Reviewerly/exHarmony huggingface dataset:
Blockchain and IoT for Insurance: A Case Study and Cyberinfrastructure Solution on Fine-Grained Transportation Insurance

In this study, we initiate a cyberinfrastructure solution by synergizing both the blockchain and Internet of Things (IoT) technologies for transportation insurance. The insurance premium related services are encapsulated in “on-chain” chaincodes to perform over the facts on vehicle's trip and driver's behavior, which are deduced through “off-chain” analytic services using the sensing data collected from vehicles' on-board sensors. A hybrid scheme coordinating both the permissioned (Hyperledger) and public (Ethereum) blockchains is proposed to exploit their respective capabilities in terms of high transaction throughput and built-in cryptocurrency. A working prototype platform is implemented with a basic premium calculation model. The prototype system is deployed across Amazon Web Services (AWSs) cloud in a real-world Internet environment. A comprehensive performance study from the aspects of throughput, latency, and resource usage under different configurations is presented to show the solution's feasibility. The design practice and research findings are concluded in consort with the experience gained for further enhancing the proposed solution and extending the functional features such as a more realistic insurance policy to be applied in generic vehicle insurance applications.
Location Privacy in Cognitive Radios With Multi-Server Private Information Retrieval

Spectrum database-based cognitive radio networks (CRNs) have become the de facto approach for enabling unlicensed secondary users (SUs) to identify spectrum vacancies in channels owned by licensed primary users (PUs). Despite its merits, the use of spectrum databases incurs privacy concerns for both SUs and PUs. Single-server private information retrieval (PIR) has been used as the main tool to address this problem. However, such techniques incur extremely large communication and computation overheads while offering only computational privacy. Besides, some of these PIR protocols have been broken. In this paper, we show that it is possible to achieve high efficiency and (information-theoretic) privacy for both PUs and SUs in database-driven CRN with multi-server PIR. Our key observation is that, by design, database-driven CRNs comprise multiple databases that are required, by the Federal Communications Commission, to synchronize their records. To the best of our knowledge, we are the first to exploit this observation to harness multi-server PIR technology to guarantee an optimal privacy for both SUs and PUs, thanks to the unique properties of database-driven CRN . We showed, analytically and empirically with deployments on actual cloud systems, that multi-server PIR is an ideal tool to provide efficient location privacy in database-driven CRN.
Evaluation of Risk-Based Re-Authentication Methods

Risk-based Authentication (RBA) is an adaptive security measure that improves the security of password-based authentication by protecting against credential stuffing, password guessing, or phishing attacks. RBA monitors extra features during login and requests for an additional authentication step if the observed feature values deviate from the usual ones in the login history. In state-of-the-art RBA re-authentication deployments, users receive an email with a numerical code in its body, which must be entered on the online service. Although this procedure has a major impact on RBA's time exposure and usability, these aspects were not studied so far. We introduce two RBA re-authentication variants supplementing the de facto standard with a link-based and another code-based approach. Then, we present the results of a between-group study (N=592) to evaluate these three approaches. Our observations show with significant results that there is potential to speed up the RBA re-authentication process without reducing neither its security properties nor its security perception. The link-based re-authentication via "magic links", however, makes users significantly more anxious than the code-based approaches when perceived for the first time. Our evaluations underline the fact that RBA re-authentication is not a uniform procedure. We summarize our findings and provide recommendations.
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder

The problem of fair classification can be mollified if we develop a method to remove the embedded sensitive information from the classification features. This line of separating the sensitive information is developed through the causal inference, and the causal inference enables the counterfactual generations to contrast the what-if case of the opposite sensitive attribute. Along with this separation with the causality, a frequent assumption in the deep latent causal model defines a single latent variable to absorb the entire exogenous uncertainty of the causal graph. However, we claim that such structure cannot distinguish the 1) information caused by the intervention (i.e., sensitive variable) and 2) information correlated with the intervention from the data. Therefore, this paper proposes Disentangled Causal Effect Variational Autoencoder (DCEVAE) to resolve this limitation by disentangling the exogenous uncertainty into two latent variables: either 1) independent to interventions or 2) correlated to interventions without causality. Particularly, our disentangling approach preserves the latent variable correlated to interventions in generating counterfactual examples. We show that our method estimates the total effect and the counterfactual effect without a complete causal graph. By adding a fairness regularization, DCEVAE generates a counterfactual fair dataset while losing less original information. Also, DCEVAE generates natural counterfactual images by only flipping sensitive information. Additionally, we theoretically show the differences in the covariance structures of DCEVAE and prior works from the perspective of the latent disentanglement.
Static Analysis of Shape in TensorFlow Programs

Machine learning has been widely adopted in diverse science and engineering domains, aided by reusable libraries and quick development patterns. The TensorFlow library is probably the best-known representative of this trend and most users employ the Python API to its powerful back-end. TensorFlow programs are susceptible to several systematic errors, especially in the dynamic typing setting of Python. We present Pythia, a static analysis that tracks the shapes of tensors across Python library calls and warns of several possible mismatches. The key technical aspects are a close modeling of library semantics with respect to tensor shape, and an identification of violations and error-prone patterns. Pythia is powerful enough to statically detect (with 84.62% precision) 11 of the 14 shape-related TensorFlow bugs in the recent Zhang et al. empirical study - an independent slice of real-world bugs.
Fast Training of Deep Learning Models over Multiple GPUs

This paper proposes FastT, a transparent module to work with the TensorFlow framework for automatically identifying a satisfying deployment and execution order of operations in DNN models over multiple GPUs, for expedited model training. We propose white-box algorithms to compute the strategies with small computing resource consumption in a short time. Recently, similar studies have been done to optimize device placement using reinforcement learning. Compared to those works which learn to optimize device placement of operations in several hours using large amounts of computing resources, our approach can find excellent device placement and execution order within minutes using the same computing node as for training. We design a list of scheduling algorithms to compute the device placement and execution order for each operation and also design an algorithm to split operations in the critical path to support fine-grained (mixed) data and model parallelism to further improve the training speed in each iteration. We compare FastT with representative strategies and obtain insights on the best strategies for training different types of DNN models based on extensive testbed experiments.
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism

We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model training with large, high-dimensional samples, which can make training much more costly and even infeasible due to excessive memory usage. We solve these challenges by extensively applying hybrid parallelism throughout the end-to-end training pipeline, including both computations and I/O. Our hybrid-parallel algorithm extends the standard data parallelism with spatial parallelism, which partitions a single sample in the spatial domain, realizing strong scaling beyond the mini-batch dimension with a larger aggregated memory capacity. We evaluate our proposed training algorithms with two challenging 3D CNNs, CosmoFlow and 3D U-Net. Our comprehensive performance studies show that good weak and strong scaling can be achieved for both networks using up to 2K GPUs. More importantly, we enable training of CosmoFlow with much larger samples than previously possible, realizing an order-of-magnitude improvement in prediction accuracy.
IOT connected with e-learning

In the networking field, Internet of Things (IoT) can be referred to technological e-learning advancements. It is seen that through the internet, nowadays it becomes easier to be connected with the real world affairs by being acknowledges about the all-around happenings. Even everyone is aware about communicating with each other all over world. ‘Things’ can be specified as the objects which are connected through internet.Over the internet, the specific kind of interconnection of different things can serve its capability through the information which can be utilized in receiving and sending of evaluated data. It is said to be specialized in almost every field that can be determined in numerous manner for implementing wide range of applications format. Wide ranges can be mentioned aseducation, business, transportation, agriculture, healthcare and management. In a generalized manner, in this article specifically and mainly the discussion is being held over the Internet of Things (IoT). Specific emphasis on E-learning is said to be implemented as a source of information applied for its readers. By the utilization of smart learning as the IOT the smart techniques can also be represented shown by e-learning methods.
Infrared Action Detection in the Dark via Cross-Stream Attention Mechanism

Action detection plays an important role in the field of video understanding and attracts considerable attention in the last decade. However, current action detection methods are mainly based on visible videos, and few of them consider scenes with low-light, where actions are difficult to be detected by existing methods, or even by human eyes. Compared with visible videos, infrared videos are more suitable for the dark environment and resistant to background clutter. In this paper, we investigate the temporal action detection problem in the dark by using infrared videos, which is, to the best of our knowledge, the first attempt in the action detection community. Our model takes the whole video as input, a Flow Estimation Network (FEN) is employed to generate the optical flow for infrared data, and it is optimized with the whole network to obtain action-related motion representations. After feature extraction, the infrared stream and flow stream are fed into a Selective Cross-stream Attention (SCA) module to narrow the performance gap between infrared and visible videos. The SCA emphasizes informative snippets and focuses on the more discriminative stream automatically. Then we adopt a snippet-level classifier to obtain action scores for all snippets and link continuous snippets into final detection results. All these modules are trained in an end-to-end manner. We collect an Infrared action Detection (InfDet) dataset obtained in the dark and conduct extensive experiments to verify the effectiveness of the proposed method. Experimental results show that our proposed method surpasses the state-of-the-art temporal action detection methods designed for visible videos, and it also achieves the best performance compared with other infrared action recognition methods on both InfAR and Infrared-Visible datasets.
Inverse Queuing Model-Based Feedback Control for Elastic Container Provisioning of Web Systems in Kubernetes

Container orchestration platforms such as Kubernetes and Kubernetes-derived KubeEdge (called Kubernetes-based systems collectively) have been gradually used to conduct unified management of Cloud, Fog, and Edge resources. Container provisioning algorithms are crucial to guaranteeing quality of services (QoS) of such Kubernetes-based systems. However, most existing algorithms focus on placement and migration of fixed number of containers without considering elastic provisioning of containers. Meanwhile, widely used linear-performance-model-based feedback control or fixed-processing-rate-based queuing model on diverse platforms cannot describe the performance of containerized Web systems accurately. Furthermore, a fixed reference point used by existing methods is likely to generate inaccurate output errors incurring great fluctuations encountered with large arrival-rate changes. In this article, a feedback control method is designed based on a combination of varying-processing-rate queuing model and linear-model to provision containers elastically which improves the accuracy of output errors by learning reference models for different arrival rates automatically and mapping output errors from reference models to the queuing model. Our approach is compared with several state-of-art algorithms on a real Kubernetes cluster. Experimental results illustrate that our approach obtains the lowest percentage of service level agreement (SLA) violation (decreasing no less than 8.44 percent) and the second lowest cost.
Pulling Back the Curtain on the Wizards of Oz

The Wizard of Oz method is an increasingly common practice in HCI and CSCW studies as part of iterative design processes for interactive systems. Instead of designing a fully-fledged system, the 'technical work' of key system components is completed by human operators yet presented to study participants as if computed by a machine. However, little is known about how Wizard of Oz studies are interactionally and collaboratively achieved in situ by researchers and participants. By adopting an ethnomethodological perspective, we analyse our use of the method in studies with a voice-controlled vacuum robot and two researchers present. We present data that reveals how such studies are organised and presented to participants and unpack the coordinated orchestration work that unfolds 'behind the scenes' to complete the study. We examine how the researchers attend to participant requests and technical breakdowns, and discuss the performative, collaborative, and methodological nature of their work. We conclude by offering insights from our application of the approach to others in the HCI and CSCW communities for using the method.
The Importance of Body Part Labeling to Enable Enterprise Imaging: A HIMSS-SIIM Enterprise Imaging Community Collaborative White Paper

Abstract In order for enterprise imaging to be successful across a multitude of specialties, systems, and sites, standards are essential to categorize and classify imaging data. The HIMSS-SIIM Enterprise Imaging Community believes that the Digital Imaging Communications in Medicine (DICOM) Anatomic Region Sequence , or its equivalent in other data standards, is a vital data element for this role, when populated with standard coded values. We believe that labeling images with standard Anatomic Region Sequence codes will enhance the user’s ability to consume data, facilitate interoperability, and allow greater control of privacy. Image consumption—when a user views a patient’s images, he or she often wants to see relevant comparison images of the same lesion or anatomic region for the same patient automatically presented. Relevant comparison images may have been acquired from a variety of modalities and specialties. The Anatomic Region Sequence data element provides a basis to allow for efficient comparison in both instances. Interoperability—as patients move between health care systems, it is important to minimize friction for data transfer. Health care providers and facilities need to be able to consume and review the increasingly large and complex volume of data efficiently. The use of Anatomic Region Sequence , or its equivalent, populated with standard values enables seamless interoperability of imaging data regardless of whether images are used within a site or across different sites and systems. Privacy—as more visible light photographs are integrated into electronic systems, it becomes apparent that some images may need to be sequestered. Although additional work is needed to protect sensitive images, standard coded values in Anatomic Region Sequence support the identification of potentially sensitive images, enable facilities to create access control policies, and can be used as an interim surrogate for more sophisticated rule-based or attribute-based access control mechanisms. To satisfy such use cases, the HIMSS-SIIM Enterprise Imaging Community encourages the use of a pre-existing body part ontology. Through this white paper, we will identify potential challenges in employing this standard and provide potential solutions for these challenges.
Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods

Abstract Tissue microarray (TMA) core images are a treasure trove for artificial intelligence applications. However, a common problem of TMAs is multiple sectioning, which can change the content of the intended tissue core and requires re-labelling. Here, we investigate different ensemble methods for colorectal tissue classification using high-throughput TMAs. Hematoxylin and Eosin (H&amp;E) core images of 0.6 mm or 1.0 mm diameter from three international cohorts were extracted from 54 digital slides (n = 15,150 cores). After TMA core extraction and color enhancement, five different flows of independent and ensemble deep learning were applied. Training and testing data with 2144 and 13,006 cores included three classes: tumor, normal or “other” tissue. Ground-truth data were collected from 30 ngTMA slides (n = 8689 cores). A test augmentation is applied to reduce the uncertain prediction. Predictive accuracy of the best method, namely Soft Voting Ensemble of one VGG and one CapsNet models was 0.982, 0.947 and 0.939 for normal, “other” and tumor, which outperformed to independent or ensemble learning with one base-estimator. Our high-accuracy algorithm for colorectal tissue classification in high-throughput TMAs is amenable to images from different institutions, core sizes and stain intensity. It helps to reduce error in TMA core evaluations with previously given labels.
Learning and Evaluating Representations for Deep One-Class Classification

We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build classifiers using generative or discriminative models on learned representations. In particular, we present a novel distribution-augmented contrastive learning method that extends training distributions via data augmentation to obstruct the uniformity of vanilla contrastive representations, yielding more suitable representations for one-class classification. Moreover, we argue that classifiers inspired by the statistical perspective in generative or discriminative models are more effective than existing approaches, such as an average of normality scores from a surrogate classifier. In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks. The framework does not only learn a better representation, but it also permits building one-class classifiers that are more faithful to the target task. Finally, we present visual explanations, confirming that the decision-making process of our deep one-class classifier is intuitive to humans.
A Recommender System for Healthy Food Choices: Building a Hybrid Model for Recipe Recommendations using Big Data Sets

Advances in Big Data analytics and machine learning have offered intangible benefits across many areas of one's health.One such area is a move towards healthier lifestyle choices such as one's diet. Recommender systems apply techniques that can filter information and narrow that information down based on user preferences or user needs and help users choose what information is relevant. Commonly adopted across e-commerce sites, social networking and entertainment industries, recommender systems can also support nutrition-basedhealth management, offering individuals more food options, not only based on one's preferred tastes but also on one's dietary needs and restrictions.This research presents the design, implementation and evaluation of three recommender systems using content-based, collaborative filtering and hybrid recommendation models within the nutrition domain.
Computing Professionals for Social Responsibility: The Past, Present and Future Values of Participatory Design

Values play a central role in technology design. But beyond acknowledging the politics of technology, questions remain around where those values are coming from, which values we need, and how they play out and shape the socio-technical systems we create. New challenges such as the climate crisis and societal polarization call for technologists to become part of the public and political arena. This results in a new sense of responsibility, but the closing of CPSR, the Computing Professionals for Social Responsibility, has left a gap. Today, across tech workers, academics and computing professionals, there is a renewed sense of urgency for engaging the public and politics to change course in how computing shapes society.
Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models.

The field of language modelling has been largely dominated by autoregressive models, for which sampling is inherently difficult to parallelize. This paper introduces two new classes of generative models for categorical data such as language or image segmentation: Argmax Flows and Multinomial Diffusion. Argmax Flows are defined by a composition of a continuous distribution (such as a normalizing flow), and an argmax function. To optimize this model, we learn a probabilistic inverse for the argmax that lifts the categorical data to a continuous space. Multinomial Diffusion gradually adds categorical noise in a diffusion process, for which the generative denoising process is learned. We demonstrate that our models perform competitively on language modelling and modelling of image segmentation maps.
A weighted randomized Kaczmarz method for solving linear systems

The Kaczmarz method for solving a linear system $Ax = b$ interprets such a system as a collection of equations $\left \langle a_i, x\right \rangle = b_i$, where $a_i$ is the $i$-th row of $A$. It then picks such an equation and corrects $x_{k+1} = x_k + \lambda a_i$ where $\lambda$ is chosen so that the $i$-th equation is satisfied. Convergence rates are difficult to establish. Strohmer & Vershynin established that if the order of equations is chosen randomly (with likelihood proportional to the size of $\|a_i\|^2_{\ell ^2}$), then $\mathbb {E}~ \|x_k - x\|_{\ell ^2}$ converges exponentially. We prove that if the $i$-th row is selected with likelihood proportional to $\left |\left \langle a_i, x_k \right \rangle - b_i\right |^{p}$, where $0<p<\infty$, then $\mathbb {E}~\|x_k - x\|_{\ell ^2}$ converges faster than the purely random method. As $p \rightarrow \infty$, the method de-randomizes and explains, among other things, why the maximal correction method works well.