Reviewerly/exHarmony huggingface dataset:
Ultrasound Image Super-Resolution with Two-Stage Zero-Shot CycleGAN

Abstract Medical ultrasound imaging is widely used in clinical diagnosis because of its non-invasive, convenient and quick characteristics. However, due to its low image contrast, multiple artifacts, noise and lack of paired high-resolution and low-resolution image data sets, the task of super-resolution reconstruction of medical ultrasound images is more challenging. In this paper, the Two-Stage GAN network model was adjusted by CycleGAN generation and unsupervised learning methods, and the Two-Stage ZSSR (“Zero-Shot” Super-Resolution) CycleGAN network was proposed. The objective evaluation indexes PSNR and SSIM were raised to 40.8079 and 0.9953. The visual effect was also significantly improved.
UMFA: a photorealistic style transfer method based on U-Net and multi-layer feature aggregation

We propose a photorealistic style transfer network to emphasize the natural effect of photorealistic image stylization. In general, distortion of the image content and lacking of details are two typical issues in the style transfer field. To this end, we design a framework employing the U-Net structure to maintain the rich spatial clues, with a multi-layer feature aggregation (MFA) method to simultaneously provide the details obtained by the shallow layers in the stylization processing. In particular, an encoder based on the dense block and a decoder form a symmetrical structure of U-Net are jointly staked to realize an effective feature extraction and image reconstruction. In addition, a transfer module based on MFA and “adaptive instance normalization” is inserted in the skip connection positions to achieve the stylization. Accordingly, the stylized image possesses the texture of a real photo and preserves rich content details without introducing any mask or postprocessing steps. The experimental results on public datasets demonstrate that our method achieves a more faithful structural similarity with a lower style loss, reflecting the effectiveness and merit of our approach.
A2GAN: A Deep Reinforcement-Based Learning Algorithm for Risk-Aware in Finance

Financial data suffer from missing, unlabeled and unbalanced data, thus weakening the performance of decision-making systems. In addition, the aim of financial institutions is not only to find decision-making models that achieve high scores for the standard metrics (e.g., AUC, accuracy, F-score) but to reduce the risk from miss-classification cases. This paper addresses these problems by proposing a novel framework inspired by reinforcement learning, specifically actor-critic, for decision-making and implementing generative adversarial networks for imputing missing data, as well as utilizing the unlabeled dataset. Moreover, by taking advantage of reinforcement learning, the trained model is calibrated using a customizable reward function, which can be designed for different purposes of financial institutions. We evaluate the framework via real-world financial datasets that only have a small amount of labeled data and exhibit missing data. Our experiment shows promising results where the financial risk is dramatically reduced without too much sacrifice on standard metrics.
Video summary generation by visual shielding compressed sensing coding and double-layer affinity propagation

Video summary technology based on keyframe extraction is an effective means to rapidly access video content. Traditional video summary generation technology requires high video resolution, which poses a problem as most existing studies have no targeted solutions for videos that are subject to privacy protection. We propose a novel keyframe extraction algorithm for video data in the visual shielding domain, named visual shielding compressed sensing coding and double-layer affinity propagation (VSCS-DAP). VSCS-DAP involves three main steps. First, the video is compressed by compressed sensing technology to provide a visual shielding effect (protecting the privacy of monitored figures), while the data volume is significantly reduced. Then, pyramid histogram of oriented gradients (PHOG) features are extracted from the compressed video to be clustered by the first step affinity propagation (AP) to gain the summaries of the first stage. Finally, the PHOG and Hist fusion features are extracted from the keyframes of the first stage, and they cluster the fused PHOG-Hist features by the second step AP algorithm to obtain the final output summaries. Experimental results obtained on two common video datasets show that our method exhibits advantages including low redundancy and few missing frames, low computational complexity, strong real-time performance, and robustness to vision-shielded video.
Hoare and He’s Unifying Theories of Programming

chapter Share on Hoare and He’s Unifying Theories of Programming Author: Jim Woodcock Search about this author Authors Info & Claims Theories of Programming: The Life and Works of Tony HoareOctober 2021 Pages 285–316https://doi.org/10.1145/3477355.3477369Online:02 October 2021Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Product Quantizer Aware Inverted Index for Scalable Nearest Neighbor Search

The inverted index is one of the most commonly used structures for non-exhaustive nearest neighbor search on large-scale datasets. It allows a significant factor of acceleration by a reduced number of distance computations with only a small fraction of the database. In particular, the inverted index enables the product quantization (PQ) to learn their codewords in the residual vector space. The quantization error of the PQ can be substantially improved in such combination since the residual vector space is much more quantization-friendly thanks to their compact distribution compared to the original data. In this paper, we first raise an unremarked but crucial question; why the inverted index and the product quantizer are optimized separately even though they are closely related? For instance, changes on the inverted index distort the whole residual vector space. To address the raised question, we suggest a joint optimization of the coarse and fine quantizers by substituting the original objective of the coarse quantizer to end-to-end quantization distortion. Moreover, our method is generic and applicable to different combinations of coarse and fine quantizers such as inverted multi-index and optimized PQ.
Chapter 13: Boddington: An Enigmatic Giant Archean Gold-Copper (Molybdenum-Silver) Deposit in the Southwest Yilgarn Craton, Western Australia

Abstract Boddington is a giant, enigmatic, and atypical Archean Au-Cu deposit hosted in a small, remnant greenstone belt within granite-gneiss and migmatite of the Southwest terrane of the Yilgarn craton, Western Australia. Primary Au and Cu (and Mo) mineralization consists of a network of thin fractures and veins, controlled by shear zones, and dominantly hosted by early dioritic intrusions and their immediate wall rocks, which comprise felsic to intermediate-composition volcanic and volcaniclastic rocks. The pre-~2714 Ma host rocks are typically steeply dipping and strongly deformed, with early ductile and overprinting brittle-ductile fabrics, and have been metamorphosed at mid- to upper greenschist facies. Features consistent with porphyry-style mineralization, classic orogenic shear zones, and intrusion-related Au-Cu-Bi mineralization are all recognized, giving rise to a variety of genetic interpretations. It is clear that Boddington does not fit any classic Archean orogenic gold deposit model, having a general lack of quartz veins and iron carbonate alteration, a Cu (Mo and Bi) association, zoned geochemical anomalism, and evidence of high-temperature, saline ore-forming fluids. Detailed petrographic, geochemical, and melt inclusion studies suggest a late-stage ~2612 Ma, monzogranite intrusion as one of the principal sources of the mineralizing fluids. However, there is also local evidence for older, perhaps protore, porphyry-style Cu (±Au) in the dioritic intrusions and patchy, locally high-grade, orogenic-style gold mineralization associated with enclosing shear zones and brittle-style deformation, which was focused on the relatively competent dioritic intrusions. The relative contributions of metals from these components to the system may not be resolvable. It appears that the Boddington deposit has been a locus for multiple episodes of intrusion, alteration, and mineralization over an extended period of time, as has been demonstrated in a number of other large Canadian and Australian gold deposits, including the Golden Mile near Kalgoorlie.
Indian Sign language Recognition Using Color Space Model and Thresholding

Sign language is for deaf and mute people. Population as large as India's provides for an enormous section of people using Indian sign language to communicate. Unfortunately, a major chunk of the population does not understand sign language which limits communication between those with disabilities and wider population. We are proposing a system to harness this communication chasm. First the images are taken from webcam in RGB color space. Preprocessing and semantic segmentation are applied on the input image. By employing a simple background, the RGB segmented image is transformed to gray scale and background noise is removed. Otsu's segmentation method is used to segment the image. Convolutional Neural Networks were employed, utilizing skin segmented hand images as the input. On the training data, a classification accuracy of 99.33% was attained for 36 static hand gestures from Indian Sign Language. The above mentioned model performed significantly well in real time implementation.
Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains

In triple negative breast cancer (TNBC) treatment, early prediction of pathological complete response (PCR) from chemotherapy before surgical operations is crucial for optimal treatment planning. We propose a novel deep learning-based system to predict PCR to neoadjuvant chemotherapy for TNBC patients with multi-stained histopathology images of serial tissue sections. By first performing tumor cell detection and recognition in a cell detection module, we produce a set of feature maps that capture cell type, shape, and location information. Next, a newly designed spatial attention module integrates such feature maps with original pathology images in multiple stains for enhanced PCR prediction in a dedicated prediction module. We compare it with baseline models that either use a single-stained slide or have no spatial attention module in place. Our proposed system yields 78.3% and 87.5% of accuracy for patch-, and patient-level PCR prediction, respectively, outperforming all other baseline models. Additionally, the heatmaps generated from the spatial attention module can help pathologists in targeting tissue regions important for disease assessment. Our system presents high efficiency and effectiveness and improves interpretability, making it highly promising for immediate clinical and translational impact.
RESEARCH ON FUZZY SCHEDULING OF CLOUD COMPUTING TASKS BASED ON HYBRID SEARCH ALGORITHMS AND DIFFERENTIAL EVOLUTION

In the process of task scheduling, due to the large search space of resources, it takes a long time to allocate appropriate resources for tasks, which results in the increase of execution time and execution cost of algorithms. For this reason, this paper proposes a cloud computing task fuzzy scheduling strategy based on hybrid search algorithm and differential evolution, which incorporates the classification based on normal distribution and a variety of mutation strategies on the basis of standard differential evolution algorithm. In mutation strategy, the individual difference vector assigns priority to each task and arranges tasks according to resource allocation rules. It improves the slow convergence speed and easy to fall into local optimum of standard difference algorithm, and can effectively solve the task scheduling problem of cloud computing. A cloud computing task scheduling algorithm with time and cost constraints is designed and tested in a simulation environment. The experimental results show that the algorithm can not only shorten the task processing time and reduce the execution cost, but also fully satisfy the users’ actual quality of service requirements by adjusting the weights of time and cost factors.
DeepSIM: Deep Semantic Information-Based Automatic Mandelbug Classification

Understanding and predicting types of bugs are of practical importance for developers to improve the testing efficiency and take appropriate steps to address bugs in software releases. However, due to the complex conditions under which faults manifest and the complexity of the classification rules, the automatic classification of Mandelbugs is a difficult task. In this article, we present a deep semantic information-based Mandelbug classification method that combines a semantic model with a deep learning classifier and makes use of both labeled and unlabeled bug reports. By training the bug report semantic model on millions of bug reports, each word in the text of a bug report is represented as a word embedding that preserves the semantic relationship among the words. Then, a convolutional neural network model is designed to capture the high-level features of bug reports to obtain a more accurate classification. Moreover, the effects of the semantic model size and domain on the classification results are investigated, and the quality of word embeddings is evaluated by analyzing several important parameters.
LDSP: Shopping with Cryptocurrency Privately and Quickly under Leadership

LDSP is a layer-2 cryptocurrency payment system that supports a dynamic and distributed setup with scalability and payer privacy. Different from the statekeeping merchant consortium assumed by a recent layer-2 solution Snappy (NDSS 2020), its core idea is to rely on leaders selected from the merchants to lead a mini-consortium and ensure the fungibility of the LDSP coins. Similar to Snappy, payers can transfer coins off-chain, enjoying low-latency transactions, while the merchants are assured that they will receive the coins. Privacy-wise, Snappy requires all merchants to check the whole customer transaction history, which is not necessary for LDSP.
Towards Accountability in Machine Learning Applications: A System-testing Approach

A rapidly expanding universe of technology-focused startups is trying to change and improve the way real estate markets operate. The undisputed predictive power of machine learning (ML) models often plays a crucial role in the 'disruption' of traditional processes. However, an accountability gap prevails: How do the models arrive at their predictions? Do they do what we hope they doâ€”or are corners cut?Training ML models is a software development process at heart. We suggest to follow a dedicated software testing framework and to verify that the ML model performs as intended. Illustratively, we augment two ML image classifiers with a system testing procedure based on local interpretable model-agnostic explanation (LIME) techniques. Analyzing the classifications sheds light on some of the factors that determine the behaviour of the systems.
Towards Accurate Cross-Domain in-Bed Human Pose Estimation

Human behavioral monitoring during sleep is essential for various medical applications. Majority of the contactless human pose estimation algorithms are based on RGB modality, causing ineffectiveness in in-bed pose estimation due to occlusions by blankets and varying illumination conditions. Long-wavelength infrared (LWIR) modality based pose estimation algorithms overcome the aforementioned challenges; however, ground truth pose generations by a human annotator under such conditions are not feasible. A feasible solution to address this issue is to transfer the knowledge learned from images with pose labels and no occlusions, and adapt it towards real world conditions (occlusions due to blankets). In this paper, we propose a novel learning strategy comprises of two-fold data augmentation to reduce the cross-domain discrepancy and knowledge distillation to learn the distribution of unlabeled images in real world conditions. Our experiments and analysis show the effectiveness of our approach over multiple standard human pose estimation baselines <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Virtual Smash Room for Venting Frustration or Just Having Fun: Participatory Design of Virtual Environments in Digitally Reinforced Cancer Rehabilitation

Background Cancer rehabilitation is central for helping patients and relatives create a functional everyday life based on the changes in life conditions. The needs are highly individual and include physical, mental, and social challenges. Cancer rehabilitation programs offer coping strategies, including guidelines on how to handle emotions. Objective This paper presents a participatory design activity where patients in cancer rehabilitation use a virtual smash room, which is a virtual environment where the user can break things, mainly porcelain or glass items such as vases or plates. The objective is to understand attitudes to, and some effects of, using this application, as well as eliciting ideas of other virtual environments that would be desired. Methods The virtual environment presented here, the virtual smash room, was designed at the request of a patient with cancer who wanted a tool for venting frustration. In this virtual environment, the user can break porcelain, vases, and plates. Patients participating in a week-long cancer rehabilitation program tested the virtual smash room and reported their experiences through a questionnaire. The questionnaire comprised three sections: (1) a subset of the Intrinsic Motivation Inventory (IMI), (2) a subset of the Virtual Reality Symptoms Questionnaire (VRSQ), and (3) a free-text response section. Results A total of 101 responses were gathered. The results from the IMI questions showed that the participants found the virtual experience enjoyable (mean 4.52, maximum 5, SD 0.73), and it helped them retain their focus (mean 4.44, maximum 5, SD 0.74). The VRSQ revealed that there were only minor symptoms related to general discomfort (5.9%, n=6), fatigue (5.9%, n=6), nausea (3.0%, n=3), and tired eyes (8.9%, n=9), while several participants experienced dizziness (22.8%, n=23). Since only postmeasurements were gathered, nothing could be concluded about the prevalence of these symptoms before testing. The free-text responses indicated that the user group had many ideas for other virtual environments to use in cancer rehabilitation. Conclusions This study presents a concept of using virtual reality in the cancer rehabilitation process and exemplifies activities of patient participation in the design process. Virtual reality has potential in being both distracting and enjoyable, while certain aspects of cybersickness might be especially important to consider for a user group already experiencing physical and mental issues. The results will act as input in the process of further designing virtual applications in digitally reinforced cancer rehabilitation.
DyMMer 2.0: A Tool for Dynamic Modeling and Evaluation of Feature Model

Managing dynamic variability has motivated several researchers to combine Dynamic Software Product Lines (DSPLs) practices with runtime variability mechanisms. By combining these approaches, a DSPL acquires important features, ranging from the ability to reconfigure by changing the context, adding or removing features, crash recovery, and re-adaptation based on changes in the model’s features. Feature model (FM) is an important artifact of a DPSL and there is a lack of tools that support the modeling of this artifact. We have extended the DyMMer tool for modeling FM of DSPLs from an adaptation mechanism based on MAPE-K to solve this problem. We migrated the DyMMer tool to a web version and incorporated new features: (i) modeling of FMs from SPLs and DSPLs, (ii) development of an adaptation mechanism for FM of DSPLs, (iii) repository of FMs, (iv) inclusion of thresholds for measures, and (v) user authentication. We believe that this tool is useful for research in the area of DSPLs, and also for dynamic domain modeling and evaluation.
Location-Based Modelling And Analysis Of Threats By Using Text Mining

Location-Based Modelling and Analysis of Threats By Using Text Mining is process of analysis of threat and criminal behavior and help to the police to find the location of the criminal activities using some text mining techniques and machine learning algorithms. Now, in digital world, Computer crime such as identity threat, cybercrime, terrorist attack, rap, kidnaping, robbery has increase because of increase a digitalization has become a significant issue for police and law enforcement agencies Many identities threat occurs through social media and that's way crime are increases. This study helps to detect the all-criminal behavior-based activity and post on social media using different techniques and construct pattern sets based on domain knowledge from social media reports or newspapers and relevant literatures. In addition, our research include previous years crime activity is use for our model to train and that data is pre-process using tokenization for meaningful data. Some techniques such as feature extraction include emoticons and synonyms, while in classification Extra tree Classifier for analyses the criminal behavior from social media.
Predictive Modelling of Highly Multiplexed Tumour Tissue Images by Graph Neural Networks

The progression and treatment response of cancer largely depends on the complex tissue structure that surrounds cancer cells in a tumour, known as the tumour microenvironment (TME). Recent technical advances have led to the development of highly multiplexed imaging techniques such as Imaging Mass Cytometry (IMC), which capture the complexity of the TME by producing spatial tissue maps of dozens of proteins. Combining these multidimensional cell phenotypes with their spatial organization to predict clinically relevant information is a challenging computational task and so far no method has addressed it directly. Here, we propose and evaluate MULTIPLAI, a novel framework to predict clinical biomarkers from IMC data. The method relies on attention-based graph neural networks (GNNs) that integrate both the phenotypic and spatial dimensions of IMC images. In this proof-of-concept study we used MULTIPLAI to predict oestrogen receptor (ER) status, a key clinical variable for breast cancer patients. We trained different architectures of our framework on 240 samples and benchmarked against graph learning via graph kernels. Propagation Attribute graph kernels achieved a class-balanced accuracy of 66.18% in the development set (N = 104) while GNNs achieved a class-balanced accuracy of 90.00% on the same set when using the best combination of graph convolution and pooling layers. We further validated this architecture in internal (N = 112) and external test sets from different institutions (N = 281 and N = 350), demonstrating the generalizability of the method. Our results suggest that MULTIPLAI captures important TME features with clinical importance. This is the first application of GNNs to this type of data and opens up new opportunities for predictive modelling of highly multiplexed images.