Reviewerly/exHarmony huggingface dataset:
Weighting of BLEG data with drainage and catchment properties to enhance Au anomalies

This research aims to take the effect of drainage sediment dilution into consideration by using catchment weighted-bulk leachable extractable gold (CW-BLEG) data in order to enhance and recognize Au anomalies. As a case study, an area in the Eskisehir region in Western Turkey was selected. In this area, gold-bearing crystalline quartz veins, of probable orogenic type, containing up to 20 g/t Au, occur in low-grade schists and marbles of the Karakaya Complex in the vicinity of the Sogut area. To classify the final geochemical maps of the BLEG data to differentiate Au anomalies from background, the singularity model was applied. After that, in order to take the sediments dilution effect (DE) into account, the other important factors in the study area such as the sinuosity index (SI) of the stream sediments per catchment, and the topography slope and area of each catchment were taken into consideration and implemented on the BLEG data. To do so, the CW-BLEG models based on the relation between the Au concentrations and each factor (i.e., C × sinuosity as DE-sinuosity, C × slope as DE-slope, and C × area as DE-area) were generated. Because of the insufficient number of known mineral deposits within the study area to evaluate the effectiveness of the models statistically, the models generated were compared with each other and studied using cross-validation. It demonstrated that the DE-area and DE-sinuosity models work better than the other model in recognizing strong and very strong anomalies considering the known mineral deposits. The cross-validation results show that the DE-area model provides a more robust result compared the other models. Considering the models generated, the very strong and strong Au anomalies in the western part of the study area are corresponded to the known gold mineralization (e.g., Sogut and Mayislar) hosted by the Karakaya Complex, and the anomalies in the eastern and central parts of the study area are associated with the Damdaca mineralization.
Modern trends in the digitalization of education

Abstract The process of education digitalization has been on its way for a long time. However, a strong surge of interest arose during the period of temporary communication restrictions associated with Covid-19 and the widespread transition to a completely remote format of interaction between all participants in educational process. Digitalization has affected all levels of education and directly the activities of students, teachers, administrators, or stakeholders. The purpose of the study is to evaluate the current state of education digitalization. The objectives of the research are to study trends in the global and domestic online educational market, identify the features of digital educational activities implementation in the Russian Federation, and comparatively analyze the willingness of secondary and higher education to put digital distance learning into practice.
Evaluating Remote Virtual Hands Models on Social Presence in Hand-based 3D Remote Collaboration

This study investigates the effects of a virtual hand representation on the user experience including social presence during hand-based 3D remote collaboration. Although a remote hand appearance is a critical parts of a hand-based telepresence, it has been rarely studied in comparison to studies on the self-embodiment of virtual hands in a 3D environment. Thus, we conducted a user study comparing the three virtual hands models (Skeleton, Low Polygon and Realistic) while performing a remote collaborative task based on the American Sign Language (ASL) using both Augmented Reality (AR) and Virtual Reality (VR) environments. We found that the realistic type was perceived as the most sense of being together, human-like, and trustable representation. The low polygon model could also convey a clear sign and moderate level of social presence. Although the system was configured asymmetrically in AR and VR, little difference in perception was found except for the participant's mental load and message understanding. We then discuss the results and suggest design implications for future hand-based 3D telepresence systems.
Deep learning based Blockchain solution for preserving privacy in future vehicles

The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today’s generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system.
Human Machine Interaction Platform for Home Care Support System

There has been a tremendous increase in the costs of caring for older adults owing to the fact that societies are aging around around the world. This has led to a decrease in the number of caregivers who are able to assist. Investigative studies indicate that older adults require social as well as physical support for their well-being which prompted researchers to use social and cognitive robots and advanced human machine interaction devices. However, most of these studies have shortcomings when it comes to providing means of a natural interaction with the machine. With speech being the most natural way for human communication and the huge developments in the Internet of Things and smart homes, equipping a robotic system with powerful natural speech interaction capabilities to maintain a conversation with an elderly while being linked to other smart home devices shows a promising direction. This paper describes a scalable and expandable system with main goal of designing a natural speech-enabled system for older adults that is capable of linking to multiple active agents with minimal integration efforts. The system makes use of the power of commercially available digital assistant systems, integrated with an intelligent conversational agent, robotics, and smart wearables. The main advantage of the system is that it could provide a portion of the population, namely older adults and the disabled, the flexibility of interacting naturally with powerful social robots in smart home environments, hence providing them with much needed independence.
Hierarchical data generator based on tree-structured stick breaking process for benchmarking clustering methods

A new variant of Hierarchical Cluster Analysis is gaining interest in the field of Machine Learning, called Object Cluster Hierarchy. Being still at an early stage of development, the lack of tools for systematic analysis of Object Cluster Hierarchies inhibits further improvement of this concept. In this paper we address this issue by proposing a generator of synthetic hierarchical data that can be used for benchmarking Object Cluster Hierarchy generation methods. The article presents a thorough empirical and theoretical analysis of the generator and provides guidance on how to control its parameters. The conducted experiments show the usefulness of the data generator capable of producing a wide range of differently structured data. Furthermore, datasets that represent the most common types of hierarchies are generated and made available to the public for benchmarking, along with the developed generator (http://kio.pwr.edu.pl/?page_id=396).
Multiple Object Tracking for Similar, Monotonic Targets

As a vital and extremely hot branch of computer vision, multiple object tracking is widely used in security monitoring and biological behavior research. At present, the majority of the multiple object tracking algorithms are composed of two stages: detection and data association. There is often mutual interference between targets in each frame, which causes great trouble to object detection and correlation. Especially for the microscopic particles, detection and tracking always is a tricky business because of its similar appearance and irregular movement. In this paper, a multiple object tracking algorithm based on two-step detection is proposed for microscopic non-fluorescent labeled object with simple structure, monotonous features, even no obvious features for learning. The specific operation is: first threshold segment each image in video to extract targets from the background, and perform morphological operations on the obtained graph to separate the slightly adhesion targets to avoid incorrect tracking, then estimate object states by Kalman Filter and use Hungarian algorithm for data association, definitively achieve the goal of multiple object tracking. Experiments show that our algorithm is better than the general method, meeting the tracking task of multiple non-fluorescent labeled particles commendably.
Effective Breast Cancer Recognition Based on Fine-Grained Feature Selection

Early detection and diagnosis of breast cancer are crucial to improve the survival rates of patients. Hence, pathologists and radiologists need a computer-aided diagnosis system to assist their clinical diagnoses effectively and efficiently. However, most breast cancer recognition models are faced with the sample scarcity problem, which results in serious overfitting and lowers recognition performance. To alleviate the sample scarcity problem, a simple, effective model called “refinement, correlation, adaptive” (RCA) for breast cancer recognition is proposed from the perspective of fine-grained feature selection. An innovative multiview efficient range-based gene selection algorithm is proposed to complete the first-layer feature “refinement,” which contributes to suppressing the noisy information in the original feature space. Then, more-discriminant but low-dimensional information among heterogeneous features is mined through the second-layer cross-modal “correlation” mining. Feature dimensions are reduced to a reasonable value that fits the sample size well and alleviates the overfitting problem. Finally, the last-layer decision-tree-guided “adaptive” feature selection is completed using the gradient boosting decision tree algorithm. The RCA model was validated on two well-known datasets. The experimental results demonstrate that the proposed RCA model can address the sample scarcity problem well. It outperforms state-of-the-art baselines, especially in terms of accuracy and the area of the Kiviat diagram. The largest performance improvements of the metrics are 2.39% and 1121, respectively. Moreover, an online diagnosis system based on the RCA model is proposed. It provides rapid and effective breast cancer recognition, which should make clinical diagnoses more convenient and narrow the gap between theoretical research and practical application.
‘You wouldn’t get that from watching TV!’: Exploring audience responses to virtual reality non-fiction in the home

Consumer virtual reality (VR) headsets (e.g. Oculus Go) have brought VR non-fiction (VRNF) within reach of at-home audiences. However, despite increase in VR hardware sales and enthusiasm for the platform among niche audiences at festivals, mainstream audience interest in VRNF is not yet proven. This is despite a growing body of critically acclaimed VRNF, some of which is freely available. In seeking to understand a lack of engagement with VRNF by mainstream audiences, we need to be aware of challenges relating to the discovery of content and bear in mind the cost, inaccessibility and known limitations of consumer VR technology. However, we also need to set these issues within the context of the wider relationships between technology, society and the media, which have influenced the uptake of new media technologies in the past. To address this work, this article provides accounts by members of the public of their responses to VRNF as experienced within their households. We present an empirical study – one of the first of its kind – exploring these questions through qualitative research facilitating diverse households to experience VRNF at home, over several months. We find considerable enthusiasm for VR as a platform for non-fiction, but we also find this enthusiasm tempered by ethical concerns relating to both the platform and the content, and a pervasive tension between the platform and the home setting. Reflecting on our findings, we suggest that VRNF currently fails to meet any ‘supervening social necessity’ (Winston, 1996, Technologies of Seeing: Photography, Cinematography and Television. British: BFI.) that would pave the way for widespread domestic uptake, and we reflect on future directions for VR in the home.
Subtraction Gates: Another Way to Learn Long-Term Dependencies in Recurrent Neural Networks

Recurrent neural networks (RNNs) can remember temporal contextual information over various time steps. The well-known gradient vanishing/explosion problem restricts the ability of RNNs to learn long-term dependencies. The gate mechanism is a well-developed method for learning long-term dependencies in long short-term memory (LSTM) models and their variants. These models usually take the multiplication terms as gates to control the input and output of RNNs during forwarding computation and to ensure a constant error flow during training. In this article, we propose the use of subtraction terms as another type of gates to learn long-term dependencies. Specifically, the multiplication gates are replaced by subtraction gates, and the activations of RNNs input and output are directly controlled by subtracting the subtrahend terms. The error flows remain constant, as the linear identity connection is retained during training. The proposed subtraction gates have more flexible options of internal activation functions than the multiplication gates of LSTM. The experimental results using the proposed Subtraction RNN (SRNN) indicate comparable performances to LSTM and gated recurrent unit in the Embedded Reber Grammar, Penn Tree Bank, and Pixel-by-Pixel MNIST experiments. To achieve these results, the SRNN requires approximate three-quarters of the parameters used by LSTM. We also show that a hybrid model combining multiplication forget gates and subtraction gates could achieve good performance.
PABC: A Patent Application System Based on Blockchain

Under the protection of the patent system, an inventor can patent his or her invention, to make an economic profit from the patent, thus encouraging further invention.However, a patent is territorial.To get an international patent, the invention should be patented several times in all target patent offices, even with the help of international conventions like PCT, leading to inefficiency, expensiveness, and uncertainty.Therefore, we construct an international patent application system based on a permissioned blockchain named PABC.Patent applications can be approved or rejected without relying on one patent office.Each patent office in a country or region manages a peer node of the system, reaching agreements of all the applications.First, we build the model of the system and detail the three-layer architecture of PABC Core.Then, we implement PABC on a permissioned blockchain platform -Hyperledger Fabric.In addition, experiments about its throughput, transaction latency, and failure rate were carried out.The results show that PABC can meet current needs.
Polarimetric Calculation Method of Global Pixel for Underwater Image Restoration

The estimation of the degree of polarization of backscattered light and target light is the key to realize scattering removal in underwater polarimetric imaging. In this paper, a new polarization scattering removal method is proposed by realizing polarimetric calculation of the target light with each pixel of the scene automatically, which is conducive to the restoration of the details of underwater scene. The global model of pixel level is established by utilizing the gradient prior of the polarization information of the total radiance image. Different from the previous study that the degree of polarization of the target light is a constant, the global calculation method can solve the global variable image of the degree of polarization of the target light and it is used for reconstruction experiments of underwater real-world scene to verify the effectiveness of the algorithm. The experimental results demonstrate that the global pixel calculation method has obvious effect on image detail recovery of the underwater scene to realize the clear underwater vision. The influence of the backscattered light on underwater imaging is well suppressed and image contrast has been significantly improved, which provides a new method for enhancing performance of underwater polarimetric imaging with better vision.
Artificial Intelligence (AI)-Centric Management of Resources in Modern Distributed Computing Systems

Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centers are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Internet of Things (IoT)-driven applications are producing a huge amount of data that requires real-time processing and fast response. Managing these resources efficiently to provide reliable services to end-users or applications is a challenging task. The existing Resource Management Systems (RMS) rely on either static or heuristic solutions inadequate for such composite and dynamic systems. The advent of Artificial Intelligence (AI) due to data availability and processing capabilities manifested into possibilities of exploring data-driven solutions in RMS tasks that are adaptive, accurate, and efficient. In this regard, this paper aims to draw the motivations and necessities for data-driven solutions in resource management. It identifies the challenges associated with it and outlines the potential future research directions detailing where and how to apply the data-driven techniques in the different RMS tasks. Finally, it provides a conceptual data-driven RMS model for DCS and presents the two real-time use cases (GPU frequency scaling and data centre resource management from Google Cloud and Microsoft Azure) demonstrating AI-centric approaches' feasibility.
Construct a robust least squares support vector machine based on L<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1478" altimg="si380.svg"><mml:msub><mml:mrow /><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math>-norm and L<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1490" altimg="si406.svg"><mml:msub><mml:mrow /><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:math>-norm

Despite some Lp-norm LSSVMs own feature selection and prediction ability, they still suffer from two common issues. (i) They always ignore edge points because the L2-norm metric is used to measure the classification error of the training samples. The edge points are important in some practical application or on the datasets that are non-independent and identically distributed (non-i.i.d). (ii) They spend higher computational time and storage space for the large scale datasets. In order to solve the above two shortcomings while retaining the feature selection ability, we adopt L∞-norm to measure the classification error of training samples and still use Lp-norm (0<p<1) to measure the maximum margin between two parallel support planes, then obtain a novel LSSVM classifier, denoted as Lp-L∞-LSSVM. Our Lp-L∞-LSSVM owns three advantages: (1) L∞-norm on empirical risk ensures the effective recognition of edge points, thereby improving the robustness and generalization ability of the classifier. (2) Lp-norm on structural risk possess feature selection ability, whether for the linear or non-linear separable case and is suitable for the small samples size (SSS) problem. (3) Inspired by the sequential minimal optimization (SMO) algorithm, we designed an iterative heuristic algorithm by breaking the large quadratic programming problem (QPP) into a series of smallest possible QPPs, which can avoid high time-consuming. This algorithm not only ensures the convergence of optimum solution but also consumes lower computational time and storage space for large scale datasets. Finally, extensive numerical experiments once again verify the above opinions and show the outstanding classification performance and feature selection ability simultaneously.
Forward Secure Public Key Encryption with Keyword Search for Cloud-assisted IoT

The Internet of Things (IoT) features a mechanism that extends connectivity to diverse computing devices, such as smart phones, commodity sensors, and appliances. Due to the huge quantity of data generated by the IoT devices, they are likely to be stored and managed by the cloud these days. However, because of the privacy concern about the sensitive data, encryption techniques are typically adopted by the cloud. In order to enable searching over encrypted data for multiple data senders in the cloud, public key encryption with keyword search (PEKS) has been proposed as one variant of searchable encryption (SE). Unfortunately, existing PEKS schemes are vulnerable to adaptive file-injection attack due to the lack of forward privacy. In this paper, we propose a forward secure PEKS scheme based on hierarchical identity-based encryption for cloud-assisted IoT environments. While the existing schemes incur to the data receiver a storage overhead that increases linearly with the number of data senders, our scheme incurs only a constant cost. The experimental analysis with Amazon EC2 and Raspberri Pi shows that our scheme is two to five times more efficient than the previous schemes, which makes our scheme more suitable for multiple data senders in the cloud-assisted IoT environments.
Blurred lines: Practical and theoretical implications of a DAW-based pedagogy

Digital audio workstations (DAWs) occupy a prominent space in the creative arts. Songwriters, composers, producers, and audio engineers use a combination of software and virtual instruments to record and make music. Educators increasingly find DAWs useful for teaching concepts in signal flow, acoustics and sound synthesis, and to model analogue processes. As the creative industries shift to primarily software-based platforms, the identities, roles, and responsibilities of the participants intersect and blur. Similarly, networked technologies change the space and place of creative activity. Now, the ‘studio’ exists virtually anywhere. For educators working with students, these changing paradigms present a series of challenges. This article explores the DAW’s possibilities across three areas: space and place, theory and identity, and pedagogy. The article advocates for a less technocratic model of teaching and learning with DAWs in favour of an approach that cultivates a balance of aesthetic awareness and creativity.