Reviewerly/exHarmony huggingface dataset:
Parameter Measurement of $M$ -Ary PSK Signals With Finite Rate of Innovation

Measurement of parameters of M -ary phase shift keying (MPSK) signals is significant for radar and communication systems. Traditional parameter measurement methods require very high sampling rates and heavy processing to estimate the key parameters of amplitude, carrier frequency, phases, and discontinuity locations. We propose a multichannel cooperative sampling system using the finite rate of innovation sampling theorem to address this problem. In our two-channel system, the main channel estimates the MPSK amplitude and carrier frequency using a nonlinear process and time-staggered sampling structure, while the cooperative channel determines phases and discontinuity locations with an annihilating filter using the main channel's estimated carrier frequency. Our system enables recovery of these parameters from as few as 2K+6 samples from MPSK signals with K segments. We also present a hardware platform implementing the structure. The simulation and experimental results demonstrate the effectiveness and robustness of the proposed method.
Cloud based health monitoring and abnormality detection using smart mirror

Background/Objective: To provide an efficient predictive technique to foresee future workload as well as to handle the resources efficiently by performing hybrid auto scaling for Cloud applications.Cloud applications might expertise completely different workload at different times, automatic provisioning has to work with efficiency at any point of time.Auto scaling is a feature of cloud computing that potentially scale the resources in line on demand.Considering this expectation, they are generally categorized into Reactive scaling which adds or reduces resources based on a fixed threshold value.The predictive scaling is used provide necessary scaling actions beforehand.Methods/Statistical Analysis: To perform the hybrid auto scaling (reactive plus predictive auto scaling), a time series technique should be used.Auto-regressive Moving Average (ARMA) model, the Exponential Smoothing (ES) model, the Autoregressive model (AR), the Moving Average model (MA) and the Trend-Adjusted Exponential Smoothing (TAES), Auto Regressive Integrated Moving Average (ARIMA) Time-series model, Naïve bayes algorithm, Recurrent Neural Network-Long Short Term Memory (RNN-LSTM), Independent Recurrent Neural Network (IndRNN) are time series techniques used to foresee the future workload.To find the effectiveness of predictive techniques, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) performance metrics are evaluated.Findings: Based on the evaluation, IndRNN gives the minimum error rate.IndRNN is used to predict the future resource requisites in order to ascertain adequate resource are available ahead of time.Application: The predicted result from IndRNN method is integrated on private cloud to autoscale the resources for cloud applications.
An Empirical Study on Combining Source Selection and Transfer Learning for Cross-Project Defect Prediction

Software defect prediction (SDP) can help software developers and quality assurance personnel to effectively predict software fault proneness. Recently, researchers have proposed a lot of methods to improve the predicting results, especially under a within-project defect prediction (WPDP) setting. However, cross-project defect prediction (CPDP) is difficult because of the data distribution difference between source and target projects. Transfer learning model has been proven that it can effectively reduce the data distribution difference. By the intuition, if the better source is selected, we can get better prediction performance based on transfer learning model. In this paper, we conducted an empirical study on source selection including feature selection and source project selection for CPDP, and then combined source selection with popular transfer learning model TCA+ in CPDP. Finally, the result shows that the combining technique MZTCA+ can effectively improve the state-of-the art CPDP models, such as TCA+, LT, Dycom, TDS.
User Requirement Solicitation for an Information Retrieval System Applied to Dutch Grey Literature in the Archaeology Domain

In this paper, we present the results of user requirement solicitation for a search system of grey literature in archaeology, specifically Dutch excavation reports. This search system uses Named Entity Recognition and Information Retrieval techniques to create an effective and effortless search experience. Specifically, we used Conditional Random Fields to identify entities, with an average accuracy of 56%. This is a baseline result, and we identified many possibilities for improvement. These entities were indexed in ElasticSearch and a user interface was developed on top of the index. This proof of concept was used in user requirement solicitation and evaluation with a group of end users. Feedback from this group indicated that there is a dire need for such a system, and that the first results are promising.
Efficient Real-Time Integrity Auditing With Privacy-Preserving Arbitration for Images in Cloud Storage System

Cloud storage provides an inexpensive and effective means for the storage and management of images, which in turn occupy a huge proportion and are usually stored in an archived mode.Considering the security and efficiency requirements of cloud images, an efficient real-time integrity audit scheme is in urgent need.However, existing solutions cannot be directly applied since they do not take the characteristics of cloud images into account and thus take enormous computations, communications, and storage to generate, transfer, and store authentication data.Moreover, the result of auditing cannot be used as evidence to prove the guilt of cloud service provider since the verifier whom is specified by the client may hide its misbehavior.Reversible watermarking is a potential way to achieve lightweight real-time audit for cloud images without introducing permanent distortion.Nevertheless, existing algorithms cannot provide stable capacity for authentication data of fixed length.In addition, it entails security problems once it is used to solve the fairness problem.This paper proposes an efficient real-time integrity audit scheme specific to cloud images with fair arbitration support.The scheme is based on the presented adaptive reversible watermarking algorithm which provides a fixed embedding capacity for images to embed authentication data.To address fairness problem under the proposed mechanism, we adopt Diffie-Hellman key exchange scheme to design a new challenge-response protocol under the established simplified consensus mechanism, so that replay attack resistance and privacy-preserving fair arbitration are achieved.The security analysis and performance evaluation show that the proposed scheme has provable security and greatly enhanced efficiency comparing with the state of the art.
H$^2$—A Hybrid Heterogeneity Aware Resource Orchestrator for Cloud Platforms

Cloud computing services are provided through datacenters that consume significant amount of energy and contribute to global warming. Largely, resources are unused due to low service demand, in principal, wasting energy and resources. Energy could be saved through efficient resource management systems; that is of utmost importance, particularly, when service providers use various kinds of sand-boxing technologies, such as bare-metal, virtual machine and/or containers to provide quality services to users. The lack of a single resource manager creates questions on infrastructure energy efficiency and service performance. In this paper, we investigate the possibility of using a single resource manager to manage datacenter heterogeneous resources, energy, and performance (hence cost) efficiently. Our evaluation suggests that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 30.47% energy could be saved at cost of only 2.14% loss in performance through using a single scheduler instead of multiple schedulers.
A new matrix form to generate all 3 × 3 involutory MDS matrices over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>

In this paper, we propose a new matrix form to generate all 3×3 involutory and MDS matrices over F2m and prove that the number of all 3×3 involutory and MDS matrices over F2m is (2m−1)2⋅(2m−2)⋅(2m−4), where m>2. Moreover, we give 3×3 involutory and MDS matrices over F23, F24 and F28 defined by the irreducible polynomials x3+x+1, x4+x+1 and x8+x7+x6+x+1, respectively, by considering the minimum XOR count, which is a metric used in the estimation of hardware implementation cost. Finally, we provide the maximum number of 1s in 3×3 involutory MDS matrices.
Application of Statistical Data Processing for Solving the Problem of Face Recognition by Using Principal Components Analysis Method

The system of face detection and recognition in the photo and video streams have been considered in this paper. Firstly, we took a number of images of each person to create our own database by using webcam. In these images, Viola-Jones algorithm is used for faces detection and then these images have been standardized and obtained our own database. The programs are implemented for modelling face recognition experiments by using the principal component analysis (PCA) method in MATLAB software environment. In this article, the statistical data processing method is used to improve the efficiency of facial recognition by using the principal components analysis. As a result of these experiments, more accurate results were obtained and the comparison results of face recognition before and after using the statistical data processing method are shown in this article. Experiments with other databases have confirmed the efficiency of statistical data processing.
Detecting Feature-Interaction Symptoms in Automotive Software using Lightweight Analysis

Modern automotive software systems are large, complex, and feature rich; they can contain over 100 million lines of code, comprising hundreds of features distributed across multiple electronic control units (ECUs), all operating in parallel and communicating over a CAN bus. Because they are safety-critical systems, the problem of possible Feature Interactions (FIs) must be addressed seriously; however, traditional detection approaches using dynamic analyses are unlikely to scale to the size of these systems. We are investigating an approach that detects static source-code patterns that are symptomatic of FIs. The tools report Feature-Interaction warnings, which can be investigated further by engineers to determine if they represent true FIs and if those FIs are problematic. In this paper, we present our preliminary toolchain for FI detection. First, we extract a collection of static “facts” from the source code, such as function calls, variable assignments, and messages between features. Next, we perform relational algebra transformations on this factbase to infer additional “facts” that represent more complicated design information about the code, such as potential information flows and data dependencies; then, the full collection of “facts” is matched against a curated set of patterns for FI symptoms. We present a set of five patterns for FIs in automotive software as well a case study in which we applied our tools to the Autonomoose autonomous-driving software, developed at the University of Waterloo. Our approach identified 1,444 possible FIs in this codebase, of which 10% were classified as being probable interactions worthy of further investigation.
ONEDAY Shoes

Personalization of shoes is of increasing importance to designers, design researchers, and manufacturers as mass customization progresses towards ultra personalized product service systems. Many attempts have been made to design co-creation platforms that allow end users to personalize their own shoes. Those co-creation platforms primarily concentrate on color selection. This research takes a different approach and designs a toolkit for maker-oriented users to co-manufacture their own shoes. The toolkit was designed in different levels and deployed to makers via crowdsharing worldwide. Backers were surveyed before deployment and interviewed after two years to understand personalization over a larger amount of time with the research product. We find that users who have greater bespoke tools and materials in their toolkits are more likely to personalize their shoes while co-manufacturing. The research provides guidelines for researchers and designers creating toolkits, designing personalization product service systems/configurators and engaging in tangible bespoke processes.
A new approach to practical function-private inner product encryption

Functional encryption (FE) is a novel paradigm supporting restricted decryption keys for a function f that allow one to learn f(xj) from the encryptions of messages xj. A natural and practical security requirement for FE is to keep not only messages x1,…,xq, but also functions f1,…fq, excluding inevitable information {fi(xj)}i,j∈[q], confidential from encryption and decryption keys for any polynomial a priori unknown number q, where fi and xj are adaptively chosen by adversaries. This security requirement is called full function privacy. In this paper, we focus on function-private FE for inner product functionality in the private key setting (referred to as inner product encryption (IPE)). To the best of our knowledge, only two approaches have been proposed for fully function-private IPE schemes in the private key setting. One is to employ a general transformation from (non-function-private) FE for general circuits (Brakerski and Segev, 2015 [20]). This approach requires computationally intensive cryptography tools, such as indistinguishability obfuscation (for non-function-private FE for general circuits), meaning it is inefficient. The other approach is more practical. It directly constructs an IPE scheme by using dual-pairing vector spaces (DPVS) (Bishop et al., 2015 [21], Datta et al., 2016 [22], and Tomida et al., 2016 [32]). We present a novel approach for practical function-private IPE schemes that does not employ DPVS, but instead uses generalizations of the Brakerski-Segev transformation. Our generalizations of the Brakerski-Segev transformation are easily combinable with existing (non-function-private) IPE schemes, as well as (non-function-private) FE schemes for general circuits on several levels of security. The proposed IPE scheme achieves better performance compared to the schemes proposed by Bishop et al. and Datta et al. The proposed IPE scheme preserves the same security level as previous schemes under the same complexity assumptions. Compared to the scheme proposed by Tomida et al., our scheme has comparable performance in terms of the size of ciphertext and decryption keys, but superior performance in terms of the size of the master key.
Designing an Efficient Cloud Management Architecture for Sustainable Online Lifelong Education

As online learning and e-learning are prevalent and widely used in education, it is important to design an efficient and reliable information system for storing learning data and providing on-demand learning services. In this paper, we design a cloud-based information system architecture for online lifelong education. Since a cloud system is based on virtualization technology, we propose a virtual resource management scheme—virtual machine allocation and monitoring nodes assignment. With the proposed cloud-based architecture, we can build and operate an e-learning information system for online lifelong education, which requires efficiency, reliability, and persistence. The evaluation results show that our proposed method can deal with more tasks for e-learning (requests for learning management system (LMS) navigations, text learning contents, text and media learning contents, and video learning contents) while introducing 48× fewer service level agreement (SLA) violations than the existing method.
Data Partitioning Scheme for Efficient Distributed RDF Querying Using Apache Spark

The rapid growth of semantic data in the form of Resource Description Framework (RDF) triples demands an efficient, scalable, and distributed storage and parallel processing strategies along with high availability and fault tolerance for its management and reuse. There are three open issues with distributed RDF data management systems that are not well addressed altogether in existing work. First is the querying efficiency, second, solutions are optimized for certain types of query patterns and don't necessarily work well for all types of query patterns, and the third is concerned with reducing pre-processing and data loading times. To address these issues, we propose a relational partitioning scheme called Subset Property Table (SPT) for RDF data that further partitions the existing Property Table approach into subsets of tables to minimize query input and join operation. We combine SPT with another existing model Vertical Partitioning (VP) for storing RDF datasets and demonstrate that our proposed combined (SPT + VP) approach outperforms state-of-the-art systems based on in-memory processing engine in a distributed environment.
Design as a Discipline for Postdigital Learning and Teaching: Bricolage and Actor-Network Theory

Digital technologies for learning and teaching have promised much in higher education (HE). There has become, however, a dualism between digital and non-digital and a technological determinism which in some cases promotes digital technologies as being innately superior to the non-digital. There is pressure on universities to provide learning and teaching in new ways in the face of regulation, as well as increased numbers and diversity of students. The postdigital perspective allows for the appropriate approaches and tools to be used. Design for learning and teaching in HE has developed interventions which promote use of digital resources, but for some have not yet met the promise of ‘enhancing’ learning. Moving outside of education, approaches from design as a discipline are sketched out, including design thinking and the epistemology of design. All of these show how designers (in general) go about their work. How designs come about can be analysed by using the framework of people (epistemology), processes (praxeology) and products (phenomenology) in design. Actor-network theory is used as an approach across each stage of this framework, and those designing in HE are encouraged to be bricoleurs, using a variety of tools for the job at hand and to think of the designs as assemblages. The ideas described here are useful for the practices of those involved in the design of learning and teaching.
Deep Review Sharing

Review-Based Software Improvement (RBSI for short) has drawn increasing research attentions in recent years. Relevant efforts focus on how to leverage the underlying information within reviews to obtain a better guidance for further updating. However, few efforts consider the Projects Without sufficient Reviews (PWR for short). Actually, PWR dominates the software projects, and the lack of PWR-based RBSI research severely blocks the improvement of certain software. In this paper, we make the first attempt to pave the road. Our goal is to establish a generic framework for sharing suitable and informative reviews to arbitrary PWR. To achieve this goal, we exploit techniques of code clone detection and review ranking. In order to improve the sharing precision, we introduce Convolutional Neural Network (CNN) into our clone detection, and design a novel CNN based clone searching module for our sharing system. Meanwhile, we adopt a heuristic filtering strategy to reduce the sharing time cost. We implement a prototype review sharing system RSharer and collect 72,440 code-review pairs as our ground knowledge. Empirical experiments on hundreds of real code fragments verify the effectiveness of RSharer. RSharer also achieves positive response and evaluation by expert developers.
Detection method for auto guide vehicle’s walking deviation based on image thinning and Hough transform

Auto guide vehicle’s position deviation always appears in its walking process. Current edge approaches applied in the visual navigation field are difficult to meet the high-level requirements of complex environment in factories since they are easy to be affected by noise, which results in low measurement accuracy and unsteadiness. In order to avoid the defects of edge detection algorithm, an improved detection method based on image thinning and Hough transform is proposed to solve the problem of auto guide vehicle’s walking deviation. First, the image of lane line is preprocessed with gray processing, threshold segmentation, and mathematical morphology, and then, the refinement algorithm is employed to obtain the skeleton of the lane line, combined with Hough detection and line fitting, the equation of the guide line is generated, and finally, the value of auto guide vehicle’s walking deviation can be calculated. The experimental results show that the methodology we proposed can deal with non-ideal factors of the actual environment such as bright area, path breaks, and clutters on road, and extract the parameters of the guide line effectively, after which the value of auto guide vehicle’s walking deviation is obtained. This method is proved to be feasible for auto guide vehicle in indoor environment for visual navigation.
A Visual Approach to Query Formulation for Systematic Search

Knowledge workers (such as healthcare information professionals, patent agents and legal researchers) need to create and execute search strategies that are accurate, repeatable and transparent. The traditional solution offered by most database vendors is to use proprietary line-by-line 'query builders'. However, these offer limited support for error checking or query optimisation, and their output can often be compromised by errors and inefficiencies. Using the healthcare domain for context, we demonstrate a new approach to search strategy formulation in which concepts are expressed as objects on a two-dimensional canvas, and relationships are articulated using direct manipulation. This approach eliminates many sources of syntactic error, makes the query semantics more transparent, and offers new ways to optimise, save and share search strategies and best practices