Reviewerly/exHarmony huggingface dataset:
RMLANet: Random Multi-Level Attention Network for Shadow Detection and Removal

This paper addresses the problem of shadow detection and shadow removal from a single image. Despite awareness of utilizing both local and global contexts, previous works only aggregate features level by level in a coarse-to-fine manner. To overcome this problem, we present RMLANet, a novel Random Multi-Level Attention Network. To be specific, we first design a shuffled multi-level feature aggregation module to fuse the multi-level features and the guiding features using the self-attention mechanism. Nevertheless, the computational complexity of dense self-attention is unaffordable when processing high-resolution inputs. We argue that dense attention between any pixel pair is unnecessary due to the local consistency in images. Then we further propose a sparse attention mechanism to reduce the number of attention pairs, which greatly reduces the computational complexity. Through extensive experiments on four shadow detection and three shadow removal benchmark datasets, our proposed RMLANet achieves superior performance over current state-of-the-art approaches for both shadow detection and shadow removal. Codes are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LeipingJie/RMLANet</uri> .
Vehicle Selection and Resource Allocation for Federated Learning-Assisted Vehicular Network

To exploit the massive amounts of onboard data in vehicular networks while protecting data privacy and security, federated learning (FL) is regarded as a promising technology to support enormous vehicular applications. Despite that FL has great potential to improve the architecture of intelligent vehicular networks, the mobility of the vehicles and the dynamic nature of wireless channels make the integration of FL and vehicular networks more challenging. In this paper, we propose a vehicle mobility- and channel dynamic-aware FL (MADCA-FL) scheme to fit vehicular networks and enhance learning performances. This novel scheme enables the RSU to select appropriate vehicles and weightedly average the local models. Afterward, MADCA-FL formulates a problem to maximize the model accuracy while assuring the latency and energy restrictions, by jointly optimizing the computation and communication resources. With a mixed- integer non-linear programming structure, the problem is NP-hard. Firstly, we utilize the successive convex approximation algorithm to handle the non-convexity, and then apply the Lagrange multiplier method and the block coordinate descent method to obtain the optimal solution. Extensive experiments are conducted to confirm the effectiveness of our proposed scheme.
Configuration Optimization with Limited Functional Impact

Dealing with a large configuration space is a complex task for developers, especially when configurations must comply with both functional constraints and non-functional goals. In this paper, we introduce an approach to optimize any set of performance indicators for an existing configuration, while meeting functional requirements. The efficiency of this approach is assessed by exhaustively optimizing a configurable system, and by analyzing how the algorithm navigates through the configuration space. This approach proves especially efficient at optimizing configurations through a minimal number of changes, thus limiting the impact on their functional behavior.
Extended Reality (XR) in QM

Extended reality (XR) is an umbrella term encapsulating augmented reality (AR), virtual reality (VR), mixed reality (MR), and everything in between. Although AR and VR offer a wide range of revolutionary experiences, the same underlying technologies are powering XR. XR applications are used in industries like healthcare, defense, education, construction, engineering, gaming, or manufacturing (Lang & Müller, 2020).
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata

Access to the proper infrastructure is critical when performing medical image segmentation with Deep Learning. This requirement makes it difficult to run state-of-the-art segmentation models in resource-constrained scenarios like primary care facilities in rural areas and during crises. The recently emerging field of Neural Cellular Automata (NCA) has shown that locally interacting one-cell models can achieve competitive results in tasks such as image generation or segmentations in low-resolution inputs. However, they are constrained by high VRAM requirements and the difficulty of reaching convergence for high-resolution images. To counteract these limitations we propose Med-NCA, an end-to-end NCA training pipeline for high-resolution image segmentation. Our method follows a two-step process. Global knowledge is first communicated between cells across the downscaled image. Following that, patch-based segmentation is performed. Our proposed Med-NCA outperforms the classic UNet by 2% and 3% Dice for hippocampus and prostate segmentation, respectively, while also being 500 times smaller. We also show that Med-NCA is by design invariant with respect to image scale, shape and translation, experiencing only slight performance degradation even with strong shifts; and is robust against MRI acquisition artefacts. Med-NCA enables high-resolution medical image segmentation even on a Raspberry Pi B+, arguably the smallest device able to run PyTorch and that can be powered by a standard power bank.
Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology

Dense prediction tasks such as segmentation and detection of pathological entities hold crucial clinical value in computational pathology workflows. However, obtaining dense annotations on large cohorts is usually tedious and expensive. Contrastive learning (CL) is thus often employed to leverage large volumes of unlabeled data to pre-train the backbone network. To boost CL for dense prediction, some studies have proposed variations of dense matching objectives in pre-training. However, our analysis shows that employing existing dense matching strategies on histopathology images enforces invariance among incorrect pairs of dense features and, thus, is imprecise. To address this, we propose a precise location-based matching mechanism that utilizes the overlapping information between geometric transformations to precisely match regions in two augmentations. Extensive experiments on two pretraining datasets (TCGA-BRCA, NCT-CRC-HE) and three downstream datasets (GlaS, CRAG, BCSS) highlight the superiority of our method in semantic and instance segmentation tasks. Our method outperforms previous dense matching methods by up to 7.2% in average precision for detection and 5.6% in average precision for instance segmentation tasks. Additionally, by using our matching mechanism in the three popular contrastive learning frameworks, MoCo-v2, VICRegL, and ConCL, the average precision in detection is improved by 0.7% to 5.2%, and the average precision in segmentation is improved by 0.7% to 4.0%, demonstrating generalizability. Our code is available at https://github.com/cvlab-stonybrook/PLM_SSL .
Continuously evolving dropout with multi-objective evolutionary optimisation

Dropout is an effective method of mitigating over-fitting while training deep neural networks (DNNs). This method consists of switching off (dropping) some of the neurons of the DNN and training it by keeping the remaining neurons active. This approach makes the DNN general and resilient to changes in its inputs. However, the probability of a neuron belonging to a layer to be dropped, the 'dropout rate', is a hard-to-tune parameter that affects the performance of the trained model. Moreover, there is no reason, besides being more practical during parameter tuning, why the dropout rate should be the same for all neurons across a layer. This paper proposes a novel method to guide the dropout rate based on an evolutionary algorithm. In contrast to previous studies, we associate a dropout with each individual neuron of the network, thus allowing more flexibility in the training phase. The vector encoding the dropouts for the entire network is interpreted as the candidate solution of a bi-objective optimisation problem, where the first objective is the error reduction due to a set of dropout rates for a given data batch, while the second objective is the distance of the used dropout rates from a pre-arranged constant. The second objective is used to control the dropout rates and prevent them from becoming too small, hence ineffective; or too large, thereby dropping a too-large portion of the network. Experimental results show that the proposed method, namely GADropout, produces DNNs that consistently outperform DNNs designed by other dropout methods, some of them being modern advanced dropout methods representing the state-of-the-art. GADroput has been tested on multiple datasets and network architectures.
Deep-Learning based Trust Management with Self-Adaptation in the Internet of Behavior

Internet of Behavior (IoB) has emerged as a new research paradigm within the context of digital ecosystems, with the support for understanding and positively influencing human behavior by merging behavioral sciences with information technology, and fostering mutual trust building between humans and technology. For example, when automated systems identify improper human driving behavior, IoB can support integrated behavioral adaptation to avoid driving risks that could lead to hazardous situations.
Deep forest auto-Encoder for resource-Centric attributes graph embedding

Graph embedding is an important technique used for representing graph structure data that preserves intrinsic features in a low-dimensional space suitable for graph-based applications. Graphs containing node attributes and weighted links are commonly employed to model various real-world problems and issues in computer science. In recent years, a hot research topic has been the exploitation of diverse information, including node attributes and topological semantic information, in graph embedding. However, due to limitations in deep learning based on neural networks, such information has not been fully utilized nor adequately integrated in existing models, leaving graph embedding unsatisfactory, especially for large resource graphs (e.g., knowledge graphs and task interaction graphs). In this study, we introduce a resource-centric graph embedding approach based on deep random forests learning, which reconstructs graphs using a deep autoencoder to achieve high effectiveness. To accomplish this, our approach employs three key components. The first component is a preprocessor driven by graph similarity, alongside modularity and self-attention modules, to comprehensively integrate graph representation. The second component utilizes local graph information structures to enhance the raw graph. Finally, we integrate diverse information using multi-grained scanning and dual-level cascade forests in the deep learning extractor and generator, ultimately producing the final graph embedding. Experimental results on seven real-world scenarios show that our approach outperforms state-of-the-art embedding methods.
A feature selection framework for anxiety disorder analysis using a novel multiview harris hawk optimization algorithm

Machine learning (ML) has demonstrated its ability to exploit important relationships within data collection, which can be used in the diagnosis, treatment, and prediction of outcomes in a variety of clinical contexts. Anxiety mental disorder analysis is one of the pending difficulties that ML can help with. A thorough study is demanded to gain a better understanding of this illness. Since the anxiety data is generally multidimensional, which complicates processing and as a result of technology improvements, medical data from several perspectives, known as multiview data (MVD), is being collected. Each view has its own data type and feature values, so there is a lot of diversity. This work introduces a novel preprocessing feature selection (FS) approach, multiview harris hawk optimization (MHHO), which has the potential to reduce the dimensionality of anxiety data, hence reducing analytical effort. The uniqueness of MHHO originates from combining a multiview linking methodology with the power of the harris hawk optimization (HHO) method. The HHO is used to identify the lowest optimal MVD feature subset, while multiview linking is utilized to find a promising fitness function to direct the HHO FS while accounting for all data views' heterogeneity. The complexity of MHHO is O(THL2), where T is the number of iterations, H is the number of involved harris hawks, and L is the number of objects. Using two publicly available anxiety MVDs, MHHO is validated against ten recent rivals in its category. The experimental findings show that MHHO has a considerable advantage in terms of convergence speed (converging in less than ten iterations), subset size (removing 75% of the views; reducing feature size by 66%), and classification accuracy (approaching 100%). Furthermore, statistical analyses reveal that MHHO is statistically different from its competitors, bolstering its applicability. Finally, feature importance is evaluated, shedding light on the most anxiety-inducing characteristics. The likelihood of developing additional disorders (such as depression or stress) is also investigated.
Evaluation of machine learning methods for impostor detection in web applications

Applying machine learning (ML) methods to multi-factor authentication is becoming increasingly popular. However, there is no comprehensive methodology to evaluate biometric systems based on machine learning in the literature. This paper proposes a general methodology for evaluation the ML-based systems for impostor recognition/detection using biometric traits. This includes creation of learning and testing sets with appropriate size balance (proportion) between these sets, selecting the number of instances coming from different users, evaluation of the influence of the impostors number on their detection rate, and the impact of the number of records representing user’s behavior. In addition, we propose how the real data (possibly affected by account takeover attempts) could be used to extend the enrollment data to support the impostor detection. The proposed approach was used for a systematic comparison of an extensive set of ML and statistical methods. For some of them, the false acceptance rate (FAR) close to zero and false rejection rate (FRR) smaller than 0.05 in a supervised experiment were accomplished, proving the merit of certain ML-based approaches. Moreover, using the method proposed in the paper, a classifier trained on experimental data achieved FAR below 0.05 on the real-world data collected at an actual financial web page.
Efficient quantum feature extraction for CNN-based learning

Hybrid quantum-classical algorithms provide a promising way to harness the power of current quantum devices. In this framework, parametrized quantum circuits (PQCs) which consist of layers of parametrized unitaries can be considered as a kind of quantum neural networks. Recent works have begun to explore the potential of PQCs as general function approximators. In this work, we propose a quantum-classical deep network structure to enhance model discriminability of convolutional neural networks (CNNs). In CNNs, the convolutional layer uses linear filters to scan the input data followed by a nonlinear operation. Instead, we build PQCs, which are more potent function approximators, with more complex structures to capture the features within the receptive field. The feature maps are obtained by sliding the PQCs over the input in a similar way as CNN. We also give a training algorithm for the proposed model. Through numerical simulation, the proposed hybrid models demonstrate reasonable classification performance on MNIST and Fashion-MNIST (4-classes). In addition, we compare the performance of models in different settings. The results demonstrate that the model with high-expressibility ansaetze achieves lower cost and higher accuracy, but exhibits a “saturation” phenomenon.
Artificial intelligence of things (<scp>AIoT</scp>) data acquisition based on graph neural networks: A systematical review

Summary The power of artificial intelligence of things (AIoT) stems from adapting machine learning (ML) and artificial intelligence (AI) models into abundant intelligent IoT fields, based on a large data stream with different formats, sizes, and timestamps generated by massive numbers of heterogeneous sensors. On the one hand, data acquisition is the fundamental basis for any AIoT systems, but data sensed by massive IoT devices may be noisy and even contain adversarial samples. On the other hand, ensuring the efficiency and robustness in data acquisition is vitally important for data‐driven ML and AI. Recently, besides perceiving ability, the literature has witnessed great development of empowering things with learning and reasoning ability through deep learning models, including recurrent neural networks (RNNs) and/or convolutional neural network (CNNs). However, the existing works have one significant weakness: fail to explicitly leverage the geospatial implications and latent connections among sensors for high‐quality data acquisition and quality control. Graphs are intrinsically suitable for representing the dependencies and inter‐relationships between AIoT data sensing devices. Due to the ability of capturing the complex interactive relationships between nodes and producing high‐level representations of the graph input, graph neural networks (GNNs) have exploded onto various ML and AI fields, to learn from graph‐structured data. Our review covers the latest progresses in GNN for the fundamental atomic task of data acquisition in AIoT. Instead of surveying the abundant GNN schemes in vertically various IoT sensing applications, this paper systematically reviews the horizontal infrastructure that all AIoT fields should have, that is, AIoT data acquisition, based on GNN and other related emerging AI factors. Our contributions include the following aspects: Provide the latest progresses in GNN for the horizontal task of data acquisition in AIoT, propose the unified GNN pipeline based on encoder–decoder paradigm, and systematically categorize and summarize the emerging technologies helpful to address the issues in AIoT data acquisition, especially the noisy and adversarial data, and point out some future directions about GNN‐based AIoT data acquisition.
Interpreting machine for ambiguous figures

Quantum theory presents a unique scenario pertaining to collapse processes. A device that measures variables incompatible with those being detected collapses randomly into one of the states defined by the measuring device. The distinction that a collapsed output is not an accurate description of reality but rather a random selection from a set of values derived from the measuring device allows us to utilize the collapse process to propose a scheme wherein a machine becomes capable of performing interpreting processes. We present herein a basic schematic of a machine that demonstrates the principle of interpretation relying on the polarization phenomenon of photons. The operation of the device is demonstrated using an ambiguous figure. We believe that building an interpreting device can contribute to the field of AI.
AI-augmented usability evaluation framework for software requirements specification in cyber physical human systems

A number of research in Information and Communication Technology (ICT) have shown that usability is an important goal for cyber-physical human systems for wider acceptance by their end-users. To evaluate the usability of a system under design, usability evaluation methods predominantly rely on subject matter experts or testers' assessments. However, integrating Artificial Intelligence (AI) technology and existing usability evaluation methods' processes can aid to evolve more effective user-centred designs. In this paper, we conceptualize an AI-augmented usability evaluation framework (AIUEF) that aims at replacing ‘end-users’ with ‘personas’ to evaluate requirements involving human-computer interaction (HCI) for a given Software Requirements Specification (SRS). We present a blueprint of AIUEF establishing a theoretical basis for an automated usability evaluation of HCI requirements for a given SRS with an aim to improve the usability of a system under design.
Enhancing Breast Cancer Classification in Histopathological Images through Federated Learning Framework

In recent decades, the mortality rate of breast cancer in females is rapidly increasing because of unawareness and failed to detect in earlier stages. In existing, several studies are attempted to develop a robust mechanism for detecting breast cancers from the given input samples. However, they are not as much effective because of several limitations and the secured sharing of sensitive medical images is still a challenging problem faced by medical sector. Thus, the proposed study aims to introduce an automated disease diagnosis system using federated learning and deep learning which automates and speed up the process efficiently. The five crucial steps that involved in the proposed study are image acquisition, encryption, optimal key generation, secured data storing and disease classification. Initially, the required input medical images are gathered in the image acquisition stage. Then, to afford more confidentiality, the gathered medical samples are encrypted through an Extended ElGamal Image Encryption (E-EIE) method. Here, the efficiency of encryption process is enhanced by generating the suitable keys in optimal manner with the help of Improved Sand Cat Swarm Optimization (I-SCSO) algorithm. Next, the security of encrypted images are improvised by utilizing federated learning flower (FLF) framework for storage purpose. This framework has the ability to transmit the medical images with higher security. Finally, the stored images are decrypted and performs disease classification by using convolutional capsule twin attention tuna optimal network (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net) model. The available loss in the proposed classifier is reduced by fine-tuning the parameters using chaotic tuna swarm optimization (CTSO) algorithm. For simulation analysis, the proposed study used Python software and the experimental analysis is carried out by using BreakHis Database. The simulation results shows that the proposed study obtained higher performance in terms of accuracy (95.68%), recall (95.6%), precision (95.66%), F-measure (95.63%), specificity (95.6%) and kappa coefficient (95.26%).