Reviewerly/exHarmony huggingface dataset:
Watching the BiG artifacts: Exposing DeepFake videos via Bi-granularity artifacts

Recent years have witnessed significant advances in AI-based face manipulation techniques, known as DeepFakes, which has brought severe threats to society. Hence, an emerging and increasingly important research topic is how to detect DeepFake videos. In this paper, we propose a new DeepFake detection method based on Bi-granularity artifacts (BiG-Arts). We observe that the most of DeepFake video generation can commonly introduce bi-granularity artifacts: the intrinsic-granularity artifacts and extrinsic-granularity artifacts. Specifically, the intrinsic-granularity artifacts are caused by a common series of operations in model generation such as up-convolution or up-sampling, while the extrinsic-granularity artifacts are introduced by a common step in post-processing that blends the synthesized face to original video. To this end, we formulate DeepFake detection as multi-task learning problem, to simultaneously predict the intrinsic and extrinsic artifacts. Benefiting from the guidance of detecting Bi-granularity artifacts, our method is notably boosted in both within-datasets and cross-datasets scenarios. Extensive experiments are conducted on several DeepFake datasets, which corroborates the superiority of our method. Our method has been contributed as a part of the solution to achieve the Top-1 rank in DFGC competition (https://competitions.codalab.org/competitions/29583).
Learning fusion feature representation for garbage image classification model in human–robot interaction

Garbage image classification often suffers from three aspect challenges: complex image background, same-shape category, and low-quality image. The existing machine vision methods have excellent learning capabilities. However, they require powerful computational resources. In this work, an efficient garbage image classification network (GScbamKL-Net) is proposed in this work to address the problems mentioned. The proposed network is designed from the following three aspects. First, the new network unit with group convolution and channel shuffle is designed. This unit can significantly reduce the number of parameters of the model and achieve good performance. Second, the CBAM attention mechanism, which can extract key features by weighting the output features in space and channel, is added to the network unit. Furthermore, the LeakyReLu function is introduced as the activation function model. A label smoothing function is constructed as the loss function. It can mitigate the errors and effects of sample imbalance and obtain a good nonlinear transformation effect. The normal garbage images and garbage images using infrared imaging technology were tested respectively. Experimental results show that the proposed GScbam-Net has excellent classification performance while maintaining its lightweight.
CRF-GCN: An effective syntactic dependency model for aspect-level sentiment analysis

Attention-based models have been widely used in aspect-level sentiment analysis owing to their contextual semantic-alignment capabilities. However, these models lack a mechanism that can incorporate syntactic tendencies. Thus, attention-based neural networks are less accurate when dealing with complex syntactic relationships and long-span syntactically dependent utterances. To solve this problem, a neural network model combining a conditional random field and a graph convolutional network is proposed. The model uses a conditional random field chain to extract the opinion span of an aspect-specific word, integrates the contextual information within the opinion span to the global nodes through a multilayer graph convolutional network using the improved position decay function, and predicts the aspect-specific sentiment polarity labels by computing the vector expressions of the global nodes. This study addresses the problem of fluctuations in model accuracy when multiple aspect words exist in an utterance by introducing global nodes into a graph convolutional network. The proposed model performed well in the experiments on all four datasets tested.
Short-term photovoltaic power prediction with similar-day integrated by BP-AdaBoost based on the Grey-Markov model

A photovoltaic (PV) power prediction based on similar-day, Grey-Markov model and AdaBoost is proposed for the impact of similar days on solar output power and the limit of historical data. First, select similar days for target days using dual 2D calculation. Four groups of similar-day were selected through Euclidean distance (ED) and grey relational analysis (GRA), with irradiance and temperature as similar variables. Second, a grey model (GM) was used to predict PV output power based on four sets of similar-day data; the prediction errors of the GM (1,1) were corrected by Markov chains (MC) to obtain four groups of prediction results. Finally, the four sets of predictions are integrated with BP-AdaBoost to obtain better prediction results. The proposed method is validated with the actual data of PV plants. The simulation results show that the integrated prediction results outperform the prediction results of the Grey-Markov model based on similar-day, which achieves complementary information and can provide important reference information for grid-connected PV power plants. Finally, comparing the proposed method (SD-BPA-GM) with decision trees (DT), back propagation neural networks (BP), fully-connected neural networks (FNN), long-term memory networks (LSTM), and convolutional neural networks (CNN), respectively, SD-BPA-GM shows better accuracy.
Explainable artificial intelligence and interpretable machine learning for agricultural data analysis

Artificial intelligence and machine learning have been increasingly applied for prediction in agricultural science. However, many models are typically black boxes, meaning we cannot explain what the models learned from the data and the reasons behind predictions. To address this issue, I introduce an emerging subdomain of artificial intelligence, explainable artificial intelligence (XAI), and associated toolkits, interpretable machine learning. This study demonstrates the usefulness of several methods by applying them to an openly available dataset. The dataset includes the no-tillage effect on crop yield relative to conventional tillage and soil, climate, and management variables. Data analysis discovered that no-tillage management can increase maize crop yield where yield in conventional tillage is <5000 kg/ha and the maximum temperature is higher than 32°. These methods are useful to answer (i) which variables are important for prediction in regression/classification, (ii) which variable interactions are important for prediction, (iii) how important variables and their interactions are associated with the response variable, (iv) what are the reasons underlying a predicted value for a certain instance, and (v) whether different machine learning algorithms offer the same answer to these questions. I argue that the goodness of model fit is overly evaluated with model performance measures in the current practice, while these questions are unanswered. XAI and interpretable machine learning can enhance trust and explainability in AI.
An evolutionary stacked generalization model based on deep learning and improved grasshopper optimization algorithm for predicting the remaining useful life of PEMFC

Accurate prediction of the future degradation trend (FDT) and remaining useful life (RUL) of proton exchange membrane fuel cell (PEMFC) is crucial in the prognosis and health management (PHM) of PEMFC. Therefore, this study develops a stacked generalization model (SGM) to predict the FDT and RUL of the PEMFC. Firstly, maximum information coefficient (MIC) and Akaike information criterion (AIC) are used to select input variables to obtain key information and reduce the difficulty of model training. Then use improved grasshopper optimization algorithm (GOA) to optimize the hyperparameters of the SGM to further improve the prediction performance of the model. Finally, according to the dataset after feature selection, improved GOA (IGOA) optimized SGM (M/AIC-IGOA-SGM) is established to predict FDT and RUL. Among them, IGOA is obtained by introducing Chebyshev chaotic mapping initialization, chaotic decreasing factor, and a spiral position update mechanism to GOA. The base models of SGM are support vector regression (SVR) and gated recurrent unit (GRU), and the meta-model is regularized extreme learning machine (RELM). In the comparative experiments under two different current conditions, the superiority of the proposed model is verified, and the effectiveness of M/AIC, IGOA, and SGM is discussed.
Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels

Recent methods in computational pathology have trended towards semi- and weakly-supervised methods requiring only slide-level labels. Yet, even slide-level labels may be absent or irrelevant to the application of interest, such as in clinical trials. Hence, we present a fully unsupervised method to learn meaningful, compact representations of WSIs. Our method initially trains a tile-wise encoder using SimCLR, from which subsets of tile-wise embeddings are extracted and fused via an attention-based multiple-instance learning framework to yield slide-level representations. The resulting set of intra-slide-level and inter-slide-level embeddings are attracted and repelled via contrastive loss, respectively. This resulted in slide-level representations with self-supervision. We applied our method to two tasks- (1) non-small cell lung cancer subtyping (NSCLC) as a classification prototype and (2) breast cancer proliferation scoring (TUPAC16) as a regression prototype-and achieved an AUC of 0.8641 ± 0.0115 and correlation (R2) of 0.5740 ± 0.0970, respectively. Ablation experiments demonstrate that the resulting unsupervised slide-level feature space can be fine-tuned with small datasets for both tasks. Overall, our method approaches computational pathology in a novel manner, where meaningful features can be learned from whole-slide images without the need for annotations of slide-level labels. The proposed method stands to benefit computational pathology, as it theoretically enables researchers to benefit from completely unlabeled whole-slide images.
Coherence-aware context aggregator for fast video object segmentation

Semi-supervised video object segmentation (VOS) is a highly challenging problem that has attracted much research attention in recent years. Temporal context plays an important role in VOS by providing object clues from the past frames. However, most of the prevailing methods directly use the predicted temporal results to guide the segmentation of the current frame, while ignoring the coherence of temporal context, which may be misleading and degrade the performance. In this paper, we propose a novel model named Coherence-aware Context Aggregator (CCA) for VOS, which consists of three modules. First, a coherence-aware module (CAM) is proposed to evaluate the coherence of the predicted result of the current frame and then fuses the coherent features to update the temporal context. CAM can determine whether the prediction is accurate, thus guiding the update of the temporal context and avoiding the introduction of erroneous information. Second, we devise a spatio-temporal context aggregation (STCA) module to aggregate the temporal context with the spatial feature of the current frame to learn a robust and discriminative target representation in the decoder part. Third, we design a refinement module to refine the coarse feature generated from the STCA module for more precise segmentation. Additionally, CCA uses a cropping strategy and takes small-size images as input, thus making it computationally efficient and achieving a real-time running speed. Extensive experiments on four challenging benchmarks show that CCA achieves a better trade-off between efficiency and accuracy compared to state-of-the-art methods. The code will be public.
Local Linear Embedding with Adaptive Neighbors

Dimensionality reduction is one of the most important techniques in the field of data mining. It embeds high-dimensional data into a low-dimensional vector space while keeping the main information as much as possible. Locally Linear Embedding (LLE) as a typical manifold learning algorithm computes neighborhood preserving embeddings of high-dimensional inputs. Based on the thought of LLE, we propose a novel unsupervised dimensionality reduction model called Local Linear Embedding with Adaptive Neighbors (LLEAN). To achieve a desirable dimensionality reduction result, we impose adaptive neighbor strategy and adopt a projection matrix to project data into an optimal subspace. The relationship between every pair-wise data is investigated to help reveal the data structure. Augmented Lagrangian Multiplier (ALM) is devised in optimization procedure to effectively solve the proposed objective function. Comprehensive experiments on toy data and benchmark datasets have been done and the results show that LLEAN outperforms other state-of-the-art dimensionality reduction methods.
SVeriFL: Successive verifiable federated learning with privacy-preserving

With federated learning, one of the most notable features is that it can update global model parameter without using the users' local data. However, various security and privacy problems still exist in the process of federated learning. The problem of devising a secure and verifiable federated learning framework, so as to obtain high performance federated learning model and protect right and interests of participants has not been sufficiently studied, the malicious server may conduct dishonest data aggregation and return incorrect aggregated gradients to all the participants. What is more, the server with ulterior motives may return correct aggregated results to some participants, but return wrong results to the specific participant. To solve the above problems, we propose the SVeriFL, a successive verifiable federated learning with privacy-preserving in this work. In specific, an elaborately designed protocol based on BLS signature and multi-party security is introduced, such that the integrity of parameter uploaded by participant and correctness of aggregated results of server can be verified; the consistency of aggregation results received from server between any multiple participants can also be testified. Moreover, the CKKS approximate homomorphic encryption is used to protect data privacy of the participant. Experimental results and analyses validate the practical performance and computation efficiency of the presented SVeriFL.
Replay-Attack Detection and Prevention Mechanism in Industry 4.0 Landscape for Secure SECS/GEM Communications

Starting from the First Industrial Revolution to the current and Fourth Industrial Revolution (or Industry 4.0), various industrial machines are present in the market and manufacturing companies. As standardized protocols have become increasingly popular, more utilities are switching to Internet Protocol (IP)-based systems for wide-area communication. SECS/GEM is one of the standards that permit industries to collect information directly from the machines, either using RS323 or TCP/IP communication. TCP/IP communication is becoming more critical than ever, especially given our accelerated digital transformation and increasing reliance on communication technologies. The growth of IT is accelerating with cyberthreats as well. In contrast, security features in the SECS/GEM protocol may be neglected by some companies as it is only used in factories and not mostly used in the outside world. However, communication of SECS/GEM is highly susceptible to various cyberattacks. This paper analyzes the potential replay-attack cyberattacks that can occur on a SECS/GEM system. In replay attacks, this paper supposes an adversary that wants to damage an operation-based control system in an ongoing condition. The adversary has the ability to capture messages to watch and record their contents for a predetermined amount of time, record them, and then replay them while attacking in order to inject an exogenous control input undetected. The paper’s objectives are to prove that SECS/GEM communication is vulnerable to cyberattack and design a detection mechanism to protect SECS/GEM communications from replay attacks. The methodology implements a simulation of the replay-attack mechanism on SECS/GEM communication. The results indicate that the design mechanism detected replay attacks against SECS/GEM communications and successfully prevented them.
Virtual and Augmented Reality versus Traditional Methods for Teaching Physiotherapy: A Systematic Review

The use of virtual worlds in health-related education is increasingly popular, but an overview of their use in physiotherapy education is still needed. The aim of this review was to analyse the use of virtual and augmented reality (VR/AR) compared to traditional methods for teaching physiotherapy. A systematic review was performed up to October 2022 in PubMed, Web of Science, Scopus, CINAHL, and PsycInfo. The quality appraisal and risk of bias were assessed by the Joana Briggs Institute checklist and the Cochrane Collaboration's RoB Tool 2.0, respectively. A total of seven randomised and non-randomised controlled studies were included, involving 737 students. VR/AR-based teaching approaches included simulation and virtual worlds, and were conducted through immersive head-mounted displays, AR-based applications, and 3D visualisations. Three studies were focused on teaching anatomy content, two on clinical decision making skills, and the rest were focused on pathology, physiotherapy tasks or exercise performance, and movement analysis of lower limbs. Inconclusive results were found in terms of learning satisfaction and academic performance, showing VR/AR-based teaching models to be equally effective as traditional methods for teaching physiotherapy. We encourage researchers and teachers to include games in their VR/AR-based teaching approaches to enhance interaction and active learning in physiotherapy education.
Blockchain-empowered security and privacy protection technologies for smart grid

With the rapid development in society of the economy and of computational technologies, it is particularly important to build a secure, efficient and reliable smart grid architecture to provide users with high-quality electricity services. However, data collection and energy trading in public networks creates security and privacy challenges in smart grids. Blockchain technologies have the excellent characteristics of decentralization, immutability and traceability, which can resolve the security, integration and coordination problems faced by the traditional centralized networks for smart grids. The goal of this paper is to introduce and compare blockchain-based technologies in addressing the problems of privacy protection, identity authentication, data aggregation and electricity pricing for the data collection and power energy trading processes in smart grids. In addition, the existing challenges and future research directions of smart grids are discussed.
Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems

Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine algorithm (SCA) and artificial bee colony (ABC) to address engineering design optimization problems. The SCA is a recently developed metaheuristic algorithm with many advantages, such as good search ability and reasonable execution time, but it may suffer from premature convergence. The enhanced SCA search equation is proposed to avoid this drawback and reach a preferable balance between exploitation and exploration abilities. In the proposed hybrid method, named HSCA, the SCA with improved search strategy and the ABC algorithm with two distinct search equations are run alternately during working on the same population. The ABC with multiple search equations can provide proper diversity in the population so that both algorithms complement each other to create beneficial cooperation from their merger. Certain feasibility rules are incorporated in the HSCA to steer the search towards feasible areas of the search space. The HSCA is applied to fifteen demanding engineering design problems to investigate its performance. The presented experimental results indicate that the developed method performs better than the basic SCA and ABC. The HSCA accomplishes pretty competitive results compared to other recent state-of-the-art methods.
A neuro‐evolutionary approach for software defined wireless network traffic classification

Abstract Accurate network traffic classification is an essential and challenging issue for wireless network management and survivability. Existing network traffic classification algorithms, on the other hand, cannot meet the required specifications of real networks' in terms of user privacy control overhead, latency, and above all, classification speed. For wireless network traffic classification, machine learning‐based and hybrid optimization techniques have been deployed. This paper takes a software‐defined wireless network (SDWN) architecture for network traffic classification into account. Because the proposed scheme is perfectly contained within the network controller,the SDWN controller's higher processing capability, global visibility, and programmability can be used to achieve real‐time, adaptive, and precise traffic classification. In this paper, a neuro‐evolutionary approach is proposed in which the feed forward neural network (FFNN) is the base classifier and particle swarm optimization (PSO) is used to train the FFNN to accurately classify traffic while minimizing communication overhead between the controller and the SDWN switches. Simulation experiments were conducted by acquiring real‐world internet datasets to test the efficacy of the proposed scheme. The results and the state‐of‐the‐art comparisons show that the proposed approach has outperformed in terms of accuracy in wireless traffic classification.
SARWAS: Deep ensemble learning techniques for sentiment based recommendation system

Identifying user preferences is a complex operation, which makes its automa- tion challenging, and existing recommendation systems that rely on one of the parameters ratings or reviews are incapable of performing effectively. In this work, an ensemble based deep learning method is used to develop a recommen- dation model in which both ratings and reviews are analyzed simultaneously. The first step is to identify the features required in our work which is reviews and ratings of a product. Next step is to conduct sentiment analysis on reviews to get numerical values for subsequent processing which gives output in terms of polarity and subjectivity, where polarity identifies the emotions expressed in reviews ranging from −1 to +1 and subjectivity identifies the relevance of par- ticular review. Reviews and ratings as features used in this work as inputs for ensemble based deep neural network. For this, both the inputs need to be in same range so scaling is done. The recommendation method used in this work is based on deep learning which employs back propagation neural networks with many hidden layers and varying nodes, facilitating rapid learning. In this pa- per, few selected representative deep learning architectures with varied amounts of layers concealed to improve the learning capability of this model. However, model of the recommendation system can yet be improved, such as the inabil- ity to explain the deep learning recommendation system, which diminishes its credibility. We assessed our work based on hidden layer architectural variations using measures such as precision, loss, and execution time.