Reviewerly/exHarmony huggingface dataset:
A trinocular vision measurement method of visible cameras with wide field of view

In this paper, a trinocular vision measurement method of visible cameras with wide field of view is proposed for the limitations of binocular stereo vision technology, such as blind spot in some areas with large measuring angles, mismatching of feature points location and limited field of view. Firstly, three visible light cameras arranged in an equilateral triangle arrangement are used to obtain images with a field of view of 60&deg;&times;60&deg;. Then, the measurement method based on trinocular stereo vision is researched: FAST algorithm is used to detect the feature points of the three images and the method of tri-crossing polar line constraint is used to match the feature points. Then the RANSAC method is used to calculate the position and attitude with the world coordinate system. After all, the proposed method is demonstrated by some experimental results. This method provides a solution for the relative pose measurement of noncooperative target in complex scenes/sparse features, and the problem of high matching rate based on image similarity matching in binocular stereo vision measurement can be avoided by using the tri-crossing polar line constraint, thus higher pose resolution accuracy can be obtained.
Document Management System using Blockchain and Inter Planetary File System

Blockchain Technology has been evolving from being a secure and immutable ledger of transactions for cryptocurrencies to a programmable interactive environment for building distributed reliable applications. This system proposes a secure platform to facilitate trustworthy document verification and management using Blockchain and Interplanetary File System (IPFS) to store data in the distributed and decentralized environment which is persistent and does not have any file size constraints. IPFS system provides a unique hash of the document based on the content of the document. The system uses smart contracts to record all the hashes for all the documents stored in the IPFS system. The system can efficiently and securely capture, manage and validate documents and prevent any malicious modification to the data.
Resource allocation, scheduling and auto-scaling algorithms for enhancing the performance of cloud using Grey Wolf Optimization and Fuzzy rules

Cloud computing technology is playing a major role in the industry and real-life, for providing fast services such as data sharing and allocating the cloud resources that are paid and truly required. In this scenario, the cloud users are scheduled according to the rule-based systems for attempting to automate the matching between computing requirements and resources. Even though, the majority auto-scaling algorithms only helped as indicators for simple resource utilization and also not considered both cloud user needs and budget concerns. For this purpose, we propose a new model which is the combination of auto-scaling algorithms, resource allocation and scheduling for allocating the appropriate resources and scheduled them. This model consists of three new algorithms namely Grey Wolf Optimization and Fuzzy rules based Resource allocation and Scheduling Algorithm (GWOFRSA), Auto-Scaling Algorithm for Cloud based Web Application (ASACWA) and Auto-Scaling Algorithm for handling Distributed Computing Tasks (ASADCT). Here, we introduce new auto-scaling algorithms for enhancing the performance of cloud services. In this work, the optimization technique is used to predict the cloud server workload, resource requirements and it also uses fuzzy rules for monitoring the resource utilization and the size of virtual machine allocation process. According to the workload prediction, the completion time is estimated for each cloud server. The experiments are conducted by using a simulator called CloudSim environment of Java programming and compared with the existing works available in this direction in terms of resource utilization and enhance the cloud performance with better Quality of Service of Virtual Machine allocation, Missed Deadline, Demand Satisfaction, Power Utilization, CPU Load and throughput.
Electoral Campaign and Hate Speech in Print Media: A Case Study of January 19, 2015 Punch Advert

The hallmark of advanced democracies all over the world is the fact that the incumbent has no influence over the elections as well as no advantage over other contestants. The campaign for the 2015 presidential election will go down in as one of the most tension-laden in the political history of Nigeria. The use of Hate speech before, during and after the general election became notorious to an extent that one would think that Nigeria might witness another civil war. The paper did an analysis of the January, 19th 2005 advert on The Punch Newspaper. The research design adopted was the Content Analysis. The paper discovered that the advert should not have been allowed because it promoted hate speech and was against the ethics of Nigeria Press Council. The study therefore concluded that the advert was inimical to democracy and against the ethics of journalism. It recommended that the media organisation should make deliberate efforts to rightly inform the people by way of their editorial policy as contained in Sections 22 and 39 of the 1999 Constitution.
Application of dynamic decision method for automatic allocation of address in airport IoT node-device

With the rapid development of the airport, the application of the Internet of things is becoming more and more popular. And the scale of the Internet of things (IoT) system is also becoming larger, in order to better manage and monitor all node-device in IoT system, the node-device is required to provide a unique access address. Since all node-devices are set according to the default factory address, in the early stage of the construction of the IoT system, the address of the node device needs to be modified one by one, which is with large workload, error and inefficient. In the later maintenance of the system, the address of all node-device needs to be reconfigured in different ways, which not only requires huge workload, but also needs to suspend the operation of the device. The method described in this paper, which can make each node-device configure its address by itself with dynamic intelligent address allocation strategy and in the case of not suspending the equipment, Improving the efficiency of system construction and equipment maintenance in the later stage, and also provide technical support for the construction of smart airport. The method in this paper effectively complete a series of work of automatic address allocation and modification, through the functional verification of practical application, and improve the efficiency of node-device address setting and maintenance of the IoT system in the initial and later stages of construction.
Engineering resilient systems cloud computing architecture (ECCA): a collaborative and secure analysis framework

Design and tradespace analysis of increasingly complex systems often require proprietary models from multiple industrial sources. These multi-partner collaborations require assurances that each company’s intellectual property will remain protected from unauthorized access or disclosure to competitors. In this paper, we present the security risks and considerations for these collaborations, and propose a cloud-based framework to address these concerns. The Engineering Resilient Systems (ERS) Cloud Computing Architecture (ECCA) system enables secure collaboration in the Department of Defense community, providing an infrastructure restricting access to connectable containerized assets: models and simulations, each of which remain isolated and maintained on the owner’s own cloud. ECCA further prevents unauthorized access to sensitive assets and infection by malicious media with an access control system supported by secure gRPC messaging and access agreements. We demonstrate the capability to connect disparate models for a simplified combat simulation and then how the new multi-model scenario enables additional capabilities in tradespace analysis.
Point de vue : une leçon tirée du confinement, l’histopathologie en visioconférence

The use of videoconferencing had increased significantly during lockdown. During this period, videoconferencing has been used in the pathological department of pathology (Timone university hospital, Marseille, France) for academic, diagnosis and referral. We provide our point of view regarding the use of this tool. As discussing slides through videoconferencing is a new and specific activity, we have also summarised specific recommendations for practical remote histopathology meetings.
TransBidiFilter: Knowledge Embedding Based on a Bidirectional Filter

A large-scale knowledge base can support a large number of practical applications, such as intelligent search and intelligent question answering. As the completeness of the information in a knowledge base may have a direct impact on the quality of downstream applications, its automatic completion has become a crucial task for many researchers and practitioners. To address this challenge, the knowledge representation learning technology which represents entities and relations as low-dimensional dense real value vectors has been developed rapidly in recent years. Although researchers continue to improve knowledge representation learning models using an increasingly complex feature engineering, we find that the most advanced models can be outdone by simply considering interactions from entities to relations and that from relations to entities without requiring huge number of parameters. In this work, we present a knowledge embedding model based on a bidirectional filter called TransBidiFilter. By learning the global shared parameter set based on the traditional gate structure, TransBidiFilter captures the restriction rules from entities to relations and that from relations to entities respectively. It achieves better automatic completion ability by modifying the standard translation-based loss function. In doing so, though with much fewer discriminate parameters, TransBidiFilter performs better than state-of-the-art baselines of semantic discriminate models on most indicators on many datasets.
Solidarity, critique and techno-science: Evaluating Rorty’s pragmatism, Freire’s critical pedagogy and Vattimo’s philosophical hermeneutics

Abstract The critique of metaphysics can often entail a critique of liberalism. Rorty sought a revolutionary paradigm shift in philosophy and the broader humanities, by linking the rejection of metaphysics to a justification for liberal democracy and reformism. He believed that the recognition of socio-historical contingency concerning interpretations of fundamental values and of truth, combined with a humanities education, would create a sense of solidarity that would motivate reforms. Freire argues that a dialogic form of education is as important as the humanities’ content . For Freire, people liberated by a critical education based on dialogue rather than a passive reception of information, can develop a radical critique of capitalism. Vattimo argues that while Heidegger saw techno-science as being the final phase in metaphysical domination, the contemporary development of information and communications technology creates a ‘Babel-like’ pluralism that undermines the ‘violence’ of metaphysic’s totalising thought. This can allow for the development of a post-metaphysical ‘weak communism’ that improves social justice. Rorty and Freire help to show that it is education, rather than technological developments, that can motivate a post-metaphysical politics of solidarity, and Vattimo and Freire are correct to argue that replacing reformism with radical critique is needed for social justice, although Vattimo’s weak communism only provides limited social justice.
IHF: Industrial High Frequency Operation Queue Discovery Algorithm

When using traditional PrefixSpan algorithm to complete industrial Internet high-frequency operation sequence mining tasks, there are problems such as low efficiency and disordered output results. Based on the PrefixSpan algorithm, we propose an IHF algorithm (Industrial High Frequency operation queue discovery algorithm) suitable for mining high-frequency operation sequences of the Industrial Internet. The algorithm reduces the scanning operation of the database by measuring the sequence length in the system, and reduces the generation of candidate sequences by constraining the sequence projection. The accuracy and performance of the IMF algorithm are verified through experiments.
Handwriting and Hand-Sketched Graphics Detection Using Convolutional Neural Networks

Handwriting and hand-sketched graphics carry rich information to reveal the insights of the physical and emotional state of the writer. Before analyzing the personal traits of handwriting and hand-sketched graphics, detecting them from the image is the most immediate subproblem in handwritten document analysis and understanding. In this paper, we introduce two Convolutional Neural Networks (CNN) based methods to extract multimodal information (handwriting and hand-sketched graphics) from questionnaire documents. A Connectionist Text Proposal Network (CTPN) based method is proposed to detect handwriting. The first stage employs the VGG-16 model to generate the convolutional feature maps of the original images. Then the second stage adopts a BLSTM based detector to predict the scores of candidate zones. An instance segmentation method using the Mask Region Convolutional Neural Network (Mask-RCNN) is also proposed to solve hand-sketched graphics detection problem. The Mask-RCNN based approach has two parts: the backbone and the head. The backbone is to extract the features over the original image, and the head is to perform bounding boxes regression and mask prediction. At first, a simple Region Proposal Network (RPN) is adopted to generate the proposals of hand-sketched graphics efficiently. Then, the Region of Interest (RoI) features of the above proposals are fed into the Fast-RCNN branch and the mask branch to obtain the bounding boxes and the graphics segmentation results. The best handwriting detection performance of 200 test questionnaire images is that the precision rate is 99.5%, the recall rate is 99.2% and the F-measure score is 99.4%. The best detection performance of hand-sketched graphics is that the precision rate is 99.0%, the recall rate is 99.5% and the F-measure score is 99.3%. Experiments demonstrate that the proposed methods achieve promising results in both handwriting and hand-sketched graphics detection tasks.
Traffic Forecasting and Decision-making of Investment and Construction of Tourism Highway under the Background of Artificial Intelligence

Passenger traffic forecasting and decision-making of investment and construction of the tourism highway under the background of artificial intelligence is studied in this paper. As an important information asset, big data is expected to provide people with comprehensive, accurate, and real-time business insights and decision guidance. The value of big data lies in its decision-making usefulness, and the knowledge contained in it can be discovered through analysis and miming can provide decision support for various practical applications. Forecasting is based on past and present conditions to then make predictive judgments about future development. This paper used the prediction model to construct the framework of decision making. The experimental results have proven their effectiveness.
Hyperspectral Image Restoration for Non-additive Noise

This work is about a curious phenomenon. Among hyperspectral image (HSI) restoration methods, most of them suppose the observed HSI is the superposition of clean component and noise component. However, in real world applications, the components are often non-additive. The non-additivity admits the uniqueness of attribute. Especially each pixel of image belongs to either clean component or noise component. To separate components robustly, this work proposes a general HSI restoration framework. The novelty of this work lies in exploiting non-additivity of components by a binary mask matrix. Two special cases are discussed. To solve the proposed models, efficient alternating minimization algorithms are developed. Compared with gradient-type algorithms, our algorithms are easy to implement as there is no need to tune optimization parameters like step sizes. The experimental results over simulated and real datasets verified the performance of the proposed method.
Subspace Alignment based Adapted GLRT Detector and Its Application in Marine Current Turbine

Most traditional methods can detect signals in noise by detecting the change of mean value of test statistics. But these methods assume that the signal parameter is known without considering unpredictability of the actual industrial process. Generalized Likelihood Ratio Test (GLRT) detector, one of most widely used detector, can replace unknown parameters with the results of maximum likelihood estimation. However, the classical detector cannot change with the working conditions. Also it can't solve the troubles of dynamic variation using prior knowledge, which lead to a very high rate of missing detection. To overcome this shortcoming, an adapted GLRT detector is proposed based on subspace alignment to highlight weak signal characteristics, then a mechanism changes time series model is established by Detrended Fluctuation Analysis (DFA). Experimental date is used to illustrate the effectiveness of the proposed detector by using three-phase stator current of Marine Current Turbine (MCT) under complex working conditions. The test result shows the proposed detector can extract weak features, and has good feature portability.
An Integrated Deep Architecture for Lesion Detection in Breast MRI

Complex nature of medical images and tedious process of data exploration calls for the development of Computer Aided Detection (CADe) methods to ease the process of lesion detection. Recent deep learning-based object detectors from computer vision are adapted to the creation of CADe lesion detectors. This research starts with state-of-the-art object detectors, namely Faster R-CNN, YOLO v2 and Grad-CAM, to determine the location of lesions in Magnetic Resonance Images of breast. A series of experiments are conducted to find the best set up for maximizing the Average Precision (AP) of each method. Consequently, AP values of 0.6993 and 0.7651 are obtained for Faster R-CNN and YOLO v2 respectively. Taking into consideration the pros and cons of each method, we propose different integration architectures in order to overcome the shortcomings of each algorithm, hence enhancing the overall lesion detection performance. The integrated architectures succeed to obtain an AP value up to 0.8097 while providing explainable reasoning that is essential for medical CADe.
Rational Manager in Bitcoin Mining Pool: Dynamic Strategies to Gain Extra Rewards

Participants of the Bitcoin system form mining pools, which have become the leading institutions of the Bitcoin mining economy, to smooth their reward of mining. Many attacks towards mining pools have been proposed. Block Withholding attack is an attacker splitting some of the power to mine in a pool, submitting shares while withholding blocks, which is one of the most famous and original attacks. Few pieces of research pay attention to the case that managers work rationally to gain extra rewards themselves at the same time of countering withholding attack. However, some different reward functions have been proposed to avoid rational miners' withholding. In this paper, we offer a model that a rational manager gain extra rewards and incentivize miners not to withhold blocks by applying a dynamic mining strategy. We conduct quantitive analysis and simulations to verify the availability and effectiveness of our attacks. We show the attack benefits the miners in the pool in some circumstances, and further discuss improvement for our attack.
Student Conference Electronic Submission System

Scientific student conferences make it possible for undergraduate students to present their research in front of a committee of experts. These events usually use proprietary online solutions for submissions and paper management, making it difficult for students to move freely among them and always have up-to-date information.The SConES project aims to ensure a single online platform for announcing and applying to student conferences. Although the authors use Transylvanian conferences as an inspiration point for functionalities, the proposed system may be used on an international scale. The target audience of the project consists of student conference organizers, applying students, their supervising professors/tutors, and committee members. The platform provides the opportunity to browse conferences and view detailed data. Students/authors are additionally able to view their submitted papers, follow status changes and edit their personal information.The current paper outlines the architecture of the project, the communication between components, main functionalities with technologies/tools used during development.
A case study on using heavy-hitters in interconnect bypass fraud

Nowadays, fraudsters are continually trying to explore technical gaps in telecom companies to get some profit. The high cost of international termination rates in Telecom Companies, and mainly because of their high asymmetry property, attracts the attention of fraudsters. In this paper, we explore the application of three deterministic algorithms and one probabilistic, that combined can help to identify possible abnormal behaviors. Interconnect Bypass Fraud (IBF) is on the top three (worldwide), most common frauds in the telecommunication domain. Typically, the Telecom Companies can detect IBF by the occurrence of bursts of calls, repetitions, and mirror behaviors from specific numbers. The goal of our work is to discover as soon as possible numbers with abnormal behaviors and based on this assumption we developed: ( i ) the lossy count algorithm with fast forgetting technique; and ( ii ) the single-pass hierarchical heavy hitter algorithm that also contains a forgetting technique; as well as the application of the HyperLogLog sketches, and the application of sticky sampling algorithm. We applied the four algorithms in two real datasets and did a parameter sensitivity analysis. The results show that our two proposals (Lossy Counting with fast forgetting and the Hierarchical Heavy Hitters) can capture the most recent abnormal behaviors, faster than the baseline algorithms. Nonetheless, these four algorithms combined can make the fraud task more difficult and can complement the techniques used by the Telecom Company.
Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition

To enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy. The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image classification and speech recognition is increasing. This study implements an adaptive method for applying the particle filter technique with a gradient descent optimizer to improve model learning performance. Using a pretrained model helps reduce the computational time to deploy an image classification model and uses a simple deep convolutional neural network for speech recognition. The applied method results in a higher speech recognition accuracy score—89.693% for the test dataset—than the conventional method, which reaches 89.325%. The applied method also performs well on the image classification task, reaching an accuracy of 89.860% on the test dataset, better than the conventional method, which has an accuracy of 89.644%. Despite a slight difference in accuracy, the applied optimizer performs well in this dataset overall.