Reviewerly/exHarmony huggingface dataset:
The Impact of Human Capital on Product Innovation

This research study is focused on the influence of human capital on innovation in SMEs. The human capital factors are the independent variables: employee’s education, employee’s development, and employee’s challenge, while the dependent variable is product innovation. Data of manufacturing and service firms in the economies of Africa and the Middle-East are taken from the World Bank Enterprise Survey database through a stratified random sampling. A sampled of 33,977 firms are observed in 53 countries. The methodology employed in this study is the quantitative research approach. A pairwise correlation, regression model and forecasting model are used for the analyses of the data. The findings from the analyses show satisfactory levels of significance with pvalues below 1% even though not all the Pearson correlation coefficients of the independent variables with the dependent variable are high. The goodness-of-fit, in terms of R-squared, Root MSE, and mean percentage error, found from the regression of human capital with product innovation is slightly poor. This is due to the loose relation between independent and dependent variables in real life and partly to the data available for this research. However, human capital acquired from skilled and formal training of employees has a more positive and significant influence on SMEs to innovate products. The findings have implications on managerial decisions and governmental policies. The results also give insight into the limitations of the research and areas for further investigations.
Opinion-unaware blind picture quality measurement using deep encoder–decoder architecture

Recently, deep-learning-based blind picture quality measurement (BPQM) metrics have gained significant attention. However, training a robust deep BPQM metric remains a difficult and challenging task because of the limited number of subject-rated training samples. State-of-the-art full-reference (FR) picture quality measurement (PQM) metrics are in good agreement with human subjective quality scores. Therefore, they can be employed to approximate human subjective quality scores to train BPQM metrics. Inspired by this, we propose a deep encoder–decoder architecture (DEDA) for opinion-unaware (OU) BPQM that does not require human-labeled distorted samples for training. In the training procedure, to avoid overfitting and to ensure the independency of the training and testing samples, we first construct 6,000 distorted pictures and use their objective quality/similarity maps obtained using a high-performance FR-PQM metric for distorted pictures as training labels. Subsequently, an end-to-end map between the distorted pictures and their objective quality/similarity maps (labels) is learned, represented as the DEDA that takes the distorted picture as the input and outputs its predicted quality/similarity map. In the DEDA, the pyramid supervision training strategy is used, which applies supervised learning over three scale layers to efficiently optimize the parameters. In the testing procedure, the quality/similarity maps of the testing samples that can help localize distortions can be predicted with the trained DEDA architecture. The predicted quality/similarity maps are then gradually pooled together to obtain the overall objective quality scores. Comparative experiments on three publicly available standard PQM datasets demonstrate that our proposed DEDA metric is in good agreement with subjective assessment compared to previous state-of-the-art OU-BPQM metrics.
Deeper Understanding of Constrained Quantum Annealing from the Perspective of the Localization Phenomena

Kudo (2020) studied the physical properties of constrained quantum annealing (CQA), which is a quantum annealing that searches only for solution space satisfying the constraints of a combinatorial ...
Deformable Registration of Coronary Arteries with Topological Constraints for Image-Guided Vascular Interventions

2D/3D registration of preoperative computed tomography angiography with intra-operative X-ray angiography improves image guidance in percutaneous coronary intervention. However, previous registration methods are inaccurate and time-consuming due to simple deformation and iterative optimization, respectively. In this paper, we propose a novel method for non-rigid registration of coronary arteries based on a point set registration network, which predicts the complex deformation field directly without iterative optimization. In order to maintain the structure of coronary arteries, we advance the classical point set registration network with a loss function containing global and local topological constraints. The method was evaluated on ten clinical data, and it achieved a median chamfer distance of 73.60 pixels with a run time of less than 1s on CPU. Experimental results demonstrate that the proposed method is highly accurate and efficient.
The Regulation of Third Party Campaigning in UK Elections

Abstract This article looks at the regulation of third parties in UK election law. During the 2019 general election campaign, media reports noted an increase in non‐party organisations spending money on electoral advertisements on social media. Such advertisements raised a number of ethical questions, related to spending, transparency, and the content of the messages. Despite such recent concerns, third party electoral activity in the UK is not new, and the existing legal framework regulates campaign spending. That framework has its roots in Victorian‐era election law and has been periodically updated. This article will look at the challenges in designing laws to regulate third party electoral activity, as a difficult line has to be drawn to ensure the laws are effective, while at the same time not imposing too many burdens on independent political activity. Moreover, the move to digital campaigning poses some further challenges, such as monitoring compliance by third party campaigners. While there are no simple solutions to some of the issues raised by third party electoral activity, this article will note some of the measures that could at least improve the transparency of such campaigning.
Analysis of Docker Performance in Cloud Environment

In the present scenario, the technology is going very different, and this difference lies on different–different platforms. The platform is assured of reliability, consistency, and quickness. These bundles of quality are called container (Docker and LXC). Container helps to produce operational efficiency, version control, developer productivity, and environment consistency. The technical industry is adopting the container technology in both internal and commercial uses. In this paper, we analyze the performance of Docker by using different applications or tools in cloud environment.
Geometry and Reconstruction of Bipartite Quantum Correlations

The first part of this thesis provides a mathematical description for bipartite quantum correlations, aiming to analyze the geometry of several sets of correlations. We explain why quantum entanglement can be used to simulate shared randomness: Cloc(Γ) ⊆ Cqd(Γ) for a sufficiently large d. The known bound for this dimension d in the literature is d ≥ dim(Cloc(Γ))+1, but we improve this by showing that the inclusion is always true for d ≥ dim(Cloc(Γ)). For the proof of this bound, we show that the set Cprivate(Γ) of correlations using private randomness is connected, which allows the use of an improved version of Caratheodory’s Theorem. In the second part of this thesis, we define and analyze a see-saw method to determine the state and measurement operators that reconstruct both the correlation itself as its entanglement dimension, by solving consecutive semidefinite programs. One of the strengths of the algorithm is its generality: it applies to different dimensions, question sets, and answer sets. Some numerical experiments demonstrated that the method can indeed reconstruct quantum correlations, although some highly entangled correlations failed to be reconstructed due to the computationallimitations. The numerical experiments motivated several new theorems, for example the fact that every correlation with |A|= 1 or |B|= 1 has entanglement dimension 1, which means that it can be written as a private randomness correlation. The proof of this result is based on the earlier described improvement for the dimension d.
Improving Website by Analysis of Web Server Logs Using Web Mining Tools

Web log data obtained from the server of the website is the key source of enormous hidden information. This information can be obtained from the analysis of web log data. Analysis of web log data is very useful for the management and improvement of the website. It also plays a vital role in the security measures of a website. Analysis of web log data is also very important for analysis of user’s behavior and for the maintenance purpose of website. This analysis is possible by many available web usage mining tools. In this paper, we have used Web Log Expert tool for analysis. Web log Expert gives lots of reports and graphs to gain insight into web log data.
Compression Detection of Audio Waveforms Based on Stacked Autoencoders

With the easy acquisition of digital recordings, the field of audio forensics has become increasingly prominent. Detection of audio compression history is an important issue in the field of audio forensics. In this paper, a detection framework is proposed to detect whether a given audio waveform is an original waveform or a decompressed one. We extract the spectrum features from the frequency domain and then adopt a stacked autoencoder to effectively detect the frame-level audio fragments to distinguish between the original audio frames and the decompressed audio frames. Then, a majority voting algorithm is applied to make the final decision for an audio clip. Our analysis focuses on multi-time compressed audio, including single compression, double compression, triple compression and even four-time compression in three kinds of compression formats. The experimental results show that the proposed framework can effectively detect multi-time compressed audio. Furthermore, the proposed framework can also estimate the compression bitrate.
A Glasses-Based Holographic Tabletop for Collaborative Monitoring of Aerial Missions

This paper describes the development of a HoloLens application for experimenting the ability to collaboratively monitor flight tests by a shared holographic-like tabletop approach. The situational awareness arising from a high sense of presence deriving from the glasses-based holographic representation of the flying scenario leads to effective decision making. Moreover, the optical see-through MR approach to in-site collaboration makes inter-person communication as easy as in the reality. Finally, the shared holographic representation virtually recreated in the in-between space among participants promises a visually coherent basis for “look here” collaboration style. A flexible architecture is proposed for this application separating the core app from the data feeds for a slimmer development-deployment process, and technologies and data source to be used for the realization are reviewed. Finally, the paper reports the results of experimental use of the system collected in a couple of flight tests held in our Center, and allows readers to draw a perspective pathway to future MR developments.
A Collaborative Filtering Algorithm Based on the User Characteristics and Time Windows

Collaborative filtering algorithm is a widely used recommendation algorithm. In the traditional collaborative filtering algorithm, a single user similarity calculation method is usually considered, and the user’s own attribute characteristics are not used as the basis of neighbor user selection. At the same time, in the process of recommendation, user’s interest is considered to be static and given the same weight in different time periods, without thinking the dynamic changes of user’s interest. For above problems, this paper proposes a collaborative filtering algorithm based on the user characteristics and time windows. Firstly, a collaborative filtering algorithm based on item rating and user’s own attribute characteristics is proposed in the process of calculating similarity. Secondly, the dynamic time windows are divided according to the Ebbinghaus forgetting curve to reflect the user’s short-term interests in the recommendation process, the concept of time function is added to assign different time weights to user interests in different periods in the process of interest fusion. Finally, through experimental analysis, the recommended effect of the algorithm is significantly improved compared with the traditional collaborative filtering recommendation algorithm.
DINGO: An Ontology for Projects and Grants Linked Data

We present DINGO (Data INtegration for Grants Ontology), an ontology that provides a machine readable extensible framework to model data relative to projects, funding, actors, and, notably, funding policies in the research landscape. DINGO is designed to yield high modeling power and elasticity to cope with the huge variety in funding, research and policy practices, which makes it applicable also to other areas besides research where funding is an important aspect. We discuss its main features, the principles followed for its development, its community uptake, its maintenance and evolution.
A Fragile Watermarking Algorithm Based on Audio Content and Its Moving Average

A fragile audio watermarking scheme for content authentication was proposed in the paper. First, two proper positive integers are chosen. Then, the audio signal is segmented. Two moving averages are computed according to the chosen integers in each segment. The watermark information obtained from audio segment is embedded at crosses of the two moving averages. The watermark can be blindly extracted without original signal when authentication is needed. Subjective and object tests show that the fragile watermarking scheme is inaudible and sensitive\e against MP3 compression, additive noise, re-sampling, re-quantization, low-pass filtering, etc. Especially, it can detect and localize the tampered regions effectively.
ELITR: European Live Translator.

ELITR (European Live Translator) project aims to create a speech translation system for simultaneous subtitling of conferences and online meetings targetting up to 43 languages. The technology is tested by the Supreme Audit Office of the Czech Republic and by alfaview®, a German online conferencing system. Other project goals are to advance document-level and multilingual machine translation, automatic speech recognition, and automatic minuting.
Performance Analysis of Color Image Watermarking with Lifting Scheme

To prevent the illegal copying and sharing of digital content, the need for security for digital data is highly demanded. Among various available techniques, digital watermarking is an effective solution to protect confidential data. This paper presents the performance analysis of color image watermarking based on lifting scheme in which color image watermark has been used. Peak signal-to-noise ratio (PSNR), Structural similarity (SSIM) index and normalized correlation coefficient (NCC), etc. have been used as fidelity parameters to test the effectiveness of presented work both quantitatively and qualitatively.
Blockchain Integrated Secured Scenarios in Advanced Wireless Networks

The wireless networks are nowadays quite susceptible towards the assorted assaults, and thereby the needs arise to secure the overall scenarios. The Blockchain technology is one of the prominent and high-performance approaches that can be used for the integration of security with wireless networks to enforce a greater degree of security and overall performance. Blockchain refers to the high-performance and security-aware technology in which a digital ledger is maintained. The digital ledger is quite transparent, and there is no scope of any manipulations in the records by the intermediates or any administrator. The records of all the transactions are logged in the Blockchain ledger, and the operations are committed finally with different protocols and algorithms which cannot be hacked by the third-party intrusions.
Formal Specification of a Security Framework for Smart Contracts

As smart contracts are growing in size and complexity, it becomes harder and harder to ensure their correctness and security. Due to the lack of isolation mechanisms a single mistake or vulnerability in the code can bring the whole system down, and due to this smart contract upgrades can be especially dangerous. Traditional ways to ensure the security of a smart contract, including DSLs, auditing and static analysis, are used before the code is deployed to the blockchain, and thus offer no protection after the deployment. After each upgrade the whole code need to be verified again, which is a difficult and time-consuming process that is prone to errors. To address these issues a security protocol and framework for smart contracts called Cap9 was developed. It provides developers the ability to perform upgrades in a secure and robust manner, and improves isolation and transparency through the use of a low level capability-based security model. We have used Isabelle/HOL to develop a formal specification of the Cap9 framework and prove its consistency. The paper presents a refinement-based approach that we used to create the specification, as well as discussion of some encountered difficulties during this process.
Comparing Post-editing based on Four Editing Actions against Translating with an Auto-Complete Feature

This article describes the results of a workshop in which 50 translators tested two experimental translation interfaces, as part of a project which aimed at studying the details of editing work. In this work, editing is defined as a selection of four actions: deleting, inserting, moving and replacing words. Four texts, machine-translated from English into European Portuguese, were post-edited in four different sessions in which each translator swapped between texts and two work modes. One of the work modes involved a typical auto-complete feature, and the other was based on the four actions. The participants answered surveys before, during and after the workshop. A descriptive analysis of the answers to the surveys and of the logs recorded during the experiments was performed. The four editing actions mode is shown to be more intrusive, but to allow for more planned decisions: although they take more time in this mode, translators hesitate less and make fewer edits. The article shows the usefulness of the approach for research on the editing task.
Usability of an Input Modality for AR

The ability to overlay useful information into the physical world has made augmented reality (AR) a popular area of research within industry for maintenance and assembly tasks. However, the current input modalities for state-of-the-art technologies such as the Microsoft HoloLens have been found to be inadequate within the environment. As part of AugmenTech, an AR guidance system for maintenance, we have developed a tactile input module (TIM) with a focus on ease of use in the field of operation. The TIM is formed by 3D printed parts, off-the-shelf electronic components and an ESP-8266 microcontroller. A within-subjects controlled experiment was conducted to evaluate the usability and performance of the module against existing HoloLens input modalities and the Avatar VR glove from NeuroDigital. A System Usability Scale (SUS) score of 81.75 and low error count demonstrated the TIM’s suitability for the factory environment.
Mask detection algorithm based on pyramid box

The novel coronavirus outbreak there was no parallel in history. In early 2020, the outbreak of an unprecedented coronavirus outbreak could effectively block the spread of the epidemic in the crowd in the public areas of the society if it could use artificial intelligence technology to detect people without masks in the crowded area. This paper introduces the face mask image detection algorithm, mainly based on the face recognition algorithm pyramid box to complete, describes how to use Baidu's paddlehub mask detection project, through the use of Python language to complete the mask detection of multiple images and get accurate detection data. The accuracy rate of face detection based on the pyramid box mask detection model is not satisfactory. It can also be deployed to the server or even the mobile terminal to achieve rapid real-time detection. With the resumption of work of enterprises, I believe that the face mask detection scheme can solve many pain points that need to be solved for many enterprises, communities and manufacturers.
A Discriminative Projection and Representation-Based Classification Framework for Face Recognition

The sparse representation-based classifier (SRC) has been developed and verified as having great potential for real-world face recognition. In this paper, we propose a discriminative projection and representation-based classification (DPRC) method to enhance the discriminant ability of the SRC. The proposed method first obtains a discriminative projection matrix not only maximizing the ratio of the distance within interclass over the distance within intraclass, but also minimizing the linear approximation error within intraclass. Then it maps the original data onto the discriminative space, and adopts an SRC method to obtain the final solution. An inexact augmented Lagrangian method of multiplier is proposed for finding the optimal representation vector in our framework, and a proximal alternating minimization method is adopted to the iteration subproblems of the proposed method. The proposed method is proven to have the subsequence convergence property. Experimental results on Yale, ORL, and AR face image databases demonstrate that, compared with some existing feature extraction methods based on the SRC, the proposed DPRC method is more efficient.
Evaluation and comparison of global-feature-based and local-feature-based segmentation algorithms in intracranial visual pathway delineation

Intracranial visual pathway is related to the effective transmission of visual signals to brain. It was not only the target organ of diseases but also the organs at risk in radiotherapy thus its delineation plays an important role in both diagnosis and treatment planning. Traditional manual segmentation method suffered from time- and labor- consuming as well as intra- and inter- variability. In order to overcome these problems, state-of-the-art segmentation models were designed and various features were extracted and utilized, but it's hard to tell their effectiveness on intracranial visual pathway delineation. It's because that these methods worked on different dataset and accompanied with different training tricks. This study aimed to research the contribution of global features and local features in delineating the intracranial visual pathway from MRI scans. The two typical segmentation models, 3D UNet and DeepMedic, were chosen since they focused on global features and local features respectively. We constructed the hybrid model through serially connecting the two mentioned models to validate the performance of combined global and local features. Validation results showed that the hybrid model outperformed the individual ones. It proved that multi scale feature fusion was important in improving the segmentation performance.
TEACHERS’ TRAINING TO TRIGGER THE UNIVERSITY’S DIGITAL TRANSITION

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 2246-2254ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.0610Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain