Reviewerly/exHarmony huggingface dataset:
Hash-processing of universal quantification-like queries dealing with requirements and prohibitions

This paper is focused on flexible universal quantification-like queries handling simultaneously positive and negative preferences (requirements or prohibitions). We emphasise the performance improvement of the considered operator by proposing new variants of the classical hash-division algorithm. The issue of answers ranking is also dealt with. We target in our work the in memory databases systems (also called main-memory database systems) with a very large volume of data. In these systems, all the data is primarily stored in the RAM of a computer. We have introduced a parallel implementation of the operator that takes into account the data skew issue. Our empirical analysis for both sequential and parallel versions shows the relevance of our approach. They demonstrate that the new processing of the mixed operator in a main-memory database achieves better performance compared to the conventional ones, and becomes faster through parallelism.
Arbit Hyoju Listings

Listings of Arbit Hyoju which is located in Milan Chowk, Ward No. 9, Bharatpur Metropolitan City, Chitwan, Pradesh 3 Nepal
Zero-one laws for provability logic: Axiomatizing validity in almost all models and almost all frames.

It has been shown in the late 1960s that each formula of first-order logic without constants and function symbols obeys a zero-one law: As the number of elements of finite models increases, every formula holds either in almost all or in almost no models of that size. Therefore, many properties of models, such as having an even number of elements, cannot be expressed in the language of first-order logic. For modal logics, limit behavior for models and frames may differ. Halpern and Kapron proved zero-one laws for classes of models corresponding to the modal logics K, T, S4, and S5. In this paper, we prove zero-one laws for provability logic with respect to both model and frame validity. Moreover, we axiomatize validity in almost all relevant finite models and in almost all relevant finite frames, leading to two different axiom systems. In the proofs, we use a combinatorial result by Kleitman and Rothschild about the structure of almost all finite partial orders. On the way, we also show that a previous result by Halpern and Kapron about the axiomatization of almost sure frame validity for S4 is not correct. Finally, we consider the complexity of deciding whether a given formula is almost surely valid in the relevant finite models and frames.
An Overview of Continuous Device-to-device Authentication Techniques for the Internet of Things

Internet of things (IoT) has become an integral part of our daily life. However, this technology exposes many security concerns. With the increasing focus on security and privacy, IoT device authentication becomes a crucial requirement to protect devices against any unauthorised access. Although static authentication ensures the legitimacy of the user or device at the beginning of each session, it does not account for session hijacking that could occur after the initial authentication. Continuous authentication is a more robust authentication technique to ensure security throughout the session. Also, it ensures fast authentication for frequent messages sent during the session. In this research, we analyse the security threats in the context of device-to-device communication. We elaborate on the main authentication requirements for IoT. Additionally, we survey major contributions in authentication and compare them based on the predefined requirements. We believe that the findings of this paper provide valuable information to researchers by presenting new directions for future research.
Digital solutions to improve basic service provision to the urban poor

Africa’s urban population is expected to double from now until 2050, while most of this growth will be concentrated in informal settlements, where over 63 per cent of the urban population in least-developed countries already live This poses challenges to basic service provision as cities struggle to meet the demands of rapidly growing urban populations Unless city authorities and utilities find innovative ways to include informal settlements in service provision, water shortages, lack of sanitation, unreliable power and insufficient waste management will remain a reality for most of the urban poor As COVID-19 demonstrates, the reliability and inclusivity of basic services is a critical determinant of societies’ resilience in the face of external shocks Mobile-enabled digital solutions are uniquely placed to address these challenges The expansion of mobile connectivity in developing countries has enabled the proliferation of digital solutions that can make essential services more accessible and affordable For instance, the spread of mobile money throughout Africa, is enabling innovative business models like pay-as-you-go (PAYG) to make vital services accessible to low-income populations Innovations like smart metering, PAYG, big data, GIS and the Internet of Things (IoT) can be applied to a range of impactful use cases, such as deploying smart grids, coordinating sanitation services, monitoring water pipe leakages, mitigating peak traffic flow or managing waste flows This section explores the potential for these mobile-enabled innovations to provide solutions to pressing challenges facing the urban poor © 2020, Institut Veolia Environnement All rights reserved
Feedback in Imitation Learning: Confusion on Causality and Covariate Shift

Imitation learning practitioners have often noted that conditioning policies on previous actions leads to a dramatic divergence between held out error and performance of the learner in situ. Interactive approaches can provably address this divergence but require repeated querying of a demonstrator. Recent work identifies this divergence as stemming from a confound in predicting the current action, and seek to ablate causal aspects of current state using tools from causal inference. In this work, we argue instead that this divergence is simply another manifestation of covariate shift, exacerbated particularly by settings of feedback between decisions and input features. The learner often comes to rely on features that are strongly predictive of decisions, but are subject to strong covariate shift. Our work demonstrates a broad class of problems where this shift can be mitigated, both theoretically and practically, by taking advantage of a simulator but without any further querying of expert demonstration. We analyze existing benchmarks used to test imitation learning approaches and find that these benchmarks are realizable and simple and thus insufficient for capturing the harder regimes of error compounding seen in real-world decision making problems. We find, in a surprising contrast with previous literature, but consistent with our theory, that naive behavioral cloning provides excellent results. We detail the need for new standardized benchmarks that capture the phenomena seen in robotics problems.
Enhancing Resolution of Artifact Image using AR-CNN and SRGAN

Abstract The super-resolution strategy recreates a higher-resolution image or arrangement from the observed low resolutions (LR) images. As super-resolution has been created for over three decades, both multi-casing and single-outline super-resolution has critical applications in our everyday life. Existing super-resolution strategies have a few constraints. The artifact is additionally an issue in compression of an image. With artifacts, the high-resolution image is most noticeably terrible to see. In this paper, we try to build up a strategy to make the high-resolution image and expelling artifacts that happen in compression. At first, we use a convolutional neural network that is used to reduce artifacts on images. Then reducing artifacts we send the low resolutions images to super-resolution generative adversarial network to produce high-resolution images. In this way, we get high-resolution images without artifacts. We compare with artifact high-resolution images and without artifact high-resolution images that we get from a super-resolution generative adversarial network (SRGAN). We use a super-resolution generative adversarial network after using artifact reduction CNN (AR-CNN) to get a high-resolution image. We use artifact reduction CNN to remove the artifacts. Keywords: Super-resolution, Artifacts, Artifact Reduction CNN, Super-resolution Generative Adversarial Network
The analysis of stego image visual quality for a data hiding scheme based on two-layer turtle shell matrix

In 2018, Xie et al. proposed a novel data hiding scheme based on two-layer turtle shell matrix. In their scheme, they claimed that their stego image visual quality is superior to that of the state-of-the-art methods, regardless of the features of cover images. In this research note, we make a theoretical analysis of stego image quality for Xie et al.'s (2018) method based on the symmetrical characteristic of the matrix. Furthermore, we found that their simulation outcomes do not reveal the fact that their embedding capacity is larger than those of the data hiding methods proposed previously under the same stego image visual quality. More simulations are made to reveal that our experimental outcomes coincide with the results of our theoretical analysis. Furthermore, the experimental results made by Xie et al. (2018) have also been corrected.
Medical video-based cryptography model to improve the security and transmission time

This paper proposes a medical video-based visual cryptography model using graph-based coherence shape Lagrange interpolation (G-CSLI). This G-CSLI model reduces the noise on medical video while generating shares and minimises the transmission time by minimising the computational complexity during secret sharing. At first, a probabilistic polynomial-time model is used with the objective of minimising computational complexity during secret sharing. Here, the similarity is measured based on the luminance and structure. Finally, a Lagrange-interpolation scheme is applied with the objective of minimising the transmission time and improves the security. Overall, the proposed model minimises the computational complexity while secret sharing and performs experimental evaluation on factors such as security, transmission time and noise in medical video-based visual cryptography. The experimentation shows that the proposed model is able to reduce the computational complexity while secret sharing by 13.11% and reduce the transmission time by 39.03% compared to the state-of-the-art algorithms.
Provably Secure Authentication Approach for Data Security in Cloud Using Hashing, Encryption, and Chebyshev-Based Authentication

Secure and efficient authentication mechanism becomes a major concern in cloud computing due to the data sharing among cloud servers and users through the internet. This paper proposed an efficient hashing, encryption, and Chebyshev (HEC)-based authentication in order to provide security among data communication. With the formal and informal security analysis, it has been demonstrated that the proposed HEC-based authentication approach provides data security more efficiently in the cloud. The proposed approach amplifies the security issues and ensures the privacy and data security to the cloud user. Moreover, the proposed HEC-based authentication approach makes the system more robust and secured and has been verified with multiple scenarios. However, the proposed authentication approach requires less computational time and memory than the existing authentication techniques. The performance revealed by the proposed HEC-based authentication approach is measured in terms of computation time and memory as 26 ms, and 1,878 bytes for 100 Kb data size, respectively.
Enhancing security by two-way decryption of message passing of EMR in public cloud

Encryption is one of the most critical and mandatory technique to provide security in outsourced data. Message passing is the most unsecure place of transfer of crucial information and these messages passing are made end-to-end encryption to avoid a centralised security agent to access the data. Existing methods of encryption only provide end-to-end encryption which is not feasible for certain situations like implementing authentication. When the end-to-end encryption is made, the messages are always not known to the central authentication agent like CBI, in this paper, we provide a two-side decryption algorithm that can be decrypted by two entities (one receiver and the other is the central authentication agent) thus improving the message passing security by allowing the centralised authentication agent to read the transferring words. We implemented this two-way decryption in trip database dataset, and the experiment results prove that our proposed algorithm improves the security of message passing of electronic medical records in public cloud while comparing with existing encryption algorithms. Thus we made with quantitative research based on the previous techniques compared based on the various types of key generation occurs in the algorithms.
An analysis of Mandarin emotional tendency recognition based on expression spatiotemporal feature recognition

In order to overcome the problem of high recognition error rate in traditional emotional tendency recognition methods, a Mandarin emotional tendency recognition method based on expression spatiotemporal feature recognition is proposed. This method extracts the spatiotemporal features of the expression of the research object, and uses the data fusion technology to fuse the extracted feature vector. This paper constructs the standard database of Mandarin emotional tendency recognition, and takes the database as the standard of emotional tendency recognition. The fusion feature vector is matched with the standard feature in the database to get the result of Mandarin emotional tendency recognition. The experimental results show that compared with the traditional method for Mandarin emotional tendency recognition, the recognition method based on the spatiotemporal feature of expression can reduce the recognition error by about 75%.
A near lossless three-dimensional medical image compression technique using 3D-discrete wavelet transform

In the field of medical image processing, there are several imaging modalities that emerged and produced high quality of images that imposes to greater amount of storage space as well as bandwidth for transmission. Therefore, there is always a great need to develop compression techniques on such images. Moreover, they produce three-dimensional images by sequence of 2D slices and process each as like 2D image. Therefore, it is essential that every medical image processing operation needs to converge on 3D space (three-dimensional image). In this paper, we provide the wavelet-based image compression technique that can be directly applied on the 3D images itself. We apply 3D DWT on a 3D volume and the resultant coefficients are taken to further compression process (like thresholding, entropy encoding). The inverse processes are performed for decompression to reconstruct the images. The effects of the results based on different wavelets are assessed and the results of lossy mode are compared in terms to lossless mode.
Application of educational research and innovation towards acquisition of quality education to meet global demand in education

Education is the mother of many disciplines such as curriculum, research and innovation which necessitates knowledge generation and societal transformation globally. This paper examines the relevance of educational research and innovation towards acquisition of quality education which is the crux of the matter in the educational industry. It highlighted the concept of educational research, relationship that exist between educational research, innovation and quality education, and reveals their contributions towards solving educational and other societal problems, their challenges were x-rayed and meaningful recommendations were made to meet global demand.
Digital Watermarking of Compressed Videos Using Larger Dimension 2D Error Correcting Codes for Higher Embedding Capacity

This paper proposes a novel method of digital watermarking of MPEG videos using 2-dimensional error-correcting codes (2D ECC). The motion vectors of the video to be protected are used as the cover....
Multi-scale neighbourhood based-tree binary pattern: a new feature descriptor for face recognition

This research paper proposes the novel face recognition (FR) descriptor for pose and expression variations so-called multi-scale neighbourhood based-tree binary pattern (MNB-TBP). To produce the entire feature size of the MNB-TBP descriptor, two local descriptors are introduced. These two local descriptors are called as NB-TBP and mean (Mn)NB-TBP. The methodologies adopted for NB-TBP and MnNB-TBP descriptors are totally novel and are based on tree structure. The multi-scale feature extraction and combination of both the proposed descriptors gives the emergence of MNB-TBP descriptor. To produce the compact and robust feature for classification principal component analysis (PCA) is applied further. Finally classification is performed by support vector machines (SVM) and nearest neighbour (NN). The three challenging databases used for the performance evaluation are Olivetti Research Laboratory (ORL), Georgia Technology (GT) and Faces94. The proposed FR approach achieves very effective results which comprehensively outperforms the several state-of-art approaches from the literature.
Technology Adoption and the Middle-Income Trap: Lessons from the Middle East and East Asia

This paper documents the existence of a for the Middle East and North Africa region. It argues that the economic woes of the Middle East and North Africa offer new insights into the debate on the trap which has thus far focused on the East Asia and Pacific region. The results are two-folds. First, non-parametric regressions show that the average rate of economic growth in the Middle East and North Africa has not only been significantly lower than that in the East Asia and Pacific region, but it has also tended to drop at an earlier level of income. Second, econometric results point to Middle East and North Africa having experienced a relatively slow pace of technology adoption in general-purpose technologies. The paper concludes that barriers to the adoption of general-purpose technologies related to the lack of contestability in key sectors could constitute an important channel of transmission for the middle-income trap.
Performance analysis of biorthogonal filter design using the lifting-based scheme for medical image transmission

To minimise the storage space and for fast transfer of the digital images, it is necessary for the medical images to undergo image compression. There are various techniques in which the images are being diagnosed, based on that the image compression being performed. The choice of the filters in the image compression is an issue which affects the quality of the image. Hence, a novel biorthogonal filter using the lifting scheme has been developed. The proposed architecture gives the same characteristics of second generation wavelets. The proposed architecture is designed using MATLAB for different medical images and the PSNR, SNR, MSE, BPP having a compression of 96.85%. The verified model is synthesised using Xilinx Spartan-6 FPGA with the speed of −3. The results show that the designed method works at a frequency of 252.89 MHz, and absorbs the power 0.036 W for 2.5 volts with temperature at 25.2°C.
EDUCATION IN THE DIGITAL AGE

The World Health Organization has declared Covid-19 as a pandemic that has posed a contemporary threat to humanity. This pandemic has successfully forced global shutdown of several activities, including educational activities.The COVID-19 pandemic was a forceful reminder that education plays an important role in delivering not just academic learning, but also in supporting physical and emotional well-being. Education and the acquisition of skills are crucial to solving some of the world’s most pressing problems. Education is a basic right that promotes other rights such as health and civic participation. It is key to unlocking the developmental potential of children, communities, and countries. An educated workforce can help lift people out of poverty, reduce premature mortality, strengthen gender equality, and promote civic participation. Children must also learn skills that can be flexible and adaptable in the age of uncertainty and economic change. Workers will need breadth of skills such as literacy and numeracy as well as the ability to think critically and to solve problems collaboratively. In the digital age, citizens must be prepared to respond to the challenges presented by globalization, climate change, health epidemics, and economic uncertainty. The world needs to urgently rethink the way education is done, how it is delivered, and what skills children will need in a digital age to become healthy and productive members of society. Many agree that the delivery of education to marginalized children must be improved. It is evident that online learning is different from emergency remote teaching, online learning will be more sustainable while instructional activities will become more hybrid provided the challenges experienced during this pandemic are well explored and transformed to opportunities. However, there is little consensus on how to do so in a way that best reflects the challenges and opportunities facing young people today and on why education systems have largely failed to adapt to ensure that all children are receiving the support and learning opportunities they need.
Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation

Dense depth estimation is essential to scene-understanding for autonomous driving. However, recent self-supervised approaches on monocular videos suffer from scale-inconsistency across long sequences. Utilizing data from the ubiquitously copresent global positioning systems (GPS), we tackle this challenge by proposing a dynamically-weighted GPS-to-Scale (g2s) loss to complement the appearance-based losses. We emphasize that the GPS is needed only during the multimodal training, and not at inference. The relative distance between frames captured through the GPS provides a scale signal that is independent of the camera setup and scene distribution, resulting in richer learned feature representations. Through extensive evaluation on multiple datasets, we demonstrate scale-consistent and -aware depth estimation during inference, improving the performance even when training with low-frequency GPS data.
Provably secure authenticated content key distribution framework for IoT-enabled enterprise digital rights management systems

The internet of things (IoT) is one of the fastest growing technology, which is helping the enterprise industry. However, it brings greater challenges to privacy and security. Moreover, online piracy of content is an emerging issue. Thus, authorised content access is required. Enterprise digital rights management (E-DRM) systems aim to manage the access of electronic records necessitates establishing a standard set of access requirements, binding those access requirements to the electronic records, and computing the permission criteria. In this chain of control, efficient authenticated access to electronic documents is a crucial task. To address the issues, a protocol is designed. Security of the proposed scheme is proved in the random oracle model. Validation of security is done using 'Automated Validation of Internet Security Protocols and Applications (AVISPA)', which indicates that the protocol is safe. Moreover, the comparative study shows that it has a desirable attribute of efficiency.