Reviewerly/exHarmony huggingface dataset:
Multi-factor authentication for users of non-internet based applications of blockchain-based platforms

Attacks targeting several millions of non-internet based application users are on the rise. These applications such as SMS and USSD typically do not benefit from existing multi-factor authentication methods due to the nature of their interaction interfaces and mode of operations. To address this problem, we propose an approach that augments blockchain with multi-factor authentication based on evidence from blockchain transactions combined with risk analysis. A profile of how a user performs transactions is built overtime and is used to analyse the risk level of each new transaction. If a transaction is flagged as high risk, we generate n-factor layers of authentication using past endorsed blockchain transactions. A demonstration of how we used the proposed approach to authenticate critical financial transactions in a blockchain-based asset financing platform is also discussed.
Image Decomposition using Geometric Region Colour Unmixing

In this paper, we propose a new geometric approach for image decomposition which aims to combine the advantages of two state of the art techniques. Given an input image, we first compute a palette of colours from the image and use it to split the RGB space into a number of different regions. Depending on which region a given pixel lies in, different geometric methods are used to unmix the pixel’s colour into a number of colours, where each colour is associated with a different layer. The layers created are smooth and homogeneous in colour, and have no reconstruction error when recombined. Our layer decomposition technique is fast to compute and the layers created can be used successfully in several applications, including layer compositing and recolouring.
Deep Texture Exemplar Extraction Based on Trimmed T-CNN

Texture exemplar has been widely used in synthesizing 3D movie scenes and appearances of virtual objects. Unfortunately, conventional texture synthesis methods usually only emphasized on generating optimal target textures with arbitrary sizes or diverse effects, and put little attention to automatic texture exemplar extraction. Obtaining texture exemplars is still a labor intensive task, which usually requires carefully cropping and post-processing. In this paper, we present an automatic texture exemplar extraction based on Trimmed Texture Convolutional Neural Network (Trimmed T-CNN). Specifically, our Trimmed T-CNN is filter banks for texture exemplar classification and recognition. Our Trimmed T-CNN is learned with a standard ideal exemplar dataset containing thousands of desired texture exemplars, which were collected and cropped by our invited artists. To efficiently identify the exemplar candidates from an input image, we employ a selective search algorithm to extract the potential texture exemplar patches. We then put all candidates into our Trimmed T-CNN for learning ideal texture exemplars based on our filter banks. Finally, optimal texture exemplars are identified with a scoring and ranking scheme. Our method is evaluated with various kinds of textures and user studies. Comparisons with different feature-based methods and different deep CNN architectures (AlexNet, VGG-M, Deep-TEN and FV-CNN) are also conducted to demonstrate its effectiveness.
Trade Openness, Institutions and Financial Development in Sub-Saharan Africa

The study aims to determine the connection between trade openness, institutions and financial development for 26 countries in sub-Saharan Africa over the period 1982-2016. The analysis relies on system GMM estimation with 5-year (non-overlapping) averaged data. The results reveal no evidence of a significant impact of trade openness on financial development. There is, however, evidence of a positive (though statistically weakly significant) impact of institutional quality on the development of the financial sector. There is also no evidence of a significant joint impact of the trade openness and institution quality on financial sector development.
A Blockchain Using Proof-of-Download

Distributed consensus and Blockchains are popular among the cryptocurrencies where no one except the coin users, owns the data and transactions. No different to open source repositories, where the data belongs to the users. In this work, we present a way of having a repository for software packages in a Blockchain with distributed consensus, supported by the idea of the also demonstrated proof-of-download.
Robotic Platform for Recovering Injured People in Smart Home

This paper presents the development of a search and rescue robot. It aims to lay the foundation for future works regarding the development of health care robots that can be used in the Smart Home - Smart City environment. At this stage, only the proof of concept and some preliminary results are presented, but they are very promising.
Research of Agricultural Big data

With the development of economy and the popularization of Internet, the development of agricultural big data is the inevitable trend of agricultural development, which is gradually changing people’ s lives. But there are many problems in the process of rural development. This paper will analyze the agricultural big data, from the meaning, significance, research status, problems and solutions of agricultural big data, in order to explore new ways for agricultural development, so as to promote the development of rural economy, improve the living standards of farmers and contribute to the construction of agricultural modernization.
Off-chain Bitcoin Trading Puzzle

We use the internal trading records of a major Bitcoin exchange leaked by hackers to detect and characterize wash trading — a type of market manipulation in which a single trader clears her own limit orders to “cook” transaction records. Our finding provides direct evidence for the widely-suspected “fake volume” allegation against cryptocurrency exchanges, which has so far only been backed by indirect estimation. We then use our direct evidence to evaluate various indirect techniques for detecting the presence of wash trades and find measures based on Benford’s law, trade size clustering, lognormal distributions, and structural breaks to be useful, while ones based on power law tail distributions to give opposite conclusions. We also provide suggestions to effectively apply various indirect estimation techniques.
An anomaly detection method based on double encoder–decoder generative adversarial networks

Purpose This study aims to address the challenge of training a detection model for the robot to detect the abnormal samples in the industrial environment, while abnormal patterns are very rare under this condition. Design/methodology/approach The authors propose a new model with double encoder–decoder (DED) generative adversarial networks to detect anomalies when the model is trained without any abnormal patterns. The DED approach is used to map high-dimensional input images to a low-dimensional space, through which the latent variables are obtained. Minimizing the change in the latent variables during the training process helps the model learn the data distribution. Anomaly detection is achieved by calculating the distance between two low-dimensional vectors obtained from two encoders. Findings The proposed method has better accuracy and F1 score when compared with traditional anomaly detection models. Originality/value A new architecture with a DED pipeline is designed to capture the distribution of images in the training process so that anomalous samples are accurately identified. A new weight function is introduced to control the proportion of losses in the encoding reconstruction and adversarial phases to achieve better results. An anomaly detection model is proposed to achieve superior performance against prior state-of-the-art approaches.
Edge pre-processing of traffic surveillance video for bandwidth and privacy optimization in smart cities

Traffic surveillance is essential for regulating traffic and reducing congestion. Nonetheless, the video data generated is non-structured, high-volume, bandwidth intensive and also raises privacy concerns. Edge computing solves these issues by preprocessing the data locally. In this paper we propose a fast, realtime vehicle detection and tracking method running on an edge device. The device sends only a single high resolution image of each detected vehicle accompanied with its trajectory. Allowing not only lower bandwidth requirements for smart city cameras, but also improved privacy, as only minimum required data is sent for post-processing. We implement object detection and tracking on an edge device, validate it in a real city environment and compare it to a state-of-the-art deep learning method using a publicly available benchmark data set where our method shows speed increase of up to 24 times.
Detection of Phishing Websites using Machine Learning

Phishing sends malicious links or attachments through emails that can perform various functions, including capturing the victim's login credentials or account information. These emails harm the victims, cause money loss, and identity theft. In this paper, we contribute to solving the phishing problem by developing an extension for the Google Chrome web browser. In the development of this feature, we used JavaScript PL. To be able to identify and prevent the fishing attack, a combination of Blacklisting and semantic analysis methods was used. Furthermore, a database for phishing sites is generated, and the text, links, images, and other data on-site are analyzed for pattern recognition. Finally, our proposed solution was tested and compared to existing approaches. The results validate that our proposed method is capable of handling the phishing issue substantially.
Vector field path-following control for a small unmanned ground vehicle with Kalman filter estimation

Industry 4.0 accelerates the growth of unmanned technology that reduces the labor cost and creates high automation in manufacturing system. The automated guided vehicle which is capable of transferring materials or executing tasks without human intervention becomes a necessary system for modern unmanned factories. The study explores the guidance and control design to accomplish the common task of path-following control for unmanned ground vehicles (UGV). A complete design method is presented that includes the lateral-directional autopilot, the vector field guidance for path-following, and multi-sensor fusion. The lateral-directional autopilot produces the low-level control action, the higher level guidance indicates the course direction of UGV at every spatial point based on the lateral path error, and the accurate UGV position relies on the estimate obtained by dynamically fusing sensors with extended Kalman filter. The design parameters in every stage are analyzed theoretically first and then fine-tuned in practice. The process is clearly described in this study, and the field test results are discussed in details to verify the performance of the proposed method and demonstrate the superiority over others.
Design of a new BUS for low power reversible computation

Optimization of device power can be achieved using reversible logic computation and this technique can be applied to a variety of low power applications such as optical computing, nanotechnology, Complementary Metal Oxide Semiconductor (CMOS), Very Large-Scale Integrated Circuits (VLSI) design and many more. Basic and universal gates are the elementary building blocks of digital system. In this paper, a new reversible Basic, Universal and Special (BUS) gate is proposed that is available as a single gate with multiple functionalities as basic (AND, OR & NOT), universal (NAND & NOR) and special gate (EXOR). The proposed BUS gate is implemented on Field Programmable Gate Array (FPGA) and simulated using 180 nm and 90 nm CMOS process technologies. Manchester adder and C17 circuit of ISCAS’85 (International Symposium on Circuits and Systems-1985) benchmark suite using BUS gate are designed and verified using Electronic Design Automation (EDA) tools. There is a power reduction of about 64.41% and 14.06% at 180 nm and 90 nm CMOS process technologies respectively in reversible BUS gate as compared to conventional CMOS-based designs. Thus, this paper provides a threshold to build more complex arithmetic systems using reversible logic, thus increasing the performance of computing systems with low power dissipation.
Analysis of computer teaching pattern based on outlier data mining and machine learning

Under the background of the development of the new era, with the arrival of the big data era and the development of computer technology, people are more and more inclined to use data to analyze all kinds of problems encountered in teaching. Different educational data models are constructed through data analysis. By statistics of different educational data and analysis using correlation models, the relationship between different variables and the intensity of interaction in these activities can be determined. Using computer to complete some teaching tasks and construct a certain teaching mode can improve the efficiency of teachers’ teaching and students’ learning to a certain extent. In the process of discussing the use of computer in classroom teaching, we should analyze how to use computer to carry out new forms of teaching activities and how to evaluate the teaching quality of computer teaching. On the one hand, this paper summarizes the current situation of the development of digital teaching mode in colleges and universities in China; on the other hand, it analyzes the research results of computer teaching by professionals in the new era; Finally, the knowledge of data mining is expounded.
A Collaborative Decision Making Approach for Multi-Unmanned Combat Vehicles based on the Behaviour Tree

Aiming at the difficulty of effective behaviour decision-making in multi-unmanned combat vehicle systems under complex environments and multi-task conditions, based on analyzing existing unmanned vehicle behaviour decision systems, this paper proposes a multi-unmanned combat vehicle cooperative behaviour decision method based on the behaviour tree. In this paper, we describe the behaviour tree modeling method for a multi-unmanned combat vehicle collaborative behavioural decision system, analyze the general process of behavioural tree modeling, and demonstrate the effectiveness of the method by implementing a multi-unmanned combat vehicle collaborative behavioural decision based on the Robomaster AI robot.
The use of digital technologies in the professional training of cadets

Abstract The modern education system should provide the society with a confident transition to the digital era. Digitalization contributes to the immediate response to any emergency. Consequently, the introduction of digital technologies plays an important role in the training of rescuers. The significance and ways of introducing digital technologies in the process of professional training of universities teaching fire-safety specialists have been analyzed in the article. It is emphasized that the system of training future specialists requires the use of innovative methods of training. The experience of leading universities is analyzed, where various training programs, electronic textbooks and simulation facilities are used. The role of pedagogical technologies based on students’ own activity, interactive communication, teamwork, group and individual reflection is growing. At the stage of the specialist’s training, the situation of possible emergency situations is simulated and the student is placed in conditions in which the personal qualities necessary for professional activity will be developed. The variety of forms for organizing educational activities in the conditions of digitalization will increase significantly, if they acquire a dynamic character (shift groups, spatially distributed training teams, various scenarios of rapid transition from group to individual activities and vice versa).
Performance Evaluation of Solar Tracking System

Today's scenario of growth for the demand of energy and concern for environmental benefits, has created a need for using renewable energy for meeting the demand for power production. Solar energy has emerged to be of utmost important Renewable Energy Sources (RES). The use of solar collector systems in many power plants has eased the conversion of solar energy into electrical power as compared to other sources. Since there is significant movement of the sun's angle w.r.t. earth's surface, which results in reducing the efficiency of the collector and the power delivered by solar systems. So, there lies a need for the design of such suntracking system that improves the panel efficiency by continuous tracking of the sun throughout the day. The present work explores distinct types of solar tracking system and its implementation for evaluating the performance of the solar power system. Tests were conducted on Parabolic Trough Collector (PTC) demonstrated at National Institute of Solar Energy (NISE), India. The present work consists of three sections wherein the first section deals with analyzing the solar radiation for Rajasthan station that represents hot & dry climatic conditions. The second section determines the distinct types of the solar tracking system and its implementation and the third section evaluates the energy yield for solar tracking system. It has been observed from the performance evaluation that the one-axis PTC with azimuth tracking performs better than the other configuration of the solar tracking system.
desc2tag: A Reinforcement Learning Approach to Mashup Tag Recommendation

Tags are critical sources of data for search, browsing and information retrieval. Manual selection of tags, over the years, have not been very effective. This paper introduces an approach to automatic mashup tag recommendation, based on reinforcement learning (RL). Our RL approach is able to carry out effective exploratory actions to automatically extract the and recommend tags for mashups. We perform experiments to evaluate our proposed method. Results from our experiments show that, the recommended mashup tags improve performance on the information retrieval task.
Design of Refined Segmentation Model for Underwater Images

Image segmentation is a technique to separate the background and the object of study from the image. For underwater image segmentation, the traditional method cannot meet the requirements of complex image segmentation due to its slow correction speed and large error. The image segmentation method based on level set is an image segmentation algorithm based on geometric contour model. This method is more stable than traditional method, simple operation and accurate result. At present, the level set algorithm has achieved good results in medical image segmentation and other non-underwater image segmentation, but the research of underwater image segmentation is still in its infancy. An improved level set algorithm for image segmentation is proposed. In this paper, the characteristics of underwater images are firstly analyzed, and the core principles of curve evolution and level set method are described in detail. Then, an improved level set algorithm is proposed to achieve accurate segmentation of underwater close-up images. In the experimental part, we test and analyze the effectiveness of the algorithm on the underwater image set containing a variety of organisms, and demonstrate the effectiveness of the algorithm on the representative of the jellyfish image with complex texture. At the same time, the segmentation results of this algorithm and Chan-Vese algorithm are compared experimentally. The actual results show that the level set algorithm can effectively complete the fine segmentation of underwater close-up images and has strong robustness to the interference of complex textures. At present, the algorithm is still very sensitive to underwater light interference. In the future, we will continue to improve the work of this paper and try to reduce the sensitivity of the algorithm to light.
Reliable, Fair and Decentralized Marketplace for Content Sharing Using Blockchain

Content sharing platforms such as Youtube and Vimeo have promoted pay per view models for artists to monetize their content. Yet, artists remain at the mercy of centralized platforms that control content listing and advertisement, with little transparency and fairness in terms of number of views or revenue. On the other hand, consumers are distanced from the publishers and cannot authenticate originality of the content. In this paper, we develop a reliable and fair platform for content sharing without a central facilitator. The platform is built as a decentralized data storage layer to store and share content in a fault-tolerant manner, where the peers also participate in a blockchain network. The blockchain is used to manage content listings and as an auditable and fair marketplace transaction processor that automatically pays out the content creators and the storage facilitators using smart contracts. We demonstrate the system with the blockchain layer built on Hyperledger Fabric and the data layer built on Tahoe-LAFS, and show that our design is practical and scalable with low overheads.
FinnSentiment: a Finnish social media corpus for sentiment polarity annotation

Abstract Sentiment analysis and opinion mining are essential tasks with many prominent application areas, e.g., when researching popular opinions on products or brands. Sentiments expressed in social media can be used in brand name monitoring and indicating fake news. In our survey of previous work, we note that there is no large-scale social media data set with sentiment polarity annotations for Finnish. This publication aims to remedy this shortcoming by introducing a 27,000-sentence data set annotated independently with sentiment polarity by three native annotators. We had three annotators annotate the whole data set, which provides a unique opportunity for further studies of annotator behavior over the sample annotation order. We analyze their inter-annotator agreement and provide two baselines to validate the usefulness of the data set.
Comparative Analysis of Ensembles Created Using Diversity Measures of Regressors

The paper presents a method for creating homogeneous and heterogeneous ensembles using three measures of regressor diversity, namely chi-square statistic, correlation coefficient and disagreement. Regressors included in the ensemble were selected from a pool of 100, 200 and 300 models generated using algorithms included in the WEKA data mining software package, such as M5P, M5Rules, Random Tree, and REPTree. Using three different measures, the degree of diversity of each pair of regressors was determined, and then 40 models with the highest diversity value were selected to construct ensembles. The obtained regressor ensembles were compared in terms of accuracy of prediction with models created using standard machine learning techniques such as linear regression, k-nearest neighbors, bagging and random forest. For comparative experiments, a real-world data set of purchase and sale transactions of residential real estate taken from a cadastral system was used. The research showed the usefulness of the proposed method for building ensembles.