Reviewerly/exHarmony huggingface dataset:
Enhanced algorithmic modelling and architecture in deep reinforcement learning based on wireless communication Fintech technology

Financial technology" or "FinTech" refers to use of information technologies to derive financial solutions. FinTech is now widely regarded as a hotly debated blend of financial services and information technology. A combination of AI and IoT approaches will significantly increase the extraction of valuable financial data as well as provide better services to customers. This research proposed a novel protocol in data transmission using fuzzy rule-based secure transmission with data optimization technique using deep reinforcement neural network by wireless communication. This technique for data transmission will reduce the credit risk and enhances the data optimization by fuzzy rule-based protocol with DRNN. The logic utilized is fuzzy logic, which is a multi-valued logic with truth values for variables ranging from 0 to 1. When truth values range from entirely true to completely false, it is used. The end user's searching experience is improved by fuzzy logic-based semantic search, which finds and retrieves exact matching files for relevant search files provided by user. When exact matches aren't possible, method leverages semantic similarities to determine most relevant matches. A grading method is utilized to minimize number of false positives. For this, proposed technique was used, which may decide on a collection of suitable storage servers on which the data must be saved and resulting in a reduction in execution time while maintaining a higher level of security. The experimental results show the execution time of 54 %, network performance of 96 %, the overall complexity of 71 %, data optimization of 95 % and end-end delay of 67 %.
Ensembled masked graph autoencoders for link anomaly detection in a road network considering spatiotemporal features

Road anomaly detection aims to find a small group of roads that are exceptional with respect to the rest of the physical links in a transportation network, posing great challenges for spatial data mining and urban infrastructure management applications. Road links involve complex features from both spatial and temporal perspectives, but existing detection methods mostly rely on the topological features of road networks because they face difficulty in capturing the spatial and temporal characteristics of road networks. To overcome this difficulty, we constructed an ensembled autoencoder framework for road anomaly detection by combining spatiotemporal graph learning and graph masking methods, and our main focus was to effectively detect the topological and attribute anomalies mapped by spatiotemporal features in a road network based on traffic flow data. To model local contexts, we performed random masking on the input graph to obtain heterogeneous masked subgraphs. On this basis, a spatiotemporal graph learning model was used to extract topological and traffic flow features from masked subgraphs. Then, we carried out feature reconstructions for each subgraph and calculated the anomaly scores for every node in the graph. Finally, an ensemble model was designed to integrate the anomaly detection results from multiple autoencoders that were trained on the subgraphs to improve feature learning. Our experimental results show that the proposed method is superior to traditional topological analysis methods, machine learning methods (DBScan, isolation forest, LOF, and one-class SVM), and recent deep learning methods (GAE and VGAE).
On the robustness of sparse counterfactual explanations to adverse perturbations

Counterfactual explanations (CEs) are a powerful means for understanding how decisions made by algorithms can be changed. Researchers have proposed a number of desiderata that CEs should meet to be practically useful, such as requiring minimal effort to enact, or complying with causal models. In this paper, we consider the interplay between the desiderata of robustness (i.e., that enacting CEs remains feasible and cost-effective even if adverse events take place) and sparsity (i.e., that CEs require only a subset of the features to be changed). In particular, we study the effect of addressing robustness separately for the features that are recommended to be changed and those that are not. We provide definitions of robustness for sparse CEs that are workable in that they can be incorporated as penalty terms in the loss functions that are used for discovering CEs. To carry out our experiments, we create and release code where five data sets (commonly used in the field of fair and explainable machine learning) have been enriched with feature-specific annotations that can be used to sample meaningful perturbations. Our experiments show that CEs are often not robust and, if adverse perturbations take place (even if not worst-case), the intervention they prescribe may require a much larger cost than anticipated, or even become impossible. However, accounting for robustness in the search process, which can be done rather easily, allows discovering robust CEs systematically. Robust CEs make additional intervention to contrast perturbations much less costly than non-robust CEs. We also find that robustness is easier to achieve for the features to change, posing an important point of consideration for the choice of what counterfactual explanation is best for the user. Our code is available at: https://github.com/marcovirgolin/robust-counterfactuals.
Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system

Degradation prediction is an important part of proton exchange membrane fuel cells (PEMFCs) prognostic research, and it is essential for prolonging the durability of PEMFCs. An accuracy and efficient degradation prediction of PEMFCs method is proposed, which combines extreme learning machine based on auto-encoder (ELM-AE) and fuzzy extension broad learning system (FEBLS). The ELM-AE inherits the fast learning ability of ELM while retaining the dimensionality reduction and feature learning ability of AE. Specifically, the proposed FEBLS consists of three parts, the Takagi–Sugeno–Kang (TSK) fuzzy subsystems, enhancement layer, and extension layer. First, a set of TSK fuzzy subsystems are utilized to replace the mapping nodes of the existing broad learning system (BLS), where the latent representation of the power time series of PEMFCs are processed. Then, the outputs of all fuzzy subsystems are sent to the next layer for nonlinear transformation to obtain enhancement nodes. After that, the enhancement nodes are further fed into the latest layer for further nonlinear transformation to obtain the extension nodes. Finally, the accuracy and efficiency of the proposed ELM-AE-FEBLS method are verified by two different aging datasets of PEMFCs. Compared with ELM, ELM based on particle swarm optimization, and BLS methods, the comparison analysis shows that ELM-AE-FEBLS of multi-input variables with power time series has more competitive in accuracy. Therefore, the proposed method can accurately predict the remaining useful life of fuel cells.
Detection of Road Objects Based on Camera Sensors for Autonomous Driving in Various Traffic Situations

Successfully detecting road objects from camera sensors in various traffic environments would significantly facilitate the development of driving safety applications for autonomous driving. However, object occlusion, bad weather, and small objects in images would restrict the performance of object detection approaches based on camera sensors. To resolve these issues, a lightweight neural network modified convolutional block attention mechanism (MCBAM)-CenterNet was proposed. In this approach, convolutional block attention module (CBAM) was used to find the focus in an image to enhance the detection capability. The lite-hourglass module was then adopted specifically for the detection of cars and pedestrians. A large-scale realistic road object detection dataset BDD100K that includes various traffic images from camera sensors was adopted in this article to evaluate the effectiveness of our proposed MCBAM-CenterNet. The evaluation results show that MCBAM-CenterNet can effectively detect road objects under different scenarios.
Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations

Deep Learning has reached human-level performance in several medical tasks including classification of histopathological images. Continuous effort has been made at finding effective strategies to interpret these types of models, among them saliency maps, which depict the weights of the pixels on the classification as an heatmap of intensity values, have been by far the most used for image classification. However, there is a lack of tools for the systematic evaluation of saliency maps, and existing works introduce non-natural noise such as random or uniform values. To address this issue, we propose an approach to evaluate the faithfulness of the saliency maps by introducing natural perturbations in the image, based on oppose-class substitution, and studying their impact on evaluation metrics adapted from saliency models. We validate the proposed approach on a breast cancer metastases detection dataset PatchCamelyon with 327,680 patches of histopathological images of sentinel lymph node sections. Results show that GradCAM, Guided-GradCAM and gradient-based saliency map methods are sensitive to natural perturbations and correlate to the presence of tumor evidence in the image. Overall, this approach proves to be a solution for the validation of saliency map methods without introducing confounding variables and shows potential for application on other medical imaging tasks.
Practical initialization of the Nelder–Mead method for computationally expensive optimization problems

Abstract Black-box optimization (BBO) algorithms are widely employed by practitioners to address computationally expensive real-world problems such as automatic tuning of machine learning models and evacuation route planning. The Nelder–Mead (NM) method is a well-known local search heuristic for BBO that has been applied to solve many real-world problems from way back because of its promising performance. However, this method has a strong dependence on initialization due to its local search tendency. Nevertheless, a discussion on the proper initialization of the NM method is limited to the recent study by Wessing (Optim Lett 13(4):847–856, 2019), which is solely based on an analysis using the simple sphere function. In this study, we take a further step to improve Wessing’s result by massively investigating how the initialization affects the search performance in views of the initial simplex size and shape and a constraint handling method that is employed on 24 BBO benchmarking problems. Based on the numerical results, we present the empirical best practice for the initialization of the NM method for cases involving a limited evaluation budget.
Challenges in Implementing Low-Latency Holographic-Type Communication Systems

Holographic-type communication (HTC) permits new levels of engagement between remote users. It is anticipated that it will give a very immersive experience while enhancing the sense of spatial co-presence. In addition to the newly revealed advantages, however, stringent system requirements are imposed, such as multi-sensory and multi-dimensional data capture and reproduction, ultra-lightweight processing, ultra-low-latency transmission, realistic avatar embodiment conveying gestures and facial expressions, support for an arbitrary number of participants, etc. In this paper, we review the current limitations to the HTC system implementation and systemize the main challenges into a few major groups. Furthermore, we propose a conceptual framework for the realization of an HTC system that will guarantee the desired low-latency transmission, lightweight processing, and ease of scalability, all accompanied with a higher level of realism in human body appearance and dynamics.
Mel spectrogram-based audio forgery detection using CNN

In this time of technology, digital speech can be created and falsified by a very diverse of hardware and software technologies. Audio copy-move forgery is an audio forgery technique that goals to create forged audio by hiding undesirable words or repeating wanted words in identical speech. Therefore, audio authentication has been a necessary requisition. In this study, an effective approach to spectral images based on audio copy-move forgery detection using convolutional neural networks (CNN) with data augmentation is proposed. There are only a few handcrafted methods conducted for the detection of audio copy-move forgery. None of the existing works on audio copy-move forgery detection has proposed deep feature learning from speech recording with Mel spectrogram. This is the first method to employ deep learning with Mel spectrogram of audio for the detection of audio copy-move forgery. The proposed CNN architecture classifies the suspicious Mel spectrogram images into two classes: original and forged. The proposed CNN system is successfully trained on these Mel spectrogram image feature extraction. The proposed algorithm has been tested on our datasets generated from Arabic Speech Corpus and TIMIT speech database. The results show the effectiveness, robustness of post-processing operations, and high accuracy of the proposed approach compared to other studies.
Block based visually secure image encryption algorithm using 2D-Compressive Sensing and nonlinearity

In this paper, a block division based visually secure encryption algorithm is proposed for color images. Each block is sparsified through an optimal orthogonal transformation. Block based 2D-Compressive Sensing is applied to collect the compressed samples which best represent the signal, sampling is done through a measurement matrix (MM) which is generated by a 2D-SLIM map. MM is optimized through the proposed optimization algorithm which minimizes the mutual coherence between sparsified basis and MM. These compressed samples are further encrypted through a DNA XOR operation. The compressed-encrypted information is decomposed into the four sub images which are embedded into the host image through integer wavelet transform. SHA-256 is used to generate the seed value of the chaotic map. At the receiver end, the secret information is extracted with the correct knowledge of the keys. The numerical results and robustness of the proposed work is analyzed and the superiority of the developed algorithm is shown by comparing it with some recent published papers.
Adaptive weighted curvature-based active contour for ultrasonic and 3T/5T MR image segmentation

Image segmentation is a complex and core technique for disease diagnosis or image-guided surgery in the medical image domain. However, low-quality images, such as images with weak edges and intensity inhomogeneities, may bring considerable challenges for radiologists. In this paper, we propose an adaptive weighted curvature-based active contour model by coupling heat kernel convolution and adaptively weighted high-order total variation for medical image segmentation to improve diagnosis effectiveness. To reduce the computational complexity, the heat kernel convolution operation is applied to approximate the perimeter of a segmentation curve. In addition, the weighted parameter included in the high-order total variation term can be automatically evaluated based on an adaptive input image to emphasize local structures and increase segmentation accuracy. Since the proposed method is a smoothing optimization model, the alternating direction method of multipliers is introduced to translate the original problems into several easily solvable subproblems. The numerical experimental results on ultrasonic and 3T/5T MRI datasets demonstrate that the proposed model is quite efficient and robust compared with several traditional segmentation methods.
Link-RGBD: Cross-Guided Feature Fusion Network for RGBD Semantic Segmentation

Significant advances have been made in the development of deep convolutional networks for RGBD semantic segmentation. However, the full extraction and utilization of depth information to assist semantic segmentation remain challenging. In this study, we propose the Link-RGBD module to fuse RGB and depth features using an innovative interactive attention mechanism to augment the representations of the objects of interest. The attention map gotten from RGB and depth branch is regarded as a weight for value (v) in the other branch in order to achieve cross-fusion. This enhances the coordinates and feature information and augments the representations of the objects of interest. We evaluated the effectiveness of the proposed method and achieved competitive results on the challenging SUN RGB-D and NYUDv2 semantic segmentation benchmarks. Its pixel accuracy is 0.2% higher than that of DeepLabv3+, reaching 83.1% on the SUN RGB-D benchmark. Moreover, its mean IoU score is 0.3% higher than that of DeepLabv3+, reaching 49.5% on the NYUDv2 benchmark. We further constructed a robot test system to evaluate the performance of the proposed method in a real scenario. The results show that the proposed method can support the completion of the moving forward and grasping task.
FlexCNN: An End-to-end Framework for Composing CNN Accelerators on FPGA

With reduced data reuse and parallelism, recent convolutional neural networks (CNNs) create new challenges for FPGA acceleration. Systolic arrays (SAs) are efficient, scalable architectures for convolutional layers, but without proper optimizations, their efficiency drops dramatically for reasons: (1) the different dimensions within same-type layers, (2) the different convolution layers especially transposed and dilated convolutions, and (3) CNN’s complex dataflow graph. Furthermore, significant overheads arise when integrating FPGAs into machine learning frameworks. Therefore, we present a flexible, composable architecture called FlexCNN, which delivers high computation efficiency by employing dynamic tiling, layer fusion, and data layout optimizations. Additionally, we implement a novel versatile SA to process normal, transposed, and dilated convolutions efficiently. FlexCNN also uses a fully pipelined software-hardware integration that alleviates the software overheads. Moreover, with an automated compilation flow, FlexCNN takes a CNN in the ONNX 1 representation, performs a design space exploration, and generates an FPGA accelerator. The framework is tested using three complex CNNs: OpenPose, U-Net, and E-Net. The architecture optimizations achieve 2.3× performance improvement. Compared to a standard SA, the versatile SA achieves close-to-ideal speedups, with up to 5.98× and 13.42× for transposed and dilated convolutions, with a 6% average area overhead. The pipelined integration leads to a 5× speedup for OpenPose.
Incomplete data processing method based on the measurement of missing rate and abnormal degree: Take the loose particle localization data set as an example

Aiming at the problem of incomplete data such as outliers and missing values in machine learning, an incomplete data processing method based on the measurement of missing rate and abnormal degree was proposed in this paper. In this method, the outlier problem that are easy to be ignored was fully considered, and the Bisection method in the data structure field was applied to reduce the interval length and find the data distribution law. And then, an incomplete data processing model for static-structure or fixed-structure data set in a certain field or research direction was constructed. In the model establishment stage, the rules of the outlier processing part were first explored, including the boundary condition for outlier processing, and the relative applicable condition of using the direct-discarding method for outliers. Then, the rules of the missing value processing part were later explored, including the boundary condition for missing value processing, the relative applicable condition of using the direct-discarding method for missing values, the division of different missing rate intervals and the corresponding applicable numerical filling methods. In this research, the loose particle localization data set was taken as the test object, and the applicable incomplete data processing model was established. Specifically, the model can process outliers with the boundary condition of 23%, and the relative applicable condition of using the direct-discarding method for outliers is 2%. While the boundary condition that can process missing values is 67%, and the relative applicable condition of using the direct-discarding method for missing values is 3%. In the range of 3% to 67%, the range of missing rate that processing by statistical-filling methods is 3% to 7%, and the range of missing rate that processing by kNN prediction model is 7% to 67%. The applicable incomplete data processing model was verified on multiple loose particle localization data sets. Test results show that the prediction accuracy achieved by the classification learner based on the parameter-optimized Random Forest has significantly improved on the data set before and after processing, and the average improvement is 5.04%, which effectively proves the feasibility and practicability of the method proposed in this paper. Theoretically, it can be extended and applied to the processing of incomplete data sets in similar fields or with same structures, and has important reference significance for the research on incomplete data processing in machine learning. In addition, it has important practical value for improving the data completeness and data regularity of feature data sets in specific application fields.
Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study

The use of artificial intelligence (AI) at the edge is transforming every aspect of the lives of human beings from scheduling daily activities to personalized shopping recommendations. Since the success of AI is to be measured ultimately in terms of how it benefits human beings, and that the data driving the deep learning-based edge AI algorithms are intricately and intimately tied to humans, it is important to look at these AI technologies through a human-centric lens. However, despite the significant impact of AI design on human interests, the security and trustworthiness of edge AI applications are not foolproof and ethicalneither foolproof nor ethical; Moreover, social norms are often ignored duringin the design, implementation, and deployment of edge AI systems. In this paper, we make the following two contributions: Firstly, we analyze the application of edge AI through a human-centric perspective. More specifically, we present a pipeline to develop human-centric embedded machine learning (HC-EML) applications leveraging a generic human-centric AI (HCAI) framework. Alongside, we also analyzediscuss the privacy, trustworthiness, robustness, and security aspects of HC-EML applications with an insider look at their challenges and possible solutions along the way. Secondly, to illustrate the gravity of these issues, we present a case study on the task of human facial emotion recognition (FER) based on AffectNet dataset, where we analyze the effects of widely used input quantization on the security, robustness, fairness, and trustworthiness of an EML model. We find that input quantization partially degrades the efficacy of adversarial and backdoor attacks at the cost of a slight decrease in accuracy over clean inputs. By analyzing the explanations generated by SHAP, we identify that the decision of a FER model is largely influenced by features such as eyes, alar crease, lips, and jaws. Additionally, we note that input quantization is notably biased against the dark skin faces, and hypothesize that low-contrast features of dark skin faces may be responsible for the observed trends. We conclude with precautionary remarks and guidelines for future researchers.