Reviewerly/exHarmony huggingface dataset:
CYBERSECURITY AND MANAGEMENT'S ETHICAL RESPONSIBILITIES: THE CASE OF EQUIFAX AND UBER

The primary subject matter of this case concerns ethical decision making.The case has a difficulty level of four, appropriate for senior level.The case is designed to be taught in one class period of approximately 50 -75 minutes and is expected to require 2-3 hours of outside preparation by students. CASE SYNOPSISProtecting sensitive customer data and company information has proven to be problematic for some companies.As a result, companies across all industries must be alert to security vulnerabilities that leave them exposed to hackers.This is best illustrated by Equifax, a credit reporting bureau, and Uber, a ride-sharing company, that disclosed data breaches to the public in 2017.These cyber-attacks were far-reaching, with the Equifax breach affecting approximately 145 million customers and the Uber attack affecting 57 million customers and drivers.Management of both companies delayed notifying the public of the data breach on a timely basis and corporate executives may have benefited financially from this inaction.This raises the question as to management's ethical responsibility after experiencing a cyber-attack.The case study focuses on the actions taken by company management to address the respective data breaches.It does not address steps that could be taken by the companies to reduce cybersecurity risk.Students are asked to use the normative, deontological, and consequentialism ethical theories to assess managements' responses to the cyber-attacks.
A Theoretically Grounded Question Answering Data Set for Evaluating Machine Common Sense

ABSTRACT Achieving machine common sense has been a longstanding problem within Artificial Intelligence. Thus far, benchmark data sets that are grounded in a theory of common sense and can be used to conduct rigorous, semantic evaluations of common sense reasoning (CSR) systems have been lacking. One expectation of the AI community is that neuro-symbolic reasoners can help bridge this gap towards more dependable systems with common sense. We propose a novel benchmark, called Theoretically Grounded common sense Reasoning (TG-CSR), modeled as a set of question answering instances, with each instance grounded in a semantic category of common sense, such as space, time, and emotions. The benchmark is few-shot i.e., only a few training and validation examples are provided in the public release to avoid the possibility of overfitting. Results from recent evaluations suggest that TG-CSR is challenging even for state-of-the-art statistical models. Due to its semantic rigor, this benchmark can be used to evaluate the common sense reasoning capabilities of neuro-symbolic systems.
Entanglement Management through Swapping over Quantum Internets

Quantum Internet has the potential to support a wide range of applications in quantum communication and quantum computing by generating, distributing, and processing quantum information. Generating a long-distance quantum entanglement is one of the most fundamental functions of a quantum Internet to facilitate these applications. However, entanglement is a probabilistic process, and its success rate drops significantly as distance increases. Entanglement-swapping is an efficient technique used to address this challenge. How to efficiently manage the entanglement through swapping is a fundamental yet challenging problem. This article considers two entanglement-swapping methods: (1) Bell state measurement (BSM) entanglement-swapping: a classic entanglement-swapping method that is able to fuse two successful quantum links, (2) Greenberger-Horne-Zeilinger (GHZ) measurement entanglement-swapping: a more general and efficient swapping method which is capable of fusing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> successful quantum links. The goal is to maximize the entanglement rate for multiple quantum processor unit (QPU) pairs over the quantum Internet with a general topology. Two efficient entanglement management protocols are proposed which respectively make use of the unique properties of BSM and GHZ. Evaluation results highlight that the proposed protocols outperform the existing ones.
Artificial intelligence: risks and perspectives of culture

The article presents an analysis of the concept of “artificial intelligence” (AI) in its relationship with socio-cultural prerequisites. Having arisen at a certain stage in the development of technological culture, AI breaks out into the sphere of independent existence, representing both a progressive form and a threat to human existence. Threats to cultural identity arise, interethnic and intercultural ties are weakened; a person is deprived of cultural and historical memory, is torn off from national roots and traditions. AI covers a significant landscape of human skills, being implemented in management, investment activities, and programming. It is an intermediary in social interaction, is present in science, translation, fine arts, theorem proving, recognizes speech and images. However, artificial intelligence does not invade the sphere of emotions, empathy, understanding of art and culture; it is an exclusively human sphere, which is deformed in the context of globalization. The relevance of the study is due to the search for harmonious links between the “superreality” and man, his creative thinking and freedom of self-realization. The novelty lies in the emphasis on changes in the methodology of scientific research: natural science, humanitarian and engineering knowledge, merging, form a new sphere of knowledge. In it, ethical and cultural principles are assumed to be fundamental. The factors of culture transformation at the stage of the information society are identified: ideas about the threats of AI in the public mind; the emergence of “roboethics” norms, security strategies from the field of artificial intelligence; the transformation of society into a communicative one. The purpose of the study: to consider the complex of consequences of the introduction of AI into the space of human existence, to identify the threats and risks arising from its possible total spread. Using the axiological approach, semiotic analysis, a change in the sign-symbolic system, and then the meanings and values of culture, was revealed. The author comes to the conclusion that artificial intelligence is a reality that is visible and understandable to mankind. It is able to mimic human cognitive abilities. Facilitating and improving human life, this phenomenon can become a tool for manipulating people’s consciousness. It is shown that the philosophical and anthropological approach makes it possible to comprehend the problem of artificial intelligence included in the field of a holistic view of the relationship between the spiritual essence of man and the world. It is concluded that AI should be controlled by the joint efforts of the scientific community. The quantitative growth of communications does not lead to a close emotional connection, there is a depersonalization of contacts, a passion for self-presentation. The problem of personal identity remains open, the solution of which we see in the appeal to traditional value orientations based on the spiritual content of culture.
Synthetic Tabular Data Based on Generative Adversarial Networks in Health Care: Generation and Validation Using the Divide-and-Conquer Strategy

Background Synthetic data generation (SDG) based on generative adversarial networks (GANs) is used in health care, but research on preserving data with logical relationships with synthetic tabular data (STD) remains challenging. Filtering methods for SDG can lead to the loss of important information. Objective This study proposed a divide-and-conquer (DC) method to generate STD based on the GAN algorithm, while preserving data with logical relationships. Methods The proposed method was evaluated on data from the Korea Association for Lung Cancer Registry (KALC-R) and 2 benchmark data sets (breast cancer and diabetes). The DC-based SDG strategy comprises 3 steps: (1) We used 2 different partitioning methods (the class-specific criterion distinguished between survival and death groups, while the Cramer V criterion identified the highest correlation between columns in the original data); (2) the entire data set was divided into a number of subsets, which were then used as input for the conditional tabular generative adversarial network and the copula generative adversarial network to generate synthetic data; and (3) the generated synthetic data were consolidated into a single entity. For validation, we compared DC-based SDG and conditional sampling (CS)–based SDG through the performances of machine learning models. In addition, we generated imbalanced and balanced synthetic data for each of the 3 data sets and compared their performance using 4 classifiers: decision tree (DT), random forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LGBM) models. Results The synthetic data of the 3 diseases (non–small cell lung cancer [NSCLC], breast cancer, and diabetes) generated by our proposed model outperformed the 4 classifiers (DT, RF, XGBoost, and LGBM). The CS- versus DC-based model performances were compared using the mean area under the curve (SD) values: 74.87 (SD 0.77) versus 63.87 (SD 2.02) for NSCLC, 73.31 (SD 1.11) versus 67.96 (SD 2.15) for breast cancer, and 61.57 (SD 0.09) versus 60.08 (SD 0.17) for diabetes (DT); 85.61 (SD 0.29) versus 79.01 (SD 1.20) for NSCLC, 78.05 (SD 1.59) versus 73.48 (SD 4.73) for breast cancer, and 59.98 (SD 0.24) versus 58.55 (SD 0.17) for diabetes (RF); 85.20 (SD 0.82) versus 76.42 (SD 0.93) for NSCLC, 77.86 (SD 2.27) versus 68.32 (SD 2.37) for breast cancer, and 60.18 (SD 0.20) versus 58.98 (SD 0.29) for diabetes (XGBoost); and 85.14 (SD 0.77) versus 77.62 (SD 1.85) for NSCLC, 78.16 (SD 1.52) versus 70.02 (SD 2.17) for breast cancer, and 61.75 (SD 0.13) versus 61.12 (SD 0.23) for diabetes (LGBM). In addition, we found that balanced synthetic data performed better. Conclusions This study is the first attempt to generate and validate STD based on a DC approach and shows improved performance using STD. The necessity for balanced SDG was also demonstrated.
Trial Assessment of Online Learners’ Engagement with 360-Degree Architecture Videos

In recent years, with the development of massive open online courses (MOOCs) and extended reality (XR), the use of XR within MOOCs is becoming more feasible. Aside from making simulations possible, XR can support learning in domains where spatial awareness can be critical, such as in architecture. An intermediate technology to XR is 360-degree videos embedded in MOOCs that can be rendered in two-dimensional view (2D) via web browsers or in three-dimensional (3D) view (i.e., volumetric) with the use of a head-mounted display (HMD). When rendered in 3D, a more immersive learning environment may be achieved as the field of view restrictions in 2D format are removed. However, whether the additional dimension can enhance the learning experience, may it be in performance or satisfaction, is yet to be investigated. This study used a short learning module using contents from an existing edX architecture MOOC in a pre-test/post-test randomized mixed methods experiment where learners watch 360-degree videos via a web browser or with an HMD while being observed. Results indicate that while HMD usage may appear to elicit more engagement, the measured learned outcomes between the two groups do not significantly differ. Since purchasing an HMD for online learning is an expense, suggestions for improving the 3D experience were derived from learner interviews. These include better scrutiny of the purpose and alignment of 360-degree video content with the lessons and more robust beta-testing before course release to the public.
Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments

The software design of autonomous vehicles (AVs) incorporates artificial intelligence (AI) characteristics to enhance their safety and overall driving performance. Central to vehicle's operation is the ability to reason effectively in complex and uncertain environments. However, traditional logical systems, such as monotonic logic, often struggle to handle the inherent uncertainties and exceptions encountered in real-world scenarios. This paper proposes the utilization of non-monotonic logic in order to enhance the reasoning capabilities of autonomous vehicles. By incorporating non-monotonic reasoning, vehicles can navigate intricate traffic scenarios, make plausible inferences, and adapt their decisions when faced with conflicting information. This research aims to provide a comprehensive review of non-monotonic logic's application in autonomous vehicles, highlighting its advantages over traditional logical systems and its potential impact on safety and performance. Additionally, through this research, we seek to contribute to the advancement of autonomous driving technology by enhancing the reasoning capabilities of vehicles in various scenarios such as car following related to critical safety events. To overcome the limitations of previous works in car-following models, particularly in their assumptions of homogeneous drivers, the personalized cognitive agent is proposed. The personalized cognitive agent is incorporating heterogeneous driving behaviors, based on individual user preferences, characteristics, and needs. The goal of a personalized cognitive agent is to provide tailored and customized experiences to cognitive vehicles, taking into account the unique requirements and individual preferences of occupants inside autonomous vehicles.
Efficient Key Generation on Lattice Cryptography for Privacy Protection in Mobile IoT Crowdsourcing

To face urgent concern of privacy leakage on mobile crowdsourcing, some Lattice-Based Cryptographic (LBC) schemes have been applied to the cloud-fog-edge data sharing platform for privacy protection. As an important factor of LBC’s security, current key generation usually involves Preimage Gaussian Sampling for Lattice Trapdoor (PGS-LT) to sample short preimage vector from dual lattice. However, there lacks researches on the implementation of PGS-LT according to entities’ computation and storage capacities. To address this issue, we present a fast double-perturbation scheme that is applied to the cloud-fog-edge data sharing platform. Firstly, we design a fast spherical G-lattice sampling algorithm including two samplers: G-perturbation sampler and G-lattice sampler. Among them, the fast non-spherical G-lattice sampling algorithm is extended to arbitrary bases, and deployed on the G-lattice sampler. Meanwhile, the G-perturbation sampler is designed to sample G-perturbation for converting the non-spherical distribution of output G-lattice vector to the spherical one. Secondly, we optimize the assignment of computational tasks in PGS-LT by considering entities’ abilities in the cloud-fog-edge platform. Moreover, we analyze three types of delegated preimage sampling in terms of Gaussian quality and complexity. The analysis and experimental results show that fast spherical G-lattice sampling provides high Gaussian quality of output vector. Meanwhile, in the aspect of complexity, the G-perturbation sampler has lower time & space complexity than the existing works. The G-lattice sampler remains good performance as it only involves extra integer multiplications in linear time complexity.
Prediction of solar irradiance using convolutional neural network and attention mechanism-based long short-term memory network based on similar day analysis and an attention mechanism

<h2>Abstract</h2> As one of the future's most promising clean energy sources, solar energy is the key to developing renewable energy. The randomness of solar irradiance can affect the efficiency of photovoltaic power generation, which makes photovoltaic power generation planning extremely difficult. The main goal of this study is to accurately predict solar irradiance and establish a prediction model with meteorological characteristics to improve prediction accuracy. This paper proposes a convolutional neural network (CNN) and attention mechanism-based long short-term memory network (A-LSTM) to predict solar irradiance the next day. In addition, the prediction accuracy is further improved by combining similar day analyses. A similar day prediction model is constructed by selecting solar energy data from Andhra Pradesh, India. The experimental results show that the method proposed in this paper can predict solar irradiance more accurately, providing a new idea for photovoltaic power generation planning.
Coarse-to-Fine Homography Estimation for Infrared and Visible Images

Homography estimation for infrared and visible images is a critical and fundamental task in multimodal image processing. Recently, the coarse-to-fine strategy has been gradually applied to the homography estimation task and has proved to be effective. However, current coarse-to-fine homography estimation methods typically require the introduction of additional neural networks to acquire multi-scale feature maps and the design of complex homography matrix fusion strategies. In this paper, we propose a new unsupervised homography estimation method for infrared and visible images. First, we design a novel coarse-to-fine strategy. This strategy utilizes different stages in the regression network to obtain multi-scale feature maps, enabling the progressive refinement of the homography matrix. Second, we design a local correlation transformer (LCTrans), which aims to capture the intrinsic connections between local features more precisely, thus highlighting the features crucial for homography estimation. Finally, we design an average feature correlation loss (AFCL) to enhance the robustness of the model. Through extensive experiments, we validated the effectiveness of all the proposed components. Experimental results demonstrate that our method outperforms existing methods on synthetic benchmark datasets in both qualitative and quantitative comparisons.
Tiny-YOLOv7: Tiny Object Detection Model for Drone Imagery

With the rapid development of drones, tiny object detection in drone-captured scenarios has become a challenge task. However, the altitude of the drone changes while flying lead to the scale of the object changes dramatically. In addition, drones flying quickly cause motion blur on the densely tiny objects. In order to address the two issues mention above, we propose Tiny-YOLOv7. In order to detect multi-scale objects, we replace the original prediction heads with transformer prediction heads. For the motion blur issue, we propose DBS module to extract more visual elements and maintain computational cost of model. The DBS module consists of Dynamic Region-Aware Convolution (DRConv), Batch Normalization and Silu modules. On scenarios with dense objects, we additionally incorporate the Convolutional Block Attention Model (CBAM) to find the attention region of dense objects. Tiny-YOLOv7 is an effective and elegant method for handing tiny object detection. We validate our model through extensive experiments on VisDrone2021 and DOTA-v1.0 datasets. The results show that our method obtains remarkable improvements over the other models. In VisDrone2021 dataset, the mAP result of our method is 39.22 $$\%$$ , which is higher than SOTA method by 1.07 $$\%$$ . Furthermore, experiments on dataset DOTA-v1.0 demonstrate generalization of the propose model.
Multi-teacher Self-training for Semi-supervised Node Classification with Noisy Labels

Graph neural networks (GNNs) have achieved promising results for semi-supervised learning tasks on the graph-structured data. However, most existing methods assume that the training data are with correct labels, but in the real world, the graph-structured data often carry noisy labels to reduce the effectiveness of GNNs. To address this issue, this paper proposes a new label correction method, called multi-teacher self-training (MTS-GNN for short), to conduct semi-supervised node classification with noisy labels. Specifically, we first save the parameters of the model training in the earlier iterations as teacher models, and then use them to guide the processes, including model training, noisy label removal, and pseudo-label selection, in the later iterations of the training process of semi-supervised node classification. As a result, based on the guidance of the teacher models, the proposed method achieves the model effectiveness by solving the over-fitting issue, improves the accuracy of noisy label removal and the quality of pseudo-label selection. Extensive experimental results on real datasets show that our method achieves the best effectiveness, compared to state-of-the-art methods.
Survey on Integration of Consensus Mechanisms in IoT-based Blockchains

While IoT systems are increasingly present in different areas of society, ensuring their data&amp;rsquo;s privacy, security, and inviolability becomes paramount. In this direction, Blockchain has been used to protect the security and immutability of data generated by IoT devices and sensors. At the heart of Blockchain solutions, consensus algorithms are crucial in ensuring the security of creating and writing data in new blocks. Choosing which consensus algorithms to utilise is critical because of a fundamental tradeoff between their security strength and response time. However, recent surveys of consensus mechanisms for IoT-based Blockchain focused on individually using and analysing these algorithms. Investigating the integration between these algorithms to address IoT-specific requirements better is a promising approach. In this context, this paper presents a literature review that explains and discusses consensus algorithms in IoT environments and their combinations. The review analyses eight dimensions that help understand existing proposals: ease of integration, scalability, latency, throughput, power consumption, configuration issues, integrated algorithms, and adversary tolerance. The final analysis also suggests and discusses open challenges in integrating multiple consensus algorithms considering the particularities of IoT systems.
Aerodynamic effect for collision-free reactive navigation of a small quadcopter

Abstract The small footprint of tiny multirotor vehicles is advantageous for accessing tight spaces, but their limited payload and endurance impact the ability to carry powerful sensory and computing units for navigation. This article reports an aerodynamics-based strategy for a ducted rotorcraft to avoid wall collisions and explore unknown environments. The vehicle uses the minimal sensing system conventionally conceived only for hovering. The framework leverages the duct-strengthened interaction between the propeller wake and vertical surfaces. When incorporated with the flight dynamics, the derived momentum-theory-based model allows the robot to estimate the obstacle’s distance and direction without range sensors or vision. To this end, we devised a flight controller and reactive navigation methods for the robot to fly safely in unexplored environments. Flight experiments validated the detection and collision avoidance ability. The robot successfully identified and followed the wall contour to negotiate a staircase and evaded detected obstacles in proof-of-concept flights.