Reviewerly/exHarmony huggingface dataset:
INFLUENCE OF REPUTATION AGENTS ON UNIVERSITY PROFILE FORMATION

It is obvious that reputation is an important factor in the success of any organization, including those working in the field of education. In the era of digital transformation the importance of reputation is not reduced, but the process of its formation and strengthening (as well as protection) acquires a new content. In particular, in the digital era the number of sources of information and communication channels and, consequently, reputational risks grow. It is important to consider that the organization has no influence over many sources of information, but, at the same time, the information in those sources is important for representatives of the target audiences, especially for applicants. On that basis, it is important for educational organizations to make the right managerial decisions related to reputation and brand. The paper studies the practices of higher education institutions in the process of managing reputational risks in connection with the activities of third-party reputational agents. In particular, it is noted that the presence in the ratings at high positions should not be a goal in itself for a university, but a natural consequence of its successful work. Therefore, managerial decisions should contribute to forming the reputation implyed by leadership in educational and scientific spheres, and that work should be carried out in cooperation with reputational agents
Reading Fluency of Students, Tagbilaran City College, Tagbilaran City, Bohol

Reading is an essential macro skill everyone should develop, especially students. The primary thrust of this study was to determine the student's reading fluency in Tagbilaran City College, Tagbilaran City, Bohol, for Academic Year 2019-2020. This quantitative research involved 256 first-year students in all degree programs. This study utilized the free reading speed test software of the Ace Reader program as a tool to determine reading fluency in terms of speed and comprehension. As to the findings, it revealed that the respondents are Instructional readers. Moreover, the study found that there is no significant relationship between students’ profiles and reading fluency. There is no significant difference in reading fluency between male and female respondents. However, there is a significant variance in the reading fluency of the respondents among complexity levels. Furthermore, the post hoc analysis using the Pairwise Comparisons showed that the respondents have a faster reading speed in the Critical, Interpretive, and Applied Complexity levels. Also, it revealed that the respondents have the highest comprehension at the Literal level and the lowest at the Critical level. The study also showed that the respondents have better reading fluency in the Literal and Interpretive levels than in the Applied and Critical levels. The study concluded that the respondents tend to read faster as the complexity level increases; however, they tend to comprehend less as the complexity level increases. Hence, the respondents' fluency decreased as the complexity level increased.
Segmentation Comparison with FCN Based approach and YOLO Based approach

Semantic segmentation, the computer-vision task in which we label specific regions of an image according to what’s being shown, attracted a lot of attention from the community. Many industries require more accurate and efficient image segmentation, such as autonomous driving, medical diagnosis, virtual reality systems, etc. This paper introduces the reader to the basics of semantic segmentation and reviews the deep learning methods applied to semantic segmentation. First, the relevant models about semantic segmentation and the methodology used are introduced, followed by a review of the details of Poly-YOLO and how it differs from the traditional method FCN. Then, the reader is shown the steps of training Poly-YOLO. Finally, the semantic segmentation results of the trained Poly-YOLO are shown and discussed.
Black Cloud Generation Method for Privacy Protection in Fall Detection

Fall detection is an important application in computer vision. However, the image data for fall detection contain unprotected privacy. For example, the silhouette of a person who falls may divulge information about whether the person is disabled. In addition, privacy, such as the age and height of the fallen person, will also be apparent in the image. To tackle these issues, this paper proposes a novel approach, called Black Cloud for Falling Detection (BCFD), to protect the privacy information of the fallen person. We introduce a new image segmentation method to estimate the contour of the human body image, and we generate 'black clouds' to accurately cover the complete human images. The detector was conducted using the contour information to obtain the fallen person. We also propose a method to construct a privacy-protected fall detection dataset and provide a sample dataset. The proposed method can protect the privacy information precisely compared with the state-of-the-art methods. We have validated our method on the proposed PPFD-sample dataset, observing 90.2% precision and 81.8% recall. Experimental results demonstrate that our approach can protect image privacy effectively in the fallen person detection task.
Three-Dimensional Offset Vector for Panoptic Segmentation

Panoptic segmentation has become a research hotspot in the field of computer vision. In this paper, we propose a novel and efficient method for panoptic segmentation. Based on semantic segmentation architecture DeepLabv3+, we predict a three-dimensional offset vector for each pixel, and produce a unified panoptic segmentation mask by combining semantic prediction. Previous bottom-up panoptic segmentation methods only consider offset vector in a horizontal plane, i.e. a two-dimensional space. In contrast, we consider offset vector in both horizontal plane and vertical direction, and predict an offset vector in three-dimensional space for each pixel. Offset vector in vertical direction is used to determine whether a pixel belongs to a foreground object, while offset vector in horizontal plane is used to determine which foreground object the foreground pixel belongs to. In this way, we can predict offset vectors for both background and foreground object pixels at the same time, and effectively model foreground pixels clustering process. During inference, we can produce a unified panoptic segmentation mask by combining the foreground pixels clustering result and semantic prediction. We demonstrate our method’s effectiveness on Cityscapes dataset, and obtain competitive results.
Pseudo Random Masked AutoEncoder for Self-supervised Learning

Masked AutoEncoder (MAE) has recently been proposed and has shown its effectiveness as a vision learner by an elegant asymmetric encoder-decoder design, which significantly optimizes both the pre-training efficiency and fine-tuning accuracy. In this paper, we propose a Pseudo Random Masking (PRM) strategy based on local gradient information instead of using random masking strategy in MAE. Our Pseudo Random Masked AutoEncoder (PRMAE) can preserve significant batches in the images and successfully improves the performances of MAE. Experimental results show that our method performs better than the previous work.
Multi Languages Pattern Matching-Based Scraping of News and Articles Websites

Web scraping refers to the extraction of data from a specific website. Every website will include web pages and each page has a source code containing HTML tags that show a representation of the data. The problem with any scraping method for a web page is how the page is structured, each page has a different structure. That’s why the process of data extraction requires more knowledge about web page structure. To solve such a problem users should know the content and the structure of any page. In this paper, we propose a multi-language pattern mining technique to scrap news and articles websites by recognising title, body and thumbnail image based on a content structure pattern. This approach is an improvement for our previous work that has been done before. By using the same method we have added thumbnail Image as new parameters to be extracted. This approach can be applied to several data-sets, in our case we have prepared 550 web pages as a dataset to test it in both languages Arabic and English.
An Overview and Current Status of Blockchains Performance

Since the introduction of Bitcoin and Ethereum, blockchain introduced new ways to handle payment and dealing with financial transactions. From 2009 till now, several blockchain systems were developed. However, the main issue for each blockchain is to know where to use it and what the best use case fits with it. Transaction per second (TPS) and confirmation time (CT) give the possibility to evaluate the speed and performance of each blockchain. It demonstrates the speed of blockchain in processing any transaction and saving it to the ledger. This work reviews and presents the current status of the state-of-the-art blockchain performance.
Distribution of the Training Data Over the Shortest Path Between the Servers

In this paper, we present a method that supports multi-server learning with distributed data, by collecting selected data from the neighbors of training servers, the proposed training scheme can train multiple models on multiple servers located at specific points. By collecting selected data from the neighbors of the training servers, the proposed training scheme can train multiple models on multiple servers located at specific points, then we will create a method to share the learning tasks by exchanging the data on the shortest path between the servers.
D-insta: A Decentralized Image Sharing Platform

Due to the covid-19 pandemic, people have moved toward digitization and using digital technologies in their daily life. For instance, photographers and artists use social media platforms or stock photo websites to showcase their art to people to get recognition and credit. Since social media platforms attract people more than stock photo websites, we consider incorporating the stock photo website features into the social media platforms. Currently, such platforms are running in a centralized fashion where their proprietary algorithms mask most of the content to which some users and advertisement posts are given more priority. Due to the centralization, such hidden algorithms create trust issues among the users along with other issues such as single point of failure, identity theft, etc. This causes genuine artists and photographers to lose their interest and motivation. Providing due credit to the authors and deserved recognition are significant concerns for photographers who share images on stock photo websites or social media platforms. In this paper, we propose a decentralized image-sharing platform/application utilizing blockchain and a distributed file storage system to address all these issues. The proposed platform leverages Ethereum-based smart contracts to maintain trust as deployed smart contracts are immutable, and the logic written in them is publicly available. We leverage a distributed file storage system to solve the blockchain scalability issue in terms of storage.
The Digital Humanities Trend in Chinese Film History: A Case Study of Filmmaker Lvban

The application of data algorithms improves the human perception of knowledge structures and provides interdisciplinary information storage and analysis tools for humanities-based academic research. Using computer-assisted tools, data visualization is applied to texts and images, which are then utilized to investigate film noumenon and film style. This paper, based on Lvban’s films and their associated literary texts, found that Before the Arrival of the New Director was a week keyword in previous research; however, it can serve as a future point of research area for Lvban cluster analysis. Returning to research on Lvban’s film style using the image itself, the author proposes a new experimental method for film research. Using computer-assisted tools CiteSpace and Darwin, this paper demonstrates that studies on Lvban are transitioning from the investigation of individual cases to the exploration of Lvban’s identity, film style, and film aesthetic.
Programming Paradigms for the Cloud Continuum

The Cloud continuum is a notion for the distributed infrastructure able to run third party software on heterogeneous hardware ranging from the high performance core Cloud data centres, through smaller fog data centres, down to resource constrained Edge servers. Beyond that, one will often have Internet of Things (IoT) devices on a wired or wireless infrastructure for data collection and actuation. The application components should be allocated along this continuum in order to optimize the application performance which is a combined measure of communication speed and computational power. However, most applications are not written with distribution in mind, and may not even be component based. This paper discusses various programming paradigms and how they can be extended to support distribution and embedding into the microservice architecture supporting tomorrow’s Cloud applications.
Performance Analysis of a BESU Permissioned Blockchain

We present a set of tests on a real permissioned blockchain where nodes are maintained by different independent public organizations in various geographic sites. Such configuration sets up real working conditions where a permissioned blockchain is not ruled and run by a single entity. Used platform is Ethereum-Hyperledger BESU implemented through docker technology. We compared standard “Caliper” tests against more detailed and customizable tests executed by launching different transactions to stimulate the answer of the entire network under typical working conditions. Results show that, unexpectedly, not all blockchain nodes work equivalently: under certain conditions, only some nodes contribute to validate transactions and to include them into blocks, while others only append empty blocks to the ledger. This work has a twofold purpose. First, behind the analysis of the specific permissioned blockchain, the aim is to investigate and detect general issues and pitfalls related to this kind of blockchain. Second, providing and improving a tool that can be customized for performance analysis.
A Decentralized Architecture for Electric Vehicle Charging Platform

Hybrid and fully electric vehicles (EV’s) have been drawing the attention of consumers and manufacturers due to the increase in fuel prices, the environmental impact that the burning of fossil fuels causes and the low volume of maintenance that vehicles in this category present. However, there are some issues that still keep consumers away from this type of vehicle, among them is the sales value, which is still much higher compared to combustion models (impacted by the cost of components such as batteries and other related items) and, the battery recharge time, which in the best case scenario, can take around 30 min, which is much higher than the time it takes to refuel a combustion vehicle. The scenario presented, where the driver needs to wait a certain period for the recharge of his EV’s, presents a business opportunity where personalized products and services can be offered through data sharing. In this work, the architecture of a platform is proposed, which allows the connection between the driver and companies interested in offering personalized services while he waits for the electric vehicle to be recharged. The architecture in question is based on the concept of web 3.0 where the driver has full control over the data that will be shared, unlike other architectures where user data is centralized and under the domain of companies with no guarantee of security, traceability, or immutability of this information.
Next Generation Mobile Sensors: Review Regarding the Significance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

The increasing usage of mobile devices amounts to around 6.8 billion by 2022. This implies a substantial increase in the quantity of personal data that are managed. The paper surveys the most relevant contributions that pertain to human activity, behavioural patterns detection, demographics, health and body parameters. Moreover, significant aspects regarding data privacy are also analyzed. The paper also defines relevant research questions and challenges.
Recommender Systems in the Museum Sector: An Overview

Recommendation Systems are used in hundreds of applications, each with particular rules, with affinity and, at times, points of departure from pure recommendation. The applications we will analyze often exploit hybrid approaches, attempting to reduce or completely eliminate the negative effects of pure methods. The first factor to consider when designing a Recommendation System is the application domain, as it has an important effect on the algorithmic approach that should be adopted. In this paper we provide an overview of the typologies of recommender systems that are applied in the museum sector.
Trusted and only Trusted. That is the Access!

Security concerns should always be considered when deploying distributed systems that deal with sensitive data. Generally, the software components responsible for storing these sensitive data are protected, having access control systems to allow or deny external requests. A Policy Enforcement Point (PEP) Proxy is one of these systems which allows or denies access to protected data by checking if the requester is authorized and has permission to access. Despite these two validations about the requester (authorization and data access permission), the traditional PEP Proxy does not guarantee anything more about the requester which will process the data. This work proposes an improvement to the PEP Proxy protection in a way that it can also verify if the requester runs on a Trusted Execution Environment (TEE) application. A TEE is responsible for trusted computing, processing data in a protected region of memory, which is tamper-resistant and isolated from external resources, and keeping code and data protected even if the operating system is hacked. The Trusted PEP Proxy (TruPP) performs the remote attestation (RA) process to guarantee that the requester runs on a TEE. We created a Coloured Petri Net (CPN) model to help validate our proposal by checking some security properties.
Attendance System via Internet of Things, Blockchain and Artificial Intelligence Technology: Literature Review

Many researchers have make efforts on creating several attendance systems to keep track of student attendance in school which is also part of academic curriculum that will have great impact in student academic performance. There have been different technologies such as RFID, face recognition, biometric, web-based attendance system etc. To capture attendance in a way that’s better than the traditional method of pen and paper or roll call system. Making this attendance record available on blockchain network and applying artificial intelligence algorithm for prediction or classification in correlation with the data captured from student attendance purpose. Several articles, papers and journals has been reviewed from different research plethora of research publishers’ database through internet such as Google scholar, IEEExplore, Springerslink etc. This will make it easy for comparing several attendance systems with respect to internet of things, blockchain and artificial intelligence to address the research gap in this aspect.
Co-evolution Genetic Algorithm Approximation Technique for ROM-Less Digital Synthesizers

A new polynomial approximation technique is presented using the concept of co-evolution genetic algorithm. In the proposed technique each polynomial coefficient is considered as a set of independent populations rather being considered as one. These populations co-evolve as they try to optimize the spurious-free dynamic range (SFDR) of a direct digital frequency synthesizer (DDFS). Obtained SFDR from these optimized polynomials have outperformed that of the polynomials evaluated using deterministic approaches. Optimized polynomials for 2nd and 3rd order approximations have achieved 62 dBc and 82 dBc after hardware implementation. Also in the paper a simple genetic algorithm has been explained for optimal pipeline level insertion for a specific hardware implementation of polynomial functions in FPGA platforms.
Utility Function Creator for Cloud Application Optimization

Cloud computing promises unlimited elasticity and allows Cloud applications to scale or change the configuration in response to demand. To automate the application management one must capture the goals and preferences of the application owner, and the most flexible way to represent these is as a utility function. However, it is often difficult for the application owner to define a such mathematical function. Therefore, we propose the Utility Function Creator, a software component that can create the utility function for Cloud application optimization based on a set of predefined utility function policies as well as by template utility function shapes.
TEATOM: A True Zero Touch Intent Based Multi-cloud Framework

The modern day network is a software centric, virtualized network that can be built on COTS hardware or on any public clouds. Despite the openness such ecosystems bring in, there are practical challenges in achieving this network and get the benefit out of it. A large reason for this is the large number of components, complex configurations and an entire new set of skill required. An elegant solution to this problem is end to end automation of rollout and operations of such networks. This paper delves into TEATOM as a framework that can enable such an automation journey that can lead to zero touch operations and maintenance.
Based and confused: Tracing the political connotations of a memetic phrase across the web

Current research on the weaponisation of far-right discourse online has mostly focused on the dangers of normalising hate speech. However, this often operates on questionable assumptions about how far-right terms retain problematic meanings over time and across different platforms. Yet contextual meaning-change, we argue, is key to assessing the normalisation of problematic but fuzzy terms as they spread across the Web. To redress this, our article traces the changing meaning of the term based, a word that was appropriated from Black Twitter to become a staple of online far-right slang in the mid-2010s. Through a quali-quantitative cross-platform approach, we analyse the evolution of the term between 2010 and 2021 on Twitter, Reddit and 4chan. We find that while the far right meaning of based partially survived, its meaning changed and was rendered diffuse as it was adopted by other communities, afforded by a repurposable kernel of meaning to based as ‘not caring about what other people think’ and ‘being true to yourself’ to which different (political) connotations were attached. This challenges the understanding of far-right memes and hate speech as carrying a single and persistent problematic message, and instead emphasises their varied meanings and subcultural functions within specific online communities.