has been at the crux of stigmatization and marginalization by the larger society, resulting in dignity erosion. India presents a unique context where e-sports have recently seen large-scale investments, a massive userbase, and appreciable demand for gaming as a career option. Yet the apprehension towards gaming is salient among parents and non-gamers who engage in the de-dignification of gamers, by advocating the discourse of violence promotion via video games. Even the government is relentless in banning games due to data privacy issues. Thus, the current study explores the experiences of gamers and how they navigate these de-dignifying circumstances. The study follows an exploratory qualitative approach where in-depth interviews are used as data collection tools guided by a semi-structured questionnaire. A total of 25 individuals were interviewed comprising casual gamers, professional gamers, and individuals who are indirectly impacted by gaming including parents, relatives, and friends of gamers. Thematic analysis via three-level coding is used to arrive at broad themes (categories) and their sub-themes. The results indicate that the de-dignification of gamers results from attaching stereotypes of introversion, aggression, low intelligence, and low aspirations to them. It is interesting to note that the intensity of de-dignification varies and is more salient in violent shooting games which are perceived to require low cognitive resources to master. The moral disengagement of gamers while playing violent video games becomes the basis for de-dignification. Findings reveal that circumventing de-dignification required gamers to engage in several tactics that included playing behind closed doors, consciously hiding the gamer identity, rationalizing behavior by idolizing professionals, bragging about achievements within the game, and so on. Theoretically, it contributes to dignity and social identity literature by focusing on stereotyping and stigmatization. From a policy perspective, improving legitimacy toward gaming is expected to improve the social standing of gamers and professionals. For practitioners, it is important that proper channels of promotion and communication are used to educate the non-gamers so that the stereotypes blur away.

Keywords: dignity, social identity, stereotyping, video games

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Authors: Bulatbayeva A. A., Maxutova I. O., Ergalieva A. N.

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This article presents a study of the practice of self-assessment of the quality of training cadets in a military higher specialized educational institution. The research was carried out by means of a questionnaire survey aimed at identifying the degree of satisfaction of cadets with the organization of the educational process, quality of teaching, the quality of the organization of independent work, and the system of their assessment. In general, the results of the study are of an intermediate nature. Proven tools will be incorporated into the planning and effective management of the learning process. The results of the study can be useful for the administrators and managers of the military education system for teachers of military higher educational institutions for adjusting the content and technologies of training future specialists. The publication was prepared as part of applied grant research for 2020-2022 by order of the Ministry of Education and Science of the Republic of Kazakhstan on the topic "Development of a comprehensive methodology for assessing the quality of education of graduates of military special educational institutions."

Keywords: teaching quality, quality satisfaction, learning management, quality management, process approach, classroom learning, interactive technologies, teaching quality

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Authors: Hala A. Hassan, Vasiliki K. Tsiouri, Konstantinos E. Konstantinos

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Fugitive particulate matter (PM) is a major source of airborne pollution in the Middle East countries. The meteorological conditions and topography of the area make it highly susceptible to wind-blown particles which raise many air quality concerns. Air quality tools such as field monitoring, emission factors, and dispersion modeling have been used in previous research studies to analyze the release and impacts of fugitive PM in the region. However, these tools have been originally developed based on experiments made for European and North American regions. In this work, an experimental campaign was conducted on April-May 2014 in a construction site in Doha city, Qatar. The ultimate goal is to evaluate the applicability of the existing emission factors for construction sites in dry and arid areas like the Middle East. This publication was made possible by a NPRP award [NPRP 7-649-2-241] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Keywords: particulate matter, emissions, fugitive, construction, air pollution

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Authors: Nikita Valluri, Vatcharaporn Esichaikul

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With the popularity of data-driven decision making on the rise, this study focuses on providing an alternative outlook towards the process of decision-making. Combining quantitative and qualitative methods rooted in the social sciences, an integrated framework is presented with a focus on delivering a much more robust and efficient approach towards the concept of data-driven decision-making with respect to not only Big data but also 'Thick data', a new form of qualitative data. In support of this, an example from the retail sector has been illustrated where the framework is put into action to yield insights and leverage business intelligence. An interpretive approach to analyze findings from both kinds of quantitative and qualitative data has been used to glean insights. Using traditional Point-of-sale data as well as an understanding of customer psychographics and preferences, techniques of data mining along with qualitative methods (such as grounded theory, ethnomethodology, etc.) are applied. This study’s final goal is to establish the framework as a basis for providing a holistic solution encompassing both the Big and Thick aspects of any business need. The proposed framework is a modified enhancement in lieu of traditional data-driven decision-making approach, which is mainly dependent on quantitative data for decision-making.

Keywords: big data, customer behavior, customer experience, data mining, qualitative methods, quantitative methods, thick data

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Authors: O. A. Adeyeye, E. R. Sadiku, Periyar Selvam Sellamuthu, Anand Babu Perumal, Reshma B. Nambiar

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Mango is a tropical fruit which is often labelled as ‘super-fruit’ because of its unquantifiable benefits to human beings. However, despite its great importance, mango is a seasonal fruit and only very few off-seasonal cultivars are available in the market for consumption. Therefore, to overcome the seasonal variation and to increase the shelf-life of mango fruits, different drying methods are considered. In this study, freeze drying and tray drying methods were used to preserve two different cultivars of mango from South Africa. Moisture content, total soluble solid, ascorbic acid, total phenol content (TPC), antioxidant activity (DPPH) and organoleptic tests were carried out on the samples before and after drying. The effects of different edible preservatives and selected packaging materials used were analyzed on each sample. The result showed that freeze drying method is the best method of preserving the selected cultivar.

Keywords: postharvest, Mangos, cultivar, total soluble solid, total phenol content, antioxidant

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Authors: Boukhatem Mohammed Belkaid, Lahdir Mourad

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In this paper, we propose a new encryption system for security issues meteorological images from Meteosat Second Generation (MSG), which generates 12 images every 15 minutes. The hybrid encryption scheme is based on AES and RSA algorithms to validate the three security services are authentication, integrity and confidentiality. Privacy is ensured by AES, authenticity is ensured by the RSA algorithm. Integrity is assured by the basic function of the correlation between adjacent pixels. Our system generates a unique password every 15 minutes that will be used to encrypt each frame of the MSG meteorological basis to strengthen and ensure his safety. Several metrics have been used for various tests of our analysis. For the integrity test, we noticed the efficiencies of our system and how the imprint cryptographic changes at reception if a change affects the image in the transmission channel.

Keywords: AES, RSA, integrity, confidentiality, authentication, satellite MSG, encryption, decryption, key, correlation

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Authors: Takahiro Nishigaki, Katsumi Nitta, Takashi Onoda

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In this paper, we present a method of applying Independent Topic Analysis (ITA) to increasing the number of document data. The number of document data has been increasing since the spread of the Internet. ITA was presented as one method to analyze the document data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis (ICA). ICA is a technique in the signal processing; however, it is difficult to apply the ITA to increasing number of document data. Because ITA must use the all document data so temporal and spatial cost is very high. Therefore, we present Incremental ITA which extracts the independent topics from increasing number of document data. Incremental ITA is a method of updating the independent topics when the document data is added after extracted the independent topics from a just previous the data. In addition, Incremental ITA updates the independent topics when the document data is added. And we show the result applied Incremental ITA to benchmark datasets.

Keywords: text mining, topic extraction, independent, incremental, independent component analysis

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Authors: Sami Kabir, Sadek Hossain Khoka

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Open Government Data (OGD) refers to all data produced by government which are accessible in reusable way by common people with access to Internet and at free of cost. In line with “Digital Bangladesh” vision of Bangladesh government, the concept of open data has been gaining momentum in the country. Opening all government data in digital and customizable format from single platform can enhance e-governance which will make government more transparent to the people. This paper presents a well-in-progress case study on OGD portal by Bangladesh Government in order to link decentralized data. The initiative is intended to facilitate e-service towards citizens through this one-stop web portal. The paper further discusses ways of collecting data in digital format from relevant agencies with a view to making it publicly available through this single point of access. Further, possible layout of this web portal is presented.

Keywords: e-governance, one-stop web portal, open government data, reusable data, web of data

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Authors: Mahsa Hadipour Jahromy, Sara Rezaii

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Parkinson’s disease (PD) results primarily from the death of dopaminergic neurons in the substantia nigra. Soon after the discovery of levodopa and its beneficial effects in chronic administration, debilitating involuntary movements observed, termed levodopa-induced dyskinesia (LID) with poorly understood pathogenesis. Polyphenol-rich compounds, like pomegranate, provided neuroprotection in several animal models of brain diseases. In the present work, we investigated whether pomegranate has preventive effects following 4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced dopaminergic degenerations and the potential to diminish LID in mice. Mice model of PD was induced by MPTP (30 mg/kg daily for five consecutive days). To induce a mice model of LID, valid PD mice were treated with levodopa (50 mg/kg, i.p) for 15 days. Then the effects of chronic co-administration of pomegranate juice (20 ml/kg) with levodopa and continuing for 10 days, evaluated. Behavioural tests were performed in all groups, every other day including: Abnormal involuntary movements (AIMS), forelimb adjusting steps, cylinder, and catatonia tests. Finally, brain tissue sections were prepared to study substantia nigra changes and dopamine neuron density after treatments. With this MPTP regimen, significant movement disorders revealed in AIMS tests and there was a reduction in dopamine striatal density. Levodopa attenuates their loss caused by MPTP, however, in chronic administration, dyskinesia observed in forelimb adjusting step and cylinder tests. Besides, catatonia observed in some cases. Chronic pomegranate co-administration significantly improved LID in both tests and reduced dopaminergic loss in substantia nigra. These data indicate that pomegranate might be a good adjunct for preserving dopaminergic neurons in the substantia nigra and reducing LID in mice.

Keywords: levodopa-induced dyskinesia, MPTP, Parkinson’s disease, pomegranate

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Authors: O. A. Adeyeye, E. R. Sadiku, Selvam Sellamuthu Periyar, Babu Perumal Anand, B. Nambiar Reshma

Abstract:

Mango is a tropical fruit which is often labelled as ‘super-fruit’ because of its unquantifiable benefits to human beings. However, despite its great importance, mango is a seasonal fruit, and only very few off-seasonal species are available in the market for consumption. Therefore, in order to overcome the seasonal variation and to increase the shelf-life of mango fruits, different drying methods are considered In this study, freeze drying and tray drying methods were used to preserve two different cultivars of mango from South Africa. Moisture content, total soluble solid, ascorbic acid, total phenol content (TPC), antioxidant activity (DPPH) and organoleptic tests were carried out on the samples before and after drying. The effects of different edible preservatives and selected packaging materials used were analyzed on each sample. The result showed that freeze drying method is the best method of preserving the selected cultivar.

Keywords: postharvest, mangos, cultivar, total soluble solid, total phenol content, antioxidant

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Authors: Lauren B. Birney

Abstract:

The project utilizes the Billion Oyster Project (BOP-CCERS) place-based “restoration through education” model to promote computational thinking in NYC high school teachers and their students. Key learning standards such as Next Generation Science Standards and the NYC CS4All Equity and Excellence initiative are used to develop a computer science curriculum that connects students to their Harbor through hands-on activities based on BOP field science and educational programming. Project curriculum development is grounded in BOP-CCERS restoration science activities and data collection, which are enacted by students and educators at two Restoration Science STEM Hubs or conveyed through virtual materials. New York City Public School teachers with relevant experience are recruited as consultants to provide curriculum assessment and design feedback. The completed curriculum units are then conveyed to NYC high school teachers through professional learning events held at the Pace University campus and led by BOP educators. In addition, Pace University educators execute the Summer STEM Institute, an intensive two-week computational thinking camp centered on applying data analysis tools and methods to BOP-CCERS data. Both qualitative and quantitative analyses were performed throughout the five-year study. STEM+C – Community Based Restoration STEM Hubs. STEM Hubs are active scientific restoration sites capable of hosting school and community groups of all grade levels and professional scientists and researchers conducting long-term restoration ecology research. The STEM Hubs program has grown to include 14 STEM Hubs across all five boroughs of New York City and focuses on bringing in-field monitoring experience as well as coastal classroom experience to students. Restoration Science STEM Hubs activities resulted in: the recruitment of 11 public schools, 6 community groups, 12 teachers, and over 120 students receiving exposure to BOP activities. Field science protocols were designed exclusively around the use of the Oyster Restoration Station (ORS), a small-scale in situ experimental platforms which are suspended from a dock or pier. The ORS is intended to be used and “owned” by an individual school, teacher, class, or group of students, whereas the STEM Hub is explicitly designed as a collaborative space for large-scale community-driven restoration work and in-situ experiments. The ORS is also an essential tool in gathering Harbor data from disparate locations and instilling ownership of the research process amongst students. As such, it will continue to be used in that way. New and previously participating students will continue to deploy and monitor their own ORS, uploading data to the digital platform and conducting analysis of their own harbor-wide datasets. Programming the STEM Hub will necessitate establishing working relationships between schools and local research institutions. NYHF will provide introductions and the facilitation of initial workshops in school classrooms. However, once a particular STEM Hub has been established as a space for collaboration, each partner group, school, university, or CBO will schedule its own events at the site using the digital platform’s scheduling and registration tool. Monitoring of research collaborations will be accomplished through the platform’s research publication tool and has thus far provided valuable information on the projects’ trajectory, strategic plan, and pathway.

Keywords: environmental science, citizen science, STEM, technology

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Authors: Andreea Anamaria Anghel, Flaviu Mihai Frigura-Lliasa, Attila Simo

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In recent years, there has been a renewed interest in revitalizing the historical area of Timisoara, a city located in western Romania, with a focus on preserving its architectural heritage while also promoting sustainable urban development. This has led to several initiatives aimed at improving public spaces, promoting sustainable transport, and encouraging the use of green infrastructure, such as green interior courtyards, to enhance the livability and sustainability of the area. A preliminary study regarding history, characteristics and current condition was carried out by the authors regarding these interior courtyards in the historical areas of Timisoara, the European Capital of Culture, in 2023, highlighting their potential to contribute to the sustainable development of the city. Modern interventions in interior historical courtyards should aim to preserve the historic character of these spaces while also promoting their sustainable and functional use in the 21st century. By doing so, these courtyards can continue to serve as vital urban oases and cultural landmarks for generations to come.

Keywords: architectural heritage, green interior courtyards, public spaces, sustainable development

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Authors: Gholamreza Farrokhi, Behzad Sani

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The goals of sustainable agricultural are development, improved nutrition, and food security. Sustainable agriculture must be developed that will meet today’s needs for food and other products, as well as preserving the vital natural resource base that will allow future generations to meet their needs. Sustainable development requires international cooperation and the effective use of technology. Access to sustainable sources of food will remain a preeminent challenge in the decades to come. Based upon current practice and consumption, agricultural production will have to increase by about 70% by 2050 to meet demand. Nuclear techniques are used in developing countries to increase production sustainably by breeding improved crops, enhancing livestock reproduction and nutrition, as well as controlling animal and plant pests and diseases. Post-harvest losses can be reduced and safety increased with nuclear technology. Soil can be evaluated with nuclear techniques to conserve and improve soil productivity and water management.

Keywords: food safety, food security, nuclear techniques, sustainable agriculture, sustainable future

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Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

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Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

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Authors: Fadwa Chtioui

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Vital Pulp Therapy (VPT) is nowadays challenging the deep-rooted dogma of root canal treatment, being the only therapeutic option for permanent teeth diagnosed with irreversible pulpitis or carious pulp exposure. Histologic and clinical research has shown that compromised dental pulp can be treated without the full removal or excavation of all healthy pulp, and the outcome of the partial or full pulpotomy followed by a Tricalcium-Silicate-based dressing seems to show promising results in maintaining pulp vitality and preserving affected teeth in the long term. By reviewing recent advances in the techniques of VPT and their clinical effectiveness and safety in permanent teeth with irreversible Pulpitis, this work provides a new understanding of pulp pathophysiology and defense mechanisms and will reform dental practitioners' decision-making in treating irreversible pulpits from root canal therapy to vital pulp therapy by taking advantage of the biological effects of Tricalcium Silicate materials.

Keywords: irreversible pulpitis, vital pulp therapy, pulpotomy, Tricalcium Silicate

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Authors: C. B. Abhilash, Kavi Mahesh

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Human beings are the most advanced species on earth; it's all because of the ability to communicate and share information via human language. In today's world, a huge amount of data is available on the web in text format. This has also resulted in the generation of big data in structured and unstructured formats. In general, the data is in the textual form, which is highly unstructured. To get insights and actionable content from this data, we need to incorporate the concepts of text mining and natural language processing. In our study, we mainly focus on Interesting data through which interesting facts are generated for the knowledge base. The approach is to derive the analytics from the text via the application of natural language processing. Using semantic web Resource framework descriptors (RDF), we generate the triple from the given data and derive the interesting patterns. The methodology also illustrates data integration using the RDF for reliable, interesting patterns.

Keywords: RDF, interestingness, knowledge base, semantic data

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Authors: Kitty Kioskli, Theofanis Fotis, Nineta Polemi

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The European Union (EU) directive Artificial Intelligence (AI) Act in Article 9 requires that risk management of AI systems includes both technical and human oversight, while according to NIST_AI_RFM (Appendix C) and ENISA AI Framework recommendations, claim that further research is needed to understand the current limitations of social threats and human-AI interaction. AI threats within social contexts significantly affect the security and trustworthiness of the AI systems; they are interrelated and trigger technical threats as well. For example, lack of explainability (e.g. the complexity of models can be challenging for stakeholders to grasp) leads to misunderstandings, biases, and erroneous decisions. Which in turn impact the privacy, security, accountability of the AI systems. Based on the NIST four fundamental criteria for explainability it can also classify the explainability threats into four (4) sub-categories: a) Lack of supporting evidence: AI systems must provide supporting evidence or reasons for all their outputs. b) Lack of Understandability: Explanations offered by systems should be comprehensible to individual users. c) Lack of Accuracy: The provided explanation should accurately represent the system's process of generating outputs. d) Out of scope: The system should only function within its designated conditions or when it possesses sufficient confidence in its outputs. Biases may also stem from historical data reflecting undesired behaviors. When present in the data, biases can permeate the models trained on them, thereby influencing the security and trustworthiness of the of AI systems. Social related AI threats are recognized by various initiatives (e.g., EU Ethics Guidelines for Trustworthy AI), standards (e.g. ISO/IEC TR 24368:2022 on AI ethical concerns, ISO/IEC AWI 42105 on guidance for human oversight of AI systems) and EU legislation (e.g. the General Data Protection Regulation 2016/679, the NIS 2 Directive 2022/2555, the Directive on the Resilience of Critical Entities 2022/2557, the EU AI Act, the Cyber Resilience Act). Measuring social threats, estimating the risks to AI systems associated to these threats and mitigating them is a research challenge. In this paper it will present the efforts of two European Commission Projects (FAITH and THEMIS) from the HorizonEurope programme that analyse the social threats by building cyber-social exercises in order to study human behaviour, traits, cognitive ability, personality, attitudes, interests, and other socio-technical profile characteristics. The research in these projects also include the development of measurements and scales (psychometrics) for human-related vulnerabilities that can be used in estimating more realistically the vulnerability severity, enhancing the CVSS4.0 measurement.

Keywords: social threats, artificial Intelligence, mitigation, social experiment

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Authors: Dina Ahmad Alkhodary

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This study aimed to investigate the practices of Data Mining on the telecommunication companies in Jordan, from the viewpoint of the respondents. In order to achieve the goal of the study, and test the validity of hypotheses, the researcher has designed a questionnaire to collect data from managers and staff members from main department in the researched companies. The results shows improvements stages of the telecommunications companies towered Data Mining.

Keywords: data, mining, development, business

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Authors: Min Liu, Thanapauge Chamaratana

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Integrating ecotourism into urban borderlands holds significant potential for promoting sustainable development, enhancing cross-border cooperation, and preserving cultural and natural heritage. This study aims to evaluate the current status and strategic measures for sustainable ecotourism development in the border urban areas of Xishuangbanna, leveraging the unique opportunities and challenges presented by its policy and geographical location. Employing a qualitative research approach, the exploratory study utilizes documentary research, observation, and in-depth interviews with 20 key stakeholders, including local government officials, tourism operators, community members, and tourists. Content analysis is conducted to interpret the collected data. The findings reveal that Xishuangbanna holds significant potential for ecotourism due to its rich biodiversity, cultural heritage, and strategic location along the Belt and Road Initiative route. The integration of ecotourism can drive economic growth, create employment opportunities, and foster a deeper appreciation for conservation efforts. By promoting ecotourism practices, the region can attract environmentally conscious travelers, thereby contributing to global sustainability goals. However, challenges such as inadequate infrastructure, limited community involvement, and environmental concerns are also identified. The study recommends enhancing ecotourism development in urban borderlands through integrated planning, stakeholder collaboration, and sustainable practices. These measures are essential to ensure long-term benefits for both the local community and the environment. Moreover, the study underscores the importance of a holistic approach to ecotourism development, which balances economic, social, and environmental priorities to achieve sustainable outcomes for urban borderlands.

Keywords: ecotourism, sustainable tourism, urban, borderland

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Authors: Amal M. Alrayes

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Data and system quality play a central role in organizational success, and the quality of any existing information system has a major influence on the effectiveness of overall system performance.Given the importance of system and data quality to an organization, it is relevant to highlight their importance on organizational performance in the Kingdom of Bahrain. This research aims to discover whether system quality and data quality are related, and to study the impact of system and data quality on organizational success. A theoretical model based on previous research is used to show the relationship between data and system quality, and organizational impact. We hypothesize, first, that system quality is positively associated with organizational impact, secondly that system quality is positively associated with data quality, and finally that data quality is positively associated with organizational impact. A questionnaire was conducted among public and private organizations in the Kingdom of Bahrain. The results show that there is a strong association between data and system quality, that affects organizational success.

Keywords: data quality, performance, system quality, Kingdom of Bahrain

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Authors: Ghaemi Reza, Abdollahi Hamid, Dashti Elham

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Cloud computing poses a diversity of challenges in data mining operation arising out of the dynamic structure of data distribution as against the use of typical database scenarios in conventional architecture. Due to immense number of users seeking data on daily basis, there is a serious security concerns to cloud providers as well as data providers who put their data on the cloud computing environment. Big data analytics use compute intensive data mining algorithms (Hidden markov, MapReduce parallel programming, Mahot Project, Hadoop distributed file system, K-Means and KMediod, Apriori) that require efficient high performance processors to produce timely results. Data mining algorithms to solve or optimize the model parameters. The challenges that operation has to encounter is the successful transactions to be established with the existing virtual machine environment and the databases to be kept under the control. Several factors have led to the distributed data mining from normal or centralized mining. The approach is as a SaaS which uses multi-agent systems for implementing the different tasks of system. There are still some problems of data mining based on cloud computing, including design and selection of data mining algorithms.

Keywords: cloud computing, data mining, computing models, cloud services

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Authors: Amy Gooden, Meshandren Naidoo

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Genetic research and transfers of big data are not confined to a particular jurisdiction, but there is a lack of clarity regarding the legal requirements for importing and exporting such data. Using direct-to-consumer genetic testing (DTC-GT) as an example, this research assesses the status of data sharing into and out of South Africa (SA). While SA laws cover the sending of genetic data out of SA, prohibiting such transfer unless a legal ground exists, the position where genetic data comes into the country depends on the laws of the country from where it is sent – making the legal position less clear.

Keywords: cross-border, data, genetic testing, law, regulation, research, sharing, South Africa

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Authors: Amol M. Jadhav, Sharad S. Chudhari, S. S. Khedkar

Abstract:

This review presents a through survey of research paper work on the experimental analysis, FEM Analysis & simulation of the metal spinning process. In this literature survey all the papers being taken from Elsevier publication and most of the from journal of material processing technology. In a last two decade or so, metal spinning process gradually used as chip less formation for the production of engineering component in a small to medium batch quantities. The review aims to provide include into the experimentation, FEM analysis of various components, simulation of metal spinning process and act as guide for research working on metal spinning processes. The review of existing work has several gaps in current knowledge of metal spinning processes. The evaluation of experiment is thickness strain, the spinning force, the twisting angle, the surface roughness of the conventional & shear metal spinning process; the evaluation of FEM of metal spinning to path definition with sufficient fine mesh to capture behavior of work piece; The evaluation of feed rate of roller, direction of roller,& type of roller stimulated. The metal spinning process has the more flexible to produce a wider range of product shape & to form more challenge material.

Keywords: metal spinning, FEM analysis, simulation of metal spinning, mechanical engineering

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Authors: Asaiel Alohaly

Abstract:

A theoretical framework for corporate governance is needed to bridge the gap between the corporate governance of private companies and State-owned Enterprises (SOEs). The two dominant models, being shareholder and stakeholder, do not always address the specific requirements and challenges posed by ‘hybrid’ companies; namely, previously national bodies that have been privatised bffu t where the government retains significant control or holds a majority of shareholders. Thus, an exploratory theoretical study is needed to identify how ‘hybrid’ companies should be defined and why the state model should be acknowledged since it is the less conspicuous model in comparison with the shareholder and stakeholder models. This research focuses on ‘the state model of corporate governance to understand the complex ownership, control pattern, goals, and corporate governance of these hybrid companies. The significance of this research lies in the fact that there is a limited available publication on the state model. The outcomes of this research are as follows. It became evident that the state model exists in the ecosystem. However, corporate governance theories have not extensively covered this model. Though, there is a lot being said about it by OECD and the World Bank. In response to this gap between theories and industry practice, this research argues for the state model, which proceeds from an understanding of the institutionally embedded character of hybrid companies where the government is either a majority of the total shares or a controlling shareholder.

Keywords: corporate governance, control, shareholders, state model

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Authors: Tejinder Singh

Abstract:

Information system is the flow of data from different levels to different directions for decision making and data operations in information system (IS). Data can be violated by different manner like manual or technical errors, data tampering or loss of integrity. Security system called firewall of IS is effected by such type of violations. The flow of data among various levels of Information System is done by networking system. The flow of data on network is in form of packets or frames. To protect these packets from unauthorized access, virus attacks, and to maintain the integrity level, network security is an important factor. To protect the data to get pirated, various security techniques are used. This paper represents the various security techniques and signifies different harmful attacks with the help of detailed data analysis. This paper will be beneficial for the organizations to make the system more secure, effective, and beneficial for future decisions making.

Keywords: information systems, data integrity, TCP/IP network, vulnerability, decision, data

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Authors: Tamas Jancso, Andrea Podor, Eva Nagyne Hajnal, Peter Udvardy, Gabor Nagy, Attila Varga, Meng Qingyan

Abstract:

The paper deals with the conditions and circumstances of integration of remotely sensed data for rural environmental monitoring purposes. The main task is to make decisions during the integration process when we have data sources with different resolution, location, spectral channels, and dimension. In order to have exact knowledge about the integration and data fusion possibilities, it is necessary to know the properties (metadata) that characterize the data. The paper explains the joining of these data sources using their attribute data through a sample project. The resulted product will be used for rural environmental analysis.

Keywords: remote sensing, GIS, metadata, integration, environmental analysis

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Authors: Emine Laarouchi, Daniela Cancila, Laurent Soulier, Hakima Chaouchi

Abstract:

Cyber-Physical Systems as drones proved their efficiency for supporting emergency applications. For these particular applications, travel time and autonomous navigation algorithms are of paramount importance, especially when missions are performed in urban environments with high obstacle density. In this context, however, safety properties are not properly addressed. Our ambition is to optimize the system safety level under autonomous navigation systems, by preserving performance of the CPS. At this aim, we introduce genetic algorithms in the autonomous navigation process of the drone to better infer its trajectory considering the possible obstacles. We first model the wished safety requirements through a cost function and then seek to optimize it though genetics algorithms (GA). The main advantage in the use of GA is to consider different parameters together, for example, the level of battery for navigation system selection. Our tests show that the GA introduction in the autonomous navigation systems minimize the risk of safety lossless. Finally, although our simulation has been tested for autonomous drones, our approach and results could be extended for other autonomous navigation systems such as autonomous cars, robots, etc.

Keywords: safety, unmanned aerial vehicles , CPS, ACPS, drones, path planning, genetic algorithms

Procedia PDF Downloads 177