Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Load Balancing Algorithm in Cloud Computing

Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence over the Internet. To provide computing services to users in efficient manner, there is a need of effective utilization of resources across the VM. But in today’s world there are billions of users who are dependent on cloud computing services this leads to a huge no. of traffic across data centers. While serving users’ request, some data center will have a maximum number of users' task traffic and some data center will have less amount of traffic. These problems create unbalancing of loads across the data centers. To rectify this problem, we need a load balancer or load balancing technique. Load Balancing technique is a mechanism of redistributing users' task/request across the data centers so that no virtual machine should have maximum no. of users' task traffic, less no. of users’ task traffic or no users’ request. The effectiveness of a load balancing technique depends upon the algorithms used behind it. The cloud analyst simulator is such a tool which uses various types of algorithms, for example—throttled algorithm, round robin algorithm, etc., and different service broker policy like closest data center, and some other parameters (e.g., data center configuration), with the help of cloud analyst we can find out data center service request time and total cost for particular data center. The objective of this paper is to compare the service request time and total cost (VM cost and total data transfer cost) of a data center by using existing load balancing algorithms with different service broker policies.
Applications, Issues, and Deep Learning Analysis of Electronic Health Records

Electronic Health Records (EHR) have evolved as a potential tool that has shown significant application in secondary purposes, e.g., research. EHR maintains valuable health information for hospitals in digital form, including demographic details, medical lab reports, procedures, medications, diagnosis codes, and more. Deep learning techniques such as Feed Forward Neural Network (FFNN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and more have been utilized to propose models for EHR analysis. In this paper, we present the current state-of-the-art of deep learning application in medical tasks for EHR data. We present the exemplary performance of DL models in healthcare applications such as disease prediction, hospital readmission, future risk prediction, and many more. We also identify various applications of EHR, which include information extraction, representation learning, outcome prediction, computational phenotyping, and medical data de-identification. The study also introduces various issues and challenges related to EHR, and a theoretical comparison of recent deep learning (DL) models for EHR analysis.
Casting a Vision of Communal and Public Life of the Church for the Post-Pandemic Korean-American Churches: Focusing on Veli-Matti Kärkkäinen’s Communion and Public Ecclesiology

I engage with Veli-Matti Kärkkäinen’s communion and public ecclesiologies in casting a vision of communal and public life of post-pandemic Korean-American churches. For Kärkkäinen, the trinitarian economy of salvation constitutes the ontological ground of a church’s communal life in the wider society. The theologian advances a communal ecclesiology that celebrates irreducible particularities in unity of the Church while upholding a public ecclesiology through creative mutual interaction with other sectors of society in the imitatio Trinitatis. I discuss how his ecclesiological proposals may contribute to Korean-American churches by enhancing their socio-economic accountability, intercultural mutual respect, and intergenerational unity, while remaining deeply rooted in the Gospel of Christ.
The Impact of Using Games on Speaking Skills Development in English Language Classes at Ho Chi Minh City University of Food Industry, Vietnam

The assignment focuses on speaking skills, one of the most important skills that the learners expect to achieve. This work used qualitative methodology together with methods such as questionnaires, observation, and semi-structured interviews to discover the influence of games in speaking classes. The data collected was analyzed in a descriptive way. The results from this innovation prove that using games in speaking English is one of the effective methods to help students be more confident and remember vocabulary. The university the researcher carried out her research is located in Tan Phu District called Ho Chi Minh City University of Food Industry (HUFI). It is a moderate campus where the researcher has spent nearly six years teaching English.
The Investigation on Recent Applications of Artificial Intelligence in Second Language Acquisition

AI technology is not a new concept in the field of SLA and EFL. However, with the booming of technology development, AI technology has achieved many improvements in many aspects. The research is conducted with the goal of revealing the recent milestone of applying AI technology in activities for improving students’ SLA process in EFL classrooms. Moreover, through the gathered data from previous, the researcher wants to identify the recent and future development trends of AI technology in the SLA support process with the hope that this information will contribute to the improvement of EFL classroom activities by stimulating EFL teachers/ lecturers to apply such type of technologies in their classroom.
Try not to build roof-down

Abstract Identify and follow the logical progression of your project’s components. If stuck: are you still following that logical progression?
Efficient DFA-Resistant AES Hardware Based on Concurrent Fault Detection Scheme

This paper presents an efficient AES encryption/decryption hardware architecture with a fault detection scheme. The proposed hardware detects faults during encryption and decryption immediately to counter fault injection attacks such as differential fault analysis (DFA). The proposed hardware employs a fault detection scheme to verify the computation correctness and detect faults, where the datapath is divided into two sub-blocks of linear and nonlinear parts. An inverse function is executed one clock cycle after a half-of-round function. The hardware combines the proposed fault detection scheme with state-of-the-art datapath optimization techniques to achieve both efficient AES encryption/decryption and resistance to DFA. We showed through logic synthesis and simulation that the proposed AES hardware achieves 92% higher area-throughput efficiency and 47% lower power consumption than conventional hardware.
Panoptic-Level Image-to-Image Translation for Object Recognition and Visual Odometry Enhancement

Image-to-image translation methods have progressed from only considering the image-level information to integrating the global- and instance-level information. However, only the foreground instances are refined, and the background semantics are taken as an entire feature, which causes a substantial loss of the semantic information in the translation. Additionally, the insufficient quality of the translated semantic regions also leads to an unsatisfactory performance of the object recognition or visual odometry tasks in which the translated images/videos are further used. In this paper, we propose a novel generative adversarial network for panoptic-level image-to-image translation (PanopticGAN). The proposed method has three advantages: (1) the extracted panoptic perception (i.e., the foreground instances and background semantic regions) as content codes are aligned with the sampled panoptic style codes, which considers the panoptic-level information to avoid the semantic information loss, and the latent space of each object has a rich fusion of content and style codes to generate the higher-fidelity results; (2) a feature masking module is proposed to extract the representations within each object contour by masks for sharpening the object boundaries; (3) the improved fidelity of the translated semantic regions further contributes to enhancing the performance of the object recognition or visual odometry tasks that the translated images/videos are used in. In this paper, we also annotate a compact panoptic segmentation dataset for the thermal-to-color translation task. Extensive experiments are conducted to demonstrate the effectiveness of our PanopticGAN over the latest methods.
Trusted and Trustworthy Algorithmic Fiduciaries

The Digital Age is speedily transforming into the Algorithmic Age, where algorithms carry on or contribute to a significant number of important social, economic, and technical decision-making. Apart from platform dominance, private governance, and surveillance capitalism, private and public use of algorithmic decision-making faces an increasingly severe legitimacy crisis of not being trusted or trustworthy. Three phases of Algorithmic Fiduciaries will accompany us into the ultimate human-centered symbiosis relations individually between human and intelligent algorithms. Trusted and trustworthy algorithmic fiduciaries are vitally important in maximizing the joint potentials and capabilities to significantly benefit the algorithms and the humanities.This paper consists of three parts. First, it proposes the idea of “algorithmic fiduciary” and argues that the trusted and trustworthy algorithmic fiduciary is important. Second, it researches government policies and regulations, industry norms, civil society proposals, and other actions within the Government-Company-NGO Triangular on algorithmic decision-making in the European Union and the United States. Third, it evaluates how these actions impact trusted and trustworthy algorithmic fiduciaries.
Continual Vocabularies to Tackle the Catastrophic Forgetting Problem in Machine Translation

Neural Machine Translation (NMT) models are rarely decoupled from their vocabularies, as both are often trained together in an end-to-end fashion. However, the effects of the catastrophic forgetting problem are highly dependent on the vocabulary used. In this work, we explore the effects of the catastrophic forgetting problem from the vocabulary point of view and present a novel method based on a continual vocabulary approach that decouples vocabularies from their NMT models to improve the cross-domain performance and mitigate the effects of the catastrophic forgetting problem. Our work shows that the vocabulary domain plays a critical role in the cross-domain performance of a model. Therefore, by using a continual vocabulary capable of exploiting subword information to construct new word embeddings we can mitigate the effects of the catastrophic forgetting problem and improve the performance consistency across domains.
Test-Time Augmentation for Document Image Binarization

Document binarization is a well-known process addressed in the document image analysis literature, which aims to isolate the ink information from the background. Current solutions use deep learning, which requires a great amount of annotated data for training robust models. Data augmentation is known to reduce such annotation requirements, and it can be used in two ways: during training and during prediction. The latter is the so-called Test Time Augmentation (TTA), which has been successfully applied for general classification tasks. In this work, we study the application of TTA for binarization, a more complex and specific task. We focus on cases with a severe scarcity of annotated data over 5 existing binarization benchmarks. Although the results report certain improvements, these are rather limited. This implies that existing TTA strategies are not sufficient for binarization, which points to interesting lines of future work to further boost the performance.
Hierarchical Line Extremity Segmentation U-Net for the SoccerNet 2022 Calibration Challenge - Pitch Localization

This paper describes the methodology our team followed for our submission to the SoccerNet Calibration Challenge - Soccer Pitch Markings and Goal Posts Localization. The goal of the challenge is to detect the extremities of soccer pitch lines present in the image. Our method directly infers the line extremities’ localization in the image, using a Deep Learning Convolutional Neural Network based on U-Net with a hierarchical output and unbalanced loss weights. The hierarchical output contains three outputs composed of different segmentation masks. Our team’s name is 2Ai-IPCA and our method achieved third place in the Pitch Localization task of the SoccerNet Calibration Challenge, with 71.01%, 76.18%, and 77.60% accuracies for the 5 pixel, 10 pixel, and 20 pixel thresholds respectively, and a global average accuracy score of 73.81% on the challenge set.
Predicting the Subjective Responses’ Emotion in Dialogues with Multi-Task Learning

Anticipating the subjective emotional responses of the user is an interesting capacity for automatic dialogue systems. In this work, given a piece of a dialog, we addressed the problem of predicting the subjective emotional response of the upcoming utterances (i.e. the emotion that will be expressed by the next speaker when the speaker talks). For that, we also take into account, as input, the personality trait of the next speaker. We compare two approaches: a Single-Task architecture (ST) and a Multi-Task architecture (MT). Our hypothesis is that the MT architecture can learn a richer representation of the features that are important to predict emotional reactions. We tested both models using the Personality EmotionLines Dataset (PELD), which is the only publicly available dataset in English that provides individual information about the participants. The results show that our proposed MT approach outperforms both the ST and the state-of-the-art approaches in predicting the subjective emotional response of the next utterance.
Mobile–Based Media as a Means of Fostering the Development of Listening Skills

Of the four communication abilities, listening is one of the most challenging, but compared to the other three (reading, writing, and speaking), it has gotten far less attention in English language instruction. Also, because students appear to sit quietly in class and listen to conversations, listening is frequently characterized as a passive skill. As language teachers, we work hard to establish the conditions necessary for learning and success for our students. At the same time, we confront challenges that may be damaging to the learning process. This unquestionably applies to how our students use their mobile devices. Almost all students own at least one mobile device. However, students still choose to use smart gadgets for entertainment—whether they are watching movies, listening to music, or playing video games—and it appears that they truly have no idea how to utilize them for learning. This paper will show how to use a mobile phone to teach and learn listening skills. It will do this by conducting literature research. The researcher has come to the conclusion that teaching English to students through the use of mobile devices can help them better comprehend the material being covered or contextualize it to help them apply it more effectively in conversations. Therefore, using mobile devices and other portable technology for the study is an excellent way to enhance the efficacy and quality of learning English.
Quantum computer wins face-off

IBM's Eagle quantum computer has bested a supercomputer at a complex calculation
Unjustified Threats in the Online Space

Abstract Unjustified threats to bring proceedings for intellectual property infringement are restrained by the Intellectual Property (Unjustified Threats) Act 2017 (‘the 2017 Act’). This chapter considers the interplay between that legislation and the reporting and takedown regimes of online selling platforms. Prior to the 2017 Act, the question of that interplay had been considered by the UK courts in a number of interim decisions, but the question was not explored subsequently until a patent decision in 2022. This chapter considers the nature of the various threats to sue in respect of intellectual property infringement made in the online space and argues that takedown notices on online sales platforms constitute threats pursuant to the 2017 Act, and tentatively suggests how takedown regimes could be modified to make it clear whether a threat of litigation is intended.
One Does Not Simply Upload a Meme—Internet Memes and the Parody Exemption in Europe

Abstract Internet memes have become ubiquitous. The number of meme templates is increasing exponentially and anyone can create new memes in a few seconds on dozens of dedicated websites for free. Memes are a genre based on the reappropriation of existing works and, as such, a topic of interest for copyright law. However, memes are also a popular form of expressing ideas. While some memes do not have more pretence than to make the viewer laugh, many are intended to convey a political, social, or cultural message. Keeping the freedom to make memes was part of the reason why some EU citizens took to the streets to prevent the enactment of the EU DSM Directive in 2018. Memes are therefore a modern illustration of the fundamental tension between copyright protection and freedom of expression. This contribution examines whether and when the use of original works of authorship to create memes constitutes copyright infringement in the EU, in particular by examining whether the parody exemption of the InfoSoc Directive can represent a useful defence against an infringement lawsuit.
Frustration is a fuzzy teacher

Abstract Some frustrations suggest trying another approach. Others indicate low energy or just a boring task. Telling them apart means learning from them.
On Performance Evaluation of AI Models in Detecting Malicious Traffics

The power communication system has a vital impact on the stability, reliability, and security of power system operations. If adversaries gain access to the power communication network, they may obtain sensitive information or initiate attacks by malicious applications. To ensure the security of the power communication system, it is important to effectively identify possible security threats from malicious traffic. To this end, this paper proposes a packet bytes-based text Convolutional Neural Network (textCNN) AI model. In particular, the model can automatically extract the essential characteristics of various malicious traffic from massive network data. To demonstrate its performance, we evaluate the model with realistic open data set and compare it with several classical deep learning models. Extensive experiment results show that the proposed approach can identify malicious traffic with an accuracy of 99.84%, which is 0.57%, 1.48%, and 1.66% higher than that of LSTM, 1D-CNN, and 2D-CNN respectively. Moreover, regarding specific malicious applications identification, it can achieve the F1-score up to 99.39%, which is 5.21%, 12.84%, and 13.71% higher than that of LSTM, 1D-CNN, and 2D-CNN respectively.
Discovering Security Gaps Using the Google Dorks

Privacy management in terms of data security has become a legal and compliance area, which has also affected the rules and regulations that need to be changed.Which is that data that is sent to encrypted classes and appears in the form of an algorithm within databases protected by multiple server gateways.The IT industry is well aware of the illegal forces that go after such worthy information, including competing competitors, hackers, ransom-seekers, or even well-informed traders. In view of these consequences, there is a great demand for the foundations for the enforcement of cybersecurity standards, policies and standards, and technical solutions. Knowing that databases include the personal information (PII) of individuals (customers, visitors, vendors, legal, patients, defense officers, etc.), they make themselves known about the capabilities and confidentiality that cybersecurity experts have to maintain. Data under the PII category includes names, identities, contacts, addresses, social security number, financial instrument details held, etc., which when stolen, hacked, or lost may lead to legal violations, making cybersecurity experts deal with many data breaches and attacks.Here, cyber security experts play the main role. Their duty includes developing security protocols while building software systems and server configurations. Apart from that, they focus heavily on monitoring systems, software, networks, and data centers, and they look for vulnerabilities for expected risks, vulnerabilities, and vulnerabilities that can be categorized as the margin created for a cyberattack or any other breaches or data dumps. it's happening. Besides, the difficult issue for them is the increasing number of cyberattacks that often cause damage to servers or tamper with databases, while for cybersecurity experts it is difficult and time consuming.Hence, in addressing such critical challenges associated with cybersecurity, the research project relies on evaluating Google Dorks' effective use of cybersecurity experts in saving time, as well as exploring information that can limit and eliminate hacking practices. Also, address advanced search techniques that may be more suitable for experts. Concerning, the research has focused on interfering with the qualitative methodology and objective elucidation of the results in order to structure the conclusions.
A Free Speech-Based Response to Media Polarization

The degree of polarization, distrust, and animosity in the political elements of our social media have reached remarkable levels. But any attempt to address the relevant problems that requires legal enforcement inevitably raises serious issues of free speech and free press. Relying instead on fact checkers has its own numerous, substantial, and inevitable limitations, some of which parallel the very problems sought to be addressed. More promising are political social media reforms, akin to that proposed herein, that are voluntary, generally pluralistic and critical, reflective of the relevant social science evidence, and unlikely to raise problems of free speech and freedom of the press.
APPLICATION OF NEURAL NETWORKS IN FACE RECOGNITION

This article discusses the relevance of the use of neural networks in image recognition, various structures of neural networks, such as convolutional, multi–layered and multitasking neural networks, their areas of application and provides examples of the use of different structures in solving various tasks.
Study and Automatic Translation of Toki Pona

In this work, we explore the use of Neural Machine Translation models for Toki Pona, a small, low resourced, and minimalistic language. We tackle the problem by employing the transformer model and transfer learning. Despite the challenges posed by the language’s limited resources and the scarcity of available data, we demonstrate that a high-accuracy machine translator can be developed for Toki Pona using these advanced techniques. Through transfer learning from existing models, we were able to adapt to the unique characteristics of Toki Pona and overcome the challenges of limited data. Our results show the potential for applying these methods to other under-resourced languages, providing a valuable tool for increasing accessibility and communication in these communities.