Reviewerly/exHarmony huggingface dataset:
Geolocation of photovoltaic farms using Geographic Information Systems (GIS) with Multiple-criteria decision-making (MCDM) methods: Case of the Ecuadorian energy regulation

Human needs and their production processes require energy services to be developed. In recent years, there has been a great interest in counteracting the use of fossil fuels to satisfy these needs. As part of the worldwide proposed initiatives, there is the use of renewable resources. In Latin America, renewable energy represents a large share of electric energy generation sources. In the case of the Ecuadorian energy regulation, it is necessary to generate at least 1 MW to be considered as a solar farm. Under this framework, Ecuador has a project for installing 91 photovoltaic power plants, fifteen of which will be solar farms and the rest solar power plants with relatively low generation capacity. Currently, in Ecuador, photovoltaic projects are randomly distributed in the territory which shows a lack of adequate criteria for their location. The first step to promote the use of solar sources is identifying the potential, which can be estimated with the use of spatial tools such as Geographic Information Systems (GIS) with Multiple-criteria decision-making (MCDM). This research aims to locate appropriate sites for installing photovoltaic solar farms based on the Ecuadorian energy regulation and combining GIS with MCDM techniques. Thus, nine factors and four restrictions were used for the analysis. Factors were weighted using the Analytic Hierarchy Process method. Once weighted, seven MCDMs were applied to select sites with solar potential. Subsequently, an analysis of seven results was performed using the Pearson correlation coefficient, followed by the absolute error analysis. By the coefficient of Pearson, it is demonstrated that there are methods with a high correlation between them. It is explained by the fact that they have many pixels with similar values, but these values are independent of geographical location. Regarding the results, Loja and part of the provinces in the center north of the country, Pichincha, Santo Domingo de los Tsáchilas, and Cotopaxi are the most adequate because of its great global solar radiation, wind speed, and temperature to cold the solar panels.
Checkerboard artifacts free convolutional neural networks

It is well-known that a number of convolutional neural networks (CNNs) generate checkerboard artifacts in both of two processes: forward-propagation of upsampling layers and backpropagation of convolutional layers. A condition for avoiding the artifacts is proposed in this paper. So far, these artifacts have been studied mainly for linear multirate systems, but the conventional condition for avoiding them cannot be applied to CNNs due to the non-linearity of CNNs. We extend the avoidance condition for CNNs and apply the proposed structure to typical CNNs to confirm whether the novel structure is effective. Experimental results demonstrate that the proposed structure can perfectly avoid generating checkerboard artifacts while keeping the excellent properties that CNNs have.
Path Planning Control for 3-Omni Fighting Robot Using PID and Fuzzy Logic Controller

This paper addresses a comparison between some control methods of three Omni wheels firefighting robot due to the variety of maneuverability. To achieve path planning for firefighting robot to reach a specific point with the shortest path, a kinematics model of omni wheel robot is applied with some control algorithms based on PID controller, Fuzzy logic controller and self-tuned PID using fuzzy logic techniques. Hardware prototype has been tested to validate the simulation results.
An Efficient Bit-Flip Resilience Optimization Method for Deep Neural Networks

Deep neural networks usually possess a high overall resilience against errors in their intermediate computations. However, it has been shown that error resilience is generally not homogeneous within a neural network and some neurons might be very sensitive to faults. Even a single bit-flip fault in one of these critical neuron outputs can result in a large degradation of the final network output accuracy, which cannot be tolerated in some safety-critical applications. While critical neuron computations can be protected using error correction techniques, a resilience optimization of the neural network itself is more desirable, since it can reduce the required effort for error correction and fault protection in hardware. In this paper, we develop a novel resilience optimization method for deep neural networks, which builds upon a previously proposed resilience estimation technique. The optimization involves only few steps and can be applied to pre-trained networks. In our experiments, we significantly reduce the worst-case failure rates after a bit-flip fault for deep neural networks trained on the MNIST, CIFAR-10 and ILSVRC classification benchmarks.
I3D-LSTM: A New Model for Human Action Recognition

Abstract Action recognition has already been a heated research topic recently, which attempts to classify different human actions in videos. The current main-stream methods generally utilize ImageNet-pretrained model as features extractor, however it’s not the optimal choice to pretrain a model for classifying videos on a huge still image dataset. What’s more, very few works notice that 3D convolution neural network(3D CNN) is better for low-level spatial-temporal features extraction while recurrent neural network(RNN) is better for modelling high-level temporal feature sequences. Consequently, a novel model is proposed in our work to address the two problems mentioned above. First, we pretrain 3D CNN model on huge video action recognition dataset Kinetics to improve generality of the model. And then long short term memory(LSTM) is introduced to model the high-level temporal features produced by the Kinetics-pretrained 3D CNN model. Our experiments results show that the Kinetics-pretrained model can generally outperform ImageNet-pretrained model. And our proposed network finally achieve leading performance on UCF-101 dataset.
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders

The Variational Auto-Encoder (VAE) is a popular method for learning a generative model and embeddings of the data. Many real datasets are hierarchically structured. However, traditional VAEs map data in a Euclidean latent space which cannot efficiently embed tree-like structures. Hyperbolic spaces with negative curvature can. We therefore endow VAEs with a Poincare ball model of hyperbolic geometry as a latent space and rigorously derive the necessary methods to work with two main Gaussian generalisations on that space. We empirically show better generalisation to unseen data than the Euclidean counterpart, and can qualitatively and quantitatively better recover hierarchical structures.
CXPlain: Causal Explanations for Model Interpretation under Uncertainty

Feature importance estimates that inform users about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and interpreting machine-learning models. However, providing fast and accurate estimates of feature importance for high-dimensional data, and quantifying the uncertainty of such estimates remain open challenges. Here, we frame the task of providing explanations for the decisions of machine-learning models as a causal learning task, and train causal explanation (CXPlain) models that learn to estimate to what degree certain inputs cause outputs in another machine-learning model. CXPlain can, once trained, be used to explain the target model in little time, and enables the quantification of the uncertainty associated with its feature importance estimates via bootstrap ensembling. We present experiments that demonstrate that CXPlain is significantly more accurate and faster than existing model-agnostic methods for estimating feature importance. In addition, we confirm that the uncertainty estimates provided by CXPlain ensembles are strongly correlated with their ability to accurately estimate feature importance on held-out data.
A Pixel-Based Digital Medical Images Protection Using Genetic Algorithm with LSB Watermark Technique

In recent years, especially when these images are transmitted via a network, digital image security has attracted attention. The 2D barcode is designed to encrypt patient information. In this paper, we proposed a grayscale medical image encryption technology based on the characteristics of genetic algorithm (GAS) using LSB technology. The patient information will be converting into 2D barcode and after that, the original image is embedded with 2D barcode using LSB technique. The LSB technique is more suitable for medical imaging. The resulting image is subjected to a genetic algorithm. In this purpose design, we combined the genetic with LSB technique to make the patient image and its information more secured.
A Novel Approach for Seamless Probabilistic Photovoltaic Power Forecasting Covering Multiple Time Frames

Uncertainty in the upcoming production of photovoltaic (PV) plants is a challenge for grid operations and also a source of revenue loss for PV plant operators participating in electricity markets, since they have to pay penalties for the mismatch between contracted and actual productions. Improving PV predictability is an area of intense research. In real-world applications, forecasts are often needed for different time frames (horizon, update frequency, etc.) and are derived by dedicated models for each time frame (i.e., for day ahead and for intra-day trading). This can result in both different forecasted values corresponding to the same horizon and discontinuities among time-frames. In this paper we address this problem by proposing a novel seamless probabilistic forecasting approach able to cover multiple time frames. It is based on the Analog Ensemble (AnEn) model, however it is adapted to consider the most appropriate input for each horizon from a pool of available input data. It is designed to be able to start at any time of day, for any forecast horizon, making it well-suited for applications like continuous trading. It is easy to maintain as it adapts to the latest data and does not need regular retraining. We enhance short-term predictability by considering data from satellite images and in situ measurements. The proposed model has low complexity compared to benchmark models and is trivially parallelizable. It achieves performance comparable to state-of-the-art models developed specifically for the short term (i.e., up to 6 hours) and the day ahead. The evaluation was carried out on a real-world case comprising three PV plants in France, over a period of one year.
Constrained Reinforcement Learning Has Zero Duality Gap

Autonomous agents must often deal with conflicting requirements, such as completing tasks using the least amount of time/energy, learning multiple tasks, or dealing with multiple opponents. In the context of reinforcement learning~(RL), these problems are addressed by (i)~designing a reward function that simultaneously describes all requirements or (ii)~combining modular value functions that encode them individually. Though effective, these methods have critical downsides. Designing good reward functions that balance different objectives is challenging, especially as the number of objectives grows. Moreover, implicit interference between goals may lead to performance plateaus as they compete for resources, particularly when training on-policy. Similarly, selecting parameters to combine value functions is at least as hard as designing an all-encompassing reward, given that the effect of their values on the overall policy is not straightforward. The later is generally addressed by formulating the conflicting requirements as a constrained RL problem and solved using Primal-Dual methods. These algorithms are in general not guaranteed to converge to the optimal solution since the problem is not convex. This work provides theoretical support to these approaches by establishing that despite its non-convexity, this problem has zero duality gap, i.e., it can be solved exactly in the dual domain, where it becomes convex. Finally, we show this result basically holds if the policy is described by a good parametrization~(e.g., neural networks) and we connect this result with primal-dual algorithms present in the literature and we establish the convergence to the optimal solution.
Federated Adversarial Domain Adaptation

Federated learning improves data privacy and efficiency in machine learning performed over networks of distributed devices, such as mobile phones, IoT and wearable devices, etc. Yet models trained with federated learning can still fail to generalize to new devices due to the problem of domain shift. Domain shift occurs when the labeled data collected by source nodes statistically differs from the target node's unlabeled data. In this work, we present a principled approach to the problem of federated domain adaptation, which aims to align the representations learned among the different nodes with the data distribution of the target node. Our approach extends adversarial adaptation techniques to the constraints of the federated setting. In addition, we devise a dynamic attention mechanism and leverage feature disentanglement to enhance knowledge transfer. Empirically, we perform extensive experiments on several image and text classification tasks and show promising results under unsupervised federated domain adaptation setting.
A Distributed Storage and Computation k-Nearest Neighbor Algorithm Based Cloud-Edge Computing for Cyber-Physical-Social Systems

The k-nearest neighbor (kNN) algorithm is a classic supervised machine learning algorithm. It is widely used in cyber-physical-social systems (CPSS) to analyze and mine data. However, in practical CPSS applications, the standard linear kNN algorithm struggles to efficiently process massive data sets. This paper proposes a distributed storage and computation k-nearest neighbor (D-kNN) algorithm. The D-kNN algorithm has the following advantages: First, the concept of k-nearest neighbor boundaries is proposed and the k-nearest neighbor search within the k-nearest neighbors boundaries can effectively reduce the time complexity of kNN. Second, based on the k-neighbor boundary, massive data sets beyond the main storage space are stored on distributed storage nodes. Third, the algorithm performs k-nearest neighbor searching efficiently by performing distributed calculations at each storage node. Finally, a series of experiments were performed to verify the effectiveness of the D-kNN algorithm. The experimental results show that the D-kNN algorithm based on distributed storage and calculation effectively improves the operation efficiency of k-nearest neighbor search. The algorithm can be easily and flexibly deployed in a cloud-edge computing environment to process massive data sets in CPSS.
Optimal path search and control of mobile robot using hybridized sine-cosine algorithm and ant colony optimization technique

Purpose This paper aims to incorporate a hybridized advanced sine-cosine algorithm (ASCA) and advanced ant colony optimization (AACO) technique for optimal path search with control over multiple mobile robots in static and dynamic unknown environments. Design/methodology/approach The controller for ASCA and AACO is designed and implemented through MATLAB simulation coupled with real-time experiments in various environments. Whenever the sensors detect obstacles, ASCA is applied to find their global best positions within the sensing range, following which AACO is activated to choose the next stand-point. This is how the robot travels to the specified target point. Findings Navigational analysis is carried out by implementing the technique developed here using single and multiple mobile robots. Its efficiency is authenticated through the comparison between simulation and experimental results. Further, the proposed technique is found to be more efficient when compared with existing methodologies. Significant improvements of about 10.21 per cent in path length are achieved along with better control over these. Originality/value Systematic presentation of the proposed technique attracts a wide readership among researchers where AI technique is the application criteria.
Noise-Induced Barren Plateaus in Variational Quantum Algorithms

Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether the noise on NISQ devices places any fundamental limitations on the performance of VQAs. In this work, we rigorously prove a serious limitation for noisy VQAs, in that the noise causes the training landscape to have a barren plateau (i.e., vanishing gradient). Specifically, for the local Pauli noise considered, we prove that the gradient vanishes exponentially in the number of layers $L$. This implies exponential decay in the number of qubits $n$ when $L$ scales as $\operatorname{poly}(n)$, for sufficiently large coefficients in the polynomial. These noise-induced barren plateaus (NIBPs) are conceptually different from noise-free barren plateaus, which are linked to random parameter initialization. Our result is formulated for an abstract ansatz that includes as special cases the Quantum Alternating Operator Ansatz (QAOA) and the Unitary Coupled Cluster Ansatz, among others. In the case of the QAOA, we implement numerical heuristics that confirm the NIBP phenomenon for a realistic hardware noise model.
Deep residual pooling network for texture recognition

Current deep learning-based texture recognition methods extract spatial orderless features from pre-trained deep learning models that are trained on large-scale image datasets. These methods either produce high dimensional features or have multiple steps like dictionary learning, feature encoding and dimension reduction. In this paper, we propose a novel end-to-end learning framework that not only overcomes these limitations, but also demonstrates faster learning. The proposed framework incorporates a residual pooling layer consisting of a residual encoding module and an aggregation module. The residual encoder preserves the spatial information for improved feature learning and the aggregation module generates orderless feature for classification through a simple averaging. The feature has the lowest dimension among previous deep texture recognition approaches, yet it achieves state-of-the-art performance on benchmark texture recognition datasets such as FMD, DTD, 4D Light and one industry dataset used for metal surface anomaly detection. Additionally, the proposed method obtains comparable results on the MIT-Indoor scene recognition dataset. Our codes are available at https://github.com/maoshangbo/DRP-Texture-Recognition.
Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks

A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. Traditional measures of image quality (IQ) have been employed to optimize and evaluate these methods. However, the objective evaluation of IQ for the DNN-based denoising methods remains largely lacking. In this work, we evaluate the performance of DNN-based denoising methods by use of task-based IQ measures. Specifically, binary signal detection tasks under signal-known-exactly (SKE) with background-known-statistically (BKS) conditions are considered. The performance of the ideal observer (IO) and common linear numerical observers are quantified and detection efficiencies are computed to assess the impact of the denoising operation on task performance. The numerical results indicate that, in the cases considered, the application of a denoising network can result in a loss of task-relevant information in the image. The impact of the depth of the denoising networks on task performance is also assessed. The presented results highlight the need for the objective evaluation of IQ for DNN-based denoising technologies and may suggest future avenues for improving their effectiveness in medical imaging applications.
Blockchain-enabled decentralized identity management: The case of self-sovereign identity in public transportation

Identity management has been ripe for disruption over the past few years due to recurring incidents of data breaches that have led to personal information leaks and identity theft. The rise of blockchain technology has paved the way for the development of self-sovereign identity (SSI)—a new class of user-controlled resilient identity management systems that are enabled by distributed ledger technology. This paper examines how SSI management can be used in a public transportation sector that spans different operators in multiple countries. Specifically, the paper explores how a blockchain-based decentralized identity management system can draw on the SSI framework to provide high-level security and transparency for all involved parties in public transportation ecosystems. Accordingly, building on analyses of the existing public transportation ticketing solutions, we elicited requirements of a comparable system based on the SSI principles. Next, we developed a low-fidelity prototype to showcase how passengers can utilize standardized travel credentials that are valid across different transportation networks in Europe. The proposed system eliminates the need for multiple travel cards (i.e., one for each transportation provider) and empowers individuals to have better control over the use of their identities while they utilize interoperable ticketing systems across Europe. Overall, building on the public transportation case, we offer a proof-of-concept that shows how individuals can better manage their identity credentials via the SSI framework.
CONTRA: Defending Against Poisoning Attacks in Federated Learning

Federated learning (FL) is an emerging machine learning paradigm. With FL, distributed data owners aggregate their model updates to train a shared deep neural network collaboratively, while keeping the training data locally. However, FL has little control over the local data and the training process. Therefore, it is susceptible to poisoning attacks, in which malicious or compromised clients use malicious training data or local updates as the attack vector to poison the trained global model. Moreover, the performance of existing detection and defense mechanisms drops significantly in a scaled-up FL system with non-iid data distributions. In this paper, we propose a defense scheme named CONTRA to defend against poisoning attacks, e.g., label-flipping and backdoor attacks, in FL systems. CONTRA implements a cosine-similarity-based measure to determine the credibility of local model parameters in each round and a reputation scheme to dynamically promote or penalize individual clients based on their per-round and historical contributions to the global model. With extensive experiments, we show that CONTRA significantly reduces the attack success rate while achieving high accuracy with the global model. Compared with a state-of-the-art (SOTA) defense, CONTRA reduces the attack success rate by 70% and reduces the global model performance degradation by 50%.