Reviewerly/exHarmony huggingface dataset:
EUIEF: Enhancement of Underexposed Images via Exposure Fusion

Images captured in inappropriate light conditions can often result in flat color saturation, bad contrast and weakly illuminated images. In this paper, we propose a framework named as Enhancement of underexposed images via exposure fusion (EUIEF), to solve the problems associated with underexposed images. First, the enhanced input image is obtained by proposed Luminous Transform (LT) which enhances the low light images on the basis of their luminous value. Secondly, an enhanced input image is generated by Invert-Image enhancement method, which works by inverting a weakly illuminated image and then applying optimized enhancement algorithm on the inverted low light image. The enhancement of inverted low light image is carried out with the help of global atmospheric light, intensity of original scene objects and the amount of light reflected by the objects. The final enhanced low light image is the result of fusion technique which blends both enhanced images. Our work was evaluated on publicly available LOL dataset. The results suggest that the images enhanced by proposed EUIEF method possess better quality in terms of contrast enhancement, naturalness level and detail enhancement compared to existing methods. To validate the EUIEF, both qualitative and quantitative comparisons are conducted.
Error Correction for Correlated Quantum Systems

Modeling open quantum systems is a difficult task for many experiments. A standard method for modeling open system evolution uses an environment that is initially uncorrelated with the system in question, evolves the two unitarily, and then traces over the bath degrees of freedom to find an effective evolution of the system. This model can be insufficient for physical systems that have initial correlations. Specifically, there are evolutions \(\rho _S=\text {tr}_E(\rho _{SE})\rightarrow \rho _S'=\text {tr}_E(U\rho _{SE}U^\dagger )\) which cannot be modeled as \(\rho _S=\text {tr}_E(\rho _{SE})\rightarrow \rho _S'=\text {tr}_E(U\rho _S\otimes \rho _E U^\dagger )\). An example of this is \(\rho _{SE}= \left| {\varPhi ^+} \right\rangle \left\langle {\varPhi ^+} \right| \) and \(U_{SE}=\text {CNOT}\) with control on the environment. Unfortunately, there is no known method of modeling an open quantum system which is completely general. We first present some restrictions on the availability of completely positive (CP) maps via the standard prescription. We then discuss some implications a more general treatment would have for quantum control methods. In particular, we provide a theorem that restricts the reversibility of a map that is not completely positive (NCP). Let \(\varPhi \) be NCP and \(\tilde{\varPhi }\) be the corresponding CP map given by taking the absolute value of the coefficients in \(\varPhi \). The theorem shows that the CP reversibility conditions for \(\tilde{\varPhi }\) do not provide reversibility conditions for \(\varPhi \) unless \(\varPhi \) is positive on the domain of the code space.
Serverless Computation with NuLambda

In recent years, the use of serverless computing and, in particular, Function as a Service (FaaS) has increased as an execution model where there is no clear server management performed by the user as on virtual machines. Instead, the cloud provider dynamically allocates resources to the function invocations and a corresponding charge is introduced depending on the execution time and shared memory, as demonstrated by AWS Lambda. We are launching nuLambda, which is an open-source platform for building future apps. We provide some features of serverless computation also the many research challenges that need to be addressed in the use of these systems for a microservice event-driven architecture design with services that meet freely and transparently. We described a deployment architecture such that individual components scale elastically and developed a thread-safe Docker-based sandboxing tool to safely execute user code. We also included a brief study of web applications that has been added to better motivate other features of serverless application construction.
A Unified-Model via Block Coordinate Descent for Learning the Importance of Filter

Deep Convolutional Neural Networks (CNNs) are increasingly used in multimedia retrieval, and accelerating Deep CNNs has recently received an ever-increasing research focus. Among various approaches proposed in the literature, filter pruning has been regarded as a promising solution, which is due to its advantage in significant speedup and memory reduction of both network model and intermediate feature maps. Many works have been proposed to find unimportant filters, and then prune it for accelerating Deep CNNs. However, they mainly focus on using heuristic methods to evaluate the importance of filters, such as the statistical information of filters (e.g., prune filter with small $\ell_2$-norm), which may be not perfect. In this paper, we propose a novel filter pruning method, namely A Unified-Model via Block Coordinate Descent for Learning the Importance of Filter (U-BCD). The importance of the filters in our U-BCD is learned by optimizing method. We can simultaneously learn the filter parameters and the importance of filters by block coordinate descent method. When applied to two image classification benchmarks, the effectiveness of our U-BCD is validated. Notably, on CIFAR-10, our U-BCD reduces more than 57% FLOPs on ResNet-110 with even 0.08% relative accuracy improvement, and also achieve state-of-the-art results on ILSVRC-2012.
Humanities Education in the Russian Higher School under Digital Revolution: Success Factors and Ways of Optimisation

The purpose of the study is to find ways to optimise the humanities education in modern higher school in the digital society. The article examines the experience of the leading Russian universities in the field of the humanities from the viewpoint of its potential use by other educational institutions to improve the quality of their work. Scientific novelty consists in identifying the basic parameters of the educational process, the improvement of which can contribute to the optimisation of modern higher school as a whole. The result of the study amounts to recommendations for optimising the educational process during teaching the humanities at a university-type institution, which can be useful in developing a strategy for the innovative restructuring of the humanities education system in the modern Russian higher school.
Comprehensive View of Low Light Image/Video Enhancement Centred on Deep Learning

In computer vision, enhancing the image/video quality is strongly influenced by darkness and the noise is considered as an important challenge. Currently, different methods are used for performing image/video enhancement. However, deep learning-based methodologies have introduced huge changes in image/video quality improvement. This paper introduces more classic image/video enhancement network in recent years. Additionally, the elementary understanding of CNN components, current challenges, comparison of traditional enhancement with deep learning methods along with their benefits and drawbacks are analysed.
Research on Image Liquid Level Measurement Technology Based on Hough Transform

Based on the camera imaging model and the storage tank measurement environment, the corresponding relationship between the real liquid level height in the image and the liquid level contour radius is deduced in this paper. For the collected images, grayscale and morphological processing are performed first, and the Sobel operator is used for edge recognition. According to the situation, the corrosion calculation is used to reduce the subsequent calculation, and then the circle is detected by the optimized Hough transform to obtain the radius of the circle. Finally, the liquid level information is obtained according to the measurement model. Experimental results show that the maximum absolute error of the system is 2.99mm, and the maximum reference error is 0.75%. The system has certain theoretical and practical significance.
Trace-Based Workload Generation and Execution

Although major cloud providers have captured and published workload executions in the form of traces, it is not clear how to use them for workload generation on a wide range of existing platforms. A methodological challenge that remains is to generate and execute realistic datacenter workloads on any infrastructure, using information from available traces. In this paper, we propose Tracie, a methodology addressing this challenge, and introduce the tool supporting its implementation. We present all the necessary steps starting from a trace up to workload execution: analysis of datacenter traces, extraction of parameters, application selection, and scaling of a workload to match the capabilities of the underlying infrastructure. Our evaluation validates that Tracie can generate executable workloads that closely resemble their trace-based counterparts. For validation, we correlate the recorded system metrics of a trace against the actual execution. We find that the average system metrics of synthetic workloads differ at most 5% compared to the trace and that they are highly correlated at 70% on average.
Bilingual Summarization of English and Arabic Genetic Diseases Texts

Health Literacy aims at empowering patients to take better decisions about their health. The quality of Health Literacy for patients with genetic diseases can be enriched via facilitating the bilingual retrieval of summaries about the genetic diseases texts from the net. This paper proposes helps translator to achieve this task by utilizing NLP and Recurrent Neural Network (RNN) techniques for two tasks: generating abstractive summarizations and making Arabic-English translation. Both summarization and translation tasks require training sets that can be built from English summaries corpus and Arabic-English parallel corpora. The English summaries corpus is built from Orphadata while the parallel corpora is built from Wiki articles. The corpus is utilized for generating the English summaries from the Wiki articles, and the corpora is utilized for translating these summaries into Arabic. This paper defines the research problem. Then, it investigates a set of objectives to solve the problem. After that, it presents a literature review of the tasks in the objectives. Finally, it discusses the proposed solution for the problem from the following aspects: the required corpora, the system architecture, the RNN memory cell components architectures, the proposed software for the implementation, and the system evaluation.
Practice and innovation of teaching management in the post-epidemic era — Informatization Teaching Management

The novel coronavirus pneumonia epidemic has brought many challenges to education and teaching, as well as new opportunities. The new crown epidemic has promoted the rapid development of online teaching. Online teaching is inseparable from the update and progress of information-based teaching management methods. Education management in China has entered the “post-epidemic era.” Based on the data analysis of the post-epidemic teaching questionnaire and multiple teaching management cases, this paper concludes that the new information-based teaching management is based on the combination of network technology and text, a combination of multiple teaching methods, and teaching technology and offline. Combination of information technology and teaching management, it can produce better teaching effect, increase flexibility and personalized teaching experience for teaching.
The impact of service quality and trust on public loyalty in the Jordanian Ministry of Water and Irrigation

This study aimed to examine the impact of service quality and trust on public loyalty in the Jordanian Ministry of Water and Irrigation. The survey questionnaire was gathered from 378 citizens who review departments of the Jordanian Ministry of Water and Irrigation. The study used the software SmartPLS (version 3.3.2) to test the study hypotheses. The results of this study indicated that service quality had a positive direct effect on public loyalty. Moreover, the results of this study indicated that trust had a positive direct effect on public loyalty. Regarding moderating effect, the results of the current study indicated that trust did not moderate the relationship between service quality and public loyalty. This study recommends the decision-makers and managers in the Jordanian ministry of water and irrigation provide a good service quality that suits the needs and aspirations of the citizens. Future studies may apply the model of the current study in other sectors and organizations such as the banking sector or industrial sector.
Uniqueness verification of blue fraction as a parameter of spectral irradiance quantification

The spectral distribution of global irradiance is measured in the optimal tilt (45° south) plane of array for the calender year 2020 in Grimstad, Norway. In this work the spectral distribution is quantified in terms of the Blue Fraction (BF). The uniqueness of BF has been tested and reported using the same methodology used for uniqueness verification of average photon energy (APE). The results show that the coefficient of correlation between the APE and BF was 0.98, meaning a possible bijective relation between these two parameters whereby both indicate a similar spectrum. The percentage contribution of integrated irradiance (Rc) was calculated for 25 nm wavelength bins for the BF region 350-650 nm. Spectra are grouped by their BF value in steps of 0.02, each group containing measurements falling within ±0.01 of the central value. The corresponding standard deviation (SD) varied from 0.05% to 0.21% for the spectra denoted by BF values from 0.44 to 0.60. The coefficient of variation (CV) was found as high as 12% for the wavelengths below 450 nm while it remained relatively low from 450-650 nm, meaning a low dispersion of Rc values around the mean value. The results indicate that the uniquenss of BF can be statistically confirmed for the measured wavelength range.
Reuse for mass personalisation through feature models and similarities

This tutorial explores the impact of the socio-economic trends of customization and personalization on software reuse and describes a product similarity evaluation process to support the management of a product line.
Managing a digital revolution

This chapter examines the Internet booms and the growth of the Information and Communication Technology (ICT) in Malaysia and how it impacts Malaysia's national security. It also examines the nature and substance of Malaysia’s cybersecurity frameworks. The chapter discusses Malaysia’s strategy to manage the multitude of cybersecurity challenges within its border, including the emerging trend of security threats such as cyber warfare, cyberterrorism, and cybercrime. The development of ICT and the Internet sectors have definitely brought about massive changes in Malaysian economy, industries, trades, social, and political life. Hyper-connectivity, open and borderless access to information highways, and users’ ability to be anonymous exposed users, including in Malaysia to cybersecurity threats, domestically and externally. While Malaysia envisages being a developed nation by 2020, through systematic economic program and developments, it also needs to deal with current realities posed by myriad challenges in its socio-political realities as well as in the cyberspace.
Query answering in circumscribed OWL2 profiles

Abstract This paper partially bridges a gap in the literature on Circumscription in Description Logics by investigating the tractability of conjunctive query answering in OWL2’s profiles. It turns out that the data complexity of conjunctive query answering is coNP-hard in circumscribed $\mathcal {E}{\mathscr{L}}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>E</mml:mi> <mml:mi>L</mml:mi> </mml:math> and DL-lite , while in circumscribed OWL2-RL conjunctive queries retain their classical semantics. In an attempt to capture nonclassical inferences in OWL2-RL, we consider conjunctive queries with safe negation. They can detect some of the nonclassical consequences of circumscribed knowledge bases, but data complexity becomes coNP-hard. In circumscribed $\mathcal {E}{\mathscr{L}}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>E</mml:mi> <mml:mi>L</mml:mi> </mml:math> , answering queries with safe negation is undecidable.
Development of Automatic Speech Recognition for Xitsonga Using Subspace Gaussian Mixture Model

Speech is the most common and efficient form of communication between people. Automatic speech recognition (ASR) or speech-to-text (SST) system is the translation of spoken language into text. In this paper, an automatic speech recognition system for the under-resourced South African language, Xitsonga, is developed to help its mother tongue users who cannot speak the English language. The development of speech technology applications focuses on improving the accuracies of the systems. The challenge is to improve the word error rate systems for under resourced languages. We use the Subspace Gaussian Mixture Model (SGMM) technique with the Kaldi toolkit to develop the automatic speech recognition for Xitsonga using National Centre for Human Language Technology (NCHLT) corpus. The size of the used corpus is 56.26 hours. The word error rate (WER) achieved is 38.61% which is promising given the size of the data.
A Networked Social Virtual Reality Learning Environment Platform for Special Education

Delivering curriculum using desktop-based virtual learning environment (VLE) technologies in a collaborative group setting has been shown to reduce the social skill limitations of students with learning disabilities. However, the lack of the immersiveness and effective generalization of acquiring knowledge and skills among students remains a critical challenge in the interactive tools used in current VLEs. In this paper, we present a networked social virtual reality learning environment (VRLE) system viz., vSocial that has been redesigned based on iterative user feedback and developed in order to leverage the latest advances in integration of smart devices such as VR headsets for virtual content delivery. We describe a comparative study to evaluate technology trade-offs in the development process of transitioning from a VLE to a VRLE, from both technological and user (e.g., student/instructor) perspectives. Lastly, we outline open issues in using VRLEs which include: system complexity, emotion recognition, cybersickness and system sustainability.
resentation attack detection for face recognition using remote photoplethysmography and cascaded fusion

Spoofing (presentation) attacks are important threats for face recognition and authentication systems, which try to deceive them by presenting an image or video of a different subject, or by using a 3D mask. Remote (non-contact) photoplethysmography (rPPG) is useful for liveness detection using a facial video by estimating the heart-rate of the subject. In this paper, we first compare the presentation attack detection performance of three different rPPG-based heart rate estimation methods on four datasets (3DMAD, Replay-Attack, Replay-Mobile, and MSU-MFSD). We also present a cascaded fusion system, which utilizes a multistage ensemble of classifiers using rPPG, motion-based (including head-pose, eye-gaze and eye-blink), and texture-based features. Experimental results show that the proposed method outperforms several other presentation attack detection methods in the literature, which utilize rPPG.
DSCNN: Dimension Separable Convolutional Neural Networks for Character Recognition Based on Inertial Sensor Signal

Most existing researches on air-writing recognition based on inertial sensors only employ acceleration and angular velocity data to recognize characters. The main characteristics of acceleration and angular velocity data are concentrated in two parts, namely, the change of signal characteristics over time and the correlation between different dimensions of signal. However, existing models usually cannot effectively extract these two types of features. In this paper, we propose a dimension separable convolutional neural networks (DSCNN) that uses one-dimensional convolution and group convolution to fully mine the signal characteristics that change over time, the unique mutual information between different dimensions, and the global characteristics of the signal. Based on DSCNN, we design a model suitable for the character recognition of the sensor signal and conduct experiments on the three databases: DB1, DB2 and DB3. The experimental results show that our method compared with the previous methods not only has a certain improvement in the case of mixed-user, but also achieves a high accuracy under the condition of user-independent, which proves that the model has good generalization performance. As the average time of single-character recognition is very short regardless of GPU or CPU conditions, our method also has good real-time performance.
Neural Network eXplainable AI Based on Paraconsistent Analysis - an Initial Approach

Para-consistent logic is a non-classical logic whose foundations allow the treatment of contradictions without invalidating the conclusions. This paper presented an attempt of using Annotated Para-consistent Analysis (APA) for supporting eXplained Artificial Intelligence (XAI) on Neural-Networks. For the study case, it was presented a situation where a binary classification model was able to correctly recognize one label but not the other with the form of the principle of explosion (p\(\wedge \lnot \)p). By plotting the linear output max-min scaled data into the paraconsistent reticulate, it was possible to properly understand that the selected architectures were being able to predict. This result is early support for using APA for Neural-Network XAI, corroborating with this paper hypothesis.
What Have We Learned from OpenReview?

Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote openness in peer review process. The paper, (meta) reviews, rebuttals, and final decisions are all released to public. We collect 5,527 submissions and their 16,853 reviews from the OpenReview platform. We also collect these submissions’ citation data from Google Scholar and their non-peer-reviewed versions from arXiv.org. By acquiring deep insights into these data, we have several interesting findings that could help understand the effectiveness of the public-accessible double-blind peer review process. Our results can potentially help writing a paper, reviewing it, and deciding on its acceptance.
A Structure Based Speckle Filtering Method for Ocean SAR Images

Synthetic aperture radar (SAR) images are subjected to speckle noise. Ocean SAR imagery contains complex and delicate features. It is much more difficult to suppress the speckle in some case. Efficient speckle reduction requires a big homogeneous area to average the pixels. At the same time, the subtle features have to be maintained. Here we propose a simple but efficient method which incorporates the minimum mean-square error (MMSE) estimator and the non-local mean method. The speckle can be suppressed much more efficiently as more homogeneous pixels can be found by using the non-local mean method. The subtle structures can be preserved by using the MMSE estimator.