Reviewerly/exHarmony huggingface dataset:
Hierarchical recurrent neural networks for graph generation

Graph generation is widely used in various fields, such as social science, chemistry, and physics. Although the deep graph generative models have achieved considerable success in recent years, some problems still need to be addressed. First, some models learn only the structural information and cannot capture the semantic information. Second, many existing models cannot be applied to large-scale datasets owing to a complex training process and a large number of parameters to be learned. Third, most of the existing models require domain-specific rules to be set up, resulting in poor generalization. To address the aforementioned problems, we propose a domain-agnostic model with hierarchical recurrent neural networks, named GHRNN, which learns the distribution of graph data for generating new graphs. First, we utilize the minimum depth-first-search code to convert a graph into a unique edge sequence that naturally generalizes to various graphs. Then, we adopt the edge-level recurrent neural network (RNN) module to learn the global structure of a graph and the element-level RNN module to obtain semantic information, such as node labels, edge labels, and the timestamp of nodes. The element-level RNN module with a fixed and small number of time steps in GHRNN can efficiently scale to large datasets. Finally, we generate a large number of graphs and evaluate the effectiveness of our model by using 11 different metrics on diverse datasets ranging from molecules to citation networks. Extensive experimental results indicate that GHRNN can capture complex dependencies and generate the most realistic and diverse graphs, in contrast to several state-of-the-art baselines.
COVID-19 Confirmed Cases Prediction in China Based on Barnacles Mating Optimizer-Least Squares Support Vector Machines

Abstract The Covid19 has significantly changed the global landscape in every aspect including economy, social life, and many others. After almost two years of living with the pandemic, new challenges are faced by the research community. It may take some time before the world can be declared as totally safe from the virus. Therefore, prediction of Covid19 confirmed cases is vital for the sake of proper prevention and precaution steps. In this study, a hybrid Barnacles Mating Optimizer with Least Square Support Vector Machines (BMO-LSSVM) is proposed for prediction of Covid19 confirmed cases. The employed data are the Covid19 cases in China which are defined in daily periodicity. The BMO was utilized to obtain optimal values of LSSVM hyper-parameters. Later, with the optimized values of the hyper-parameters, the prediction task will be executed by LSSVM. Through the experiments, the study recommends the superiority of BMO-LSSVM over the other identified hybrid algorithms.
Big Data Analysis with Momentum Strategy on Data-driven Trading

Data-driven trading approaches have become more widespread in quantitative hedge funds due to the availability of large amount of financial data and the popularization of quantitative trading strategies. One of the most popular trading strategy is called momentum strategy which seeks the opportunity of market volatility by buying the stock when the price goes up and selling them as soon as there is a sign showing they would go down. Although this concept is clear to the majority, building one concrete momentum strategy remains a problem to every investor. In response to this concern, in this paper, we propose a the detailed algorithm to build a concrete momentum strategy. First, we compare the performance of four basic momentum strategies with respect to their return in S&P 500 index and its option investment. Then we optimize the momentum trading strategy by adding one extra accelerating factor and then enhance with decision tree investment method so as to capture more market trading opportunities and become more profitable. From the experiment, we find that straddle hedging strategy is proved to effectively lower the draw-down risk. In addition, although using options rather than stocks would largely raise the portfolio variance, it will significantly avoid the bad performance of momentum strategy during the bear market.
Learning by Ignoring the Most Wrong

Abstract Imprecise probabilities (IP) are an increasingly popular way of reasoning about rational credence. However they are subject to an apparent failure to display convincing inductive learning. This paper demonstrates that a small modification to the update rule for IP allows us to overcome this problem, albeit at the cost of satisfying only a weaker concept of coherence.
The Strategy of Media Education Development of a Future Journalist as a Factor of Professionalism in the Digital Economy

The article examined one of the priority areas of social development — media education. The transition to a new technological system has caused major changes in media education. Digitalization processes influence the system of higher vocational education and highlight the fact that media education should be aimed at developing students' skills relevant to new digital environment. The originality of the research is the fact that media education is one of the factors that ensures that students are able to adjust to innovative economy of the Russian Federation. The study examined the regional demand for trained media literate professionals. The research proposed a model of media education for students majoring in journalism and a special educational program to develop professional competencies taking into account the media discourse in the region. The article discussed one of the components of the model of strategically oriented media education — the integrative course of additional vocational education. We found that graduates who participated in media education program not only improved their professional competencies defined by the government standard, but also acquired additional skills such as the ability to form media culture of the target audience. This kind of focus of educational efforts increased their competitiveness in the regional labor market. The proposed model of media education of future journalists, which integrates information and communication environment of the university in sync with current industry requirements; media technologies as part of the core subjects, and the advanced training program that focuses on the management of media literacy of the audience, has shown its effectiveness. Teaching methodology presented by the authors develops value and belief system that has ongoing professional media education at its core. It also fosters the development of mass media culture in digital environment.
Characteristics and determinants of population acceptance of COVID-19 digital contact tracing: a systematic review.

As recently outlined in the WHO-ECDC Indicator framework (1) to evaluate the public health effectiveness of digital proximity tracing solutions, one of the main barriers to digital contact tracing (DCT) is population acceptance, which, in turns, is influenced by digital literacy, attitudes and practice. DCT came to public prominence during the COVID-19 pandemic but evidence on its population acceptance have not been comprehensively analyzed. Methods: We carried out a systematic review (PROSPERO: CRD42021253668) following the PRISMA guidelines to collect, systematize and critically appraise the available evidence on population DCT acceptance. Original studies reporting on different measures of population DCT acceptance were included.The systematic review was based on 41 articles meeting our a priori defined inclusion criteria, comprising aa total of 186144 surveyed subjects, 50000 tweets, 5025 Reddit posts and 714 written comments. Data extraction and synthesis required a qualitative outcome grouping, performed ex-post, in 14 different benchmarks components. They constitute a narrative analysis of actionable points for public health policy. Population acceptance is a key component of DCT effective adoption and infection control during infectious diseases outbreaks. Assessing DCT acceptance's determinants in different settings, populations an cultural contexts it is of fundamental importance to inform the planning, implementation and monitoring of public health interventions. The results of our in-depth qualitative and quantitative analysis will provide context for prospective improvements and actionable items and should guide future research aimed at exploring how digitalization can serve people-centred care.
Survey of NISQ Era Hybrid Quantum-Classical Machine Learning Research

Quantum computing is a rapidly growing field that has received a significant amount of support in the past decade in industry and academia. Several physical quantum computers are now freely available to use through cloud services, with some implementations supporting upwards of hundreds of qubits. These advances mark the beginning of the Noisy Intermediate-Scale Quantum (NISQ) era of quantum computing, paving the way for hybrid quantum-classical systems. This work provides an introductory overview of gate-model quantum computing through the Visual IoT/Robotics Programming Language Environment and a survey of recent applications of NISQ era quantum computers to hybrid quantum-classical machine learning.
Definition of an FHIR-based multiprotocol IoT home gateway to support the dynamic plug of new devices within instrumented environments

Abstract Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020, there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection over multiple protocols (e.g. Bluetooth, MQTT, CoAP, ZigBEE, etc.) the interpretation, as well as the harmonization of the data format that derive from the existing huge amounts of heterogeneous IoT medical devices. In this respect, this study aims at proposing an advanced Home Gateway architecture that offers a unique data collection module, supporting direct data acquisition over multiple protocols (i.e.BLE, MQTT) and indirect data retrieval from cloud health services (i.e. GoogleFit). Moreover, the solution propose a mechanism to automatically convert the original data format, carried over BLE, in HL7 FHIR by exploiting device capabilities semantic annotation implemented by means of FHIR resource as well. The adoption of such annotation enables the dynamic plug of new sensors within the instrumented environment without the need to stop and adapt the gateway. This simplifies the dynamic devices landscape customization requested by the several telemedicine applications contexts (e.g. CVD, Diabetes) and demonstrate, for the first time, a concrete example of using the FHIR standard not only (as usual) for health resources representation and storage but also as instrument to enable seamless integration of IoT devices. The proposed solution also relies on mobile phone technology which is widely adopted aiming at reducing any obstacle for a larger adoption.
Going beyond Cryptocurrencies: The Social Representation of Blockchain

This study investigates the social representation of Blockchain from the perspective of professionals in Brazil, herein considered as a proxy for emerging markets, and then compares the results found with the existing academic literature on the concept of Blockchain. To do that, the social representation theory was applied, operationalized through the words evocation technique. Security, bitcoin and decentralization were the categories located in the central nucleus of the social representation of Blockchain, while innovation, data, network, cryptocurrency, and technology were the categories located in the peripheral system. Based on the results obtained, there was a perceived strong association of Blockchain with bitcoin, one of its applications, and a dissonance between the existing academic literature and the perception of Brazilian professionals about the concept of Blockchain, as the latter is a privilege of the technical and operational issues of Blockchain to the detriment of its strategic potential. This dissonance can cause Blockchain initiatives to have results below expectations. Finally, Brazilian professionals did not realize the potential for inclusion of Blockchain in an emerging market such as Brazil and did not notice the need and relevance of a specific legal governance for Blockchain, an issue also forgotten by academia.
A novel Paradigm for Access Control Trust in IoT Applications: A Distributed Cross-Communication Approach

How to best secure communications between IoT devices remains a challenging research area. However, the heterodox nature of individual IoT devices and its limited capabilities demand a more centralized, authority-derivative nature of SSL/TLS for the IoT ecosystem. While a number of experimental protocols have been proposed, many of these techniques are either derivative of SSL/TLS on some layer, or lack either enough ubiquity to be widely applied to a live ecosystem, or verifiable results. This paper proposes a distributed system for securing end-to-end cross-device communications; Access Control Trust A Distributed Cross-Communication scheme (ACT-DCC) using a localized web of self-determinate nodes in a network. As opposed to distributed systems, which typically rely on blockchain or centrally signed certificates, the proposed scheme instead operates on an iterative, decentralized method, which combines pathfinding techniques alongside an algorithmic interpretation of hash-chaining and ledger-based storage. An initial network device carries an original key, which is naturally spread to additional connected devices on the same network in a distributed, web-like fashion. As more devices join the distributed network, the strength of the network grows exponentially through the volume of edge devices available in the web. The proposed scheme shows significant improvement as compared to other cross-device communication schemes, and encourages better-individualized decentralization, and less locally stored data.
Human Resource Management and Technology Development in Artificial Intelligence Adoption in the UAE Energy Sector

Abstract Artificial Intelligence (AI) is proved a very effective technique in reducing complexity and making suitable quick decisions for achieving success. Artificial Intelligence is an emerging area and growing fast. It is used successfully in various fields. In UAE also, AI is being used in several sectors. Now the government of UAE has promoted AI in the energy sector to get the maximum of its benefits. Hence, this study evaluated various indicators that affect the promotion of AI in the energy section of the UAE. With the help of a questionnaire survey, 350 questionnaire forms were analyzed to prioritize the parameters affecting AII adoption. From the analysis results it was found that “Organization use AI to provide effective business innovation”, “Organizations use AI to align with its business strategy”, and “Organization use AI to improve the levels of production” are the key motivating factor to adopt AI. Significant parameters of AI technology include are; AI Technology is user friendly, AI Technology is able to improve the quality of the work and AI Technology fits well with the tasks involved; are reported as significant technological parameters to adopt AI. On the other hand, Referral person is required if facing difficulty with AI technology, and teammate support in using AI technology are essential parameters of the human resource management which affect AI adoption in the energy sector of the UAE. In addition, reliability and normality tests validated the data. Hence, these findings can be used to promote AI and understand the situation for making proper decisions.
Calibration of Short-Wave InfraRed (SWIR) hyperspectral imaging using Diffuse Reflectance Infrared Fourier Transform spectroscopy (DRIFTS) to obtain continuous logging of mineral abundances along sediment cores

Sediment cores are a major source of sub-surface data for a variety of environmental research endeavors, as well as mining and civil engineering applications. In paleo-environmental studies, fluctuations in mineral abundances along sediment cores are used to study climate change, sea-level variations, sediment dynamics, and landscape evolution. Increasing reliance on such proxies spurs the need for reliable and time-saving analytical tools and procedures. Diffuse Reflectance Infrared Fourier Transform spectroscopy (DRIFTS) is a rapid, non-destructive and accurate tool for quantification of mineral abundances. We use DRIFTS to calibrate Short-Wave InfraRed (SWIR) hyperspectral images (HSIs) of a sediment core, in order to convert point-specific DRIFTS measurement of mineral abundances into a continuous record of mineral abundances along the core. Initially, single-phase and mineral assemblages of quartz, calcite, and clay mineral are created and measured by DRIFTS to establish calibration curves to convert DRIFTS data into mineral abundances, as %wt of bulk sediment samples. Mineral abundances are then measured by DRIFTS in a set of 60 samples from a core drilled into the Albegna River delta, Tuscany, Italy in the Fall of 2020. SWIR-HSIs of the core are then obtained and averaged over the areas corresponding to the samples taken for point-specific DRIFTS measurements. Least squares regressions are established between SWIR-HSI and DRIFTS data over these areas. This calibration is then used to convert SWIR-HSIs of the entire core into a continuous prediction of quartz, calcite, and clay mineral abundances along the core. The continuous record highlights sharp changes in mineral composition, smeared out in the discontinuous DRIFTS record. The advantages and limitations of the method, as well as its applicability to other sedimentary environments are discussed.
Towards facial recognition using likelihood ratio approach to facial landmark indices from images

We propose a novel approach aimed at facial comparison in forensic context. We employed an automatic approach to detect facial landmarks, and then selected independent facial indices extracted from a subset of these landmarks. As it is difficult to compare the morphometric indices due to pose variations, some statistically reliable method is required for face comparison. The present work demonstrates the use of likelihood ratios (LRs) to assess the grouping of facial images based on the morphometric indices, in a database of 40 persons, with 10 facial images each; in different poses, expressions, illumination and background. Using the criterion LR> 1, for correct grouping, and only considering indices that are not correlated, a true positive (TP) rate of 85% and false positive (FP) rate of 25% was obtained. We have also calculated the performance metrics for assessment of the validity and reliability of the likelihood ratio approach and the log likelihood ratio cost has been found to be 0.26. As compared to more abstract face identification methods, the proposed approach is relevant in forensic context, as it is based on interpretable morphometric features of faces. The likelihood ratio approach being a statistical approach is reliable as it leverages the variations due to pose variations in the face images, offers a good quality segregation of faces and hence is a promising technique for use in forensic facial identification.
Evaluating The Sunshine Duration Characteristics In Association With Other Climate Variables

Many processes such as for industrial production, heating, cooling, health, education and etc. are depended on and use the energy.In recent years, the consumption of energy has been increased over the world.Fossil energy sources cannot meet the increase in energy demand.So, renewable energy sources especially solar energy become important in this way.The applications with solar energy present clear and environment friendly systems.Sunshine duration which is used for estimating the solar energy is very crucial parameter for human life.Climate variables in atmospheric environment have influences on sunshine duration.This study aims to demonstrate the relations between daily sunshine duration and other climate variables such as cloud amount, atmospheric pressure, maximum air temperature, minimum air temperature, relative humidity, precipitation and wind speed.The data of all climate variables were obtained from the measurements of ground based observation station belonging to the Turkish State Meteorological Service.The long term records based on daily measurements were examined for Rize meteorological station.The daily observation values for sunshine duration and other climate variables were transformed to mean montly values.A 51 years database was statistically analyzed by using the method named as Pearson Correlation Analysis (%95 confidence interval).The obtained results over this analysis were shown that sunshine duration was associated with other climate variables.The grade of these relations was changed according the different time periods.The amount of relations were obtained generally in moderate, good and weak degrees as statistically significant (p<0.05).Sunshine duration was highly negatively correlated with cloud amount generally except the exceptional circumstances such as moderate for December.The correlations for sunshine duration and the other climate variables were found as negatively for relative humidity, atmospheric pressure and precipitation and positively for wind speed, maximum and minimum air temperature.
Personalized Federated Learning with Semisupervised Distillation

Heterogeneous data and models pose critical challenges for federated learning. However, the traditional federated learning framework, which trains the global model by transferring model parameters, has major limitations; it requires that all participants have the same training model architectures, and the trained global model does not guarantee accurate projections for participants’ personal data. To solve this problem, we propose a new federal framework named personalized federated learning with semisupervised distillation (pFedSD), which ensures the privacy of the participants’ model architectures and improves the communication efficiency by transmitting the model’s predicted class distribution rather than model parameters. First, the server adopts the adaptive aggregation method to reduce the weight of low-quality model predictions for the model’s predicted class distributions uploaded by all clients, which helps to improve the quality of the aggregation of the prediction class distribution. Then, the server sends it back to the clients for local training to obtain the personalized model. We finally conducted experiments on different datasets (MNIST, FMNIST, and CIFAR10), and the results show that the model performance of pFedSD exceeds the latest federated distillation algorithms.
Detecting Smishing Attacks Using Feature Extraction and Classification Techniques

Phishing scams via SMS have become a common phenomenon due to the widespread use of smartphones and the availability of mobile Internet technologies. Identifying a phishing SMS via analyzing unstructured short texts is a challenging issue in the domain of AI-driven cybersecurity. Machine learning-based techniques integrated with natural language processing have massive potentials to identify differentiating patterns between phishing and legitimate SMS. In this paper, we have experimented with several state-of-the-art machine learning algorithms on a benchmark dataset. Also, NLP-based feature extraction and feature selection steps are incorporated to build an automated phishing detection strategy. Support vector machine classifier when applied after feature extraction and feature selection has outperformed the tenfold cross-validation score of 98.27%, F1-score of 99.08% for legitimate SMS, and accuracy of 98.39%. The performance of the tested methods has been evaluated through popular evaluation metrics on a benchmark dataset.