Reviewerly/exHarmony huggingface dataset:
Super-Resolution of Synthetic Aperture Radar Complex Data by Deep-Learning

One of the greatest limitations of Synthetic Aperture Radar imagery is the capability to obtain an arbitrarily high spatial resolution.Indeed, despite optical sensors, this capability is not just limited by the sensor technology.Instead, improving the SAR spatial resolution requires large transmitted bandwidth and relatively long synthetic apertures that for regulatory and practical reasons are impossible to be met.This issue gets particularly relevant when dealing with Stripmap mode acquisitions and with relatively low carrier frequency sensors (where relatively large bandwidth signals are more difficult to be transmitted).To overcome this limitation, in this paper a deep learning based framework is proposed to enhance the SAR image spatial resolution while retaining the complex image accuracy.Results on simuated and real SAR data demonstrate the effectiveness of the proposed framework.
Phase unwrapping algorithm for a segmented phase based on iterative pseudo-phase inpainting

Segmented phase unwrapping is an intractable problem in the phase-shifting technique. To solve the problem, this Letter presents an iterative pseudo-phase inpainting algorithm (IPPI). By means of image inpainting, the IPPI can be used to realize the pseudo-phases connecting each other among these phase islands. The error points in the pseudo-phases can be reduced by iterations of phase inpainting with the assistance of the reference pseudo-phase obtained by introducing the numerical carrier frequency and using the 2D Fourier transform. Compared with other methods, the proposed algorithm does not have to do any processing on the effective area of the wrapped phase, which ensures the authenticity of the result. The simulated and experimental verifications show that the proposed method not only possesses high precision, but also can be applied to a segmented phase with severe noise.
Tracking the Functions of AI as Paradata &amp;amp; Pursuing Archival Accountability

While a familiar term in fields like social science research and digital cultural heritage, &#x2018;paradata&#x2019; has not yet been introduced conceptually into the archival realm. In response to an increasing number of experiments with machine learning and artificial intelligence, the InterPARES Trust AI research group proposes the definition of paradata as &#x2018;information about the procedure(s) and tools used to create and process information resources, along with information about the persons carrying out those procedures.&#x2019; The utilization of this concept in archives can help to ensure that AI-driven systems are designed from the outset to honor the archival ethic, and to aid in the evaluation of off-the-shelf automation solutions. An evaluation of current AI experiments in archives highlights opportunities for paradata-conscious practice
Ethereum Architecture and Overview

The Ethereum blockchain is a fascinating platform that uses innovative smart contract capability to empower decentralized applications in almost all major business sectors. Pioneered by Vitalik Buterin and several cofounders, Ethereum has gone through the milestones of initial launch, stable coins, ICO (Initial Coin Offering), DeFi (Decentralized Finance), DAO (Decentralized Autonomous Organization), NFT (nonfungible tokens), and L2 (Layer 2) for scalability. The Ethereum blockchain and infrastructure are transitioning to POS (proof of stake) with sharding and are on the way to impose big impacts on CBDC (Central Bank Digital Currency) and enterprise blockchains with promising potential. After years of astronomical growth, Ethereum has reached a market cap of $400 billion, just trailing behind Bitcoin. Compared with bitcoin, the Ethereum blockchain has advantages such as supporting the Ethereum Virtual Machine (EVM) and smart contracts, supporting more diverse use cases, and being more nimble by changing consensus from POW to POS to save energy costs.
Graphene and its Applications. Study on The Development Trends in Research and on the Implementation Potential using Big Data and Information Refining Methods

This paper presents the results of a study on development trends for graphene technologies and on the applications of this material, which is a dynamically growing field of Nano technological research. The study uses information refinement methods and analytical methods using big data tools, as well as statistical methods and machine learning-based algorithms. The aim of the study was to determine the potential of graphene application and identification of change trends in this field, based on the previously defined fields of technology. The results of the study indicate the potential of analytical tools - the greater the more specific, specialized knowledge in the field is involved. The study identified trends related to graphene applications, mainly in the field related to energy solutions. Research dedicated to applications in electric engineering include not only batteries, but also transistors, magnetic field sensors, fuel cells, electric conductors and generators.
Now is the time to strengthen cyber defences

Thanks in part to the Covid-19 pandemic, cybercrime is at an all-time high and many organisations are struggling to cope. A large number of companies have had to grapple with security strategies for the first time, and those that already had plans in place found them being tested to the full.
Uncertainty Guided Collaborative Training for Weakly Supervised and Unsupervised Temporal Action Localization

In weakly supervised (WSAL) and unsupervised temporal action localization (UAL), the target is to simultaneously localize temporal boundaries and identify category labels of actions with only video-level category labels (WSAL) or category numbers in a dataset (UAL) during training. Among existing methods, attention based methods have achieved superior performance in both tasks by highlighting action segments with foreground attention weights. However, without the segment-level supervision on the attention weight learning, the quality of the attention weight hinders the performance of these methods. In this paper, we propose a novel Uncertainty Guided Collaborative Training (UGCT) strategy to alleviate this problem, which mainly includes two key designs: (1) The first design is an online pseudo label generation module, in which the RGB and FLOW streams work collaboratively to learn from each other. (2) The second design is an uncertainty aware learning module, which can mitigate the noise in the generated pseudo labels. These two designs work together to promote the model performance effectively and efficiently by exchanging information between RGB and FLOW streams. Extensive experimental results on two benchmark datasets with three attention based methods demonstrate the effectiveness of the proposed method, e.g, more than 7.0% performance gain for mAP@IoU=0.5 on THUMOS14 dataset.
Hand Interaction Toolset for Augmented Reality Environments

As Augmented and Virtual Reality (AR/VR) technologies become nowadays very popular, there is the need for better interaction in these environments. In this paper, a toolset that facilitates the process of creating interaction in AR and VR environments is presented. The toolset is mainly targeted on mobile devices as the majority of AR and VR applications are exported. The toolset is divided into two main parts; the first part presents the components that are utilized inside the Unity 3D game engine, and the second part involves a webservice to handle the hand tracking task. The separation of hand tracking into a different part allows changes of the algorithms without the need to update the entire package. To evaluate the proposed toolset, a scenario of usage was fabricated in which users -with a computer science background- were shared a package and were asked to complete three tasks and evaluate their experience of using the toolset to create applications. Furthermore, the resulting applications were tested in real case scenarios to examine the overall performance and experience.
A Cloud-Based Mobile Augmented Reality Application for BIM Collaboration

The complexity and the fragmentation of the construction industry require interprofessional collaboration and effective real-time management of information exchange throughout the construction process. Augmented Reality (AR), Building Information Modeling (BIM) and Cloud Computing have proven to be highly promising in enhancing performance while reducing costs in the AEC domain. This paper presents a mobile Open-Source application named "CollaBIM" providing visualization, collaboration, and interaction functionalities with a BIM model. Collaboration is the focus of this work by using a Cloud service allowing notification, display of the schedule, visualization of updated data and images. The application is developed under Unity and uses a BIM model resulting from a Scan-To-BIM process. It is executable on Android mobile devices and can be easily integrated into the work processes of the construction sector.
A real-time model for COVID19 face-mask identification with “YOLOv4”

At the beginning of 2020 WHO declared COVID19 as an epidemic; healthcare industries experts and academicians from worldwide are working in the directions to surveillance the daily behaviors of the citizens to combat the COVID-19 cases. In India, we thank the government for performing its outperformed active measures and spontaneous compliance to follow the policy of wearing masks when moving out to any public places; it entails active real-time monitoring to supervise the citizens by governments. In this process, real-time face-mask identification is a very challenging task of computer vision. And the absence of accurate datasets for this problem is a critical hard problem to solve. To address this bottleneck, we are proposing our real-time deep learning face-mask identification technique with annotated class labels with bounding boxes which have its real-time application to assist the governments to control and prevent the spread of these epidemics in its supervision. Our model is very robust and effective to classify the real-time images and videos for face mask detection with accuracy and average precision. The proposed model substitutes the manual surveillance with the object detection method using YOLOv4 supported on a deep learning approach to monitor the crowd accurately even if they change their respective locations. The experiment identify or classify the object within any dataset to distinguish the images or videos with two class labels such as &#x201C;with-mask&#x201D; and &#x201C;without-mask&#x201D; with approximately 98.26&#x0025; accuracy, mAP of 68.28&#x0025;, recall of 77&#x0025;, and precision of 57&#x0025;.
WordAlchemy: A Transformer-based Reverse Dictionary

A reverse dictionary takes a target word's description as input and returns the words that fit the description. Reverse Dictionaries are useful for new language learners, anomia patients, and for solving common tip-of-the-tongue problems (lethologica). Currently, there does not exist any Reverse Dictionary provider with support for any Indian Language. We present a novel open-source cross-lingual reverse dictionary system with support for Indian languages. In this paper, we propose a transformer-based deep learning approach to tackle the limitations faced by the existing systems using the mT5 model. This architecture uses the Translation Language Modeling (TLM) technique, rather than the conventional BERT's Masked Lanagage Modeling (MLM) technique.
Modeling and analysis of the decentralized interactive cyber defense approach

Powered by the Internet and the ever-increasing level of informatization, the cyberspace has become increasingly complex and its security situation has become increasingly grim, which requires new adaptive and collaborative defense technologies. In this paper, we introduced an extended interactive multi-agent decision model for decentralized cyber defense. Based on the significant advantages of the cooperative multi-agent decision-making, the decentralized interactive decision model DI-MDPs and the corresponding interaction and retrieval algorithms are proposed. Then, we analyzed the interactive decision by the calculation and update processes of three matrices, the stability and evolutionary equilibrium of the proposed model are also analyzed. Finally, we evaluated the performance of the proposed algorithms based on open data sets and standard test environments, the experimental results shown that the proposed work will be more applicable in cyber defense.
Bitcoin network measurement and a new approach to infer the topology

Bitcoin has made an increasing impact on the world's economy and financial order, which attracted extensive attention of researchers and regulators from all over the world. Most previous studies had focused more on the transaction layer, but less on the network layer. In this paper, we developed BNS(Bitcoin Network Sniffer), which could find and connect nodes in the Bitcoin network, and made a measurement in detail. We collected nearly 4.1 million nodes in 1.5 hours and identified 9,515 reachable nodes. We counted the reachable nodes' properties such as: service type, port number, client version and geographic distribution. In addition, we analyzed the stability of the reachable nodes in depth and found nearly 60% kept stable during 15 days. Finally, we proposed a new approach to infer the Bitcoin network topology by analyzing the Neighbor Addresses of Adjacent Nodes and their timestamps, which had an accuracy over 80%.
Lightweight blockchain fuzzy decision scheme through MQTT and Fibonacci for sustainable transport

The unprecedented progress in field of IoT enabled rapid developments in the vehicle intelligent transportation systems and most of these provide services in a centralized way. However, the centralized system architecture is vulnerable to the external attacks as a result both information and equipment are prone to eavesdropping and destruction. Therefore, there is a trend to apply blockchain technology to the vehicle intelligent transportation systems in order to achieve sustainable transportation. Nevertheless, the system is so great and very sophisticated and the ultimate task will be harder to implement. In view of this, an attempt is made in this paper to propose a lightweight fuzzy decision blockchain scheme through MQTT and Fibonacci, and through this scheme, the extent of blockchain server can be scaled and easy to deploy. Also through MQTT, reliable communication and transmission of blockchain can be realized. LF-BC is formed by using DH and Fibonacci transformation to enhance security, and F-PBFT consensus algorithm can reduce the communication overhead and improve the fault tolerance tremendously. Using LF-BC scheme, the experimental results show that the fault tolerance rate is significantly improved by 22.3%, and the sustainable safety and reliability of the vehicle intelligent transportation system is increased consumedly. At the same time, the feasibility of the scheme is also verified by taking specific cases.
Word Segmentation by Component Tracing and Association (CTA) Technique

Word-level segmentation is a very important step in many document analysis systems. This is because word is the most important unit in any language systems. Word segmentation of handwritten documents is a very challenging task due to cursive nature of handwriting, overlap, touching and crossing of adjacent words, non-straight baselines, and cluttering among many others. Of these challenges, crossing is the most difficult challenge to deal with. This paper proposes a novel offline word-level segmentation technique for handwritten documents that addresses the challenges of touching and crossing of words. The main contribution of the paper is junction branch association (JBA) method that specifically handles touching and crossing words where many other proposed methods fail. The proposed method has been evaluated with ICDAR2009 and ICDAR2013 benchmark datasets of handwritten scripts. Also, crossing words extracted from FireMaker dataset of handwritten documents have been used to specifically evaluate performance of JBA method in segmenting crossing words.
The hercynian imposed deformations in the Golets Vysochaishii deposit structure (Baikal-Patom belt), &lt;sup&gt;40&lt;/sup&gt;Ar/&lt;sup&gt;39&lt;/sup&gt;Ar data

The paper presents the results of the detailed structural analysis and 40 Ar/ 39 Ar dating of the deformations in the Golets Vysochaishii deposit area. The geological and structural studies showed the folded-deformation sequence from large lying folds through axial-plane cleavage formation and cleavage to quartz-filled fractures. The 40 Ar/ 39 Ar dating of syntectonic sericite yielded the ages of two Hercynian tectonic impulses – 340 and 320 Ma. This stage is characterized by the earlier-known successive complication of the Early Paleozoic fold-nappe structure of the area. Analysis has been made on four samples taken from different sites and characterized by different sets of deformational structures. The 40 Ar/ 39 Ar age of 340 Ma reflects the formation of small-size folds as a result of interlayer sliding and sliding along the cleavage axial planes. The 40 Ar/ 39 Ar age of 320 Ma reflects the formation of discretely manifested crenulation cleavage and low-Au, low-sulfidation quartz veins oriented obliquely to the elements of bedding and axial-plane cleavage.
Integrated classification schemas to interlink cultural heritage collections over the web using LOD technologies

Libraries, archives and museum collections are now being published over the web using LOD technologies. Many of them have thematic intersections or are related to other web subjects and resources such as authorities, sites for historic events, online exhibitions, or to articles in Wikipedia and its sibling resources DBpedia and Wikidata. The full potential of such published initiatives using LOD rests heavily on the meaningful interlinking of such collections. Within these contextual vocabularies and classifications, schemas are important, as they provide meaning and context to heritage data. This paper proposes comprehensive classification schemas - a Culturally Relevant Relationships (CRR) vocabulary and a classification schema of types of heritage objects - to order, integrate and provide structure to cultural heritage data brought about with the publication of heritage collections as LOD.
Utilization of Microsoft Excel in Assisting the Preparation of Financial Reports at MI Ngalian Tirto Pekalongan

The role of Information and Communication Technology is very much needed for school institutions as their provision in dealing with the changes and demands of the times in the era of the Industrial Revolution 4.0. Operating a computer is part of the hard skills. Students and teachers need to have skills in operating the programs contained in Microsoft Windows and the program that is most often used is Microsoft Excel. This particular program can support daily work as it makes processing data and compiling financial reports easier. Through Microsoft Excel, financial reports can be prepared simply with a shorter cycle, from journaling to simple financial reports. The purpose of this community service is to provide training on the process of preparing financial reports through Microsoft Excel in order to streamline administrative performance. The method used in this service is in the form of training and direct practice. The subject of this training is the financial treasurer section of MI Ngalian Tirto. The results of this service indicate that this training provides knowledge for school treasurers regarding simple accounting processes by utilizing excel so that in preparing financial reports can be better.
Joint remote implementation of operators

Abstract We propose a deterministic protocol for three distant parties to cooperate so that two parties can implement their secret unitary operators on the third party’s secret quantum state via shared quantum channel assisted by cross-Kerr nonlinearities. The operators are of general form while the state may be encoded either in spatial degree of freedom or polarization degree of freedom. The quantum channel is served by a three-photon hyperentangled state establishing the minimum of consumed photon number for this type of task. This protocol can be named joint remote implementation of operators which is necessary for distributed quantum tasks throughout a quantum network.
Implementing DevSecOps Pipeline for an Enterprise Organization

DevSecOps is an organizational software engineering culture and practice that aims at unifying software development (Dev), security (Sec) and operations (Ops). DevSecOps is an extension of DevOps, which is considered as a means to intertwine development, operation and security. The main characteristic of DevSecOps is to improve customer outcomes and mission value by automating, monitoring, and applying security at all phases of the software lifecycle: plan, develop, build, test, release, deliver, deploy, operate, and monitor. Continuous practices, i.e., continuous integration, delivery, and deployment, are the software development industry practices that enable organizations to frequently and reliably release new features and products. DevSecOps means thinking about application and infrastructure security from the start. With the increasing interest in the literature on continuous practices, it is important to systematically review and synthesize the approaches, tools, challenges, and practices reported for adopting and implementing continuous practices. This paper aimed at systematically reviewing the state of the art of continuous practices to classify approaches and tools, identify challenges and practices in this regard, and identify the gaps for future research. We used the systematic literature review method for reviewing the peer reviewed papers on continuous practices published between 2004 and June 1, 2016. We applied the thematic analysis method for analysing the data extracted from reviewing 69 papers selected using predefined criteria. Conclusion: Although DevSecOps is getting increasing attention by industry, it is still in its infancy and needs to be promoted by both academia and industry.
On the Fixed-Interval Smoothing for Jump Markov Nonlinear Systems

This paper considers the fixed-interval smoothing for jump Markov systems. An optimal backward-time recursive equation for computing the joint posterior of the state vector and model index is established first. A suboptimal algorithm is then developed to approximate the new Bayesian smoother under nonlinear state-space models with additive Gaussian noise. The proposed method utilizes the well-known assumed density filtering with Gaussian assumption and the expression for the quotient of two Gaussian densities to compute the smoothing posterior. It eliminates the need for finding the inverse of the state dynamics and can handle singular process noise covariance, compared with several existing multiple model smoothers. Promising results are obtained in simulations using a maneuvering target tracking task.