Reviewerly/exHarmony huggingface dataset:
On the Design of Federated Learning in Latency and Energy Constrained Computation Offloading Operations in Vehicular Edge Computing Systems

With the advent of smart vehicles, several new latency-critical and data-intensive applications are emerged in Vehicular Networks (VNs). Computation offloading has emerged as a viable option allowing to resort to the nearby edge servers for remote processing within a requested service latency requirement. Despite several advantages, computation offloading over resource-limited edge servers, together with vehicular mobility, is still a challenging problem to be solved. In particular, in order to avoid additional latency due to out-of-coverage operations, Vehicular Users (VUs) mobility introduces a bound on the amount of data to be offloaded towards nearby edge servers. Therefore, several approaches have been used for finding the correct amount of data to be offloaded. Among others, Federated Learning (FL) has been highlighted as one of the most promising solving techniques, given the data privacy concerns in VNs and limited communication resources. However, FL consumes resources during its operation and therefore incurs an additional burden on resource-constrained VUs. In this work, we aim to optimize the VN performance in terms of latency and energy consumption by considering both the FL and the computation offloading processes while selecting the proper number of FL iterations to be implemented. To this end, we first propose an FL-inspired distributed learning framework for computation offloading in VNs, and then develop a constrained optimization problem to jointly minimize the overall latency and the energy consumed. An evolutionary Genetic Algorithm is proposed for solving the problem in-hand and compared with some benchmarks. The simulation results show the effectiveness of the proposed approach in terms of latency and energy consumption.
Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran

The cuurent study was performed to predict spatial distribution of some heavy metals (Ni, Fe, Cu, Mn) in western Iran, using environmental covariates and applying two machine learning methods comprised Random forest (RF), and Cubist. In this respect, a combination of different input environmental variables (remote sensing data, topographic attributes, thematic maps and soil properties) were used in modeling under four scenarios (I: remote sensing data (RS); II: RS + topographic attributes resulted from digital elevation model (DEM); III: RS + topographic attributes + thematic maps; IV: RS + topographic attributes + thematic maps +soil properties). The maps of Euclidean distance from mines and roads as well as the geology map have been used as thematic maps. A total of 346 soil samples were taken using stratified random sampling from the surface layers (0–20 cm depth) of the studied area and selected heavy metals (Ni, Fe, Cu, Mn), and soil properties were measured in the laboratory. RF and Cubist models were used to predict soil heavy metals in four scenarios. The results indicated that the best prediction accuracy was achieved for the fourth scenario (IV) when all input variables were combined to predict selected heavy metals. Moreover, two models showed different capability for various metals. According to our results, the random forest model had a high accuracy in predicting Ni (R2 = 0.67) and Cu (R2 = 0.60), In contrast, the Cubist model had a higher accuracy in predicting Mn (R2 = 0.55). For predicting Fe, both models provided a similar accuracy (R2 = 0.73). This study proved the high capability of machine learning methods to use easily available environmental data to predict studied heavy metals in the large scale that are essential for decision making in sustainable management in agricultural and environmental concerns.
Task-Oriented Multi-User Semantic Communications for VQA

Semantic communications focus on the transmission of semantic features. In this letter, we consider a task-oriented multi-user semantic communication system for multimodal data transmission. Particularly, partial users transmit images while the others transmit texts to inquiry the information about the images. To exploit the correlation among the multimodal data from multiple users, we propose a deep neural network enabled semantic communication system, named MU-DeepSC, to execute the visual question answering (VQA) task as an example. Specifically, the transceiver for MU-DeepSC is designed and optimized jointly to capture the features from the correlated multimodal data for task-oriented transmission. Simulation results demonstrate that the proposed MU-DeepSC is more robust to channel variations than the traditional communication systems, especially in the low signal-to-noise (SNR) regime.
Multi-threshold image segmentation using a multi-strategy shuffled frog leaping algorithm

Medical image segmentation, which is a complex and fundamental step in medical image processing, can help doctors make more precise decisions on patient diagnosis. Although multi-threshold image segmentation is the most exceptionally fundamental image segmentation technology, it requires complex computing and tends to yield unsatisfactory segmentation results, leading to its limited applications. To solve this problem, in this study, an ensemble multi strategy-driven shuffled frog leaping algorithm with horizontal and vertical crossover search (HVSFLA) is designed for multi-threshold image segmentation. Specifically, a horizontal crossover search enables different frogs to exchange information and guarantee the compelling exploration of each frog. Meanwhile, a vertical crossover search can make frogs in stagnation continue to search actively. Therefore, a better balance between diversification and intensification can be ensured. To evaluate its performance, HVSFLA was compared with a range of state-of-the-art algorithms using CEC 2017 benchmark functions. Furthermore, the performance of HVSFLA was also proved on several Berkeley segmentation datasets 500 (BSDS500). Finally, the proposed algorithm was applied to breast invasive ductal carcinoma cases based on multi-threshold segmentation technique using a non-local means 2D histogram integrated with Kapur’s entropy. The experimental results demonstrate that the proposed HVSFLA outperforms a broad array of similar competitors, and thus it has a great potential to be used for medical image segmentation.
Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for various imaging related tasks such as artificial image generation to support AI training. GANs are especially useful for medical imaging-related tasks where training datasets are usually limited in size and heavily imbalanced against the diseased class. We present a systematic review, following the PRISMA guidelines, of recent GAN architectures used for medical image analysis to help the readers in making an informed decision before employing GANs in developing medical image classification and segmentation models. We have extracted 54 papers that highlight the capabilities and application of GANs in medical imaging from January 2015 to August 2020 and inclusion criteria for meta-analysis. Our results show four main architectures of GAN that are used for segmentation or classification in medical imaging. We provide a comprehensive overview of recent trends in the application of GANs in clinical diagnosis through medical image segmentation and classification and ultimately share experiences for task-based GAN implementations.
A novel image encryption scheme based on pseudo-random coupled map lattices with hybrid elementary cellular automata

In this paper, a hybrid elementary cellular automata (HECA) composed of two chaotic global rules is designed to enhance the chaotic properties of the pseudo-random coupled map lattices (PRCML) model based on the Chirikov standard map (CSM). Theory analyses and simulations indicate that the PRCML-HECA model exhibits superior chaos. Subsequently, a novel image encryption scheme based on this model is implemented. The scheme consists of key seed generation, encryption, and decryption. A series-wound PRCML-HECA model is designed to generate the key seed related to the plain-image, initial key, and timestamp. This process is nonlinear and irreversible, ensuring the proposed scheme can resist the chosen-plaintext/ciphertext attacks. Furthermore, a dynamic substitution box changed with the key seed is also included in the proposed scheme. Moreover, plenty of statistical and security analyses, such as diffusion, histogram, correlation analyses, etc., are introduced in this paper. The results prove that the proposed image encryption scheme based on the PRCML-HECA model can resist differential and statistical attacks. And the scheme also possesses strong robustness and high efficiency.
2D Federated Learning for Personalized Human Activity Recognition in Cyber-Physical-Social Systems

The proliferation of the Internet of Things (IoT), wearable computing, and social media technologies bring forward the realization of the so-called Cyber-Physical-Social Systems (CPSS), which is capable of offering intelligent services in many different aspects of our day-to-day life. While CPSS offer a wide variety of data from wearable devices, challenges such as data silos and secure data sharing still remain. In this study, a 2-Dimensional Federated Learning (2DFL) framework, including the vertical and horizontal federated learning phases, is designed to cope with the insufficient training data and insecure data sharing issues in CPSS during a secure distributed learning process. Considering a specific application of Human Activity Recognition (HAR) across a variety of different devices from multiple individual users, the vertical federated learning scheme is developed to integrate shareable features from heterogeneous data across different devices into a full feature space, while the horizontal federated learning scheme is developed to effectively aggregate the encrypted local models among multiple individual users to achieve a high-quality global HAR model. A computationally efficient somewhat homomorphic encryption (SWHE) scheme is then improved and applied to support the parameter aggregation without giving access to it, which enables heterogeneous data sharing with privacy protection across different personal devices and multiple users in building a more precise personalized HAR model. Experiments are conducted based on two public datasets. Comparing with three conventional machine learning methods, evaluation results demonstrate the usefulness and effectiveness of our proposed model in achieving faster and smoother convergence, with better precision, recall, and F1 scores for HAR applications in CPSS.
Feature-specific mutual information variation for multi-label feature selection

Recent years has witnessed urgent needs for addressing the curse of dimensionality regarding multi-label data, which attracts wide attention for feature selection. Feature relevance terms are often constructed depending on the amount of information contributed by selected features or candidate features to the label set in previous multi-label feature selection approaches based on information theory. Although it is important to consider the amount of information, they ignore both the changed ratio for the undetermined amount of information and the changed ratio for the established amount of information, two types of changed ratios regarding feature relevance evaluation cannot be underestimated. To this end, we devise a new feature relevance term, Relevance based on Weight (RW), which is based on two types of changed ratios. Both two types of changed ratios have positive or negative impacts regarding feature relevance evaluation. A novel multi-label feature selection approach, Relevance based on Weight Feature Selection (RWFS), is proposed based on RW. To verify the effectiveness, the proposed approach is compared to eight state-of-the-art multi-label approaches on thirteen real-world data sets. The experimental results present that RWFS approach has superior performance than other eight compared approaches.
Relation between prognostics predictor evaluation metrics and local interpretability SHAP values

Maintenance decisions in domains such as aeronautics are becoming increasingly dependent on being able to predict the failure of components and systems. When data-driven techniques are used for this prognostic task, they often face headwinds due to their perceived lack of interpretability. To address this issue, this paper examines how features used in a data-driven prognostic approach correlate with established metrics of monotonicity, trendability, and prognosability. In particular, we use the SHAP model (SHapley Additive exPlanations) from the field of eXplainable Artificial Intelligence (XAI) to analyze the outcome of three increasingly complex algorithms: Linear Regression, Multi-Layer Perceptron, and Echo State Network. Our goal is to test the hypothesis that the prognostics metrics correlate with the SHAP model's explanations, i.e., the SHAP values. We use baseline data from a standard data set that contains several hundred run-to-failure trajectories for jet engines. The results indicate that SHAP values track very closely with these metrics with differences observed between the models that support the assertion that model complexity is a significant factor to consider when explainability is a consideration in prognostics.
Sketch Your Own GAN

Can a user create a deep generative model by sketching a single example? Traditionally, creating a GAN model has required the collection of a large-scale dataset of exemplars and specialized knowledge in deep learning. In contrast, sketching is possibly the most universally accessible way to convey a visual concept. In this work, we present a method, GAN Sketching, for rewriting GANs with one or more sketches, to make GANs training easier for novice users. In particular, we change the weights of an original GAN model according to user sketches. We encourage the model’s output to match the user sketches through a crossdomain adversarial loss. Furthermore, we explore different regularization methods to preserve the original model’s diversity and image quality. Experiments have shown that our method can mold GANs to match shapes and poses specified by sketches while maintaining realism and diversity. Finally, we demonstrate a few applications of the resulting GAN, including latent space interpolation and image editing.
Spatial Uncertainty-Aware Semi-Supervised Crowd Counting

Semi-supervised approaches for crowd counting attract attention, as the fully supervised paradigm is expensive and laborious due to its request for a large number of images of dense crowd scenarios and their annotations. This paper proposes a spatial uncertainty-aware semi-supervised approach via regularized surrogate task (binary segmentation) for crowd counting problems. Different from existing semi-supervised learning-based crowd counting methods, to exploit the unlabeled data, our proposed spatial uncertainty-aware teacher-student framework focuses on high confident regions’ information while addressing the noisy supervision from the unlabeled data in an end-to-end manner. Specifically, we estimate the spatial uncertainty maps from the teacher model’s surrogate task to guide the feature learning of the main task (density regression) and the surrogate task of the student model at the same time. Besides, we introduce a simple yet effective differential transformation layer to enforce the inherent spatial consistency regularization between the main task and the surrogate task in the student model, which helps the surrogate task to yield more reliable predictions and generates high-quality uncertainty maps. Thus, our model can also address the task-level perturbation problems that occur spatial inconsistency between the primary and surrogate tasks in the student model. Experimental results on four challenging crowd counting datasets demonstrate that our method achieves superior performance to the state-of-the-art semi-supervised methods. Code is available at : https://github.com/smallmax00/SUA_crowd_counting
GAN-Control: Explicitly Controllable GANs

We present a framework for training GANs with explicit control over generated facial images. We are able to control the generated image by settings exact attributes such as age, pose, expression, etc. Most approaches for manipulating GAN-generated images achieve partial control by leveraging the latent space disentanglement properties, obtained implicitly after standard GAN training. Such methods are able to change the relative intensity of certain attributes, but not explicitly set their values. Recently proposed methods, designed for explicit control over human faces, harness morphable 3D face models (3DMM) to allow fine-grained control capabilities in GANs. Unlike these methods, our control is not constrained to 3DMM parameters and is extendable beyond the domain of human faces. Using contrastive learning, we obtain GANs with an explicitly disentangled latent space. This disentanglement is utilized to train control-encoders mapping human-interpretable inputs to suitable latent vectors, thus allowing explicit control. In the domain of human faces we demonstrate control over identity, age, pose, expression, hair color and illumination. We also demonstrate control capabilities of our framework in the domains of painted portraits and dog image generation. We demonstrate that our approach achieves state-of-the-art performance both qualitatively and quantitatively.
Unsupervised feature selection via adaptive autoencoder with redundancy control

Unsupervised feature selection is one of the efficient approaches to reduce the dimension of unlabeled high-dimensional data. We present a novel adaptive autoencoder with redundancy control (AARC) as an unsupervised feature selector. By adding two Group Lasso penalties to the objective function, AARC integrates unsupervised feature selection and determination of a compact network structure into a single framework. Besides, a penalty based on a measure of dependency between features (such as Pearson correlation, mutual information) is added to the objective function for controlling the level of redundancy in the selected features. To realize the desired effects of different regularizers in different phases of the training, we introduce adaptive parameters which change with iterations. In addition, a smoothing function is utilized to approximate the three penalties since they are not differentiable at the origin. An ablation study is carried out to validate the capabilities of redundancy control and structure optimization of AARC. Subsequently, comparisons with nine state-of-the-art methods illustrate the efficiency of AARC for unsupervised feature selection.
Blockchain in healthcare and IoT: A systematic literature review

Blockchain technology is a highly regarded technology that possesses a plethora of exciting features. This paper analyzes trends and highlights the potential benefits of blockchain deployment in IoT and healthcare. According to the literature, blockchain technology is mostly utilized for data management operations in healthcare and IoT, specifically to improve data security, which includes data integrity, access control, and privacy preservation. In both areas, six distinct types of data security preservation strategies are applied. Additionally, publications highlight how blockchain and IoT, including health IoT, can be used in an integrative way. Three integration mechanisms were seen to accomplish this goal. These solutions range from fully integrating blockchain into data exchanges between IoT devices to using it solely for metadata storage. The most frequently covered area of IoT is a smart city, where blockchain is utilized to improve real-time data sharing, and electricity trading, and so on. Additionally, it is learned that, despite the numerous benefits of blockchain in healthcare, authors typically use it for drug supply chain management and data management purposes in order to avoid counterfeiting and empower patients with regard to their data, respectively.
Should Robots Replace Teachers? Mobilisation of AI and Learning Analytics in Education

The purpose of this study was to determine how artificial intelligence (AI) impacts education. The study's focus was restricted to the use and impact of artificial intelligence in administration, education, and learning, and was founded on a narrative and framework created via exploratory research for assessing AI. The use of literature review as a research strategy and methodology helped in accomplishment of study's goal, which was achieved making use of a qualitative research technique. AI is an area of research in which computers, robots, and other technologies are programmed to exhibit human-like intelligence, as characterised by cognitive skills such as learning and adaptation, as well as decision-making capabilities. According to the findings, AI has been widely embraced and used in a number of ways by organisations, especially educational institutions, in a range of circumstances and settings. AI began with computer technology, progressed to intelligent online and web-based education, and finally, embedded computer systems were used to perform instructional tasks autonomously or collaboratively with teachers, in conjunction with other technologies such as humanoid robots and web-based chatbots. These platforms have allowed instructors to improve the overall quality of their instructional work while also improving their efficiency and speed for administrative duties such as evaluating students' work. However, since the systems are based on machine learning and adaptability, curriculum and materials can be customised to meet the unique requirements of individual students, resulting in improved absorption and retention and a more pleasant overall learning experience.
The Fifth Industrial Revolution: How Harmonious Human–Machine Collaboration is Triggering a Retail and Service [R]evolution

This manuscript draws attention to the dawn of the Fifth Industrial Revolution (5IR) and highlights its potential for addressing a host of issues within retail and service domains. With a retailing and service perspective, the authors outline the meaning of the 5IR, according to a 2 × 2 framework that categorizes retailers and service providers by their embrace of human–machine collaborations. They also propose an expanded definition of stakeholders in the 5IR (companies, employees, customers, and society). Merging digital, physical, and biological technologies promises enhanced well-being for societal actors across the board. By outlining these likely implications of the 5IR for retailing and services, this article establishes a roadmap for how the (r)evolution is likely to progress and offers a set of key research questions that emerge as a result.