Reviewerly/exHarmony huggingface dataset:
Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Mine in Gansu Province, China as an example

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model. This method uses three techniques (window offset, scaling, and rotation) to enhance the number of training data for the model. A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits. Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area. This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset, meaning that the deep-learning method can be effectively used for deposit prospecting prediction. Using soil active geochemical measurement data, this method was applied in the Daqiao area, Gansu Province, for which seven favorable gold prospecting target areas were predicted. The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3‒4 g/t. In 2020, the project team drilled and verified the K prediction area, and found 66 m gold mineralized bodies. The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.
Towards a Holistic DLT Architecture for IIoT: Improved DAG for Production Lines

The Industrial Internet of Things (IIoT) aims to greatly improve the existing production procedures, enhancing customer experiences, reducing costs and increasing efficiency. IIoT will make a significant impact on existing business models in several areas. However, IIoT has several issues related to the security of the information and the excessive centralization which creates single point of failures, poor performance and scalability. Therefore, we introduce an Industry 4.0 case scenario, and, on top of it we propose a multi-layer distributed ledger architecture for IIoT that combines various types of Distributed Ledger Technologies (DLTs) in order to deliver a lightweight and efficient solution for Industry 4.0. However, in this paper we only focus on the design of the first layer of the architecture. We design a lightweight Directed Acyclic Graph (DAG) DLT architecture and propose several improvements regarding the lightweight devices participation, data storage, cryptography and consensus.
Lookahead adversarial learning for near real-time semantic segmentation

Semantic segmentation is one of the most fundamental problems in computer vision with significant impact on a wide variety of applications. Adversarial learning is shown to be an effective approach for improving semantic segmentation quality by enforcing higher-level pixel correlations and structural information. However, state-of-the-art semantic segmentation models cannot be easily plugged into an adversarial setting because they are not designed to accommodate convergence and stability issues in adversarial networks. We bridge this gap by building a conditional adversarial network with a state-of-the-art segmentation model (DeepLabv3+) at its core. To battle the stability issues, we introduce a novel lookahead adversarial learning (LoAd) approach with an embedded label map aggregation module. We focus on semantic segmentation models that run fast at inference for near real-time field applications. Through extensive experimentation, we demonstrate that the proposed solution can alleviate divergence issues in an adversarial semantic segmentation setting and results in considerable performance improvements (+5% in some classes) on the baseline for three standard datasets.
Research on the Application of Social Network Service in Resource Sharing of Ideological and Political Education in Colleges

This paper takes the retrieval demand of Ideological and political education resources in colleges as the background, studies the system architecture, working principle, key technology, workflow and other related theories and technologies of vertical search engine, and designs an educational resource sharing system based on social network service mode. On one hand, the vector space model is constructed by extracting the semantic information of education and curriculum, and the topic search engine architecture is used to solve the problem of topic crawler topic concept set expression and URL importance judgment; on the other hand, a vertical search engine for educational resources is implemented and integrated by Herltix, Htmlparser and Lucene. Finally, by testing the function and performance of educational resource sharing system, it shows that the system can run well and achieve certain results.
EBFT: A Hierarchical and Group-Based Byzantine Fault Tolerant Consensus Algorithm

Consensus algorithm is a key component of the blockchain technology, and also a hot topic in distributed system research. Under the background that blockchain technology has entered the development stage of consortium blockchain, the Practical Byzantine Fault Tolerance (PBFT), proposed by Miguel and Liskov in 1999, occupies an vital position. Although PBFT has many advantages, it does not scale well because of O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) communication complexity. In order to solve this drawback, we present a hierarchical and group-based BFT consensus algorithm — efficient BFT (EBFT). It utilizes a novel network topology to effectively reduce communication times among nodes and provides O(n) communication complexity. The experimental results show that EBFT optimizes the consensus process involving large-scale nodes, so that the consortium blockchain can be applied to a wider range of application scenarios.
Text Anchor Based Metric Learning for Small-Footprint Keyword Spotting

Keyword Spotting (KWS) remains challenging to achieve the trade-off between small footprint and high accuracy. Recently proposed metric learning approaches improved the generalizability of models for the KWS task, and 1D-CNN based KWS models have achieved the state-of-the-arts (SOTA) in terms of model size. However, for metric learning, due to data limitations, the speech anchor is highly susceptible to the acoustic environment and speakers. Also, we note that the 1D-CNN models have limited capability to capture long-term temporal acoustic features. To address the above problems, we propose to utilize text anchors to improve the stability of anchors. Furthermore, a new type of model (LG-Net) is exquisitely designed to promote long-short term acoustic feature modeling based on 1D-CNN and self-attention. Experiments are conducted on Google Speech Commands Dataset version 1 (GSCDv1) and 2 (GSCDv2). The results demonstrate that the proposed text anchor based metric learning method shows consistent improvements over speech anchor on representative CNN-based models. Moreover, our LG-Net model achieves SOTA accuracy of 97.67% and 96.79% on two datasets, respectively. It is encouraged to see that our lighter LG-Net with only 74k parameters obtains 96.82% KWS accuracy on the GSCDv1 and 95.77% KWS accuracy on the GSCDv2.
Efficient Communication Topology via Partially Differential Privacy for Decentralized Learning

Decentralized learning (DL) allows IoT devices to exchange local model updates with only their neighboring devices instead of sending their model updates to a central server for aggregation. However, current DL frameworks cannot support the emerging Social IoT(SIoT) paradigm since SIoT devices exchange model updates with only social neighbors based on specific social relations (e.g., ownership and parental relationships). Conversely, sharing model updates with non-social neighbors can improve training performance but may violate social relations. Differential privacy (DP) is thus engaged with DL to ensure data security, while excessive devices engaging DP may downgrade the training performance. However, most research neglects the effect of neighbor selection for each device based on social networks, physical networks, and DP. Therefore, in this paper, we explore the non-trivial relation among the above factors to present a DL framework, DeepPrivacy, and prove its convergence rate and DP. Then, we formulate a novel optimization problem, CoTOPO, to find an efficient communication topology <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for model updates exchange among devices in DL, and propose an algorithm, AutoTag, for CoTOPO. Last, experiment results manifest that DeepPrivacy and AutoTag combined outperform the state of the art in terms of convergence rate and physical training time significantly on CIFAR10 and FMNIST.
Integrating a dialog system with an intelligent tutoring system for a 3D virtual laboratory

This paper addresses the challenge of integrating a dialog system with an ITS created for supporting procedural training in a 3D virtual environment. To this end, we first describe the desired features of the dialog to be provided to students in such system. Then, we explain some technical issues of our proposal such as the architecture; the design of the dialog manager; and the construction of the answers to the students’ questions by leveraging an ontology related to the virtual world. Next, we present a pilot study with students to validate our approach. In this pilot study, we utilized a prototype that encompasses a 3D virtual laboratory. In the last seven years, students have used this virtual laboratory to perform a practice guided by an automatic tutor without dialog capacity. So, the evaluated prototype represents an extension of this automatic tutor that aims to give it the dialog capacity in the context of the practice.
Exploring Button Design for Low Contrast User Interfaces

Mainstream display technologies have progressed towards higher resolutions and contrast levels. However, for IoT applications, where displays are embedded in the environment, there are requirements to optimize displays for lower power consumption or minimal environmental impact, potentially sacrificing contrast or switching time. We approach this issue by considering the spatial design of UI components. As a first step, we explore switch type UI components (e.g., check box, toggle switch) and evaluate the performance of 4 alternative spatial designs at low contrast levels. As a contribution, we open the issue of spatial UI design for low contrast and demonstrate an approach for its evaluation.
INVESTOR ATTENTION AND HERDING IN THE CRYPTOCURRENCY MARKET DURING THE COVID-19 PANDEMIC

This study examines the relationship between investor attention and herding effects in the cryptocurrency market by employing the vector autoregression and quantile regression models. Furthermore, we examine whether the COVID-19 pandemic affected herding behaviour in cryptocurrencies. Using the daily closing price and Google search volume of the five leading cryptocurrencies, the paper finds that herding in the cryptocurrency market decreases with an increase in investor attention for the overall sample. The results for the COVID-19 period indicate that the impact of investor attention on the herding effect decreases due to increased attention to the pandemic. This study is one of the initial attempts to examine the impact of investor attention on herding in cryptocurrencies.
Measuring Digital Competence: An Exploratory Study Mapping Digital Competence Profiles of Sri Lankan English Language Teachers

The overall vision for 21st century learning has incorporated digitalisation as a key focus in teaching and learning practices. In Sri Lanka, however, despite major initiatives taken to improve digital competency of teachers, only minor improvement was noted. Using the DigCompEdu assessment tool, this study investigates to what extent Sri Lankan English language teachers are digitally competent. The results aim to inform national initiatives to facilitate the shift towards a bottom-up process, informed by actual realities based on skills and competences. The DigCompEdu 22-item quantitative survey was used to sample 40 English language teachers working within the public education system. Overall, the study finds that not even 50% of the sample is at one competency band. It is recommended that for substantial changes to occur, a more varied and individualised teacher-training is recommended, using the DigCompEdu as a diagnostic guide.
Real Time Web Based Information using Natural Language Processing (NLP) Algorithm

With the creation use of the Web and other substance application spaces, the game plans in both substance mining and Natural language preparing have been expanding. Executives in content mining have perceived that Natural language planning the undertaking to disconnect a enormous target of Natural language overseeing is to actualize inside PCs the propensity to get a handle on an average human language or standard language. It is related to the field of PC human correspondence. One of the motivations of Natural language overseeing is for the general people whose territory to web information is involved in a general sense by their need to use the assistance and working structure. Standard language goes under the zone of man-made skillful limit with the target of appreciation and making immense verbalizations in human language. Man-made thinking is the motivation behind detainment of a machine to duplicate sharp human direct.Therefore Natural language overseeing jobs Artificial Intelligence and is used to recover information in data mining. This paper shows an appraisal on Natural language arranging and its approaches.
Depth Map Super-Resolution Based on Dual Normal-Depth Regularization and Graph Laplacian Prior

The edge information plays a key role in the restoration of a depth map. Most conventional methods assume that the color image and depth map are consistent in edge areas. However, complex texture regions in the color image do not match exactly with edges in the depth map. In this paper, firstly, we point out that in most cases the consistency between normal map and depth map is much higher than that between RGB-D pairs. Then we propose a dual normal-depth regularization term to guide the restoration of depth map, which constrains the edge consistency between normal map and depth map back and forth. Moreover, considering the bimodal characteristic of weight distribution that exists in depth discontinuous areas, a reweighted graph Laplacian regularizer is proposed to promote this bimodal characteristic. And this regularization is incorporated into a unified optimization framework to effectively protect the piece-wise smoothness(PWS) characteristics of depth map. By treating depth image as graph signal, the weight between two nodes is adapted according to its content. The proposed method is tested for both noise-free and noisy cases, and is compared against the state-of-the-art methods on both synthesis and real captured datasets. Extensive experimental results demonstrate the superior performance of our method compared with most state-of-the-art works in terms of both objective and subjective quality evaluations. Specifically, our method is more effective on edge areas and more robust to noises.
Two-Level Private Information Retrieval

In the conventional robust <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> -colluding private information retrieval (PIR) system, the user needs to retrieve one of the possible messages while keeping the identity of the requested message private from any <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> colluding servers. Motivated by the possible heterogeneous privacy requirements for different messages, we consider the ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N, T_{1}: K_{1}, T_{2}: K_{2}$</tex> ) two-level PIR system, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{1}$</tex> messages need to be retrieved privately against <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T_{1}$</tex> colluding servers, and all the messages need to be retrieved privately against <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T_{2}$</tex> colluding servers where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T_{2}\leq T_{1}$</tex> . We obtain a lower bound to the capacity by proposing a novel coding scheme, namely the non-uniform successive cancellation scheme. A capacity upper bound is also derived. The gap between the upper bound and the lower bound is analyzed, and shown to vanish when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T_{1}=T_{2}$</tex> .
Review of Association Mining Methods for the Extraction of Rules Based on the Frequency and Utility Factors

Association rule defines the relationship among the items and discovers the frequent items using a support-confidence framework. This framework establishes user-interested or strong association rules with two thresholds (i.e., minimum support and minimum confidence). Traditional association rule mining methods (i.e., apriori and frequent pattern growth [FP-growth]) are widely used for discovering of frequent itemsets, and limitation of these methods is that they are not considering the key factors of the items such as profit, quantity, or cost of items during the mining process. Applications like e-commerce, marketing, healthcare, and web recommendations, etc. consist of items with their utility or profit. Such cases, utility-based itemsets mining methods, are playing a vital role in the generation of effective association rules and are also useful in the mining of high utility itemsets. This paper presents the survey on high-utility itemsets mining methods and discusses the observation study of existing methods with their experimental study using benchmarked datasets.
An augmented reality approach to learning about the force of gravity

Abstract The experimental investigation of Newton’s law of universal gravitation requires expensive and sensitive equipment. Therefore, it is often not possible to conduct such an experiment within a typical physics class. An alternative is to investigate the universal gravitation law by using simulations. In this paper, we are presenting such an augmented reality simulation (GravityAR). The simulation has been developed for mobile devices and runs on an Android operating system, making it very accessible for students and teachers at all educational levels. It allows the user to investigate how the gravitational force between two spherical bodies depends on their masses and mutual distance. Additionally, the simulation can be used to determine the value of the gravitational constant ( G ).
Whole-Body Control on Non-holonomic Mobile Manipulation for Grapevine Winter Pruning Automation

Mobile manipulators that combine manipulability and mobility, are increasingly being used for various unstructured application scenarios in the field, e.g. vineyards. Therefore, coordinated motion of the manipulator and mobile base is an essential feature of the overall performance. In this paper, we explore a whole-body coordinated motion controller of a robot which is composed of a 2-DoFs non-holonomic wheeled mobile base with a 7-DoFs manipulator (non-holonomic wheeled mobile manipulator, NWMM). This robotic platform is designed to efficiently undertake complex grapevine pruning automation. In this control framework, a task priority coordinated motion of the NWMM is guaranteed. Tasks with lower priority are projected into the null space of the top-priority tasks so that higher-priority tasks are completed without interruption from lower-priority tasks. The proposed controller was evaluated in a grapevine spur pruning experiment scenario.
Smart_Auth: User Identity Authentication Based on Smartphone Motion Sensors

Nowadays, the smartphone saves a lot of the private information of users. The traditional authentication methods, such as passwords or patterns, have some limitations, so it is not enough for legitimate users to protect personal privacy. In this paper, we propose a user identity authentication system for smartphone users called Smart_Auth. Multiple motion sensors of the smartphone are used to collect data, and a deep learning network is employed to realize smartphone user identity authentication. We invite 11 users to participate experiment. The legitimate user performs circle actions, and the illegitimate user performs random actions. The sampling frequency of smartphone motion sensors is 200Hz, and about 7260 samples are collected. The system achieves a 99.18% accuracy of legitimate users authentication and prevents 98.18% of illegitimate users from intruding into the smartphone.
FaasRS: Remote Sensing Image Processing System on Serverless Platform

Big data processing is now the primary mission in remote sensing processing, fortunately, cloud computing provides a feasible approach to perform it efficiently. But the work of resource provisioning, scheduling, and scaling is still inevitable in most cloud computing solutions, it poses a considerable challenge to data analyst. The emerging serverless architecture presents a new paradigm to provide a cloud service, the user only needs to upload function codes and leaves all the other server management jobs to the service provider. It reveals a new possibility of remote sensing processing. This paper presents FaasRS, a framework to process remote sensing images upon serverless platform. FaasRS is built on AWS Lambda, it exposes only simple APIs to operate images, and builds DAG for user’s algorithm. FaasRS splits task by splitting the image into small tiles based on geospatial region, and uses each Lambda worker to perform the computation for one tile. To reduce the redundant operations, we also make optimizations based on the algorithm DAG. FaasRS shows favorable performance and scalability in our evaluation. In the comparison with Spark and Ray, FaasRS shows a significant performance improvement in different type of RS processing jobs.
Organizational Commitment and Its Impact on Job Performance

This study aimed to identify the availability level of organizational commitment (OC) among employees in the Jordanian commercial banks, as well as to measure its impact on their job performance (JP). To achieve the objective of the study, 257 copies of the questionnaire were distributed to a random sample of employees at all administrative levels in the Jordanian commercial banks. The results of the study have indicated that OC is available in Jordanian banks with a high degree. Moreover, it has showed that the emotional commitment is the most prevailing commitment. The study showed that there is a significant positive impact of OC on the JP. The study recommended that it is very important to set strategic plans to promote OC among the employees of Jordanian commercial banks.