Reviewerly/exHarmony huggingface dataset:
An Experimental Investigation of Co2 Concentration on Solar PV Performance using SVM

Solar photovoltaic Plant installation requires a rigorous calculation about the performance of Solar photovoltaic Plant under different weather condition and that of the the Solar output power has to be predicted well in advance. all these things are required at the initial designing stage of the Solar photovoltaic Plant. Rapid growth in the The interconnection of renewable sources to the traditional grid for developing sustainable environment and that of minimising the power loss required as strong assessment of solar photovoltaic system before its interconnection to the traditional grid. two main factors which affect the performance of Solar photovoltaic Plant are solar irradiance and temperature which again directly or indirectly depends upon the greenhouse content of the gas present in environment. because of the intermittent nature of the factors affecting performance of solar PV plant hence forecasting the solar power is a very difficult task. this paper investigate A comparative analysis of solar power forecasting using adaptive neural network and decision tree. the system has been investigated through matlab simulink model for Bhubaneswar in the state of Odisha, where the real time data and experimental setup has been conducted at NRRI, CUTTACK.
Approximation of daily solar radiation: A comprehensive review on employing of regression models

The energy crisis has always been one of the most important issues of recent decades. Using renewable energy sources is known as the best solution to this problem. Solar energy applications, especially in areas that benefit from high solar radiation, are very promising. For optimal use of solar energy, it is necessary to measure the amount of radiation received in each area, but this is not possible in many areas. A common practice is to estimate radiation by empirical correlations based on measuring relevant parameters. This study conducted a comprehensive review of all available daily model formats to approximate solar radiation. Totally, 191 model formats were listed and classified into eight categories, namely, sunshine-based (18), temperature-based (32), sunshine-temperature-based (3), meteorological (58), independent (23), hybrid (32), air quality index-based (11), and cloud cover-based (14) models.
Mathematical Modeling of Self Balancing Robot and Hardware Implementation

Two-wheel self-balancing robots is a area of review that may provide the future locomotion for everyday robots. The unique stability control feature which keeps the robot upright differentiates it from all other traditional forms of robots. The inverted pendulum principle provides the mathematical modeling of unstable system. This is then utilized to develop and implement a suitable stability control system that is timely, responsive and successful in achieving the entire objective. Self-balancing robot is primarily based on the principle of inverted pendulum, which are supported by wheels for back and forth movement for its balancing.
MoRE: Multi-output residual embedding for multi-label classification

Multi-label classification (MLC) is one of the challenging tasks in computer vision, where it confronts high dimensional problem both in output label and input feature spaces. This paper proposed solving MLC through multi-output residual embedding (MoRE), which learns appropriate distance metric by analyzing the residuals between input and output spaces. Unlike traditional MLC paradigms that learn relationships between label space and feature space, our proposed approach further learns a low-rank structure in residuals between input and output spaces. And it encodes such residual projection to achieve dimension reduction in label space, enhancing the performance of the proposed algorithm in processing high dimensional MLC task. Furthermore, considering the label correlations between instances and its neighbors, multiple residuals of instances neighbors are also incorporated into the proposed model to further learn more appropriate distance metric in the same way. Overall, with residual embedding learning from instances and their neighbors, the obtained metric can learn a more appropriate low-rank structure in label space to handle high dimensional problem in MLC. Experimental results on several data sets, such as Cal500, Corel5k, Bibtex, Delicious, Tmc2007, 20ng, Mirflickr and Rcv1s1, demonstrate the excellent predictive performance of MoRE among STOA methods, such as LMMO-kNN, M3MDC, KRAM, SEEM, CPLST, CSSP, FaIE.
A blind robust color image watermarking method based on discrete wavelet transform and discrete cosine transform using grayscale watermark image

Abstract With the rapid development of internet technology, digital images are easily accessed and tampered by unauthorized users. Therefore, copyright of color images has become an important challenge in nowadays. Robust color image watermarking methods have emerged to provide copyright. However, robust watermarking methods must satisfy the requirements such as robustness, imperceptibility, capacity, and security. Therefore, researchers are focused on methods using grayscale watermark instead of the binary watermark to increase capacity. Grayscale watermark based robust color image watermarking methods are mostly semi‐blind and non‐blind. However, these methods have limited utilization areas since the original image, original watermark, or side information is required. To address these drawbacks, we propose a blind robust color image watermarking method using grayscale watermark. In the proposed watermark embedding procedure, one‐level discrete wavelet transform is performed on the Cb color component. Then, scrambled watermark pixels are hidden into coefficients of discrete cosine transform (DCT) matrices of LL subband by using dividing‐based new watermark embedding formula. In the watermark extracting procedure, watermark pixels are extracted from coefficients of DCT matrices by using dividing‐based extracting formula. The performance is demonstrated against various attacks and experimental results indicate that the presented method ensures robustness while preserving imperceptibility.
Towards a robust and trustworthy machine learning system development: An engineering perspective

While Machine Learning (ML) technologies are widely adopted in many mission critical fields to support intelligent decision-making, concerns remain about system resilience against ML-specific security attacks and privacy breaches as well as the trust that users have in these systems. In this article, we present our recent systematic and comprehensive survey on the state-of-the-art ML robustness and trustworthiness from a security engineering perspective, focusing on the problems in system threat analysis, design and evaluation faced in developing practical machine learning applications, in terms of robustness and user trust. Accordingly, we organize the presentation of this survey intended to facilitate the convey of the body of knowledge from this angle. We then describe a metamodel we created that represents the body of knowledge in a standard and visualized way. We further illustrate how to leverage the metamodel to guide a systematic threat analysis and security design process which extends and scales up the classic process. Finally, we propose the future research directions motivated by our findings. Our work differs itself from the existing surveys by (i) exploring the fundamental principles and best practices to support robust and trustworthy ML system development, and (ii) studying the interplay of robustness and user trust in the context of ML systems. We expect this survey provides a big picture for machine learning security practitioners.
Graspable AI: Physical Forms as Explanation Modality for Explainable AI

Explainable AI (XAI) seeks to disclose how an AI system arrives at its outcomes. But the nature of the disclosure depends in part on who needs to understand the AI and the available explanation modalities (e.g., verbal and visual). Users’ preferences regarding explanation modalities might differ, as some might prefer spoken explanations compared to visual ones. However, we argue for broadening the explanation modalities, to consider also tangible and physical forms. In traditional product design, physical forms have mediated people’s interactions with objects; more recently interacting with physical forms has become prominent with IoT and smart devices, such as smart lighting and robotic vacuum cleaners. But how tangible interaction can support AI explanations is not yet well understood.
Performance Analysis of the Pretrained EfficientDet for Real-time Object Detection on Raspberry Pi

Recently there has been a lot of demand for deep learning models that can operate on a constrained device. When it comes to the task of object detection, EfficientDet is a well-known model. In this study, we use the integer quantization technique to perform real-time object detection on a Raspberry Pi using the popular EfficientDet family. We use the pretrained models from the TensorFlow to perform object detection for a specific task and evaluate their on-device performance. We examined the models&#x2019; performance in terms of average precision and recall, IOU, speed, and model size. When working on a Raspberry Pi, we discovered that EfficientDet1 after quantization, with a moving average decay of 0.95 and a Stochastic Gradient Descent optimizer is a good choice.
EFNN-NullUni: An evolving fuzzy neural network based on null-uninorm

Interpretability in intelligent models becomes a challenge in academic research and approaches that facilitate understanding the responses obtained in models based on artificial intelligence and machine learning. This paper presents a new logical fuzzy neuron based on the concept of null-uninorm, thus called null-unineuron to compose the architecture of an evolving neuro-fuzzy model. This new structural neuron can extract advanced fuzzy rules allowing AND and OR-connections of antecedents to better interpret and understand the analyzed problem. This three-layer model uses an evolving weighted fuzzification approach based on incremental data partitioning concepts for knowledge extraction through null-unineurons whose training procedure suits the classification of binary and multiclass patterns in an online and incremental way. The weights integrated in the evolving data partitioning algorithm belong to feature importance levels and achieve an automatic shrinkage of distance calculations along unimportant input directions (features), which in turn accounts for a soft dimension reduction and the likelihood to decrease over-fitting. The new architecture proposed in this model was, subject to pattern classification tests, being more efficient compared to related (evolving) neuro-fuzzy models in the literature. Finally, experiments on various real-world data sets proved that the evolving neuro-fuzzy model proposed in this paper can act in a simplified way in the extraction of knowledge from data while providing answers with a high degree of accuracy for pattern classification problems.
Towards Compact Autonomous Driving Perception With Balanced Learning and Multi-Sensor Fusion

We present a novel compact deep multi-task learning model to handle various autonomous driving perception tasks in one forward pass. The model performs multiple views of semantic segmentation, depth estimation, light detection and ranging (LiDAR) segmentation, and bird’s eye view projection simultaneously without being supported by other models. We also provide an adaptive loss weighting algorithm to tackle the imbalanced learning issue that occurred due to plenty of given tasks. Through data pre-processing and intermediate sensor fusion techniques, the model can process and combine multiple input modalities retrieved from RGB cameras, dynamic vision sensors (DVS), and LiDAR placed at several positions on the ego vehicle. Therefore, a better understanding of a dynamically changing environment can be achieved. Based on the ablation study, the model variant trained with our proposed method achieves a better performance. Furthermore, a comparative study is also conducted to clarify its performance and effectiveness against the combination of some recent models. As a result, our model maintains better performance even with much fewer parameters. Hence, the model can inference faster with less GPU memory utilization. Moreover, the result tends to be consistent in 3 different CARLA simulation datasets and 1 real-world nuScenes-lidarseg dataset. To support future research, we share codes and other files publicly at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/oskarnatan/compact-perception</uri> .
Mutual Learning of Joint and Separate Domain Alignments for Multi-Source Domain Adaptation

Multi-Source Domain Adaptation (MSDA) aims at transferring knowledge from multiple labeled source domains to benefit the task in an unlabeled target domain. The challenges of MSDA lie in mitigating domain gaps and combining information from diverse source domains. In most existing methods, the multiple source domains can be jointly or separately aligned to the target domain. In this work, we consider that these two types of methods, i.e. joint and separate domain alignments, are complementary and propose a mutual learning based alignment network (MLAN) to combine their advantages. Specifically, our proposed method is composed of three components, i.e. a joint alignment branch, a separate alignment branch, and a mutual learning objective between them. In the joint alignment branch, the samples from all source domains and the target domain are aligned together, with a single domain alignment goal, while in the separate alignment branch, each source domain is individually aligned to the target domain. Finally, by taking advantage of the complementarity of joint and separate domain alignment mechanisms, mutual learning is used to make the two branches learn collaboratively. Compared with other existing methods, our proposed MLAN integrates information of different domain alignment mechanisms and thus can mine rich knowledge from multiple domains for better performance. The experiments on Domain-Net, Office-31, and Digits-five datasets demonstrate the effectiveness of our method.
Online Continual Learning Via Candidates Voting

Continual learning in online scenario aims to learn a sequence of new tasks from data stream using each data only once for training, which is more realistic than in offline mode assuming data from new task are all available. However, this problem is still under-explored for the challenging class-incremental setting in which the model classifies all classes seen so far during inference. Particularly, performance struggles with increased number of tasks or additional classes to learn for each task. In addition, most existing methods require storing original data as exemplars for knowledge replay, which may not be feasible for certain applications with limited memory budget or privacy concerns. In this work, we introduce an effective and memory-efficient method for online continual learning under class-incremental setting through candidates selection from each learned task together with prior incorporation using stored feature embeddings instead of original data as exemplars. Our proposed method implemented for image classification task achieves the best results under different benchmark datasets for online continual learning including CIFAR-10, CIFAR-100 and CORE-50 while requiring much less memory resource compared with existing works.
Diagnosis and distinguishment of open-switch and current sensor faults in PMSM drives using improved regularized extreme learning machine

Three-phase voltage-source inverter fed permanent magnet synchronous motors drive system is widely applied in high power drive applications. Multiple power switches faults and sensor faults are very common in drive systems. To rapidly and accurately diagnose and distinguish power switches and sensor faults, this article introduces a data-driven diagnosis approach named regularized extreme learning machine (RELM) to distinguish the fault type and identify fault components. In order to shorten the training time of RELM, LU decomposition is used to solve the output weight matrix of the RELM. Meanwhile, the beetle swarm optimization (BSO) algorithm is designed to optimize the weights and thresholds, as well as the Tent mapping reverse learning and Levy flight group learning strategies are also introduced to improve optimization performance. Further, the database required for training is filled with the current fault indicators instead of the universal three-phase currents, which not only good for obtaining a well-learned fault classifier model but also avoid false alarms generated by the inaccurate thresholds in traditional signal-based approach. Experimental tests show that the proposed approach can rapidly and accurately diagnose and distinguish multiple power switches and sensor faults.
Human Being Detection from UWB NLOS Signals: Accuracy and Generality of Advanced Machine Learning Models

This paper studies the problem of detecting human beings in non-line-of-sight (NLOS) conditions using an ultra-wideband radar. We perform an extensive measurement campaign in realistic environments, considering different body orientations, the obstacles' materials, and radar-obstacle distances. We examine two main scenarios according to the radar position: (i) placed on top of a mobile cart; (ii) handheld at different heights. We empirically analyze and compare several input representations and machine learning (ML) methods-supervised and unsupervised, symbolic and non-symbolic-according to both their accuracy in detecting NLOS human beings and their adaptability to unseen cases. Our study proves the effectiveness and flexibility of modern ML techniques, avoiding environment-specific configurations and benefiting from knowledge transference. Unlike traditional TLC approaches, ML allows for generalization, overcoming limits due to unknown or only partially known observation models and insufficient labeled data, which usually occur in emergencies or in the presence of time/cost constraints.
Real-Time Adaptation of Context-Aware Intelligent User Interfaces, for Enhanced Situational Awareness

In this work, a novel computational approach for the dynamic adaptation of User Interfaces (UIs) is proposed, which aims at enhancing the Situational Awareness (SA) of users by leveraging the current context and providing the most useful information, in an optimal and efficient manner.By combining Ontology modeling and reasoning with Combinatorial Optimization, the system decides what information to present, when to present it, where to visualize it in the display -and how, taking into consideration contextual factors as well as placement constraints.The main objective of the proposed approach is to optimize the SA associated with the displayed UI at run-time, while avoiding information overload and induced stress.In the context of this work, we have deployed our computational approach to the use case of an Augmented Reality (AR) system for Law Enforcement Agents (LEAs).To explore the benefits and limitations of the developed system, two evaluations have been conducted.The first one was an expert-based evaluation with LEAs and User Experience (UX) experts, assessing the appropriateness of the system's decisions.The second one was a user-based evaluation involving LEAs from different agencies, estimating the SA, the mental workload and the overall UX associated with the system, through an AR simulation.The results indicate that the system enhances SA, and while not imposing workload, it provides an overall positive UX.
Morph Detection Enhanced by Structured Group Sparsity

In this paper, we consider the challenge of face morphing attacks, which substantially undermine the integrity of face recognition systems such as those adopted for use in border protection agencies. Morph detection can be formulated as extracting fine-grained representations, where local discriminative features are harnessed for learning a hypothesis. To acquire discriminative features at different granularity as well as a decoupled spectral information, we leverage wavelet domain analysis to gain insight into the spatial-frequency content of a morphed face. As such, instead of using images in the RGB domain, we decompose every image into its wavelet sub-bands using 2D wavelet decomposition and a deep supervised feature selection scheme is employed to find the most discriminative wavelet sub-bands of input images. To this end, we train a Deep Neural Network (DNN) morph detector using the decomposed wavelet sub-bands of the morphed and bona fide images. In the training phase, our structured group sparsity-constrained DNN picks the most discriminative wavelet sub-bands out of all the sub-bands, with which we retrain our DNN, resulting in a precise detection of morphed images when inference is achieved on a probe image. The efficacy of our deep morph detector which is enhanced by structured group lasso is validated through experiments on three facial morph image databases, i.e., VISAPP17, LMA, and MorGAN.
Lightweight Monocular Depth Estimation on Edge Devices

Given monocular images as inputs, monocular depth estimation (MDE) infers pixel-level depth. MDE is always a critical stage in scene sensing on edge devices. Existing MDE studies frequently employ deep neural networks (DNNs) for MDE, but they still face some problems, such as sacrificing computational complexity and efficiency in return for great precision, or losing more precision in exchange for increased efficiency. To alleviate these issues; 1) we propose an encoder–decoder network (EdgeNet) for precise and fast MDE on different edge devices. When recovering depth in the decoder, we design upsampling modules to aggregate global depth information with low computational complexity, improving the accuracy of the decoder by extracting its different ranges of depth information; 2) we develop a two-stage channel pruning method to, respectively, prune the encoder and decoder based on their characteristics. Our pruning method further reduces latency and model/computational complexity of EdgeNet, while losing little accuracy; and 3) we optimize the pruned EdgeNet to decrease graphics processing unit (GPU) scheduling overhead. The optimization accelerates MDE inference by an order of magnitude on the TX2 GPU device, when the input resolution is 224 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 224. Extensive experiments show that our strategies are effective on different edge GPU devices, when input resolutions differ in outdoor or indoor scenes. For example, compared with the state of the art, the optimized EdgeNet, respectively, reduces the GPU latency by 76.3% and 89.2% on Nano and TX2 GPU devices with 2.6% lower root mean square error when the input resolution is 128 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 416.