Reviewerly/exHarmony huggingface dataset:
Prostate Segmentation From 3D Mri Using A Two-Stage Model and Variable-Input Based Uncertainty Measure

This paper proposes a two-stage segmentation model, variable-input based uncertainty measures and an uncertainty-guided post-processing method for prostate segmentation on 3D magnetic resonance images (MRI). The two-stage model was based on 3D dilated U-Nets with the first stage to localize the prostate and the second stage to obtain an accurate segmentation from cropped images. For data augmentation, we proposed the variable-input method which crops the region of interest with additional random variations. Similar to other deep learning models, the proposed model also faced the challenge of suboptimal performance in certain testing cases due to varied training and testing image characteristics. Therefore, it is valuable to evaluate the confidence and performance of the network using uncertainty measures, which are often calculated from the probability maps or their standard deviations with multiple model outputs for the same testing case. However, few studies have quantitatively compared different methods of uncertainty calculation. Furthermore, unlike the commonly used Bayesian dropout during testing, we developed uncertainty measures based on the variable input images at the second stage and evaluated its performance by calculating the correlation with ground-truth-based performance metrics, such as Dice score. For performance estimation, we predicted Dice scores and Hausdorff distance with the most correlated uncertainty measure. For post-processing, we performed Gaussian filter on the underperformed slices to improve segmentation quality. Using PROMISE-12 data, we demonstrated the robustness of the two-stage model and showed high correlation of the proposed variable-input based uncertainty measures with GT-based performance. The uncertainty-guided post-processing method significantly improved label smoothness.
REALITY

"REALITY" is a live entertainment creation service for virtual avatars aka VTubers. It was created to enable a live entertainment ecosystem staffed with real time CG characters. The platform not only allows interaction with audience and VTubers using a professional motion capture studio, but also formatting virtual beings encouraging community engagements with a culture that is moving towards an avatar society using today's smartphone app that has real time facial capture.
A Survey on Human Behavior Recognition Using Smartphone-Based Ultrasonic Signal

With the rapid progress of the Internet of Things (IoT) technology, human behavior recognition has become an important research topic in the field of ubiquitous computing and has obtained quite a number of research achievements. Accurate human behavior recognition can enhance the quality of human–computer interaction and facilitate the development of various sensing applications. With the popularity of smartphones and the improved performance of sensors such as speakers and microphones built in smartphones, the behavior recognition technique based on ultrasound signal of the smartphone has gained more attention and achieved several research results. In this paper, we first review the common behavior recognition techniques including light, video, sound, and frequency radio and outline their main characteristics. Then, we introduce the fundamental principle of human behavior recognition based on ultrasound signals. Specifically, these systems treat speakers and microphones embedded in smartphones as the transceiver and leverage the received signal changes caused by human movement including phase differences, frequency shift, and time of flight (ToF) to recognize human behavior. Next, we investigate the state-of-the-art studies and applications and analyze the signal processing techniques such as data collection, signal preprocessing, feature description, and behavior recognition approach. Afterward, according to the purpose of these applications, we classify them into five groups and compare them in detail including hand gesture recognition, activity recognition, hand trajectory tracking, vital sign monitoring, and lip reading. Finally, we conclude by discussing the limitations, challenges, and open issues involved in behavior recognition based on ultrasound signal of smartphone.
Image super-resolution via feature-augmented random forest

Recent random-forest (RF)-based image super-resolution approaches inherit some properties from dictionary-learning-based algorithms, but the effectiveness of the features working in RF is overlooked in the literature. In this paper, we present a novel feature-augmented random forest (FARF) method for image super-resolution, where the conventional gradient-based features are proposed to augment the features used in RF, and different feature recipes are formulated on different processing stages in an RF. The advantages of our method are that, firstly, the dictionary-learning-based features are enhanced by adding gradient magnitudes, based on the observation that the non-linear gradient magnitudes are highly discriminative. Secondly, generalized locality-sensitive hashing (LSH) is used to replace principal component analysis (PCA) for feature dimensionality reduction in constructing the trees, but the original high-dimensional features are employed, instead of the compressed LSH features, for the leaf-nodes’ regressors. With the use of the original higher dimensional features, the regressors can achieve better learning performances. Finally, we present a generalized weighted ridge regression (GWRR) model for the leaf-nodes’ regressors. Experiment results on several public benchmark datasets show that our FARF method can achieve an average gain of about 0.3 dB, compared to traditional RF-based methods. Furthermore, a fine-tuned FARF model can compare to, or (in many cases) outperform, some recent state-of-the-art deep-learning-based algorithms.
Improving an Identity Authentication Management Protocol in IIoT

The Internet of Things (IoT) is where almost anything can be controlled and managed remotely. Although the IoT evolution led to quality of life enhancement, many of its devices are insecure. The lack of robust key management systems, efficient identity authentication, low fault tolerance, and many other issues lead IoT devices into being easily targeted by attackers. The aim of this research is to enhance an authentication protocol that helps in the authentication process of IoT devices and CPANs (Personal Area Network Coordinators) in an Industrial Internet of Things (IIoT) environment. To propose a solution for this issue, and to find out the optimal contribution, a comparison and analysis on the current IoT security solutions have been conducted. Based on the analysis, it is found that the superior solution was a Blockchain-based solution called BCTrust. In this paper, the aim is to improve the basis authentication protocol adopted by BCTrust. The protocol is to be improved mainly by two sides, firstly, eliminating the blacklisting mechanism that can be leveraged by attackers to negatively impact the network. Secondly, introducing a new way for generating the identifiers of the IIoT devices. This would enhance the security and efficiency of BCTrust, specifically in the generation of IIoT secret keys.
Hierarchically-Fused Generative Adversarial Network for Text to Realistic Image Synthesis

In this paper, we present a novel Hierarchically-fused Generative Adversarial Network (HfGAN) for synthesizing realistic images from text descriptions. While existing approaches on this topic have achieved impressive success, to generate 256×256 images from captions, they commonly resort to coarse-to-fine scheme and associate multiple discriminators in different stages of the networks. Such a strategy is both inefficient and prone to artifacts. Motivated by the above findings, we propose an end-to-end network that can generate 256×256 photo-realistic images with only one discriminator. We fully exploit the hierarchical information from different layers and directly generate the fine-scale images by adaptively fusing features from multi-hierarchical layers. We quantitatively evaluate the synthesized images with Inception Score, Visual-semantic Similarity and average training time on the CUB birds, Oxford-102 flowers, and COCO datasets. The results show that our model is more efficient and noticeably outperforms the previous state-of-the-art methods.
Channel-wise Temporal Attention Network for Video Action Recognition

Recently, video action recognition receives lots of attention, and deep learning based methods have achieved promising performance. Most existing methods focus on spatiotemporal information encoding to learn video representation, which ignore the relevance among channels. In this paper, we propose a novel Channel-wise Temporal Attention Network (CTAN) to explore the fine-grained key information for action recognition. First, the channel-wise attention generation module is proposed to emphasize the fine-grained informative features in each frame. Then, the temporal information aggregation module is introduced before attention generation to exploit the interaction of different frames. Finally, a discriminative video-level representation for action recognition is generated by end-to-end training. Experimental results on two benchmarks, UCF101 and HMDB51, demonstrate the effectiveness of the proposed CTAN.
Fine-Grained Dynamic Resource Allocation for Big-Data Applications

Many big-data applications are batch applications that exploit dedicated frameworks to perform massively parallel computations across clusters of machines. The time needed to process the entirety of the inputs represents the application's response time, which can be subject to deadlines. Spark, probably the most famous incarnation of these frameworks today, allocates resources to applications statically at the beginning of the execution and deviations are not managed: to meet the applications' deadlines, resources must be allocated carefully. This paper proposes an extension to Spark, called dynaSpark, that is able to allocate and redistribute resources to applications dynamically to meet deadlines and cope with the execution of unanticipated applications. This work is based on two key enablers: containers, to isolate Spark's parallel executors and allow for the dynamic and fast allocation of resources, and control-theory to govern resource allocation at runtime and obtain required precision and speed. Our evaluation shows that dynaSpark can (i) allocate resources efficiently to execute single applications with respect to set deadlines and (ii) reduce deadline violations (w.r.t. Spark) when executing multiple concurrent applications.
Codensity Games for Bisimilarity

Bisimilarity as an equivalence notion of systems has been central to process theory. Due to the recent rise of interest in quantitative systems (probabilistic, weighted, hybrid, etc.), bisimilarity has been extended in various ways: notably, bisimulation metric between probabilistic systems. An important feature of bisimilarity is its game-theoretic characterization, where Spoiler and Duplicator play against each other; extension of bisimilarity games to quantitative settings has been actively pursued too. In this paper, we present a general framework that uniformly describes game characterizations of bisimilarity-like notions. Our framework is formalized categorically using fibrations and coalgebras. In particular, our characterization of bisimilarity in terms of fibrational predicate transformers allows us to derive codensity bisimilarity games: a general categorical game characterization of bisimilarity. Our framework covers known bisimilarity-like notions (such as bisimulation metric) as well as new ones (including what we call bisimulation topology).
Technical Debt Triage in Backlog Management

Remediation of technical debt through regular refactoring initiatives is considered vital for the software system's long and healthy life. However, since today's software companies face increasing pressure to deliver customer value continuously, the balance between spending developer time, effort, and resources on implementing new features or spending it on refactoring of technical debt becomes vital. The goal of this study is to explore how the prioritization of technical debt is carried out by practitioners within today's software industry. This study also investigates what factors influence the prioritization process and its related challenges. This paper reports the results of surveying 17 software practitioners, together with follow-up interviews with them. Our results show that there is no uniform way of prioritizing technical debt and that it is commonly done reactively without applying any explicit strategies. Often, technical debt issues are managed and prioritized in a shadow backlog, separate from the official sprint backlog. This study was also able to identify several different challenges related to prioritizing technical debt, such as the lack of quantitative information about the technical debt items and that the refactoring of technical debt issues competes with the implementation of customer requirements.
Medical knowledge infused convolutional neural networks for cohort selection in clinical trials

Abstract Objective In this era of digitized health records, there has been a marked interest in using de-identified patient records for conducting various health related surveys. To assist in this research effort, we developed a novel clinical data representation model entitled medical knowledge-infused convolutional neural network (MKCNN), which is used for learning the clinical trial criteria eligibility status of patients to participate in cohort studies. Materials and Methods In this study, we propose a clinical text representation infused with medical knowledge (MK). First, we isolate the noise from the relevant data using a medically relevant description extractor; then we utilize log-likelihood ratio based weights from selected sentences to highlight “met” and “not-met” knowledge-infused representations in bichannel setting for each instance. The combined medical knowledge-infused representation (MK) from these modules helps identify significant clinical criteria semantics, which in turn renders effective learning when used with a convolutional neural network architecture. Results MKCNN outperforms other Medical Knowledge (MK) relevant learning architectures by approximately 3%; notably SVM and XGBoost implementations developed in this study. MKCNN scored 86.1% on F1metric, a gain of 6% above the average performance assessed from the submissions for n2c2 task. Although pattern/rule-based methods show a higher average performance for the n2c2 clinical data set, MKCNN significantly improves performance of machine learning implementations for clinical datasets. Conclusion MKCNN scored 86.1% on the F1 score metric. In contrast to many of the rule-based systems introduced during the n2c2 challenge workshop, our system presents a model that heavily draws on machine-based learning. In addition, the MK representations add more value to clinical comprehension and interpretation of natural texts.
Speculative and Critical Design — Features, Methods, and Practices

Abstract Speculative and Critical Design (SCD) confronts traditional design practice. Instead of reproducing and reinforcing contemporary perceptions of products and services, SCD seeks to change them, with the goal to spur a public debate e.g. on what is a preferable societal development. SCD methods comprise among others speculative narratives and design of critical objects. However, the theoretical fundament of SCD is feeble, and concepts and methods are upheld of mere examples. The article aims to present the features, methods, and practice of SCD, and locate it in the design field. Following the introduction, section 2 presents features and SCD methods and practices. Section 3 links SCD to traditional design and identifies benefits and challenges relating to its role as a supplement to today's design practice. The final section suggests topics for further SCD research. The article benefits designers in search for alternatives to commercially oriented design approaches. Findings indicate that SCD can contribute to creating new societal roles for designers, foster innovation in the relationship between designers and users, and encourage creativity through imaginative aesthetics.
Dynamic Region Division for Adaptive Learning Pedestrian Counting

Accurate pedestrian counting algorithm is critical to eliminate insecurity in the congested public scenes. However, counting pedestrians in crowded scenes often suffer from severe perspective distortion. In this paper, basing on the straightline double region pedestrian counting method, we propose a dynamic region division algorithm to keep the completeness of counting objects. Utilizing the object bounding boxes obtained by YoloV3 and expectation division line of the scene, the boundary for nearby region and distant one is generated under the premise of retaining whole head. Ulteriorly, appropriate learning models are applied to count pedestrians in each obtained region. In the distant region, a novel inception dilated convolutional neural network is proposed to solve the problem of choosing dilation rate. In the nearby region, YoloV3 is used for detecting the pedestrian in multi-scale. Accordingly, the total number of pedestrians in each frame is obtained by fusing the result in nearby and distant regions. A typical subway pedestrian video dataset is chosen to conduct experiment in this paper. The result demonstrate that proposed algorithm is superior to existing machine learning based methods in general performance.
A Deep LSTM Approach for Activity Recognition

Since 1990s, activity recognition effectual field in machine learning literature. Most of studies that relevant activity recognition, use feature extraction method to achieve higher classification performance. Moreover, these studies mostly use traditional machine learning algorithms for classification. In this paper, we focus on a deep (Long Short Term Memory) LSTM neural network for feature free classification of seven daily activities by using raw data that collected from three-dimensional accelerometer. Based on the results, the proposed deep LSTM approach can classify raw data with high performance. The results show that the proposed deep LSTM approach achieved 91.34, 96.91, 88.78, 87.58 as percent classification performance in terms of accuracy, sensitivity, specificity, F-measure respectively.
Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System

Recent advancement in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. While accuracy is important, focusing primarily on it poses an overfitting danger, exposing manufacturers to risk, ultimately hindering the adoption of these techniques. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty in a cyber-physical manufacturing system (CPMS) scenario. Then, we propose a multi-agent system architecture which leverages probabilistic machine learning as a means of achieving such criteria. We propose possible scenarios for which our architecture is useful and discuss future work. Experimentally, we implement Bayesian Neural Networks for multi-tasks classification on a public dataset for the real-time condition monitoring of a hydraulic system and demonstrate the usefulness of the system by evaluating the probability of a prediction being accurate given its uncertainty. We deploy these models using our proposed agent-based framework and integrate web visualisation to demonstrate its real-time feasibility.
GANDIVA

In this research, the authors propose a novel tree structure called GANDIVA which computes true supports of all temporal itemsets by performing a tree-based scan and eliminating the database scan which is required for SPAMINE, G-SPAMINE, MASTER, and Z-SPAMINE approaches. The idea is to construct the tree called GANDIVA which determines support of all time-stamped temporal itemsets from the constructed tree. Another important advantage of the proposed approach is that it does not require the original database to be retained in the memory after a time profiled pattern tree (GANDIVA) is constructed from the original database. The significant advantage of GANDIVA over SPAMINE, G-SPAMINE, Z-SPAMINE, and MASTER is that GANDIVA requires zero database scans after the tree construction. GANDIVA is the pioneering research to propose a novel tree-based framework for seasonal temporal data mining.
Perceptual representation of spam and phishing emails

Summary Understanding how computer users allocate attention to features of potentially dangerous emails could help mitigate costly errors. Which features are salient? How stable is attention allocation across variation in email features? We attempted to measure the mental salience of several email features common in spam and/or phishing emails. We created two email sets: one in which messages contained company logos and urgent actionable links and one without these features. Participants rated pairwise similarity of emails within each set. Multidimensional scaling (MDS) analysis was conducted to quantify psychological similarity between emails. A separate group rated the same emails for presence of five other features: important downloadable content, collecting personal information, account deletion or suspension, advertisement, and large images with clickable content. Regressing feature ratings onto the MDS coordinates revealed that similarity judgments were influenced mostly by advertisement/large images and collecting personal information, regardless of presence or absence of company logos and urgent actionable links.
A New Region Proposal Network for Far-Infrared Pedestrian Detection

An automatic region proposal network (ARPN) is proposed to generate bounding boxes with confidence scores for far-infrared (FIR) pedestrian detection. The model consists of two parts: first, the bounding boxes are predicted by the L2 loss function and a module is designed based on a convolutional neural network. This module is simple and only has two layers, each with a 1Ã-1 kernel. Second, a score map is obtained through FIR pedestrian segmentation based on a feature pyramid network. The scores are taken as the confidence levels for the predicted bounding boxes. To obtain the scores, a new labelling method is also introduced in this paper for FIR image segmentation. We can obtain the bounding boxes per pixel directly and efficiently without any manually designed hyper parameters related to the anchor boxes. To validate the model, this paper uses the LSI, CVC09, CVC14 and SCUT FIR pedestrian detection datasets in the experiments. The datasets consist of different sizes of FIR images collected from several different cameras. The datasets contain different outdoor urban scenes collected at different times, from day to night. The recall vs number of proposals, average recall and recall at IoU 0.5 are used to evaluate the proposed method and log-average miss rate is used to evaluate final detection results. Compared with other algorithms, experiments on most of the data sets also show better performance.
Modeling of Dynamics of the Company’s Share in the Solid Fuel Market

The task of identification of the interval discrete dynamic model of the company's share in the solid fuel market is considered in the paper. For building such models, it is proposed to use the methods of structural and parametric identification, which are based on the analysis of the interval data. The built mathematical model is a methodological basis for forecasting the enterprise commodity products market shares for the years to come. The advantage of such a model is its ability to be quickly adjusted to other conditions by specifying performance for the first three months of the year.