Data mining possess a significant threat to ethics, privacy, and legality, especially when we consider the fact that data mining makes it difficult for an individual or consumer (in the case of a company) to control accessibility and usage of his data. Individuals should be able to control how his/ her data in the data warehouse is being access and utilize while at the same time providing enabling environment which enforces legality, privacy and (...) ethicality on data scientists, or data engineer during data mining process. This paper review issues of legality, privacy, and ethicality in data mining, review processes of Data mining, and also proposes solution to current ethical and privacy issue in data mining. It introduces a new method which enforces data mining without infringing on the privacy of individual or consumer whose data are being used. The sole aim of this paper is to propose a new method of mining data which restricts scientists within the constraints of legality, privacy, and ethicality. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by (...) using different machine learning algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random (...) Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by (...) using different machine learning algorithm, and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

This chapter addresses the morality of two types of national security electronic surveillance (SIGINT) programs: the analysis of communication “metadata” and dragnet searches for keywords in electronic communication. The chapter develops a standard for assessing coercive government action based on respect for the autonomy of inhabitants of liberal states and argues that both types of SIGINT can potentially meet this standard. That said, the collection of metadata creates opportunities for abuse of power, and so judgments about the trustworthiness and (...) competence of the government engaging in the collection must be made in order to decide whether metadata collection is justified in a particular state. Further, the moral standard proposed has a reflexive element justifying adversary states’ intelligence collection against one another. Therefore, high-tech forms of SIGINT can only be justified at the cost of justifying cruder versions of signals intelligence collection practiced by a state’s less-advanced adversaries. (shrink)

"Data mining is not an invasion of privacy because access to data is only by machines, not by people": this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the (...) analysis will have to be made either by humans or by machines that understand. It concludes that the current data mining violates the right to privacy and should be subject to the standard legal constraints for access to private information by people. (shrink)

In light of Taiwan's extensive exposure to the Chinese Communist Party's "cognitive domain infiltration warfare," this paper proposes new response mechanisms and strategies for cybersecurity and national defense. The focus is primarily on assessing the CCP's cognitive infiltration tactics to develop policy recommendations in cybersecurity linguistics. These recommendations are intended to serve as a reference for future national defense and information security policies. Within the constraints of limited resources, this study attempts to provide an integrated analysis method combining qualitative (...) and quantitative tools. This method involves text mining in cybersecurity linguistics, focusing on "textual content" and incorporating discourse deconstruction. The aim is to enhance the success rate in identifying and defining contentious information in cognitive warfare, thereby offering policy suggestions. -/- Keywords: Cognitive Infiltration, Big Data Corpus, Cybersecurity Linguistics, Text Mining Analysis, Narrative Language Deconstruction. (shrink)

Revista Intelligence Info este o publicație trimestrială din domeniile intelligence, geopolitică și securitate, și domenii conexe de studiu și practică. -/- Cuprins: -/- EDITORIALE / EDITORIALS -/- Tiberiu TĂNASE Considerații privind necesitatea educării și formării resursei umane pentru intelligence–ul național Considerations regarding the need to educate and train human resources for national intelligence Nicolae SFETCU Epistemologia activității de intelligence Epistemology of intelligence Nicolae SFETCU Rolul serviciilor de informații în război The role of the intelligence agencies in a war Nicolae SFETCU (...) Evoluția inteligenței artificiale în domeniul securității naționale The evolution of artificial intelligence in the field of national security -/- INTELLIGENCE / INTELLIGENCE -/- Ovidiu BOUREANU O scurtă istorie a spionajului: spionajul modern de la Războiul Rece la Războiul împotriva terorii A Brief History of the Spy: Modern Spying from the Cold War to the War on Terror Ioana PĂIUȘ, Tiberiu TĂNASE Securitate și informații într-o lume în schimbare Security and intelligence in a changing world Nicolae SFETCU Activitatea de intelligence - Ciclul intelligence Intelligence Activity – Intelligence Cycle Tiberiu TĂNASE Controlul asupra Intelligence –ului Control over Intelligence Tiberiu TĂNASE Informația și nevoia de gestionare a resurselor informaționale Information and the need to manage information resources Tiberiu TĂNASE Controlul parlamentar al serviciilor de informații Parliamentary control of intelligence services Tiberiu TĂNASE Mecanisme de control democratic al activității serviciilor de informații Mechanisms of democratic control of the activity of intelligence agencies Nicolae SFETCU Analiza informațiilor Intelligence Analysis Bogdan-George RĂDULESCU Cum ar trebui să fie utilizată analiza intelligence pentru a contracara războiul cognitiv? How should intelligence analysis be utilized to counter cognitive warfare? -/- ISTORIE / HISTORY -/- Tiberiu TĂNASE Biroul de Informații al Secției Militare din Transilvania și rolul său în campania pentru apărarea României Mari The Intelligence Office of the Transylvanian Military Section and its role in the campaign for the defense of Greater Romania Stan PETRESCU Pacepa, Kuklinski, Pollard și românii Pacepa, Kuklinski, Pollard and the Romanians Tiberiu TĂNASE Contracararea de către SSI a activităților informative și acțiunilor Serviciilor Germane și Sovietice de Informații implicate în evenimentele din 21 - 23 ianuarie 1941 Countering by the SSI of the informational activities and actions of the German and Soviet Intelligence Services involved in the events of January 21-23, 1941 Tiberiu TĂNASE Alexandru Ioan Cuza, fondatorul Intelligence-lui statului modern românesc Alexandru Ioan Cuza, Founder of the Intelligence of the Modern Romanian State Tiberiu TĂNASE, Bogdan MUSCALU Puncte de vedere privind tradițiile romane în istoria intelligence-lui Viewpoints on Roman traditions in history intelligence Tiberiu TĂNASE Istoria Serviciului Român de Informații – serviciu de intelligence competitiv, și strategiile de intelligence History of the Romanian Intelligence Service – competitive intelligence service, and intelligence strategies Rodica LISEANU Reformele serviciilor secrete de la Mihail Moruzov la Eugen Cristescu, între inovație și decadență Secret service reforms from Mihail Moruzov to Eugen Cristescu Rodica LISEANU Take Ionescu – O biografie vectorială în istoria partidelor politice și în semantica diplomației Take Ionescu - A Vectorial Biography in the History of Political Parties and the Semantics of Diplomacy Remus MACOVEI Trădători și trădători Traitors and traitors -/- GEOPOLITICA / GEOPOLITICS -/- Antoaneta AVRAM Pandemia de COVID-19 și consecințele ei în peisajul anului 2022 The COVID-19 pandemic and its consequences in the landscape of 2022 Darius-Antoniu FERENȚ, Ioan MANCI Analiza conflictului ruso-cecen din perspectivă militară Analysis of the Russian-Chechen conflict from a military perspective Lisa-Maria ACHIMESCU Apusul universalismului european The decline of European universalism Emilian M. DOBRESCU Hegemonia chineză în producția de pământuri rare Chinese Hegemony in the Production of Rare Earths Ionuț-Daniel GORGOVAN ”Descurajarea integrată”. Analiză asupra strategiei SUA în Pacific "Integrated deterrence". Analysis of US Pacific Strategy Tiberiu TĂNASE, Violeta Ioana NAGÂȚ Conflictul arabo-israelian. Percepții privind cauzele și natura conflictului. Repere istorice și actualitate The Arab-Israeli conflict. Perceptions of the causes and nature of conflict. Historical landmarks and current affairs -/- SECURITATE / SECURITY -/- Tănase TIBERIU Minighid de pregătire și protecție contrainformativă Counterintelligence preparation and protection mini-guide Darius-Antoniu FERENȚ ISIS - Jocul morții - Martiri, asasinate și fascinație The Killing Game - Martyrdom, Murder and the Lure of ISIS Traian OSTAHIE Abordări generale ale conceptului de cultură de securitate For a 21st century realism Dalia Elena SILAGHI Terorismul cibernetic: o formă contemporană de terorism Cyber Terrorism: A Contemporary Form of Terrorism Paul Alexandru HELER Conflictul asimetric și consecințele sale în securitatea Asiei Asymmetric Conflict and Its Consequences in Asian Security Daria-Maria TÂRNĂVEAN Confruntarea informațională și repercusiunile sale asupra mediului actual de securitate The Information Confrontation and its Repercussions on the Current Security Environment Darius-Antoniu FERENȚ Analiza atacurilor cibernetice, precum APT (Advanced Persistent Threat) și ransomware Analysis of cyber attacks such as APT (Advanced Persistent Threat) and ransomware Daniela Georgiana GOLEA, Oana Ștefania COȘA Nevoia de adaptare managerială a noilor probleme de securitate modernă The need for managerial adaptation to new modern security issues Darius-Antoniu FERENȚ, Corneliu PREJA Implicarea NATO în domeniul apărării cibernetice NATO's involvement in cyber defence Alexandru CRISTIAN Paradigme în mediul internațional de securitate Paradigms in the international security environment Alexandru CRISTIAN Schimbarea paradigmelor în mediul internațional de securitate Changing paradigms in the international security environment Dan D. FARCAȘ Adevăr și dezinformare în fenomenul OZN Truth and disinformation in the UFO phenomenon Alexandru CRISTIAN Un nou mediu de securitate A new security environment -/- ȘTIINȚA INFORMAȚIEI / INFORMATION SCIENCE -/- Oana Ștefania COȘA Rolul structurilor de intelligence medical în atingerea obiectivul de consolidare a sistemului de sănătate The role of medical intelligence structures in achieving the objective of strengthening the health system Nicolae SFETCU Preocupări legislative în mineritul datelor Legislative concerns in data mining Nicolae SFETCU Utilizarea analiticii rețelelor sociale în intelligence Using social network analytics in intelligence Nicolae SFETCU Ingineria cunoașterii în colectarea informațiilor Knowledge Engineering in Intelligence Gathering -/- ISSN 2971 – 9089 ISSN-L 2821 – 8159, DOI: 10.58679/II26972. (shrink)

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining (...) techniques, used for rainfall prediction. In our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue (...) that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience. (shrink)

Motivation: • Breaking barriers in publishing demands a proactive attitude • Open data, open review and open dialogue in making social sciences plausible .

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (shrink)

This research presents a novel and efficient public key cryptosystem known as the Enhanced Schmidt Samoa (ESS) cryptosystem, proposed to safeguard the data of a single owner in cloud computing environments. Data storage is a one-time process in the cloud, while data retrieval is a frequent operation. Experimental results demonstrate that the ESS cryptosystem offers robust data confidentiality in the cloud, surpassing the security provided by traditional cryptosystems. The research also introduces a secure cloud framework (...) designed to accommodate both individuals and organizations accessing applications and data in the cloud. While individual users may generate and share data, organizations often involve multiple users in data sharing to support their business processes. In these scenarios, multi-user data ownership and access management become critical, requiring secure sharing of cryptographic keys among the authorized users. To address these issues and ensure data confidentiality in multi-user cloud environments, the Improved Secure Cloud Data Storage Framework (ISCDSF) is introduced. This research not only enhances data security but also provides a comprehensive framework for secure data sharing in the cloud, catering to the needs of both individual users and organizations. (shrink)

In the end of the course the student will be able to: • Understand the functioning of data mining tools and their contributions to managerial professions • Master the use of dynamic search tools on the open web and on the dark web. • Use the proper tools according to the objectives sought • Master the latest trends and innovations in Business Analytics • Analyze the opportunities offered in terms of data mining by artificial intelligence and (...) IoT. (shrink)

The modern computer environment has moved past the local data center with a single entry and exit point to a global network comprising many data centers and hundreds of entry and exit points, commonly referred as Cloud Computing, used by all possible devices with numerous entry and exit point for transactions, online processing, request and responses traveling across the network, making the ever complex networks even more complex, making traversing, monitoring and detecting threats over such an environment a (...) big challenge for Network forensic and investigation for cybercrimes. It has demanded in depth analysis using network tools and techniques to determine how best information can be extracted pertinent to an investigation. Data mining technique providing great aid in finding relevant clusters for predicting unusual activities, pattern matching and fraud detection in an environment, capable to deal with huge amount of data. The concept of network forensics in cloud computing requires a new mindset where some data will not be available, some data will be suspect, and some data will be court ready and can fit into the traditional network forensics model. From a network security viewpoint, all data traversing the cloud network backplane is visible and accessible by the cloud service provider. It is not possible to think now that one physical device will only have one operating system that needs to be taken down for investigation. Without the network forensics investigator, understanding the architecture of the cloud environment systems and possible compromises will be overlooked or missed. In this paper, we focus on the role of Network Forensic in a cloud environment, its mapping few of the available tools and contribution of Data Mining in making analysis, and also to bring out the challenges in this field. (shrink)

Historic preservation, which is a vital act for conveying people’s understanding of the past, such as events, ideas, and places to the future, allows people to preserve history for future generations. Additionally, since the historic properties are currently concentrated in urban areas, an urban-oriented approach will contribute to the issue. Hence, public awareness is a key factor that paves the way for this conservation. Public history, a history with a public audience and special methods of representation, can serve society in (...) this regard. Historical video games, which get their game objectives from the past, can be considered a form of public history, raising people’s awareness and letting them gain historical experience. While plenty of studies focused on this content with various approaches, this study takes the topic under consideration with a player-oriented focus. The research uses the Steam video game digital distribution service database to explore the existing historical games based on their genre, player scale, dimension, VR compatibility attributes, and player rating. The database includes 1,148 games published between 2002 and 2022, filtered as historical by Steam tags and sorted by player review. For the data collection, the study uses the Octoparse web scraping tool for data extraction, and for the data analysis, it uses RapidMiner Studio data mining software. The study aims to provide a framework applicable to the development of commercially playable historical video games contributing to heritage preservation by raising public awareness in the context of public history. (shrink)

Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to (...) perform a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïve Bayes, Logistic Regression, Lazy IBK (Instance-Bases learning with parameter K), Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k-NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors. (shrink)

Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for (...) the purposes of making computers understand medical natural language. (shrink)

E-Government refers to the administration of Information and Communication Technologies (ICT) to the procedures and functions of the government with the objective of enhancing the transparency, efficiency and participation of the citizens. E-Government is tough systems that require distribution, protection of privacy and security and collapse of these could result in social and economic costs on a large scale. Many of the available e-government systems like electronic identity system of management (eIDs), websites are established at duplicated databases and servers. (...) An established validation and management system could face a single failure point and the system is prone to Distributed Denial of Service Attacks (DDoS), denial of service attacks (DoS), malware and other cyber attacks. The execution of a privacy preserving and a secure decentralized system is enabled by the block chain technology. Here any third-party organizations do not have any control over the transactions of the Government. With the help of block chain technology, new and existing data are encapsulated within ledger or blocks, which are evenly distributed through the network in an enduring and sustainable way. The privacy and security of information are improved with the help of block chain technology, where distribution and encryption of data are performed through the total network. This analytical paper maps out the analysis of the security in the e-government system, utilizing the block chain technology that provides privacy and security of information and thereby enhancing the trust among the public sector. Qualitative and theoretical analysis is made for the proposed topic and implications of privacy and security of the proposed system is made. (shrink)

This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized (...) ABKS not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student (...) success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students. (shrink)

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising (...) the integrity and confidentiality of the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. Furthermore, the implementation of an optimized keyword indexing mechanism facilitates fast and accurate search operations, making the protocol well-suited for real-time IoT applications. Extensive experiments were conducted using various IoT datasets to evaluate the performance of the proposed protocol in terms of encryption speed, search efficiency, and overall security. The results demonstrate that the optimized encryption protocol significantly outperforms existing methods, offering a scalable solution that meets the stringent requirements of IoT systems. This research contributes to the field by providing a practical and secure encryption solution that addresses the unique challenges of IoT environments, paving the way for more secure and efficient IoT networks. (shrink)

The suite of secretive national security programs initiated in the US since 9/11 has created debate not only about the merits of targeted killing, torture, secret detention, cyberwar, global signals intercepts, and data-mining, but about the very secrecy in which these programs were conceived, debated by government officials, and implemented. Law must be revealed to those who are expected to comply with its demands. Law is a mere pretext for coercion if the laws permitting the government to (...) coerce people for non-compliance are concealed. The purpose of this essay is to determine whether inhabitants of a liberal state—a type of state tracing the legitimacy of its coercive actions to the consent of the governed—also need to know the internal protocols and legal findings of the government agencies ostensibly serving them. To put this question another way, what, if anything, may government agencies in liberal states keep secret? (shrink)

A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the (...) recent work taking place at the intersection of text mining and mental health so that we can (2) highlight and analyze several pressing ethical issues that are arising in this rapidly growing field and (3) suggest productive directions for how these issues might be better addressed within future interdisciplinary work to ensure the responsible development of text mining approaches in psychology generally, and in mental health fields, specifically. In Section 1, we review some of the recent literature on text-mining and mental health in the contexts of traditional experimental settings, social media, and research involving electronic health records. Then, in Section 2, we introduce and discuss ethical concerns that arise before, during, and after research is conducted. Finally, in Section 3, we offer several suggestions about how ethical oversight of text-mining research might be improved to be more responsive to the concerns mapped out in Section 2. (shrink)

Because obituaries are succinct and explicitly intended to summarize their subjects’ lives, they may be expected to include only the features that the author finds most salient but also to signal to others in the community the socially-recognized aspects of the deceased’s character. We begin by reviewing studies 1 and 2, in which obituaries were carefully read and labeled. We then report study 3, which further develops these results with a semi-automated, large-scale semantic analysis of several thousand obituaries. Geography, gender, (...) and elite status all turn out to be associated with the virtues and values associated with the deceased. (shrink)

Educational Data Mining (EDM) is a new paradigm aiming to mine and extract knowledge necessary to optimize the effectiveness of teaching process. With normal educational system work it’s often unlikely to accomplish fine system optimizing due to large amount of data being collected and tangled throughout the system. EDM resolves this problem by its capability to mine and explore these raw data and as a consequence of extracting knowledge. This paper describes several experiments on real educational (...) data wherein the effectiveness of Data Mining is explained in migration the educational data into knowledge. The experiments goal at first to identify important factors of teacher behaviors influencing student satisfaction. In addition to presenting experiences gained through the experiments, the paper aims to provide practical guidance of Data Mining solutions in a real application. (shrink)

Revista Cunoașterea Științifică este o publicație trimestrială din domeniile științei și filosofiei, și domenii conexe de studiu și practică. -/- Cuprins: -/- EDITORIALE Cunoașterea, de Nicolae Sfetcu Cuvânt introductiv pentru (și despre) „Cunoașterea Științifică”, de Adrian Klein Viitorul științei – Știința științelor, de Nicolae Sfetcu -/- ȘTIINȚE NATURALE Teste gravitaționale, de Nicolae Sfetcu Inside, and Beyond „Nothingness”, de Adrian Klein și Robert Neil Boyd Anomalii ale relativității generale, de Nicolae Sfetcu -/- ȘTIINȚE SOCIALE Fondarea Uniunii Europene și evoluția tratatelor comunității (...) europene, de Alexandru Cristian Istoria eugeniei, de Nicolae Sfetcu Contextul intrării României în al Doilea Război Mondial, de Nicolae Sfetcu Dezvoltarea capabilităților europene în domeniul managementului crizelor, de Alexandru Cristian The Security Management System from the Perspective of the Global Energy Crisis and the Extended Black Sea Region Escalating Conflict, de Daniela Georgiana Golea, Andreea Florina Radu, și Tiberiu Tănase Closed Economies, Autarchy – Failure and Economic Disaster, de Darius-Antoniu Ferenț -/- ȘTIINȚE FORMALE Ontologii de intreprindere în tehnologia blockchain, de Nicolae Sfetcu Învățarea automată a regulilor de asociere în mineritul datelor (data mining), de Drew Bentley -/- FILOSOFIE Platon: Biografia, de Nicolae Sfetcu Karl Popper și problema demarcației între știință și ne-știință, de Nicolae Sfetcu A Scientifically Acceptable Mechanism for the Reincarnation Process of the Self, de Adrian Klein -/- RECENZII CĂRȚI Lebăda Neagră, un risc asumat – Merită?, de Nicolae Sfetcu Republica lui Platon, de Nicolae Sfetcu -/- ISSN 2971 – 9070 ISSN-L 2821 – 8086, DOI: 10.58679/CS83230. (shrink)

The purpose of this paper was to examine institutional variables and the supervision of security in secondary schools in Cross River State. The study specifically sought to determine whether there was a significant influence of school population, school type and school location, on the supervision of security in public secondary schools in Cross River State. Three null hypotheses were formulated accordingly to guide the study. 360 students and 120 teachers resulting in a total of 480 respondents, constituted the (...) sample for the study. The instrument used for data collection was a questionnaire while Independent t-test was used to analyze data and test the hypotheses at .05 level of significance using Microsoft Excel version 2013. The results of the findings revealed that school population, school type and school location, all have an influence in the supervision of security in public secondary schools of Cross River State. It was also revealed that lowly populated, mixed-gender, and urban public secondary schools were more efficient in the supervision of security than their counterparts such as highly populated, single-gender and rural secondary schools. Based on the findings of this study, conclusions were drawn and recommendations were made. (shrink)

This study investigated the perception of how methods of entrepreneurial education predict the level of economic security in the Southern Senatorial District of Cross River State. It was guided by two research questions and two hypotheses, tested at a significant level of 0.05. The research design was a survey. The study had 350 participants. A 40-item "Entrepreneurship Education and Economic Security Questionnaire" (EEESQ) was validated by five experts from the University of Calabar and used in data collection. (...) The EEESQ was pilot tested on 30 respondents who were not a part of the study sample. In order to evaluate the consistency of the EEESQ, Cronbach Alpha statistics were used, and they yielded reliability values of 0.84 and 0.80 for the independent and dependent variables, respectively. Three hundred and fifty respondents responded to the instrument, which was administered. In order to find the answers to the study questions and test the hypotheses, data were analyzed using linear regression analysis at the significant level of .05. The study discovered that experiential and transdisciplinary teaching strongly predict economic security. It was recommended that educators at various stages should introduce students to real-world scenarios when teaching entrepreneurship education. (shrink)

Bulk collection involves the mining of large data sets containing personal data, often for a security purpose. In 2013, Edward Snowden exposed large scale bulk collection on the part of the US National Security Agency as part of a secret counter-terrorism effort. This effort has mainly been criticised for its invasion of privacy. I argue that the right moral argument against it is not so much to do with intrusion, as ineffectiveness for its official purpose (...) and the lack of oversight by security cleared legislators. It also lends itself to government "domination" (in Pettit's sense) of those who are subject to it. (shrink)

Cloud computing is the computing technology which provides resources like software, hardware, services over the internet. Cloud computing provides computation, software, data access, and storage services that do not require end- user knowledge of the physical location and configuration of the system that delivers the services. Cloud computing enables the user and organizations to store their data remotely and enjoy good quality applications on the demand without having any burden associated with local hardware resources and software managements but (...) it possesses a new security risk towards correctness of data stored at cloud. The data storage in the cloud has been a promising issue in these days. This is due to the fact that the users are storing their valuable data and information in the cloud. The users should trust the cloud service providers to provide security for their data. Cloud storage services avoid the cost storage services avoids the cost expensive on software, personnel maintains and provides better performance less storage cost and scalability, cloud services through internet which increase their exposure to storage security vulnerabilities however security is one of the major drawbacks that preventing large organizations to enter into cloud computing environment. This work surveyed on several storage techniques and this advantage and its drawbacks. (shrink)

Data security is, protecting data from ill- conceived get to, utilize, introduction, intrusion, change, examination, recording or destruction. Cloud computing is a sort of Internet-based computing that grants conjoint PC handling resources and information to PCs what's more, different gadgets according to necessity. It is a model that empowers universal, on-request access to a mutual pool of configurable computing resources. At present, security has been viewed as one of the best issues in the improvement of Cloud (...) Computing. The key issue in effective execution of Cloud Computing is to adequately deal with the security in the cloud applications. This paper talks about the part of cryptography in cloud computing to improve the data security. The expectation here is to get bits of knowledge another security approach with the usage of cryptography to secure information at cloud data centers. (shrink)

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only (...) be extracted from binary data where an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules. (shrink)

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple (...) cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. Future enhancements may include integrating machine learning algorithms to predict and adapt to new security threats in real time, further solidifying the reliability of cloud storage solutions. (shrink)

This study aimed to identify the digital transformation and its impact on the application of Cyber Security in the Palestinian Ministry of Interior and National Security. The study used the analytical descriptive approach. The study tool (questionnaire), and the comprehensive survey method was used, where (61) questionnaires were retrieved (87.1%), and they were unloaded and analyzed using the SPSS statistical package. The study found several results, including that there is a statistically significant correlation between all dimensions of Digital (...) transformation and the application of Cyber Security in the Ministry of Interior and National Security. And that there is an impact of Digital transformation on the application of security in the Ministry of Interior and National Security in the governorates of Gaza, and the impact coefficient was (0.897). It was found that the dimensions affecting the dependent variable of Cyber Security are dimensions (organizational structure, job description, data privacy and security, technical infrastructure required for digital transformation), as these dimensions affect (89.7%) of the variation in the application of Cyber Security. The study presented a set of recommendations, the most important of which are: The need for the Ministry's administration to provide a special budget to develop the quality of its electronic services as a lever for digital transformation. And the need for the number of workers in computer and information technology departments and units to be commensurate with the volume and quality of work to bridge the gap between the required performance and the actual performance. The ministry should set a clear methodology for exchanging data and information between the components of the ministry. And the need for the Ministry of Interior and National Security to use a protection network capable of detecting all threats. (shrink)

The security problems of Nigeria have continued to stare at her very ominously and intermittently harass her, both within and outside her shores. These have lingered on and have created a clog on the wheel of the country's progress, indeed dramatically stagnating, and to say the least, truncating the mainstay of the country's survival. Several interpretations, theories, analyses, syntheses, and jingoistic conceptualization have been propagated, all producing the same result. From scientific to technological approach, religious to ritualist approach, political (...) to social approach, the security situation rather than improving, is static and under some regimes retarded. Academic contributions, especially ideas from the humanities and indeed the discipline of history are jettisoned, in fact, quickly dusted into the waste bin. The concern of this paper is to attempt a historical investigation of the security problems in Nigeria, identifying them to show how information and communication technology can help in curbing these challenges, using the realist paradigm as a theoretical framework. This study identifies Nigerians as the cause of her security problems, who rather than face these seismic challenges head-on, abandon them – a cowardly act, ending in futility and deeper chaos. This study adopts both primary and secondary sources of data collection. It is hoped that if academic exercises are not mere, this input may create a turnaround in the security situation in Nigeria. (shrink)

Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments (...) are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data (...) security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. (shrink)

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword searches over encrypted (...) data. This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized ABKS not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

Background: Use of patients’ medical data for secondary purposes such as health research, audit, and service planning is well established in the UK. However, the governance environment, as well as public understanding about this work, have lagged behind. We aimed to systematically review the literature on UK and Irish public views of patient data used in research, critically analysing such views though an established biomedical ethics framework, to draw out potential strategies for future good practice guidance and inform (...) ethical and privacy debates. Methods: We searched three databases using terms such as patient, public, opinion, and electronic health records. Empirical studies were eligible for inclusion if they surveyed healthcare users, patients or the public in UK and Ireland and examined attitudes, opinions or beliefs about the use of patient data for medical research. Results were synthesised into broad themes using a framework analysis. Results: Out of 13,492 papers and reports screened, 20 papers or reports were eligible. While there was a widespread willingness to share patient data for research for the common good, this very rarely led to unqualified support. The public expressed two generalised concerns about the potential risks to their privacy. The first of these concerns related to a party’s competence in keeping data secure, while the second was associated with the motivation a party might have to use the data. Conclusions: The public evaluates trustworthiness of research organisations by assessing their competence in data-handling and motivation for accessing the data. Public attitudes around data-sharing exemplified several principles which are also widely accepted in biomedical ethics. This provides a framework for understanding public attitudes, which should be considered in the development in any guidance for regulators and data custodians. We propose four salient questions which decision makers should address when evaluating proposals for the secondary use of data. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data (...) security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for predictive search improvements and strengthening ABKS against emerging security vulnerabilities are future research priorities. (shrink)

In this paper we focus on what is referred to as the ‘mineness’ of experience, that is, the intimate familiarity we have with our own thoughts, perceptions, and emotions. Most accounts characterize mineness in terms of an experiential dimension, the first-person givenness of experience, that is subsumed under the notion of minimal self-consciousness or a ‘minimal self’. We argue that this account faces problems and develop an alternative account of mineness in terms of the coherence of experiences with what we (...) label an ‘embodied biography’. Building on a near consensus among consciousness researchers over the function of consciousness as integrating infor- mation, we argue that the phenomenology of mineness consists in the absence of any further thought on top of the experience itself. Finally we argue that this non-phenomenological account of mineness fits well with existing data on pathologies of mineness such as delusions of thought insertion. (shrink)

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the (...) audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas. (shrink)

This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes of data, (...) resource management complexities in optimizing computational resources, cost management strategies to balance performance with expenses, and ensuring robust data security and privacy measures. The manuscript then delves into innovative solutions and techniques aimed at addressing these challenges. It discusses advancements such as workflow automation tools and frameworks that streamline repetitive tasks, containerization technologies like Docker and Kubernetes for efficient application deployment and management, and the utilization of serverless architectures to enhance scalability and reduce operational costs. Additionally, it explores the benefits of parallel processing frameworks such as Apache Spark and Hadoop in optimizing data processing tasks. The integration of machine learning algorithms for dynamic workflow optimization and effective data management strategies in cloud environments are also examined. Through detailed case studies and application examples across various domains, the manuscript illustrates the practical implementation and outcomes of these optimization strategies. Furthermore, it discusses emerging trends in cloud technologies, the role of AI-driven automation in enhancing workflow efficiencies, and ethical considerations surrounding data science operations in cloud computing. The manuscript concludes with a summary of findings, practical recommendations for organizations seeking to enhance their data science workflows in the cloud, and insights into future research directions to address evolving challenges. (shrink)

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We (...) also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities. (shrink)

A huge amount of data is being generated everyday through different transactions in industries, social networking, communication systems etc. Big data is a term that represents vast volumes of high speed, complex and variable data that require advanced procedures and technologies to enable the capture, storage, management, and analysis of the data. Big data analysis is the capacity of representing useful information from these large datasets. Due to characteristics like volume, veracity, and velocity, big (...) class='Hi'>data analysis is becoming one of the most challenging research problems. Semantic analysis is method to better understand the implied or practical meaning of the input dataset. It is mostly applied with ontology to analyze content mainly in web resources. This field of research combines text analysis and Semantic Web technologies. The use semantic knowledge is to aid sentiment analysis of queries like emotion mining, popularity analysis, recommendation systems, user profiling, etc. A new method has been proposed to extract semantic relationships between different data attributes of big data which can be applied to a decision system. (shrink)

The Internet-of-Things (IoT) is gradually being established as the new computing paradigm, which is bound to change the ways of our everyday working and living. IoT emphasizes the interconnection of virtually all types of physical objects (e.g., cell phones, wearables, smart meters, sensors, coffee machines and more) towards enabling them to exchange data and services among themselves, while also interacting with humans as well. Few years following the introduction of the IoT concept, significant hype was generated as a result (...) of the proliferating number of IoT-enabled devices, which (according to many projections) are expected to amount to several billion in the next years. During recent years, this hype has been turning to reality, as a wave of IoT applications with significant social and economic has been emerging. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorization of analytic approaches and propose a layered taxonomy from IoT data to analytics. IoT data analysis is an integral element of any non- trivial IoT system. Nevertheless, IoT analytics are still in their infancy, as IoT data still remain largely unexploited. (shrink)

This research examines ethical issues in computational linguistics that can be applied to national defense by analyzing philosophical and security language. The increasing use of language contexts, such as intelligence and communication data analysis, raises ethical and philosophical challenges related to privacy, control, and accuracy. This research aims to identify and analyze ethical issues, especially in the use of computational linguistics in defense applications, as well as their implications for the protection of individual rights and privacy. This method (...) involves reviewing ethical technology references and analyzing the philosophical design of language and security. Deontological and utilitarian ethical theories are applied to evaluate the moral impacts and consequences of using the language of technology in a defense context. The results demonstrate that using linguistics in defense can pose privacy risks, misuse of data, and the potential for significant bias in decision-making. This often involves collecting data from multiple sources, creating challenges to ensure data is managed ethically and responsibly. Conclusion: This shows the need for a solid ethical and regulatory framework to ensure technology's fair and responsible use in context defense. Implications of the findings include the need for ongoing dialogue between technology developers, policymakers, and the public to mitigate ethical risks and ensure that application technologies comply with critical and security principles. With a comprehensive and responsible approach, technological linguistics can improve military safety and effectiveness without compromising individual rights and privacy. (shrink)

This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms (...) for predictive search improvements a. (shrink)