Reviewerly/exHarmony huggingface dataset:
Charisma and learning: designing charismatic behaviours for virtual human tutors

Charisma as defined in the Oxford Dictionary, is the compelling attractiveness or charm that can inspire devotion in others. It is a powerful device of communication because it is the primary way humans signal interpersonal closeness, willingness to communicate, and feelings toward one another. Without exception, charisma is very crucial in the learning process in individuals. It makes the teachers more influential, causing learners to listen more intently to what they have to say and subsequently boosting their learning performance at the end of the day.&#x0D; Virtual learning has become a widely adopted phenomenon all around the world, with the aid of virtual humans (computer-generated simulations of human beings), who, unlike real humans, provide consistency in the delivery of accurate knowledge, engaging role-plays, and appropriate feedback. Furthermore, pedagogical agents have played a significant role in virtual learning, and virtual tutors and companions falling under this category. Incorporating charismatic behaviors in virtual human tutors has become a quest for developers, due to their awareness on how impactful it can be in virtual learning. How can such behaviors be realized in a virtual character? Developers are incorporating charismatic traits in virtual human tutors which include: portraying a high degree of engagement with their students, proficiency in their subject matter, organized and very profound in their knowledge of the subject. Taiwanese authors researched the topic of teaching and charisma. The Statistical results obtained supported the virtual human tutors’ charisma has a positive influence on inducing the students’ learning interest.&#x0D; Feelings of isolation that may be faced by a student in online learning are overcome by the fully-present and approachable trait of the charismatic virtual tutor. The ability of a virtual human tutor to be charismatic boosts instructor immediacy; behavior that brings the instructor and the students closer together in terms of perceived distance.&#x0D; Overall, we conclude that developers’ endeavor to design charismatic behavior for virtual human tutors is advantageous to the highly advancing educational field and the use of Artificial Intelligence in education.&#x0D;
Revolutionizing education: A study on Intelligent Tutoring Systems in U.S education

Over the decades, AI has been drastically impacting health care, entertainment, finance,transportation, and many other industries, but specifically, how has it impacted educationsystems in the United States? With a better ability at examining data points, strengths, andweaknesses, in this study, we investigate how MATHia, an intelligent tutoring system, usesAI and how it has affected student performance in mathematics. AI-driven online tutoringoffers a human-like learning environment that encourages collaboration with the students inorder to learn from them and create individualized courses based on the students' emotions,progress, and learning preferences. MATHia was developed to resemble a human tutor andpersonalize learning based on each individual's area of weakness. Did these online tutorspresent a cost-effective and revolutionary solution to real-life tutors? Was MATHia effectivein enhancing students' knowledge of the mathematical concepts they had to learn? What canbe done in the future to further expand the benefits of these systems? This research answersall of these questions by taking three standpoints into consideration (educational, economic,and futuristic standpoints), each with its own unique data, problems, and solutions to theseproblems.
FreePart: Hardening Data Processing Software via Framework-based Partitioning and Isolation

Data processing oriented software, especially machine learning applications, are heavily dependent on standard frameworks/libraries such as TensorFlow and OpenCV. As those frameworks have gained significant popularity, the exploitation of vulnerabilities in the frameworks has become a critical security concern. While software isolation can minimize the impact of exploitation, existing approaches suffer from difficulty analyzing complex program dependencies or excessive overhead, making them ineffective in practice.
Flame: A Centralized Cache Controller for Serverless Computing

Caching function is a promising way to mitigate coldstart overhead in serverless computing. However, as caching also increases the resource cost significantly, how to make caching decisions is still challenging. We find that the prior "local cache control" designs are insufficient to achieve high cache efficiency due to the workload skewness across servers.
LightRidge: An End-to-end Agile Design Framework for Diffractive Optical Neural Networks

To lower the barrier to diffractive optical neural networks (DONNs) design, exploration, and deployment, we propose LightRidge, the first end-to-end optical ML compilation framework, which consists of (1) precise and differentiable optical physics kernels that enable complete explorations of DONNs architectures, (2) optical physics computation kernel acceleration that significantly reduces the runtime cost in training, emulation, and deployment of DONNs, and (3) versatile and flexible optical system modeling and user-friendly domain-specific-language (DSL). As a result, LightRidge framework enables efficient end-to-end design and deployment of DONNs, and significantly reduces the efforts for programming, hardware-software codesign, and chip integration. Our results are experimentally conducted with physical optical systems, where we demonstrate: (1) the optical physics kernels precisely correlated to low-level physics and systems, (2) significant speedups in runtime with physics-aware emulation workloads compared to the state-of-the-art commercial system, (3) effective architectural design space exploration verified by the hardware prototype and on-chip integration case study, and (4) novel DONN design principles including successful demonstrations of advanced image classification and image segmentation task using DONNs architecture and topology.
Veil: A Protected Services Framework for Confidential Virtual Machines

Confidential virtual machines (CVMs) enabled by AMD SEV provide a protected environment for sensitive computations on an untrusted cloud. Unfortunately, CVMs are typically deployed with huge and vulnerable operating system kernels, exposing the CVMs to attacks that exploit kernel vulnerabilities. Veil is a versatile CVM framework that efficiently protects critical system services like shielding sensitive programs, which cannot be entrusted to the buggy kernel. Veil leverages a new hardware primitive, virtual machine privilege levels (VMPL), to install a privileged security monitor inside the CVM. We overcome several challenges in designing Veil, including (a) creating unlimited secure domains with a limited number of VMPLs, (b) establishing resource-efficient domain switches, and (c) maintaining commodity kernel backwards-compatibility with only minor changes. Our evaluation shows that Veil incurs no discernible performance slowdown during normal CVM execution while incurring a modest overhead (2 -- 64%) when running its protected services across real-world use cases.
Explainable-DSE: An Agile and Explainable Exploration of Efficient HW/SW Codesigns of Deep Learning Accelerators Using Bottleneck Analysis

Effective design space exploration (DSE) is paramount for hardware/software codesigns of deep learning accelerators that must meet strict execution constraints. For their vast search space, existing DSE techniques can require excessive trials to obtain a valid and efficient solution because they rely on black-box explorations that do not reason about design inefficiencies. In this paper, we propose Explainable-DSE - a framework for the DSE of accelerator codesigns using bottleneck analysis. By leveraging information about execution costs from bottleneck models, our DSE is able to identify bottlenecks and reason about design inefficiencies, thereby making bottleneck-mitigating acquisitions in further explorations. We describe the construction of bottleneck models for DNN accelerators. We also propose an API for expressing domain-specific bottleneck models and interfacing them with the DSE framework. Acquisitions of our DSE systematically cater to multiple bottlenecks that arise in executions of multi-functional workloads or multiple workloads with diverse execution characteristics. Evaluations for recent computer vision and language models show that Explainable-DSE mostly explores effectual candidates, achieving codesigns of 6X lower latency in 47X fewer iterations vs. non-explainable DSEs using evolutionary or ML-based optimizations. By taking minutes or tens of iterations, it enables opportunities for runtime DSEs.
RECom: A Compiler Approach to Accelerating Recommendation Model Inference with Massive Embedding Columns

Embedding columns are important for deep recommendation models to achieve high accuracy, but they can be very time-consuming during inference. Machine learning (ML) compilers are used broadly in real businesses to optimize ML models automatically. Unfortunately, no existing work uses compilers to automatically accelerate the heavy embedding column computations during recommendation model inferences. To fill this gap, we propose RECom, the first ML compiler that aims at optimizing the massive embedding columns in recommendation models on the GPU. RECom addresses three major challenges. First, generating an efficient schedule on the GPU for the massive operators within embedding columns is difficult. Existing solutions usually lead to numerous small kernels and also lack inter-subgraph parallelism. We adopt a novel codegen strategy that fuses massive embedding columns into a single kernel and maps each column into a separate thread block on the GPU. Second, the complex shape computations under dynamic shape scenarios impede further graph optimizations. We develop a symbolic expression-based module to reconstruct all shape computations. Third, ML frameworks inevitably introduce redundant computations due to robustness considerations. We develop a subgraph optimization module that performs graph-level simplifications based on the entire embedding column context. Experiments on both in-house and open-source models show that RECom can achieve 6.61X and 1.91X over state-of-the-art baselines in terms of end-to-end inference latency and throughput, respectively. RECom's source code is publicly available at https://github.com/AlibabaResearch/recom.
VarSaw: Application-tailored Measurement Error Mitigation for Variational Quantum Algorithms

For potential quantum advantage, Variational Quantum Algorithms (VQAs) need high accuracy beyond the capability of today's NISQ devices, and thus will benefit from error mitigation. In this work we are interested in mitigating measurement errors which occur during qubit measurements after circuit execution and tend to be the most error-prone operations, especially detrimental to VQAs. Prior work, JigSaw, has shown that measuring only small subsets of circuit qubits at a time and collecting results across all such `subset' circuits can reduce measurement errors. Then, running the entire (`global') original circuit and extracting the qubit-qubit measurement correlations can be used in conjunction with the subsets to construct a high-fidelity output distribution of the original circuit. Unfortunately, the execution cost of JigSaw scales polynomially in the number of qubits in the circuit, and when compounded by the number of circuits and iterations in VQAs, the resulting execution cost quickly turns insurmountable.
Exploiting the Regular Structure of Modern Quantum Architectures for Compiling and Optimizing Programs with Permutable Operators

A critical feature in today's quantum circuit is that they have permutable two-qubit operators. The flexibility in ordering the permutable two-qubit gates leads to more compiler optimization opportunities. However, it also imposes significant challenges due to the additional degree of freedom. Our Contributions are two-fold. We first propose a general methodology that can find structured solutions for scalable quantum hardware. It breaks down the complex compilation problem into two sub-problems that can be solved at small scale. Second, we show how such a structured method can be adapted to practical cases that handle sparsity of the input problem graphs and the noise variability in real hardware. Our evaluation evaluates our method on IBM and Google architecture coupling graphs for up to 1,024 qubits and demonstrate better result in both depth and gate count - by up to 72% reduction in depth, and 66% reduction in gate count. Our real experiments on IBM Mumbai show that we can find better expected minimal energy than the state-of-the-art baseline.
CRYPTOCURRENCY MARKET IN 2021 AND 2022

Digital/virtual currencies or cryptocurrencies are digital constructs that are not issued or guaranteed by a central bank or public authority and whose value is not backed up by the assets of a particular economy, but depends solely on supply and demand. Digital currencies are linked to a legally established currency and do not correspond to the legal status of currency or money; however, they are accepted by more and more people and companies, either for the ease of use, or for speculative reasons, because they are very volatile, either for illegal payments. In the years 2021 and 2022, cryptocurrencies proved to be digital assets with a pronounced economic potential, many cryptocurrencies saw rapid increases or falls, some countries banned cryptocurrencies, others allowed them to their citizens. Central banks - such as the American Reserve (Fed) and the European Central Bank (ECB) do not approve the activity of cryptocurrency trading platforms, and several countries, including India, Romania and Turkey, have banned the circulation of cryptocurrencies on their territory
Prediksi Perubahan Iklim Ekstrem di Kota Palembang dan Kaitannya dengan Fenomena El Niño-Southern Oscillation (ENSO) Berbasis Machine Learning

The purpose of this study is to provide predictions of climate conditions and trend of temperature rise in the city of Palembang in the 21st century. The BMKG station data used, namely SK Palembang and SM SMB II from the 2000-2020 period were analyzed based on the agreement of the Expert Team for Climate Change Detection and Indices (ETCCDI). ). Analysis using google colab shows that rainfall in the city of Palembang has a fairly high variability and a decrease in the number of rainy days that occur in the city of Palembang with the trend of air temperature, namely TMAXmean and TMINmean, has increased significantly by 2.40C for 50 years. Based on the correlation analysis between rainfall and El Niño-Southern Oscillation (ENSO) with Google Colab, a negative relationship was found. The El Niño-Southern Oscillation (ENSO) index used is the Niño 3.4 index. The study concluded that although the geographical location of Palembang city is located in the Asian Monsoon area, the ENSO phenomenon does not significantly affect rainfall variability in Palembang city.
The Role of the Metaverse in Transforming Healthcare

To diagnose, treat, or perform surgical operations on a patient, conventional physical interactions between a patient and a doctor were necessary. Telehealth services have challenged this, enabling digital connections between patients and healthcare professionals. The metaverse, a virtual environment that permits interaction with digital objects and settings, is fast gaining traction in the healthcare industry. There is a strong likelihood that patient outcomes will be improved by technological advances such as artificial intelligence (AI), augmented reality (AR), the internet of things (IoT), virtual reality (VR), quantum computing, and robotics. Augmented and virtual reality technologies have progressed dramatically in recent years, and they are now routinely used in medical education, training, and surgical procedures to execute complex surgeries with extraordinary precision. However, as the metaverse grows, there are apprehensions about potential ethical and legal implications, such as cybercrime and data vulnerability. The objective of this review article is to examine potential uses of the metaverse in the field of healthcare as well as the issues that need to be resolved.
Visual Quality Assessment for $$360^\circ $$ Videos

In the last few years, with the advancement in display and rendering technologies, the consumption of VR applications is increasing, with $$360^\circ $$ videos being an example. Increasing numbers of higher-quality videos can now be watched on smartphones or HMDs using platforms such as YouTube. The overall QoE of a $$360^\circ $$ viewing session depends on the degree of immersion or presence of the user, the degree of simulator sickness he/she experiences, and the media (audio and video) quality, which also interact with each other. Therefore, assessing the quality of $$360^\circ $$ videos is an essential step in improving overall QoE. In this chapter, the impact of HMDs, encoding settings (resolution and bitrate), voting methods, and different delay values (for tile-based streaming) are discussed related to the perceived video quality for $$360^\circ $$ videos. Furthermore, conclusions are drawn from the analysis, and recommendations are provided on the improvement of the perceived visual quality.
Technical Setup

This chapter describes the pipeline of the 360 $${ }^\circ $$ video testing system and presents the details of the source sequences and processing used for the subjective tests conducted as part of this book. The source sequences include self-produced videos, in addition to sources obtained from different open as well as proprietary databases. Further, this chapter provides a brief overview of the technical environment such as HMDs, the PC, and the framework for recording behavioral data.
Introduction

In this book, first, the conventional subjective test methods such as Absolute Category Rating (ACR) (ITU-R BT.500-13. Recommendation ITU-R BT.500-13: Methodology for the subjective assessment of the quality of television pictures, 2012) and Double Stimulus Impairment Scale (DSIS) (ITU-T Rec. P.910. Recommendation ITU-T P.910: Subjective video quality assessment methods for multimedia applications, 2008) were applied for the assessment of visual quality, and a novel test method Modified-ACR (M-ACR) (A. Singla et al. (Comparison of subjective quality evaluation for HEVC encoded omnidirectional videos at different bit-rates for UHD and FHD resolution, in Proceedings of the on Thematic Workshops of ACM Multimedia. Mountain View, California, USA, 2017) is proposed during the book research. A methodological framework for 360 $${ }^\circ $$ video quality assessment is presented in this book. Second, simulator sickness is assessed to investigate the influence of different factors, such as video resolution, bitrate, session duration, and latency on the scores of simulator sickness. A simplified simulator sickness questionnaire is then proposed to assess the symptoms of simulator sickness and is more directed towards 360 $${ }^\circ $$ video. Lastly, a correlation between presence, media quality, and simulator sickness is shown.
Performance Analysis of SSQ

$$360^\circ $$ videos provide an immersive experience to users. Besides this, $$360^\circ $$ videos may lead to an undesirable effect when consumed with HMDs, referred to as simulator sickness/cybersickness. The SSQ is the most widely used questionnaire for the assessment of simulator sickness. Since the SSQ with its 16 questions was not designed for $$360^\circ $$ video related studies, the research hypothesis was that it may be simplified to enable more efficient testing for $$360^\circ $$ video. Hence, the SSQ was evaluated to reduce the number of questions asked from subjects, based on six different previously conducted studies. The reduced set of questions from the SSQ are derived using Principal Component Analysis (PCA) for each test. Pearson Correlation is analyzed to compare the relation of all obtained reduced questionnaires as well as two further variants of SSQ reported in the literature, namely VRSQ (Virtual Reality Sickness Questionnaire) and CSQ (Cybersickness Questionnaire). The analysis suggests that a reduced questionnaire with 9 out of 16 questions yields the best agreement with the initial SSQ, with less than 44% of the initial questions. Exploratory Factor Analysis (EFA) shows that the nine symptom-related attributes determined as relevant by PCA also appear to be sufficient to represent the three dimensions resulting from EFA, namely, Uneasiness, Visual Discomfort, and Loss of Balance. The simplified version of the SSQ has the potential to be more efficiently used than the initial SSQ for $$360^\circ $$ video by focusing on the questions that are most relevant for individuals, shortening the required testing time.
Evaluation of Different Constructs: Media Quality, Simulator Sickness, and Presence

QoE for omnidirectional videos comprises additional components such as simulator sickness and presence. Besides media quality, also presence and simulator sickness need to be assessed for a holistic understanding of QoE. In this chapter, a series of tests is presented comparing different test protocols to assess integral quality, simulator sickness, and presence for omnidirectional videos in one test run, using the HTC Vive Pro as the head-mounted display. For quality ratings, the five-point ACR scale was used. In addition, the well-established SSQ and Presence Questionnaire (PQ) methods were used, once in a full version, and once with only one single integral scale, to analyze how well presence and simulator sickness can be captured using only a single scale. Furthermore, the inter-relation between the ratings for quality, presence, and simulator sickness is investigated.
Simulator Sickness Assessment for $$360^\circ $$ Videos

While viewing $$360^\circ $$ videos on an HMD, users may experience symptoms of simulator sickness (or cybersickness) such as nausea, dizziness, vertigo, sweating, etc. (Kennedy et al (1993) Int J Aviat Psychol 3(3):203–220). Simulator sickness may occur during and after exposure to a Virtual Environment (VE). Hence, for a holistic picture of omnidirectional video QoE, simulator sickness should be monitored during respective tests. For the self-evaluation of simulator sickness, various long and short simulator sickness questionnaires were discussed in Sect. 2.5.2. In the following, the terms “Cybersickness” and “Simulator Sickness” will be used interchangeably. In this chapter, the main aim is to investigate the impact of different influencing factors such as video resolution, bitrate, session duration, and latency on cybersickness. At present, practically no solution exists that can efficiently eradicate the symptoms of simulator sickness from virtual environments. Therefore, in the absence of a solution, it is required to quantify and predict the amount of sickness associated with a given VE, or as investigated in this book, $$360^\circ $$ video viewing. Hence, another objective of this chapter is to present initial work on a Simulator Sickness Model SiSiMo, including a first component to predict simulator sickness scores over time. Using linear regression of short-term scores already shows promising performance for predicting the scores collected from a number of user tests.