Reviewerly/exHarmony huggingface dataset:
Protection of surveillance recordings via blockchain-assisted multimedia security

Surveillance is being pervasively used, and its recording is extensively applied in practice. Thereby, protection of surveillance recordings, which is related to multimedia security, increasingly attracts research interests. Traditional techniques, such as watermarking, cryptography and steganography, focus on analysing multimedia content, resulting in high complexity and long latency, which makes them not suitable for protecting real-time surveillance applications. In this paper, we propose a novel blockchain-assisted framework to protect the recordings in real-time surveillance applications. In the proposed framework, we design an algorithm that generates frame fingerprints, which involves real-time extraction of packets from surveillance multimedia streams, SHA3-512 functions, and the verification of frame consistency. We use a blockchain for preserving the frame fingerprints generated by our proposed algorithm. Different from existing works that use simulations to show the performance, we develop a real system using a service-oriented blockchain, virtualisation for distributed ledger technology (vDLT), and the effectiveness of this system is demonstrated.
Nonnegative Robust PCA for Background and Foreground Image Decomposition

Nowadays, video surveillance and motion detection system are widely used in various environments. With the relatively low-price cameras and highly automated monitoring system, video and image analysis on road, highway and skies becomes realistic. The key process in the analysis is to separate the useful information such as moving foreground objects from the original video sequence where Robust Principal Component Analysis (RPCA) plays an important role in extracting the foreground objects. RPCA have been widely used in data analysis and dimension reduction with applications in image recovery, information clustering and computer vision. But one drawback of RPCA lies in the fact that it does not guarantee the nonnegativity of pixels. It is important to have nonnegative foreground object since negative pixels that are not in the range between 0 and 255 are meaningless and the foreground objects are thus not visible. State-of-the-art methods do not consider the nonnegativity of the foreground object in their algorithms. This thesis focuses on the problem of extracting foreground moving object from background scenes and guarantee the nonnegativity of foreground object. This thesis proposes a method that combines RPCA and Nonnegative Matrix Factorization (NMF). It ensures the pixels that constitute the foreground object is nonnegative by using the basic model of RPCA and nonnegative components that NMF provides. The efficacy of the proposed algorithms is tested on publicly available dataset. Experiment shows in detail how the proposed algorithms achieve in recovering the foreground object with high true positive rate. Together with RPCA algorithm, the performance of recovery is compared and their advantages and disadvantages are discussed.
Una familia de polinomios de grado 8 cuyo grupo de Galois es el grupo cuaternión Q8

Let p, q be prime integers with q<p and let alfa pq:= (q+ q)(p+p). Let b pq be a real square root of alfa alfa pq. We prove that Q(beta pq) Q is a Galois exension if and only if q-1 is an square in Z and, either p-1 is an spare in Z. In the first case he Galois group Gal (Q(beta pq)Q) is isomorphic to Z4x Z2and in the second one it is isomorphic to the queternion qroup Q8. Thus, under these hypothesis, the minimal polynomial of Beta pq over Q has degree 8 and its Golois group over Q is either Z4 x Z2 or Qs
Community Fabric: Visualizing communities and structure in dynamic networks

We present Community Fabric, a novel visualization technique for simultaneously visualizing communities and structure within dynamic networks. In dynamic networks, the structure of the network is continuously evolving throughout time and these underlying topological shifts tend to lead to communal changes. Community Fabric helps the viewer more easily interpret and understand the interplay of structural change and community evolution in dynamic graphs. To achieve this, we take a new approach, hybridizing two popular network and community visualizations. Community Fabric combines the likes of the Biofabric static network visualization method with traditional community alluvial flow diagrams to visualize communities in a dynamic network while also displaying the underlying network structure. Our approach improves upon existing state-of-the-art techniques in several key areas. We describe the methodologies of Community Fabric, implement the visualization using modern web-based tools, and apply our approach to three example data sets.
Short Video Audience Identification Data Recommended by Multiple Neural Network Algorithms

Short Video is a hot new media in recent years. It has broad prospects for development, and the users are increasing rapidly. Therefore, it is significant to research and analyze mobile and audience identification data. Now, it is the Internet age, we get the useful information by collecting a large number of user behavior records. Traditional media platforms have problems such as serious information overload or slow transmission speed. So, in the big data era, how to generate interest in the short video industry through the analysis of precise audiences has become an urgent problem to be solved. The authors use survey and audience identification data recommended by multiple neural network algorithms to analysis. The research shows that the young and middle-aged people are mainly users and producers. The users’ interest, high-quality educational content and entertainment are the potential factors affecting the popularity of video.
Where Did The Car Go? : Smart cities, calm technology and the future of autonomous cars

Urbanization has been a growing trend in the past fifty years. Cities are now transforming into smart cities, spaces whose infrastructure comprises an embedded digital layer. Hardware collects real ...
Design of Resource Matching Model for Intelligent Education System Based on Machine Learning

The educational revolution brought by new technology is making a robust progress. Artificial intelligence andintelligent education lead the innovation of education and teaching, so they have become an inevitable trend of theeducational informationizationdevelopment. With the rise of big data in education, how to analyze a large amountof data to support accurate prediction is a new topic in the era of artificial intelligence. As an important branch ofartificial intelligence, machine learning can meet the requirements for the analysis and prediction of educationalbig data. Therefore, based on a series of questions, such as “why to analyze, what to analyze, how to analyze, whyto apply”, the applicablenessof machine learning and wisdom education was discussedby analyzing the actionobjects, process, specific methods, and stakeholders. Through summarizing the case studies of machine learningeducation applications based on real data in recent years, it was found that the current application of machinelearning education is mainly concentrated in student modeling, student behavior modeling, learning behaviorprediction, early warning of dropout risk, learning support, as well as evaluation and resource recommendation.Then starting from the perspectiveof crossover, technology, and teaching, some suggestions were put forward forthe educational application and innovation of machine learning based on the framework of intelligent education.
COVID-19 data sharing and organization through blockchain and decentralized models

The pandemic due to Covid-19 has highlighted and sometimes amplified some critical issues affecting the global health system. From the beginning, it was clear that having an overview of the pandemic situation was one of the main objectives to be achieved to fight the Virus. In this sense, technologies based on decentralized systems, such as Blockchain, can be effective in collecting data since they can store information in a secure and scalable way while respecting people's privacy. This work aims to propose a framework based entirely on decentralized systems that can solve management and collaborative analysis of sensitive data, such as those related to the Coronavirus pandemic. In particular, the model is proposed as a tool to improve the efficiency of all those organizations that have found themselves on the front line to deal with the spread of the Virus in the world, such as hospitals and research institutes. In addition, it aims to improve communications between organizations at the global level to facilitate a fair and profitable exchange of knowledge to fight the pandemic. Copyright © 2021 for this paper by its authors.
Assessing the measurement model of negotiation styles and marital satisfaction among couples in Nigeria

This study investigates the measurement model of negotiation styles and marital satisfaction among married couple in Nigeria. To achieve the purpose a cross-sectional survey design was adopted with a sample of 385 married couples in Kwara state Nigeria. The instruments used are tagged ‘Negotiation Styles and Marital Satisfaction Scales’ which contained 40 items spread among the 8 sub-constructs, a 4 level Likert type was adopted throughout the scale. The data collected was entered into Microsoft Excel 2016 and SPSS 20 for data cleaning and filtered to the SmartPLS 3. The data was checked for normality using two indicators: Skewness and Kurtosis. The data analysis was conducted using Partial least square structural equation modelling approaches with SmartPLS 3 software. The measurement models were evaluated by items loading, composite reliability (CR) and average variance extracted (AVE). The results revealed that the measurement model assessed in this study showed acceptability with validity and reliability tests results presented in the preceding sections. The convergent validity was examined through the factor loadings, composite reliability (CR), and average variance extracted (AVE). Thus with the satisfaction of all the measurement requirements, it can be concluded that the developed Negotiation Styles and Marital Satisfaction Scales validated in this study can be used to assess the Negotiation Styles and Marital Satisfaction Scales among married couples in Nigeria.
Small Targets Detection for Transmission Tower Based on SRGAN and Faster RCNN

Background: Power line inspection is essential to ensure the safe and stable operation of the power system. Object detection for tower equipment can significantly improve inspection efficiency. However, due to the low resolution of small targets and limited features, the detection accuracy of small targets is not easy to improve. Objective: This study aimed to improve the tiny targets’ resolution while making the small target's texture and detailed features more prominent to be perceived by the detection model. Methods: In this paper, we propose an algorithm that employs generative adversarial networks to improve small objects' detection accuracy. First, the original image is converted into a superresolution one by a super-resolution reconstruction network (SRGAN). Then the object detection framework Faster RCNN is utilized to detect objects on the super-resolution images. Results: The experimental results on two small object recognition datasets show that the model proposed in this paper has good robustness. It can especially detect the targets missed by Faster RCNN, which indicates that SRGAN can effectively enhance the detailed information of small targets by improving the resolution. Conclusion: We found that higher resolution data is conducive to obtaining more detailed information of small targets, which can help the detection algorithm achieve higher accuracy. The small object detection model based on the generative adversarial network proposed in this paper is feasible and more efficient. Compared with Faster RCNN, this model has better performance on small object detection.
Investigation and hazard implication of 1604 Quanzhou earthquake using modern simulation with literature intensity

The 1604 Quanzhou earthquake occurred offshore southeastern China in the Taiwan Strait and with magnitude estimated ranging from 7.5 to 8.0, which is the largest earthquake observed in the low seismicity zone of the Taiwan strait for the last 400 years.According to literature, this earthquake caused several casualties and severe damage along the coast of southeastern China.Although no document reported the impact of this earthquake on Taiwan, the impact of an earthquake like the 1604 Quanzhou event is of great concern for the region considering the densely populated areas on both sides of the Taiwan Strait.In this study, we review the literature and past studies of this earthquake, and we simulate the level of ground shaking through various models based on past studies.Our results show that the 1604 Quanzhou earthquake had a magnitude of M w 8.1 with maximum slip of 16 -20 m on an asperity, and with an average stress drop of 10 MPa.The comparison of the simulation to the developed response spectra suggests that this earthquake brought a larger long period motion (< 0.4 Hz) to Taiwan compared to that from the regional recorded crustal earthquakes.The long period motion from an earthquake in this magnitude scale warrants attention regarding seismic hazard mitigation for high-rise buildings for cities across the Taiwan Strait.
Graph2Speak: Improving Speaker Identification using Network Knowledge in Criminal Conversational Data

Criminal investigations mostly rely on the collection of speech conversational data in order to identify speakers and build or enrich an existing criminal network. Social network analysis tools are then applied to identify the most central characters and the different communities within the network. We introduce two candidate datasets for criminal conversational data, Crime Scene Investigation (CSI), a television show, and the ROXANNE simulated data. We also introduce the metric of conversation accuracy in the context of criminal investigations. By re-ranking candidate speakers based on the frequency of previous interactions, we improve the speaker identification baseline by 1.2% absolute (1.3% relative), and the conversation accuracy by 2.6% absolute (3.4% relative) on CSI data, and by 1.1% absolute (1.2% relative), and 2% absolute (2.5% relative) respectively on the ROXANNE simulated data.
Incidencia del uso de las TIC en los resultados académicos

Pedagogical trends are subject to the development of information and communication technologies to strengthen the skills of students in basic primary education. Therefore, the study aimed to determine the impact of ICT on the activities and academic performance of fourth and fifth grade students, and consequently, a questionnaire was designed and applied to 500 students from different public and private educational institutions in the municipality of Ocana, concluding that more than 50% of the students use the Google search engine, manage files in folders and save the information. It also concluded that technological tools have a high impact on the training and learning of students.
MANAGING SCHOOLS DURING THE COVID-19 PANDEMIC: SOME ORGANIZATIONAL CULTURE CONSIDERATIONS

The Corona virus pandemic has led to a global lockdown which resulted in schools closure and interruption on teaching and learning. For health-related reasons schools have to adhere to recommendations that limit the spread of the virus and these include; social distancing,  limiting the use of cooperative learning strategies in classrooms as well as strict regulation of collaborative working teams in schools. These changes to how work is carried out in schools indicate a need to adapt school organizational cultures to the new way of doing things. Changes to how schools are managed, to curriculum and instructional design, and to how performance is measured in schools are a starting point. This paper looks at some of the changes that need to be implemented on school cultures in order to keep schools safe from the virus, as well as to maintain high levels of efficiency particularly during times of uncertainty.
Stochastic Gradient in Learning Decision Sequences

In this chapter the known, classic and more recent approaches to learning decisions and decision sequences that are based on various versions are reviewed. They are presented in a unified manner in order to compare them from the point of view of their applicability to learn relatively long decision sequences of the length, say, of dozens or hundreds.
Analisa Sistem Distribusi Air Bersih Tahun 2020-2040 Di Desa Negeri Lama Kecamatan Baguala, Kota Ambon, (Studi Kasus RT 01, 02/RW 01 Dan RT 09/RW 04)

Negeri Lama Village, Baguala District, Ambon City, has a spring that is often consumed by the local community. At RT 01.02 / RW 01 and RT 09 / RW 04 there is a spring that is often consumed by the local community for their daily needs. however, they experience problems so that people find it difficult to get clean water. One of the main problems that occurred in the village of Negeri Lama, especially in RT 01.02 / RW 01 and RT 09 / RW 04, was the lack of raw water sources and unequal service and supply of raw water so that it had not been fully utilized. This made the author want to review the problems that occurred in the Old Country village. The purpose of writing this thesis is to find out the results of observations in Negeri Lama Village, especially in RT 01, 02 / RW 01 and RT 09 / RW 04 to find out the water needs of the population for the next 20 years from 2020 to 2040. and the projected population in The plan year is analyzed using the Least Squere Method, the Geometric Method, and the Arithmetic Method. and reviewing the problem of the need for clean water in Negeri Lama Village by calculating the time for distributing and filling water evenly. The results of the survey and analysis of clean water needs in Negeri Lama Village for 2020-2040 show that the available water debit at the spring in Negeri Lama Village RT 01.02 / RW 01 and RT 09 / RW 04 is = 1.20 l/s. The amount of available population water needs only until 2036, namely = 1.1994 l/s and the population water needs that are not met, namely from 2037-2040 is = 1308.3 l / s. so that the water needs of residents in the old country villages RT 01 02 / RW 01 and RT 09 / RW 04 are not fulfilled, because the discharge at the spring is not able to meet the water needs of the population for the next 20 years. and to overcome this problem, periodicals for supplying water to the community must be implemented and distribution restrictions in each RT so that the water needs of the population can be met evenly.
Perpustakaan, Dashboatd System P Perancangan dan Implementasi Dashboard Sistem Informasi UPT.Badan Perpustakaan Umum Siak Kecil Kabupaten Bengkalis Menggunakan Metodologi Prototyping

Public libraries are places that collect collections of books, printed materials and other records for the benefit of the general public. Public libraries can also be interpreted as various educational institutions for the general public by providing various kinds of scientific information, on the management of library book data, both those that are often borrowed and those that cannot be returned and the types of books in the library or recording book borrowing transactions such as book records still use the traditional way, this is one of the channels that is exceeded to meet and monitor the reading interest of visitors in Siak Kecil sub-district. On this basis, a Library System Dashboard will be developed using the Proptotyping methodology. This system is built using PHP (Hypertext Processor) as programming language and MySQL as database. Once done, blackbox testing can show that the system is running as expected. Based on the user acceptance test (UAT) that has been carried out, it shows that 94% of the systems built are well accepted and can be the head of the library in managing data and helping decision making.
Why the Folsom Point Was Fluted: Implications from a Particular Technofunctional Explanation

Technofunctional explanations generally appeal to a mechanical design feature intended to enhance penetration and killing power and/or intended to facilitate physical union between the stone point and other wood or osseous haft components. The haft was made from a longitudinally split shaft or rod, shaped slightly on the distal end to provide a smooth contour for penetration and tapered on the proximal end to fit into a drilled hole in the main shaft. The haft served as a reinforcing splint along each face of the stone point, guarding the stone point against breakage from bending forces. Although pertinent details are less well documented through experimentation and archaeological study, it is probable that a high failure rate also occurred during production of Folsom ultrathin bifaces. The fluting explanation discussed here provides no insight regarding the extreme metric and technological similarity in Folsom points across the great expanse of its known distribution.
Robust and Efficient Algorithms for Federated Learning and Distributed Computing

Author(s): Reisizadeh, Amirhossein | Advisor(s): Pedarsani, Ramtin | Abstract: Training a large-scale model over a massive data set is an extremely computation and storage intensive task, e.g. training ResNet with hundreds of millions of parameters over the data set ImageNet with millions of images. As a result, there has been significant interest in developing distributed learning strategies that speed up the training of learning models. Due to the growing computational power of the ecosystem of billions of mobile and computing devices, many future distributed learning systems operate based on storing data locally and pushing computation to the network edge. Unlike traditional centralized machine learning environments, however, machine learning at the edge is characterized by significant challenges including (1) scalability due to severe constraints on communication bandwidth and other resources including storage and energy, (2) robustness to stragglers, and edge failures due to slow edge nodes, (3) models generalizing to non-i.i.d. and heterogeneous data.In this thesis, we focus on two important distributed learning frameworks: Federated Learning and Distributed Computing, with a shared goal in mind: how to provably address the critical challenges in such paradigms using novel techniques from distributed optimization, statistical learning theory, probability theory, and communication and coding theory to advance the state-of-the-art in efficiency, resiliency, and scalability. In the first part of the thesis, we devise three methods to mitigate communication cost, straggler resiliency and robustness to heterogeneous data in federated learning paradigms. Our main ideas are to employ model compression, adaptive device participation and distributionally robust minimax optimization, respectively for such challenges. We characterize provable improvements for the proposed algorithms in terms of convergence speed, expected runtime, and generalization gaps.Moving on to the second part, we consider important instances of distributed computing frameworks such as distributed gradient aggregation, matrix-vector multiplication and MapReduce-type computing tasks and propose several algorithms to mitigate the aforementioned bottlenecks in these settings. The key idea in our designs is to introduce redundant and coded computation in an elaborate fashion in order to benefit in communication cost and the total runtime. We also support our theoretical results in both parts by significant improvements in numerical experiments.