Reviewerly/exHarmony huggingface dataset:
Energy-Efficient Solution for Smart Lighting Through IoT

Building lighting is an important energy consumer in the building sector and moreover, it is linked to building comfort. The convergence points between energy efficiency and visual comfort in buildings can be achieved in different ways. In this paper, we explore the use of energy-efficient solutions for smart lighting through IoT. To test out our approach we have developed an application that was deployed in a real residential building. On the physical level, the communication was made through the KNX / TP protocol, while on the higher level, the communication was made through the KNX / IP protocol. The solution shows real potential in terms of increasing the energy efficiency of the building and integration with other digital technologies.
Impact of the Cyber Hygiene Intelligence and Performance (CHIP) Interface on Cyber Situation Awareness and Cyber Hygiene

A common theme across cybersecurity solutions is a lack of transparency for end-users. Our prototype, Cyber Hygiene Intelligence & Performance (CHIP), was purposefully designed to improve end users’ cyber hygiene and cyber situation awareness. We begin by addressing current cybersecurity training solutions, their inability to continuously impact user cyber hygiene and cyber situation awareness (CSA), and how end users’ needs for transparency are overlooked. We then illustrate the major stages of our design process for the medium-fidelity CHIP prototype, including defining, ideation, prototyping, and analysis. For each design stage, we describe our methodologies, major decision points, design considerations, and outcomes. We then highlight our between-groups survey experiment that measured cyber hygiene and CSA for an experimental group, who received CHIP notifications while completing web-based tasks, compared to a control group, who completed the same tasks without any notifications. Our findings show promise for a web application-based solution like CHIP to increase cyber hygiene and CSA for specific cyber threats.
Improving the classification of phishing websites using a hybrid algorithm

Abstract In this article, a hybrid algorithm has been proposed for the identification of phishing and legitimate websites. The dataset may have an imbalanced class distribution and may consist of irrelevant features. Therefore, in the data preprocessing, the adaptive synthetic sampling approach has been used to handle the imbalanced data. Irrelevant or redundant features are removed from the balanced data using the proposed binary version of Rao algorithms. The S‐shaped and V‐shaped transfer functions are applied for mapping continuous search space to discrete search space. Also, the results of these S‐shaped and V‐shaped transfer functions are analyzed for proposed algorithms. The performance is improved by optimizing the value of the k parameter in the kNN classifier. The dataset used in this article has been taken from the UCI machine‐learning repository. The performance of the proposed approach has been evaluated using the polygon area metric. The obtained classification accuracy is 97.044%. A comparison of the proposed hybrid algorithm with the other state‐of‐the‐art techniques is also made for validation. Moreover, the proposed approach has been compared with seven metaheuristic feature selection algorithms and six filter methods for performance analysis. Additionally, we have applied the proposed approach to URLs that are registered on the PhishTank website.
Digitalization of Education: An Urgent Need of Sustainable Teaching Process Caused by Covid 19

The study aims to identify the benefits and problematic aspects of applying e-learning technologies at Ukrainian universities, taking into account several influential factors, such as technical capabilities, the necessary skills of students and tutors, etc. Using e-learning has several advantages, such as more convenience, greater accessibility, the possibility of an individual approach, and better demonstration opportunities. The article discusses specific tools for educational digitalization, using which should increase the effectiveness and availability of training in COVID 19 pandemic conditions.
Research on Control of Computer Room Personnel Based on OpenCV

As one of the commonly used techniques of identity authentication, face recognition has great application value in computer room monitoring. Taking Raspberry Pi as the system carrier, the project adopts OpenCV image recognition library Haar feature and Face Recognition Library for the detection and recognition of the personnel entering the computer room, classifies the detection results, and optimizes the results by using the error optimization algorithm. As indicated by the final results, the OpenCV-based computer room personnel control system has an accuracy rate of 99.09%, which can be applied in actual computer room inspection system.
Gaussian Mechanisms Against Statistical Inference: Synthesis Tools

In this manuscript, we provide a set of tools (in terms of semidefinite programs) to synthesize Gaussian mechanisms to maximize privacy of databases. Information about the database is disclosed through queries requested by (potentially) adversarial users. We aim to keep part of the database private (private sensitive information); however, disclosed data could be used to estimate private information. To avoid an accurate estimation by the adversaries, we pass the requested data through distorting (privacy-preserving) mechanisms before transmission and send the distorted data to the user. These mechanisms consist of a coordinate trans-formation and an additive dependent Gaussian vector. We formulate the synthesis of distorting mechanisms in terms of semidefinite programs in which we seek to minimize the mutual information (our privacy metric) between private data and the disclosed distorted data given a desired distortion level - how different actual and distorted data are allowed to be.
Research on extraction method of centerline of large format laser stripe

Aiming at the real-time 3D reconstruction of large-format components, this paper proposes a method for extracting the centerline of laser stripes against ambient light interference. The Zhang-Suen thinning algorithm is optimized to effectively retain the data information of the laser fringe endpoints. In the process of fitting the centerline of the la-ser fringe with the least square method, a method of extracting the region of interest is proposed. Through the anal-ysis of experimental data, this method effectively improves the anti-interference ability of the algorithm, is suitable for more scanning occasions, and reduces the conditional limitation of using laser three-dimensional scanning in the industrial measurement process.
THz Super-Resolution Imaging Based on Complex Laplacian Prior Deconvolution Algorithm

Due to the long wavelength of the Terahertz (THz) wave, the imaging quality is seriously deteriorated with diffraction. To solve this problem, a simple but very effective approach based on prior knowledge and wave nature was introduced in this paper. In this prior, the image gradients are represented by Laplacian to constrain the gradient of the high-resolution image and the enhanced image when performing single image super-resolution and sharpness enhancement. Moreover, the deconvolution algorithm is expended to a complex dimension. Low-Resolution (LR) THz image was simulated by convolution the High-Resolution (HR) image with real-measured Point-Spread Function (PSF) to ensure the applicability. The numerical experiments illustrate the efficiency and effectiveness of the proposed method in terms of Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE) and Structural Similarity (SSIM). Super-Resolution (SR) results show that the proposed method has good performance in convergence and suppressing ringing or jaggy artifacts.
Design of English Learning Platform Based on Knowledge Management

research-article Design of English Learning Platform Based on Knowledge Management Share on Author: Lei Zhang Hanjiang Normal University, China Hanjiang Normal University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 1747–1751https://doi.org/10.1145/3482632.3484031Online:22 November 2021Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Context-Aware and Time-Aware Attention-Based Model for Disease Risk Prediction With Interpretability

Thanks to the huge accumulation of Electronic Health Records (EHRs), numerous deep learning based predictive models were proposed for this task. Among them, most of the existing state-of-the-art (SOTA) models were built with recurrent neural networks (RNNs). Regardless of their success, RNN-based models mainly suffer from three limitations. (i) Accuracy: the prediction accuracy of RNN-based models drops quickly as the length of EHR sequences increases. (ii) Efficiency: the recurrence property of RNN-based models makes the computation parallelization impossible, and accordingly hurts the efficiency of such models in practice. (iii) Interpretability: the outputs of RNN-based models are difficult to explain due to the unexplainable nature of deep models. In this paper, we resort to the recently advanced attention mechanism to model the dependencies between inputs and outputs, which overcomes shortages of RNN-based models in accuracy and efficiency. As for interpretability, we model the relationships with two linear mappings from the input to the output, which account for two important factors—one is for context-aware information and the other is for time-aware representation—of capturing discriminative features in learning patient’s representations. We empirically demonstrate the effectiveness of the proposed model in both accuracy and computational efficiency, meanwhile, analyze and discuss the reasonability of each explanation approach.
Quarry: An open source tool for OPC UA SPARQL queries over hybrid architectures using query rewriting

OPC UA is an open set of specifications for communication and modelling that have found extensive use in industrial automation. OPC UA information models are part of the specification and are used by asset owners and equipment vendors to describe the static parameters and time varying signals belonging to equipment in standardized ways. Such descriptions are aimed to drastically reduce the cost of integrating software managing these industrial assets. Hindering the adoption of OPC UA information models is their inherent complexity and the problematic specification of query support. Previous research has implemented the OPC UA query specification using SPARQL and found that formulating queries in SPARQL natively provides shorter queries that are easier to formulate. However, parts of the OPC UA graph are time-varying, and may index large time series data sets, which are sub-optimally handled by most available SPARQL databases. We show how OPC UA can be mapped to the Semantic Web in a way that allows a hybrid approach where time series data is stored in a specialized system and describe a query rewriting approach that allows for joint queries over static OPC UA information models and time series data. Compared to existing approaches, we argue that our approach is better suited for analytics applications that require portability across infrastructure and allows a greater range of SPARQL engines. A prototype implementation called Quarry is open sourced under the Apache 2.0 license to improve the accessibility and reproducibility of this research area.
Efficient Platform Migration of a Mainframe Legacy System Using Custom Transpilation

Legacy software systems play an important role in the economy but are known to cause high operational and maintenance costs. To reduce these costs, such systems are often migrated to modern infrastructure or languages. There exists a variety of migration strategies, however choosing the best strategy or combination of strategies given technical, economical and business constraints remains a challenging task. We observe a lack of experience reports on industrial migration projects explaining their decisions in detail. In this report, we present the case of an insurance system with 1M Source Lines of Code, running on an expensive mainframe and featuring Natural, Cobol, and Assembler code as well as an Adabas database. We elaborate on why state-of-practice migration strategies were inadequate in this case and introduce an alternative methodology, taking into account the limited budget for the migration. In this project, we use custom transpilation to translate the legacy code automatically to another programming language. In contrast to off-the-shelf transpilers, we implement an iteratively refined transpiler that is fine-tuned to the legacy code at hand. The transpiler guides its own development by pointing out instructions in the legacy code it cannot yet translate. Manual adaptions to the legacy code allow circumventing the implementation of overly complicated translation rules. This ensures the transpiler and the generated code remain lean and efficient while being able to cope with specific challenges of the system at hand. In the presented industrial case, Natural and Assembler sources were transpiled to Cobol running on Linux, combined with some adapted and rewritten Cobol and Java. We illustrate our lessons learned and provide in-depth insights into testing and debugging activities. A comparison with alternative offers by other vendors validates the economic benefits of this approach.
Research on Compression Performance Prediction of JPEG2000

Image compression is one of the potential techniques for image processing. However, the compression also takes a certain amount of time, and some algorithms are not adapted to all images. In order to improve the processing efficiency and performance, this paper studied the relation between image characteristics and Peak Signal to Noise Ratio (PSNR) to predict the compression performance. In this paper, we adopted JPEG2000 algorithm to compress, and PSNR to evaluate the image quality. The statistics of an image contains the values of mean, variance, entropy and others. Then, we drew the relation graph between each statistic calculated and PSNR, and found the statistic which is the most closely related to PSNR. Finally, we derived an explicit expression. Experimental results show that Image Activity Measure (IAM) has the closest relation with PSNR, and the expression has the average relative error of 2% - 3.0%. Meanwhile, it can be simplified by ensuring that the formula error is unchanged basically. Furthermore, we also used other images dataset for verifying the formula. Gray images all can be well predicted for JPEG2000 algorithm when Compression Ratio (CR) is 16. It indicates that a more accurate and simpler IAM-PSNR relation we had obtained. Therefore, we can predict the compression performance before compression so as to select the appropriate compression algorithm and to provide great convenience for subsequent processing.
Research on the Construction of Collaborative Governance Audit Big Data Platform

Currently, the traditional audit model has been gradually replaced by collaborative governance auditing. The effective development of collaborative governance auditing requires the construction of a big data audit platform to ensure. This article combines cloud computing related technologies, using CEPH distributed storage system, MapReduce parallel computing framework, heterogeneous computing and other technologies to try to establish a collaborative governance auditing big data platform, and analyze from the three aspects of the motivation, measures and system of the construction of the collaborative governance audit big data platform. The article divides the audit big data platform into five systems: data collection, preprocessing, storage center, analysis and visualization. The combination of collaborative governance audit and big data technology is a new attempt, with epoch-making significance and broad imagination, and provides a technical foundation for the collaborative governance audit cloud sharing platform, and provides theoretical support for the development of future big data audit work.
Sequential Fast Covariance Intersection Fusion Kalman Filter for Multi-Sensor Systems with Random One-step Measurement Delays and Missing Measurements

In order to handle the fusion estimation problem for the multi-sensor systems with random one-step measurement delays and missing measurements, a Sequential Fast Covariance Intersection (SFCI) fusion structure is presented by the augmented state technology with the fictitious noises, which can avoid large computational burden about the unknown cross-covariance matrices and is not sensitive to the sequential fusion orders. The accuracy of the presented SFCI fusion Kalman filter is higher than each of local estimators, less but close to that of the information fusion Kalman estimator weighted by matrices. The simulation example shows the effectiveness and the estimation accuracy of the proposed algorithm.
Moving load identification method of complex bridge structure based on information platform

In order to study the fatigue damage mechanism of complex bridge structures under the repeated action of moving vehicle loads, a moving load identification method of complex bridge structures based on information platform was proposed. The complex bridge structure model is established and the coupling vibration principle of vehicle and bridge is analyzed under the model. The information platform is used to collect field data, analyze the dynamic force of complex bridge structure under the action of moving load, and realize the identification of moving load of complex bridge structure from three aspects: size, speed and position of moving load. The experimental results show that the optimized moving load identification method can effectively identify the moving load of complex structures, and the identification accuracy is high.
Reconfigurable Deep Learning Accelerator Hardware Architecture Design for Sparse CNN

The design architecture proposed in this paper uses the row-sparsity -map compression method. Proposed row-leap to solve the PE balance problem. Proposed the channel-leap to increase the PE usage. The design architecture proposed in this paper can achieve 95% PE usage under the Yolo-like network. And the bandwidth is reduced by 40%.
I3rab: A New Arabic Dependency Treebank Based on Arabic Grammatical Theory

Treebanks are valuable linguistic resources that include the syntactic structure of a language sentence in addition to part-of-speech tags and morphological features. They are mainly utilized in modeling statistical parsers. Although the statistical natural language parser has recently become more accurate for languages such as English, those for the Arabic language still have low accuracy. The purpose of this article is to construct a new Arabic dependency treebank based on the traditional Arabic grammatical theory and the characteristics of the Arabic language, to investigate their effects on the accuracy of statistical parsers. The proposed Arabic dependency treebank, called I3rab, contrasts with existing Arabic dependency treebanks in two main concepts. The first concept is the approach of determining the main word of the sentence, and the second concept is the representation of the joined and covert pronouns. To evaluate I3rab, we compared its performance against a subset of Prague Arabic Dependency Treebank that shares a comparable level of details. The conducted experiments show that the percentage improvement reached up to 10.24% in UAS and 18.42% in LAS.
Descriptor Sampling VLSI Design Based on BRIEF Algorithm for Surrounding View Application

This paper presents a new descriptor sampling method based on Binary Robust Independent Elementary Features (BRIEF). The proposed method is divided into several steps, split blocks, sampling, and descriptor construction. The proposed descriptor sampling method is realized by Very Large-Scale Integration (VLSI) technique. The usage of the split blocks keeps average accuracy attaining 88.01% and reduces 75% gate counts by using the same sampling model to construct descriptions. Comparing with previous studies, this design shows lower hardware cost and higher accuracy.
Application of AR in 3D model

In recent years, with the development and application of AR technology, more and more augmented reality applications have begun to appear in education, introduction, etc. to achieve display through specific 3D models, such as popularizing human body information through human skeleton models, and introducing cars' composition information through car models. As a brand-new interactive method, augmented reality-AR system can provide more detailed information, for the human by the direct-viewing feeling, and improve the efficiency of understanding information.
Decentralized Path Planning for Multi-Objective Robot Swarm System

Abstract The multi-robot path planning aims to explore a set of non-colliding paths with the shortest sum of lengths for multiple robots. The most popular approach is to artificially decompose the map into discrete small grids before applying heuristic algorithms. To solve the path planning in continuous environments, we propose a decentralized two-stage algorithm to solve the path-planning problem, where the obstacle and inter-robot collisions are both considered. In the first stage, an obstacle- avoidance path-planning problem is mathematically developed by minimizing the travel length of each robot. Specifically, the obstacle-avoidance trajectories are generated by approximating the obstacles as convex-concave constraints. In the second stage, with the given trajectories, we formulate a quadratic programming (QP) problem for velocity control using the control barrier and Lyapunov function (CBF-CLF). In this way, the multi-robot collision avoidance as well as time efficiency are satisfied by adapting the velocities of robots. In sharp contrast to the conventional heuristic methods, path length, smoothness and safety are fully considered by mathematically formulating the optimization problems in continuous environments. Extensive experiments as well as computer simulations are conducted to validate the effectiveness of the proposed path-planning algorithm.
Cross-Cultural Differences of Designing Mobile Health Applications for Africans

With the increasing use of mobile applications in Western countries, more emphasis should be taken when designing them for the African community to increase usage. In this paper, a pilot study was conducted with 30 participants to investigate the cultural differences in the design of a mobile health application for the African community residing in Maryland, United States. It highlights the cultural factors that need to be considered when designing a mobile application for the African community. The findings revealed the differences with adoption by gender and age. Differences in age and gender were measured using cultural dimensions. Health management along with cultural markers were examined to better understand factors that affect behavioral changes in the design of a mobile health application.