Reviewerly/exHarmony huggingface dataset:
Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU

Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for every five-minute interval on Amherst weather power station. These models were evaluated using mean absolute error root means square error, mean square error, and mean absolute percentage error. It was observed that horizontal solar irradiance and water saturation deficiency had a highly proportional relationship with Solar PV electricity generation. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed solar farm. Bi-LSTM has performed the best among all models with 0.0135, 0.0315, 0.0012, and 0.1205 values of MAE, RMSE, MSE, and MAPE, respectively. Comparison with the existing methods endorses the use of our proposed RNN variants for higher efficiency, accuracy, and robustness. Multistep-ahead solar energy prediction is also carried out by exploiting hybrids of LSTM, Bi-LSTM, and GRU.
When Federated Learning Meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection

Federated learning (FL) is a technique that allows multiple participants to collaboratively train a Deep Neural Network (DNN) without the need to centralize their data. Among other advantages, it comes with privacy-preserving properties, making it attractive for application in sensitive contexts, such as health care or the military. Although the data are not explicitly exchanged, the training procedure requires sharing information about participants’ models. This makes the individual models vulnerable to theft or unauthorized distribution by malicious actors. To address the issue of ownership rights protection in the context of machine learning (ML), DNN watermarking methods have been developed during the last five years. Most existing works have focused on watermarking in a centralized manner, but only a few methods have been designed for FL and its unique constraints. In this paper, we provide an overview of recent advancements in federated learning watermarking, shedding light on the new challenges and opportunities that arise in this field.
Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

Real-time object detection based on UAV remote sensing is widely required in different scenarios. In the past 20 years, with the development of unmanned aerial vehicles (UAV), remote sensing technology, deep learning technology, and edge computing technology, research on UAV real-time object detection in different fields has become increasingly important. However, since real-time UAV object detection is a comprehensive task involving hardware, algorithms, and other components, the complete implementation of real-time object detection is often overlooked. Although there is a large amount of literature on real-time object detection based on UAV remote sensing, little attention has been given to its workflow. This paper aims to systematically review previous studies about UAV real-time object detection from application scenarios, hardware selection, real-time detection paradigms, detection algorithms and their optimization technologies, and evaluation metrics. Through visual and narrative analyses, the conclusions cover all proposed research questions. Real-time object detection is more in demand in scenarios such as emergency rescue and precision agriculture. Multi-rotor UAVs and RGB images are of more interest in applications, and real-time detection mainly uses edge computing with documented processing strategies. GPU-based edge computing platforms are widely used, and deep learning algorithms is preferred for real-time detection. Meanwhile, optimization algorithms need to be focused on resource-limited computing platform deployment, such as lightweight convolutional layers, etc. In addition to accuracy, speed, latency, and energy are equally important evaluation metrics. Finally, this paper thoroughly discusses the challenges of sensor-, edge computing-, and algorithm-related lightweight technologies in real-time object detection. It also discusses the prospective impact of future developments in autonomous UAVs and communications on UAV real-time target detection.
Siamese Trackers Based on Deep Features for Visual Tracking

Visual object tracking poses challenges due to deformation of target object appearance, fast motion, brightness change, blocking due to obstacles, etc. In this paper, a Siamese network that is configured using a convolutional neural network is proposed to improve tracking accuracy and robustness. Object tracking accuracy is dependent on features that can well represent objects. Thus, we designed a convolutional neural network structure that can preserve feature information that is produced in the previous layer to extract spatial and semantic information. Features are extracted from the target object and search area using a Siamese network, and the extracted feature map is input into the region proposal network, where fast Fourier-transform convolution is applied. The feature map produces a probability score for the presence of an object region and an object in a region, where the similarities are high to search the target. The network was trained with a video dataset called ImageNet Large Scale Visual Recognition Challenge. In the experiment, quantitative and qualitative evaluations were conducted using the object-tracking benchmark dataset. The evaluation results indicated competitive results for some video attributes through various experiments. By conducting experiments, the proposed method achieved competitive results for some video attributes, with a success metric of 0.632 and a precision metric of 0.856 as quantitative values.
Granular vortex spin-torque nano oscillator for reservoir computing

In this paper, we investigate the granularity in the free layer of the magnetic tunnel junctions (MTJ) and its potential to function as a reservoir for reservoir computing where grains act as oscillatory neurons while the device is in the vortex state. The input of the reservoir is applied in the form of a magnetic field which can pin the vortex core into different grains of the device in the magnetic vortex state. The oscillation frequency and MTJ resistance vary across different grains in a non-linear fashion making them great candidates to be served as the reservoir's outputs for classification objectives. Hence, we propose an experimentally validated area-efficient single granular vortex spin-torque nano oscillator (GV-STNO) device in which pinning sites work as random reservoirs that can emulate neuronal functions. We harness the nonlinear oscillation frequency and resistance exhibited by the vortex core granular pinning of the GV-STNO reservoir computing system to demonstrate waveform classification.
The Effects of Latency and In-Game Perspective on Player Performance and Game Experience

Previous work shows that high latency, a prolonged delay between player in- and system output, negatively affects player experience and performance. However, previous work also comes to contrary conclusions about how the in-game perspective alters the latency sensitivity of video games. Currently, it is unclear if the in-game perspective independently modulates latency's effects. To investigate how a game's in-game perspective interacts with latency, we developed a shooting game incorporating three perspectives (First-Person-, Third-Person-, and Bird's-Eye-View). In a study, participants (N = 36) played with two levels of latency (low and high) and the three perspectives. We show that latency reduces performance and experience, independent of the perspective. Moreover, Bayesian analysis suggests that the in-game perspective does not interact with latency and does not affects performance or experience. We conclude that more robust means to categorize latency sensitivity of video games than the in-game perspective are required.
BBB security for 5-round even-Mansour-based key-alternating Feistel ciphers

Abstract In this paper, we study the security of the Key-Alternating Feistel (KAF) ciphers, a class of key alternating ciphers with the Feistel structure, where each round of the cipher is instantiated with n -bit public round permutation $$P_i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>P</mml:mi> <mml:mi>i</mml:mi> </mml:msub> </mml:math> , namely the i -th round of the cipher maps $$\begin{aligned} (X_L, X_R) \mapsto (X_R, P_i(X_R \oplus K_i) \oplus K_i \oplus X_L). \end{aligned}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mtable> <mml:mtr> <mml:mtd> <mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>X</mml:mi> <mml:mi>L</mml:mi> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>X</mml:mi> <mml:mi>R</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>↦</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>X</mml:mi> <mml:mi>R</mml:mi> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>P</mml:mi> <mml:mi>i</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>X</mml:mi> <mml:mi>R</mml:mi> </mml:msub> <mml:mo>⊕</mml:mo> <mml:msub> <mml:mi>K</mml:mi> <mml:mi>i</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>⊕</mml:mo> <mml:msub> <mml:mi>K</mml:mi> <mml:mi>i</mml:mi> </mml:msub> <mml:mo>⊕</mml:mo> <mml:msub> <mml:mi>X</mml:mi> <mml:mi>L</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>.</mml:mo> </mml:mrow> </mml:mtd> </mml:mtr> </mml:mtable> </mml:mrow> </mml:math> We have shown that our 5 round construction with independent round permutations and independent round keys achieves 2 n /3-bit security in the random permutation model, i.e., the setting where the adversary is allowed to make forward and inverse queries to the round permutations in a black box way.
Weapons powered by artificial intelligence pose a frontier risk and need to be regulated

&lt;p&gt;Major advances in artificial intelligence (AI) technology in recent years have led to the growing sophistication of military weaponry. Among the most noteworthy new devices are Lethal Autonomous Weapon Systems (L.A.W.S.): machines engineered to identify, engage and destroy targets without human control.&lt;/p&gt; &lt;p&gt;These machines, and the risks posed by their deployment, have been fodder for science fiction literature and cinema with the German film &lt;a href="https://www.theguardian.com/film/2010/oct/21/metropolis-lang-science-fiction" target="_blank"&gt;&lt;em&gt;Metropolis&lt;/em&gt;&lt;/a&gt; in 1927 and Ray Bradbury’s 1953 dystopian novel &lt;a href="https://defense.info/book-review/2019/01/fahrenheit-451-perspectives-on-the-challenges-posed-by-proliferating-artificial-intelligence/" target="_blank"&gt;&lt;em&gt;Fahrenheit 451&lt;/em&gt;&lt;/a&gt;. But L.A.W.S are not just speculation anymore. AI systems can now independently &lt;a href="https://online.maryville.edu/blog/big-data-is-too-big-without-ai/" target="_blank"&gt;use data&lt;/a&gt;, &lt;a href="https://qz.com/527008/an-algorithm-can-predict-human-behavior-better-than-humans/" target="_blank"&gt;predict human behavior&lt;/a&gt;, &lt;a href="https://www.defense.gov/Explore/News/Article/Article/2040031/artificial-intelligence-to-be-used-for-charting-intel-collection/" target="_blank"&gt;gather intelligence&lt;/a&gt;, &lt;a href="https://www.wired.com/insights/2014/08/the-new-eyes-of-surveillance-artificial-intelligence-and-humanizing-technology/" target="_blank"&gt;perform surveillance&lt;/a&gt;, &lt;a href="https://blog.marketresearch.com/8-key-military-applications-for-artificial-intelligence-in-2018" target="_blank"&gt;identify potential targets&lt;/a&gt; and &lt;a href="https://www.northropgrumman.com/what-we-do/can-artificial-intelligence-apply-gaming-to-military-strategy/" target="_blank"&gt;make tactical decisions in virtual combat&lt;/a&gt;. The use of robots in the military is becoming commonplace and the leap to fully autonomous robots being optimized for warfare is already realistic. The big question now is not what kinds of L.A.W.S could exist, but if they pose a frontier risk to humanity.&lt;/p&gt;
Memory guided Aquila optimization algorithm with controlled search mechanism for seismicity analysis of earthquake prone regions

De-clustering the seismic catalog is one of the crucial processes in determining the probability of exceeding ground motions at particular locations. Removing dependent events, such as foreshocks and aftershocks generated from mainshocks, from an earthquake catalog is known as seismicity de-clustering. This paper presents a new approach to classify seismicity using a swarm intelligence technique called the memory-guided Aquila optimizer (MGAOA). Aquila optimization is a recently reported yet popular optimization algorithm inspired by the hunting process of Aquila. In MGAOA, the Aquila searches for the prey based on the personal best history stored in the memory element, which helps MGAOA to converge faster and maintain the balance between exploration and exploitation during local and global search procedures. The search-controlled parameter is further integrated with Aquila optimization to enhance the exploitation phase of the algorithm. The effectiveness of the proposed MGAO is tested on twenty-three classical test functions and ten benchmark test suites of IEEE CEC 2021. The results show that the proposed MGAOA outperforms other algorithms regarding performance metrics and statistical tests. The MGAOA is applied to solve the problem of seismicity de-clustering using the nearest neighbor distance (NND). The NND is a widely used parameter for seismicity and consists of space–time-magnitude information. The MGAOA-based de-clustering model is used to identify the seismic clusters in highly active earthquake-prone Himalayas, California, Japan, and Indonesia regions. The results obtained by the proposed model are compared with state-of-the-art de-clustering techniques and other memory-guided swarm intelligence techniques. The obtained results in terms of aftershocks and backgrounds are evaluated with the help of cumulative plot, λ-plot, space–time plot, inter-event time versus inter-event distance plot, and statistical parameters like coefficient of variance, which show that the proposed model efficiently detects the seismic clusters, and outperforms other benchmark de-clustering techniques.
Image segmentation using active contours with image structure adaptive gradient vector flow external force

Introduction Gradient vector flow (GVF) has been proven as an effective external force for active contours. However, its smoothness constraint does not take the image structure into account, such that the GVF diffusion is isotropic and cannot preserve weak edges well. Methods In this article, an image structure adaptive gradient vector flow (ISAGVF) external force is proposed for active contours. In the proposed ISAGVF model, the smoothness constraint is first reformulated in matrix form, and then the image structure tensor is incorporated. As the structure tensor characterizes the image structure well, the proposed ISAGVF model can be adaptive to image structure, and the ISAGVF snake performs well on weak edge preservation and deep concavity convergence while possessing some other desirable properties of the GVF snake, such as enlarged capture range and insensitivity to initialization. Results Experiments on synthetic and real images manifest these properties of the ISAGVF snake.
Attention-guided graph convolutional network for multi-behavior recommendation

Multi-behavior recommendation system aims to improve recommendation performance by using the interaction data of users′ multiple behaviors. Although some methods have explored the dependencies between different behaviors, there are also existing challenges: (1) user–item interactions have complex dependencies; (2) the dependencies between multiple behaviors vary due to users′ personalized preferences. To address these challenges, we propose a new model MB-AGCN (Attention-Guided Graph Convolutional Network for Multi-Behavior Recommendation), which considers personalized interaction patterns and cross-typed behavioral interdependencies. In the MB-AGCN framework, we take the different effects of multi-behavior information on predicting user preferences into account. We first model the user multi-behavior relationships with the attention mechanism to capture the personalized multi-behavior characteristics. Then, we explore the knowledge learned from the multi-behavior relationship modeling to generate a weight matrix that guides the graph neural network to learn the complex dependencies in different types of user–item interactions and capture the relationships between different types of behaviors. A comprehensive evaluation on three real-world datasets shows that MB-AGCN consistently outperforms state-of-the-art methods. Our codes will be available at https://github.com/3endurance/MB-AGCN.
Case studies of clinical decision-making through prescriptive models based on machine learning

The development of computational methodologies to support clinical decision-making is of vital importance to reduce morbidity and mortality rates. Specifically, prescriptive analytic is a promising area to support decision-making in the monitoring, treatment and prevention of diseases. These aspects remain a challenge for medical professionals and health authorities.In this study, we propose a methodology for the development of prescriptive models to support decision-making in clinical settings. The prescriptive model requires a predictive model to build the prescriptions. The predictive model is developed using fuzzy cognitive maps and the particle swarm optimization algorithm, while the prescriptive model is developed with an extension of fuzzy cognitive maps that combines them with genetic algorithms. We evaluated the proposed approach in three case studies related to monitoring (warfarin dose estimation), treatment (severe dengue) and prevention (geohelminthiasis) of diseases.The performance of the developed prescriptive models demonstrated the ability to estimate warfarin doses in coagulated patients, prescribe treatment for severe dengue and generate actions aimed at the prevention of geohelminthiasis. Additionally, the predictive models can predict coagulation indices, severe dengue mortality and soil-transmitted helminth infections.The developed models performed well to prescribe actions aimed to monitor, treat and prevent diseases. This type of strategy allows supporting decision-making in clinical settings. However, validations in health institutions are required for their implementation.
Proactively selection of input variables based on information gain factors for deep learning models in short-term solar irradiance forecasting

As the proportion of solar power generation increases, accurate solar irradiance forecast used to connect solar power to the grid has become crucial. Multi-parameter prediction is one of the most commonly-used methods for solar irradiance forecast. Effective additional variables can improve the accuracy of the model, while invalid additional variables can lead to over fitting or under fitting of the model. To address this issue, this paper proposes the information gain factor as the basis for proactively selecting input variables. Firstly, the experiment combines 10 kinds of additional variables with GHI and inputs them into five models: auto regressive model (AR), gradient boosting decision tree (GBDT), convolutional neural network (CNN), long short-term memory (LSTM) and convolutional long short-term memory (ConvLSTM). Then, the impact of additional variables on prediction accuracy is analyzed and used as a basis for verifying the feasibility of the proposed method. Finally, the Pearson correlations and information gain factors between these variables and GHI are calculated separately. The results indicate that the information gain factor is more suitable as a basis for selecting input variables than the Pearson coefficient.
Combining the Knowledge of Experienced Programmers to Extract Useful Web Resources for Solving Programming Tasks

People with more programming experience often perform more streamlined and efficient web searches. Previous studies have shown that expert programmers spend less time searching for knowledge on the web than novices. However, which web resources are practical for expert programmers and whether they lead to efficient programming problem-solving is unknown. In this study, we aim to extract practical learning web resources for solving programming tasks from web browsing logs of programming experts. We present a web browser extension that tracks a user’s browsing history. Data is collected from ten participants with over four years of programming experience. Participants are asked to complete three text-mining tasks using the R programming language. After the experiment, participants are asked to complete a post-experiment survey to indicate which web resource assisted them in solving the problem. The results show that combining the results of web access for programmers make it possible to discover practical web resources for problem-solving. Our results demonstrate a strategy for extracting web resources helpful in solving programming tasks.
Trust-aware conditional adversarial domain adaptation with feature norm alignment

Adversarial learning has proven to be an effective method for capturing transferable features for unsupervised domain adaptation. However, some existing conditional adversarial domain adaptation methods assign equal importance to different samples, ignoring the fact that hard-to-transfer samples might damage the conditional adversarial adaptation procedure. Meanwhile, some methods can only roughly align marginal distributions across domains, but cannot ensure category distributions alignment, causing classifiers to make uncertain or even wrong predictions for some target data. Furthermore, we find that the feature norms of real images usually follow a complex distribution, so directly matching the mean feature norms of two domains cannot effectively reduce the statistical discrepancy of feature norms and may potentially induce feature degradation. In this paper, we develop a Trust-aware Conditional Adversarial Domain Adaptation (TCADA) method for solving the aforementioned issues. To quantify data transferability, we suggest utilizing posterior probability modeled by a Gaussian-uniform mixture, which effectively facilitates conditional domain alignment. Based on this posterior probability, a confidence-guided alignment strategy is presented to promote precise alignment of category distributions and accelerate the learning of shared features. Moreover, a novel optimal transport-based strategy is introduced to align the feature norms and facilitate shared features becoming more informative. To encourage classifiers to make more accurate predictions for target data, we also design a mixed information-guided entropy regularization term to promote deep features being away from the decision boundaries. Extensive experiments show that our method greatly improves transfer performance on various tasks.