Reviewerly/exHarmony huggingface dataset:
Low encoding overhead ultra-low latency streaming via HESP through sparse initialization streams

HESP, the High Efficiency Streaming Protocol [4], realizes ultra-low latencies and ultra-short start-up times by combining two feeds, the keyframe-only Initialization Stream and the ultra-low latency CMAF-CTE Continuation Stream. HESP uses a keyframe from the Initialization Stream to start playback (via keyframe injection) of the Continuation Stream extremely close to the live edge. In previous research [5], the impact of the HESP keyframe injection on the video quality has been proven to be very low or even negligible. In contrast to the trivial double encoding for each quality in the bitrate ladder, in this paper we show that the overhead of the generation of the keyframe-only Initialization Streams can be reduced. We designed an approach in which the frequency of keyframes in the Initialization Streams is defined by a trade-off between the encoding overhead and two metrics in the viewing QoE: start-up time and time that it takes to switch to the highest feasible video quality of the ABR ladder. More specifically, for each quality Qi, fi is defined such that (i) switching to Qi, either for start-up or for switching to Qi as a higher quality, takes [EQUATION] additional delay, and (ii) there always is a Qi, lower than Qcurrent (unless Qcurrent is the lowest quality) to which the player can switch down instantly, which is needed in case of network problems. The resulting impact on the viewer QoE is characterizedby occasional (whenever an ABR switch to a higher quality is needed) short intervals [EQUATION] during which playback potentially is done at a lower than feasible video quality. Based on measurements, the proposed approach results in an overhead when encoding Initialization Streams of only 15 to 20%. Compared to "standard" HESP, the viewer QoE reduction is hardly noticeable.
Predicting the Reader’s English Level From Reading Fixation Patterns Using the Siamese Convolutional Neural Network

Abstract-Among numerous functions performed by the human eye, reading is a common task that best reflects an individual's understanding and cognitive patterns. Previous studies showed that text comprehension may be determined by comprehension monitoring, a metacognitive process that evaluates and regulates the pattern of comprehension. Herein, we propose a hypothesis: an individual's cognitive pattern during reading is predictive of the level of reading comprehension. According to the criteria of the College English Test Band Six (CET-6), 80 participants (sophomore and junior) were divided into a pass group (n = 40) and a non-pass group (n = 40). Heatmaps of eye fixation counts were collected by an eye-tracker while each participant executed four reading comprehension tests. Using these heatmaps as inputs, we proposed the Siamese convolutional neural network models to predict the English level of participants. Both strategies of "Trained from scratch" and "Pretrained with fine-tuning" were employed. "Soft Voting" was applied to integrate the predictions from four tests. Results showed that the Siamese network model trained by the datasets with the cluster radius of fixation equal to 25 pixels and connection layer by L1 norm distance has a satisfactory or superior performance to other comparative experiments. The AUC values of Siamese networks trained by the two strategies reach 0.941 and 0.956, respectively. This indicates that the individual reading cognitive pattern captured by the eye-tracker can predict the level of reading comprehension through advanced deep learning models.
HECATE: Performance-Aware Scale Optimization for Homomorphic Encryption Compiler

Despite the benefit of Fully Homomorphic Encryption (FHE) that supports encrypted computation, writing an efficient FHE application is challenging due to magnitude scale management. Each FHE operation increases scales of ciphertext and leaving the scales high harms performance of the following FHE operations. Thus, rescaling ciphertext is inevitable to optimize an FHE application, but since FHE requires programmers to match the rescaling levels of operands of each FHE operation, programmers should rescale ciphertext reflecting the entire FHE application. Although recently proposed FHE compilers reduce the programming burden by automatically manipulating ciphertext scales, they fail to fully optimize the FHE application because they greedily rescale the ciphertext without considering their performance impacts throughout the entire application. This work proposes HECATE, a new FHE compiler framework that optimizes scales of ciphertext reflecting their rescaling levels and performance impact. With a new type system that embeds the scale and rescaling level, and a new rescaling operation called downscale, HECATE makes various scale management plans, analyzes their expected performance, and finds the optimal rescaling points throughout the entire FHE application. This work implements HECATE on top of the MLIR framework with a Python frontend and shows that HECATE achieves 27% speedup over the state-of-the-art approach for various FHE applications.
Promise of AI in DeFi, a Systematic Review

Decentralized Finance (DeFi) is an emerging and revolutionizing field with notable uncertainties of reliability to be used on a mass scale. On the other hand, Artificial Intelligence (AI) has proved to be a crucial helping tool in numerous domains. In this study, we present a systematic review of the utility of AI in DeFi in terms of impact, reliability, and security and conduct exhaustive analysis. The review was motivated by an in-depth investigation of recently published literature that prioritized AI and DeFi in their research. This research, like many prior studies, examined the articles in terms of impact, reliability, and security. In addition, a new relevance score is introduced to better comprehend the quality of the content. According to investigation, the combination of AI and DeFi is one of the trending research topics that lacks adequate interpretations of black-box methodologies. Furthermore, it was discovered that one of the primary issues in DeFi is security, and numerous technologies, including blockchain technology and machine learning approaches, have been used to minimize such challenges. We hope that the gap addressed throughout this review will give insights to future researchers and practitioners, ultimately leading to new research opportunities in AI to bridge the gap of trust between peers and make the integration of DeFi more agile in the near future.
Performance evolution of mitigating transient execution attacks

Today's applications pay a performance penalty for mitigations to protect against transient execution attacks such as Meltdown [32] and Spectre [25]. Such a reduction in performance directly translates to higher operating costs and degraded user experience. This paper measures the performance impact of these mitigations across a range of processors from multiple vendors and across several security boundaries to identify trends over successive generations of processors and to attribute how much of the overall slowdown is caused by each individual mitigation.
Transmedia educational project as a method of developing music teacher’ transmedia competence

The paper looks into the analysis of theoretical findings for comprehending the transmedia and transmedia competence in the light of pre-service music teachers’ training. The paper’s objectives are to substantiate the transmedia educational projects in music art and disseminate the experience of its development and implementation for enhancing pre-service music teachers’ transmedia competence. The study is based on theoretical (analysis of scientific literature, generalisation and interpretation of project results) and empirical (developing transmedia educational project, conducting the survey for studying pre-service music teachers’ interest in transmedia activities) methods. Based on the theoretical findings, the authors present three stages of doing the transmedia projects (preparatory stage, practical and creative stage, organisational and reflective stage). The experience of implementing transmedia projects is highlighted in the example of the educational project ‘Pyotr Tchaikovsky. The Life in Music’. The research effective in implementing the transmedia technology in pre-service teachers’ education.
Learning-based invalid points detection for fringe projection profilometry

Fringe Projection Profilometry (FPP) is widely used in shape retrieval for non-contact and high-speed three-dimensional (3D) measurements. Phase unwrapping is critical to FPP. Among all the proposed FPP systems, the single-camera and single-projector system projecting only single-frequency patterns is the most ideal one in terms of 3D imaging speed and system cost. However, this type of system needs robust spatial phase unwrapping (SPU) methods. A well-known problem with SPU methods is that the unwrapped result is path-dependent. Although quality-guided phase unwrapping methods achieve good results in reducing the path dependence by excluding invalid points from the unwrapping process, they still face great challenges in dealing with phase maps with phase discontinuity and motion blur. This study proposed a learningbased invalid points detection approach for FPP. A deep convolutional neural network (DCNN) is suggested to learn the discriminative features of points from the composite map of background intensity, intensity modulation, and phase map. After training, this DCNN is able to classify each point in phase maps into three classes. The training data is collected using a hand-held FPP system, which achieves 3D reconstruction via temporal phase unwrapping. The labeling of training data is automatically accomplished through a registration-orientated labeling procedure. Experiments on a large-scale dataset of real objects demonstrate that complex phase maps with motion blur and phase discontinuity can be unwrapped correctly by the flood-fill algorithm after the invalid points are detected by the proposed approach.
Teaching mechanism empowered by virtual simulation: Edge computing–driven approach

With the rapid development of network and communication techniques, the teaching forms have become diversified. To enhance the education experience and improve the teaching environment, an increasing number of educational institutions have adopted virtual simulation technology. A typical teaching mechanism is to exploit Virtual Reality (VR) technology, which affords participants an immersive experience. Unquestionably, such a VR-based mode is highly approved. However, the performance of this technology requires further optimization. On one hand, for VR 360° video, the current intraframe decision cannot adapt to rapid response demands. On the other hand, the generated data size is considerably large and fast computation may not be realized, depending on the local VR device. Therefore, this study proposes an improved teaching mechanism empowered by edge computing–driven VR, called VE4T, that involves two parts. First, an intraframe decision algorithm for VR 360° videos is devised to realize the rapid responses. Second, an edge computing framework is proposed to offload some tasks to an edge server for computation, where a task scheduling strategy is developed to check whether a task needs to be offloaded. Finally, experiments are performed using a practical teaching scenario with some VR devices. The obtained results demonstrate that VE4T is more efficient than existing mechanisms.
Deep Learning Assist IoT Search Engine for Disaster Damage Assessment

In this paper, we address the issue of disaster damage assessments using deep learning (DL) techniques. Specifically, we propose integrating DL techniques into the Internet of Things Search Engine (IoTSE) system to carry out disaster damage assessment. Our approach is to design two scenarios, Single and Complex Event Settings, to complete performance validation using four Convolutional Neural Network (CNN) models. These two scenarios are designed with three possible network services. Our experimental results confirm that all four CNN models can learn each label during the single event setting well. Whereas, with complex event settings, the CNN models have learning difficulty because multiple events have closely related labels.
Technology of Multilevel Interuniversity Indicators as a Factor for Increasing Academic Mobility. Experience Based on Russian Federal Educational Standards

Introduction. At the present time, more and more students are changing either their field of study or the university in the process of studying. This raises the problem of how to determine whether a student’s level of knowledge meets the host institution’s criteria. A simple comparison of competencies is not enough. Therefore, the authors propose a new system of comparing existing and required knowledge (competencies) at the new place of study. The purpose of this article is to present the results of research on the development and practical application of specific “competency trees” that allow for the automatic comparison and re-crediting of disciplines. Materials and Methods. The research is based on the methods of system analysis for weakly formalized problems: the method of expert evaluations and the method of the goal tree. For direct development the method of construction of binary decision trees was used. To evaluate the effectiveness of the developed method, methods of observation and comparison were used. Results. This article describes the specific steps for creating checklists based on multilevel competency indicator trees. The tables describe the four levels of competency acquisition. Based on the experiments carried out on the use of such tables for retake disciplines when transferring a student from one specialty to another, the following recommendations are made: if it is necessary to obtain a mark of the “Test” type in the Host University, the comparison is made according to the second level indicators; if it is necessary to obtain a mark of the type “Graded test/Test with a grade” in the Host University, the comparison is made according to the third level indicators; if it is necessary to obtain a mark of the “Exam” type in the Host University, the comparison is made according to the indicators of the deepest level for this indicator of the first level. The technique has been successfully tested for moving of a student within Kazan National Research Technical University named after A. N. Tupolev-KAI between the academic programs Aircraft Engineering and Applied Mathematics and Informatics. Discussion and Conclusion. The proposed multilevel system of interuniversity indicators will significantly simplify the procedure for transferring subjects for students who are moved from one study program to another at any level – whether within one university, or between different universities of the Russian Federation. The use of an automated system for comparing the level of knowledge of a student when moving from one university to another will not only reduce the time of a student and teachers, but also eliminate the human factor, bias and subjectivity in the process of making decisions about transferring, and increase the transparency of this process. All this together will contribute to the development of academic mobility of students, increasing their competitiveness in the labor market and strengthening academic interuniversity relationships both in Russia and abroad.
Hyperspectral data as a proxy for porosity estimation of carbonate rocks

Rock porosity is one of the most significant parameters in fluid-flow simulation in the context of carbonate reservoirs. The hydrocarbon industry uses porosity to assess the production potential of oil and gas in carbonate environments. Traditional methods to determine porosity are limited to discrete measurements and generally demand extra resources associated with careful analysis of logs, rock sampling and laboratory analysis. This paper investigates an alternative to estimate porosity in carbonate rocks using pointwise hyperspectral data and machine learning. The method is contiguous, does not require rock sampling and was validated in various rock plug samples collected from two distinct carbonate outcrops. The samples were analysed in the laboratory to determine ground-truth values for the effective porosity and reflectance in visible and infrared regions of the spectra. The supervised regression methods applied were able to estimate a robust relationship between the effective porosity of carbonate rocks and spectral behaviour in characteristic spectral features of carbonate, hydroxyl, molecular water and Fe/Mn. The results obtained here suggest the soundness of the indirect approach to estimate porosity with most of the models trained achieving a coefficient of determination above 0.8 and mean absolute deviation of less than 2%.KEY POINTSHyperspectral data can be used as proxy for porosity estimation in carbonate rocks.All the tested learners achieved R2 greater than 0.7.Regularised linear regression can be used to estimate porosity.Support vector regression estimation of porosity achieves a mean absolute error of 1.0249 in our dataset.
Supporting Security Requirements Engineering through the Development of The Secure Development Ontology

Security: the forgotten requirement. With the coming of the digital age comes new challenges to protect assets. Security of systems has become the top priority for many nations and companies due to the devastating results of software attacks. Often, much of security engineering is left up to the imaginations of engineers which is limited to experience; therefore, development of secure systems often requires security experts to analyze security risks, suggest security requirements, propose mitigations, and implement security requirements. Another challenge of security is the constantly shifting landscape. For example, once secure hash algorithms no longer being secure with the cheap availability of resources. This paper presents the development of the Secure Development Ontology (SDO), which has the purpose of assisting with the elicitation of security requirements and design decisions. The ontology defines 183 core entities and 32 object properties. The SDO is implemented in Web Ontology Language 2 through the Protege 5.5.0 tool. While ontologies have been used to map the security domain many have focused on subsets of the security domain or on security for after implementation of a system. Experience has shown that adding features to a development project is cheapest at the earliest stages of the development life cycle therefore this ontology focuses on security requirements, design, and implementation considerations.
Probability-based Naïve Bayes Algorithm for Email Spam Classification

Email spam, which is a type of e-spam, is one of the most common internet problems. Email is the standard mode of communication for sharing vital and official information. Most institutions and businesses prefer email to all other forms of communication because it is the most cost-effective, simple to use, easily accessible, official, and dependable. It&#x0027;s commonly utilized since it ensures that the information submitted is kept private. The majority of spam emails are sent for commercial objectives, and others may contain virus links that direct users to fraudulent websites. However, there are drawbacks to this reliable and simple mode of communication, as many people abuse it by sending unwanted and pointless messages for their benefit. These unwanted emails generate problems for the average user, such as filling the inbox with undesirable emails, making it difficult to find valuable emails, and even causing one to miss over vital and beneficial communications as a result of all these unwanted emails. As a result, a powerful email spam detector is required, one that can filter a large number of spam emails with increased accuracy while ensuring that real emails are not screened as spam. Ham consists of emails that are legally legitimate messages that people can accept. Unwanted emails that a user wants to delete are referred to as spam. The goal of this study is to use an improved and efficient classification algorithm to classify spam and ham emails. The goal of this study is to enhance the accuracy of classifying emails into two groups with minimal training. This study uses the Nave Bayes (NB) classifier to ensure that the requirements are met with minimal training and that the findings are more accurate than previous methods.
The Effect of Purge Flow Rate and Wafer Arrangement on Humidity Invasion Into a Loaded Front Opening Unified Pod (FOUP)

In recent years, the semiconductor industry has continued to advance production techniques and reduce chip feature size. The presence of unwanted moisture during the construction of these features increases the risk of chip defects and reduced yield. During production, smaller integrated circuit (IC) products are more sensitive to the presence of moisture. Therefore, increasing the efficiency of moisture control techniques is very important to decrease the product defect rate. Front opening unified pods (FOUPs) are the plastic enclosure boxes that are designed to provide a cleaner environment for semiconductor wafers during manufacturing and storage. Clean dry air (CDA) or nitrogen (N<sub>2</sub>) can purge moist air out from the FOUP. For the first time, in this study the effect of purge flow rate on the moisture removal efficiency of a loaded FOUP was examined accurately via smoke flow visualization, particle image velocimetry (PIV) and relative humidity (RH) measurement. Moreover, two different wafer arrangements of top-empty and bottom-empty FOUP were investigated. The findings show the bottom-empty FOUP arrangement results in higher purge performance compared to the top-empty FOUP arrangement. Four purge flow rates of 130, 200, 300 and 400 LPM were examined. The findings also show that when the purge flow rate was set to 200 LPM, the lowest level of relative humidity was measured inside the FOUP. Therefore, the purge flow rate of 200 LPM and the bottom- empty FOUP arrangement can minimize the humidity invasion into a loaded FOUP. The findings from this study can be beneficial to the industry to optimize the purge flow rate and to define the most efficient arrangement of the wafers inside the FOUP during manufacturing and storage. This can increase the product quality and reduce energy consumption by decreasing product defects and increasing yield.
Hotel Recommendation System using advanced efficiency and accuracy with modified BERT technique

Discovering compatible hotel for customer’s needs and economy is a tough process. These days, it helps with the availability of a sufficient amount of customer-created online reviews. This fact also helps consumers improve information processing by presenting the recommended hotel system and the direction of promising research in the tourism field. This proposal system employs hotel sentiment analysis as part of its hotel recommendation system. Although RoBERTa and BERT have almost identical architectures, the RoBERTa model exceeds the BERT model in the context of performance. Robustly Optimized BERT Pretraining Approach (RoBERTa) model is used to segregate reviews into positive, negative, and neutral. Actual reviews show different sentiments of hotel guests, and each review categorization is done considering multiple categories like location, cleanliness, service, etc. The way reviews function with user-supplied queries determines how they are categorized. The reviews are grouped using cosine similarity and fuzzy logic simultaneously. The resulting dataset is stored into Apache Kafka. In consideration of the issue of inconsistent data flow in real application scenarios, the message queue Kafka was introduced. It guarantees that real-time data input rates are smooth and consistent. In this paper, implementation of a real-time recommendation system using micro-batch data stream processing by Kafka-Spark integration is done. It uses Spark Streaming to ensure minimal data loss while concurrently storing all Kafka data for speedy recovery.