Reviewerly/exHarmony huggingface dataset:
Deepfakes, Copyright and Personality Rights an Inter-Disciplinary Perspective

This chapter concentrates on unsupervised learning, the recent rise of deepfakes, the potential role of copyright and related rights, exceptions and limitations therein, and personality rights to ensure a balanced application of the technology. Using inter-disciplinary insights from law and the technical literature, this chapter takes a holistic view on the uptake, adoption, and the future of deepfakes. Considering the centrality of personality rights to deepfakes, this chapter also explores the potential foundations of an EU-wide personality rights framework. It crucially examines the future acceptance of deepfakes, and considers the potential role of the blockchain technology to prevent the apocalypse of a potential infodemic following the widespread prevalence of deepfakes.
AICA Development Challenges

In this chapter we explore the development challenges that must be tackled before fulfilling the great potential of Autonomous Intelligent Cyberdefense Agent (AICA). We propose dividing development challenges into two kinds: the ones that are associated with the AICA engineering ecosystem and the ones that are associated with the AICA research ecosystem. This is reasonable because adequately addressing engineering challenges requires to tackling a range of research challenges. Moreover, engineering and research have different ways of thinking; in general, engineering focuses on narrower aspects and is often built on technical breakthroughs resulting from fundamental research. The engineering ecosystem has six components: design; implementation; individual test & certification; composition; composite test & certification; and deployment. The research ecosystem also accommodating six components: models; architectures; mechanisms; testing and certification; operations; and social, ethical, and legal aspects. To show how the challenges associated with these components are related to each other, we make connections between these two ecosystems by describing how tackling challenges in the research ecosystem would contribute to tackling the challenges that are encountered when engineering AICAs. We draw insights into the gaps between the state-of-the-art technology and the desired ultimate goals and propose research directions to bridge them. We hope this chapter will serve as a milestone in guiding the development (i.e., engineering and research) activities in fulfilling the vision of AICAs.
Cognitive Capacities for Designing, Operating, Supervising, and Securing Complex Systems

Despite the integration of a variety of “Industry 4.0” technologies, the active presence of human actors is required to bring their cognitive capabilities to Designing, Operating, Supervising, and Securing (DOSS) complex systems. Figure 3.1 illustrates the structure of Chap. 3 . In particular, we discuss situation awareness in Sect. 3.1, problem solving in Sect. 3.2, and collaboration in Sect. 3.3 as the three essential cognitive capabilities that distinguish a human actor from a traditional cyber-physical component in the above DOSS procedures. Examples of human roles in four DOSS procedures are introduced to illustrate these three classes of cognitive capabilities and are mapped onto the four quadrants in Fig. 3.2 based on their time-sensitivity and interactivity.
Summary and Conclusions

This book has introduced emerging cyber threats that exploit human vulnerabilities to obtain initial credentials from human users. Cognitive security is a primary concern that needs to be addressed in HCPSs. We have presented a system-scientific foundation that builds on and bridges the fields of psychology, neuroscience, data science, decision theory, and game theory. Such a foundation has led to transdisciplinary socio-technical solutions that protect humans from cognitive security threats and improve the resiliency of HCPSs. In this chapter, we summarize the book, discuss several insights, explore potential applications, and suggest potential directions for future work.
Collaboration and Negotiation

Collaboration and Negotiation is a critical high-level function of an Autonomous Intelligent Cyber-Defense Agent (AICA) that enables communication among agents, central cyber command-and-control (C2), and human operators. Maintaining the Confidentiality, Integrity, and Availability (CIA) triad while achieving mission goals requires stealthy AICA agents to exercise: (1) minimal communication as needed for avoiding detection, (2) verification of information received with limited resources, and (3) active learning during operations to address dynamic conditions. Moreover, negotiations to jointly identify and execute a Course of Action (COA) will require building consensus under distributed and/or decentralized multi-agent settings with information uncertainties. This chapter presents algorithmic approaches for enabling the collaboration and negotiation function. Strengths and limitations of potential techniques are identified, and a representative example is illustrated. Specifically, a two-tier Multi-Agent Reinforcement Learning (MARL) algorithm was implemented to learn joint strategies among agents in a navigation and communication simulation environment. Based on simulation experiments, emergent collaborative team behaviors among agents were observed under information uncertainties. Recommendations for future development are also discussed.
Response Planning

Given a perception of the environment and threat, and an overall assessment of the situation, cyber response planning is invoked to generate a course of action (COA) or multiple COAs intended to defeat the threat and minimize damage to the system. This chapter describes the characteristics of the response planning problem, which provide a context to understand the requirements for a response planner. A response planner must represent the system being defended. It must also understand the capabilities of the attacker. It must represent the system processes and functions and understand how changes to the system state can result in operational impacts. And it must represent how the set of response actions affect the state of the system and/or the ability of an attacker to compromise components. This chapter then provides an overview of the variety of computational techniques that can be employed toward accomplishing response planning goals and discusses their advantages and limitations. The chapter finishes with a description of an operational cyber response planner prototype that has been developed to bring together a set of capabilities that fulfill the described response planner requirements.
Review of System-Scientific Perspectives for Analysis, Exploitation, and Mitigation of Cognitive Vulnerabilities

Chapter 3 elaborates on three critical types of human cognitive capabilities to fulfill four classes of CPS tasks in Fig. 3.2 . For all of its advantages, the active presence of human cognition also brings vulnerabilities. Compared to computer programs and robots that strictly follow the algorithms and retain the same level of performance, human operators may violate security procedures or be prone to errors due to misaligned incentives, herding effects, inattention, fatigue, and bounded rationality. In Chap. 4 , we classify cognitive vulnerabilities into innate vulnerabilities in Sect. 4.1 and acquired vulnerabilities in Sect. 4.2 based on whether they can be mitigated through short-term external interference, including security training and mechanism design in Fig. 1.8 . For each cognitive vulnerability, we first illustrate its impact on HCPSs and how cognitive attacks can exploit it. Then, we present system-scientific perspectives to characterize the vulnerability, the attacks, and the defense methods in different security scenarios, which focus on the computational aspects of vulnerability analysis, exploitation, and mitigation in the literature.
Case Study B: AI Agents for the Tactical Edge

This chapter explores military aspects of building and deploying AI Agents for cyber defense. We concentrate on those aspects which are particularly characteristic of AI Agent deployment at the “tactical edge,” by which we mean warfighters directly involved in executing the mission. This choice of emphasis is motivated by two observations. First, the tactical edge is clearly of preeminent importance in warfare, as it is where actual fighting takes place. Second, and not coincidentally, it is where the general environment for an AI cyber defense agent is least similar to their classic, and still typical, habitat – the enterprise-scale network. The tactical edge presents manifold challenges to the deployment of AI agents as conventionally considered. These challenges stem from generally austere conditions, characterized by low availability of computing power, poor to nonexistent connectivity to the cloud and enterprise-scale resources in general, and porous borders between the cyber domain as conventionally considered and the physical and electronic warfare (EW) domains. These challenges suggest a minimalist approach to AI agents at the tactical edge, emphasizing resilience, graceful degradation, and close cooperation with human warfighters. We elaborate examples of such AI agents derived from our experience applying AI to Blue Force Spectrum Awareness (BFSA).
Case Study A: A Prototype Autonomous Intelligent Cyber-Defense Agent

The AICA International Working Group (IWG) spent 2021 collaboratively developing an initial prototype implementation of the AICA reference architecture, AICAproto21. This prototype was built using open-source software components in a containerized manner to allow for the quickest time-to-completion with maximum flexibility for future capabilities. This prototype was a fully self-contained demonstration of the ability of the agent to respond to an indicated attack with a defensive action, though the scope of scenarios was constrained due to the primary focus on the construction of the framework itself. Future work would include incorporation of computational intelligence (i.e., knowledge representation and automated reasoning components) and additional scenarios. The authors found that the chosen approach did lead to a very easy-to-scale solution that is likely to work in a cross-platform manner. Complicating factors encountered include the difficulty in constructing the framework to operate with various external systems in a generalizable way, and the likely host-system impact of needing to run multiple containers simultaneously to achieve desired functionality, especially when host systems could be low-power “things” such as drones, weapons platforms, et cetera. A critical question to answer as work on AICAproto21 and related experimentation continues is whether the effort required to build a more “ground-up” monolithic application is justified by the potential savings in resource consumption and optimization for the specified purpose.
Development of Extractor-Classifier-Regulator integrated anomaly detection model for turning process

Anomaly detection is important for safety and productivity in manufacturing systems. Together with the development in IoT, data-driven approach turns out as the most promising alternative for up-to-date anomaly detection. While applying modern A.I solutions into manufacturing for anomaly detection, however, it is often encountered that insufficient anomalous data is available for training deep-learning models. The most manufacturing systems are optimized to run at the best conditions so that anomalies rarely occur in the actual production lines. In this study, we proposed and developed an Extractor-Classifier-Regulator integrated anomaly detection model (ECR model) which combines the feature extractor, classifier and regulator in a block stacked way. This model requires the "normal" data only for training, and is capable of identifying the anomality based on the density of distribution in the extracted feature space. The framework described in the Section 2 yields that the ECR model is an end-to-end learning architecture, requires no pre-process for feature extraction and selection. The performance of the proposed model was first verified in the Section 3, by use of the phase-shifted sinusoidal waveforms generated artificially. The results suggested that our ECR model well yielded the degree of abnormality corresponding to the deviation of each variable from the "normal" datasets. The proposed model was then implemented into a turning process of Inconel 718 to detect cutting failure caused by tool wear. The experimental setup including machine tool, workpiece, tool holder, data acquisition system, time sequential signals related to the cutting process were detailed in the Section 4. After being trained by AE (Acoustic Emission) or sound signals captured in the "normal" cutting stage, the proposed model was able to derive anomaly scores which well matched with the cutting stages of "normal", "transitional" and "anomalous" corresponding to the tool wear.
A survey of set accumulators for blockchain systems

Set accumulators are cryptographic primitives used to represent arbitrarily large sets of elements with a single constant-size value and to efficiently verify whether a value belongs to that set. Accumulators support the generation of membership proofs, meaning that they can certify the presence of a given value among the elements of a set. In this paper we present an overview of the theoretical concepts underlying set accumulators, we compare the most popular constructions from a complexity perspective, and we survey a number of their applications related to blockchain technology. In particular, we focus on four different use cases: query authentication, stateless transactions validation, anonymity enhancement, and identity management. For each of these scenarios, we examine the main problems they introduce and discuss the most relevant accumulator-based solutions proposed in the literature. Finally, we point out the common approaches between the proposals and highlight the currently open problems in each field.
DEFEG: Deep Ensemble with Weighted Feature Generation

With the significant breakthrough of Deep Neural Networks in recent years, multi-layer architecture has influenced other sub-fields of machine learning including ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcForest that involves several layers of Random Forest-based classifiers. Although gcForest has outperformed several benchmark algorithms on specific datasets in terms of classification accuracy and model complexity, its input features do not ensure better performance when going deeply through layer-by-layer architecture. We address this limitation by introducing a deep ensemble model with a novel feature generation module. Unlike gcForest where the original features are concatenated to the outputs of classifiers to generate the input features for the subsequent layer, we integrate weights on the classifiers’ outputs as augmented features to grow the deep model. The usage of weights in the feature generation process can adjust the input data of each layer, leading the better results for the deep model. We encode the weights using variable-length encoding and develop a variable-length Particle Swarm Optimization method to search for the optimal values of the weights by maximizing the classification accuracy on the validation data. Experiments on a number of UCI datasets confirm the benefit of the proposed method compared to some well-known benchmark algorithms.
A satellite component contour extraction method for lightweight space mobile platforms

Purpose Owing to the complex space environment and limited computing resources, traditional and deep learning-based methods cannot complete the task of satellite component contour extraction effectively. To this end, this paper aims to propose a high-quality real-time contour extraction method based on lightweight space mobile platforms. Design/methodology/approach A contour extraction method that combines two edge clues is proposed. First, Canny algorithm is improved to extract preliminary contours without inner edges from the depth images. Subsequently, a new type of edge pixel feature is designed based on surface normal. Finally, surface normal edges are extracted to supplement the integrity of the preliminary contours for contour extraction. Findings Extensive experiments show that this method can achieve a performance comparable to that of deep learning-based methods and can achieve a 36.5 FPS running rate on mobile processors. In addition, it exhibits better robustness under complex scenes. Practical implications The proposed method is expected to promote the deployment process of satellite component contour extraction tasks on lightweight space mobile platforms. Originality/value A pixel feature for edge detection is designed and combined with the improved Canny algorithm to achieve satellite component contour extraction. This study provides a new research idea for contour extraction and instance segmentation research.
Identifying Common Barriers to Formal Disclosure of Sexual Violence

This study analyzed tweets from Twitter containing hashtags related to the #WhyIDidntReport movement, which was created to allow victims of sexual violence to share their experiences and seek support. The study aimed to identify common barriers to formal reporting of sexual violence. The study used the Twitter API and snscrape, a social network scraping tool, to gather tweets from January 1, 2020 to October 31, 2022. A total of 29,855 tweets were collected and preprocessed, and 1,500 were randomly selected for analysis to identify themes. The study found that the most common reason for not reporting was personal, followed by societal. The least common reason was criminal. The study also found that the most common subtheme for not reporting was fear of retaliation from the perpetrator, which made up 30.6% of the tweets. Another subtheme under the personal theme was ignorance of assault, which made up 15.3% of the tweets. The subtheme of considering the assault as not serious made up 12.6% of the tweets. Several recommendations were made to the prevalence of sexual violence.
Generating Human-Like Descriptions for the Given Image Using Deep Learning

One of the most prominent applications in the field of computer vision and natural language processing research is image captioner. The paper includes an exhaustive review of the literature on image captioning and the implementation using attention-based encoder-decoder model. The process of depicting an image with textual explanations is known as image captioning. The problem has seen extensive use of encoder-decoder frameworks. In this study, Deep Convolutional Neural Network (CNN) for image classification and Recurrent Neural Network (RNN) for sequence modeling are combined to build a single network that creates descriptions of images using the Microsoft Common Objects in Context Dataset (MSCOCO Dataset). Because of RNNs being computationally expensive to train and assess, memory is often restricted to a few items. By highlighting the most important components of an input image, the Attention model had been used to address this issue. The model was developed using Nvidia Quadro RTX5000 GPU (CUDA), which received the Bleu-1 score of 0.5793 for the 100 generated sentences. The captions generated by the model on the testing dataset labeled nearly all of the objects in the image and were sufficiently like the actual captions in the annotations, even on images outside of the testing dataset.
Unscented Trainable Kalman Filter Based on Deep Learning Method Considering Incomplete Information

Rapid changes of states and occurrence of data missing in power systems cause accurate state estimation very hard.In this paper, an unscented trainable Kalman filter (UTKF) with a deep learning prediction model is proposed to provide accurate state estimation under incomplete information.First, the CNN-LSTM architecture, a typical deep learning model, is applied to form a trainable prediction model (TPM), which offers more accurate prediction of states.However, sometimes states are incomplete due to data losses in transmissions.To deal with incomplete information, historical time-series states are employed and fed to the TPM in order to develop a missing data filling method.In this way, the prediction errors can be lower through the online training and parameter adjustment of the TPM.Combining with the TPM and the missing data filling method, an unscented trainable Kalman filter (UTKF) is proposed to improve the state estimation of power systems when incomplete information is involved.Finally, three cases are designed, and the simulation results show that for the prediction of states, the root mean square error (RMSE), an indicator of accuracies, is reduced by about 3 multiples, if our missing data filling method is added.Furthermore, the accuracy of state estimation is improved about 5 multiples by the proposed UTKF method, even if incomplete information is involved.
A Survey on Fingerprinting Technologies for Smartphones Based on Embedded Transducers

Smartphones are a vital technology, they improve our social interactions, provide us a great deal of information and bring forth the means to control various emerging technologies, like the numerous IoT devices that are controlled via smartphone apps. In this context, smartphone fingerprinting from sensor characteristics is a topic of high interest not only due to privacy implications or potential use in forensics investigations, but also because of various applications in device authentication. In this work we review existing approaches for smartphone fingerprinting based on internal components, focusing mostly on camera sensors, microphones, loudspeakers and accelerometers. Other sensors, i.e., gyroscopes and magnetometers, are also accounted, but they correspond to a smaller body of works. The output of these transducers, which convert one type of energy into another, e.g., mechanical into electrical, leaks through various channels such as mobile apps and cloud services, while there is little user awareness on the privacy risks. Needless to say, miniature physical imperfections from the manufacturing process make each such transducer unique. One of the main intentions of our study is to rank these sensors according to the accuracy they provide in identifying smartphones and to give a clear overview on the amount of research that each of these components triggered so far. We review the features which can be extracted from each type of data and the classification algorithms that have been used. Last but not least, we also point out publicly available datasets which can serve for future investigations.
Analysis of the dilemma of higher vocational thinking education in China under the background of “Internet+”

Abstract Network culture is a culture based on computer network information technology. With the popularity of “Internet+”, the influence of network culture on college students’ ideological views, behavioral patterns, value orientations and psychological development is also increasing. While Internet culture brings opportunities to higher education ideological and political education, it also makes education work face serious challenges. This paper proposes a collaborative knowledge graph construction method for Civic Education based on Internet + group intelligence. The core of the method is a continuously running loop that contains 3 activities: free exploration, automatic integration, and active feedback. The experimental results show that the knowledge graph fusion algorithm can effectively utilize the structural information of the knowledge graph as well as the semantic information of the nodes to form a high-quality knowledge graph fusion scheme. The collaborative approach based on the “explore-fusion-feedback” loop can improve the scale of group construction of knowledge graphs and the efficiency of individual construction of knowledge graphs, and exhibit better scalability of group scale.