Reviewerly/exHarmony huggingface dataset:
Lightweight cryptographic algorithms based on different model architectures: A systematic review and futuristic applications

Summary Lightweight cryptography is a rapidly developing research field. Its main goal is to provide security for devices with fewer resources. These limited‐resource devices implement reliable ciphers that use very little power and computation. The lightweight cipher should be built for high performance while using the fewest resources possible, such as memory and power. In this article, we compare block ciphers and several other stream ciphers based on criteria such as input size, output size, structure employed, key size, number of rounds, vulnerable attacks, chip area, gate equivalent, memory use, throughput, and security features. Moreover, this article provides a detailed analysis comparing all cryptographic algorithms and their use in day‐to‐day life activities. This paper also discusses some lightweight ciphers, stream ciphers, and hybrid ciphers. Moreover, it shows the cryptanalysis of some block ciphers like DES.
MSSDet: Multi-Scale Ship-Detection Framework in Optical Remote-Sensing Images and New Benchmark

Ships comprise the only and most important ocean transportation mode. Thus, ship detection is one of the most critical technologies in ship monitoring, which plays an essential role in maintaining marine safety. Optical remote-sensing images contain rich color and texture information, which is beneficial to ship detection. However, few optical remote-sensing datasets are open publicly due to the issue of sensitive data and copyrights, and only the HRSC2016 dataset is built for the ship-detection task. Moreover, almost all general object detectors suffer from the failure of multi-scale ship detection because of the diversity of spatial resolution and ship size. In this paper, we re-annotate the HRSC2016 dataset and supplement 610 optical remote-sensing images to build a new open source ship-detection benchmark dataset with rich multi-scale ship objects named the HRSC2016-MS dataset. In addition, we further explore the potential of a recursive mechanism in the field of object detection and propose a novel multi-scale ship-detection framework (MSSDet) in optical remote-sensing images. The success of detecting multi-scale objects depends on the hierarchical pyramid structure in the object-detection framework. However, the inherent semantic and spatial gaps among hierarchical pyramid levels seriously affect detection performance. To alleviate this problem, we propose a joint recursive feature pyramid (JRFP), which can generate semantically strong and spatially refined multi-scale features. Extensive experiments were conducted on the HRSC2016-MS, HRSC2016, and DIOR datasets. Detailed ablation studies directly demonstrated the effectiveness of the proposed JRFP architecture and also showed that the proposed method has excellent generalizability. Comparisons with state-of-the-art methods showed that the proposed method achieves competitive performance, i.e., 77.3%, 95.8%, and 73.3% mean average precision accuracy on the HRSC2016-MS, HRSC2016, and DIOR datasets, respectively.
Shadowed Type-2 Fuzzy Sets in Dynamic Parameter Adaption in Cuckoo Search and Flower Pollination Algorithms for Optimal Design of Fuzzy Fault-Tolerant Controllers

In recent, various metaheuristic algorithms have shown significant results in control engineering problems; moreover, fuzzy sets (FSs) and theories were frequently used for dynamic parameter adaption in metaheuristic algorithms. The primary reason for this is that fuzzy inference system (FISs) can be designed using human knowledge, allowing for intelligent dynamic adaptations of metaheuristic parameters. To accomplish these tasks, we proposed shadowed type-2 fuzzy inference systems (ST2FISs) for two metaheuristic algorithms, namely cuckoo search (CS) and flower pollination (FP). Furthermore, with the advent of shadowed type-2 fuzzy logic, the abilities of uncertainty handling offer an appealing improved performance for dynamic parameter adaptation in metaheuristic methods; moreover, the use of ST2FISs has been shown in recent works to provide better results than type-1 fuzzy inference systems (T1FISs). As a result, ST2FISs are proposed for adjusting the Lèvy flight (P) and switching probability (P′) parameters in the original cuckoo search (CS) and flower pollination (FP) algorithms, respectively. Our approach investigated trapezoidal types of membership functions (MFs), such as ST2FSs. The proposed method was used to optimize the precursors and implications of a two-tank non-interacting conical frustum tank level (TTNCFTL) process using an interval type-2 fuzzy controller (IT2FLC). To ensure that the implementation is efficient compared with the original CS and FP algorithms, simulation results were obtained without and then with uncertainty in the main actuator (CV1) and system component (leak) at the bottom of frustum tank two of the TTNCFLT process. In addition, the statistical z-test and non-parametric Friedman test are performed to analyze and deliver the findings for the best metaheuristic algorithm. The reported findings highlight the benefits of employing this approach over traditional general type-2 fuzzy inference systems since we get superior performance in the majority of cases while using minimal computational resources.
Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things

Natural disasters such as severe flooding can cause catastrophic losses to properties and human lives. Constant real-time water level monitoring prior to a flooding event can minimise damages and casualties. Many of the currently deployed water level monitoring systems typically use a combination of float-type or ultrasonic sensing, image processing and computer vision techniques. However, these systems incur high computing and hardware requirements, which hinder the deployment of such systems in resource-constrained and low-cost environments. The recent development of technologies empowered by the Internet of things (IoT) and edge computing have enabled real-time systems to be deployed at a significantly lower cost and a far more distributed manner. In this paper, we propose an architecture for flood monitoring using RGB-D cameras with stereoscopic capabilities to measure the water level in an open environment. Our system uses image preprocessing techniques to account for chromatic aberration due to overexposure, followed by postprocessing before the depth readings are extracted. Data processing and water level information extraction are entirely performed on an edge computing device, therefore greatly reducing the amount of data transmitted to the cloud server. We practically implemented and experimentally validated this system in the real world, under a wide range of weather and lighting conditions. Our results showed promising outcomes and demonstrated the applicability of our proposed system in a wider context.
Classification of natural images inspired by the human visual system

In this paper, a three-step model based on the integration of Deep Neural Networks (DNN) and Decision Models is introduced for image classification which is inspired by the human visual system. To make a decision about an object, many actions should be done in a hierarchical process in the brain. First, the retina receives visual stimuli and transfers them to the visual cortex in the brain. The information extracted in the visual cortex, is accumulated over time to select an appropriate response. Many of the current decision-making models do not show how each image is converted into useful information for the decision model. Some models have used neural networks to convert each image into the information needed in the decision-making model; however, the role of the retina is ignored among these models. In this paper, a combination of retina inspired filters, CNN-based description and accumulator-based decision model is used to classify images. This model's structure resembles the human brain due to the usage of the DoG filter bank as retina inspired filter in the first stage of it. This model shows a significant improvement in accuracy in comparison to other models; furthermore, its performance is acceptable even with the small sample training set.
A three-dimensional text image watermarking model based on multilayer overlapping of extracted two-dimensional information

• Formation mechanism of 3D text image watermarking is proposed. • A 3D text image watermarking model is realized based on multilayer overlapping. • Key parameters are confirmed for building the 3D text image watermarking. • Selection function for the optimal regions of watermarks embedded is established. Aiming at the problem that watermark information is easy to be lost in two-dimensional text image watermarking, a three-dimensional text image watermarking model is proposed based on multilayer overlapping of extracted two-dimensional information, and a specific method is accordingly realized by means of embedding, extracting and overlapping of multiple watermarks in sequence. We discuss the influence of the sequence order, positional distancing and color difference of the extracted multiple two-dimensional information on displaying a three-dimensional text watermark image. In addition, considering the crucial evaluation indices of the invisibility and robustness for the watermarking algorithm, the selection method for superior embedding regions of multiple watermarks is also constructed to enhance the robust performance of watermarks under the premise of effective invisibility. Moreover, we embed the multiple two-dimensional information into the host image by using the undecimated dual-tree complex wavelet transforms - bidiagonal singular value decomposition algorithm. In this way, the extracted multiple two-dimensional information is automatically overlapped to generate the three-dimensional text watermarking according to the optimal matching parameters. We use standard image processing data set to carry out experiments, the results show that the peak signal to noise ratio before and after embedded watermarks is higher than 39 dB, and the peak signal to noise ratio between the original watermarks and the extracted watermarks is more than 37 dB, which is superior to the current reported watermark model. Therefore, it is suggested that the proposed model shows good invisibility and robustness and can reduce the loss of watermark during transmission and attack as much as possible.
Intelligent facial expression recognition and classification using optimal deep transfer learning model

Facial expression is commonly utilized by humans to deliver their mood and emotional state to other people. Facial expression recognition (FER) becomes a hot research area in recent days, and it is a tedious process owing to the presence of high intra-class variation. The conventional methods for FEC are mainly based on handcrafted features with a classification model trained on image or video datasets. Since the facial datasets involve large variations in the images and comprise partial faces, it is needed to design automated FER models. The latest advancements in artificial intelligence (AI) and deep learning (DL) models find useful for better understanding of facial emotions related to face images. In this aspect, this paper presents an intelligent FER using optimal deep transfer learning (IFER-DTFL) model. The proposed IFER-DTFL technique aims to detect the face and identify the facial expressions automatically. The IFER-DTFL technique encompasses a three state process: face detection, feature extraction, and expression classification. In addition, a mask RCNN model is used for the detection of faces. Moreover, the Adam optimizer with Densely Connected Networks (DenseNet121) model is employed for feature extraction process. Furthermore, the weighted kernel extreme learning machine (WKELM) model is utilized to classify the facial expressions. A comprehensive set of simulations were carried out on benchmark dataset and the results are inspected under varying aspects. The experimental results pointed out the supremacy of the IFER-DTFL technique over the other recent techniques interms of several performance measures.
A three-stage GAN model based on edge and color prediction for image outpainting

Human beings have a natural ability to perceive invisible things around them based on materials. This paper examines the problems encountered during image outpainting, which is widely utilized in computational photography, image editing and computer graphics. Although considerable progress has been made in image outpainting, problems such as semantic ambiguity, structural confusion and poor quality still arise. Inspired by the idea that humans draw a boundary contour, paint it with color, and then fill the content, we propose a three-stage GAN model, defined as the ECPIO network. The first-stage model is an edge prediction network (EP-net) that is used to predict the edge map of missing areas. The second-stage model is a color prediction network (CP-Net) that is utilized to predict the color map in the missing area, and the third-stage model is an image outpainting network (IO-Net) that is employed to generate the outpainting results. Since edge and semantics constrain the extended area, we introduce edge and semantic information to the discriminator to guide the image generation. Our method achieved a good performance with a PSNR of 27.15 and an SSIM of 0.78 for the Cityscapes dataset. The experimental results for other public datasets also show that our method can recover accurate semantic content and accurate structure information of missing regions. In addition, our method achieves good effects in semantic coherence, structure vision, color vision and texture vision. Our method has a wide range of intelligent image processing systems, such as computer photography, image editing, and image restoration.
AcME—Accelerated model-agnostic explanations: Fast whitening of the machine-learning black box

In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the users wait. In this paper, we propose Accelerated Model-agnostic Explanations (AcME), an interpretability approach that quickly provides feature importance scores both at the global and the local level. AcME can be applied a posteriori to each regression or classification model based on tabular data. Not only AcME computes feature ranking, but it also provides a what-if analysis tool to assess how changes in features values would affect model predictions. We evaluated the proposed approach on synthetic and real-world datasets, also in comparison with SHapley Additive exPlanations (SHAP), the approach we drew inspiration from, which is currently one of the state-of-the-art model-agnostic interpretability approaches. We achieved comparable results in terms of quality of produced explanations while reducing dramatically the computational time and providing consistent visualization for global and local interpretations. To foster research in this field, and for the sake of reproducibility, we also provide a repository with the code used for the experiments.
Efficient privacy-preserving aggregation for demand side management of residential loads

An effective tool used in practice to maintain network balance is residential demand side management (DMS). However, privacy concerns related to the processing of user personal consumption data often result in a slow wide-scale adoption and acceptance. Various general purpose cryptographic techniques are available, such as homomorphic encryption and secure multi-party computation (MPC), to address these privacy-concerns. Unfortunately, these come at a significant computation price which makes them not applicable in many practical scenarios. In this work, a privacy-preserving aggregation algorithm, based on additive random shares and a combination of well-established symmetric and asymmetric key cryptography methods is proposed and compared with additive homomorphic encryption (AHE) techniques and state-of-the-art MPC protocols. The results show that generic techniques such as homomorphic encryption come with significant computational and communication cost, especially at the users side. On the other hand, MPC approaches provide better performance and resilience to dropouts for very large networks but entail a complex communication among users. In this context, the proposed additive random shares algorithm was the most balanced choice for DSM, with a good performance, simpler information flow and the possibility of adding redundant intermediary parties for enhanced resilience.
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks

Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks (CNNs), aggregate the message of nodes' neighbors and structure information to acquire expressive representations of nodes for node classification, graph classification, and link prediction. Previous studies have indicated that node-level GNNs are vulnerable to Membership Inference Attacks (MIAs), which infer whether a node is in the training data of GNNs and leak the node's private information, like the patient's disease history. The implementation of previous MIAs takes advantage of the models' probability output, which is infeasible if GNNs only provide the prediction label (label-only) for the input.
Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Abstract Process synthesis experiences a disruptive transformation accelerated by artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state‐of‐the‐art actor‐critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from process graphs. In particular, the graph neural networks are implemented within the agent architecture to process the states and make decisions. We implement a hierarchical and hybrid decision‐making process to generate flowsheets, where unit operations are placed iteratively as discrete decisions and corresponding design variables are selected as continuous decisions. We demonstrate the potential of our method to design economically viable flowsheets in an illustrative case study comprising equilibrium reactions, azeotropic separation, and recycles. The results show quick learning in discrete, continuous, and hybrid action spaces. The method is predestined to include large action‐state spaces and an interface to process simulators in future research.
Generating fuzzy graph based Multi-document summary of text based learning materials

Online learning, also known as E-Learning enables the teaching–learning process to be done using technologies. In any educational context, all efforts must be made to ensure that learners carry out their tasks in the proper way. Learning materials may contain a huge amount of content related to a course or any topic. If the content length, in text materials is more, the learners may find it difficult to read and understand the entire content. If summaries are provided for the text-based learning materials, then the learners will have a quick understanding of the purpose and the major focus of the topic. This work aims to provide a summary of the text-based learning materials related to a common topic using maximal cliques and itemset mining with fuzzy graph to remove the uncertainty that occur while calculating the node weight. The summary covers all concepts of the text without redundancy. Through ROUGE evaluation, the summary of this work outperforms the existing itemset-based summarizer and the graph- based summarizers LexRank (LR), Bushy Path (BP), and Aggregate Similarity (AS).
Combining simple and less time complex ML models with multivariate empirical mode decomposition to obtain accurate GHI forecast

Accurate solar irradiance forecasting requires several climatological variables whose seasonal variation affects the stability of the solar irradiance forecast. A less time complex ensemble model has been integrated with multivariate empirical mode decomposition (MEMD) to resolve the non-linearity and non-stationarity of meteorological variables into several intrinsic mode functions (IMFs). The ensemble model combines three base models (kNN-distance-based nonlinear model, DTR-tree-based nonlinear model, ridge-regularized linear model) to ensure good prediction accuracy with less time complexity. For better generalization and reliability of the model, the results have been validated for three Indian locations, viz. Roorkee, Greater Noida, and Gangtok. The MEMD-stacked model outperformed the modern and more complex ML techniques with a minimum % RMSE (% MAE) reduction of 48.51 (37.52) compared to RF, 47.43 (35.11) compared to ANN, and 46.46 (29.36) compared to LSTM. Moreover, in a seasonal analysis, the reported highest RMSE by the proposed model (53.57 W/m2) for the monsoon season was significantly less than that of LSTM (102.36 W/m2). This paper reflects the potential to combine simpler, less time-complex ML algorithms with MEMD to obtain more accurate GHI forecasts with lesser time complexity vis-à-vis the more popular complex ML models.
A deep neural framework for real-time vehicular accident detection based on motion temporal templates

<h2>Abstract</h2> Vehicular accident prediction and detection has recently garnered curiosity and large amounts of attention in machine learning applications and related areas, due to its peculiar and fascinating application potentials in the development of Intelligent Transportation Systems (ITS) that play a pivotal role in the success of emerging smart cities. In this paper, we present a new vision-based framework for real-time vehicular accident prediction and detection, based on motion temporal templates and fuzzy time-slicing. The presented framework proceeds in a stepwise fashion, starting with automatically detecting moving objects (i.e., on-road vehicles or roadside pedestrians), followed by dynamically keep tracking of the detected moving objects based on temporal templates, clustering and supervised learning. Then, an extensive set of local features is extracted from the temporal templates of moving objects. Finally, an effective deep neural network (DNN) model is trained on the extracted features to detect abnormal vehicle behavioral patterns and thus predict an accident just before it occurs. The experiments on real-world vehicular accident videos demonstrate that the framework can yield mostly promising results by achieving a hit rate of 98.5% with a false alarm rate of 4.2% that compare very favorably to those from existing approaches, while still being able to deliver delay guarantees for realtime traffic monitoring and surveillance applications.
Exemplar-Free Online Continual Learning

Targeted for real world scenarios, online continual learning aims to learn new tasks from sequentially available data under the condition that each data is observed only once by the learner. Though recent works have made remarkable achievements by storing part of learned task data as exemplars for knowledge replay, the performance is greatly relied on the size of stored exemplars while the storage consumption is a significant constraint in continual learning. In addition, storing exemplars may not always be feasible for certain applications due to privacy concerns. In this work, we propose a novel exemplar-free method by leveraging nearest-class-mean (NCM) classifier where the class mean is estimated during training phase on all data seen so far through online mean update criteria. We focus on image classification task and conduct extensive experiments on benchmark datasets including CIFAR-100 and Food-1k. The results demonstrate that our method without using any exemplar outperforms state-of-the-art exemplar-based approaches with large margins under standard protocol (20 exemplars per class) and is able to achieve competitive performance even with larger exemplar size (100 exemplars per class).