Reviewerly/exHarmony huggingface dataset:
A Novel Iterative Rigid Image Registration Algorithm Based on the Newton Method

In recent years, Image Registration has attracted lots of attention due to its capabilities and numerous applications. Various methods have been exploited to map two images with the same concept but different conditions. Considering the finding of the mentioned map as an optimization problem, mathematical-based optimization methods have been extensively employed due to their real-time performances. In this paper, we employed the Newton method to optimize two defined cost functions. These cost functions are Sum of Square Difference and Cross-Correlation. These presented algorithms have fast convergence and accurate features. Also, we propose an innovative treatment in order to attend to one of the free parameter-rotations or scale as a sole variable and the other one as the constant value. The assignment is replaced through the iterations for both parameters. The intuition is to turn a two-variable optimization problem into a single variable one in every step. Our simulation on benchmark images by the means of Root Mean Square Error and Mutual Information as the goodness criteria, that have been extensively used in similar studies, has shown the robustness and affectivity of the proposed method.
Assessment of Load in Cloud Computing Environment Using C-means Clustering Algorithm

Load balancing is an important concept which helps to raise the throughput. It helps to increase the performance of the system by assigning virtual machine for the execution of user request in less time. It assists in raising user satisfaction level and reducing user response time. As we know, the demand for cloud services raises day by day, which lead to load balancing as a major problem. The technique helps to distribute the workload among all available servers so that user can get their resource in less time. Cloud load balancing introduced the distribution of workload traffic and demands that reside over the Internet. It takes advantage of the cloud extensibility and possesses alertness to meet redirected workload demands and to improve long-term opportunity. The purpose of load balancing is to continue the system constancy and develop the performance considerably. We propose a load balancing technique by using c-means clustering to handle tasks efficiently which conserve less amount of energy.
Social Development of Young People in the Conditions of Development of Information and Communication Technologies

The article is devoted to the analysis of the social development of young people in the conditions of development of information and communication technologies. We considered the increasing influence of information and computer technology at the beginning of the XXI century on the Russian youth. We focused on the unification of sociocultural characteristics of young people in different continents, countries, and instruments of mass consumer culture through the process of globalization. In our opinion, the entry of the Internet and information-computer technologies into social life seriously changes the face of the process of social development of young people. Currently, the successful social development of a young person is no longer conceivable without effective integration into the global information space. We made the conclusion that there is a systemic imbalance in the criteria for the social development of Russian youth at the beginning of the 21st century, which threatens not only the process of social development of young people but also, more broadly, the stability of society in terms of continuity and the change of generations.
IMPROVED PERFORMANCE OF CRYPTOSYSTEM BASED ON EDWARDS AND TWISTED EDWARDS ELLIPTIC CURVES USING SOME TECHNIQUES OF AIVM

This paper is on some efficient algorithms implementation of points addition and points doublingfor the Edwards elliptic curves using techniques of ancient mathematics. In particular, we improve timings inthe cryptographic operations for the Edwards elliptic curves. We use some techniques of ancient mathematicsnamely Urdhva-triyagbhaym for multiplication, Dvandva-yoga for squaring of any digits. We introducecryptographic formulae (Point Addition and Point Doubling) for Edwards elliptic curves. We discuss that thetechniques of the ancient mathematics-based scheme show better performance in speed, processing time andpower consumption of multipliers compared to the conventional method. The coding and synthesis are done inMATLAB for the algorithm of multiplications and squaring using 16-bits and 32-bits. The effect of someAncient Mathematical techniques over elliptic curves was investigated and the obtained results are explained inthe form of tables and graphs. We discuss the significance of these optimizations for elliptic curve cryptography.
From the Parking Lot to Your Gate: A Need-Centered Approach for Optimizing User Experience in Automated Valet Parking System

Major improvements in autonomous driving allow the realization of automated valet parking. However, parking is known to be a challenging and complex task with high cognitive demand, and many challenges remain within the area of human factors, for example, whether the user trust automated driving vehicle systems and whether the system is understandable and safe. Therefore, we present a need-centered approach and the involved “UX framework”, which can optimize user experience in the automated valet parking system. We create Persona models through surveys and interviews to explore pain points and users’ needs, and use Kano model to categorize and prioritize these needs from three levels: security, understandability, and controllability, then reflect them in each function of the system to build the information architecture. Meanwhile, we also consider the corresponding services about One-dimensional and Attractive quality. Furthermore, we put forward the “UX framework” and 5 principles for the interaction design of the automated valet parking system, which will provide references for designers and practitioners.
Digital Employee using Capabilities of Artificial Intelligence to Human Intelligence

Using the concept of Artificial Intelligence, a virtual assistant named "Gabriel" has been Developed to aid in education, market, business and many other fields.[4] The uniqueness of this bot is that it is programmed in python and is stored in raspberry pi providing the user friendly environment by moving along with user. It uses numerous python Libraries to help perform various functions that enable the Assistant to assist its user in day to Day activities. The Assistant can convert text to speech and vice versa using pyttsx3 and speech Recognition libraries respectively. Also, it can scrape the information from Wikipedia and visit any Website. It can be used to surf YouTube and visit any YouTube channel. The Assistant Comes with games such as flappy bird, tic-tac-toe and snake developed by the programmer. Also, the Assistant has an in-built calculator that too developed by the programmer. This Research is to develop an assistant who is highly compatible with human life. This employee comes with a self-made library called ‘the_gmail_sender’ that sends Gmail taking Voice input from the user
Adaptive Model Updating Correlation Filter Tracker with Feature Fusion

Aiming at the poor accuracy of a single feature in the challenging scenarios, as well as the failure of tracking caused by partial or complete occlusion and background clutter, a correlation filter tracking algorithm based on feature fusion and model adaptive updating is proposed. On the basis of the background-aware correlation filter, the proposed algorithm firstly introduces the CN feature and integrates with the HOG feature to improve the accuracy of tracking. Then, the Average Peak-to-Correlation Energy (APCE) is introduced, and the results of object tracking are fed back to the tracker through the ratio changes. The tracker is adaptively updated, which improves the robustness of the algorithm to occlusion and background clutter. Finally, the proposed algorithm is experimented on the self-build ship dataset. The experimental results show that the algorithm can adapt well to complex scenes, such as object occlusion and background clutter. Compared to the state-of-the-art trackers, the average precision of the proposed tracker is improved by 2.3%, the average success rate is improved by 2.9%, and the average speed is about 18 frames per second.
Learning and Interpreting Multi-Multi-Instance Learning Networks

We introduce an extension of the multi-instance learning problem where examples are organized as nested bags of instances (e.g., a document could be represented as a bag of sentences, which in turn are bags of words). This framework can be useful in various scenarios, such as text and image classification, but also supervised learning over graphs. As a further advantage, multi-multi instance learning enables a particular way of interpreting predictions and the decision function. Our approach is based on a special neural network layer, called bag-layer, whose units aggregate bags of inputs of arbitrary size. We prove theoretically that the associated class of functions contains all Boolean functions over sets of sets of instances and we provide empirical evidence that functions of this kind can be actually learned on semi-synthetic datasets. We finally present experiments on text classification, on citation graphs, and social graph data, which show that our model obtains competitive results with respect to accuracy when compared to other approaches such as convolutional networks on graphs, while at the same time it supports a general approach to interpret the learnt model, as well as explain individual predictions.
Explore on Voice Parameters of Social Robots Applied in Education Industry

The language communication of social robots has a great significance meaning for improving the robot's intelligence and humanity.This research explores the impact of the dialogue type and voice parameter settings of social robots in the education industry on their perceived personality traits and acceptance, and constructs the principles and models of voice applications for robots that are suitable for the education industry.The study included the influence of the educational robot's dialogue type and speech parameters, gender , fundamental frequency, and speech rate.Through the control of robot's speech and dialogue content, to measure the personality of the robot during the interaction between the subjects and the robots.The experiment invited 32 subjects to conduct VHRI robot experiments, which was found that there is a significant interactive effects among the dialogue types and voice parameters of social robots used in the education industry.In addition, the gender of sound and fundamental frequency can improve the personal characteristics of robot extroversion (E), agreeableness (A),conscientiousness (C), openness (O), and anti-neurotic (N).Speech rates can improve the robot's agreeableness(A), conscientiousness (C), anti-neurotic (N), openness (O) and other characteristics.This study confirmed that the robot's dialogue types and voice parameter setting can be used to improve the robot's perceptual personality traits to improve its humanity.On this basis , the results will help social robots play their professional role in the application of education better and get better integrate into human society ,at the same time, it provides references for the voice parameters design of social robotics in different industries in the future.
Research on the Problems and Countermeasures of Computer Security under Cloud Computing Background

Abstract Loud computing is a network-based modern computer technology, it is a new computing service model. The application of cloud computing has brought great changes to computers in many aspects, and also brought many new directions and opportunities. However, the development of cloud computing has also brought a certain impact on computer security. Based on above, the article expounds the cloud computing features and computer security firstly, then explores computer security issues and measures under cloud computing in details. We hope to solve practical security problems through research on computer security issues.
Information Literacy Competence in Connection with Religiosity toMaintain Social Cohesion among Santri of Ali Maksum IslamicBoarding School, KrapyakYogyakarta – Indonesi

Maintaining social cohesion among society has required people, both individually and collectively, including the Santri. Information literacy (IL) competence and religiosity has become factors that can contribute to maintain the social cohesion. The objective of this research is to find out the effect of information literacy competence and religiosity of those Santri toward the social cohesion. This effect is going to be mapped into three points, namely: first, the influence of information literacy competence on social cohesion; second, the influence of religiosity toward social cohesion; and third, the the influence of information literacy and religiosity simultaneously on social cohesion among the Santri of Ali Maksum Islamic Boarding School in Krapyak, Yogyakarta. Research sampling uses a Slovin technique which results in 182 respondents from the Santri. Researchers used a Likert scale to score for each answer of questionnaire and used SPSS as a data processing tool. Based on the results of data processing tool, the results show that information literacy competence significantly influence the social cohesion because the t-count value > t table value is 5.740 > 1.97331. It is also the same significant influence by religiosity toward the social cohesion as seen from the t-count value > t table by 6.061 > 1.97331. Therefore, information literacy and religiosity, both partially and simultaneously, influenced the social cohesion in significant way as shown on F count > F table, that is 5.669 > 3.05. Due to difference of significant influence between independent variables, it needs more exploration by other further studies
On Autonomous Deployment of Micro Aerial Field Vehicles

In this thesis, I am going to investigate the control, navigation and path planning frame-works forMicro Aerial Vehicles (MAVs), both mathematically and algorithmically. Inorder to deploy them in c ...
Active Classification of Cold-Start Users in Large Sparse Datasets

Many applications need to perform classification on large sparse datasets. Classifying the cold-start users who have very few feedbacks is still a challenging task. Previous work has applied active learning to classification with partially observed data. However, for large and sparse data, the number of feedbacks to be queried is huge and many of them are invalid. In this paper, we develop an active classification framework that can address these challenges by leveraging online Matrix Factorization models. We first identify a step-wise data acquisition heuristic which is useful for active classification. We then use the estimations of online Probabilistic Matrix Factorization to compute this heuristic function. In order to reduce the number of invalid queries, we further estimate the probability that a query can be answered by the cold-start user with online Poisson Factorization. During active learning, a query is selected based on the current knowledge learned in these two online factorization models. We demonstrate with real-world movie rating datasets that our framework is highly effective. It not only gains better improvement in classification, but also reduces the number of invalid queries.
All Guides: Library Services: COVID-19: General Public Access

The Consortium Library is actively monitoring the coronavirus (COVID-19) situation and adjusting services as necessary. This guide contains relevant information related to the library's current operations and will be updated as we receive new information. Options available to members of the public.
UNIVERSITY AND ACADEMIC MUSEUMS OF RUSSIA IN MODERN CULTURAL SPACE

A brief analysis of some Russian university and academic museums history formation, their ongoing roles and missions is given. The significance of university and academic museums in scientific, educational and outreach processes, in preservation, maintenance and development of a common cultural space is substantiated. Some features of modern university and academic museums functioning, problems of inter-museum interaction and possible solutions are discussed.
Peningkatan Keterampilan Menulis Teks Berita Menggunakan Metode Partisipatori dengan Teknik ATTL (Amati, Tanya, Tulis, Laporkan) pada Kelas XI RPL-1 SMK Wikrama I Kabupaten Semarang

ABSTRACTNugrahawan Ari Widhiyanto, A 310120092. Improvement of News Text Writing Skills Using Participatory Methods with ATTL Techniques (observe, ask, write, report) on Class XI RPL-1 Vocational School Wikrama 1 Vocational School Semarang Regency Semarang Odd Semester Academic Year 2018/2019.The skills of writing news texts for students of class XI RPL-1 SMK Wikrama 1 Semarang Regency are still low. The results obtained have not yet reached the specified KKM, which is 75. This is due to the lack of student interest in participating in learning, students have not been able to include news items in writing, and the strategies used by teachers are less attractive so students experience difficulties in writing activities.The problems examined in this study are (1) whether the use of participatory methods with ATTL techniques can improve news text writing skills in class XI RPL-1 SMK Wikrama 1 Semarang District? and (2) how is the change in behavior of students of class XI RPL-1 SMK Wikrama 1 Semarang Regency after participating in learning using the pasticipatory method with the ATTL technique?This research was conducted in three stages, namely prasiklus, cycle I and cycle II with a target grade average or minimum completeness, which is 75. The subject of this research was the skill of writing news texts on class XI RPL-1 students at SMK Wikrama 1 Semarang Regency Collection data in cycle I and cycle II using test and non-test techniques.Based on the results of data analysis on pre-cycle, cycle I and cycle II, it is known that there is an increase in completeness obtained by students in learning to write news texts. The results of completeness in pre-cycle of 72.09% with an average value of 70.0 in the category enough.Test completion in the first cycle was 76.70% with an average value of 75.25 in the good category. In the second cycle, students' completeness reached 83.72% with an average score of 82.81 and included in both categories. There was an increase from pre-cycle to cycle I of 4.61% and an increase in the average value of 5.25 points while from cycle I to cycle II of 7.02% and an increase in the average value of 7.56. Learning to write news texts using the pasticipatory method with ATTL techniques is able to change behavior of the students of class XI RPL-1 SMK Wikrama 1 Semarang Regency. These changes are like students who were initially unprepared, less enthusiastic, and less active in participating in learning to be ready, excited, happy, and enjoying learning. Students also appear to be more active and not ashamed to ask the interviewees when facing difficulties.Keywords: news text writing skills, participatory methods, ATTL techniques.
SCIENCE COMMUNICATION DURING THE 2019-2020 SOUTHWEST PUERTO RICO EARTHQUAKE SEQUENCE

Clarity, conciseness and significance are key characteristics for science communication during times of disaster. The challenge is to adapt a data-based message for the general public while maintaining the same depth intended for peers. This presentation will describe the experience of a local geologist and professor as a science communicator during the 2019-2020 Southwest Puerto Rico Earthquake Sequence. The role consisted of communicating how science explains the origin of earthquakes, why seismic activity occurred in Southwest Puerto Rico and addressing questions and concerns from Puerto Rico residents and officials. Science communication practices during this period included the assessment of conflicting information, support through expert opinion and delivery of educational content. Conflicting information emerging from social media, pseudoscience and general public perception included science misconceptions, conspiracy theories and ideas of fatalism. When working with conflicting information, the message was redirected towards basic seismology concepts, regional geology and necessary actions to reduce risk. Expert opinion was offered to support local and federal agencies to make decisions during periods of disaster response. Communicating educational content required the evaluation of daily events and the emerging general public opinion on relevant topics to provide a clear and detailed message on topics such as the frequency and magnitude of seismic events, Puerto Rico’s past seismic history and triggered effects including landslides, liquefaction and subsidence. This experience highlights the need for existing and timely developed products, collaboration with known media personalities, anticipation of possible concerns from the public and clear talking points. But more essentially, the importance of delivering the message in a way that inspires security and hope.
Vision and inertial data fusion for collaborative robotics

Robots capable of engaging in collaborative behaviours with humans, widely known as cobots, are characterized by incredibly complex requirements and are one of today's major challenges in the robotics field. In order to meet the rather strict accuracy requirements needed to ensure human safety and to gather context information useful for intelligent human-robot collaboration, these robots must adequately localize human operators who move freely in the robotic workplaces. In today’s industrial environments, this objective can be achieved by adopting sophisticated sensory devices like lasers, ultrasounds or vision systems. However, human tracking can be particularly difficult in presence of occluding factors that could severely affect vision-based or light-based approaches and in unconstrained conditions like crowded spaces. This thesis analyzes the integration of inertial measurement units and a vision system in order to improve the human localization for collaborative robotics purposes. More in detail, this work first shows how the human upper body can be independently reconstructed by means of an inertial motion capture system and of a stereoscopic vision system. In order to take advantage of both types of sensors, the measurements of these systems are then combined using a two-step Kalman filter fusion algorithm. The approach is first validated by simple calibration movements. Then, some complex movements are considered in order to verify the effectiveness of the framework. In particular, two different categories of movements are experimentally tested: i) short movements where the subject comes back to a rest condition every few seconds and ii) long movements where the subject performs a long motion task without going back to the rest position until the end. Experimental results show that the presence of IMU sensors in addition to cameras can compensate for the typical drift of IMU sensors and effectively improve the spatial perception of the robot. This result could be of great interest not only for direct interaction tasks between humans and robots, but also in the characterization of advanced robotic cells, where human behaviour can be gradually learned and the use of IMU sensors can be finally disregarded, in favour of a pure three-dimensional vision reconstruction.
From quantum computing to quantum-inspired computations

Shanghai University (SHU) and Huawei company, Fangrunhua Lecture Hall, Baoshan Campus, Shanghai University, from 21 - 22 September, 2019.