Reviewerly/exHarmony huggingface dataset:
The Curious Case of Stablecoins—Balancing Risks and Rewards?

ABSTRACT Stablecoins is a blockchain-driven innovation and a new subset of crypto assets. Even though they could transform how payments are made, regulators paid little attention to them until recently. The announcement of the Libra project in 2019 elevated stablecoins to the top of the regulatory agenda. Libra’s global scale and its capacity to reach billions of potential users through a user-centric social network platform that is already seamlessly integrated within the lives of the global population and the potential impact of a global yet fast and cheap payment solution raised many issues and concerns among authorities related to not only financial stability, monetary policy, and competition, but also money laundering, financing of terrorism, and others. Addressing stablecoins has proven challenging for many regulators as they face a difficult task of balancing financial stability, with innovation. This paper analyzes how the official perception of stablecoins has evolved, from dismissiveness and underestimation to serious concern. It evaluates existing regulatory responses, highlights regulatory dilemmas, and makes recommendations regarding future regulatory approaches. To reap the benefits of stablecoin innovation, regulators need to take a broader long-term view of stablecoins beyond the perceived risks and embrace their advantages. Regulations should not stifle this innovation but support a diverse ecosystem of stablecoins and foster competition.
Blockchain-Based Trusted Property Transactions in the Built Environment: Development of an Incubation-Ready Prototype

Blockchain can be introduced to use cases in the built environment where reliability of transaction records is paramount. Blockchain facilitates decentralised, cryptographically secure, trustworthy, and immutable recordkeeping of transactions. However, more research is urgently required to understand the process and complications in implementing blockchain solutions in the built environment. This paper demonstrates a methodology for developing a blockchain system starting from problem analysis, selection of blockchain platform, system modelling, prototype development, and evaluation. The evolutionary prototyping model was selected as the software development methodology for the use case of property transactions. A systematic process protocol involving the multi-criteria decision-making method, Simple Multi Attribute Rating Technique (SMART), was used to select Hyperledger Fabric as the most suitable blockchain platform for the prototype. The system architecture facilitates a simplified, lean property transaction process implemented through chaincode (smart contract) algorithms and graphical user interfaces. System evaluation through test cases allowed iterative improvements, leading to an incubation-ready software prototype. The contribution to knowledge of this paper is in the demonstration of the process to follow to implement a blockchain solution for a specific domain. The findings provide the foundation for developing proofs of concept for other potential applications of blockchain in the built environment.
Language Identification of Intra-Word Code-Switching for Arabic–English

Multilingual speakers tend to mix different languages in text and speech; a phenomenon referred to by linguists as “code-switching” (CS). Also, speakers switch between morphemes from various languages in the same word (intra-word CS). User-generated texts on social media are informal and contain a fair amount of different types of CS data. This data needs to be investigated and analyzed for several linguistic tasks. Language Identification (LID) is one of the important tasks that should be tackled for intra-word CS data. LID involves segmenting mixed words and tagging each part with its corresponding language ID. This work aimed at creating the first annotated Arabic–English (AR–EN) corpus for the CS intra-word LID task along with a web-based application for data annotation. We implemented two baseline models using Naïve Bayes and Character BiLSTM for AR–EN text. Our main model was constructed using segmental recurrent neural networks (SegRNN). We investigated the usage of different word embeddings with SegRNN. The highest LID system for tagging the entire data-set was obtained using SegRNN alone, achieving an F1-score of 94.84% and was able to recognize mixed words with F1-score equal to 81.15%. Besides, the model of the SegRNN with FastText embeddings achieved the highest results equal to 81.45% F1-score for tagging the mixed words.
Prospects of using innovations in post-pandemic higher education

The prerequisites of this study were the practices of using innovative educational methods and technologies in higher education during the global COVID-19 pandemic, as well as their potential prospects for using already in the post-pandemic period. The purpose of the study was to assess the prospects and give innovative forecasts used in the framework of distance learning in post-pandemic higher education. To achieve this goal, such methods were used: analysis, inductive-deductive method, comparison method, as well as experimental and forecasting methods. The main result of the study is the conclusion that the prospects for using innovations of the pandemic period in post-pandemic higher education take place mainly in organizing the educational process in a hybrid (or a mixed) format, which involves the simultaneous use of traditional and innovative teaching methods and technologies. At the same time, among the innovative educational technologies that emerged during the pandemic and promising in the post-pandemic period, the following aspects were identified: remote lectures; remote seminars using online whiteboards, online video conferences, virtual and augmented reality technologies; interactive online learning technologies; remote technologies for organizing the educational process. Traditional educational technologies, in turn, included the following: personal consultations with a teacher; personal communication with a supervisor on student research papers; interactive practical classes, as well as a number of other technologies that are built on direct communication between students within a group or on communication between students and a teacher.
Cryptography as the Means to Protect Fundamental Human Rights

Cryptography is traditionally considered as a main information security mechanism, providing several security services such as confidentiality, as well as data and entity authentication. This aspect is clearly relevant to the fundamental human right of privacy, in terms of securing data from eavesdropping and tampering, as well as from masquerading their origin. However, cryptography may also support several other (legal) requirements related to privacy. For example, in order to fulfil the data minimisation principle—i.e., to ensure that the personal data that are being processed are adequate and limited only to what is necessary in relation to the purposes for which they are processed—the use of advanced cryptographic techniques such as secure computations, zero-knowledge proofs or homomorphic encryption may be prerequisite. In practice though, it seems that the organisations performing personal data processing are not fully aware of such solutions, thus adopting techniques that pose risks for the rights of individuals. This paper aims to provide a generic overview of the possible cryptographic applications that suffice to address privacy challenges. In the process, we shall also state our view on the public “debate” on finding ways so as to allow law enforcement agencies to bypass the encryption of communication.
Management Innovation of Kuliah Kerja Praktek (KKP)

This study aims to provide innovation management for inputting practical work titles online based on Laravel. The rapid development of the positive number of Covid-19 has made the world restless, including the country of Indonesia, which has gone through more than 1 year of the pandemic period starting from March 2020 to May 2021. The surge in positive cases of Covid-19 has had a significant impact on the sustainability of the institution's teaching process. education. Covid-19 is forcing digital transformation towards the 4.0 education era. Technology is the only answer that can help during the pandemic, with technology the teaching and learning process continues, meetings, discussions and other things continue online. With digital transformation, conventional methods have been replaced, one of which is inputting the title ofPractical Job Training (KKP) at Raharja University which utilizes the ability of Ms. Excel. This offline KKP title input service must be replaced, given the existence of Large-scale Social Restrictions which require students to make long-distance contact with the KKP title inputting administrator and reduce the service for filling out forms which are quite time-consuming and the occurrence of data input errors. The method used in this research is the Agilemethod Scrum Tire which is a reference in working on Laravel-based technology. With the presence of this technology, it becomes a finding in replacing conventional methods and breakthroughs in the all-digital pandemic. The application of online KKP input can provide convenience and efficiency in the proces.
Web crawling based context aware recommender system using optimized deep recurrent neural network

Abstract Recommendation systems are obtaining more attention in various application fields especially e-commerce, social networks and tourism etc. The top items are recommended based on the ability of recommender system which predict the future preference out of the available items. Because of the internet, the people in the current society has too many options that’s why the recommendation system is very essential. The recommendation is achieved by the particular users who predict the ratings for numerous items and recommend those items to other users. Majorly, content and collaborative filtering techniques are employed in typical recommendation systems to find user preferences and provide final recommendations. But, these systems commonly lacks to take growing user preferences in various contextual factors. Context aware recommendation systems consider various contextual parameters into account and attempt to catch user preferences appropriately. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. Therefore, in this paper an effective deep learning based context aware recommendation model is proposed which can be act as an efficient recommender system by showing minimum error during recommendation. Initially, the dataset is pre-processed using Natural Language Tool Kit (NLTK) in Python platform. After pre-processing, the TF–IDF and word embedding model is used for every pre-processed reviews to extract the features and contextual information. The extracted feature is considered as an input of density based clustering to group the negative, neutral and positive sentiments of user reviews. Finally, deep recurrent neural Network (DRNN) is employed to get the most preferable user from every cluster. The recurrent neural network model parameter values are initialized through the fitness computation of Bald Eagle Search (BES) algorithm. The proposed model is implemented using NYC Restaurant Rich Dataset using Python programming platform and performance is evaluated based on the metrics of accuracy, precision, recall and compared with existing models. The proposed recommendation model achieves 99.6% accuracy which is comparatively higher than other machine learning models.
A Novel Random Error Approximate Adder-Based Lightweight Medical Image Encryption Scheme for Secure Remote Monitoring of Health Data

In the present global scenario, social distancing is an inevitable one. The need for social distancing and advancements of technology to facilitate the patients and doctors around the world mandated the telemedicine and remote monitoring of patience details as the pivotal way to diagnose the disease. In this, it is essential to transmit the patient’s information such as X-ray and scan images of them to the doctor in the remote location. Preventing the medical data from the technological adversaries is the need of the hour. Infinitesimal attacks in medical images may cost human lives. This work proposes a lightweight, secure medical image encryption scheme for the remote monitoring of health data. The proposed encryption scheme uses computationally less complex weighted shift approximate adder (WSAA)–based encryption logic. The scheme uses a 256-bit key for the encryption process that strengthens the encryption and robust against various attacks. The proposed encryption scheme deploys the WSAA for diffusing the pixel values. A unique way of key distribution for pixel-wise encryption within the image is proposed that avoids the need for separate logic for the pixel-wise confusion. The proposed Encryption scheme is evaluated for its entropy and horizontal, vertical, diagonal correlation, histogram, key space, and sensitivity. Experimental results affirm that the proposed scheme significantly good with less computational complexity. The peak signal-to-noise ratio (PSNR) value of the decrypted image is infinity, and this matches the ideal requirement of the medical encryption scheme.
Semi-Automatic Locating of Cryptographic Operations in Side-Channel Traces

Locating a cryptographic operation in a side-channel trace, i.e. finding out where it is in the time domain, without having a template, can be a tedious task even for unprotected implementations. The sheer amount of data can be overwhelming. In a simple call to OpenSSL for AES-128 ECB encryption of a single data block, only 0.00028% of the trace relate to the actual AES-128 encryption. The rest is overhead. We introduce the (to our best knowledge) first method to locate a cryptographic operation in a side-channel trace in a largely automated fashion. The method exploits meta information about the cryptographic operation and requires an estimate of its implementation’s execution time.The method lends itself to parallelization and our implementation in a tool greatly benefits from GPU acceleration. The tool can be used offline for trace segmentation and for generating a template which can then be used online in real-time waveformmatching based triggering systems for trace acquisition or fault injection. We evaluate it in six scenarios involving hardware and software implementations of different cryptographic operations executed on diverse platforms. Two of these scenarios cover realistic protocol level use-cases and demonstrate the real-world applicability of our tool in scenarios where classical leakage-detection techniques would not work. The results highlight the usefulness of the tool because it reliably and efficiently automates the task and therefore frees up time of the analyst.The method does not work on traces of implementations protected by effective time randomization countermeasures, e.g. random delays and unstable clock frequency, but is not affected by masking, shuffling and similar countermeasures.
Real-Time Face Mask Detection using Deep Learning

The outbreak of COVID-19 has taught everyone the importance of face masks in their lives. SARS-COV-2(Severe Acute Respiratory Syndrome) is a communicable virus that is transmitted from a person while speaking, sneezing in the form of respiratory droplets. It spreads by touching an infected surface or by being in contact with an infected person. Healthcare officials from the World Health Organization and local authorities are propelling people to wear face masks as it is one of the comprehensive strategies to overcome the transmission. Amid the advancement of technology, deep learning and computer vision have proved to be an effective way in recognition through image processing. This system is a real-time application to detect people if they are wearing a mask or are without a mask. It has been trained with the dataset that contains around 4000 images using 224x224 as width and height of the image and have achieved an accuracy rate of 98%. In this research, this model has been trained and compiled with 2 CNN for differentiating accuracy to choose the best for this type of model.It can be put into action in public areas such as airports, railways, schools, offices, etc. to check if COVID-19 guidelines are being adhered to or not.
On some operations on soft topological spaces

The aim of the article is to point out a one-to-one correspondence between soft topological spaces over a universe U with respect to a parameter set E and topological ones on the Cartesian product E x U. From this point of view, all soft topological terms, soft operations, soft functions and properties of soft topological spaces are actually topological concepts. Because the set valued mappings and set valued analysis have great application potential, it is necessary to look for their meaningful use with respect to standard topological methods and set valued analysis procedures.
A Blockchain-Based Auto Insurance Data Sharing Scheme

Auto electronic insurance policy and electronic maintenance list record the entire process of auto owners purchasing auto insurance and repairs after accident, respectively. They play a vital role in auto owners’ applications for claims and insurance company’s judgment on whether to settle the claims. However, the privacy of insurance policy and the “information island” resulting from the nonsharing of data between users make the claim has low efficiency. The notable features of blockchain technology are decentralization and tamper-proof, which can well solve data sharing and privacy protection. This paper proposes a blockchain-based auto insurance data sharing scheme to improve the existing auto insurance claim system. The scheme includes four main bodies: auto owner, insurance company, 4S Shop, and government authority. In the proposed scheme, the data sharing of authorized users is realized through proxy reencryption. Finally, we have analyzed the security and performance of the solution. The analysis results show that the proposed scheme can meet many security features such as user access control and data tamper resistance and has an ideal calculation and communication cost.
Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios

Abstract In this paper we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of Grammatical Evolution. In particular, we extend previous work, obtaining mathematical expressions to model glucose levels in the blood of diabetic patients. Here we use a multi-objective Grammatical Evolution approach based on the NSGA-II algorithm, considering the root-mean-square error and an ad-hoc fitness function as objectives. This ad-hoc function is based on the Clarke Error Grid analysis, which is useful for showing the potential danger of mispredictions in diabetic patients. In this work, we use two datasets to analyse two different scenarios: What-if and Agnostic , the most common in daily clinical practice. In the What-if scenario, where future events are evaluated, results show that the multi-objective approach improves previous results in terms of Clarke Error Grid analysis by reducing the number of dangerous mispredictions. In the Agnostic situation, with no available information about future events, results suggest that we can obtain good predictions with only information from the previous hour for both Grammatical Evolution and Multi-Objective Grammatical Evolution.
EfficientNet-B0 Based Monocular Dense-Depth Map Estimation

Monocular depth estimation is a hot research topic in autonomous car driving. Deep convolution neural networks (DCNN) comprising encoder and decoder with transfer learning are exploited in the proposed work for monocular depth map estimation of two-dimensional images. Extracted CNN features from initial stages are later upsampled using a sequence of Bilinear UpSampling and convolution layers to reconstruct the depth map. The encoder forms the feature extraction part, and the decoder forms the image reconstruction part. EfficientNetB0, a new architecture is used with pretrained weights as encoder. It is a revolutionary architecture with smaller model parameters yet achieving higher efficiencies than the architectures of state-of-the-art, pretrained networks. EfficientNet-B0 is compared with two other pretrained networks, the DenseNet-121 and ResNet50 models. Each of these three models are used in encoding stage for features extraction followed by bilinear method of UpSampling in the decoder. The Monocular image is an ill-posed problem and is thus considered as a regression problem. So the metrics used in the proposed work are F1-score, Jaccard score and Mean Actual Error (MAE) etc., between the original and the reconstructed image. The results convey that EfficientNet-B0 outperforms in validation loss, F1-score and Jaccard score compared to DenseNet-121 and ResNet-50 models.
Bayesian Optimization over Permutation Spaces

Optimizing expensive to evaluate black-box functions over an input space consisting of all permutations of d objects is an important problem with many real-world applications. For example, placement of functional blocks in hardware design to optimize performance via simulations. The overall goal is to minimize the number of function evaluations to find high-performing permutations. The key challenge in solving this problem using the Bayesian optimization (BO) framework is to trade-off the complexity of statistical model and tractability of acquisition function optimization. In this paper, we propose and evaluate two algorithms for BO over Permutation Spaces (BOPS). First, BOPS-T employs Gaussian process (GP) surrogate model with Kendall kernels and a Tractable acquisition function optimization approach to select the sequence of permutations for evaluation. Second, BOPS-H employs GP surrogate model with Mallow kernels and a Heuristic search approach to optimize the acquisition function. We theoretically analyze the performance of BOPS-T to show that their regret grows sub-linearly. Our experiments on multiple synthetic and real-world benchmarks show that both BOPS-T and BOPS-H perform better than the state-of-the-art BO algorithm for combinatorial spaces. To drive future research on this important problem, we make new resources and real-world benchmarks available to the community.