Reviewerly/exHarmony huggingface dataset:
A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple Myeloma cancer diagnosis

Multiple Myeloma (MM) is a malignancy of plasma cells. Similar to other forms of cancer, it demands prompt diagnosis for reducing the risk of mortality. The conventional diagnostic tools are resource-intense and hence, these solutions are not easily scalable for extending their reach to the masses. Advancements in deep learning have led to rapid developments in affordable, resource optimized, easily deployable computer-assisted solutions. This work proposes a unified framework for MM diagnosis using microscopic blood cell imaging data that addresses the key challenges of inter-class visual similarity of healthy versus cancer cells and that of the label noise of the dataset. To extract class distinctive features, we propose projection loss to maximize the projection of a sample’s activation on the respective class vector besides imposing orthogonality constraints on the class vectors. This projection loss is used along with the cross-entropy loss to design a dual branch architecture that helps achieve improved performance and provides scope for targeting the label noise problem. Based on this architecture, two methodologies have been proposed to correct the noisy labels. A coupling classifier has also been proposed to resolve the conflicts in the dual-branch architecture’s predictions. We have utilized a large dataset of 72 subjects (26 healthy and 46 MM cancer) containing a total of 74996 images (including 34555 training cell images and 40441 test cell images). This is so far the most extensive dataset on Multiple Myeloma cancer ever reported in the literature. An ablation study has also been carried out. The proposed architecture performs best with a balanced accuracy of 94.17% on binary cell classification of healthy versus cancer in the comparative performance with ten state-of-the-art architectures. Extensive experiments on two additional publicly available datasets of two different modalities have also been utilized for analyzing the label noise handling capability of the proposed methodology. The code will be available under https://github.com/shivgahlout/CAD-MM.
An automatic method for segmentation of liver lesions in computed tomography images using deep neural networks

Liver cancer is one of the major causes of death by cancer. The early detection of lesions in the liver provides a better chance of treatment and cure of the disease. Computed tomography (CT) is one of the most used imaging techniques for the detection and diagnosis of liver lesions. However, the manual segmentation of liver and tumors, aside from being time-consuming, can still cause errors and may vary among specialists. Because of this hard work, computer-aided detection (CAD) and computer-aided diagnosis (CADx) systems have been developed to assist specialists in the detection and characterization of lesions in the liver and reduce the required time for diagnosis. The automatic segmentation of these lesions is a complex task since they present variability in contrast, shape, size, and location. In this work, a method to automatically segment liver lesions in CT images is proposed. The proposed method, which presents two deep convolutional neural networks (CNN) models, consists of five main steps: (1) image acquisition, (2) image pre-processing, (3) initial segmentation using RetinaNet, (4) lesion segmentation using U-Net, and (5) segmentation refinement. The proposed method was evaluated using a set of 131 CT images from the LiTS dataset, and the best result obtained a matthews correlation coefficient (MCC) of 83.62%, a sensitivity of 83.86%, a specificity of 99.96%, a Dice coefficient of 82.99%, a volumetric overlap error (VOE) of 27.89%, and a relative volume difference (RVD) of 1.69%. We show in our method that the problem of segmentation of liver lesions in CT images can be efficiently solved through the use of deep CNNs to define the scope of the problem and to precisely segment lesions.
Developing Prospective Chemistry Teachers’ TPACK–A Comparison between Students of Two Different Universities and Expertise Levels Regarding Their TPACK Self-Efficacy, Attitude, and Lesson Planning Competence

The aim of this study was to investigate how to develop prospective chemistry teachers' TPACK and thus qualify them for the effective use of educational technology in their future chemistry teaching. For this purpose, we developed and evaluated a university seminar for the professional development of preservice chemistry teachers. We implemented this seminar at two German universities with students of two different expertise levels and courses of study. One group of students consisted of master students with an imminent internship semester while the other comprised bachelor students. In order to find out how preservice teacher training for digital literacy should be structured we measured the participants' development considering their TPACK competence in lesson planning, TPACK self-efficacy, and attitude. The results revealed that the seminar is suitable for increasing the participants' TPACK since we were able to improve the students' skills to implement educational technology into their lesson plan. Yet, the residual comparison between both groups shows a significantly higher increase in the master students' abilities. Regarding the TPACK self-efficacy and attitude, however, we were able to measure a significant increase for only the master students. Based on our findings, we suggest to allocate a seminar on educational technology in direct link to an internship and at a time during their studies when the teacher candidates have already had the opportunity to gain some prior knowledge in content, pedagogy, and subject didactics.
Teachers’ Beliefs about the Role of Digital Educational Resources in Educational Practice: A Qualitative Study

Information and Communication Technologies and Digital Educational Resources have undergone a rapid evolution and have been swiftly introduced into educational contexts. Teachers play a key role in integrating these technological resources into the classroom. The objective of the present study was to determine the value that teachers attribute to digital resources in their educational practice. Based on a qualitative methodology, the necessary information was obtained via an open-ended interview, in which a Spanish school’s Early Childhood and Primary Education teachers participated. The results revealed that teachers value the integration of digital resources into the classroom, though no consensus was reached as to the suitable level of integration. Use satisfaction was mainly related to student motivation. Certain problems or limitations also came to light, however, linked to students’ digital training. An important conclusion according to the perception of teachers is that the integration of digital resources in their educational practice was significant and improved the quality of the educational process.
Constriction coefficient based particle swarm optimization and gravitational search algorithm for multilevel image thresholding

Abstract Image segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. This work introduces a new image segmentation method based on the constriction coefficient‐based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image act as searcher agents of the CPSOGSA algorithm. The optimal number of thresholds is determined using Kapur's entropy method. The effectiveness and applicability of CPSOGSA in image segmentation is accomplished by applying it to five standard images from the USC‐SIPI image database, namely Aeroplane, Cameraman, Clock, Lena, and Pirate. Various performance metrics are employed to investigate the simulation outcomes, including optimal thresholds, standard deviation, MSE (mean square error), run time analysis, PSNR (peak signal to noise ratio), best fitness value calculation, convergence maps, segmented image graphs, and box plot analysis. Moreover, image accuracy is benchmarked by utilizing SSIM (structural similarity index measure) and FSIM (feature similarity index measure) metrics. Also, a pairwise non‐parametric signed Wilcoxon rank‐sum test is utilized for statistical verification of simulation results. In addition, the experimental outcomes of CPSOGSA are compared with eight different algorithms including standard PSO, classical GSA, PSOGSA, SCA (sine cosine algorithm), SSA (salp swarm algorithm), GWO (grey wolf optimizer), MFO (moth flame optimizer), and ABC (artificial bee colony). The simulation results clearly indicate that the hybrid CPSOGSA has successfully provided the best SSIM, FSIM, and threshold values to the benchmark images.
Salient Objects in Clutter

In this paper, we identify and address a serious design bias of existing salient object detection (SOD) datasets, which unrealistically assume that each image should contain at least one clear and uncluttered salient object. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. However, these models are still far from satisfactory when applied to real-world scenes. Based on our analyses, we propose a new high-quality dataset and update the previous saliency benchmark. Specifically, our dataset, called Salient Objects in Clutter (SOC), includes images with both salient and non-salient objects from several common object categories. In addition to object category annotations, each salient image is accompanied by attributes that reflect common challenges in common scenes, which can help provide deeper insight into the SOD problem. Further, with a given saliency encoder, e.g., the backbone network, existing saliency models are designed to achieve mapping from the training image set to the training ground-truth set. We therefore argue that improving the dataset can yield higher performance gains than focusing only on the decoder design. With this in mind, we investigate several dataset-enhancement strategies, including label smoothing to implicitly emphasize salient boundaries, random image augmentation to adapt saliency models to various scenarios, and self-supervised learning as a regularization strategy to learn from small datasets. Our extensive results demonstrate the effectiveness of these tricks. We also provide a comprehensive benchmark for SOD, which can be found in our repository: https://github.com/DengPingFan/SODBenchmark.
Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Our earlier Low-on-Latency (dubbed as LoL) solution offered an accurate bandwidth prediction and rate adaptation algorithm tailored for live streaming applications that targeted an end-to-end latency of up to two seconds. While LoL was a significant step forward in multi-bitrate low-latency live streaming, further experimentation and testing showed that there was room for improvement in three areas. First, LoL used hard-coded parameters computed from an offline training process in the rate adaptation algorithm and this was seen as a significant barrier in LoL’s wide deployment. Second, LoL’s objective was to maximize a collective QoE function. Yet, certain use cases have specific objectives besides the singular QoE and this had to be accommodated. Third, the adaptive playback speed control failed to produce satisfying results in some scenarios. Our goal in this paper is to address these areas and make LoL sufficiently robust to deploy. We refer to the enhanced solution as LoL <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> , which has been integrated to the official dash.js player in v3.2.0.
Graph Neural Networks for Fast Node Ranking Approximation

Graphs arise naturally in numerous situations, including social graphs, transportation graphs, web graphs, protein graphs, etc. One of the important problems in these settings is to identify which nodes are important in the graph and how they affect the graph structure as a whole. Betweenness centrality and closeness centrality are two commonly used node ranking measures to find out influential nodes in the graphs in terms of information spread and connectivity. Both of these are considered as shortest path based measures as the calculations require the assumption that the information flows between the nodes via the shortest paths. However, exact calculations of these centrality measures are computationally expensive and prohibitive, especially for large graphs. Although researchers have proposed approximation methods, they are either less efficient or suboptimal or both. We propose the first graph neural network (GNN) based model to approximate betweenness and closeness centrality. In GNN, each node aggregates features of the nodes in multihop neighborhood. We use this feature aggregation scheme to model paths and learn how many nodes are reachable to a specific node. We demonstrate that our approach significantly outperforms current techniques while taking less amount of time through extensive experiments on a series of synthetic and real-world datasets. A benefit of our approach is that the model is inductive, which means it can be trained on one set of graphs and evaluated on another set of graphs with varying structures. Thus, the model is useful for both static graphs and dynamic graphs. Source code is available at https://github.com/sunilkmaurya/GNN_Ranking
In Tension with Progression: Grasping the Frictional Tendencies of Speculative, Critical, and other Alternative Designs

Speculative design, critical design, and other alternative designs have emerged as popular approaches and burgeoning traditions within HCI and design research. While examples of this work abound, comparatively little theory exists for grasping alternative designs, and for explicating their relation to other types of design and to design in general. In response this paper develops the key concepts of progressional design, frictional design, and design as prefiguration. The progressional conceptualization of design holds that designs have a primary purpose, and that purpose is to ultimately converge toward and ideally arrive at production. The frictional conceptualization of design radically relaxes teleological assumptions and productional expectations. Frictional designs prefigure possibilities that are compellingly resistant to further progression and final production. Prefiguration grounds both progression and friction in the idea that designs are partial, provisional, and potentially preliminary actualizations of possible futures. To illustrate frictional design, this paper outlines a framework of 5 frictional tendencies: diverging, opposing, accelerating, counterfactualizing, and analogizing. These tendencies represent ways in which frictional designs are directionally in tension with the arrow-like vector of progressional design. Several additional concepts are discussed in conclusion to further explicate more nuanced relational potentials between friction and progression: transproductional uses, teleological ambiguity, and relational multiplicity.
Deep Learning in Pancreatic Tissue: Identification of Anatomical Structures, Pancreatic Intraepithelial Neoplasia, and Ductal Adenocarcinoma

Identification of pancreatic ductal adenocarcinoma (PDAC) and precursor lesions in histological tissue slides can be challenging and elaborate, especially due to tumor heterogeneity. Thus, supportive tools for the identification of anatomical and pathological tissue structures are desired. Deep learning methods recently emerged, which classify histological structures into image categories with high accuracy. However, to date, only a limited number of classes and patients have been included in histopathological studies. In this study, scanned histopathological tissue slides from tissue microarrays of PDAC patients (n = 201, image patches n = 81.165) were extracted and assigned to a training, validation, and test set. With these patches, we implemented a convolutional neuronal network, established quality control measures and a method to interpret the model, and implemented a workflow for whole tissue slides. An optimized EfficientNet algorithm achieved high accuracies that allowed automatically localizing and quantifying tissue categories including pancreatic intraepithelial neoplasia and PDAC in whole tissue slides. SmoothGrad heatmaps allowed explaining image classification results. This is the first study that utilizes deep learning for automatic identification of different anatomical tissue structures and diseases on histopathological images of pancreatic tissue specimens. The proposed approach is a valuable tool to support routine diagnostic review and pancreatic cancer research.
AutoVAS: An automated vulnerability analysis system with a deep learning approach

Owing to the advances in automated hacking and analysis technologies in recent years, numerous software security vulnerabilities have been announced. Software vulnerabilities are increasing rapidly, whereas methods to analyze and cope with them depend on manual analyses, which result in a slow response. In recent years, studies concerning the prediction of vulnerabilities or the detection of patterns of previous vulnerabilities have been conducted by applying deep learning algorithms in an automated vulnerability search based on source code. However, existing methods target only certain security vulnerabilities or make limited use of source code to compile information. Few studies have been conducted on methods that represent source code as an embedding vector. Thus, this study proposes a deep learning-based automated vulnerability analysis system (AutoVAS) that effectively represents source code as embedding vectors by using datasets from various projects in the National Vulnerability Database (NVD) and Software Assurance Reference Database (SARD). To evaluate AutoVAS, we present and share a dataset for deep learning models. Experimental results show that AutoVAS achieves a false negative rate (FNR) of 3.62%, a false positive rate (FPR) of 1.88%, and an F1-score of 96.11%, which represent lower FNR and FPR values than those achieved by other approaches. We further apply AutoVAS to nine open-source projects and detect eleven vulnerabilities, most of which are missed by the other approaches we experimented with. Notably, we discovered three zero-day vulnerabilities, two of which were patched after being informed by AutoVAS. The other vulnerability received the Common Vulnerabilities and Exposures (CVE) ID after being detected by AutoVAS.
Impact of ICT Adoption and Governance Interaction on Food Security in West Africa

The impact of the interaction of governance and information and communication technologies (ICT) adoption on food security in West Africa is investigated in this study. The study engaged the system generalised method of moments (GMM) approach on a panel data of 15 West African countries. The data used for the study are obtained from the world development indicators (WDI) and world governance indicators (WGI), for the period 2005 to 2018. The findings show that good governance (government effectiveness and efficient anti-corruption control) can boost food security by between 12% and 20%. Furthermore, the findings show that a 1% rise in ICT adoption may boost food security by 12% to 15%. In explaining the level of food security, the relationship between governance and ICT adoption is positive and significant. This implies that ICT and governance (government effectiveness) interaction may have about 15% positive influence on food security, while ICT and control of corruption interaction may positively influence the level of food security by 8%. The study concludes by recommending that to enhance food security in West Africa, effectiveness in governance and ICT adoption are crucial.
Articulating Soma Experiences using Trajectories

In this paper, we reflect on the applicability of the concept of trajectories to soma design. Soma design is a first-person design method which considers users’ subjective somatic or bodily experiences of a design. Due to bodily changes over time, soma experiences are inherently temporal. Current instruments for articulating soma experiences lack the power to express the effects of experiences on the body over time. To address this, we turn to trajectories, a well-known concept in the HCI community, as a way of mapping this aspect of soma experience. By showing trajectories through a range of dimensions, we can articulate individual experiences and differences in those experiences. Through analysis of a set of soma experience designs and a set of temporal dimensions within the experiences, this paper demonstrates how trajectories can provide a practical conceptual framing for articulating the temporal complexity of soma designs.
Heterogeneous type-specific entity representation learning for recommendations in e-commerce network

In heterogeneous e-commerce recommender systems, the type and attribute information of users and products contain rich semantics, which can benefit the prediction and explanation of user ratings of interesting items. Existing studies include collaborative and content-based recommendations that mainly capture semantic features by considering user–item interactions or behavioral history records, which ignores the explanatory role of the product type and attribute. In this paper, we first propose an attentional attribute and interaction method used to model the semantic embeddings of users and items. We then construct a type-specific matrix to exploit heterogeneous type-specific information to learn user and item representations. The incorporated heterogeneous type information helps capture a user’s latent features that solve the sparsity problem of user–item interactions for the recommender systems. Further, the rating relationship of the nodes is predicted through the translation mechanism based on user and items’ type-specific representations. Extensive experimental results on real-world datasets demonstrate the superior performance of the proposed model over several state-of-the-art methods and show the visual interpretability for rating behaviors in e-commerce recommender systems. • A novel heterogeneous type-specific entity representation method is proposed. • A hybrid translation-based relation embedding is designed for rating prediction. • An explainable strategy based on entity characteristics is proposed.
Controllable, frightening, or fun? Exploring the gendered dynamics of smart home technology preferences in the United Kingdom

The use of interconnected digital technologies and data offer new and perhaps profound opportunities across the world, whilst holding promises to enhance productivity growth and improved citizen wellbeing. In this research, we explore how people perceive the opportunities and drawbacks of smart homes and smart home technologies via the lens of gender. The study aims to identify what factors shape differing gendered perceptions and the implications this has for future sustainability and technological design. We address these issues through a rigorous mixed methods research design including a nationally representative survey in the United Kingdom (n = 1,032) as well as three focus groups in London, Manchester, and Surrey (n = 18 respondents). We identify the gendered nature of smart homes inductively across five different dimensions: (1) knowledge, awareness, and adoption patterns; (2) housekeeping and daily domestic life; (3) environmental sustainability; (4) trust and risk tolerance; and (5) emotions and feelings. We conclude with policy implications as well as how these findings point the way towards future research.