Reviewerly/exHarmony huggingface dataset:
Percepción de los estudiantes sobre el uso de Zoom en las clases de inglés como lengua extranjera

La pandemia COVID-19 ha tenido un impacto en muchas industrias, pero principalmente en el campo educativo. La educación se llevó a cabo de forma remota a causa del COVID-19, utilizando algunas aplicaciones, como Zoom. Esta investigación tuvo como objetivo analizar las opiniones de un grupo de estudiantes de inglés como lengua extranjera en una escuela secundaria sobre la aplicación Zoom utilizada en las clases en línea. Se trata de un estudio transversal en el que se eligieron enfoques cualitativos y cuantitativos para este estudio. Se utilizaron entrevistas y cuestionarios para reunir los datos y la población estaba integrada por 28 estudiantes. Para obtener los resultados de la investigación, se analizaron la entrevista y los cuestionarios que permitieron conocer la percepción de los estudiantes sobre el uso de la aplicación Zoom en las clases de inglés y la entrevista ayudó a identificar los desafíos que enfrentaron los estudiantes en las clases de Zoom. Ambos resultados mostraron que el internet es un factor importante para el progreso de la clase, sin embargo existen factores externos, que son distractores y que pueden limitar el proceso de aprendizaje.&#x0D; Abstract&#x0D; The COVID-19 pandemic has had an impact on many industries, but mainly on the educational field. Education was carried out remotely due to COVID-19, using some applications, such as Zoom. This research aimed to analyze the opinions of a group of students of English as a Foreign Language in a secondary school about the Zoom application used in online classes. This is a cross-sectional study where qualitative and quantitative methods were used to acquire the information. Interviews and questionnaires were used to gather the data. The population consisted of 28 students. To obtain the results of the research, the interview script was analyzed which allowed us to know the students' perception about the use of Zoom application in English classes and with respect to the interview helped identify the challenges faced by the students in Zoom classes. Both results showed that the internet is an important factor for class progress, however there are external factors, which are distracting and can limit the learning process.&#x0D; Keywords: Education; Zoom application; Innovation; Internet
Advance sharing of quantum shares for quantum secrets

Secret sharing is a cryptographic scheme to encode a secret to multiple shares being distributed to participants, so that only qualified sets of participants can restore the original secret from their shares. When we encode a secret by a secret sharing scheme and distribute shares, sometimes not all participants are accessible, and it is desirable to distribute shares to those participants before a secret information is determined. Secret sharing schemes for classical secrets have been known to be able to distribute some shares before a given secret. Lie et al. found a ((2, 3))-threshold secret sharing for quantum secrets can distribute some shares before a given secret. However, it is unknown whether distributing some shares before a given secret is possible with other access structures of secret sharing for quantum secrets. We propose a quantum secret sharing scheme for quantum secrets that can distribute some shares before a given secret with other access structures.
The challenges of Digital Evidence usage in Deepfake Crimes Era

This research paper discusses the challenges of digital evidence usage in Deepfake crimes era in both the Egyptian and US Legislations. There is no doubt about the importance of keeping pace with the Law with behaviors that pose a threat to fundamental interests that deserve protection, especially in an era when information technology is instantaneously accelerating towards the creation of many modern technologies that raise many concerns, since artificial intelligence algorithms have helped to think about a large number of issues that did not exist a few years ago, such as the ease of processing big data and simultaneous machine translation, and one of those algorithms is Deepfake, which was classified as the most dangerous among artificial intelligence algorithms on Cybersecurity threats. With the complexity of Investigations of computer related crimes, due to the obstacles in gathering the evidence. The researcher seeks, after discussing the essence of digital evidence, stating its types, forms, characteristics, sources, principles, and challenges facing its application, as well as comparing between the laws regulating the digital evidence nationally, internationally concerning (Budapest convention) and The US federal rules of digital evidence, to present and set recommendations to reduce the risks and challenges of these crimes, and to assist the legislator in addressing the shortcomings in Egyptian laws.&#x0D; Keywords: Digital evidence, Deepfake, Cybercrime, Digital privacy &amp; Cybersecurity.
An Optimized Role-Based Access Control Using Trust Mechanism in E-Health Cloud Environment

In today’s world, services are improved and advanced in every field of life. Especially in the health sector, information technology (IT) plays a vigorous role in electronic health (e-health). To achieve benefits from e-health, its cloud-based implementation is necessary. With this environment’s multiple benefits, privacy and security loopholes exist. As the number of users grows, the Electronic Healthcare System’s (EHS) response time becomes slower. This study presented a trust mechanism for access control (AC) known as role-based access control (RBAC) to address this issue. This method observes the user’s behavior and assigns roles based on it. The AC module has been implemented using SQL Server, and an administrator develops controls for users with roles and access to multiple EHS modules. To validate the user’s trust value, A .net-based framework has been introduced. The framework of e-health proposed in this research ensures that users can protect their data from intruders and other security threats.
Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving

Conditional Imitation learning is a common and effective approach to train autonomous driving agents. However, two issues limit the full potential of this approach: (i) the inertia problem, a special case of causal confusion where the agent mistakenly correlates low speed with no acceleration, and (ii) low correlation between offline and online performance due to the accumulation of small errors that brings the agent in a previously unseen state. Both issues are critical for state-aware models, yet informing the driving agent of its internal state as well as the state of the environment is of crucial importance. In this paper we propose a multi-task learning agent based on a multi-stage vision transformer with state token propagation. We feed the state of the vehicle along with the representation of the environment as a special token of the transformer and propagate it throughout the network. This allows us to tackle the aforementioned issues from different angles: guiding the driving policy with learned stop/go information, performing data augmentation directly on the state of the vehicle and visually explaining the model's decisions. We report a drastic decrease in inertia and a high correlation between offline and online metrics.
PDG: A Prefetcher for Dynamic Graph Updating

Dynamic graphs can be utilized to model many real-world applications like social media analysis in which the connections and entities evolve continuously. Hence, the processing of dynamic graphs is gaining increasing popularity. However, prior dynamic graph processing systems mainly focus on the optimization of graph analytics but overlook graph updating which manages the evolving graph structure and presents a unified view to graph analytics. Since graph updating operates on evolving graphs and involves a large number of irregular memory accesses, it poses a substantial influence on the performance of dynamic graph processing systems. In this work, we observe that graph updating is mainly bottlenecked by a frequent indirect memory access pattern *(*(BAi+offset)). The pattern is inherent to the typical graph updating from the incoming edge stream to the base data store organized with either an adjacent list or a compressed sparse row. With this observation, we propose a novel Prefetcher for Dynamic Graph updating abbreviated as PDG. PDG is a lightweight pipelined instruction-based prefetcher specialized for graph updating and it is also compatible with the irregular memory access pattern BAi widely used in graph analytics. In addition, it leverages a monitor of the instruction queue to decide the appropriate timing of prefetching to make the best use of the cache. According to our experiments, PDG achieves 1.60×, 1.26× and 1.30× performance speedup compared to three representative prefetchers respectively with negligible hardware overhead in graph updating.
Accident Detection from Drone Footage

This research project aimed to address the issue of unreported accidents surpassing the time limit for reporting by leveraging drone surveillance in the context of smart cities and transportation. The project involved utilizing drone-captured images as input for an accident detection model, which incorporated a modified Intersection over Union (IoU) algorithm optimized with background subtraction to accommodate the resource limitations of an embedded environment. The implementation was conducted on a Raspberry Pi 4 B model, integrating a pre-trained model imported from a high-performance computer and layered with an anomaly detection algorithm. This algorithm not only indicated the presence of accidents in each frame but also provided a percentage estimation of accident presence along with identification of any additional anomalies suggestive of accidents occurring outside the current frame. To mitigate computational complexity, frame skipping was employed, skipping every other frame. The primary stage of the research yielded satisfactory results, demonstrating the model’s viability in contemporary society.
Currency Authentication using Color Based Processing

The process of currency authentication is crucial to prevent counterfeit currency from being circulated in the economy. Traditional methods of currency authentication such as watermark, security thread and microprint are no longer sufficient due to the high-tech printing technology available to counterfeiters. To overcome this, image processing techniques have been employed to develop a reliable and efficient method for currency authentication. In this study, we propose a currency authentication system using image processing. The system involves capturing images of the currency and analyzing them using various image processing algorithms. The currency is identified and localized in the image using template matching technique. It improves accuracy and efficiency by examining security features, spotting fake notes, and preserving financial integrity.
Comparative Analysis of Face Recognition Algorithms: Evaluating LBPH and FaceNet with Haar Cascade

Facial recognition is a widely utilized technology in numerous industries, such as security, retail, delivery, and criminal detection. However, it encounters significant challenges when dealing with low-resolution images and videos. To achieve accurate facial recognition, researchers have explored various techniques, including LBPH, Face++, and ReKognition. Despite advancements, there remains considerable room for improving recognition accuracy. LBPH, a feature-based method, demonstrates remarkable proficiency in recognizing faces from diverse viewpoints. In contrast, deep learning approaches like FaceNet and Haar Cascade have emerged as prominent techniques. Haar Cascade specifically focuses on face recognition. This study evaluates the performance of LBPH face recognition system in comparison with a combined approach employing FaceNet and Haar Cascade using real-time video data.
Sentiment Analysis of Employee Feedback Data

Emotional analysis is the disclosure of data on products that aids a company in comprehending the public's perceptions of its product, product, or service. The goal of this work is to develop an emotional analysis model that aids in identifying positive and negative emotions in the textual reviews provided by the CMR Institute of Technology, Bangalore. The polarity is compared using the Python-based Text Blob and Vader sentiment analyzers. For evaluation, a variety of machine learning techniques are applied. A thorough evaluation of the college's success will be possible with the aid of the intellectual response analysis.
Navigating Data Scarce Environments in Computer Vision

Abstract Sumedh Datar FBCS is a senior software engineer with expertise in computer vision. He explores ways in which an absence of data may not necessarily result in AI being unable to identify new objects correctly.
BSeVS – A Smart e-Voting System based on Blockchain Technology

Security and privacy has always been a major concern for wider adoption of electronic voting systems since the election results have both major economic and political influences all around the world. The challenges of an e-voting system like anonymity, secrecy, verifiability and authenticity is yet to be fixed and can be resolved by utilizing the Blockchain technology. Blockchain technology can improve voting process security, transparency, immutability, and dependability. Blockchain is a distributed, digital, consensus-based system for securely storing information. In this paper, a Blockchain based Smart e-Voting System (BSeVS) is implemented, which is utilizing the Hash functions and Elliptic Curve Cryptography to resolve the issues of the e-voting scheme. It enhances the end to end safety of e-voting mechanism without compromising the integrity and authenticity of voting environment. The results show that the BSeVS is performing better than existing voting schemes in terms of security and time complexity.
Automatic Text Summarization based Web Application

Text summarization is an important task in natural language processing that aims to condense large bodies of text into short but coherent summaries. This study provides an overview of text summarization techniques, including extractive, abstractive, and hybrid approaches. We explore various methods employed within each approach and discuss their advantages and disadvantages. Furthermore, we delve into the challenges faced by researchers in the field of text summarization and highlight potential avenues for future research. We introduce an all in one web application for text summarization that encompasses all aspects of text summarization.
The Art of Feeling and Sensing with Technology

Abstract Camille Baker is an artist who uses technology to explore our emotions. Georgia Smith talks to her about her work, her hopes and her passions.
Framework for securing information on helpdesk systems based on blockchain and IPFS technologies

In traditional help desk systems there are vulnerabilities that allow the alteration of data without it’s manager’s knowledge. Previous studies show that a high percentage of help desk staff are resources without the experience or skills necessary for managing a highly sensitive system. This paper proposes a framework for the implementation of decentralized blockchain and Interplanetary File System (IPFS) technologies to ensure the integrity and traceability of information recorded in conventional help desk systems. The framework is composed of three phases (1) entity abstraction, (2) deployment of decentralized technologies and (3) implementation of decentralized technologies. The study was validated through experiments in controlled environments and the measurement of different variables and indicators. The results showed that the combination of decentralized blockchain and IPFS technologies makes it possible to secure the information recorded in help desk systems.
Fake news and digital politics

The rise of deliberate disinformation - nowadays often referred to as fake news - has sparked societal as well as academic debates. Fake news’ connection to digital politics and particular elections is the focus of this chapter. The term fake news has effectively been weaponized by political actors to attack a variety of news media. The chapter looks at how politicians have been associated with the term, both in terms of labelling fake news, producing fake news and creating a legal framework to forbid fake news. We also cover what characterizes consumers of fake news. Despite the heightened attention to this phenomenon, important aspects are still only marginally understood, and the chapter suggest three pathways for future research regarding fake news and digital politics: politicians’ impact on users’ trust in media outlets, the effect of different types of corrections, and platforms’ role in fighting fake news.
Regulation of election communication

This chapter describes the rules which protect the fairness and legitimacy of election campaigns in democracies. It describes how fundamental rights, journalism professional rules, broadcast licensing and election laws seek to maintain a level playing field in elections and ensure that public opinion cannot be manipulated. The chapter describes how recent developments in the technology of campaigns and particularly the increasing importance of social media have undermined previously existing rules, for example through the use of campaign services provided by companies such as Cambridge Analytica. The increasing datafication of election campaigns has led to widespread concern with fragmentation, manipulation, distortion and corruption of election campaigns which are reviewed in this chapter. The role of dominant platforms and foreign actors is also explored and a review is made of current directions in research and public policy aiming to restore public trust in campaigns.
Four elements of higher education as student self-formation

The essential character of higher education is the reflexive self-formation of students. Students work on themselves, to a varying extent, in relation to their evolution, goals and projects. The essential elements of higher education as self-formation are the autonomy of the learner, reflexive agency, the will to learn and immersion in knowledge. Immersion in collective knowledge distinguishes self-formation in higher education from other domains. Self-formation is both a researchable phenomenon and a norm to be achieved. The provision of opportunities and resources for self-formation is more fundamental to institutional higher education than is the fostering of employability, which partly depends on other social sectors, but student self-formation is foundational to graduate work and graduate social contributions in other spheres. Self-formation is enhanced by certain productive conditions, including pedagogy, communicative relations in higher education, student mobility including cross-border mobility, formative activities beyond the classroom and institution-based services.
Political communication about data

The relationship between political communication and data is complex. Much of the attention has been placed on computational propaganda, where data-driven systems actively change the form, scope and targeting of political communication. This chapter, however, emphasizes political communication about data. Using examples from the Covid-19 pandemic, it focuses on three main areas: statistical rhetoric - the use of numbers by politicians for specific purposes and goals; open data discourses - how datasets are used to emphasize transparency, accountability and trustworthiness of institutions that produce them, but also how this data is used to frame phenomena being quantified; transforming data - when algorithms are communicated by those who create them to talk of fairness and objectivity in algorithmic processes. Taken together, we need to see political talk about data as an ensemble of communication about numerical facts, data and algorithms.
Algorithms, power and digital politics

This chapter unpacks the relations of algorithms and power - how algorithms shape and impact key political processes and how algorithms themselves have and exert power in these processes. Algorithms are not neutral or objective, they are omnipresent in digital politics, and few citizens understand how algorithms work. As a result, there is a massive power asymmetry materializing in algorithms - the power asymmetry between those who create, shape and maintain algorithms and those who only use them, sometimes knowingly, mostly unknowingly, or are subjected to algorithmic decision-making. The chapter starts with a discussion of algorithms in digital politics, regarding information, persuasion and opinion formation, personalization, polarization and radicalization. We then connect algorithms to the classic definition of power: any opportunity to impose one’s will on others, even against their resistance. Finally, the chapter delineates and discusses the power of algorithms, power through algorithms, and power over algorithms.
Online content moderation during conflict

War increasingly has a digital dimension. Recent conflicts around the world have underlined the role of social media spaces in providing channels to fight propaganda and denounce atrocities but also facilitating governments to share disinformation or hate speech that contribute to inflaming conflicts. In this chapter, we underline how social media spaces are critical channels influencing conflicts focusing particularly on online hate and disinformation. Issues of censorship, online hate speech, and disinformation during conflict are a huge concern given their potential to exacerbate violence and erode election integrity. Yet how and when social media companies respond has long been an overlooked and under-addressed problem, particularly in countries beyond their priority markets. This chapter underlines the role of social media in moderating conflicts through artificial intelligence systems and the inequalities in this process. The first part of this chapter underlines the role of content moderation in times of war. The second part explores the inequalities in the field of social media and conflicts. We conclude by underlining potential paths to address this situation, particularly though systems of oversight, information intervention and crisis protocols.
An Interactive Showcase of Touch'n'Draw: Rapid 3D Sketching with Fluent Bimanual Coordination

In perspective drawing, designers express 3D shapes by drawing auxiliary lines that construct surfaces and drawing design curves on them. However, drawing auxiliary lines can be challenging, and too many of them can make the drawing difficult to understand. To address these issues, we present a novel 3D sketching system that allows the user to quickly and easily create instant auxiliary lines and instant sketch surfaces for drawing desired 3D curves with fluent bimanual touch and pen interactions. We will produce concept sketches using our system to showcase its potential usefulness.
ForceField: Visualizing Intermaterial Interaction through Floor and Depth Sensing

ForceField realizes the measurement of intermaterial contact forces across any surface within a room-scale environment without the need for additional device attachments to bodies or objects. It achieves this by acquiring a 3D model through a depth sensor and capturing ground pressure through a floor sensor, and then reconstructing contact forces between surfaces where no sensors are attached, such as when a hand pushes a desk, or people push against each other, from information on force and geometry. Through this unobtrusive environmental measurement approach, ForceField opens up the possibility of highly physical spatial interactions in domains such as healthcare, sports, and entertainment.