Reviewerly/exHarmony huggingface dataset:
A Substitution Box for Lightweight Ciphers to Secure Internet of Things

The Internet of Things is a resource-constrained device that demands lightweight cryptographic solutions to achieve high performance and optimal security. In lightweight ciphers Substitution Box (S-box) plays an important role as it enables confusion property. However, it is one of the costlier operations. The design and construction of such S-boxes for Internet of Things (IoT) devices are crucial..Hence, we propose a 4-bit, highly Nonlinear, Bijective, Balanced S-box called Feather S-box to enable confusion in lightweight ciphers. The hardware performance of Feather S-Box is analysed in terms of Area and Critical Path-Delay cost. While examining the Area Delay-Product and Power-Delay Product, it shows 23% and 19% lower than PRESENT cipher and 12% lesser than GIFT and KATAN ciphers. The security analysis of the proposed S-Box is also done in terms of Nonlinearity, Bijective, Balanced, Global Avalanche characteristics, resistance to Algebraic attack, Side-channel attacks, Differential and Linear Cryptanalysis. The Feather S-box also exhibts good cryptographic properties such as Nonlinearity and immunity against Algebraic attacks. Moreover, it offers good resistance against Side-channel attack, Differential and Linear Cryptanalysis. We also observed that the Feather S-box has the highest immunity against Differential and Linear Cryptanalysis except for the SKINNY cipher.
Exploring 360-degree virtual reality videos for CSR communication: An integrated model of perceived control, telepresence, and consumer behavioral intentions

Recent years have seen 360-degree virtual reality (VR) being used as a valuable tool in communicating corporate social responsibility (CSR) campaigns. Built upon the wealth of literature on stimulus–organism–response (S–O–R) theory, this study proposed to test a model integrating perceived control of the 360-degree CSR VR technology, telepresence, engagement with VR 360-degree video (VR engagement), CSR advocacy, and purchase intentions. To that end, a nationally representative sample was recruited for a survey that exposed participants to a digital CSR campaign that uses 360-degree VR to increase mental health awareness. Results showed a significant association between participants' perceived control over the VR 360-degree CSR campaign and telepresence as well as their engagement with the VR 360-degree video. Moreover, telepresence and VR engagement were significant mediators in predicting consumers’ willingness to promote social initiatives (CSR advocacy) and purchase intentions. Theoretical and practical implications of the study were discussed.
Fiber-detector subsystem loss comparison for a ground-based photon-counting optical receiver

In this paper we present a comparative study of the losses associated with fiber-coupled single-photon detectors in two different configurations, each with the goal of receiving a pulsed-position modulated signal with a maximum data rate of 267 Mbps. First, we consider a 7x1 few-mode-fiber (FMF) photonic lantern coupled to seven individual superconducting nanowire single photon detectors (SNSPDs). In the second configuration we assess a single FMF coupled to a 16-channel monolithic SNSPD array. In each case we measure and compare combined fiber coupling, SNSPD blocking, and system efficiency losses under emulated atmospheric turbulence conditions. We address subsystem impact on link performance and analyze feasibility for future NASA lunar and deep-space optical communications missions.
Cloud computing virtual machine consolidation based on stock trading forecast techniques

In Cloud Computing, the virtual machine scheduling in datacenters becomes challenging when trying to optimize user-service requirements and, at the same time, efficient resource management. Clumsy load management results in host overloads that trigger a continuous flow of virtual machine (VM) migrations to correct this situation, thus negatively impacting the Service Level Agreement (SLA), resource availability and energy consumption. The present paper explores the combined use of trend analysis techniques with time series forecasting techniques broadly used in stock markets, to improve VM-to-host consolidation. The main goal is to provide an efficient estimate of the near future trend of virtual machine resource usage and host availability. This information improves the scheduler's decisions when determining the correct VM to be migrated and the candidate host to allocate it to. The results have demonstrated that it is possible to reduce the number of migrations by up to 75% while obtaining a reduction in the SLA violations by up to 60%. The results also showed noticeable improvements regarding the reduction of energy consumption. The migration decisions based on predictions of near-future resource usage trends using stock trading techniques showed a decrease in network usage, thus obtaining an energy saving of up to 16%.
Quantum Volume for Photonic Quantum Processors

Defining metrics for near-term quantum computing processors has been an integral part of the quantum hardware research and development efforts. Such quantitative characteristics are not only useful for reporting the progress and comparing different quantum platforms but also essential for identifying the bottlenecks and designing a technology road map. Most metrics such as randomized benchmarking and quantum volume were originally introduced for circuit-based quantum computers and were not immediately applicable to measurement-based quantum computing (MBQC) processors such as in photonic devices. In this Letter, we close this long-standing gap by presenting a framework to map physical noises and imperfections in MBQC processes to logical errors in equivalent quantum circuits, whereby enabling the well-known metrics to characterize MBQC. To showcase our framework, we study a continuous-variable cluster state based on the Gottesman-Kitaev-Preskill (GKP) encoding as a near-term candidate for photonic quantum computing, and derive the effective logical gate error channels and calculate the quantum volume in terms of the GKP squeezing and photon transmission rate.
Autonomous anomaly detection on traffic flow time series with reinforcement learning

This study develops an autonomous artificial intelligence (AI) agent to detect anomalies in traffic flow time series data, which can learn anomaly patterns from data without supervision, requiring no ground-truth labels for model training or knowledge of a threshold for anomaly definition. Specifically, our model is based on reinforcement learning, where an agent is built by a Long-Short-Term-Memory (LSTM) model and Q-learning algorithm to incorporate sequential information in time series data into policy optimization. The key contribution of our model is the development of a novel unsupervised reward learning algorithm that automatically learns the reward for an action taken by the agent based on the distribution of data, without requiring a manual specification of a reward function. To test the performance of our model, we conduct a comprehensive set of experimental study on both real-world data from Brisbane city, Australia, and synthetic data simulated according to the distribution of real-world data. We compare the performance of our model against three state-of-the-art models, and the experimental results show that our model outperforms the other models in different parameter settings, with around 90% precision, 80% recall, and 85% F1 score.
Transformer for authenticating the source microphone in digital audio forensics

In recent years, digital audio has played a significant role in providing information used as evidence in criminal investigations. The authenticity of an audio recording must be proven for it to be admitted into evidence in a legal proceeding. In this paper, we propose a robust system based on transformer deep learning architecture for identifying the source microphone with high accuracy, which is applicable in digital audio forensics. To evaluate the proposed model, we use the Audio Forensic Dataset for Digital Multimedia Forensics (AF-DB) and the King Saud University speech database (KSU-DB) datasets. Experiments show that the proposed model achieves state-of-the-art classification accuracy on the two databases. The model achieves an overall accuracy of 94.18% and 84.19% for Inter-model and Intra-model microphone classification, respectively, for the AF-DB and 99.38% for the KSU-DB speech dataset.
Integrating Users’ Contextual Engagements with Their General Preferences: An Interpretable Followee Recommendation Method

Users’ contextual engagements can affect their decisions about who to follow on online social networks because engaged (versus disengaged) users tend to seek more information about the interested topic and are more likely to follow relevant accounts successively. However, existing followee recommendation methods neglect to consider contextual engagement by only relying on users’ general preferences. In the light of the chronological characteristic of the user’s following behavior, we draw on the engagement theory and propose an interpretable algorithm, namely preference-engagement latent Dirichlet allocation (PE-LDA), which integrates users’ contextual engagements with their general preferences for followee recommendation. Specifically, we suggest that if engaged in the current interest, a user will be more likely to select a followee relevant to that interest. If not, the user tends to select a followee according to their general preference. To implement this framework, we extend the original LDA by (1) introducing an indicator to represent whether the user is engaged in the current interest or not and (2) allowing a potential dependency between a user’s successive interests to describe the condition of contextual engagement. We conduct extensive experiments using a real-world Twitter data set. Results demonstrate the superior performance of PE-LDA compared with several existing methods. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71702066, 71802192, 71832010, 72172112, and 72272152]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1284 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0172 ) at ( http://dx.doi.org/10.5281/zenodo.7460938 ).
Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Abstract In order to improve the global optimization of particle swarm optimization (PSO), enhance the performance of PSO in dealing with these complex, high‐dimensional, multimodal optimization problems, and furthermore, promote the optimization effect in reliability optimization applications, a direct position updating‐based trying‐mutation PSO (DTPSO) was proposed. In this algorithm, the direct position updating strategy and trying‐mutation strategy were designed, which could effectively maintain the population diversity, balance the exploitation and exploration, and increase the probability of obtaining global optimal solution. After being verified and compared by nine complex test functions, the rationality of algorithm design and the excellent global optimization performances were proved. In the reliability optimization, the reliability redundancy allocation and reliability allocation were optimized by different advanced improved algorithms, the comparison results proved the stability and optimization performance of DTPSO. Due to its stable and efficient global optimization performance, it was concluded the proposed DTPSO could provide effective technical support for practical applications such as reliability optimization.
A methodology for using a multilevel perspective framework to analyze complex systems

Misalignment between the different levels of complex societal systems (e.g. socio-technical, socio-ecological) are often a barrier to transitioning these systems toward more sustainable behavior. One way to understand alignment is through the Multilevel Perspective Theory of sustainable transitions in sociotechnical systems (MLP) which posits that when a regime (the current “ways of doing” something) exhibits behavior that negatively impacts its surrounding economic, social and environmental landscape, that landscape will exert pressure on the regime to change. This in turn opens the window of opportunity for niche developments such as novel technology adoption or policy changes. Therefore, understanding the relationships between the landscape, the regime and emerging niches can help align these different levels of perspective to facilitate a successful transition toward more sustainable practices. This paper describes a methodology for mapping complex systems such as agricultural systems based on MLP theory, and incorporates a fourth “user” level (e.g. the farm) into this multilevel framework (MLP+ F). The methodology is illustrated with a case study example of North Carolina’s lagoon and sprayfield swine waste management regime. By integrating well-established techniques for data collection and analysis, the case study engages a diverse panel of local experts through a modified Delphi approach to identify the relevant concepts and their cause-effect relationships for the current regime. These concepts and relationships are then categorized and organized into a multilevel, multi-perspective conceptual model using fuzzy cognitive maps. The conceptual model identifies how stakeholder groups align in their perception of the system, and provides a foundation for future qualitative, semi-quantitative, and visual analyses of the system and how it can be changed through alternative scenarios. Although this methodology was initially developed for agricultural applications, it has potential for application to other complex societal issues where understanding stakeholder alignment and the potential for sustainability transitions through change are needed.
Human Activity Detection Using Smart Wearable Sensing Devices with Feed Forward Neural Networks and PSO

Hospitals must continually monitor their patients’ actions to lower the chance of accidents, such as patient falls and slides. Human behavior is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behavior is challenging, since it is hard to forecast how individuals will think or act in response to a certain event. Mobility tracking depends on intelligent monitoring systems that apply artificial intelligence (AI) applications referred to as “categories”. Because motion sensors, such as gyroscopes and accelerometers, output unconnected data that lack labels, event detection is a vital task. The fall feature parameters of tridimensional accelerometers and gyroscope sensors are presented and used, and the classification technique is based on distinguishing characteristics. This study focuses on the age-old problem of tracking turbulence in motion to improve detection precision. We trained the model, considering that detection accuracy is limited by factors such as the subject’s mass, velocity, and gait style. This is performed by employing an experimental dataset. When we used the sophisticated technique of particle swarm optimization (PSO) in combination with a four-stage forward neural network (4SFNN) to forecast four different types of turbulent motion, we observed that the total prediction accuracy was 98.615% accurate.
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks

Deep neural networks (DNNs) are widely used for computer vision tasks. However, it has been shown that deep models are vulnerable to adversarial attacks—that is, their performances drop when imperceptible perturbations are made to the original inputs, which may further degrade the following visual tasks or introduce new problems such as data and privacy security. Hence, metrics for evaluating the robustness of deep models against adversarial attacks are desired. However, previous metrics are mainly proposed for evaluating the adversarial robustness of shallow networks on the small-scale datasets. Although the Cross Lipschitz Extreme Value for nEtwork Robustness (CLEVER) metric has been proposed for large-scale datasets (e.g., the ImageNet dataset), it is computationally expensive and its performance relies on a tractable number of samples. In this article, we propose the Adversarial Converging Time Score (ACTS), an attack-dependent metric that quantifies the adversarial robustness of a DNN on a specific input. Our key observation is that local neighborhoods on a DNN’s output surface would have different shapes given different inputs. Hence, given different inputs, it requires different time for converging to an adversarial sample. Based on this geometry meaning, the ACTS measures the converging time as an adversarial robustness metric. We validate the effectiveness and generalization of the proposed ACTS metric against different adversarial attacks on the large-scale ImageNet dataset using state-of-the-art deep networks. Extensive experiments show that our ACTS metric is an efficient and effective adversarial metric over the previous CLEVER metric.
A Three-Dimensional Analytical Framework: Textual Analysis and Comparison of Chinese and US Energy Blockchain Policies

With the development of blockchain technology in various fields, attempts have been made by the US and China to apply it to the energy industry. This study constructed a three-dimensional policy analysis framework of “policy instrument–innovation value chain–policy level” and added the industry field research perspective. It summarises the energy blockchain policies of China and the US from 2016 to 2022 and compares the differences between the two countries. This study shows that both countries pay attention to the application of environmental-based policy tools and that there is an imbalance between the internal structure of supply- and demand-based policy tools. The energy blockchain policies are more focused on application. China and the US lack policy support in basic research and exploitation, respectively. The US energy blockchain policy distribution at the policy level is relatively uniform, while China’s energy blockchain policy has an inverted pyramid structure, with many policies at the strategic level. From the perspective of industry, the energy blockchain policies of China and the US mainly involve the new-generation information technology industry and the new energy industry. The US should reform its energy policy structure and give full play to various policy tools, while China should strengthen basic research to lay the foundation for the practice of the energy blockchain.
Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing

Real-world nonhomogeneous haze brings challenges to image restoration. More efforts are needed to remove dense haze and thin haze simultaneously and efficiently. However, most existing dehazing methods do not pay attention to the complex distributions of haze and usually suffer from a low runtime speed. To tackle such problems, we present an efficient re-parameterization residual attention network (RRA-Net), whose design has three key aspects. Firstly, we propose a training-time multi-branch residual attention block (MRAB), where multi-scale convolutions in different branches cope with the nonuniformity of haze and are converted into a single-path convolution during inference. It also features local residual learning with improved spatial attention and channel attention, allowing dense and thin haze to be attended to differently. Secondly, our lightweight network structure cascades six MRABs followed by a long skip connection with attention and a fusion tail. Overall, our RRA-Net only has about 0.3M parameters. Thirdly, two new loss functions, namely the Laplace pyramid loss and the color attenuation loss, help train the network to recover details and colors. The experimental results show that the proposed RRA-Net performs favorably against state-of-the-art dehazing methods on real-world image datasets, including both nonhomogeneous haze and dense homogeneous haze. A runtime comparison under the same hardware setup also demonstrates the superior efficiency of the proposed network.
A Fast Circle Detection Algorithm Based on Circular Arc Feature Screening

Circle detection is a crucial problem in computer vision and pattern recognition. In this paper, we propose a fast circle detection algorithm based on circular arc feature screening. In order to solve the invalid sampling and time consumption of the traditional circle detection algorithms, we improve the fuzzy inference edge detection algorithm by adding main contour edge screening, edge refinement, and arc-like determination to enhance edge positioning accuracy and remove unnecessary contour edges. Then, we strengthen the arc features with step-wise sampling on two feature matrices and set auxiliary points for defective circles. Finally, we built a square verification support region to further find the true circle with the complete circle and defective circle constraints. Extensive experiments were conducted on complex images, including defective, blurred-edge, and interfering images from four diverse datasets (three publicly available and one we built). The experimental results show that our method can remove up to 89.03% of invalid edge points by arc feature filtering and is superior to RHT, RCD, Jiang, Wang, and CACD in terms of speed, accuracy, and robustness.
Visual Attention Adversarial Networks for Chinese Font Translation

Currently, many Chinese font translation models adopt the method of dividing character components to improve the quality of generated font images. However, character components require a large amount of manual annotation to decompose characters and determine the composition of each character as input for training. In this paper, we establish a Chinese font translation model based on generative adversarial network without decomposition. First, we improve the method of image enhancement for Chinese character images. It helps the model learning structure information of Chinese character strokes to generate font images with complete and accurate strokes. Second, we propose a visual attention adversarial network. By using visual attention block, the network catches global and local features for constructing details of characters. Experiments demonstrate our method generates high-quality Chinese character images with great style diversity including calligraphy characters.
Rough Entanglement: Not Enough Fourier

Abstract This work presents a new form of entanglement known as rough entanglement. It is based on the quantum Fourier transform (QFT) and is an alternative to those used in quantum information processing for generating maximally and nonmaximally entangled states. The roughly entangled pairs are used in the quantum teleportation protocol, where, the performance achieved in the reconstruction of the teleported state is evaluated. The aforementioned teleportation is implemented on a simulator and a 5‐qubit physical machine from the IBM Q Experience inventory. Emphasis is placed on the teleportation of computational basis states (CBS) commonly used in quantum key distribution protocols. Finally, in previous works, it is shown that to generate Bell states the intervention of two QFT blocks is required, while the rough version requires only one. The lack of a second layer or QFT block in its constitution gives the name to this new type of entanglement.