Reviewerly/exHarmony huggingface dataset:
Research on Dynamic Political Sentiment Polarity Analysis of Specific Group Twitter Based on Deep Learning Method

Abstract The article introduce a method to analyzing the dynamic political sentiment polarity of American politicians’ tweets within a fixed period of time to assist analysts in judging the direction of American politics and the future trend of China-US relations. This research is applied in the field of think tanks or intelligence analysis. The service targets are intelligent analysts, and the data is the tweet text data sent by a specific group in a fixed time period. We propose an architecture that combines multiple deep learning models and use a dedicated tweet data set to construct a specific group to obtain an sentiment polarity multi-classifier, and then introduce the time characteristics of tweets, and finally obtain the dynamic political sentiment polarity of politicians. The US politician tweets data set proposed in this article verifies that the proposed comprehensive architecture is better than traditional deep learning methods in this task, and the accuracy of the classifier verification set reaches 80.66%. According to the sentiment polarity judgments of 20 US governors and senators, the success rate is 75%. The analysis of individual dynamic political sentiment polarity can provide effective help and intelligence support for analysts. The method in this paper effectively uses a variety of deep learning techniques to assist analysts to obtain more accurate dynamic political sentiment polarity from massive Twitter text data.
A Comparative Study for Obstacle Avoidance Inverse Kinematics: Null-Space Based vs. Optimisation-Based

Obstacle avoidance for robotic manipulators has attracted much attention in robotics literature, and many algorithms have been developed. In this paper, two algorithms are explored which perform obstacle avoidance within the inverse kinematics calculation process. The first algorithm applies a velocity to the null-space of the manipulator’s Jacobian matrix which directs the manipulator away from obstacles. The second algorithm uses an optimisation-based approach to calculate joint positions which incorporates constraints to prevent the manipulator from violating obstacle boundaries. Applying obstacle avoidance at the inverse kinematics level of the control process is particularly applicable to teleoperation and allows the robotic manipulator to react to obstacles at a faster rate without involving a path planner which operates on a slower cycle time. The two algorithms were implemented for a direct comparison in terms of obstacle avoidance capability and processing times. It was found that the processing time of the null-space method was substantially quicker than the optimisation-based algorithm. However, the null-space method did not guarantee collision avoidance which may not be suitable for safety critical applications without supervision.
Elliptic-Curve Cryptography

Elliptic-curve cryptography (ECC) is a public-key cryptography approach based on the algebraic structure of elliptic curves over finite fields. ECC can be used in cryptography applications and primitives, such as key agreements, digital signatures, and pseudo-random generators. They can be used for operations such as encryption, an operation that is achieved through a combination between key agreements with a symmetric encryption scheme. Some other interesting usages can be seen in several tries of integer factorization algorithms that are based on elliptic curves (EC), with applications in cryptography, such as Lenstra Elliptic-Curve Factorization (L-ECC) [1].
A Novel Higher-order Weisfeiler-Lehman Graph Convolution.

Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs.
Appendix to "Circuits, Cycles, Configurations: an Interaction Model of Web Comics"

Appendix of the Circuits, Cycles, Configurations: an Interaction Model of Web Comics paper presented at ICIDS2020 conference.
AMI: An Auditory Machine Intelligence Algorithm for Predicting Sensory-Like Data

In this paper, we present the results of our experiments using a new biologically constrained machine intelligence algorithm based on neural processing in the auditory cortex called auditory machine intelligence (AMI). This algorithm is an online learning technique for predicting sensory time series data i.e. data that comes in streams or a sequential order. The AMI algorithm is particularly inspired by the mismatch negativity effect which provides important evidence that the brain learns a statistical structure of the world it senses. We show through a number of experiments with popular benchmarks, how this algorithm may be applied in a real world sense. The results of these experiments have also been compared with two very popular techniques that have been used for time series predictions and are very encouraging.
RegulaTOR: A Powerful Website Fingerprinting Defense.

Website Fingerprinting (WF) attacks are used by passive, local attackers to determine the destination of encrypted internet traffic by comparing the sequences of packets sent to and received by the user to a previously recorded data set. As a result, WF attacks are of particular concern to privacy-enhancing technologies such as Tor. In response, a variety of WF defenses have been developed, though they tend to incur a high bandwidth and latency overhead or require additional infrastructure, making them difficult to implement in practice. Some lighter-weight defenses have been presented as well; still, they attain only moderate effectiveness against recently published WF attacks. In this paper, we aim to present a realistic and novel defense, Regulator, that demonstrates improved overhead and high effectiveness against current WF attacks. In the closed-world setting, this defense reduces the accuracy of the state-of-the-art attack, Tik-Tok, against lightweight defenses from 66% to 22.9%. To achieve this performance, it requires minimal added latency and a bandwidth overhead 38.1% less than the leading lightweight defense. In the open-world setting, Regulator limits a precision-tuned Tik-Tok attack to an F-score of .087, compared to .625 for the best comparable lightweight defense.
Bologna Process as a Factor of Higher Education System Reformation Policy in Modern Russia

Based on the analysis of scholarly discourse on the results of Bologna Declaration goals and principles’ incorporation into reformation of higher education system in Russia and through their own experience of teaching in higher education institutions, the authors come to conclusion that commitment to accession into the world educational space was caused not so much by real domestic needs to improve home higher education system but by political factors as a result of slow-response impact of a liberal reformation strategy of the 1990s. Expediency to concentrate intellectual resources of university academia necessary for digitalization of educational process in compliance with the aims and needs of economy and the whole society is substantiated.
Integrating RBAC, MIC, and MLS in Verified Hierarchical Security Model for Operating System

Designing a trusted access control mechanism of an operating system (OS) is a complex task if the goal is to achieve high level of security assurance and guarantees of unwanted information flows absence. Even more complex it becomes when the integration of several heterogeneous mechanisms, like role-based access control (RBAC), mandatory integrity control (MIC), and multi-level security (MLS) is considered. This paper presents results of developement of a hierarchical integrated model of access control and information flows (HIMACF), which provides a holistic integration of RBAC, MIC, and MLS preserving key security properties of all those mechanisms. Previous version of this model is called MROSL~DP-model. Now the model is formalized using Event-B formal method and its correctness is formally verified. In the hierarchical representation of the model, each hierarchy level (module) corresponds to a separate security control mechanism, so the model can be verified with less effort reusing the results of verification of lower level modules. The model is implemented in a Linux-based operating system using the Linux Security Modules infrastructure.
Fluorite deposits in China: Geological features, metallogenic regularity, and research progress

ABSTRACT Fluorite is one of the important mineral raw materials in the industry. In China, it is mainly distributed in the provinces and regions such as Hunan, Zhejiang, Jiangxi, Inner Mongolia, Fujian, and Henan provinces, boasting huge reserves and large numbers of deposits. However, most of the fluorite deposits are on a small or medium scale. The main fluorite deposits in China were studied in this paper. Their geological features and metallogenic regularity were summarized and compared. Meanwhile, based on their main genetic factors including metallogenic fluid sources and main metallogenic geological processes, they were divided into two groups, namely meso-epithermal deposits and magmatic-hydrothermal deposits. Furthermore, based on the prospecting achievements and research progress obtained in fluorite deposits in recent years, prospecting potential predictions were made for the metallogenic prospect areas and major prospecting areas of fluorite in China. This aims to provide a theoretical basis and direction for future fluorite prospecting in China.
THE EXPEDIENCY OF INTRODUCING “EDUCATION TECHNOLOGIES AND LEARNING IN THE DIGITAL AGE” COURSE INTO EDUCATIONAL PLAN OF THE FUTURE PROFESSIONALS OF INFORMATION TECHNOLOGIES

The article analyzes international experience of the diverse set of aspects of education technologies and digitalized education study at the higher education establishments. Carnegie Mellon University offers “Master of Educational Technology and Applied Learning Science” program for graduated bachelors. “Design educational games” is one of the examples of the courses offered. Stanford graduate School of Education offers “Education, design and technologies” course. Stanford University offers “Creating effective online and blended courses”. University of Maryland provides set of courses, such as “Instructional Design: Digital Media, New Tools and Technology”. Pennsylvania University offers “Introduction to Online and Blended Teaching”. Massachusetts Institute of Technologies offers “Design and Development of Educational Technology”. The analysis of international experience led to the conclusion, that “Education technologies and learning in the digital age” course is reasonable to introduce into educational plan of the technical specialties’ students and future professionals of information technologies in particular. The reasons to introduce the course for the future professionals of information technologies are the possibility to design and implement course and diploma works in the educational area (educational software development, educational games development, etc.), work at the educational software development companies, conduct PhD research in the area of educational technologies. The course prepares students to the future life-long learning using educational technologies. “Education technologies and learning in the digital age” course is oriented to the 121 “Software engineering”, 122 “Computer science”, 123 “Computer engineering, ”125 “Cybersecurity”, 126 “Information systems and technologies” and is based on the learning the main terms and principles of the education technologies and digital age education approaches. The approximate list of topics to learn in the course is provided. The advantages of the introducing the course are discussed. Keywords: education technologies, EduTech, digital age, education, digitalization of education, higher education establishments.
Magnetometry, Acoustical and Inertial Indoor-Positioning in Healthcare

Introduction . The problem of localization of moving objects inside buildings becomes more urgent in healthcare. Tracking the movements of patients in real time allows one to provide them with timely medical support in case of sharp deterioration in their vital signs. It is especially important to track the location of patients undergoing a surgery, since the risk of death due to postoperative complications for them is extremely high. Using indoor-positioning technologies in telemedicine systems can solve the problem, thereby reducing the mortality rate of patients and improving the quality of medical care. Aim. To study the applicability of magnetometry, inertial and acoustic technologies for patient’s localization in a hospital. Materials and methods. The analysis of domestic and foreign scientific sources devoted to indoor-positioning based on the above technologies was carried out. Material published not earlier than 2016, was chosen for the analysis. Most of the papers were published in journals with impact-factor not lower than 3. Results. After analyzing the information received, it was concluded that none of the technologies can be used independently. Inertial sensors possess high accuracy, but over time, the measurement error increases. There-fore, the sensors need to regular correction. Indoor-positioning based on geomagnetism is hampered by interference that can be induced by the operation of magnetic resonance imaging scanners and X-ray equipment, which are usually used in medical facilities. Active magnetometry does not allow to keep track of moving objects due to specific of hardware used. Ultrasound-based positioning can be complicated by ultrasonography apparatuses interference. Using an audible sound creates noise pollution and exerts a negative impact on patient’s health. Also, acoustic technologies are unable to provide a secure communication channel for data exchange. Conclusion. It is recommended to combine the reviewed positioning technologies with other technologies in order to correct the indicated disadvantages.
Important qualities of a perfect human in youth education

The article examines the important qualities of an ideal person, such as generosity, nobility, kindness, generosity. The science of futuwvat is analyzed, and on the basis of the sources the generality of generosity is substantiated, which is its practical expression. The importance of these qualities in the upbringing of modern youth is emphasized.
A Deep Learning Based Analysis of the Big Five Personality Traits from Handwriting Samples Using Image Processing

Handwriting Analysis has been used for a very long time to analyze an individual’s suitability for a job, and is in recent times, gaining popularity as a valid means of a person’s evaluation. Extensive Research has been done in the field of determining the Personality Traits of a person through handwriting. We intend to analyze an individual’s personality by breaking it down into the Big Five Personality Traits using their handwriting samples. We present a dataset that links personality traits to the handwriting features. We then propose our algorithm - consisting of one ANN based model and PersonaNet, a CNN based model. The paper evaluates our algorithm’s performance with baseline machine learning models on our dataset. Testing our novel architecture on this dataset, we compare our algorithm based on various metrics, and show that our novel algorithm performs better than the baseline Machine Learning models.
Trust Assessment on Streaming Data: A Real Time Predictive Approach

IoT data, that most often carry a temporal dimension, can be exploited from an analysis perspective or from a forecasting one. In this paper, we propose a predictive approach to address the problem of data trustworthiness in a data stream generated by a Smart Home application. We describe an online Ensemble Regression model that performs prediction in assigning a trust score to a target temporal value in real-time. Experiments conducted with data retrieved from the UCI ML repository demonstrate the performance of the model, while assessing data accuracy.
A comparative study of word embedding methods for early risk prediction on the Internet

We built a system to participate in the eRisk 2019 T1 Shared Task. The aim of the task was to evaluate systems for early risk prediction on the internet, in particular to identify users suffering f ...
Content moderation modulation

Deliberating on how to regulate---or not regulate---online speech in the era of evolving social media.
Museum Digital Ulos Berbasis Android

Museum is an educational institution where tangible (fossils, artifacts) and intangible (values, traditions, norms) are displayed. The museum in Medan has various range of but there has been no inventory of the Ulos as a Bataknese traditional ethnic cloth yet as one of the cultural identities of native people in North Sumatera, Indonesia. By using Android platform, this study aims to analyze and explain various patterns and motifs of the Batak Ulos in 6 (six) regencies: Karo, Pak-pak, Simalungun, Toba Samosir, Tapanuli, and Mandailing. The problems were (1) designing a digital Ulos museum using Android software, (2) describing and explaining the various motifs of Ulos representating each region, and (3) explaining noble values contained in Ulos. Therefore, people nowadays can safe time and distance as well as easily find information about the use, the meaning, and the function of each Ulos. This study uses the ADDIE (Analysis Design - Development - Implement - Evaluate) method. The results of this study are in the form of an android-based application that can be distributed offline to students, teachers, and the general public. In the future, this research is expected to develop applications that can be accessed online through various Playstores.
When Simple Hash Functions Suffice

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ON/OFF switch control of smart home prototype using palm and fist hand gesture

Human beings looking forward to living in a modern and comfortable environment like smart houses. In this study, an effective user-friendly smart home prototype designed with low cost. The prototype has a set of Light Emitting Diode (LED). The LEDs in prototype considered as home appliances and the control (ON/OFF) of the LED depends on recognizing palm and fist hand gestures in real-time video. A palm and fist gestures recognized using a new suggested algorithm to recognize gestures based on the aspect ratio of minor and major axes of the detected hand. The hand is detected based on the Viola-Jones method using positive (hand) and negative (non-hand) image datasets. The location and size of positive images are determined automatically in the newly introduced algorithm using skin colour detection. LEDs in prototype controlled with very fast time, where the response time of LEDs to take action was 0.52 sec.
Multi-Objective Vibration-Based Particle-Swarm-Optimized Fuzzy Controller With Application to Boundary-Following of Mobile-Robot Simulation Environment

This paper presents a multi-objective vibration-based particle-swarm-optimization (MO-VBPSO) algorithm with enhanced exploration ability and convergence performance, for training fuzzy-controller (FC) to achieve robot control. The MO-VBPSO applies a reference point-based leader selection schema that assigns leaders for MO-PSOs' searching optimal parameters of the FC. Besides, the MO-VBPSO framework is integrated with a vibration factor to strengthen the exploration ability for resolving the local minima issue, which is inspired by the amplitude of the Firework Algorithm (FWA). The evaluation of MO-VBPSO focuses on the effect of the vibration factor by applying it to training a mobile robot in a simulation environment. The evaluation results are discussed concerning exploration ability, convergence performance, and performance stability. Experimental results reveal that the proposed MO- VBPSO lifts the performance of robot training significantly.
A Secure Data Sharing Scheme Based on Blockchain for Industrial Internet of Things Using Consensus Algorithm

This chapter discusses the rate of growth and dominance in this technology across several stages of evolution in industrial revolutions. It also discusses the current scenario in every related industrial sector and commercial production industries of blockchain and also in several other fields. The term Industrial Revolution 4.0 was first coined by a set of representing members from various fields to succeed the German Federal Government at the Hannover fair in 2011, which was a step towards the High Tech Strategy of 2020. A decentralized distributed ledger that stores the origin of a digital asset is called a blockchain. It can also be defined as a structure that stores transactional records of the public in several databases, known as the chain, connected in a peer to peer network through peer to peer nodes. The supply chain industry provides a drastic change in blockchain innovation; several trades and activities are observed by a fixed decentralized ledger.
Chebyshev polynomials and Pell equations over finite fields

We shall describe how to construct a fundamental solution for the Pell equation x2 − my2 = 1 over finite fields of characteristic p ≠ 2. Especially, a complete description of the structure of these fundamental solutions will be given using Chebyshev polynomials. Furthermore, we shall describe the structure of the solutions of the general Pell equation x2 − my2 = n.
On the Task Approach in Artificial Intelligence and Cognitive Sciences

This work continues a series of publications on the task approach in artificial intelligence. As noted earlier, the agent-based approach described in the monograph by Stuart Russell and Pieter Norwig “Artificial Intelligence. The Modern Approach", may be more argumentatively presented within the framework of the task approach. This paper will show that not only the problems of the bases of mathematics and artificial intelligence, but also many cognitive functions performed by humans and analyzed in cognitive sciences, can also be described and studied within the framework of the task approach. In particular, this paper shows that the analogue of the concept of task in cognitive sciences is the concept of goal and that the Functional Systems Theory (FST), which describes purposeful behavior, can be presented as the brain's solution of tasks to achieve goals and satisfaction of needs. It gives the chance to compare directly the tasks of artificial intellect with natural cognitive processes and, thereby, to reveal the list of those tasks of "natural" intellect and schemes of their solution which can be successfully used for the solution of artificial intelligence tasks.