Reviewerly/exHarmony huggingface dataset:
Application of Sine Cosine Egret Swarm Optimization Algorithm in Gas Turbine Cooling System

Gas turbine cooling system is a typical multivariable, strongly coupled, nonlinear, and uncertain MIMO system. In order to solve the control problem of pressure, flow, and temperature of the system, an intelligent approach is necessary and more appropriate. The current system control mainly depends on the experience of the staff, which exists problems such as high labor intensity, low work efficiency and low control accuracy. Lack of accurate models make parameters tune difficultly, and ordinary control methods are difficult to control complex gas turbine cooling system. In this paper, the system transfer function model is built based on the field data obtained under different working conditions and system identification method. The diagonal matrix decoupling method is used to weaken the correlation between variables and achieve independent control among variables. When optimizing the parameters of the controller, Sine Cosine Egret Swarm Optimization Algorithm is proposed. Egret Swarm Optimization Algorithm is composed of Sit-And-Wait strategy, random walk, and encirclement strategy. The sit-and-wait strategy is prone to premature convergence, which makes the optimized parameters unsuitable for gas turbine cooling system. Sine Cosine Algorithm is introduced to randomly use the sine-cosine function for the pseudo-gradient of the weights of the observation equation, thus expanding the search range of the population. Friedman tests prove that the deviation of SE-ESOA is within the allowable range. The results show that the result of Sine Cosine Egret Swarm Optimization Algorithm is more stable and accurate, and it is more suitable for gas turbine cooling system, which solve the pressure, flow, and temperature control problems of complex systems.
Investigation of the Effect of Albedo in Photovoltaic Systems for Urban Applications: Case Study for Spain

Rooftop photovoltaic generation can help cities become key players in the transition to clean energy. The optimal solar photovoltaic production on rooftops depends on two angles: tilt angle and azimuth angle. It is accepted in all studies that the ideal orientation of photovoltaic modules is toward the south (north) in the northern hemisphere (south). In contrast, the determination of the optimum tilt angle is more complex, and there are different equations for its calculation. Most of these equations do not take albedo into account. In this work, 47 Spanish province capitals representing the most populated cities have been studied with different equations for the calculation of the optimum annual tilt angle (Technical report by the Spanish Institute for the Diversification and Saving of Energy (IDAE), Lorenzo’s and Jacobson’s equation) and different types of albedo. Accounting for the geographical and the meteorological conditions of the cities, we analyzed the optimum tilt angle through a Mathematica© optimization code. The influence that different variables have on optimum tilt angle has been quantified by means of the term relative energy harvested. The use of the equations as a function of latitude increases the annual relative energy harvested by increasing the albedo. When the albedo is 0.2, the annual relative energy harvested is very similar in all equations. Comparing to the method that maximizes the total irradiation incident on a tilted surface, the minimum and maximum value of the percentage of relative energy harvested per year were 0.01 and 2.50% for the IDAE guideline, 0.00 and 2.38% for Lorenzo’s equation, 0.00 and 2.46% for Jacobson’s equation. A simplified polynomial regression model to estimate optimum tilt angle as a function of latitude, altitude and albedo has been proposed as well.
Why So Toxic?

Chatbots are used in many applications, e.g., automated agents, smart home assistants, interactive characters in online games, etc. Therefore, it is crucial to ensure they do not behave in undesired manners, providing offensive or toxic responses to users. This is not a trivial task as state-of-the-art chatbot models are trained on large, public datasets openly collected from the Internet. This paper presents a first-of-its-kind, large-scale measurement of toxicity in chatbots. We show that publicly available chatbots are prone to providing toxic responses when fed toxic queries. Even more worryingly, some non-toxic queries can trigger toxic responses too. We then set out to design and experiment with an attack, ToxicBuddy, which relies on fine-tuning GPT-2 to generate non-toxic queries that make chatbots respond in a toxic manner. Our extensive experimental evaluation demonstrates that our attack is effective against public chatbot models and outperforms manually-crafted malicious queries proposed by previous work. We also evaluate three defense mechanisms against ToxicBuddy, showing that they either reduce the attack performance at the cost of affecting the chatbot's utility or are only effective at mitigating a portion of the attack. This highlights the need for more research from the computer security and online safety communities to ensure that chatbot models do not hurt their users. Overall, we are confident that ToxicBuddy can be used as an auditing tool and that our work will pave the way toward designing more effective defenses for chatbot safety.
Mobile Cyber Forensic Investigations of Web3 Wallets on Android and iOS

Constant advancements in technology have a significant impact on our everyday lives and the ecosystem in which we live. The growing popularity of cryptocurrencies (e.g., Bitcoin and Ethereum), along with Non-Fungible Tokens (NFTs), which are founded on blockchain technology, has opened the way for these blockchain projects to be integrated into a wide range of other kinds of applications (apps). Today, cryptocurrencies are used as a popular method of payment online; however, their popularity on the dark Web is also increasing. For example, they can be used to buy and perform various illegal activities among criminals due to their anonymity. Web3 cryptocurrency wallets, used to store cryptocurrencies, have not been studied as thoroughly as many other apps from a digital forensic perspective on mobile devices, given the increasing number of these services and apps today for many platforms, including the leading mobile operating systems (i.e., iOS and Android). Therefore, the purpose of this research is to guide investigators to unlock the full potential of popular cryptocurrency Web3 wallets, Trust Wallet and Metamask, to understand what can be recovered, and to look at areas where there are knowledge gaps. We digitally analyzed and forensically examined two mobile wallets that do not require any personal identifiers to register and are widely used for Web3 cryptocurrencies on Android and iOS devices. We review the digital evidence we have collected and discuss the implications of the forensic tools we have used. Finally, we propose a proof of concept extension to the iOS Logs, Events, And Plists Parser (iLEAPP) tool to automatically recover artifacts.
Implications of the “momentum” theory of digitalization in accounting: Evidence from Ash Cloud

We aim to demystify digitalization in accounting (DIA) based on the case study of Ash Cloud, a digital factory in Shenzhen, China. From the perspective of dynamic capabilities, we develop the “momentum” theory of DIA to illustrate that firm and executive characteristics drive digital transformation and organizational capabilities. Ash Cloud’s CEO values and cultivates an organizational culture of transparency and openness, while the firm is characterized by cost pressures. Organizational capabilities shape digitalization in business processes and different approaches to DIA. Our findings suggest that the core competence of Ash Cloud is its capability for systems integration, which includes knowledge of redesign, reconfiguration and redefinition. Ash Cloud stands out because of its knowledge extending beyond the firm’s boundaries.
A study on the effect of input data length on a deep-learning-based magnitude classifier

Abstract. The rapid characterisation of earthquake parameters such as its magnitude is at the heart of earthquake early warning (EEW). In traditional EEW methods, the robustness in the estimation of earthquake parameters has been observed to increase with the length of input data. Since time is a crucial factor in EEW applications, in this paper we propose a deep-learning-based magnitude classifier based on data from a single seismic station and further investigate the effect of using five different durations of seismic waveform data after first P-wave arrival: 1, 3, 10, 20 and 30 s. This is accomplished by testing the performance of the proposed model that combines convolution and bidirectional long short-term memory units to classify waveforms based on their magnitude into three classes: “noise”, “low-magnitude events” and “high-magnitude events”. Herein, any earthquake signal with magnitude equal to or above 5.0 is labelled as “high-magnitude”. We show that the variation in the results produced by changing the length of the data is no more than the inherent randomness in the trained models due to their initialisation. We further demonstrate that the model is able to successfully classify waveforms over wide ranges of both hypocentral distance and signal-to-noise ratio.
Multi-Chaos-Based Lightweight Image Encryption-Compression for Secure Occupancy Monitoring

With the advancement of camera and wireless technologies, surveillance camera-based occupancy has received ample attention from the research community. However, camera-based occupancy monitoring and wireless channels, especially Wi-Fi hotspot, pose serious privacy concerns and cybersecurity threats. Eavesdroppers can easily access confidential multimedia information and the privacy of individuals can be compromised. As a solution, novel encryption techniques for the multimedia data concealing have been proposed by the cryptographers. Due to the bandwidth limitations and computational complexity, traditional encryption methods are not applicable to multimedia data. In traditional encryption methods such as Advanced Encryption Standard (AES) and Data Encryption Standard (DES), once multimedia data are compressed during encryption, correct decryption is a challenging task. In order to utilize the available bandwidth in an efficient way, a novel secure video occupancy monitoring method in conjunction with encryption-compression has been developed and reported in this paper. The interesting properties of Chebyshev map, intertwining map, logistic map, and orthogonal matrix are exploited during block permutation, substitution, and diffusion processes, respectively. Real-time simulation and performance results of the proposed system show that the proposed scheme is highly sensitive to the initial seed parameters. In comparison to other traditional schemes, the proposed encryption system is secure, efficient, and robust for data encryption. Security parameters such as correlation coefficient, entropy, contrast, energy, and higher key space prove the robustness and efficiency of the proposed solution.
Explore Contextual Information for 3D Scene Graph Generation

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this article, we propose a framework fully exploring contextual information for the 3D SGG task, which attempts to satisfy the requirements of fine-grained entity class, multiple relation labels, and high accuracy simultaneously. Our proposed approach is composed of a Graph Feature Extraction module and a Graph Contextual Reasoning module, achieving appropriate information-redundancy feature extraction, structured organization, and hierarchical inferring. Our approach achieves superior or competitive performance over previous methods on the 3DSSG dataset, especially on the relationship prediction sub-task.
Reconnoitering the significance of security using multiple cloud environments for conveyance applications with blowfish algorithm

Abstract In recent years the process of transportation needs a highly effective traffic system in order to monitor all consumer goods as many goods are left out at different locations. To handle such moving cases cloud platform is highly helpful as with respect to geographical location the goods are mapped in correct form. However incorporation of single cloud platform does not provide sufficient amount of storage about all goods thus a multiple cloud platform is introduced in proposed system. As multiple cloud platform is provided the security features of each data base system is also checked and enhanced using encryption keys. Moreover for proper operating conditions of multiple cloud platforms an analytical model is designed that synchronizes necessary data at end system. The defined analytical model focuses on solving multiple objectives that are related to critical energy problems where demand problems are reduced. Further the encryption process is carried out using Improved BlowFish Algorithm (IBFA) by allocating proper resources with decryption keys. To validate the effectiveness of proposed method five scenarios are considered where all scenario outcomes proves to be much higher than existing models by an average of 43%.
Spatio-Temporal Collaborative Module for Efficient Action Recognition

Efficient action recognition aims to classify a video clip into a specific action category with a low computational cost. It is challenging since the integrated spatial-temporal calculation (e. g., 3D convolution) introduces intensive operations and increases complexity. This paper explores the feasibility of the integration of channel splitting and filter decoupling for efficient architecture design and feature refinement by proposing a novel spatio-temporal collaborative (STC) module. STC splits the video feature channels into two groups and separately learns spatio-temporal representations in parallel with decoupled convolutional operators. Particularly, STC consists of two computation-efficient blocks, i.e., [Formula: see text] and [Formula: see text], where they extract either spatial ( S· ) or temporal ( T· ) features and further refine their features with either temporal ( ·T ) or spatial ( ·S ) contexts globally. The spatial/temporal context refers to information dynamics aggregated from temporal/spatial axis. To thoroughly examine our method's performance in video action recognition tasks, we conduct extensive experiments using five video benchmark datasets requiring temporal reasoning. Experimental results show that the proposed STC networks achieve a competitive trade-off between model efficiency and effectiveness.
Real-Time UAV Patrol Technology in Orchard Based on the Swin-T YOLOX Lightweight Model

Using unmanned aerial vehicle (UAV) real-time remote sensing to monitor diseased plants or abnormal areas of orchards from a low altitude perspective can greatly improve the efficiency and response speed of the patrol in smart orchards. The purpose of this paper is to realize the intelligence of the UAV terminal and make the UAV patrol orchard in real-time. The existing lightweight object detection algorithms are usually difficult to consider both detection accuracy and processing speed. In this study, a new lightweight model named Swin-T YOLOX, which consists of the advanced detection network YOLOX and the strong backbone Swin Transformer, was proposed. Model layer pruning technology was adopted to prune the multi-layer stacked structure of the Swin Transformer. A variety of data enhancement strategies were conducted to expand the dataset in the model training stage. The lightweight Swin-T YOLOX model was deployed to the embedded platform Jetson Xavier NX to evaluate its detection capability and real-time performance of the UAV patrol mission in the orchard. The research results show that, with the help of TensorRT optimization, the proposed lightweight Swin-T YOLOX network achieved 94.0% accuracy and achieved a detection speed of 40 fps on the embedded platform (Jetson Xavier NX) for patrol orchard missions. Compared to the original YOLOX network, the model accuracy has increased by 1.9%. Compared to the original Swin-T YOLOX, the size of the proposed lightweight Swin-T YOLOX has been reduced to two-thirds, while the model accuracy has slightly increased by 0.7%. At the same time, the detection speed of the model has reached 40 fps, which can be applied to the real-time UAV patrol in the orchard.
A New Interval Type-2 Fuzzy Aggregation Approach for Combining Multiple Neural Networks in Clustering and Prediction of Time Series

Inspired by how some cognitive abilities affect the human decision-making process, the proposed approach combines neural networks with type-2 fuzzy systems. The proposal consists of combining computational models of artificial neural networks and fuzzy systems to perform clustering and prediction of time series corresponding to the population, urban population, particulate matter (PM2.5), carbon dioxide (CO2), registered cases and deaths from COVID-19 for certain countries. The objective is to associate these variables by country based on the identification of similarities in the historical information for each variable. The hybrid approach consists of computationally simulating the behavior of cognitive functions in the human brain in the decision-making process by using different types of neural models and interval type-2 fuzzy logic for combining their outputs. Simulation results show the advantages of the proposed approach, because starting from an input data set, the artificial neural networks are responsible for clustering and predicting values of multiple time series, and later a set of fuzzy inference systems perform the integration of these results, which the user can then utilize as a support tool for decision-making with uncertainty.
Deep Learning-Based Cyber–Physical Feature Fusion for Anomaly Detection in Industrial Control Systems

In this paper, we propose an unsupervised anomaly detection method based on the Autoencoder with Long Short-Term Memory (LSTM-Autoencoder) network and Generative Adversarial Network (GAN) to detect anomalies in industrial control system (ICS) using cyber–physical fusion features. This method improves the recall of anomaly detection and overcomes the challenges of unbalanced datasets and insufficient labeled samples in ICS. As a first step, additional network features are extracted and fused with physical features to create a cyber–physical dataset. Following this, the model is trained using normal data to ensure that it can properly reconstruct the normal data. In the testing phase, samples with unknown labels are used as inputs to the model. The model will output an anomaly score for each sample, and whether a sample is anomalous depends on whether the anomaly score exceeds the threshold. Whether using supervised or unsupervised algorithms, experimentation has shown that (1) cyber–physical fusion features can significantly improve the performance of anomaly detection algorithms; (2) the proposed method outperforms several other unsupervised anomaly detection methods in terms of accuracy, recall, and F1 score; (3) the proposed method can detect the majority of anomalous events with a low false negative rate.
An fMRI Dataset for Concept Representation with Semantic Feature Annotations

The neural representation of concepts is a focus of many cognitive neuroscience studies. Prior works studying concept representation with neural imaging data have been largely limited to concrete concepts. The use of relatively small and constrained sets of stimuli leaves open the question of whether the findings can generalize other concepts. We share an fMRI dataset in which 11 participants thought of 672 individual concepts, including both concrete and abstract concepts. The concepts were probed using words paired with images in which the words were selected to cover a wide range of semantic categories. Furthermore, according to the componential theories of concept representation, we collected the 54 semantic features of the 672 concepts comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences by crowdsourcing annotations. The quality assessment results verify this as a high-quality neuroimaging dataset. Such a dataset is well suited to study how the brain represents different semantic features and concepts, creating the essential condition to investigate the neural representation of individual concepts.
The Classicality and Quantumness of the Driven Qubit–Photon–Magnon System

The hybrid architecture of the driven qubit–photon–magnon system has recently emerged as a promising candidate for novel quantum technologies. In this paper, we introduce the effective wave-function of a superconducting single qubit and a magnon mode contained within a cavity resonator and an external field. The non-classicality of the magnon and resonator modes are investigated by using the negative values of the Wigner function. Additionally, we discuss the non-classicality of the qubit state via the Wigner–Yanase skew information. We find that the mixture angle of the qubit–resonator plays a controllable role in non-classicality. However, the strength of the magnon–photon increases the non-classical behaviour of the system.
Sequential Order-Aware Coding-Based Robust Subspace Clustering for Human Action Recognition in Untrimmed Videos

Human action recognition (HAR) is one of most important tasks in video analysis. Since video clips distributed on networks are usually untrimmed, it is required to accurately segment a given untrimmed video into a set of action segments for HAR. As an unsupervised temporal segmentation technology, subspace clustering learns the codes from each video to construct an affinity graph, and then cuts the affinity graph to cluster the video into a set of action segments. However, most of the existing subspace clustering schemes not only ignore the sequential information of frames in code learning, but also the negative effects of noises when cutting the affinity graph, which lead to inferior performance. To address these issues, we propose a sequential order-aware coding-based robust subspace clustering (SOAC-RSC) scheme for HAR. By feeding the motion features of video frames into multi-layer neural networks, two expressive code matrices are learned in a sequential order-aware manner from unconstrained and constrained videos, respectively, to construct the corresponding affinity graphs. Then, with the consideration of the existence of noise effects, a simple yet robust cutting algorithm is proposed to cut the constructed affinity graphs to accurately obtain the action segments for HAR. The extensive experiments demonstrate the proposed SOAC-RSC scheme achieves the state-of-the-art performance on the datasets of Keck Gesture and Weizmann, and provides competitive performance on the other 6 public datasets such as UCF101 and URADL for HAR task, compared to the recent related approaches.