Reviewerly/exHarmony huggingface dataset:
CNN Filter based Text Region Segmentation from Lecture Video and Extraction using NeuroOCR

Lecture videos are rich with textual information and to be able to understand the text is quite useful for larger video understanding/analysis applications. Though text recognition from images have been an active research area in computer vision, text in lecture videos has mostly been overlooked. In this paper, text extraction from lecture videos are focused. For text extraction from different types of lecture videos such as slides, whiteboard lecture videos, paper lecture videos, etc. The text extraction, the text regions are segmented in video frames and extracted using recurrent neural network based OCR. And finally, the extracted text is converted into audio for ease of convenience. The designed algorithm is tested on different videos from different lectures. The experimental results show that the proposed methodology is quite efficient over existing work.
Design and Validation of a Meter Band Rate in OpenFlow and OpenDaylight for Optimizing QoS

Technological developments in the Internet and communications have created a vastly complex and dynamic context with diverse heterogeneous networks and fast growth of mobile devices and multimedia.As the Internet becomes the primary mode of communication for many organisations there is requirement to enhance quality of service (QoS) from heterogeneous systems and networks.Traditional networks such as TETRA have become increasingly incapable of addressing the demand for media rich, bandwidth intensive traffic flows and applications.Mission-critical multimedia over new generation mobile networks face QoS constraints.This research explores a novel solution for quality of service performance for streaming mission-critical video data in OpenFlow SDN networks.A Meter Band Rate Evaluation (MBE) mechanism is advanced that improves the native QoS capability of OpenFlow and OpenDaylight.The MBE is a physical component added to the OpenFlow meter table to evaluate and dynamically adjust traffic rates and allows the traffic volume to be specified relative to other traffic in the network.Its design and development are presented, and the mechanism is verified through a simulated experiment in an SDN testbed.The results identified that QoS performance experienced a significant percentage increase when the MBE was active.These findings contribute a novel Meter Band Rate Evaluation mechanism that extends the native capability of OpenFlow and OpenDaylight to enhance the efficiency of QoS provision.
GID-Net: Detecting Human-Object Interaction with Global and Instance Dependency

Since detecting and recognizing individual human or object are not adequate to understand the visual world, learning how humans interact with surrounding objects becomes a core technology. However, convolution operations are weak in depicting visual interactions between the instances since they only build blocks that process one local neighborhood at a time. To address this problem, we learn from human perception in observing HOIs to introduce a two-stage trainable reasoning mechanism, referred to as GID block. GID block breaks through the local neighborhoods and captures long-range dependency of pixels both in global-level and instance-level from the scene to help detecting interactions between instances. Furthermore, we conduct a multi-stream network called GID-Net, which is a human-object interaction detection framework consisting of a human branch, an object branch and an interaction branch. Semantic information in global-level and local-level are efficiently reasoned and aggregated in each of the branches. We have compared our proposed GID-Net with existing state-of-the-art methods on two public benchmarks, including V-COCO and HICO-DET. The results have showed that GID-Net outperforms the existing best-performing methods on both the above two benchmarks, validating its efficacy in detecting human-object interactions.
NOLGP: A Novel Optimized Large-Scale Graph Partitioning Algorithm

In big data era, large-scale graphs with billions of vertices and trillions of edges have emerged in wide range of applications, such as social networks and knowledge graphs. For efficient analysis to a large-scale graph on big data/distributed platforms, a key pre-step is to divide vertices/edges of the graph into balanced partitions, i.e., kway balanced partitions. Although several graph partitioning algorithms have been proposed trying to solve this NP-hard problem, the existing algorithms suffer from different defects, such as poor partitioning efficiency and quality, and falling into local optima. To address these issues, this paper first models the problem of k-way partitioning large-scale graph as a multi-objective optimization problem, and proposes a novel optimized algorithm based on the introduced improved label propagation algorithm and a set of optimal strategies. We conducted experiments on real-world large-scale graphs, and the theoretical analysis and experimental results show that our algorithm outperforms the state-of-the-art algorithms, in both partitioning quality and efficiency.
The Effect Of Cooperative Learning Models Type Of Picture And Picture On The Ability To Read The Beginning In Class 1 Students Basic School

This study aims to determine the difference in the effect on the ability to read the beginning by using Model Coopertive Learning Type of picture and picture in grade 1 SDN Sukapura III. This type of research is Experimentation. The design used in this study is Nonequivalent Control Group Design.The population used in this study were all students of SDN Sukapura III. The sample used was class 1 SDN Sukapura III, amounting to 50 students, where class 1 A numbered 25 students as Experiment class and class 1 B amounted to 25 students as control class. Data collection techniques used in this study were in the form of a preliminary reading test using five indicators, namely the accuracy of writing the sound, the reasonableness of pronunciation, the reasonableness of intonation, fluency, sound clarity. Test instruments used to determine the ability to read the beginning is to use an oral test or pratik reading the beginning. This test consists of an initial test (pretest) and a testpost-action (posttest). The results of the study showed Pretest the average value of the experimental class was 50.24 and the control class was 51.04. After treatment of learning using the Cooperative Learning Model Type Picture and Picture, the results of data analysis in the experimental class Postest with an average of 86.24 and the value of the control class with an average of 70.88. Based on the hypothesis test shows that the significant value is 0,000. Due to the significant value of 0.000 &lt; α = 0.05, then H 0 accepted, meaning that this proves that there is a difference between the Model Cooperative Learning mode Picture and picture of the ability to read beginning in grade 1 to grade not use the Model Cooperative Learning mode Picture and picture. From the results empirically that it can be concluded sump wa there are differences in the effect of M odel Cooperative Learning Type of picture and picture of the ability to read beginning in grade 1 SDN Sukapura III 2018/2019 academic year.
Complexity of Shapes Embedded in ${\mathbb Z^n}$ with a Bias Towards Squares.

Shape complexity is a hard-to-quantify quality, mainly due to its relative nature. Biased by Euclidean thinking, circles are commonly considered as the simplest. However, their constructions as digital images are only approximations to the ideal form. Consequently, complexity orders computed in reference to circle are unstable. Unlike circles which lose their circleness in digital images, squares retain their qualities. Hence, we consider squares (hypercubes in $\mathbb Z^n$) to be the simplest shapes relative to which complexity orders are constructed. Using the connection between $L^\infty$ norm and squares we effectively encode squareness-adapted simplification through which we obtain multi-scale complexity measure, where scale determines the level of interest to the boundary. The emergent scale above which the effect of a boundary feature (appendage) disappears is related to the ratio of the contacting width of the appendage to that of the main body. We discuss what zero complexity implies in terms of information repetition and constructibility and what kind of shapes in addition to squares have zero complexity.
Fail-safe Watchtowers and Short-lived Assertions for Payment Channels

The recent development of payment channels and their extensions (e.g., state channels) provides a promising scalability solution for blockchains which allows untrusting parties to transact off-chain and resolve potential disputes via on-chain smart contracts. To protect participants who have no constant access to the blockchain, a watching service named as watchtower is proposed -- a third-party entity obligated to monitor channel states (on behalf of the participants) and correct them on-chain if necessary. Unfortunately, currently proposed watchtower schemes suffer from multiple security and efficiency drawbacks. In this paper, we explore the design space behind watchtowers. We propose a novel watching service named as fail-safe watchtowers. In contrast to prior proposed watching services, our fail-safe watchtower does not watch on-chain smart contracts constantly. Instead, it only sends a single on-chain message periodically confirming or denying the final states of channels being closed. Our watchtowers can easily handle a large number of channels, are privacy-preserving, and fail-safe tolerating multiple attack vectors. Furthermore, we show that watchtowers (in general) may be an option economically unjustified for multiple payment scenarios and we introduce a simple, yet powerful concept of short-lived assertions which can mitigate misbehaving parties in these scenarios.
Video Caption Dataset for Describing Human Actions in Japanese

In recent years, automatic video caption generation has attracted considerable attention. This paper focuses on the generation of Japanese captions for describing human actions. While most currently available video caption datasets have been constructed for English, there is no equivalent Japanese dataset. To address this, we constructed a large-scale Japanese video caption dataset consisting of 79,822 videos and 399,233 captions. Each caption in our dataset describes a video in the form of "who does what and where." To describe human actions, it is important to identify the details of a person, place, and action. Indeed, when we describe human actions, we usually mention the scene, person, and action. In our experiments, we evaluated two caption generation methods to obtain benchmark results. Further, we investigated whether those generation methods could specify "who does what and where."
Catch the Ball: Accurate High-Speed Motions for Mobile Manipulators via Inverse Dynamics Learning

Mobile manipulators consist of a mobile platform equipped with one or more robot arms and are of interest for a wide array of challenging tasks because of their extended workspace and dexterity. Typically, mobile manipulators are deployed in slow-motion collaborative robot scenarios. In this paper, we consider scenarios where accurate high-speed motions are required. We introduce a framework for this regime of tasks including two main components: (i) a bi-level motion optimization algorithm for real-time trajectory generation, which relies on Sequential Quadratic Programming (SQP) and Quadratic Programming (QP), respectively; and (ii) a learning-based controller optimized for precise tracking of high-speed motions via a learned inverse dynamics model. We evaluate our framework with a mobile manipulator platform through numerous high-speed ball catching experiments, where we show a success rate of 85.33%. To the best of our knowledge, this success rate exceeds the reported performance of existing related systems and sets a new state of the art.
Ensuing Security in a Proposed Tertiary Institution Cloud Computing Environment: Introducing a NoHype Framework to the Private Cloud as a Way of Securing the IaaS Model

Cloud computing has reduced the large capital outlays for hardware storage and the human expense needed for its operation.As more institutions and organizations read and write (upload and download private data remotely on computer networks through an Internet connection, eliminating the need for local computer storage, data encryption from unauthorized access (intrusive hackers) becomes a priority.Tertiary institutions need for storage is a wide one, so the need for storing of data in the cloud arises simplifying data management and easing the workflow and pipeline of the staff, non-staff and students.The paper explores various research efforts, reviewed to introduce cloud to Tertiary institutions and employable methods for addressing the biggest fear of cloud adoption in educational organizations.This paper focuses on the security of data stored on the private cloud environment for tertiary institutions, proposing a security architecture for its infrastructure to employ for the satisfaction of such needs.For the purpose of this paper, the security of the private cloud is discussed with more focus on the Virtualization Infrastructure as a Service.This adopted security Architecture is integrated into the Proposed Tertiary Institution Cloud Computing Environment and ready for testing.
Bootstrapping Deep Neural Networks from Approximate Image Processing Pipelines

Complex image processing and computer vision systems often consist of a processing pipeline of functional modules. We intend to replace parts or all of a target pipeline with deep neural networks to achieve benefits such as increased accuracy or reduced computational requirement. To acquire a large amount of labeled data necessary to train the deep neural network, we propose a workflow that leverages the target pipeline to create a significantly larger labeled training set automatically, without prior domain knowledge of the target pipeline. We show experimentally that despite the noise introduced by automated labeling and only using a very small initially labeled data set, the trained deep neural networks can achieve similar or even better performance than the components they replace, while in some cases also reducing computational requirements.
Swarm intelligence versus direct cover algorithms in synthesis of Multi-Valued Logic functions

Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued Logic (MVL) is carried out using more than two discrete logic levels. In this paper, we compare two the SI-based algorithms in synthesizing MVL functions. A benchmark consisting of 50,000 randomly generated 2-variable 4-valued functions is used for assessing the performance of the algorithms using the benchmark. Simulation results show that the PSO outperforms the ACO technique in terms of the average number of product terms (PTs) needed. We also compare the results obtained using both ACO-MVL and PSO-MVL with those obtained using Espresso- MV logic minimizer. It is shown that on average, both of the SI-based techniques produced better results compared to those produced by Espresso-MV. We show that the SI-based techniques outperform the conventional direct-cover (DC) techniques in terms of the average number of product terms required.
Construction quality management

Improving the quality of construction products is a natural process that reflects the objective trend in the development of social production. It is due to the essence of market relations, considering quality as the main measure in the general perception of supply. A significant impetus to this process is given by quality management, which directs participants in the construction industry to create products that meet all the requirements.The right choice of management methods and tools enables successful implementation of projects, which in turn directly affects their competitiveness. The quality of construction products, as well as the quality of building (finishing) materials, as well as construction technologies, including design and other engineering solutions, should be much higher than the average formed. The most important function of quality management of construction products is to encourage construction industry participants to improve the quality characteristics of the products created. The managerial influence not only promotes high quality construction, but also adequately responds to all developments in the investment and construction sphere: organizational, structural, financial, technical and other changes.The article considers such a parameter of the project as quality, compares the experience of implementing the quality management system in domestic construction organizations and construction organizations of some foreign countries. The focus is on the most common methods and programs aimed at improving product quality.A study of domestic and foreign quality management and control systems has shown that the quality management and control system in foreign countries is organized more clearly and effectively than in our country, and certainly positive experience should be taken into service by our construction (and not only) companies, which is only to increase their competitiveness. As a result, the winners will be all participants of the construction project: the consumer of the end result, the customer-developer, investor, insurance companies, authorities and others.
Analyzing Supply Chain Integration through Network Analyses and Meta-Analysis

이 논문은 2019년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2019S1A5A2A03054143) This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2019S1A5A2A03054143) This research is (partially) supported by BK21PLUS Research Fund for Korea University Business School.
SISTEM INFORMASI GEOGRAFIS LAHAN PERTANIAN PADA KECAMATAN BANDAR KHALIFAH BERBASIS PEMETAAN

Bandar Khalifah Subdistrict is one of the sub-districts in Serdang Bedagai Regency. Bandar Khalifah District has various types of agricultural land. Agricultural land in Bandar Khalifah Subdistrict has various kinds of information (non-spatial data) related to the geographical area (spatial data). But this information cannot be processed optimally. Therefore, we need a system that is able to manage spatial data and non-spatial data so that it becomes an effective and efficient information. The system that is able to overcome this problem is GIS Agricultural Land in Bandar Khalifah District. GIS Agricultural Land District Bandar Khalifah was built using ArcView GIS Version 3.3. Digitizing techniques are used to manage spatial data in graphic form. Non-spatial data is managed in tabular forms. Spatial and non-spatial data will be displayed on the system interface. The process of making an interface on the system using the Avenue script programming language. GIS Agricultural Land District of Bandar Khalifah District presents both spatial and non-spatial information about the mapping of agricultural land in Bandar Khalifah District. This system can also be used to update information that can only be accessed by the admin. The interface on the system is a modification of the appearance of the ArcView GIS software interface.
Personal Educational Medium of A Creative Teacher In The Continuing Education System

Along with the sociocultural, natural and technogenic life environments in the post-industrial society, the information environment, embodied by the media and the global Internet, is becoming the leading one. In the information environment, individual activity is transferred to the Internet. It includes professional activities mediated by distance technologies, social networking, Internet communities, personal sites, blogs, twitters, collections of scientific, educational, literary and artistic publications, news, movies, concerts, meetings, etc. In this context, the dual social role of the teacher as a provider and, at the same time, consumer of educational services is highlighted. The development of general and professional competencies (especially socio-communicative) affects the success of a teacher in the field of socialization, education and upbringing of young people in the society based on innovations and high technologies. The education system demands a teacher who is able to implement innovative changes in the educational process, requires fundamentally new technological support for innovations and involves the development of the relevant norms of innovative behavior and description of the models, types and methods of disseminating innovative pedagogical experience. Such a teacher meets the requirements of the Russian national project “Our New School” and should be one of the most important resources for modernizing the education system as a whole and, in particular, in terms of continuing professional development. High-quality performance of professional labor functions (training, educating, upbringing) implies the possession of competencies in the analysis, exchange and dissemination of innovative pedagogical technologies. Professional development of a teacher is impossible without the need to transmit the positive experience to wide pedagogical communities.
Brain vasculature segmentation based on human perception criteria

In this work, we aim to accurately segment the cerebral vasculature on MRI-TOF images. This study is part of a wider project<sup>1</sup> in which we intend to characterize the arterial bifurcations in order to estimate the risk of occurrence of intra-cranial aneurysms (ICA). However, a very accurate segmentation of the vasculature is needed along the Circle of Willis (as this is where most of Intra-Cranial Aneurysms occur) prior to launch the bifurcation characterization. An imprecise segmentation of the Circle of Willis will inevitably lead to a deficient characterization, and thus an erroneous ICA risk estimation. This study was motivated by the lack of efficiency of various State of the Art segmentation methods. In this work, we try to mimic the behavior of the Human Visual System in order to correctly segment the Circle of Willis on TOF imaging of the brain. When Neuroradiologists diagnose an aneurysm on an MRI volume, they modulate the image contrast and luminance so that the vasculature is highlighted within the image. In this work, we first consider the display monitor behavior and we exploit a model that mimics the perception of contrasts by a human observer, in order to accentuate the vasculature for the last segmentation step. Indeed, thanks to this perceptual contrast enhancement, the amplitude of the vasculature moves beyond the rest of the image (parenchyma, cerebrospinal fluid,· · ·) this perceptual contrast stretching then allows to simplify the final thresholding step.