Reviewerly/exHarmony huggingface dataset:
Co-creation of knowledge using mobile technologies and digital media as pedagogical devices in undergraduate STEM education

Digital media assignments are a widely used method of assessing student learning in higher education. Despite their common use, the literature on digital media assignments has many gaps regarding theoretical frameworks to guide their design, implementation and evaluation. This research paper focuses on student attitudes towards the use of mobile technology and digital media assignments in undergraduate STEM education. The study used a set of novel theoretical frameworks to identify training needs in digital media production, development of assessment weightings, marking rubrics and student training and resources. Longitudinal data were captured over a period of 4 years (n&nbsp;= 1724) using a mixed-methods approach. Validated questionnaires measured student attitudes to digital media support and attitudes to technology, understanding of the assignment, knowledge construction and digital media for learning and career development. Open-ended questions helped gather suggestions from students for improving the assessment task. Questionnaire data were analysed by using descriptive statistics and qualitative data with thematic analysis. The results suggested that students enjoyed group work, found learning with digital media to be engaging and developed critical thinking and digital media skills. In conclusion, STEM students had a positive learning experience repurposing mobile technology as pedagogical devices that present knowledge by using a multi-modal approach mediated by digital media.
Vision-Based Safety-Related Sensors in Low Visibility by Fog

Mobile service robots are expanding their use to outdoor areas affected by various weather conditions, but the outdoor environment directly affects the functional safety of robots implemented by vision-based safety-related sensors (SRSs). Therefore, this paper aims to set the fog as the environmental condition of the robot and to understand the relationship between the quantified value of the environmental conditions and the functional safety performance of the robot. To this end, the safety functions of the robot built using SRS and the requirements for the outdoor environment affecting them are described first. The method of controlling visibility for evaluating the safety function of SRS is described through the measurement and control of visibility, a quantitative means of expressing the concentration of fog, and wavelength analysis of various SRS light sources. Finally, object recognition experiments using vision-based SRS for robots are conducted at low visibility. Through this, it is verified that the proposed method is a specific and effective method for verifying the functional safety of the robot using the vision-based SRS, for low visibility environmental requirements.
AUGMENTED REALITY SEBAGAI MEDIA PENDIDIKAN KESEHATAN UNTUK ANAK USIA SEKOLAH

Technology in the multimedia field that is currently developing is Augmented Reality (AR). Utilization of Augmented Reality as a medium for children's education provides a new perspective on the existing educational media, not only using real objects but also using virtual objects in the delivery of information. AR is a technology that combines three-dimensional virtual objects into a real three-dimensional environment and displays them in real time. AR can make delivery easier and make information more interesting especially for children. AR is expected to be able to support as one of the media for school-age children in an effort to realize health promotion. This research method uses a literature review that focuses on the use of Augmented Reality as a medium for health education for school-age children. The literature used is in the form of articles originating from national and international journals. The application of AR as an educational medium can increase children's knowledge. In Indonesia the use of AR media has not been implemented much less especially in nursing. The need for the development of this AR media in the world of nursing.
A Survey of Context-Aware Messaging-Addressing for Sustainable Internet of Things (IoT)

Nowadays, communication engineering technology is merging with the Internet of Things (IoT), which consists of numerous connected devices (referred to as things) around the world. Many researchers have shown significant growth of sensor deployments for multiple smart engineering technologies, such as smart-healthcare, smart-industries, smart-cities, and smart-transportation, etc. In such intelligent engineering technologies, sensors continuously generate a bunch of messages in the network. To enhance the value of the data in the messages, we must know the actuality of the data embedded inside the messages. For this purpose, the contextual information of the data creates a vital challenge. Recently, context-aware computing has emerged to be fruitful in dealing with sensor information. In the ubiquitous computing domain, location is commonly considered one of the most essential sources of context. However, whenever users or applications are concerned with objects, and their site or spatial relationships, location models or spatial models are necessary to form a model of the environment. This paper investigates the area of context-aware messaging and addressing services in diverse IoT applications. The paper examines the notion of context and the use of context within the data exchanged by the sensors in an IoT application for messaging and addressing purposes. Based on the importance and need for context of the information, we identify three critical categories of new IoT applications for context-aware messaging and addressing services: emergency applications, applications for guiding and reminding, and social networking applications. For this purpose, a representative range of systems is reviewed according to the application type, the technology being used, their architecture, the context information, and the services they provide. This survey assists the work of defining an approach for context-aware messaging services domain by discovering the area of context-aware messaging.
Digital Enterprise Management in Cyberspace

The article describes the results of a study of cybernetic enterprise management based on the model and technology of digital twin and the concept of IEM (intelligent enterprise managing) system.The digital twin model is a meta-model of a real enterprise and a digital enterprise, that exchange information and informationally interact with each other.Cybernetic management is a stage in the development of digital management.The article formalizes the basic principles of cybernetic management.It is shown, that the "mirroring" technology, borrowed from their theory of database replication, is based on the double enterprise model.This mirroring allows to reflect all real processes in cyberspace.In cyberspace, based on a system of standardized management and standardization rules, optimal production processes are modeled and transferred to a real enterprise.The article describes a virtual conveyor model, that is built as an analogue of value chains.It is shown, that the set of virtual conveyors forms a kind of artificial neural network.
High-Dimensional Hybrid Data Reduction for Effective Bug Triage

Owing to the ever-expanding scale of software, solving the problem of bug triage efficiently and reasonably has become one of the most important issues in software project maintenance. However, there are two challenges in bug triage: low quality of bug reports and engagement of developers. Most of the existing bug triage solutions are based on the text information and have no consideration of developer engagement, which leads to the loss of bug triage accuracy. To overcome these two challenges, we propose a high-dimensional hybrid data reduction method that combines feature selection with instance selection to build a small-scale and high-quality dataset of bug reports by removing redundant or noninformative bug reports and words. In addition, we also study the recent engagement of developers, which can effectively distinguish similar bug reports and provide a more suitable list of the recommended developers. Finally, we experiment with four bug repositories: GCC, OpenOffice, Mozilla, and NetBeans. We experimentally verify that our method can effectively improve the efficiency of bug triage.
DIHARD II is Still Hard: Experimental Results and Discussions from the DKU-LENOVO Team

In this paper, we present the submitted system for the second DIHARD Speech Diarization Challenge from the DKULENOVO team. Our diarization system includes multiple modules, namely voice activity detection (VAD), segmentation, speaker embedding extraction, similarity scoring, clustering, resegmentation and overlap detection. For each module, we explore different techniques to enhance performance. Our final submission employs the ResNet-LSTM based VAD, the Deep ResNet based speaker embedding, the LSTM based similarity scoring and spectral clustering. Variational Bayes (VB) diarization is applied in the resegmentation stage and overlap detection also brings slight improvement. Our proposed system achieves 18.84% DER in Track1 and 27.90% DER in Track2. Although our systems have reduced the DERs by 27.5% and 31.7% relatively against the official baselines, we believe that the diarization task is still very difficult.
Highly customized 1010 nm, ns-pulsed Yb-doped fiber amplifier as a key tool for on-demand single-photon generation

The development of highly customized technical devices is a decisive feature of technically complex setups, as frequently observed in quantum experiments. This paper describes the development and realization of an Yb-doped all-fiber amplifier system designed for such a special application, more specifically, an on-demand single-photon source based on four-wave mixing with rubidium Rydberg atoms. The laser is capable of generating bandwidth-limited configurable nanosecond pulses up to peak powers of >100 W and with pulse repetition frequencies (PRF) between 50 Hz and 1 MHz at selectable wavelengths (1008-1024 nm). Especially the amplification of the 1010 nm reference seed at the lower edge of the amplification range for Yb-based fibers is challenging and tends to produce amplified spontaneous emission (ASE) at higher wavelengths. To achieve high ASE suppression, particularly at low pulse repetition frequencies, two acousto-optical modulators (AOM) are utilized both for pulse picking and for temporal filtering. The synchronization between pulse repetition frequency and AOM driver signal allows pulse amplitude fluctuations to be kept below 1%, while ASE is suppressed by at least 85 dB (PRF = 1 MHz) and 65 dB (PRF = 1 kHz).
Detection and quantification of entanglement with measurement-device-independent and universal entanglement witness*

Entanglement is the key resource in quantum information processing, and an entanglement witness (EW) is designed to detect whether a quantum system has any entanglement. However, prior knowledge of the target states should be known first to design a suitable EW, which weakens this method. Nevertheless, a recent theory shows that it is possible to design a universal entanglement witness (UEW) to detect negative-partial-transpose (NPT) entanglement in unknown bipartite states with measurement-device-independent (MDI) characteristic. The outcome of a UEW can also be upgraded to be an entanglement measure. In this study, we experimentally design and realize an MDI UEW for two-qubit entangled states. All of the tested states are well-detected without any prior knowledge. We also show that it is able to quantify entanglement by comparing it with concurrence estimated through state tomography. The relation between them is also revealed. The entire experimental framework ensures that the UEW is MDI.
Digraph Signal Processing With Generalized Boundary Conditions

Signal processing on directed graphs (digraphs) is problematic, since the graph shift, and thus associated filters, are in general not diagonalizable. Furthermore, the Fourier transform in this case is now obtained from the Jordan decomposition, which may not be computable at all for larger graphs. We propose a novel and general solution for this problem based on matrix perturbation theory: We design an algorithm that adds a small number of edges to a given digraph to destroy nontrivial Jordan blocks. The obtained digraph is then diagonalizable and yields, as we show, an approximate eigenbasis and Fourier transform for the original digraph. We explain why and how this construction can be viewed as generalized form of boundary conditions, a common practice in signal processing. Our experiments with random and real world graphs show that we can scale to graphs with a few thousands nodes, and obtain Fourier transforms that are close to orthogonal while still diagonalizing an intuitive notion of convolution. Our method works with adjacency and Laplacian shift and can be used as preprocessing step to enable further processing as we show with a prototypical Wiener filter application.
Improving the Leakage Rate of Ciphertext-Policy Attribute-Based Encryption for Cloud Computing

A Leakage-Resilient Ciphertext-Policy Attribute-based Encryption (LR-CP-ABE) not only supports the fine-grained access control to encrypted data but also guarantees the security of the data under the side-channel attacks. However, the leakage rate in the existing schemes is low or related to the number of attributes. It will make these schemes suffer from continual attacks. In addition, all of them almost not consider the leakage of the users' privacy and rely on the composite order groups which will threaten the privacy security of the users and depress the users in practice. In this paper, we aim at solving the above problems and propose a scheme with the improving leakage rate in the prime order group. In the proposed scheme, an extension of the lattice-based trapdoor is used to make it achieve the maximum leakage rate $1-o(1)$ . Moreover, it achieves the anonymity which can protect the privacy of the receivers. The proposed scheme can be reduced to the standard assumption-Decision Linear (DLIN) assumption in the selective security model and resist the Chosen Plaintext Attacks (CPA security). At last, the performance comparisons are given to confirm the efficiency and security of the proposed scheme.
The relationship between human resources management practices and job engagement: The mediating role of psychological capital

The primary objective of this study is to investigate the relationship between Human Resources Management Practices (HRMP) and Job Engagement with mediating role of Psychological Capital (PsyCap) in Telecommunications companies in Jordan (Zain, Orange, Umniah).The researcher relied on descriptive analytical method to analyze and classify the data to achieve the objectives of the study and to identify the variables of the study.A total of (270) questionnaires are sent out to the top and middle management of Jordanian telecom companies in Amman (Zain, Orange, Umniah), 9 questionnaires are excluded, as they were not valid for statistical analysis.The number of questionnaires analyzed are 261 (96.7%).The results showed that there was an impact of the dimensions (Jobs analysis and description, Selection and recruitment, Training, Performance appraisal) on Job Engagement and Psychological Capital.Also, the results indicate an impact of Psychological Capital on Job Engagement and there is a partial impact of the Psychological Capital, that is, there is an impact of Human Resources Management Practices through the moderator variable (Psychological Capital).
Image encryption scheme in public key cryptography based on cubic pells quadratic case

Cryptography systems face new threats with the transformation of time and technology. Each innovation tries to contest challenges posed by the previous system by analyzing approaches that are able to provide impressive outcomes. The prime aim of this work is to urge ways in which the concept of Pell’s equation can be used in Public key Cryptography techniques.The main aim of this approach is secure and can be computed very fast. Using Cubic Pell’s equation defined in Quadratic Case, a secure public key technique for Key generation process is showcased. The paper highlights that a key generation time of proposed scheme using Pell’s Quadratic case equation is fast compared to existing methods.The strength and quality of the proposed method is proved and analyzed by obtaining the results of entropy, differential analysis, correlation analysis and avalanche effect. The superiority of the proposed method over the conventional AES and DES is confirmed by a 50% increase in the execution speed and shows that Standard diviation and Entropy analysis of proposed scheme gives immunity to guess the encryption key and also it is hard to deduce the private key from public key using Diffrential analysis.
A Hardware-Oriented Echo State Network and its FPGA Implementation

This paper proposes implementation of an Echo State Network (ESN) to Field Programmable Gate Array (FPGA).The proposed method is able to reduce hardware resources by using fixed-point operation, quantization of weights, which includes accumulate operations and efficient dataflow modules.The performance of the designed circuit is verified via experiments including prediction of sine and cosine waves.Experimental result shows that the proposed circuit supports to 200 MHz of operation frequency and facilitates faster computing of the ESN algorithm compared with a central processing unit.
A Study on Industry 4.0 Concept

Industry 4.0 is a strategic initiative recently introduced by the German government.The goal of the initiative is transformation of industrial manufacturing through digitalization and exploitation of potentials of new technologies.An Industry 4.0 production system is thus flexible and enables Individualized and customized products.The aim of this paper is to present and far-ciliate an understanding of Industry 4.0 concepts, its drivers, enablers, goals and limitations.Building blocks are described and smart factory concept is pre-scented.This paper presents a review on the advances of robotic and automation technology in achieving industry 4.0.Many companies, research centers, and universities acknowledge that robotics and automation technology is the basis of industrial manufacturing and an important driver for Industry 4.0.The current status of Industry 4.0 readiness of the German companies is presented and commented.Finally it is discussed if Industry 4.0 is really a disruptive concept or simply a natural incremental development of industrial production systems.
uPredict: A User-Level Profiler-Based Predictive Framework in Multi-Tenant Clouds

Accurate performance prediction for cloud applications is an essential component to support many cloud resource management and auto-scaling policies. However, most existing studies on performance prediction for cloud applications in multitenant clouds are at the system level and may require access to performance counters in hypervisors. In this work, we propose uPredict, a user-level profiler-based performance predictive framework for single-VM (virtual machine) applications in multitenant clouds. We designed three micro-benchmarks to assess the contention of CPUs, memory and disks in a VM, respectively. Based on the measured performance of an application and micro-benchmarks, the application and VM-specific predictive models are derived by exploiting various regression and neural network based techniques. These models can then be used to predict the application's performance using the in-situ profiled resource contention with the micro-benchmarks. We evaluated uPredict extensively with representative benchmarks from PARSEC, NAS Parallel Benchmarks and CloudSuite, on a private cloud and two public clouds. The results show that the average prediction errors are between 10.4% to 17% for various predictive models on the private cloud with high resource contention, while the errors are within 4% on public clouds. A smart load-balancing scheme powered by uPredict is presented and can effectively reduce the execution and turnaround times of the considered application by 19% and 10%, respectively.
Requirements Analysis for an Open Research Knowledge Graph

Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KGs) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective by presenting a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications and outline possible solutions.
A Medical Image Segmentation Method With Anti-Noise and Bias-Field Correction

Brain magnetic resonance images (MRI) are affected by noise and bias field, which make the traditional FCM algorithm unable to segment tissue regions of MR images accurately. Based on the above problems, this paper proposes an MR image segmentation method (MPCFCM) with anti-noise and bias field correction, which implements segmentation by point-to-plane algebraic distance constraint. Different from traditional point-based clustering methods, a hyper-center of clustering (i.e., plane) model is defined, and data clustering is completed by optimizing different planes. In addition, to realize the point clustering with plane, a key problem that how to measure the distance from point to plane needs to be solved. This paper adopts the algebraic distance as a measure function, which can avoid the nonlinear problem caused by a direct calculation of the minimum distance between a point and a plane, thus simplifying the computational complexity. In the proposed algorithm, spatial distance, local variance and gray-difference of neighbors are combined to construct a new anti-noise smoothing factor for constraining the energy function so that the algorithm has better anti-noise and retains more image details. Finally, the singular value decomposition is performed on the loss energy, some information removed is re-added to the segmented image to repair it. The experimental results show that MPCFCM algorithm can better correct bias field and eliminate noise and obtain accurate image segmentation results with more details.
Behavioral Analysis for Virtualized Network Functions: A SOM-based Approach

In this paper, we tackle the problem of detecting anomalous behaviors in a virtualized infrastructure for network function virtualization, proposing to use self-organizing maps for analyzing historical data available through a data center.We propose a joint analysis of system-level metrics, mostly related to resource consumption patterns of the hosted virtual machines, as available through the virtualized infrastructure monitoring system, and the application-level metrics published by individual virtualized network functions through their own monitoring subsystems.Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, show that our technique is able to identify specific points in space and time of the recent evolution of the monitored infrastructure that are worth to be investigated by a human operator in order to keep the system running under expected conditions.