Reviewerly/exHarmony huggingface dataset:
Bi-DNE: bilayer evolutionary pattern preserved embedding for dynamic networks

Authors:
raw_author_name: Xu Gu
raw_author_name: Xiaoliang Chen
raw_author_name: Min Yang
raw_author_name: Yajun Du
raw_author_name: Mingwei Tang

Abstract: Abstract Network embedding is a technique used to generate low-dimensional vectors representing each node in a network while maintaining the original topology and properties of the network. This technology enables a wide range of learning tasks, including node classification and link prediction. However, the current landscape of network embedding approaches predominantly revolves around static networks, neglecting the dynamic nature that characterizes real social networks. Dynamics at both the micro- and macrolevels are fundamental drivers of network evolution. Microlevel dynamics provide a detailed account of the network topology formation process, while macrolevel dynamics reveal the evolutionary trends of the network. Despite recent dynamic network embedding efforts, a few approaches accurately capture the evolution patterns of nodes at the microlevel or effectively preserve the crucial dynamics of both layers. Our study introduces a novel method for embedding networks, i.e., bilayer evolutionary pattern-preserving embedding for dynamic networks (Bi-DNE), that preserves the evolutionary patterns at both the micro- and macrolevels. The model utilizes strengthened triadic closure to represent the network structure formation process at the microlevel, while a dynamic equation constrains the network structure to adhere to the densification power-law evolution pattern at the macrolevel. The proposed Bi-DNE model exhibits significant performance improvements across a range of tasks, including link prediction, reconstruction, and temporal link analysis. These improvements are demonstrated through comprehensive experiments carried out on both simulated and real-world dynamic network datasets. The consistently superior results to those of the state-of-the-art methods provide empirical evidence for the effectiveness of Bi-DNE in capturing complex evolutionary patterns and learning high-quality node representations. These findings validate the methodological innovations presented in this work and mark valuable progress in the emerging field of dynamic network representation learning. Further exploration demonstrates that Bi-DNE is sensitive to the analysis task parameters, leading to a more accurate representation of the natural evolution process during dynamic network embedding.
The Emergency Braking Game: a game theoretic approach for maneuvering in a dense crowd of pedestrians

Authors:
raw_author_name: Zoltán Gyenes
raw_affiliation_string: Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary
raw_author_name: Emese Gincsainé Szádeczky-Kardoss
raw_affiliation_string: Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary
raw_author_name: Ladislau Bölöni
raw_affiliation_string: Department of Computer Science, University of Central Florida, 4328 Scorpius St, Orlando, FL, 32816, USA
raw_author_name: István Harmati
raw_affiliation_string: Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary

Abstract: 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

Authors:
raw_author_name: Alvin Gonzales
raw_affiliation_string: Intelligence Community Postdoctoral Research Fellowship Program, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, Illinois 60439, USA; School of Computing, Southern Illinois University Carbondale, Carbondale, Illinois 62901, USA
raw_author_name: Daniel Dilley
raw_affiliation_string: School of Physics and Applied Physics, Southern Illinois University Carbondale, Carbondale, Illinois 62901, USA
raw_author_name: Mark Byrd
raw_affiliation_string: School of Computing, Southern Illinois University Carbondale, Carbondale, Illinois 62901, USA

Abstract: 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

Authors:
raw_author_name: Gonçalo M. Quinta
raw_affiliation_string: Instituto de Telecomunicações, Lisbon, Portugal
raw_author_name: Rui André
raw_affiliation_string: Instituto de Telecomunicações, Lisbon, Portugal

Abstract: 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

Authors:
raw_author_name: Archak Purkayastha
raw_affiliation_string: Centre for Complex Quantum Systems, Aarhus University, Nordre Ringgade 1, 8000 Aarhus C, Denmark; Department of Physics, Indian Institute of Technology, Hyderabad 502284, India

Abstract: 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

Authors:
raw_author_name: Caibo Dong
raw_author_name: Dazi Li

Abstract: 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.
Image Encryption Algorithms: A Survey of Design and Evaluation Metrics

Authors:
raw_author_name: Yousef Alghamdi
raw_affiliation_string: Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA
raw_author_name: Arslan Munir
raw_affiliation_string: Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA

Abstract: 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

Authors:
raw_author_name: Mehrdad Lakestani
raw_affiliation_string: Department of Applied Mathematics, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz 33193, Iran

Abstract: 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

Authors:
raw_author_name: Gürkan Doğan
raw_affiliation_string: Department of Computer Engineering, Faculty of Engineering, Munzur University, Tunceli, Turkey
raw_author_name: Burhan Ergen
raw_affiliation_string: Department of Computer Engineering, Faculty of Engineering, Fırat University, Elazig, Turkey

Abstract: 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

Authors:
raw_author_name: Stefan Kutschera
raw_affiliation_string: Institute of Software Technology, Graz University of Technology, 8010 Graz, Austria
raw_author_name: Wolfgang Slany
raw_affiliation_string: Institute of Software Technology, Graz University of Technology, 8010 Graz, Austria
raw_author_name: Patrick Ratschiller
raw_affiliation_string: Institute of Software Technology, Graz University of Technology, 8010 Graz, Austria
raw_author_name: Sarina Gursch
raw_affiliation_string: Institute of Software Technology, Graz University of Technology, 8010 Graz, Austria
raw_author_name: Håvard Dagenborg
raw_affiliation_string: Department of Computer Science, UiT The Arctic University of Norway, 9037 Tromsø, Norway

Abstract: 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

Authors:
raw_author_name: Johann Maximilian Zollner
raw_affiliation_string: Department of Aerospace and Geodesy, Technical University of Munich, Lise-Meitner-Straße 9, 85521, Ottobrunn, Germany
raw_author_name: Martin Werner
raw_affiliation_string: Department of Aerospace and Geodesy, Technical University of Munich, Lise-Meitner-Straße 9, 85521, Ottobrunn, Germany

Abstract: 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

Authors:
raw_author_name: Anton Trusov
raw_affiliation_string: Department of Mathematical Software for Computer Science, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 119333 Moscow, Russia; Phystech School of Applied Mathematics and Informatics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; Smart Engines Service LLC, 117312 Moscow, Russia
raw_author_name: Elena Limonova
raw_affiliation_string: Department of Mathematical Software for Computer Science, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 119333 Moscow, Russia; Smart Engines Service LLC, 117312 Moscow, Russia
raw_author_name: Dmitry Nikolaev
raw_affiliation_string: Smart Engines Service LLC, 117312 Moscow, Russia; Vision Systems Laboratory, Institute for Information Transmission Problems of Russian Academy of Sciences, 127051 Moscow, Russia
raw_author_name: Vladimir V. Arlazarov
raw_affiliation_string: Department of Mathematical Software for Computer Science, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 119333 Moscow, Russia; Smart Engines Service LLC, 117312 Moscow, Russia

Abstract: 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.
Intelligent Upgrading and Transformation of Multimedia Classrooms in Universities Under the Smart Teaching Environment

Authors:
raw_author_name: Lu Ren
raw_affiliation_string: The Network Information Center, Wuhan University of Technology, Wuhan, China

Abstract: 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.