Reviewerly/exHarmony huggingface dataset:
SENext: Squeeze-and-ExcitationNext for Single Image Super-Resolution

Recent research on image and video processing using convolutional neural networks has shown remarkable improvements, especially in the area of single image super-resolution(SISR).The primary target of SISR is to recover the visually appealing high-resolution (HR) output image from the original degraded low-resolution (LR) input image.However, most recent convolutional neural networks (CNNs)based image super-resolution frameworks often used a deeper and broader network architecture that requires a sizeable computational resource, risk of overfitting, increases computational complexity, and more memory consumption, as well as takes more processing time during the evaluations.To address these issues, we have presented a Squeeze-and-ExcitationNext for Single Image Super-Resolution approach, known as SENext.In brief, the squeeze-and-excitation blocks (SEB) are used in our network architecture with a view to reduce the computational cost and adopt the channel-wise feature mappings to recalibrate the features adaptively.Furthermore, local, sub-local, and global skip connections are employed between each SEB to enable the feature reusability and stabilize training convergence smoothly.Instead of hand-designed bicubic upsampling at pre-processing step, we have performed post-upsampling at the later stage to reconstruct the high-resolution image.Extensive quantitative and qualitative experiments are performed on the benchmark test dataset, including Set5, Set14, BSDS100, Urban100, and Manga109.These experimental evaluations validate the superiority of the SENext over other deep CNN image SR methods in terms of PSNR/SSIM, FLOPs, Number of parameters, processing speed, and visually pleasing effect.
Embodied competencies and smart home technology in energy use: three ways users integrate smart heating systems in everyday practices

Abstract Smart heating systems are increasingly entering the domestic sphere. Such smart home technology (SHT) intends to provide comfort, control, and convenience in the home as well as energy efficiency and energy flexibility. However, the success of these promises depends on users’ ability to implement the SHT in everyday practices. In previous research, the importance of embodied competences acquired through previous experiences has gotten little attention. In this paper, we argue that broad scale implementation of SHT requires more knowledge on the interaction between previous experience in the formation of new competences and domestic heating practices. In this paper, we explore how users integrate new smart technologies into their everyday heating practices in different ways by focusing on embodied competences. Based on 24 qualitative household interviews with SHT users, conducted through two Danish case studies, we identified three ways of approaching and integrating SHT in heating practices. First, the reluctant appears hesitant when adopting SHT and loses interest quickly. Second, the compliant follows orders and does what is expected by the new setup. Third, the committed encompasses a playful approach and shows strong interest in SHT design. From this background, we find that embodied competences and previous experience are essential for how users adopt and integrate SHT in everyday life. For example, some users’ previous experience, here younger and technically interested individuals, may match well with the logics of SHT, and for some users, here older and less tech-interested individuals, previous experience may not match well with SHT.
Evolutionary algorithm for optimized CNN architecture search applied to real-time boat detection in aerial images

When processing the detection of boats in aerial images by neural networks, we have always been concerned about the execution time of these networks in the equipment on board the Unmanned Aerial Vehicle (UAV). Throughout its mission, the UAV will capture images that must be processed in real time. For this purpose, a network optimized for execution time is essential. This article proposes an enhanced Network Architecture Search (NAS) method for searching for time-optimized detection networks, for a given dataset, using an evolutionary algorithm. The search uses mutations as a mechanism of evolution that affect the structure of the network and the hyper-parameters of its layers. Its original fitness function allows the choice of architectures that are not very greedy in terms of operations, specifically favouring small networks whose advantages are to be fast and quick to train, thus accelerating the search algorithm. Using this method, we were able to obtain detection networks with an improved mean Average Precision (mAP) compared to the initial network (parent) but with much fewer FLoating-point OPerations (Flops): 68% of operations reduction. This induces considerable gain in terms of execution time with 50 Frames Processed per Second (FPS) in an embedded environment on a drone.
Explainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell Segmentation

Leukemia is a significant health challenge, with high incidence and mortality rates. Computer-aided diagnosis (CAD) has emerged as a promising approach. However, deep-learning methods suffer from the "black box problem", leading to unreliable diagnoses. This research proposes an Explainable AI (XAI) Leukemia classification method that addresses this issue by incorporating a robust White Blood Cell (WBC) nuclei segmentation as a hard attention mechanism. The segmentation of WBC is achieved by combining image processing and U-Net techniques, resulting in improved overall performance. The segmented images are fed into modified ResNet-50 models, where the MLP classifier, activation functions, and training scheme have been tested for leukemia subtype classification. Additionally, we add visual explainability and feature space analysis techniques to offer an interpretable classification. Our segmentation algorithm achieves an Intersection over Union (IoU) of 0.91, in six databases. Furthermore, the deep-learning classifier achieves an accuracy of 99.9% on testing. The Grad CAM methods and clustering space analysis confirm improved network focus when classifying segmented images compared to non-segmented images. Overall, the proposed visual explainable CAD system has the potential to assist physicians in diagnosing leukemia and improving patient outcomes.
Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today's technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output.
Automatic base-model selection for white blood cell image classification using meta-learning

Healthcare has benefited from the implementation of deep-learning models to solve medical image classification tasks. For example, White Blood Cell (WBC) image analysis is used to diagnose different pathologies like leukemia. However, medical datasets are mostly imbalanced, inconsistent, and costly to collect. Hence, it is difficult to select an adequate model to overcome the mentioned drawbacks. Therefore, we propose a novel methodology to automatically select models to solve WBC classification tasks. These tasks contain images collected using different staining methods, microscopes, and cameras. The proposed methodology includes meta- and base-level learnings. At the meta-level, we implemented meta-models based on prior-models to acquire meta-knowledge by solving meta-tasks using the shades of gray color constancy method. To determine the best models to solve new WBC tasks we developed an algorithm that uses the meta-knowledge and the Centered Kernel Alignment metric. Next, a learning rate finder method is employed to adapt the selected models. The adapted models (base-models) are used in an ensemble learning approach achieving accuracy and balanced accuracy scores of 98.29 and 97.69 in the Raabin dataset; 100 in the BCCD dataset; 99.57 and 99.51 in the UACH dataset, respectively. The results in all datasets outperform most of the state-of-the-art models, which demonstrates our methodology's advantage of automatically selecting the best model to solve WBC tasks. The findings also indicate that our methodology can be extended to other medical image classification tasks where is difficult to select an adequate deep-learning model to solve new tasks with imbalanced, limited, and out-of-distribution data.
Effective sample pairs based contrastive learning for clustering

As an indispensable branch of unsupervised learning, deep clustering is rapidly emerging along with the growth of deep neural networks. Recently, contrastive learning paradigm has been combined with deep clustering to achieve more competitive performance. However, previous works mostly employ random augmentations to construct sample pairs for contrastive clustering. Different augmentations of a sample are treated as positive sample pairs, which may result in false positives and ignore the semantic variations of different samples. To address these limitations, we present a novel end-to-end contrastive clustering framework termed Contrastive Clustering with Effective Sample pairs construction (CCES), which obtains more semantic information by jointly leveraging an effective data augmentation method ContrastiveCrop and constructing positive sample pairs based on nearest-neighbor mining. Specifically, we augment original samples by adopting ContrastiveCrop, which explicitly reduces false positives and enlarges the variance of samples. Further, with the extracted feature representations, we provide a strategy to construct positive sample pairs via a sample and its nearest neighbor for instance-wise and cluster-wise contrastive learning. Experimental results on four challenging datasets demonstrate the effectiveness of CCES for clustering, which surpasses the state-of-the-art deep clustering methods.
Historical Materialism: A Postdigital Philosophical Method

This chapter advances historical materialism as a postdigital research method. It wagers that historical materialism and postdigitalism are compatible—fellow travelers, companions even. We focus on Marx’s oft-misunderstood base and superstructure model which has strangely served as a stand-in for the entire marxist method in many students’ educations. Through a close textual analysis of this model (and in conversation with postdigital concepts and sensibilities) we rescue historical materialism from the determinism, stageism, and linear developmentalism so often assigned to it. Postdigital concepts help us gain a deeper understanding of historical materialism beyond its caricature in U.S. academia. Historical materialism, in turn, helps us understand capitalist relations under the postdigital era more precisely. We also excavate a serendipitous, common origin story between marxist and biological methodology. Ultimately, in a time of deepening suffering for most of the working people of the world, we sense the need to combine theoretical forces, unite disparate apparatuses, and build a united research front that can take on the postdigital challenges in front of us.
Caring Cuts: Unfolding Methodological Sensibilities in Researching Postdigital Worlds

In this chapter, we introduce the configuration of caring cuts. Composed of care and cuts, two key notions in feminist posthumanism and Actor-Network Theory (ANT), caring cuts addresses the entanglement of epistemology and ontology. By putting to work an ontology of relational materialisms, the chapter explores how to respond to the mess, emergence, and elusive objects that become centred by our concern and ways of producing knowledge with and on postdigital worlds. It means that vital methodological questions are raised for postdigital relationalities in education and elsewhere. Instead of seeking to untangle the postdigital, caring cuts is put to work to examine mundane research events with sensibilities of the world-making practices of research. We argue that caring cuts affords acknowledgements of the collective responsibilities that research practices involve and bring attention to the inevitably untidy and non-innocent character of knowledge production and making worlds researchable. With caring cuts, modest interruptions and uneventful events suggest a methodological sensibility of not too hastily putting things ‘right’ but acknowledging that other worlds are possible. This means that caring cuts invites thinking and researching more-than-digital relations anew.
Postdigital Research: Genealogies, Challenges, and Future Perspectives

This chapter addresses the Postdigital Science and Education community’s pressing need to examine its own research praxes and asks: What binds us together in postdigital research, particularly in a methodological sense? The essay outlines genealogies, challenges, opportunities, and future perspectives of postdigital research. It offers authors’ understanding of the main developments in the field and ends with an open call for dialogue. The chapter was first published in Postdigital Science and Education journal before the Call for Chapters for Postdigital Research: Genealogies, Challenges, and Future Perspectives and Constructing Postdigital Research: Method and Emancipation and was shared with all authors in this collection.
Postdigital Research and Human Agency

Postdigital research needs to steer a path between two extreme understandings of human agency; the deterministic and the instrumental. The first allows no scope for creative but critical human-nonhuman interactions and the second treats them as purely external with the digital as no more than another technology to be shaped rather than itself shaping the human. Two examples of these dynamics are examined. The first is that of the Metaverse as a current development of virtual reality and the second is the controversial area of robot rights. The aim is to highlight the problems of assuming a full presence and the danger of equating partial presence with a deficit or lack. As a counter to Chalmer’s book on the Metaverse, Esposito’s work is presented deploying the concepts of difference, determination, and opposition drawing on Deleuze to argue for an affirmative presence. In the realm of robot rights Darling’s suggestion that there are parallels between human-animal relationships and human-robot relationships reveals that empathy is essential for crossing these boundaries. Distributed agency is examined as a related alternative and then taken further to propose a more nuanced ontology drawing on the work of Bryant, Harman, and DeLanda to construct a more appropriate basis for human nonhuman relationships which avoids both the deterministic and the instrumental. As in debates about incarnation, both presence and absence are essential for understanding human agency.
Dynamic Difference Learning With Spatio–Temporal Correlation for Deepfake Video Detection

With the rapid development of face forgery techniques, the existing frame-based deepfake video detection methods have fell into a dilemma that frame-based methods may fail when encountering extremely realistic images. To overcome the above problem, many approaches attempted to model the spatio-temporal inconsistency of videos to distinguish real and fake videos. However, current works model spatio-temporal inconsistency by combining intra-frame and inter-frame information, but ignore the disturbance caused by facial motions that would limit further improvement in detection performance. To address this issue, we investigate into long and short range inter-frame motions and propose a novel dynamic difference learning method to distinguish between the inter-frame differences caused by face manipulation and the inter-frame differences caused by facial motions in order to model precise spatio-temporal inconsistency for deepfake video detection. Moreover, we elaborately design a dynamic fine-grained difference capture module (DFDC-module) and a multi-scale spatio-temporal aggregation module (MSA-module) to collaboratively model spatio-temporal inconsistency. Specifically, the DFDC-module applies self-attention mechanism and fine-grained denoising operation to eliminate the differences caused by facial motions and generates long range difference attention maps. The MSA-module is devised to aggregate multi-direction and multi-scale temporal information to model spatio-temporal inconsistency. The existing 2D CNNs can be extended into dynamic spatio-temporal inconsistency capture networks by integrating the proposed two modules. Extensive experimental results demonstrate that our proposed algorithm steadily outperforms state-of-the-art methods by a clear margin in different benchmark datasets.
Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage Retrieval

Abstract Pre-trained language models have been successful in many knowledge-intensive NLP tasks. However, recent work has shown that models such as BERT are not “structurally ready” to aggregate textual information into a [CLS] vector for dense passage retrieval (DPR). This “lack of readiness” results from the gap between language model pre-training and DPR fine-tuning. Previous solutions call for computationally expensive techniques such as hard negative mining, cross-encoder distillation, and further pre-training to learn a robust DPR model. In this work, we instead propose to fully exploit knowledge in a pre-trained language model for DPR by aggregating the contextualized token embeddings into a dense vector, which we call agg★. By concatenating vectors from the [CLS] token and agg★, our Aggretriever model substantially improves the effectiveness of dense retrieval models on both in-domain and zero-shot evaluations without introducing substantial training overhead. Code is available at https://github.com/castorini/dhr.
Evaluating View Management for Situated Visualization in Web‐based Handheld AR

Abstract As visualization makes the leap to mobile and situated settings, where data is increasingly integrated with the physical world using mixed reality, there is a corresponding need for effectively managing the immersed user's view of situated visualizations. In this paper we present an analysis of view management techniques for situated 3D visualizations in handheld augmented reality: a shadowbox, a world‐in‐miniature metaphor, and an interactive tour. We validate these view management solutions through a concrete implementation of all techniques within a situated visualization framework built using a web‐based augmented reality visualization toolkit, and present results from a user study in augmented reality accessed using handheld mobile devices.
CA-YOLO: Model Optimization for Remote Sensing Image Object Detection

The CA-YOLO (Coordinate Attention-YOLO) model has been optimized for object detection in complex remote sensing images, addressing key issues faced by multi-object detection algorithms.These issues include weak multi-scale feature learning capabilities and the challenging tradeoff between detection accuracy and model parameter complexity.Built on the framework of YOLOv5, the CA-YOLO model incorporates a lightweight coordinate attention module in the shallow layer to improve detailed feature extraction and suppress redundant information interference, while a spatial pyramid pooling-fast with a tandem construction module is implemented in the deeper layer.The model also employs a stochastic pooling strategy to fuse multi-scale key feature information from low-level to high-level layers, reducing the number of model parameters while improving inference speed.The anchor box mechanism and modified loss function have been optimized to enhance multi-scale feature learning.Results show that the CA-YOLO model outperforms the original YOLO in terms of multi-object detection accuracy, with an average mAP@0.5 accuracy improvement of 4.8% and mAP@0.5:0.95accuracy improvement of 3.8%.Additionally, the CA-YOLO model demonstrates exceptional inference speed, averaging 125 fps, which reinforces its superiority in detection accuracy, generalization ability, and overall efficiency.Notably, these improvements were achieved while maintaining the same number of parameters and complexity as other models, making the CA-YOLO model an exceptional choice for various applications.
Bitcoin and money supply relationship: An analysis of selected country economies

&lt;abstract&gt; &lt;p&gt;Bitcoin has become quite known after the 2008 economic crisis and the COVID-19 health crisis. For some, these cryptocurrencies constitute rebellion against the existing system as governments encourage uncontrolled expansions in the money supply; for some others, it is a quick source of income. Undeniably, the volume of the crypto money market has grown considerably in recent years, regardless of the reasoning of the people who invest and trade in this field. At this point, one of the most important questions to be investigated is "what variables have caused the tremendous growth in the crypto money quantities in recent years?" This study tests the assumption that changes in cryptocurrencies are affected by changes in national currencies. Thus, the Bitcoin price is the dependent variable, and M1 monetary supply changes in the USA, European Union and Japanese economies are considered independent variables. The variables in this study were tested using the time-varying Granger causality method. The results obtained from this study confirm the philosophy of Bitcoin's emergence and the possibility that it can be a hedge against the inflationary effects of money, especially after the COVID-19 pandemic.&lt;/p&gt; &lt;/abstract&gt;