Reviewerly/exHarmony huggingface dataset:
The Emergency Braking Game: a game theoretic approach for maneuvering in a dense crowd of pedestrians

Abstract We introduce an algorithm that maneuvers a vehicle through an area with randomly moving pedestrians. In non-critical situations, our strategy is to avoid pedestrians by steering, whereas dangerously moving pedestrians are avoided by braking, possibly coming to a complete stop. The distinction between non-critical and dangerous situations, as well as proof of safety, is based on a continuous optimization problem that we define. In this abstract problem, called Emergency Braking Game, one pedestrian is actively trying to collide with a continuously decelerating car. We show how to determine the outcome of the game based on the initial states of the car and the pedestrian. Using this information, our algorithm can initiate deceleration in the real scenario in time to avoid collision. The method’s safety is proven theoretically, and its efficiency is shown in simulations with randomly moving pedestrians.
Some entanglement survives most measurements

To prepare quantum states and extract information, it is often assumed that one can perform a perfectly projective measurement. Such measurements can achieve an uncorrelated system and environment state. However, perfectly projective measurements can be difficult or impossible to perform in practice. We investigate the limitations of repeated nonprojective measurements in preparing a quantum system. For an $n$-qubit system initially entangled with its environment and subsequently prepared with measurements, using a sequence of weak measurements, we show that some entanglement remains unless one of the measurement operators becomes perfectly projective through an extreme limiting process. Removing initial (unentangled) correlations between a system and its environment and the scenario where measurement outcomes are not tracked are also discussed. We present results for $n$-qubit and $n$-dimensional input states.
Multipartite entanglement from consecutive scatterings

We study how the successive scattering of spin-1/2 particles with a central spin-1/2 target particle can generate entanglement between the helicity degrees of freedom of all scattered particles, effectively producing a multipartite entangled state. We show that the bipartite entanglement between each pair of scattered particles, as quantified by the concurrence, is largest for reflected particles and decreases with the number of scatterings. We study the entanglement generation as a function of the scattered particles momenta, angular distribution, and mass ratios, and show that there is always a combination of optimal helicities and momentum, which generate the largest amount of bipartite entanglement.
Interaction-induced transition in quantum many-body detection probability

The authors introduce the concept of quantum many-body detection probability. They use this to demonstrate the possibility of observing a non-equilibrium transition in a quantum many-body system using single-shot stroboscopic measurements, bypassing the need to repeat the experiment several times to obtain expectation values.
Adaptive Evolutionary Reinforcement Learning with Policy Direction

Abstract Evolutionary Reinforcement Learning (ERL) has garnered widespread attention in recent years due to its inherent robustness and parallelism. However, the integration of Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) remains relatively rudimentary and lacks dynamism, which can impact the convergence performance of ERL algorithms. In this study, a dynamic adaptive module is introduced to balance the Evolution Strategies (ES) and RL training within ERL. By incorporating elite strategies, this module leverages advantageous individuals to elevate the overall population's performance. Additionally, RL strategy updates often lack guidance from the population. To address this, we incorporate the strategies of the best individuals from the population, providing valuable policy direction. This is achieved through the formulation of a loss function that employs either L1 or L2 regularization to facilitate RL training. The proposed framework is referred to as Adaptive Evolutionary Reinforcement Learning (AERL). The effectiveness of our framework is evaluated by adopting Soft Actor-Critic (SAC) as the RL algorithm and comparing it with other algorithms in the MuJoCo environment. The results underscore the outstanding convergence performance of our proposed Adaptive Evolutionary Soft Actor-Critic (AESAC) algorithm. Furthermore, ablation experiments are conducted to emphasize the necessity of these two improvements. It is worth noting that the enhancements in AESAC are realized at the population level, enabling broader exploration and effectively reducing the risk of falling into local optima.
Generation of normal distributions revisited

Abstract Normally distributed random numbers are commonly used in scientific computing in various fields. It is important to generate a set of random numbers as close to a normal distribution as possible for reducing initial fluctuations. Two types of samples from a uniform distribution are examined as source samples for inverse transform sampling methods. Three types of inverse transform sampling methods with new approximations of inverse cumulative distribution functions are also discussed for converting uniformly distributed source samples to normally distributed samples.
Image Encryption Algorithms: A Survey of Design and Evaluation Metrics

Ensuring confidentiality and privacy is critical when it comes to sharing images over unsecured networks such as the internet. Since widely used and secure encryption methods, such as AES, Twofish, and RSA, are not suitable for real-time image encryption due to their slow encryption speeds and high computational requirements, researchers have proposed specialized algorithms for image encryption. This paper provides an introduction and overview of the image encryption algorithms and metrics used, aiming to evaluate them and help researchers and practitioners starting in this field obtain adequate information to understand the current state of image encryption algorithms. This paper classifies image encryption into seven different approaches based on the techniques used and analyzes the strengths and weaknesses of each approach. Furthermore, this paper provides a detailed review of a comprehensive set of security, quality, and efficiency evaluation metrics for image encryption algorithms, and provides upper and lower bounds for these evaluation metrics. Finally, this paper discusses the pros and cons of different image encryption approaches as well as the suitability of different image encryption approaches for different applications.
New Vessel Extraction Method by Using Skew Normal Distribution for MRA Images

Vascular-related diseases pose significant public health challenges and are a leading cause of mortality and disability. Understanding the complex structure of the vascular system and its processes is crucial for addressing these issues. Recent advancements in medical imaging technology have enabled the generation of high-resolution 3D images of vascular structures, leading to a diverse array of methods for vascular extraction. While previous research has often assumed a normal distribution of image data, this paper introduces a novel vessel extraction method that utilizes the skew normal distribution for more accurate probability distribution modeling. The proposed method begins with a preprocessing step to enhance vessel structures and reduce noise in Magnetic Resonance Angiography (MRA) images. The skew normal distribution, known for its ability to model skewed data, is then employed to characterize the intensity distribution of vessels. By estimating the parameters of the skew normal distribution using the Expectation-Maximization (EM) algorithm, the method effectively separates vessel pixels from the background and non-vessel regions. To extract vessels, a thresholding technique is applied based on the estimated skew normal distribution parameters. This segmentation process enables accurate vessel extraction, particularly in detecting thin vessels and enhancing the delineation of vascular edges with low contrast. Experimental evaluations on a diverse set of MRA images demonstrate the superior performance of the proposed method compared to previous approaches in terms of accuracy and computational efficiency. The presented vessel extraction method holds promise for improving the diagnosis and treatment of vascular-related diseases. By leveraging the skew normal distribution, it provides accurate and efficient vessel segmentation, contributing to the advancement of vascular imaging in the field of medical image analysis.
A new CNN-based semantic object segmentation for autonomous vehicles in urban traffic scenes

Abstract Semantic segmentation is the most important stage of making sense of the visual traffic scene for autonomous driving. In recent years, convolutional neural networks (CNN)-based methods for semantic segmentation of urban traffic scenes are among the trending studies. However, the methods developed in the studies carried out so far are insufficient in terms of accuracy performance criteria. In this study, a new CNN-based semantic segmentation method with higher accuracy performance is proposed. A new module, the Attentional Atrous Feature Pooling (AAFP) Module, has been developed for the proposed method. This module is located between the encoder and decoder in the general network structure and aims to obtain multi-scale information and add attentional features to large and small objects. As a result of experimental tests with the CamVid data set, an accuracy value of approximately 2% higher was achieved with a mIoU value of 70.59% compared to other state-of-art methods. Therefore, the proposed method can semantically segment objects in the urban traffic scene better than other methods.
Incidental Data: A Survey towards Awareness on Privacy-Compromising Data Incidentally Shared on Social Media

Sharing information with the public is becoming easier than ever before through the usage of the numerous social media platforms readily available today. Once posted online and released to the public, information is almost impossible to withdraw or delete. More alarmingly, postings may carry sensitive information far beyond what was intended to be released, so-called incidental data, which raises various additional security and privacy concerns. To improve our understanding of the awareness of incidental data, we conducted a survey where we asked 192 students for their opinions on publishing selected postings on social media. We found that up to 21.88% of all participants would publish a posting that contained incidental data that two-thirds of them found privacy-compromising. Our results show that continued efforts are needed to increase our awareness of incidental data posted on social media.
Satellite Image Representations for Quantum Classifiers

Abstract Existing quantum hardware is limited in the number of bits and length of the series of operations. Nevertheless, by shifting parts of the computation on classical hardware, hybrid quantum-classical systems utilize quantum hardware for scaled-down machine learning approaches, which is quantum machine learning. Due to the theoretically possible computational speed-up of quantum computers compared to classical computers and the increasing volume and speed of data generated in earth observation, attempts are now being made to use quantum computers for satellite image processing. However, satellite imagery is too large and high dimensional, and transformations that reduce the dimensionality are necessary to fit the classical data in the limited input domain of quantum circuits. This paper presents and compares several dimensionality reduction techniques as part of hybrid quantum-classical systems to represent satellite images with up to $$256\times 256\times 3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>256</mml:mn> <mml:mo>×</mml:mo> <mml:mn>256</mml:mn> <mml:mo>×</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> values with only 16 values. We evaluate the representations of two benchmark datasets with supervised classification by four different quantum circuit architectures. We demonstrate the potential use of quantum machine learning for satellite image classification and give a comprehensive overview of the impact of various satellite image representations on the performance of quantum classifiers. It shows that autoencoder models are best suited to create small-scale representations, outperforming commonly used methods such as principle component analysis.
4.6-Bit Quantization for Fast and Accurate Neural Network Inference on CPUs

Quantization is a widespread method for reducing the inference time of neural networks on mobile Central Processing Units (CPUs). Eight-bit quantized networks demonstrate similarly high quality as full precision models and perfectly fit the hardware architecture with one-byte coefficients and thirty-two-bit dot product accumulators. Lower precision quantizations usually suffer from noticeable quality loss and require specific computational algorithms to outperform eight-bit quantization. In this paper, we propose a novel 4.6-bit quantization scheme that allows for more efficient use of CPU resources. This scheme has more quantization bins than four-bit quantization and is more accurate while preserving the computational efficiency of the later (it runs only 4% slower). Our multiplication uses a combination of 16- and 32-bit accumulators and avoids multiplication depth limitation, which the previous 4-bit multiplication algorithm had. The experiments with different convolutional neural networks on CIFAR-10 and ImageNet datasets show that 4.6-bit quantized networks are 1.5–1.6 times faster than eight-bit networks on the ARMv8 CPU. Regarding the quality, the results of the 4.6-bit quantized network are close to the mean of four-bit and eight-bit networks of the same architecture. Therefore, 4.6-bit quantization may serve as an intermediate solution between fast and inaccurate low-bit network quantizations and accurate but relatively slow eight-bit ones.
Quantum Leap and Uptake for Technological Advances in Africa in the Era of the COVID-19 Crisis

This chapter explores how the COVID-19 pandemic affected technology in Africa, focusing on technological innovations and uptake. The COVID-19 pandemic has encouraged industrial innovation and digital transformation in Africa. The COVID-19 pandemic has shown that traditional methods are ineffectual, and technology is needed. African nations need digital technology to improve healthcare, education, e-commerce, and governance. Technology narrows the digital divide by improving productivity, access to critical services, and promoting economic development. Telemedicine, e-learning, digital payments, and remote working made Africa resilient in the face of the pandemic. The chapter evaluates African technical problems and potential. Poor infrastructure, internet connectivity, high fees, and digital illiteracy are critical issues that need to be addressed. The chapter suggests that government, industry, and international organizations work together to solve these problems. COVID-19 might boost African technology. Technology may boost African economies and living standards through digital transformation if the aforementioned critical issues are overcome.
Intelligent Upgrading and Transformation of Multimedia Classrooms in Universities Under the Smart Teaching Environment

In the context of classroom revolution promoting the transformation of smart teaching environment, to solve the problems of small number of classrooms, few seats, complex equipment operation, and high management difficulty in existing smart classrooms. Based on the concept of intelligent teaching, new technologies such as high-speed networks and intelligent cloud boxes are applied to conduct practical research on the intelligent upgrading and transformation of ordinary multimedia classrooms in universities. On the base of not changing the original equipment, tables, and chairs in the classroom, utilizing the existing teaching resources of the school, adding intelligent teaching functions such as multi-channel projection, multi windows, and live streaming. Since the outbreak of the epidemic, the information technology capabilities of teachers and students and the transformation of teaching methods have greatly improved, promoting the process of intelligent classroom upgrading and transformation, and laying a good foundation for the transformation of more advanced smart classrooms.
Enhancing Zero Trust Models in the Financial Industry through Blockchain Integration: A Proposed Framework

As financial institutions navigate an increasingly complex cyber threat landscape and regulatory ecosystem, there is a pressing need for a robust and adaptive security architecture. This paper introduces a comprehensive, Zero Trust model-based framework specifically tailored for the finance industry. It encompasses identity and access management (IAM), data protection, and device and network security and introduces trust through blockchain technology. This study provides a literature review of existing Zero Trust paradigms and contrasts them with cybersecurity solutions currently relevant to financial settings. The research adopts a mixed methods approach, combining extensive qualitative analysis through a literature review and assessment of security assumptions, threat modelling, and implementation strategies with quantitative evaluation using a prototype banking application for vulnerability scanning, security testing, and performance testing. The IAM component ensures robust authentication and authorisation processes, while device and network security measures protect against both internal and external threats. Data protection mechanisms maintain the confidentiality and integrity of sensitive information. Additionally, the blockchain-based trust component serves as an innovative layer to enhance security measures, offering both tamper-proof verification and increased integrity. Through analysis of potential threats and experimental evaluation of the Zero Trust model’s performance, the proposed framework offers financial institutions a comprehensive security architecture capable of effectively mitigating cyber threats and fostering enhanced consumer trust.
Conditional Proxy Re-Encryption-Based Key Sharing Mechanism for Clustered Federated Learning

The need of data owners for privacy protection has given rise to collaborative learning, and data-related issues heterogeneity faced by federated learning has further given rise to clustered federated learning; whereas the traditional privacy-preserving scheme of federated learning using homomorphic encryption alone fails to fulfill the privacy protection demands of clustered federated learning. To address these issues, this research provides an effective and safeguarded answer for sharing homomorphic encryption keys among clusters in clustered federated learning grounded in conditional representative broadcast re-encryption. This method constructs a key sharing mechanism. By combining the functions of the bilinear pairwise accumulator and specific conditional proxy broadcast re-ciphering, the mechanism can verify the integrity of homomorphic encryption keys stored on cloud servers. In addition, the solution enables key management centers to grant secure and controlled access to re-encrypted homomorphic encryption keys to third parties without disclosing the sensitive information contained therein. The scheme achieves this by implementing a sophisticated access tree-based mechanism that enables the cloud server to convert forwarded ciphertexts into completely new ciphertexts customized specifically for a given group of users. By effectively utilizing conditional restrictions, the scheme achieves fine-grained access control to protect the privacy of shared content. Finally, this paper showcases the scheme’s security against selective ciphertext attacks without relying on random prediction.
Adaptive Influence Maximization: Adaptability via Nonadaptability

Adaptive influence maximization is an important research problem in computational social networks, which is also a typical problem in the study of adaptive processing of information and adaptive construction of objects. In this paper, we propose a new method that reduces the adaptive influence maximization problem into a nonadaptive one in a different social network, so that an adaptive optimization can be solved by those methods for nonadaptive optimization. In addition, we provide a new approximation algorithm for the submodular maximization problem with a knapsack constraint, which runs in [Formula: see text] time and has performance ratio [Formula: see text], where n is the number of nodes in the network. The ratio is better than the best known previous one with the same running time. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search &amp; Approximation Algorithms. Funding: This research is supported in part by the National Natural Science Foundation of China [Grant U20A2068].
A Multi-Task Learning and Knowledge Selection Strategy for Environment-Induced Color-Distorted Image Restoration

Existing methods for restoring color-distorted images in specific environments typically focus on a singular type of distortion, making it challenging to generalize their application across various types of color-distorted images. If it were possible to leverage the intrinsic connections between different types of color-distorted images and coordinate their interactions during model training, it would simultaneously enhance generalization, address potential overfitting and underfitting issues during data fitting, and consequently lead to a positive performance boost. In this paper, our approach primarily addresses three distinct types of color-distorted images, namely dust-laden images, hazy images, and underwater images. By thoroughly exploiting the unique characteristics and interrelationships of these types, we achieve the objective of multitask processing. Within this endeavor, identifying appropriate correlations is pivotal. To this end, we propose a knowledge selection and allocation strategy that optimally distributes the features and correlations acquired by the network from the images to different tasks, enabling a more refined task differentiation. Moreover, given the challenge of difficult dataset pairing, we employ unsupervised learning techniques and introduce novel Transformer blocks, feedforward networks, and hybrid modules to enhance context relevance. Through extensive experimentation, we demonstrate that our proposed method significantly enhances the performance of color-distorted image restoration.