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This article reconstructs the bureaucratic and legal processes that culminated in the creation of the modern national security classification system in Executive Order No. 10,290, issued by Harry Truman in 1951. It argues that classification was shaped by processes of improvisation endogenous to the federal bureaucracy, which produced the problems of overclassification, definitional vagueness, and ambiguous constitutional status that have haunted the secrecy regime until the present. In so doing, it provides new insight into the development of the modern presidency, the national security state, and American democracy, and suggests possible paths to reform the contemporary pathologies of the classification system. [ABSTRACT FROM AUTHOR]

Robust data security is of paramount importance, particularly for highly classified information. To achieve this, advanced encryption and decryption methods, along with stringent access control and physical security measures, are essential to mitigate the risk of unauthorized access and data exposure. Ghost imaging (GI), a computational imaging technique that operates indirectly, offers a distinct approach compared to traditional spatially resolved imaging techniques. By utilizing computational algorithms, GI enables indirect image reconstruction, making it well‐suited for encryption applications. This proposal introduces a new computational imaging encryption scheme that combines Fourier ghost imaging (FGI) and computational holography. The scheme utilizes an untrained‐tree‐network to generate two camouflaged computer‐generated holograms (CGHs). Integration of CGH‐based steganography and holographic authentication enhances the encoding and decoding processes of FGI encryption. The effectiveness and security of the proposed scheme are rigorously validated through numerical simulations and optical experiments. The results demonstrate promising potential for effectively merging direct and indirect imaging methods in security applications. [ABSTRACT FROM AUTHOR]

Purpose: Paper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms' nutrient source. Mold not only can damage papers' structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper. Methods: Molds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model. Results & Conclusion: In the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation. [ABSTRACT FROM AUTHOR]

The United Nations Social Development Goals ensure the achievement of food security by 2030. Food security is characterized by various aspects, however in this study we focus on economic vulnerability to food insecurity. In particular, we compared two measures: food insecurity levels as defined by the Integrated food security phase classification Global-Partners quantitative scale and those defined by the Latin American and Caribbean Food Security qualitative scale. The qualitative scale only measures nutritional status using specific questions, regardless of food expenditures, while the quantitative scale is based on the share of consumer expenditure spent on food (the ratio between food expenditure and total expenditure). From our analysis on 2018 Mexico data—therefore unaffected by the pandemic—59% of discrepancies were found between the two measures. The qualitative scale does not classify people who spend too much on food as being food insecure, so they are economically vulnerable about housing, healthcare and other important aspects of social life (access to food generate insecurity). The quantitative scale does not identify people who do not have access to high-quality nutrition because they must pay for other fixed expenses, for example for housing or healthcare. These two measures depict two aspects of economic vulnerability to food insecurity, and here we quantify this difference in a developing country. Future research should be at integrating these two aspects into one single indicator based on food and non-food expenditures, so to take into account both the two aspects by using only consumption expenditure data. [ABSTRACT FROM AUTHOR]

National food insecurity early warning systems and food policy interventions need reliable information concerning the classification of food insecurity. The aim of this paper was to produce an acute food insecurity classification in Mozambique, by: i) analyzing food insecurity indicators individually; ii) comparing it with a new integrated analysis of survey-based indicators called the "Matrix Analysis." The Matrix results show more severe classifications than the single indicators for the analyzed districts. The matrix novelty consists on a cross-tabulation of all indicators, allowing a less subjective analysis. Further research is needed on how the Matrix approach could complement national classification systems. [ABSTRACT FROM AUTHOR]

The subject of covert radio agents operating behind enemy lines during World War II has recently taken on a life of its own. In the last five years, there have been no fewer than 16 books written on the subject. It is difficult to pinpoint the reason for the lingering popularity of this genre. Perhaps it is the mystery associated with spies, secrets, clandestine radios, and sabotage, which has been the grist of historical novels and movies for over 80 years. It may also be the fact that highly classified files and documents have been slowly released to the public over the years. Several of the recent authors cite in the prefaces of their books that they are using original information recently uncovered or unclassified. [ABSTRACT FROM AUTHOR]

This article explores the covert intervention by the United States in Guatemala in 1954, which resulted in the overthrow of the country's leftist government. It examines the motivations behind the intervention, questioning whether it was driven by corporate interests or Cold War concerns. The article argues that while the United Fruit Company (UFCO) had significant influence over US decision-makers, the intervention was primarily motivated by fears of communist infiltration. It also discusses new evidence of UFCO's political influence during President Truman's tenure. The article calls for a reevaluation of the impact of business, social, and political relationships on decision-making processes and highlights the extensive influence of UFCO on US policy towards Guatemala. It concludes by discussing the role of US corporations, particularly UFCO, in Guatemala's economy and politics. [Extracted from the article]

This article explores the importance of access to previously classified materials in the field of diplomatic history, particularly in relation to the Cold War period. It discusses the efforts made by organizations like the Society for Historians of American Foreign Relations and the National Security Archive to ensure access to classified materials. The article also examines the significance of public-facing foreign policy statements, such as presidential doctrines, during the Cold War. It provides examples of three different Cold War doctrines: the Eisenhower Doctrine, the Kennedy Doctrine, and the Nixon Doctrine, highlighting their purposes and impacts. The article concludes by discussing the limitations and implications of these doctrines, emphasizing the need to study them to understand the impact of the Cold War on U.S. foreign policy. [Extracted from the article]

"The Declassification Engine: What History Reveals About America's Top Secrets" by Matthew Connelly is a thought-provoking book that sheds light on the state of American archives and access to classified information. Connelly argues that excessive classification of information by the government is hindering public understanding of political events and impeding accountability. He emphasizes the need for a rational, risk management approach to declassification in order to balance national security concerns with the public's right to know. The book also explores the historical origins of secrecy in the US and its implications for democracy. Connelly's research project at Columbia University's History Lab developed algorithms to uncover redacted information in declassified documents, revealing hidden knowledge and challenging the effectiveness of secrecy. The book raises important questions about the role of classification in avoiding accountability and the potential loss of historical records due to the overwhelming volume of classified material. [Extracted from the article]

Introduction Apps that support telemedicine on the battlefield typically run on classified devices and transmit information over classified networks, whereas the medical data the apps create and transmit are unclassified. Current systems treat these data as classified, so a cross-domain solution is required to transfer the data back to an unclassified domain, which adds delays and costs to the process of transmitting critical data needed to treat injured warfighters. To address this gap, ATC-NY developed DroidChamber, which is a software-based Android system that enables multilevel security and which runs on smartphones and tablets. DroidChamber enables warfighters to execute apps in multiple security domains without risking information leakage. Materials and Methods DroidChamber v6 is a collection of mobile device management and security containerization technologies targeting Android 6, and it applies these technologies by leveraging Linux kernel operating-system-level virtualization technologies. DroidChamber v10 takes advantage of Android Work Profiles and Samsung's Knox technology in Android 10, which provides the ability to provision separate "work" and "personal" profiles that securely isolate applications; apps within each profile can further be isolated through the built-in Android controls and the Knox Application Policy. Results DroidChamber is a software-based Android system, so it requires no added hardware to be attached or integrated with an end-user device. DroidChamber's innovation is isolation of specific Android app resources (e.g. networking) enforced with fine-grained security policies. With DroidChamber, a medic can connect their device to different security domains, giving a wider range of access to medical information in a tactical environment. Conclusions DroidChamber improves telemedicine applications by enabling the warfighter to share information without requiring a cross-domain guard that may erroneously block some data. Using DroidChamber, a warfighter can use a single mobile device to manage/transmit data at different security levels, thereby reducing the cost and complexity of a mission. [ABSTRACT FROM AUTHOR]

This article seeks to explore the relation between nuclear physics and secrecy in early Cold War Europe. After World War II, nuclear physics re-emerged from the Manhattan Project as a largely classified field. Over time, the boundary between secret and unclassified information set by the United States moved due to both political and scientific developments. This shifting boundary of secrecy is taken as a place to investigate power relations in the context of Cold War Science. The Netherlands and Norway are two countries with early nuclear programs that tried to move this boundary, in part by building a joint reactor in 1951. Whereas they requested classified information from the US in 1946, their programs developed to a point where the US made requests to classify nuclear information in Europe by 1960. Between 1954 and 1960, the joint reactor program became the site of a multilateral intelligence operation. Secrecy was used as an intelligence tool to spread nuclear disinformation to the Soviet Union. This history shows how (de)classification opened and closed windows of opportunity and sheds light on the effectiveness of classification. [ABSTRACT FROM AUTHOR]

This study analyzes the coverage of the Covid-19 pandemic in two Brazilian television news programs, specifically Fantástico from TV Globo and Domingo Espetacular from Record TV. Our objective is to study how both programs interpreted Jair Bolsonaro's positions during the crisis, marked by populist antagonism that led to a series of attacks on doctors, scientists, and intellectuals through a negationist and anti-science rhetoric. To do so, we conducted a study of a corpus of 308 news articles collected at different moments during the pandemic. The materials were scrutinized based on the intersection of content analysis and framing techniques. This approach allowed us to systematize data on the visibility of the pandemic and understand the frames of meaning proposed by the two channels in the discursivization of the former president's actions. We started with the hypothesis that the coverage exposed Record TV's alignment with the far-right government, which began during the 2018 elections, and highlighted the critical orientation assumed by TV Globo, especially regarding the figure of the president, in the face of his extremism. This sets it apart from a tradition of support for conservative rulers. The results show significant differences in the coverage of the two programs. While TV Globo openly criticized Bolsonaro's positions, aligning with the discourse of science, Record TV, affiliated with the Universal Church of the Kingdom of God, embraced a discourse in line with the negationist agenda, giving voice to anti-science speeches that normalize the populism of the then-president. [ABSTRACT FROM AUTHOR]

The evolution of social media and online platforms created more opportunities to express one's thoughts without any burden. This emergence paved the way to construct more intelligent systems but by taking advantage of these platforms some people display their vengeance by harassing or abusing comments. The attackers leak sensitive information about an individual or an organization through the internet using abusive words, hate comments, threatening information, fake news, etc. So, online shaming detection with an optimum classification mechanism becomes necessary to avoid such negative consequences. In this paper, a novel online shaming detection mechanism using a Convolutional Neural Network Bidirectional Gated Recurrent Unit-based Hybrid Archimedes Tasmanian Devil (CNNBiGRU-HATD) approach is proposed to detect and classify the offensive comments and non-offensive comments. If the comment seems offensive, it is further classified into six categories according to shaming types such as abusive, doxing, Passing Judgment, Ethnic/Religious, Joke/Sarcasm, and Whataboutery. The utilized four Twitter-based datasets are initially preprocessed and feature extracted to improve the classification performance of the CNNBiGRU-HATD approach. After accurate classification, the information from the classified shaming types is collected and authenticated. The proposed approach is simulated using the Python platform. To validate the potential capability of the proposed CNNBiGRU-HATD approach, it is related to the existing methods by various parameters. The experimental result reveals that the proposed CNNBiGRU-HATD method achieves higher accuracy of 96.5% for the labeled hate speech detection dataset. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the compliance of the accounting practices of the Savings Deposit Insurance Fund of Turkey (SDIF), which carries out bank resolution, trusteeship and liquidation services, with the generally accepted Turkish Financial Reporting Standards (TFRSs). In this context, the financial statements and footnotes in SDIF's 2021 activity report are evaluated from the perspective of published standards. It is concluded that the accounting practices used by SDIF do not comply with the TFRSs in terms of non-consolidation of subsidiaries, incorrect classification and reporting of securities, and failure to publish a complete set of financial statements. [ABSTRACT FROM AUTHOR]

certain informations cannot and must not be available to the general public because they represent information of importance for national security or their publication could lead to great damage or endangerment of persons, property and other relations. In our legal system, such information is defined as secret data, which is further categorized into several groups based on its importance and the damage that may occur as a result of its publication. The protection of such data, by determining that they are secret, and carefully controlling who and how they give access to that data, is carried out by competent authorities in accordance with the law. The matter of defining secret data, use and protection of secret data in the Republic of Serbia is codified by the Data Secrecy Act, but other regulations are also applied in specific areas, which makes the entire positive legal framework for the protection of secret data extremely extensive. In the paper, the author analyzes the current legal framework and especially emphasizes the role played by the administrative bodies and the National Assembly of the Republic of Serbia in the protection of secret data. [ABSTRACT FROM AUTHOR]

Classified information is information of vital importance to the State, which must be protected against disclosure, misuse, damage, unauthorized reproduction, destruction, loss or theft in the interest of the State. At present, there are four levels of classification. For each classification level, precise requirements are laid down for the material of the walls, partitions and ceilings of the rooms in which classified information is stored. Several types of materials are defined for each classification level. The objective of this study was to test and determine whether the different types of materials proposed for the Confidential level meet the same level of resistance. A drop weight test via pendulum was used to determine the resistance. A 50 kg weight was used to break through a 60 × 100 cm sample. The impact of the weight was on the exact center of the sample. The result of the tests was that to break through samples of different materials, large differences in the drop height of the weight were required. The most resistant was the specimen made of reinforced concrete, which required 3 impacts from a height of 80 cm to break through. On the contrary, the least resistant were the specimens made of masonry of autoclaved aerated concrete, where after 2 falls from a height of 5 cm, the sample broke into 2 parts. [ABSTRACT FROM AUTHOR]

Based on deep learning, a Dust Ablation Trail Analysis (DATA) code package is developed to detect dust ablation trails in tokamaks, which is intended to analyze a large amount data of tokamak dusts. To validate and benchmark the DATA code package, 2440 plasma jet images are exploited for the training and test of the deep learning DATA code package, since plasma jets resemble the shape and size of dust ablation clouds in tokamaks. After being trained by 1920 plasma jet images, the DATA code package is able to locate 100% plasma jets, classify plasma jets with the accuracy of >99.9%, and output image skeleton information for classified plasma jets. The DATA code package trained by the plasma jet images is also used to analyze the dust ablation trails captured in the Experimental Advanced Superconducting (EAST) tokamak with the satisfactory performance, further verifying its applicability in the fusion dust ablation investigation. Based on its excellent performance presented here, it is demonstrated that our DATA code package is able to automatically identify and analyze dust ablation trails in tokamaks, which can be used for further detailed investigations, such as the three-dimensional reconstruction of dusts and their ablation trails. [ABSTRACT FROM AUTHOR]

In this article, we highlight the importance of art and culture as elements of transformation in the contemporary city. Adopting a critical urban perspective, we developed ethnographic fieldwork between 2018 and 2019, taking as a starting point the ambivalences of art and culture in the transformation of territories. We look at three territories in Lisbon where the relationship between the city and the arts stands out: the Intendente, as a reference case of public policies that transformed a decadent space into a fashionable one; Marvila, as an old industrial space that has undergone important changes; and the hill of Santana, as a space designed for the future, where built heritage, structures and cultural and artistic events leverage the transformation process. Evidence suggests that Lisbon has replicated the prevailing neoliberal urban model. Furthermore, the social dynamics of resistance, although tenuous and often impotent to change hegemonic dynamics, is an important force in the territories. The three cases presented allow us to understand the direction of socio-territorial change, highlighting the importance of the presence of classified built heritage, structures and artistic and cultural events in valuing spaces, promoting their commodification and contributing to urban competitiveness. [ABSTRACT FROM AUTHOR]

The evolution of technology has led to the appearance of smart cities. An essential element in such cities is smart mobility that covers the subjects related to Intelligent Transportation Systems (ITS). The problem is that the ITS vulnerabilities may considerably harm the life quality and safety status of human beings living in smart cities. In fact, software and hardware systems are more exposed to security risks and threats. To reduce threats and secure software design, threat modelling has been proposed as a preventive solution in the software design phase. On the other hand, threat modelling is always criticised for being time consuming, complex, difficult, and error prone. The approach proposed in this study, that is, Automated Security Assistant of Threat Models (ASATM), is an automated solution that is capable of achieving a high level of security assurance. By defining concepts and conceptual modelling as well as implementing automated security assistant algorithms, ASATM introduces a new approach to identifying threats, extracting security requirements, and designing secure software. The proposed approach demonstrates a quantitative classification of security at three levels (insecure, secure, and threat), twelve sub‐levels (nominal scale and colour scale), and a five‐layer depth (human understandability and conditional probability). In this study, to evaluate the effectiveness of our approach, an example with various security parameters and scenarios was tested and the results confirmed the superiority of the proposed approach over the latest threat modelling approaches in terms of method, learning, and model understanding. [ABSTRACT FROM AUTHOR]

The article focuses on food security in the least developed countries, through a case study of the Democratic Republic of Congo (D.R. Congo), a low-income country with a food deficit, with one of the lowest rates of gross domestic product per capita in the world. The article presents the context after gaining independence in 1960, the last five years, starting with 2017 and the current context of D.R. Congo in 2022, briefly developing the demographic situation, which have a direct impact on food security. The study has as inputs the data provided by the Integrated Food Security Phase Classification (IPC). The D.R. Congo continues to live in a particularly unstable political, economic, and social climate. If currently the western part of the country, including the capital Kinshasa, is no longer the scene of violent clashes and demonstrations, in many other provinces armed gangs, non-governmental militias, ex-military and tribal groups are active, carrying out assaults and raids with massacres of civilians. However, in the D.R. Congo, most deaths are not caused by the violence of the ongoing conflict in the African country, but rather by malnutrition and a poor medical system. Thus, food insecurity is one of the first two factors generating deaths in the D.R. Congo. [ABSTRACT FROM AUTHOR]

Phishing is an attack that deceit online users by means of masquerading as a genuine website to pilfer their classified or personal information. This is one among the recognized cybercrime. Disparate phishing website detection systems were recently developed for the purpose of detecting the phishing websites. However, they fail to attain the desired output and are suffered from countless drawbacks like lower accuracy and higher training time. For trouncing such drawbacks, this paper proposes an effectual Hybrid Deep Learning (HDL)-centric Phishing Detection System (PDS) using the MCS-DNN classifier. At first, pre-processing is done on the input dataset for ameliorating its quality. Subsequently, clustering and feature selection (FS) are performed to lessen the processing time and elevate the accuracy using CoK-means and CM-WOA, respectively. The features which are chosen during FS are fed into the MCS-DNN classifier, which classifies the legitimate websites and phishing websites. Lastly, the K-fold cross-validations (KCV) are employed for effectively predicting the proposed system's accurateness. The outcomes highlight the robustness and predictive ability of the proposed PDS to distinguish the phishing as well as legitimate sites. [ABSTRACT FROM AUTHOR]

Background: Although visual locomotion scoring is inexpensive and simplistic, it is also time consuming and subjective. Automated lameness detection methods have been developed to replace the visual locomotion scoring and aid in early and accurate detection. Several types of sensors are measuring traits such as activity, lying behavior or temperature. Previous studies on automatic lameness detection have been unable to achieve high accuracy in combination with practical implementation in a on farm commercial setting. The objective of our research was to develop a prediction model for lameness in dairy cattle using a combination of remote sensor technology and other animal records that will translate sensor data into easy to interpret classified locomotion information for the farmer. During an 11-month period, data from 164 Holstein-Friesian dairy cows were gathered, housed at an Irish research farm. A neck-mounted accelerometer was used to gather behavioral metrics, additional automatically recorded data consisted of milk production and live weight. Locomotion scoring data were manually recorded, using a one-to-five scale (1 = non-lame, 5 = severely lame). Locomotion scores where then used to label the cows as sound (locomotion score 1) or unsound (locomotion score ≥ 2). Four supervised classification models, using a gradient boosted decision tree machine learning algorithm, were constructed to investigate whether cows could be classified as sound or unsound. Data available for model building included behavioral metrics, milk production and animal characteristics. Results: The resulting models were constructed using various combinations of the data sources. The accuracy of the models was then compared using confusion matrices, receiver-operator characteristic curves and calibration plots. The model which achieved the highest performance according to the accuracy measures, was the model combining all the available data, resulting in an area under the curve of 85% and a sensitivity and specificity of 78%. Conclusion: These results show that 85% of this model's predictions were correct in identifying cows as sound or unsound, showing that the use of a neck-mounted accelerometer, in combination with production and other animal data, has potential to replace visual locomotion scoring as lameness detection method in dairy cows. [ABSTRACT FROM AUTHOR]

The purpose of this article is to introduce two items. 1) The Civil Participation Tool, developed by the Council of Europe’s Centre of Expertise for Good Governance (2020) for the purposes of: selecting, evaluating, and classifying civil actors (stakeholders) into groups; selecting the most appropriate forms of participation in decision-making processes; providing accurate information for classified civil actors (stakeholders); developing the most appropriate strategies to activate the processes of participation for different civil actors (stakeholders) based on the assessment of their potential to engage; and enhancing the level of participation. 2) The results of educational pilot projects implemented in 6 Lithuanian municipalities in 2020. These projects confirmed the scientific hypothesis of this article: that more active civil participation in decision-making processes can be expected when the objective differences of local civil actors (stakeholders) are recognized. According to this, different forms of civil participation should be provided as well as different strategies for improving skills and increasing civil engagement in decision-making processes. [ABSTRACT FROM AUTHOR]

Location-based services (LBS) are necessary for obtaining the important details since the user needs vary based on the location. Location-based advertising (LBA) are utilized for abandoning the user location and to offer assistance by using the obtained information. Therefore, an efficient machine learning and metaheuristic-based model referred as GANM is designed for LBS. Initially, the potential location information is evaluated utilizing the geographic information system (GIS). Thereafter, significant features are computed using the location data. Obtained features are then segmented to improve the process of LBS. Adaptive network-based fuzzy inference system (ANFIS) is then utilized to efficiently classify the user data. Finally, the optimization of classified documents is achieved using the nondominated sorting genetic algorithm-III (NSGA-III). Extensive experiments are performed to validate GANM for LBS. Comparative analyses reveal that GANM outperforms the competitive LBS models in terms of F-score, accuracy, sensitivity, specificity, and area under curve by 2.2734%, 2.3981%, 2.3947%, 2.4271%, and 2.1638%, respectively. [ABSTRACT FROM AUTHOR]

This paper asks: to what extent can a dedicated or special committee with access to classified information empower parliaments to oversee major defence procurement decisions? These decisions often involve a mixture of political, military, economic and societal interests. Particularly after episodes of contestation or controversy, questions tend to arise about how to empower parliaments. The central argument in this paper is that being institutionally empowered and incentivised does not guarantee rigorous oversight. The availability of expertise is an oft-ignored factor in studies of parliamentary oversight. An analysis of oversight behaviour by the Belgian Federal Parliament during the acquisition of new fighter jets (2015–2018) shows that members of parliament also need to be capable of mobilising the necessary expertise in order to translate technical information in such a way that it allows them to influence decision-making, which is often executive-dominated. In this way, this paper contributes to managing expectations about the capacity of parliaments to cope with complex military problems. [ABSTRACT FROM AUTHOR]

Susceptible data recognition has become a fundamental requirement in any network administration system. Though, in suitable sharing and usage, the susceptible data could wipe out the user's privacy. So, susceptible data detection and its security re-assurance in a cloud-IoT (Internet of Things) integrated distributive communication network are mandatory. In this paper, the authors have anticipated novel susceptible data detection and re-assurance algorithms. The algorithms are capable to make out the identical attributes from diverse data sources which are précised by the domain expert. In the proposed method, the sensitivity scores of distinct attributes are measured as significant features for susceptible data identification and assurance. However, the distinctions of sensitivity scores will be able to distinguish the susceptible data from the non-susceptible data in a cloud-IoT integrated distributed computing environment. The authors have explicated various ways through which susceptible data may be exposed in the distributed system environment. Moreover, the authors have proposed novel algorithms for the security re-assurance of static/dynamic susceptible data. The decision tables are considered for each of the definite cases of security re-assurance in a cloud-IoT enabled distributive computing platform. These decision tables will facilitate the network managers to validate the legitimacy of the requests which are arriving from various extents of distributive internetworked systems. In this research work, the results of security re-assurance processes of static and dynamic susceptible data are authenticated through the two dimensional (2D) and three dimensional (3D) graphic representations. The two- and three-dimensional graphical representations designate that the requests initiated from inter/intra networks are being traced and the illegitimate requests are being leftover by the automated model in a cloud-IoT environment. This process will avert the attacks generated from identical internet protocol (IP) addresses. As a summing up it can be said that the research paper primarily emphasizes an innovative approach to recognizing the susceptible data in a cloud-IoT integrated distributive environment and the anticipated technique defends the susceptible data from unlawful admittance by the intruders. [ABSTRACT FROM AUTHOR]

Networks play a central role in cyber-security: networks deliver security attacks, suffer from them, defend against them, and sometimes even cause them. This article is a concise tutorial on the large subject of networks and security, written for all those interested in networking, whether their specialty is security or not. To achieve this goal, we derive our focus and organization from two perspectives. The first perspective is that, although mechanisms for network security are extremely diverse, they are all instances of a few patterns. Consequently, after a pragmatic classification of security attacks, the main sections of the tutorial cover the four patterns for providing network security, of which the familiar three are cryptographic protocols, packet filtering, and dynamic resource allocation. Although cryptographic protocols hide the data contents of packets, they cannot hide packet headers. When users need to hide packet headers from adversaries, which may include the network from which they are receiving service, they must resort to the pattern of compound sessions and overlays. The second perspective comes from the observation that security mechanisms interact in important ways, with each other and with other aspects of networking, so each pattern includes a discussion of its interactions. [ABSTRACT FROM AUTHOR]

Security bugs can catastrophically impact our increasingly digital lives. Designing effective tools for detecting and fixing software security bugs requires a deep understanding of security bug characteristics. In this paper, we conducted a comprehensive study on security bugs and proposed the classification criteria for security bug category, that is, root cause, consequence, and location. In addition, we selected 1076 bug reports from five projects (i.e., Apache Tomcat, Apache HTTP Server, Mozilla Firefox, Linux Kernel, and Eclipse) in the NVD for investigation. Finally, we investigated the correlation between the classification results and obtained some findings: (1) memory operation is the most common security bug; (2) the primary root causes of security bugs are CON (Configuration Error), INP (Input Validation Error), and MEM (Memory Error); (3) the severity of more than 40% of security bugs is high; (4) security bugs caused by INP mainly occur on web; and (5) security bugs caused by LOG (Logic Resource Error) usually lead to DoS (Denial of Service). We discussed these findings through data analysis, which can also help developers better understand the characteristics of security bugs. [ABSTRACT FROM AUTHOR]

Software defined network (SDN) decouples the network control and data planes. Despite various advantages of SDNs, they are vulnerable to various security attacks such anomalies, intrusions, and Denial-of-Service (DoS) attacks and so on. On the other hand, any anomaly and intrusion in SDNs can affect many important domains such as banking system and national security. Therefore, the anomaly detection topic is a broad research domain, and to mitigate these security problems, a great deal of research has been conducted in the literature. In this paper, the state-of-the-art schemes applied in detecting and mitigating anomalies in SDNs are explained, categorized, and compared. This paper categorizes the SDN anomaly detection mechanisms into five categories: (1) flow counting scheme, (2) information-based scheme, (3) entropy-based scheme, (4) deep learning, and (5) hybrid scheme. The research gaps and major existing research issues regarding SDN anomaly detection are highlighted. We hope that the analyses, comparisons, and classifications might provide directions for further research. [ABSTRACT FROM AUTHOR]

Summary The coupling between the power system and natural gas system becomes more and more intensive, so it is necessary to study the security of integrated power and natural gas systems. This paper proposes the concept of total load energy supply capability (TLESC) for integrated power and natural gas systems. Then, a nonlinear optimization model is presented to calculate this TLESC. Normal operation of the power system and natural gas system, and the N‐1 contingency of the power system are considered. Piecewise linearization is used to linearize nonlinear models to obtain a uniquely effective solution. Then, the impact of contingencies on the TLESC is measured by the load change rate and the contingency load rate. A method for dividing the security level of the integrated power and natural gas systems is proposed. The N‐1 check is used to judge whether the integrated power and natural gas systems are secure to operate. If the N‐1 check is not satisfied, load loss rate, threshold crossing, hazard index, and so on are used to measure the insecure operation state; If the N‐1 check is satisfied, security margin index, power flow‐gas flow index, comprehensive margin index are used to measure the secure operation state of the system. An integrated 6‐bus power system and 7‐node natural gas system, an integrated 39‐bus power system and 20‐bus natural gas system and the modified IEEE 118‐bus power system and a 40‐node natural gas system are carried out to validate the effectiveness of the proposed model and security level classification method are verified by simulation examples. [ABSTRACT FROM AUTHOR]

For over many decades, relational database model has been considered as the leading model for data storage and management. However, as the Big Data explosion has generated a large volume of data, alternative models like NoSQL and NewSQL have emerged. With the advancement of communication technology, these database systems have given the potential to change the existing architecture from centralized mechanism to distributed in nature, to deploy as cloud-based solutions. Though all of these evolving technologies mostly focus on performance guarantees, it is still being a major concern how these systems can ensure the security and privacy of the information they handle. Different datastores support different types of integrated security mechanisms, however, most of the non-relational database systems have overlooked the security requirements of modern Big Data applications. This paper reviews security implementations in today's leading database models giving more emphasis on security and privacy attributes. A set of standard security mechanisms have been identified and evaluated based on different security classifications. Further, it provides a thorough review and a comprehensive analysis on maturity of security and privacy implementations in these database models along with future directions/enhancements so that data owners can decide on most appropriate datastore for their data-driven Big Data applications. [ABSTRACT FROM AUTHOR]

Since Sirocco, a 1951 film, the United States' cinema, Hollywood, has produced many terrorism films that have portrayed Arabs and Muslims unjustly as "terrorists." After the 9/11 terrorist attacks, Hollywood's projection of prejudice and negative stereotypes of the Arabs and Muslims have been fostered. The post-9/11 period was an era in which the White House, the Pentagon, and the Central Intelligence Agency (CIA) contributed directly and indirectly to the production of several terrorism films. In Zero Dark Thirty, gifts were offered to some CIA agents in order to obtain classified information and various accesses. In Iron Man, funding and resources were provided, a green light was given, and strict regulations were imposed by the Pentagon to portray the military in a positive light. In Syriana, a CIA agent had a friendly relationship with the film screenwriter, and much of the film events were inspired by the agent's personal experiences. The post-9/11 films have been used to spread stereotyped demeaning images of the Arabs and Muslims and perpetuated a constant distortion of Muslim communities. They have therefore severely targeted Muslims and depicted them as murderers and criminals, who express the feeling of hatred towards Western civilization. [ABSTRACT FROM AUTHOR]

To share information in a multinational environment is always a big challenge. This statement is especially true when systems with different classification levels need to operate simultaneously, or different national classified systems must work together. In the first part of this article, the author analysed the different international security management procedures, the possible threats, vulnerabilities, and solutions to create the most effective information-sharing environment. The purpose of this article, as in the first part, is to analyse and evaluate the different information sharing methods and procedures that can be applied to the sharing of information between classified and non-classified networks. It also analyses the risks arising from their use and the security measures needed to remedy them. In this paper, the author collected the possible information sharing techniques and solutions and analysed the different methods to find the best result to share information in multinational environments. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of long time and low accuracy of traditional methods, a cloud computing data center information classification and storage method based on group collaborative intelligent clustering was proposed. The cloud computing data center information is collected in real time through the information acquisition terminal, and the collected information is transmitted. The optimization function of information classification storage location was constructed by using the group collaborative intelligent clustering algorithm, and the optimal solutions of all storage locations were evolved to obtain the elite set. According to the information attribute characteristics, different information was allocated to different elite sets to realize the classified storage of information in the cloud computing data center. The experimental results show that the longest time of information classification storage is only 0.6 s, the highest information loss rate is 10.0%, and the highest accuracy rate is more than 80%. [ABSTRACT FROM AUTHOR]

Learning vector quantization (LVQ) constitutes a very popular machine learning technology with applications, for example, in biomedical data analysis, predictive maintenance/quality as well as product individualization. Albeit probabilistic LVQ variants exist, its deterministic counterparts are often preferred due to their better efficiency. The latter do not allow an immediate probabilistic interpretation of its output; hence, a rejection of classification based on confidence values is not possible. In this contribution, we investigate different schemes how to extend and integrate pairwise LVQ schemes to an overall probabilistic output, in comparison with a recent heuristic surrogate measure for the security of the classification, which is directly based on LVQ's multi-class classification scheme. Furthermore, we propose a canonic way how to fuse these values over a given time window in case a possibly disrupted measurement is taken over a longer time interval to counter the uncertainty of a single point in time. Experimental results indicate that an explicit probabilistic treatment often yields superior results as compared to a standard deterministic LVQ method, but metric learning is able to annul this difference. Fusion over a short time period is beneficial in case of an unclear classification. [ABSTRACT FROM AUTHOR]

This paper introduces security assessment methodology for isolated single-workstation multilayer systems processing sensitive or classified data according with a corresponding security model for such system. The document provides a high-level tool for systematizing certain-class-systems security models development. The models based on the introduced methodology cover data confidentiality and availability attributes protection on a sufficient level. [ABSTRACT FROM AUTHOR]

The article examines some of the proffered reasons why evidentiary standards for the admission of certain types of evidence in military commissions are superior to those in Article III courts. Topics mentioned covered include miscellaneous misconceptions relating to evidence in Article III courts, the Classified Information Procedures Act passed in 1980, and prosecution of French citizen Zacarias Moussaoui for his role in the September 11, 2001 terror attacks.

Discovery is an important aspect of the civil litigation process in the United States of America, in which all parties to a lawsuit are permitted to request relevant evidence from other parties. With the rapid growth of digital content, the emerging need for "e-discovery" has created a strong demand for techniques that can be used to review massive collections both for "responsiveness" (i.e., relevance) to the request and for "privilege" (i.e., presence of legally protected content that the party performing the review may have a right to withhold). In this process, the party performing the review may incur costs of two types, namely, annotation costs (deriving from the fact that human reviewers need to be paid for their work) and misclassification costs (deriving from the fact that failing to correctly determine the responsiveness or privilege of a document may adversely affect the interests of the parties in various ways). Relying exclusively on automatic classifi- cation would minimize annotation costs but could result in substantial misclassification costs, while relying exclusively on manual classification could generate the opposite consequences. This article proposes a risk minimization framework (called MINECORE, for "minimizing the expected costs of review") that seeks to strike an optimal balance between these two extreme stands. In MINECORE (a) the documents are first automatically classified for both responsiveness and privilege, and then (b) some of the automatically classified documents are annotated by human reviewers for responsiveness (typically by junior reviewers) and/or, in cascade, for privilege (typically by senior reviewers), with the overall goal of minimizing the expected cost (i.e., the risk) of the entire process. Risk minimization is achieved by optimizing, for both responsiveness and privilege, the choice of which documents to manually review.We present a simulation study in which classes from a standard text classification test collection (RCV1-v2) are used as surrogates for responsiveness and privilege. The results indicate that MINECORE can yield substantially lower total cost than any of a set of strong baselines. [ABSTRACT FROM AUTHOR]

Today, it is important to understand that normative conflicts are at the root of the destruction of the existing system of religious security. The essential features of normative conflicts lie in the contradictions between parts of a separate system of norms and values, or between normative value systems of a different nature, but included in one dominant culture. The authors determine that to ensure religious security, it is necessary to minimize the destructive effects of normative conflicts through the removal of contradictions. The study proposes a classification of practices that make it possible to remove the severity of normative conflicts, including in the sphere of the 'secular' - 'religious' segments. Based on the principles of the anthropological approach, conflict functionalism, and marginal anthropology, the authors identify a system of authoritarian and humanistic practices of religious security. The authoritarian type of practice includes stigmatization and legal protection of the feelings of believers. Humanistic practices should include tolerance, nonviolence, and calls for a peaceful resolution of conflicts. The classification of religious security practices is based on the interpretation of the concept of norm represented by two paradigms: classical and nonclassical. The authors conclude that all types of religious security practices are actively used in the modern world. [ABSTRACT FROM AUTHOR]

The currently accepted means of categorizing household food insecurity and identifying famine or famine risk is through Integrated Food Security Phase Classification (IPC) analysis. IPC guidelines set criteria for the measurement and determination of famine, but recent analyses in famine-risk countries have faced several challenges. Commonly used indicators capture different aspects of food security and often produce very divergent estimates of the prevalence of food insecurity. This led to the usage of the Household Hunger Scale (HHS) as the presumed "anchor" indicator for IPC acute food insecurity analysis, with all other indicators of food consumption calibrated to the HHS. But because there was no gold standard, only an "anchor," this presumption had never been tested. Further, given that HHS was the only indicator that could specify the difference between the two most severe categories of IPC Phase Classification (Phase 4 "Emergency" and Phase 5 "Famine"), analysis of food insecurity at the extreme end of the IPC scale relies heavily on HHS, rather than the standard panoply of indicators used for acute food insecurity analysis at lesser levels of severity. This study sought to test how well HHS differentiates between IPC Phase 4 and Phase 5 and to investigate the validity of the usage of HHS as the "anchor" indicator across the spectrum of IPC analysis. Data was collected in seven different severely food-insecure areas of South Sudan, Kenya, and Somalia. The findings show that, overall, the HHS performs reasonably well as an "anchor indicator" across the IPC scale and is able to differentiate the majority of cases in Phases 4 and 5 but tends to over-classify (over-estimate the severity of food insecurity at the household level)—especially at the lower end of the IPC scale. At the high end of the scale, these results indicate that HHS is good at flagging highly food-insecure households but may require additional information to rigorously differentiate households in Phase 4 from those in Phase 5. The study identified ways to improve classification by HHS by some simple additional questions or observations to differentiate between Phase 4 and Phase 5. In an era of extreme scarcity of humanitarian funding, this has substantial implications for resource allocation and humanitarian prioritization. [ABSTRACT FROM AUTHOR]

• LASA fills the gap that no adversary is designed specified for sonar image, according to the real-world underwater conditions. • The performance of LASA is superior to the existing state-of-the-art adversarial attack methods (both targeted and non-targeted) in SSS image classification. • We demonstrate that the adversarial training indeed improves the robustness of CNNs against several adversarial attack methods. • LASA shows better understanding of adversarial attack on SSS images. Deep convolutional neural networks (CNNs) are extensively applied to the classification tasks for Side-scan sonar (SSS) images. However, state-of-the-art neural networks are prone to be confused by adversarial attacks that generate a tiny modification of the images, threatening the security of SSS classification. The robustness of CNN to adversarial attacks can be improved by introducing adversarial examples through adversarial training. Practical adversarial examples are often generated from elaborate adversarial attackers. For the underwater scenario of sonar, a specially designed adversarial attack method to weaken SSS image classification can make the research community better understand the weakness of CNN in this scenario and improve the security measures in a well-directed way. Thus, exploring adversarial attack methods for SSS image classification is essential. Nevertheless, the existing adversarial attack methods are designed for optical images, reflecting no physical characteristics of sonar images. To fill this gap and investigate the adversarial attack related to real-world conditions, in this paper, we propose an adversarial attack method named Lambertian Adversarial Sonar Attack (LASA). It initially leverages the Lambertian model to simulate the formation of the SSS image, factoring the image to three parameters, then updates the parameters on the direction of gradients by the chain rule. Finally, the parameters regenerate the adversarial example to fool the classifier. To validate the performance of LASA, we constructed a diversified SSS image dataset containing three categories. On our dataset, LASA reduces the Top-1 accuracy of a well-trained ResNet-101 to 7.31 % ± 0.21 (one-shot version) and 0.00% (iterative version), the success rate of targeted attack reaches 97.03 ± 2.24 , far beyond the performance of the existing state-of-the-art adversarial attack methods. Meanwhile, we show that the adversarial training using examples generated from LASA makes the classifier more robust. We expect that our methods can be applied as a benchmark of adversarial attacks on SSS images, motivating future research to design novel neural networks or defensive methods to resist real-world adversarial attacks on SSS images. [ABSTRACT FROM AUTHOR]

Current advancement in satellite sensor and remote sensing image (RSI) technologies allows high-resolution RSI, which provide spatial information about the world. In these circumstances, RSI scene classification is attracted considerable interest owing to its wide-ranging applications like environmental monitoring, urban planning, national defence security, and natural hazard detection. New deep learning (DL) achievements have resulted in the newest technology of RSISC applications; however, they lack trustworthiness and explainability. But RSIs of sensitive area is vulnerable to theft, interception, and loss due to the transmission through public channel or the storage in the cloud environment. Therefore, this study develops a mayfly optimization with deep learning (DL) based robust remote sensing scene image classification (MFODL-RRSSIC) model. The presented MFODL-RRSSIC model majorly aims to accomplish two objectives namely scene classification and security. For scene classification, the presented MFODL-RRSSIC technique employs NasNet feature extraction, MFO based hyperparameter tuning, and stacked autoencoder (SAE) classifier. Besides, the presented MFODL-RRSSIC model exploits modified deep belief network (MDBN) for intrusion detection and attaining security. A wide-ranging simulation analysis is made on popular datasets to illustrate the enhanced performance of the MFODL-RRSSIC technique. [ABSTRACT FROM AUTHOR]

These cases are used to model options that U.S. government R&D procedures could emulate, and though Srivastava acknowledges that "degrees of openness" might apply given security restrictions for government projects, American technology innovation strategies "must evolve" (23-24). With I Innovation in a Secret World i , Tina Srivastava joins Sharon Weinberger and Christian Brose in highlighting the challenging environment of research and development and procurement in the United States Department of Defense.[1] The implications inherent in government innovation are obvious and compelling - technological superiority might well be a deciding factor in future conflicts and is therefore crucial to U.S. national security. [Extracted from the article]

Today, it is important to understand that normative conflicts are at the root of the destruction of the existing system of religious security. The essential features of normative conflicts lie in the contradictions between parts of a separate system of norms and values, or between normative value systems of a different nature, but included in one dominant culture. The authors determine that to ensure religious security, it is necessary to minimize the destructive effects of normative conflicts through the removal of contradictions. The study proposes a classification of practices that make it possible to remove the severity of normative conflicts, including in the sphere of the "secular" -- "religious" segments. Based on the principles of the anthropological approach, conflict functionalism, and marginal anthropology, the authors identify a system of authoritarian and humanistic practices of religious security. The authoritarian type of practice includes stigmatization and legal protection of the feelings of believers. Humanistic practices should include tolerance, nonviolence, and calls for a peaceful resolution of conflicts. The classification of religious security practices is based on the interpretation of the concept of norm represented by two paradigms: classical and nonclassical. The authors conclude that all types of religious security practices are actively used in the modern world. [ABSTRACT FROM AUTHOR]

The article discusses the U.S. Court of Appeals for the District of Columbia Circuit's ruling in the 2017 case Dhiab v. Trump which deals with freedom of the press protections under the American Constitution's First Amendment and the court's determination that the press cannot unseal classified video recordings featuring Guantanamo Bay Detention Camp detainee Wa’el (Jihad) Dhiab. A legal right of access is addressed, along with the legal aspects of classified government records.

The article focuses on the importance of managing the relationship between intelligence community and politicians in the U.S. and discusses the President's Daily Brief (PDB), a classified and agenda informing document. Topics discussed include the Office of the Director of National Intelligence, under which the PDB is prepared; the declassification of several PDBs by the Central Intelligence Agency; and the origin of the PDBs in 1946 under former President Harry S. Truman. Also mentioned are several former Presidents and their policies on PDBs including John F. Kennedy and Lyndon B. Johnson.

Purpose - There should be one standard for those with access to classified data. Leadership by example should be required by all managers, supervisors and department heads. The paper aims to discuss this issue. Design/methodology/approach - This paper is a viewpoint and does not have a methodology. Findings - Leaders who work in the public sector have an inherent responsibility to earn and maintain the trust of the public for whom they serve. Regardless of whether one is a career government employee, a politician or a political appointee, all who have access to classified material should respect the necessity of safeguards to keep one's nation's secrets - secret. Research limitations/implications - If leaders fail to set the example, then nations risk further breaches of their classified information. Practical implications - This viewpoint applies to anyone who works in an environment dealing with classified material. Social implications - This viewpoint serves to educate the public on maintaining a single standard for those who handle classified material. Originality/value - The author has yet to see much covered in peer-reviewed publications on this topic and believe that the subject is particularly relevant at this time. [ABSTRACT FROM AUTHOR]

Trying to forecast the human behavior involves acts and knowledge of motivational theories, applicable to profile of each organization and in particular to each individual's style. The anticipation of personal attitudes has not the only aim for a passive monitoring of professional activity, but also wants to increase performance of risk avoidance, in accordance with a specific organizational environment. The emergence and development of motivational forms and values, whose projections determine social crimes, are risk factors, affecting the professional activity of the person, but also affecting the performance and stability of the institution. Moreover, if the motivation determines attitudes aimed at compromising classified information, the resulting actions may be considered as threats to national security. The prevention of such threats can only be achieved by understanding motivational mechanisms and external conditions for the perssonel that make it possible to transform some intentions into real actions. [ABSTRACT FROM AUTHOR]

Can states credibly communicate their intentions through covert policy tools, despite the absence of credibility-enhancing publicity? Most extant research suggests covert action and secrecy in general are uniquely uninformative and often used as an alternative to signaling. Yet episodes such as Richard Nixon's secret bombing of Cambodia suggest that leaders have used covert action to convey intentions and coerce adversaries. This article builds a theoretical framework for understanding signaling in the covert sphere, developing reasons why states find covert communication both intelligible (that is, the basic intended message is understandable) and credible (that is, the message is believable). We argue that two target audiences—local allies and strategic adversaries—tend to observe covert action and that the costs and risks incurred by initiating and expanding covert action credibly convey resolve. We assess our arguments empirically through careful process tracing of a set of nested covert interventions by Soviet and American leaders in conflicts in Angola and Afghanistan. Drawing on a trove of recently declassified material, we assess intentions and inferences related to covert signaling. We find that both strategic adversaries and local partners observed and drew inferences about resolve. Covert lethal aid programs thereby served as a credible indicator of resolve through three mechanisms we identify in the paper: sunk costs, counter-escalation risks, and domestic political risks. These findings have important implications for the study of coercive bargaining, secrecy, and reputation. They also shed light on an important policy tool contemporary policymakers will likely use, suggesting the kinds of effects covert action has and elucidating the basic interpretive framework needed to communicate messages with new methods like covert cyber attacks. [ABSTRACT FROM PUBLISHER]