Reviewerly/exHarmony huggingface dataset:
Real-World Robot Evolution: Why Would it (not) Work?

This paper takes a critical look at the concept of real-world robot evolution discussing specific challenges for making it practicable. After a brief review of the state of the art several enablers are discussed in detail. It is noted that sample efficient evolution is one of the key prerequisites and there are various promising directions towards this in different stages of maturity, including learning as part of the evolutionary system, genotype filtering, and hybridizing real-world evolution with simulations in a new way. Furthermore, it is emphasized that an evolutionary system that works in the real world needs robots that work in the real world. Obvious as it may seem, to achieve this significant complexification of the robots and their tasks is needed compared to the current practice. Finally, the importance of not only building but also understanding evolving robot systems is emphasised, stating that in order to have the technology work we also need the science behind it.
CloudBank: Managed Services to Simplify Cloud Access for Computer Science Research and Education

CloudBank is a cloud access entity founded to enable the computer science research and education communities to harness the profound computational potential of public clouds. By delivering a set of managed services designed to alleviate common points of friction associated with cloud adoption, Cloudbank serves as an integrated service provider to the research and education community. These services include front-line help desk support, cloud solution consulting, training, account management, cost monitoring and optimization support, and automated billing. CloudBank has a multi-cloud pay-per-use billing model and aims to serve the spectrum of cloud users from novice to advanced.
A Mathematical Model of the Feature Variability

Mathematical reason of the work is to inspire the activity to build a model of a variable property depending on the stages of knowledge and recognition mapping. An evolent is chosen along which the stages evolve. A generalized recognition mapping is constructed, which varies along the stage evolvent. It is shown that the induced variable domains form the functional spaces and collect exactly those individuals who satisfy the recognition condition. This gives rise to families of subsets that can be treated as generalized concepts. These concept spaces, in turn, can be expressed through families of dependencies on variable concepts. This is achieved by a suitable selection of displaced concepts. The cognitive reason of the work is consistent applying the method of indexical expressions, which allows the semantic model to take into account the stages of knowledge and their evolving. The resulting information graphs, as it turns out, can be interpreted as an environment for the propagation of data streams, which in this sense can control their interference. Using this basis, a toolkit for managing cognitive information can be developed. In addition, in this work, the ideas of modeling information processes by variable domains are largely inherited. The features of process inheritance are taken into account. The model solutions used are also based on the idea of channelized propagation of processes. Finally, the possibilities of jump-like transitions of processes, as well as the effects of entanglement of information processes, potentially causing cognitive interference and its propagation along the network channels, are taken into account.
Unsupervised Machine Learning in 6G Networks -State-of-the-art and Future Trends

Wireless communication systems play a very crucial role for business, commercial, health and safety applications. With the commercial deployment of fifth generation (5G), academic and industrial research focuses on the sixth generation (6G) of wireless communication systems. Artificial Intelligence (AI) and especially Machine Learning (ML), will be a key component of 6G systems. Here, we present an up-to-date review of future 6G wireless systems and the role of unsupervised ML techniques in them.
Toward Distributed, Global, Deep Learning Using IoT Devices

Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Utilizing such datasets to produce a problem-solving model within a reasonable time frame requires a scalable distributed training platform/system. We present a novel approach where to train one DL model on the hardware of thousands of mid-sized IoT devices across the world, rather than the use of GPU cluster available within a data center. We analyze the scalability and model convergence of the subsequently generated model, identify three bottlenecks that are: high computational operations, time consuming dataset loading I/O, and the slow exchange of model gradients. To highlight research challenges for globally distributed DL training and classification, we consider a case study from the video data processing domain. A need for a two-step deep compression method, which increases the training speed and scalability of DL training processing, is also outlined. Our initial experimental validation shows that the proposed method is able to improve the tolerance of the distributed training process to varying internet bandwidth, latency, and Quality of Service metrics.
The Application of Human–Computer Interaction Technology Fused With Artificial Intelligence in Sports Moving Target Detection Education for College Athlete

The purposes are to digitalize and intellectualize current professional sports training and enrich the application scenarios of motion capture technology of moving targets based on artificial intelligence (AI) and human–computer interaction (HCI) in sports training. From an educational psychology perspective, sport techniques are a cognitive ability of sports, and a tacit knowledge. However, sports technology, language, image, and other methods play an auxiliary role in sports training. Here, a General Framework of Knowledge-Based Coaching System (KBCS) is proposed using the HCI technology and sports knowledge to accomplish autonomous and intelligent sports training. Then, the KBCS is applied to table tennis training. The athletic performance is evaluated quantitatively through the calculation of the sports features, motion recognition, and the hitting stage division in table tennis. Results demonstrate that the speed calculated by the position after mosaicking has better continuity after the initial frame of the unmarked segment is compared with the end frame of the market segment. The typical serve and return trajectories in three serving modes of slight-spin, top-spin, and back-spin, as well as the trajectories of common services and return errors, are obtained through the judgment of the serving and receiving of table tennis. Comparison results prove that the serving accuracy of slight-spin and back-spin is better than that of top-spin, and a lower serve speed has higher accuracy. Experimental results show that the level distribution of the three participants calculated by the system is consistent with the actual situation in terms of the quality of the ball returned and the standard of the motion, proving that the proposed KBCS and algorithm are useful in a small sample, thereby further improving the accuracy of pose restoration of athletes in sports training.
Irish Attitudes Toward COVID Tracker App &amp; Privacy: Sentiment Analysis on Twitter and Survey Data

Contact tracing apps used in tracing and mitigating the spread of COVID-19 have sparked discussions and controversies worldwide. The major concerns in relation to these apps are around privacy. Ireland was in general praised for the design of its COVID tracker app, and the transparency through which privacy issues were addressed. However, the "voice" of the Irish public was not really heard or analysed. This study aimed to analyse the Irish public sentiment towards privacy and COVID tracker app. For this purpose we have conducted sentiment analysis on Twitter data collected from public Twitter accounts from Republic of Ireland. We collected COVID-19 related tweets generated in Ireland over a period of time from January 1, 2020 up to December 31, 2020 in order to perform sentiment analysis on this data set. Moreover, the study performed sentiment analysis on the feedback received from a national survey on privacy conducted in Republic of Ireland. The findings of the study reveal a significant criticism towards the app that relate to privacy concerns, but other aspects of the app as well. The findings also reveal some positive attitude towards the fight against COVID-19, but these are not necessarily related to the technological solutions employed for this purpose. The findings of the study contributed to the formulation of useful recommendations communicated to the relevant Irish actors.
Privacy Preserving in Online Social Networks Using Fuzzy Rewiring

Privacy concerns of users threaten the usage of online social networks (OSN). In this regard, privacy preserving of OSN emerged as a convincing solution for preserving the privacy of users and uncovering useful insights from the social network data. In this article, we propose a novel algorithm based on the fuzzy sets and rewiring algorithm for preserving the privacy of users. This article presents the algorithm called privacy-preserving rewiring algorithm (PPRA), which can be used for anonymizing the social network data. The algorithm is validated by showing its effectiveness on four real-world datasets across three major graph mining tasks. The proposed PPRA algorithm will help in preserving the privacy of users in the OSN graph while simultaneously maintaining the utility that can be generated from the OSN graph structure.
RGB Cameras Failures and Their Effects in Autonomous Driving Applications

RGB cameras are one of the most relevant sensors for autonomous driving applications. It is undeniable that failures of vehicle cameras may compromise the autonomous driving task, possibly leading to unsafe behaviors when images that are subsequently processed by the driving system are altered. To support the definition of safe and robust vehicle architectures and intelligent systems, in this paper we define the failure modes of a vehicle camera, together with an analysis of effects and known mitigations. Further, we build a software library for the generation of the corresponding failed images and we feed them to six object detectors for mono and stereo cameras and to the self-driving agent of an autonomous driving simulator. The resulting misbehaviors with respect to operating with clean images allow a better understanding of failures effects and the related safety risks in image-based applications.
FAIR and Open Data in Science: The Opportunity for IUPAC

Abstract At the start of 2020, IUPAC’s Committee on Publications and Cheminformatics Data Standards (CPCDS) formed a Task Force to propose guidelines for the dissemination and sharing of machine-readable chemical data. These guidelines would be for IUPAC to adopt internally and recommend to the wider chemistry community.
A method of predicting oil and gas resource spatial distribution based on Bayesian network and its application

The spatial distribution prediction of hydrocarbon resource is essential to reduce exploration risks and improve investment returns. Determining the location of the spatial distribution of oil and gas resources is a complex and uncertain problem. This paper systematically analyzes oil and gas exploration risk assessment and resource spatial distribution prediction technology, and proposes a method of predicting hydrocarbon spatial distribution based on averaged one-dependence estimators(AODE). Firstly, the transformation process of petroleum geological problem to mathematical model is described. Then, the classification principle and decision rule of AODE model are expounded. Finally, a case study of Sangonghe Formation in the hinterland of Junggar Basin in China is given to further illustrate the proposed method and work flow. The case prediction results not only reveal that the accuracy of the AODE model can reach 85.2% on the data set of 203 exploratory wells, which is higher than state-of-the-art methods (such as tree augmented Bayesian network method and the Mahalanobis distance method), but also point out the areas with high probability of remaining hydrocarbon resources in Sangonghe Formation, which provides decision-making basis for the next exploration. The application results show that the AODE model can effectively predict the spatial distribution of oil and gas and visualize the risk of geological exploration, thereby optimizing drilling strategy and increasing economic benefits.
UrbanRama: Navigating Cities in Virtual Reality

Exploring large virtual environments, such as cities, is a central task in several domains, such as gaming and urban planning. VR systems can greatly help this task by providing an immersive experience; however, a common issue with viewing and navigating a city in the traditional sense is that users can either obtain a local or a global view, but not both at the same time, requiring them to continuously switch between perspectives, losing context and distracting them from their analysis. In this paper, our goal is to allow users to navigate to points of interest without changing perspectives. To accomplish this, we design an intuitive navigation interface that takes advantage of the strong sense of spatial presence provided by VR. We supplement this interface with a perspective that warps the environment, called UrbanRama, based on a cylindrical projection, providing a mix of local and global views. The design of this interface was performed as an iterative process in collaboration with architects and urban planners. We conducted a qualitative and a quantitative pilot user study to evaluate UrbanRama and the results indicate the effectiveness of our system in reducing perspective changes, while ensuring that the warping doesn't affect distance and orientation perception.
Web service selection and composition based on uncertain quality of service

Abstract Web services are becoming a major utility for accomplishing complex tasks over the Internet. In practice, the end‐users usually search for Web service compositions that best meet the quality of service (QoS) requirements (i.e., QoS global constraints). Since the number of services is constantly increasing and their respective QoS is inherently uncertain (due to environmental conditions), the task of selecting optimal compositions becomes more challenging. To tackle this problem, we propose a heuristic based on majority judgment that allows for reducing the search space. In addition, we perform a constraint programming search to select the Top K compositions that fulfill the QoS global constraints. The experimental results demonstrate the high performance of our approach.
Identifying hydrothermally altered rocks using ASTER satellite imageries in Eastern Anti-Atlas of Morocco: a case study from Imiter silver mine

The Imiter silver mine in the Eastern Anti-Atlas metallogenic province, Eastern Morocco, is a world-class silver ore deposit. This region has potential for undiscovered silver mineralisation and deserves a detailed remote-sensing study. In this study, Crosta, band ratios and mixture-tuned matched-filtering (MTMF) methods were applied to ASTER remote-sensing data. Argillic, phyllic and propylitic alteration zones were identified using specialised band ratios and Crosta techniques. Sub-pixel abundances of goethite, haematite, limonite, muscovite/illite, chlorite/epidote, jarosite and kaolinite/alunite were detected using MTMF algorithm. Accordingly, several alteration zones were identified and delimited in the central, northern, southern and northeastern parts of the study region, which can be considered as high prospective zones. GPS survey, analysis of thin and polish sections, XRD and geochemical survey verified the alteration zones and sulphide mineralisation in high prospective zones. This approach can be applied in other parts of the Eastern Anti-Atlas metallogenic province to explore hydrothermal ore deposits.
Real-Time Data Selection and Merging for 3D-Image Sensing Network With Multiple Sensors

Acquiring high quality 3D-image data in real time from multiple 3D-image sensors is problematic because of the following three difficulties: 1) latency, 2) calibration, and 3) limited network bandwidth. First, the delay from sensing to getting the detection results needs to be within a few hundreds of milliseconds. Second, time calibration and spatial calibration are essential when data are merged from multiple 3D-image sensors. Lastly, due to a large volume of the data from 3D-image sensors, a part of data cannot be delivered under limited bandwidth when data are merged via the network. In this paper, to tackle the difficulties, we propose a scheme for real-time 3D-image sensing with multiple 3D-image sensors. The proposed scheme consists of a data selection method to cut down the data volume while suppressing computational complexity, and a data merging method to merge data from multiple 3D-image sensors while satisfying the required periodical deadline. We develop a prototype system to implement the proposed scheme. We measure the processing delay and confirm that the proposed scheme satisfies the delay requirement. We also measure the quality of merged data collected from multiple 3D-image sensors using the proposed scheme, by performing experiments in laboratory and real fields. From the measurement results, we verify that the proposed scheme solves the difficulties; it merges data from multiple 3D-image sensors and maintains high data quality in real time by reducing the data volume against strict bandwidth limitation in both laboratory and real fields.
Doctor Recommendation Model Based on Ontology Characteristics and Disease Text Mining Perspective

Patients can access medical services such as disease diagnosis online, medical treatment guidance, and medication guidance that are provided by doctors from all over the country at home. Due to the complexity of scenarios applying medical services online and the necessity of professionalism of knowledge, the traditional recommendation methods in the medical field are confronting with problems such as low computational efficiency and poor effectiveness. At the same time, patients consulting online come from all sides, and most of them suffer from nonacute or malignant diseases, and hence, there may be offline medical treatment. Therefore, this paper proposes an online prediagnosis doctor recommendation model by integrating ontology characteristics and disease text. Particularly, this recommendation model takes full consideration of geographical location of patients.The recommendation model takes the real consultation data from online as the research object, fully testifying its effectiveness. Specifically, this model would make recommendation to patients on department and doctors based on patients' information of symptoms, diagnosis, and geographical location, as well as doctor's specialty and their department.Utilizing crawler technique, five hospital departments were selected from the online medical service platform. The names of the departments were in accordance with the standardized department names used in real hospitals (e.g., endocrinology, dermatology, gynemetrics, pediatrics, and neurology). As a result, a dataset consisting of 20000 consultation questions by patients was built. Through the application of Python and MySQL algorithms, replacing semantic dictionary retrieval or word frequency statistics, word vectors were utilized to measure similarity between patients' prediagnosis and doctors' specialty, forming a recommendation framework on medical departments or doctors based on the above-obtained sentence similarity measurement and providing recommendation advices on intentional departments and doctors.In the online medical field, compared with the traditional recommendation method, the model proposed in the paper is of higher recommendation accuracy and feasibility in terms of department and doctor recommendation effectiveness.The proposed online prediagnosis doctor recommendation model integrates ontology characteristics and disease text mining. The model gives a relatively more accurate recommendation advice based on ontology characteristics such as patients' description texts and doctors' specialties. Furthermore, the model also gives full consideration on patients' location factors. As a result, the proposed online prediagnosis doctor recommendation model would improve patients' online consultation experience and offline treatment convenience, enriching the value of online prediagnosis data.
Security and Privacy Issues in Cloud Computing

Abstract The idea of Cloud Computing (CC) provides dynamically scalable services that are delivered over the Internet as a service. The key driver of the cloud is economic benefits, as it aims to minimize capital expenditure as well as operating expenditure. There are still some problems to be overcome in order for this to become reality. One of the important issue is security and privacy concerns, which has been addressed by many researchers but still the problem persist. Security assurance is an important driver for cloud adoption and for increasing cloud deployment. To provide a detailed overview of existing cloud security challenges and mitigation strategies, this paper provides a comprehensive survey of underlying cloud security and privacy issues and concerns along with countermeasures. Further; as a contribution in research, we have provided a framework to address the security and privacy concerns in CC. Proposed framework uses hybrid authentication mechanism for the security of CC. The study provides a deeper insight to the researchers and practitioners about CC and underlying security and privacy concerns along with countermeasures and a novel solution.
Solar Potential in Saudi Arabia for Inclined Flat-Plate Surfaces of Constant Tilt Tracking the Sun

The objective of the present work is to investigate the optimally performing tilt angles in Saudi Arabia of solar panels that follow the daily motion of the Sun. To that end, the annual energy sums are estimated for surfaces with tilt angles in the range 5°–55° at 82 locations covering all Saudi Arabia. All calculations use a surface albedo of 0.2 and a near-real value, too. It is found that tilt angles of 40°, 45°, and 50°, respectively, are optimal for the three recently defined solar energy zones in Saudi Arabia. The variation of the energy sums in each energy zone on annual, seasonal and monthly basis is given for near-real ground albedos; the analysis provides regression equations for the energy sums as functions of time. A map of the annual global inclined solar energy for Saudi Arabia is derived and presented. The annual energy sums are found to vary between 2159 kWhm−2year−1 and 4078 kWhm−2year−1. Finally, a correction factor, introduced in a recent publication, is used; it is confirmed that the relationship between the correction factor and either the tilt angle or the ground-albedo ratio has a general application and it may constitute a nomogram.