Reviewerly/exHarmony huggingface dataset:
SelfMix: A Self-adaptive Data Augmentation Method for Lesion Segmentation

Deep learning-based methods have obtained promising results in various organ segmentation tasks, due to their effectiveness in learning feature representation. However, accurate segmentation of lesions can still be challenging due to 1) the lesions provide less information than normal organs; 2) the available number of labeled lesions is more limited than normal organs; 3) the morphology, shape, and size of lesions are more diverse than normal organs. To increase the number of lesion samples and further boost the performance of various lesion segmentation, in this paper, we propose a simple but effective lesion-aware data augmentation method called Self-adaptive Data Augmentation (SelfMix). Compared with existing data augmentation methods, such as Mixup, CutMix, and CarveMix, our proposed SelfMix have three-fold advances: 1) Solving the challenges that the generated tumor images are facing the problem of distortion by absorbing both tumor and non-tumor information; 2) SelfMix is tumor-aware, which can adaptively adjust the fusing weights of each lesion voxels based on the geometry and size information from the tumor itself; 3) SelfMix is the first one that notices non-tumor information. To evaluate the proposed data augmentation method, experiments were performed on two public lesion segmentation datasets. The results show that our method improves the lesion segmentation accuracy compared with other data augmentation approaches.
An intelligent system for multi-topic social spam detection in microblogging

The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This article proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users’ behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers is compared and their effectiveness is measured via a number of robust evaluation measures. Furthermore, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules.
Test-Time Adaptation with Shape Moments for Image Segmentation

Supervised learning is well-known to fail at generalization under distribution shifts. In typical clinical settings, the source data is inaccessible and the target distribution is represented with a handful of samples: adaptation can only happen at test time on a few (or even a single) subject(s). We investigate test-time single-subject adaptation for segmentation, and propose a shape-guided entropy minimization objective for tackling this task. During inference for a single testing subject, our loss is minimized with respect to the batch normalization’s scale and bias parameters. We show the potential of integrating various shape priors to guide adaptation to plausible solutions, and validate our method in two challenging scenarios: MRI-to-CT adaptation of cardiac segmentation and cross-site adaptation of prostate segmentation. Our approach exhibits substantially better performances than the existing test-time adaptation methods. Even more surprisingly, it fares better than state-of-the-art domain adaptation methods, although it forgoes training on additional target data during adaptation. Our results question the usefulness of training on target data in segmentation adaptation, and points to the substantial effect of shape priors on test-time inference. Our framework can be readily used for integrating various priors and for adapting any segmentation network. The code is publicly available ( https://github.com/mathilde-b/TTA ).
Rethinking Breast Lesion Segmentation in Ultrasound: A New Video Dataset and A Baseline Network

Automatic breast lesion segmentation in ultrasound (US) videos is an essential prerequisite for early diagnosis and treatment. This challenging task remains under-explored due to the lack of availability of annotated US video dataset. Though recent works have achieved better performance in natural video object segmentation by introducing promising Transformer architectures, they still suffer from spatial inconsistency as well as huge computational costs. Therefore, in this paper, we first present a new benchmark dataset designed for US video segmentation. Then, we propose a dynamic parallel spatial-temporal Transformer (DPSTT) to improve the performance of lesion segmentation in US videos with higher computational efficiency. Specifically, the proposed DPSTT disentangles the non-local Transformer along the temporal and spatial dimensions, respectively. The temporal Transformer attends temporal lesion movement on different frames at the same regions, and the spatial Transformer focuses on similar context information between the previous and the current frames. Furthermore, we propose a dynamic selection scheme to effectively sample the most relevant frames from all the past frames, and thus prevent out of memory during inference. Finally, we conduct extensive experiments to evaluate the efficacy of the proposed DPSTT on the new US video benchmark dataset.
Spatial-Hierarchical Graph Neural Network with Dynamic Structure Learning for Histological Image Classification

Graph neural network (GNN) has achieved tremendous success in histological image classification, as it can explicitly model the notion and interaction of different biological entities (e.g., cell, tissue and etc.). However, the potential of GNN has not been fully unleashed for histological image analysis due to (1) the fixed design mode of graph structure and (2) the insufficient interactions between multi-level entities. In this paper, we proposed a novel spatial-hierarchical GNN framework (SHGNN) equipped with a dynamic structure learning (DSL) module for effective histological image classification. Compared with traditional GNNs, the proposed framework has two compelling characteristics. First, the DSL module integrates the positional attribute and semantic representation of entities to learn the adjacency relationship of them during the training process. Second, the proposed SHGNN can extract rich and discriminative features by mining the spatial features of different entities via graph convolutions and aggregating the semantic of multi-level entities via a vision transformer (ViT) based interaction mechanism. We evaluate the proposed framework on our collected colorectal cancer staging (CRCS) dataset and the public breast carcinoma subtyping (BRACS) dataset. Experimental results demonstrate that our proposed method yield superior classification results compared to state-of-the-arts.
ConTrans: Improving Transformer with Convolutional Attention for Medical Image Segmentation

Over the past few years, convolution neural networks (CNNs) and vision transformers (ViTs) have been two dominant architectures in medical image segmentation. Although CNNs can efficiently capture local representations, they experience difficulty establishing long-distance dependencies. Comparably, ViTs achieve impressive success owing to their powerful global contexts modeling capabilities, but they may not generalize well on insufficient datasets due to the lack of inductive biases inherent to CNNs. To inherit the merits of these two different design paradigms while avoiding their respective limitations, we propose a concurrent structure termed ConTrans, which can couple detailed localization information with global contexts to the maximum extent. ConTrans consists of two parallel encoders, i.e., a Swin Transformer encoder and a CNN encoder. Specifically, the CNN encoder is progressively stacked by the novel Depthwise Attention Block (DAB), with the aim to provide the precise local features we need. Furthermore, a well-designed Spatial-Reduction-Cross-Attention (SRCA) module is embedded in the decoder to form a comprehensive fusion of these two distinct feature representations and eliminate the semantic divergence between them. This allows to obtain accurate semantic information and ensure the up-sampling features with semantic consistency in a hierarchical manner. Extensive experiments across four typical tasks show that ConTrans significantly outperforms state-of-the-art methods on ten famous benchmarks.
Where is Vincent? Expanding our emotional selves with AI

In what ways could the future of emotional bonds between humans and conversational AI change us? To explore this question in a multi-faceted manner, designers, engineers, philosophers as separate focus groups were given a design fiction probe— a story of a chatbot's disappearance from a person's life. Though articulated in discipline-specific ways, participants expressed similar concerns and hopes: 1) caring for a machine could teach people to emotionally care for themselves and others, 2) the boundary between human and non-human emotions may become blurred when people project their own emotions onto AI, e.g., a bot's "breakdown" as one's own, and 3) people may then intertwine their identities with AI through emotions. We consider ethical ramifications of socially constructed emotions between humans and conversational agents.
Is Alexa like a computer? A search engine? A friend? A silly child? Yes.

In this provocation, we analyze metaphors used by 14 end-users in semi-structured interviews to describe their interactions with Voice User Interfaces (VUIs). We identified four key metaphor groups—computer, search engine, friend, and silly child—that can help us understand how individual end-users perceive VUIs differently based on the conversational context. A consideration of these four groups draws attention to the issues with reductionist implementations of system personas and role-based performances and the problems of fixating on humanness as a metaphor. Fortunately, using metaphor analysis can also help in ideating solutions to these issues.
Mixture 2D Convolutions for 3D Medical Image Segmentation

Three-dimensional (3D) medical image segmentation plays a crucial role in medical care applications. Although various two-dimensional (2D) and 3D neural network models have been applied to 3D medical image segmentation and achieved impressive results, a trade-off remains between efficiency and accuracy. To address this issue, a novel mixture convolutional network (MixConvNet) is proposed, in which traditional 2D/3D convolutional blocks are replaced with novel MixConv blocks. In the MixConv block, 3D convolution is decomposed into a mixture of 2D convolutions from different views. Therefore, the MixConv block fully utilizes the advantages of 2D convolution and maintains the learning ability of 3D convolution. It acts as 3D convolutions and thus can process volumetric input directly and learn intra-slice features, which are absent in the traditional 2D convolutional block. By contrast, the proposed MixConv block only contains 2D convolutions; hence, it has significantly fewer trainable parameters and less computation budget than a block containing 3D convolutions. Furthermore, the proposed MixConvNet is pre-trained with small input patches and fine-tuned with large input patches to improve segmentation performance further. In experiments on the Decathlon Heart dataset and Sliver07 dataset, the proposed MixConvNet outperformed the state-of-the-art methods such as UNet3D, VNet, and nnUnet.
Technical, economical and environmental feasibility study of a photovoltaic system under the climatic condition of north Iraq

The energy, economical and environmental performances of the 1 MWp photovoltaic power plant have been numerically investigated under the climatic condition of Zakho city/Iraq using reality metrological measurements. The results of the PV system have been compared with those of other available power plants that used fossil fuels under identical power values. The findings displayed that the investment in PV technology is very feasible in Zakho city compared to other power plants. The economic results displayed that the payback time is nearly 7 years to recover the primary expense of the plant and the payback energy time is 2 years to recover the embedded energy of PV panels. The cost of a PV plant is nearly 10 times less than that of the other fossil fuel power plants. The employment of the PV plant leads to reduce CO2 emissions between 600 and 2000 tCO2. Finally, the results illustrated that the use of the PV system in Zakho city is feasible technically, economically and environmentally.
RouteChain: Towards Blockchain-based secure and efficient BGP routing

Routing on the Internet is defined among autonomous systems (ASes) based on a weak trust model where it is assumed that ASes are honest. While this trust model strengthens the connectivity among ASes, it also results in an attack surface that can be exploited to hijack the routing paths. One such attack is known as the BGP prefix hijacking, in which a malicious AS broadcasts IP prefixes that belong to a target AS, thereby hijacking its traffic. In this paper, we propose RouteChain: a blockchain-based secure BGP routing system that counters BGP hijacking and maintains a consistent view of the Internet routing paths. Towards that, we leverage provenance assurance and tamper-proof properties of blockchains to augment trust among ASes. We group ASes based on their geographical (network) proximity and construct a bi-hierarchical blockchain model that detects false prefixes prior to their spread over the Internet. We evaluate RouteChain using three different consensus protocols and show its effectiveness by drawing a case study with the Youtube hijacking of 2008. Our results show that RouteChain can efficiently detect false prefix announcements and prevent BGP attacks over the Internet.
Swin-VoxelMorph: A Symmetric Unsupervised Learning Model for Deformable Medical Image Registration Using Swin Transformer

Deformable medical image registration is widely used in medical image processing with the invertible and one-to-one mapping between images. While state-of-the-art image registration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on computer vision tasks. Existing models neglect to employ attention mechanisms to handle the long-range cross-image relevance in embedding learning, limiting such approaches to identify the semantically meaningful correspondence of anatomical structures. These methods also ignore the topology preservation and invertibility of the transformation although they achieve fast image registration. In this paper, we propose a novel, symmetric unsupervised learning network Swin-VoxelMorph based on the Swin Transformer which minimizes the dissimilarity between images and estimates both forward and inverse transformations simultaneously. Specifically, we propose 3D Swin-UNet, which applies hierarchical Swin Transformer with shifted windows as the encoder to extract context features. And a symmetric Swin Transformer-based decoder with patch expanding layer is designed to perform the up-sampling operation to estimate the registration fields. Besides, our objective loss functions can guarantee substantial diffeomorphic properties of the predicted transformations. We verify our method on two datasets including ADNI and PPMI, and it achieves state-of-the-art registration accuracy while maintaining desirable diffeomorphic properties.
A case study of NeuralProphet and nonlinear evaluation for high accuracy prediction in short-term forecasting in PV solar plant

Prediction of the energy, active production from the PV solar plant is a challenge in cloudy weather or with clouds over the solar plant; therefore, it has impact in the planning of the power system, especially in the season analysis and prediction accuracy adjustments, for example in holidays. In 2022, some authors published some analysis associated to horizontal pyranometers and the limits in the evaluation of the data, the Mean Bias Error of Daily Solar Irradiation average (MIEave) ranges from 0.17% to 2.86% associated a sudden change in the weather, it increases the "risk of misestimating the potential electricity generation" with short-term error of more than 50% and the Global Horizontal Irradiance (GHI) has a mean bias error (MBE) of at least ±8% [1]. In this research article, a novel proposal for short-term forecasting combines the satellite with meteorological station data and statistical model associated to the new seasonality analysis by using two approaches: i) NeuralProphet, Ridge regression, ii) Long Short-Term Memory with convolutional neural networks. Besides, it requires three KPI as feedback, it is the mean absolute error (MAE), relative Root mean square error (RMSE), and mean absolute percentage error (MAPE). The results demonstrate a MAPE of 5.93% and a computational time 852.10 s and the comparison with new predictions methods from 2019 to 2021. This research article illustrates the new approach with the forecasting method in a case of the PV solar plant in Peru and proves the robustness and seasonality results, and new short-terms improvements associated to external influence as cloudy conditions and resource availability. Our findings are an improvement of the model MAPE 12.14%-5.93%; even compared with the literature and currently models as ARIMA-LSTM with 10.57%, LSTM with NN and G, SARIMA and SVM considering Gaussian White Noise with 8.14% and Prophet with SVM with 8.81%.
A Local Perturbation Generation Method for GAN-Generated Face Anti-Forensics

Although the current generative adversarial networks (GAN)-generated face forensic detectors based on deep neural networks (DNNs) have achieved considerable performance, they are vulnerable to adversarial attacks. In this paper, an effective local perturbation generation method is proposed to expose the vulnerability of state-of-the-art forensic detectors. The main idea is to mine the fake faces’ areas of common concern in multiple-detectors’ decision-making, then generate local anti-forensic perturbations by GANs in these areas to enhance the visual quality and transferability of anti-forensic faces. Meanwhile, in order to improve the anti-forensic effect, a double- mask (soft mask and hard mask) strategy and a three-part loss (the GAN training loss, the adversarial loss consisting of ensemble classification loss and ensemble feature loss, and the regularization loss) are designed for the training of the generator. Experiments conducted on fake faces generated by StyleGAN demonstrate the proposed method’s advantage over the state-of-the-art methods in terms of anti-forensic success rate, imperceptibility, and transferability. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/imagecbj/A-Local-Perturbation-Generation-Method-for-GAN-generated-Face-Anti-forensics</uri> .
Mobile assisted language learning in learning English through social networking tools: An account of Instagram feed-based tasks on learning grammar and attitude among English as a foreign language learners

Advancement of social media in the modern era provides a good incentive for researchers to unleash the potential of social networking (SN) tools in order to improve education. Despite the significant role of social media in affecting second/foreign language (L2) learning processes, few empirical studies have tried to find out how Instagram feed-based tasks affect learning grammar structure. To fill this lacuna of research, the current study set forth to delve into the influence of Instagram feed-based tasks on learning grammar among English as a foreign language (EFL) learners. In so doing, a sample of 84 intermediate EFL learners were randomly divided into experimental and control groups. The learners in the control group received regular online instruction via webinar platforms. In contrast, the learners in the experimental group were exposed to Instagram feed-based tasks. Data inspection applying one-way ANCOVA indicated that the learners in the experimental group outperformed their counterparts in the control group. The results highlighted the significant contributions of Instagram feed-based tasks in fostering learning grammar. Furthermore, EFL learners' positive attitudes toward using Instagram Feed-based Tasks in Learning Grammar was concluded. The implications of this study may redound to the benefits of language learners, teachers, curriculum designers, as well as policy makers in providing opportunities for further practice of Instagram feed-based tasks in language learning and teaching.
Scale Estimation for Design Decisions in Virtual Environments: Understanding the Impact of User Characteristics on Spatial Perception in Immersive Virtual Reality Systems

User spatial perception in different virtual environments may vary based on specific user characteristics and the features of the Virtual Reality (VR) system. This research explored the impacts of user characteristics such as age, gender, and design knowledge on spatial decision-making by comparing an Immersive Virtual Reality Interactive Environment (IVRIE) with a traditional Virtual Reality system (also known as desktop-based Virtual Reality system, abbreviated herein as the DT system). Users’ spatial perceptions when using IVRIE and a DT system were studied with regard to the features of the different systems, including the types of immersion and interaction, users’ perceptions of human body scale, and how the environments were explored. The factors affecting the two systems included texture variation, type of enclosure, and spatial function. Inferential testing using quantitative data was applied to identify differences between the two systems in terms of participants’ actual design outcomes. The results showed that based on the type, spatial characteristics, and texture of spaces, perception filters could have both active and inactive roles in impacting the spatial decision-making of participants between the two systems. In addition, between the two systems, participant characteristics had more impact on size variations for both types of spaces—fully enclosed and corridors—for accommodating larger groups.