Reviewerly/exHarmony huggingface dataset:
Context-Aware Data Association for Multi-Inhabitant Sensor-Based Activity Recognition

Recognizing the activities of daily living (ADLs) in multi-inhabitant settings is a challenging task. One of the major challenges is the so-called data association problem: how to assign to each user the environmental sensor events that he/she actually triggered? In this paper, we tackle this problem with a contextaware approach. Each user in the home wears a smartwatch, which is used to gather several high-level context information, like the location in the home (thanks to a micro-localization infrastructure) and the posture (e.g., sitting or standing). Context data is used to associate sensor events to the users which more likely triggered them. We show the impact of context reasoning in our framework on a dataset where up to 4 subjects perform ADLs at the same time (collaboratively or individually). We also report our experience and the lessons learned in deploying a running prototype of our method.
Data‐Driven Facial Simulation

Abstract In Visual Effects, the creation of realistic facial performances is still a challenge that the industry is trying to overcome. Blendshape deformation is used to reproduce the action of different groups of muscles, which produces realistic static results. However, this is not sufficient to generate believable and detailed facial performances of animated digital characters. To increase the realism of facial performances, it is possible to enhance standard facial rigs using physical simulation approaches. However, setting up a simulation rig and controlling material properties according to the performance is not an easy task and could take a lot of time and iterations to get it right. We present a workflow that allows us to generate an activation map for the fibres of a set of superficial patches we call pseudo‐muscles . The pseudo‐muscles are automatically identified using k‐means to cluster the data from the blendshape targets in the animation rig and compute the direction of their contraction (direction of the pseudo‐muscle fibres). We use an Extended Position–Based Dynamics solver to add physical simulation to the facial animation, controlling the behaviour of simulation through the activation map. We show the results achieved using the proposed solution on two digital humans and one fantastic cartoon character, demonstrating that the identified pseudo‐muscles approximate facial anatomy and the simulation properties are properly controlled, increasing the realism while preserving the work of animators.
Research on the application of computer data mining technology in the era of big data

Abstract With the wide application of computer technology, the position of computer technology in various fields is gradually higher. Accordingly, the amount of data generated by people’s activities on the Internet is also increasing. With the continuous progress of information technology, the data that originally belonged to chicken ribs in people’s eyes has more value. At this time, the era of big data is coming. Firstly, this paper analyzes the main methods and advantages of data mining. Then, this paper analyzes the function of data mining technology. Finally, the main application of data mining technology is proposed.
Load Balancing for Sharded Blockchains

Sharding is an approach to designing a highly scalable blockchain. A sharded blockchain achieves parallelism by dividing consensus nodes (validators) into groups called shards and making them process different transactions in each shard. In this paper, we economically analyze users' behavior on sharded blockchains and identify a phenomenon that users' accounts and smart contracts eventually get concentrated in a few shards, making shard loads unfair. This phenomenon leads to bad user experiences, such as delays in transaction inclusions and increased transaction fees. To solve the above problem, we propose a load balancing framework in sharded blockchains in which accounts and contracts are frequently reassigned to shards to reduce the difference of loads between shards. We formulate the contract reassignment as an optimization problem and present the algorithm to solve it. Further, we apply the framework to an existing sharding design (Ethereum 2.0) and modify the protocol to do load balancing. Finally, we simulate the protocol and observe smaller transaction delays and fees.
A Panoramic View of Content-based Medical Image Retrieval system

Content-Based Image Retrieval (CBIR) system for medical applications is one of the trending research fields in computer vision and Digital image processing over the last 20 years. Content-Based Image Retrieval is a technique to perform retrieval of the images from a large database. The CBIR technique is used in different domains such as medical images, weather forecasting and remote sensing. But, the application of CBIR system in the medical field is of utmost importance as it can help in the diagnosis and treatment of the diseases by studying the medical images. It allows indexing of images, retrieval of images and understanding of large collections of image data. This paper presents a review of the already existing Content-based Medical Image Retrieval (CBMIR) systems and techniques used mainly for the feature selection and classification used in these systems.
A Comparative Study and enhancement of classification techniques using Principal Component Analysis for credit card dataset

The following research reveals the significance of modified classification in estimating new trends. Rigorous evaluation of different classification algorithms viz. Logistic Regression, Decision Tree, K-Nearest Neighbor and Naive Bayesian is explored in this paper. These findings forecast the finest techniques for discovery of potential defaulters which can be adapted by banking institutions. Our main motive is to compare the performance measures between original dataset and original dataset on which principal component is applied. The reason to use the principal component was to evaluate its impact on the performance of the algorithms used while dealing with the dataset. Different algorithms can be compared on the basis of various criterions such as Accuracy, Precision, Fl-Score, Recall, ROC. Successful contrast between these attributes would yields a efficient model for the given dataset. Logistic regression is then found to be the most efficient method for this particular dataset.
Trans-Causalizing NAT-Modeled Bayesian Networks

Conditional independence encoded in Bayesian networks (BNs) avoids combinatorial explosion on the number of variables. However, BNs are still subject to exponential growth of space and inference time on the number of causes per effect variable in conditional probability tables. A number of space-efficient local models exist that allow efficient encoding of dependency between an effect and its causes, and can also be exploited for improved inference efficiency. We focus on the Nonimpeding Noisy-AND Tree (NIN-AND Tree or NAT) models because of multiple merits. We present a novel framework, trans-causalization of NAT-modeled BNs, by which causal independence embedded in NAT models is exploited for more efficient inference. We show that trans-causalization is exact and yields polynomial space complexity. We demonstrate significant efficiency gain on inference based on lazy propagation and sum-product networks.
Double-stream atrous network for shadow detection

Recently, shadow detectors based on Deep Convolutional Neural Networks (DCNN) have lifted the detection performances to a new height. However, the correct detection of mild and very dark shadows remains to be a difficult task. In this work, we propose a new model called Double-stream Atrous Network (DSAN) for shadow detection. It has a double-streams framework: the pooling stream extracts high-level features and the residual stream incorporates low-level features with high-level feature maps from the pooling stream in each single step. We also design new modules such as Atrous Convolution Module (ACM), Multi-layer Atrous Pooling Module (MLAPM), and Cross-stream Residual Module (CSRM) to extract shadow features effectively. On two shadow datasets, our DSAN outperforms several popular shadow detectors based on DCNN.
Multi‐party key generation protocol for the identity‐based signature scheme in the IEEE P1363 standard for public key cryptography

Identity-based cryptography (IBC) is considered as a promising mechanism in the Internet of Things and ad-hoc networks, providing lightweight authentication and powerful access control. However, it suffers from two inherent problems, i.e. key escrow and the requirement of a secure channel, which are not always good properties in many realistic scenarios. Thus, an efficient key issuing protocol in a distributed setting without the assumption of the secure channel is needed. In this study, the authors give special attention to the IBC standardised in IEEE P1363 and design a multi-party setup and key issuing protocol for it. Their protocol is proven to be malicious secure by simulation under weaker assumptions. Contrast to prior works that rely on a trusted party for key distribution or the strong assumption of a secure channel, they provide the first practical solution for the distributed architectures.
Review of Optical Devanagari Character Recognition Techniques

Optical character recognition techniques are capable of automatic translating of document images into equivalent character codes, so it helps in saving human energy as well as cost. These techniques can play a key role to improve or enhance the interaction between human and machine in many applications such as postal automation, signature verification, recognition of city names and automatic bank cheque processing/reading. This paper gives a review of various techniques explored for Devanagari word/text and isolated character recognition in the past few years. Different challenges to optical character recognition are also presented in this work. In the end, practical aspects towards the development of a robust optical character recognition system has been discussed along with directions for future research.
A new index for information gain in the Bayesian framework⁎

In data-driven dynamical modeling, precise estimation of the parameters of large models from limited data has been considered a challenging task. The precision of the parameter estimates is highly dependent upon the information contained in the data; Loss of practical identifiability and sloppiness in the model structure are major challenges in estimating parameters precisely and closely related to the information contained in the data. Therefore, quantifying information is an important step in data-driven modeling. Quantifying information is a well-studied problem in the frequentist approach, where Fisher Information is one of the widely used metrics. However, Fisher Information computed via maximum likelihood estimation cannot accommodate any known prior knowledge about the parameters. Prior knowledge of the parameters along with informative experiments will improve the precision of the estimates. Bayesian estimation accommodates prior information in the form of a p.d.f. There has been very little work in the literature for quantifying information in the Bayesian framework. In this work, we introduce a new method for estimating information gain in the Bayesian framework using what is known as the Bhattacharyya coefficient. It is seen that the bounds of the coefficient have an insightful interpretation naturally in terms of information gain on the parameter of interest. We also demonstrate using case studies that the information gain of each parameter is an indication of loss of practical identifiability and sloppy parameters. It is also shown that the proposed information gain can be used as a model selection tool in black-box identification.
Irina: Accelerating DNN Inference with Efficient Online Scheduling

DNN inference is becoming prevalent for many real-world applications. Current machine learning frameworks usually schedule inference tasks with the goal of optimizing throughput under predictable workloads and task arrival patterns. Yet, inference workloads are becoming more dynamic with bursty queries generated by various video analytics pipelines which run expensive inference only on a fraction of video frames. Thus it is imperative to optimize the completion time of these unpredictable queries and improve customer experience.
Revenue-Sharing based Computation-Resource Allocation for Mobile Blockchain

In this paper, we investigate the revenue-sharing based computation-resource allocation for mobile Blockchain, in which conventional mobile devices (i.e., the edge-computing users, EUs) can acquire computation-resources from the edge-server (ES) for increasing their chances of winning the mining game in Blockchain. Meanwhile, as a compensation to the ES, the EUs adopt the revenue-sharing mechanism by sharing parts of their respective rewards to the ES. We formulate a joint optimization of the ES's computation-resource allocation to the EUs and the EUs' revenue-sharing to the ES, with the objective of maximizing the system-reward of all EUs and the ES. Despite the non-convexity of the joint optimization problem, we propose an algorithm based on the cyclic block coordinate descent (CBCD) to solve the problem. Our algorithm decouples the original problem into two subproblems and alternatively optimizes the EUs' revenue-sharing and ES's computation-resource allocation until reaching convergence. For each subproblem, we also propose an efficient algorithm for solving it. Numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms, as well as the performance advantage of our proposed revenue-sharing based computation-resource allocation for mobile Blockchain.
A Comparative Evaluation of Geospatial Semantic Web Frameworks for Cultural Heritage

Recently, many Resource Description Framework (RDF) data generation tools have been developed to convert geospatial and non-geospatial data into RDF data. Furthermore, there are several interlinking frameworks that find semantically equivalent geospatial resources in related RDF data sources. However, many existing Linked Open Data sources are currently sparsely interlinked. Also, many RDF generation and interlinking frameworks require a solid knowledge of Semantic Web and Geospatial Semantic Web concepts to successfully deploy them. This article comparatively evaluates features and functionality of the current state-of-the-art geospatial RDF generation tools and interlinking frameworks. This evaluation is specifically performed for cultural heritage researchers and professionals who have limited expertise in computer programming. Hence, a set of criteria has been defined to facilitate the selection of tools and frameworks. In addition, the article provides a methodology to generate geospatial cultural heritage RDF data and to interlink it with the related RDF data. This methodology uses a CIDOC Conceptual Reference Model (CRM) ontology and interlinks the RDF data with DBpedia. Although this methodology has been developed for cultural heritage researchers and professionals, it may also be used by other domain professionals.
The effects of transition style for collaborative view sharing in immersive Virtual Reality

New low-cost Virtual Reality (VR) technology is enabling large numbers of engineers and designers to be outfitted with VR tools. This, in turn, is making feasible new tools such as immersive, collaborative, design environments that reflect the collaborative environment of drafting tables while still providing all the power of modern, computer-based, design tools. However, before such an environment can be fully realized, basic issues surrounding collaboration in VR such as cybersickness and disorientation must be more fully addressed. This work presents the results of an experimental study conducted to characterize the trade-offs between different styles of transitioning collaborators to a shared view in an immersive virtual environment. The experimental task consisted of asking participants to identify in which quadrant of the car specific components were located. Quadrant identification was performed after a participant’s virtual location was transitioned to a shared view location focused on the component to identify. The study was conducted with 45 participants who provided 1890 transition response datapoints. Analysis of these results suggests that, in many cases, collaborators are willing to experience some mild cybersickness in order to reduce their disorientation and improve both confidence and accuracy of component location identification. The results also support the recommendation that the best practice is to provide support for multiple transition styles and allow collaborators to select their preference.
Improving covariance-regularized discriminant analysis for EHR-based predictive analytics of diseases

Linear Discriminant Analysis (LDA) is a well-known technique for feature extraction and dimension reduction. The performance of classical LDA however, significantly degrades on the High Dimension Low Sample Size (HDLSS) data for the ill-posed inverse problem. Existing approaches for HDLSS data classification typically assume the data in question are with Gaussian distribution and deal the HDLSS classification problem with regularization. However, these assumptions are too strict to hold in many emerging real-life applications, such as enabling personalized predictive analysis using Electronic Health Records (EHRs) data collected from an extremely limited number of patients who have been diagnosed with or without the target disease for prediction. In this paper, we revised the problem of predictive analysis of disease using personal EHR data and LDA classifier. To fill the gap, in this paper, we first studied an analytical model that understands the accuracy of LDA for classifying data with arbitrary distribution. The model gives a theoretical upper bound of LDA error rate that is controlled by two factors: (1) the statistical convergence rate of (inverse) covariance matrix estimators and (2) the divergence of the training/testing datasets to fitted distributions. To this end, we could lower the error rate by balancing the two factors for better classification performance. Hereby, we further proposed a novel LDA classifier De-Sparse that leverages De-sparsified Graphical Lasso to improve the estimation of LDA, which outperforms state-of-the-art LDA approaches developed for HDLSS data. Such advances and effectiveness are further demonstrated by both theoretical analysis and extensive experiments on EHR datasets https://www.overleaf.com/project/5d2728c718f6ff3b2bcf5991 .
Evaluating asynchronous Schwarz solvers on GPUs

With the commencement of the exascale computing era, we realize that the majority of the leadership supercomputers are heterogeneous and massively parallel. Even a single node can contain multiple co-processors such as GPUs and multiple CPU cores. For example, ORNL’s Summit accumulates six NVIDIA Tesla V100 GPUs and 42 IBM Power9 cores on each node. Synchronizing across compute resources of multiple nodes can be prohibitively expensive. Hence, it is necessary to develop and study asynchronous algorithms that circumvent this issue of bulk-synchronous computing. In this study, we examine the asynchronous version of the abstract Restricted Additive Schwarz method as a solver. We do not explicitly synchronize, but allow the communication between the sub-domains to be completely asynchronous, thereby removing the bulk synchronous nature of the algorithm. We accomplish this by using the one-sided Remote Memory Access (RMA) functions of the MPI standard. We study the benefits of using such an asynchronous solver over its synchronous counterpart. We also study the communication patterns governed by the partitioning and the overlap between the sub-domains on the global solver. Finally, we show that this concept can render attractive performance benefits over the synchronous counterparts even for a well-balanced problem.
A novel second-order cone programming support vector machine model for binary data classification

The support vector machine is a classification approach in machine learning. The second-order cone optimization formulation for the soft-margin support vector machine can ensure that the misclassification rate of data points do not exceed a given value. In this paper, a novel second-order cone programming formulation is proposed for the soft-margin support vector machine. The novel formulation uses the l2-norm and two margin variables associated with each class to maximize the margin. Two regularization parameters α and β are introduced to control the trade-off between the maximization of margin variables. Numerical results illustrate that the proposed second-order cone programming formulation for the soft-margin support vector machine has a better prediction performance and robustness than other second-order cone programming support vector machine models used in this article for comparision.
Integrated encryption in dynamic arithmetic compression

A variant of adaptive arithmetic coding is proposed, adding cryptographic features to this classical compression method. The idea is to perform the updates of the frequency tables for characters of the underlying alphabet selectively, according to some randomly chosen secret key K. We give empirical evidence that with reasonably chosen parameters, the compression performance is not hurt, and discuss also aspects of how to improve the security of the system being used as an encryption method. To keep the paper self-contained, we add a short description of the arithmetic coding algorithm that is necessary to understand the details of the new suggested method.
Rotation-invariant NCC for 2D color matching of arbitrary shaped fragments of a fresco

The proposed RINCCAS method, an abbreviation of the paper title, was originally developed to participate in DAFNE (Digital Anastylosis of Frescos challeNgE) race, June-July 2019. The method consists of two phases. Phase 1 extends the classic Normalized Cross Correlation (NCC) for template matching of arbitrary curvilinear 2D shapes of fragments that are assumed belonging to a fresco as perceived in a photograph. For this purpose, each fragment is approximated by one (up to several, but not overlapping) Maximal & Axes-Collinear Inner Rectangles (MACIRs). The extension also includes rotation invariance and vector compatibility of NCC in respect to the three (RGB) color channels. The high positioning accuracy makes it possible to identify eventual/existing spurious fragments in Phase 2 of RINCCAS, as follows − by HSV scheme for color differences, and by accurate recognition of overlaps among the fragments. The first phase is ‘log-cubically’ complex in speed, estimated on the average size of the MACIRs of the fragments. For some DAFNE tasks, the 1st phase of RINCCAS requires high computational resources (HPC), while a conventional PC is sufficient for its 2nd phase, even in the case of multiple interactive optimization of the ratio between true and spurious fragments.