Reviewerly/exHarmony huggingface dataset:
A Survey on QoE-Oriented VR Video Streaming: Some Research Issues and Challenges

With the advent of the information age, VR video streaming services have emerged in large numbers in scenarios such as immersive entertainment, smart education, and the Internet of Vehicles. People are also demanding an increasing number of virtual-reality (VR) services, and service providers must ensure a good user experience. Therefore, the quality of the VR user’s experience is receiving increasing attention from academia and industry. The review in this paper focuses on a comprehensive summary of the current state of quality-of-experience (QoE) technologies applied to VR video streaming. First, we review the main influencing factors of QoE and VR video streaming. Second, the user QoE for VR evaluation is discussed. Third, the modeling of QoE for VR video streaming, the QoE-oriented VR optimization problem, and enabling techniques of machine learning for VR video streaming improvement are summarized. Lastly, we present current challenges and possible future research directions.
Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta

The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it reflects enough inter-patient variability. This paper addresses the problem of generating virtual patient cohorts of thoracic aorta geometries that can be used for in-silico trials. In particular, we focus on the problem of generating a cohort of patients that meet a particular clinical criterion, regardless the access to a reference sample of that phenotype. We formalize the problem of clinically-driven sampling and assess several sampling strategies with two goals, sampling efficiency, i.e., that the generated individuals actually belong to the target population, and that the statistical properties of the cohort can be controlled. Our results show that generative adversarial networks can produce reliable, clinically-driven cohorts of thoracic aortas with good efficiency. Moreover, non-linear predictors can serve as an efficient alternative to the sometimes expensive evaluation of anatomical or functional parameters of the organ of interest.
An Attention-Based Mood Controlling Framework for Social Media Users

In this digital age, social media is an essential part of life. People share their moments and emotions through it. Consequently, detecting emotions in their behavior can be an effective way to determine their emotional disposition, which can then be used to control their negative thinking by making them see the positive aspects of the world. This study proposes an emotion detection-based mood control framework that reorganizes social media posts to match the user's mental state. An emotion detection model based on Attention mechanism, Bidirectional Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN) has been proposed which can detect six emotions from Bangla text with 66.98% accuracy. It also demonstrates how emotion detection frameworks can be implemented in other languages as well.
Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding

A key solution to temporal sentence grounding (TSG) exists in how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence description.Existing methods mainly leverage vanilla soft attention to perform the alignment in a single-step process.However, such single-step attention is insufficient in practice, since complicated relations between inter-and intra-modality are usually obtained through multi-step reasoning.In this paper, we propose an Iterative Alignment Network (IA-Net) for TSG task, which iteratively interacts inter-and intra-modal features within multiple steps for more accurate grounding.Specifically, during the iterative reasoning process, we pad multi-modal features with learnable parameters to alleviate the nowhere-toattend problem of non-matched frame-word pairs, and enhance the basic co-attention mechanism in a parallel manner.To further calibrate the misaligned attention caused by each reasoning step, we also devise a calibration module following each attention module to refine the alignment knowledge.With such iterative alignment scheme, our IA-Net can robustly capture the fine-grained relations between vision and language domains step-bystep for progressively reasoning the temporal boundaries.Extensive experiments conducted on three challenging benchmarks demonstrate that our proposed model performs better than the state-of-the-arts.
TEACh: Task-Driven Embodied Agents That Chat

Robots operating in human spaces must be able to engage in natural language interaction, both understanding and executing instructions, and using conversation to resolve ambiguity and correct mistakes. To study this, we introduce TEACh, a dataset of over 3,000 human-human, interactive dialogues to complete household tasks in simulation. A Commander with access to oracle information about a task communicates in natural language with a Follower. The Follower navigates through and interacts with the environment to complete tasks varying in complexity from "Make Coffee" to "Prepare Breakfast", asking questions and getting additional information from the Commander. We propose three benchmarks using TEACh to study embodied intelligence challenges, and we evaluate initial models' abilities in dialogue understanding, language grounding, and task execution.
AltibbiVec: A Word Embedding Model for Medical and Health Applications in the Arabic Language

In recent years, the utilization of natural language processing (NLP) and Machine Learning (ML) techniques in clinical decision support systems have shown their ability in improving and automating the diagnosis process and reduce the potential for clinical errors.NLP in the Arabic language is more intricate due to several limitations, such as the lack of datasets and analytical resources compared to other languages like English.However, a clinical decision support system in the Arabic context is of significant importance.A fundamental process in NLP is extracting features from textual data via text embedding.Word embedding is a representation of words in a numeric format that encodes the statistic, semantic, or context information.Building a neural word embedding model requires hundreds of thousands of data instances to find hidden patterns of relationships within sentences.Essentially, extracting relevant and informative features promotes the performance of the learning algorithms.The objective of this paper is to propose an Arabic neural-based word embedding model in the medical and healthcare context (called "AltibbiVec").Around 1.5 million medical consultations and questions are used to train the embedding model.Three different embedding models are developed and compared, which are based on Word2Vec, FastText, and GloVe.The trained models are evaluated by different criteria, including the word clustering and the similarity of words.Besides, they are evaluated by performing a specialty-based question classification.Word2Vec and FastText capture sufficiently the semantics of text more than GloVe.Hence, they are recommended for utilization in healthcare NLP applications.The developed models are available on Github repository: https://github.com/altibbi-com/AltibbiVec.
Multiple instance convolutional neural network with modality-based attention and contextual multi-instance learning pooling layer for effective differentiation between borderline and malignant epithelial ovarian tumors

Malignant epithelial ovarian tumors (MEOTs) are the most lethal gynecologic malignancies, accounting for 90% of ovarian cancer cases. By contrast, borderline epithelial ovarian tumors (BEOTs) have low malignant potential and are generally associated with a good prognosis. Accurate preoperative differentiation between BEOTs and MEOTs is crucial for determining the appropriate surgical strategies and improving the postoperative quality of life. Multimodal magnetic resonance imaging (MRI) is an essential diagnostic tool. Although state-of-the-art artificial intelligence technologies such as convolutional neural networks can be used for automated diagnoses, their application have been limited owing to their high demand for graphics processing unit memory and hardware resources when dealing with large 3D volumetric data. In this study, we used multimodal MRI with a multiple instance learning (MIL) method to differentiate between BEOT and MEOT. We proposed the use of MAC-Net, a multiple instance convolutional neural network (MICNN) with modality-based attention (MA) and contextual MIL pooling layer (C-MPL). The MA module can learn from the decision-making patterns of clinicians to automatically perceive the importance of different MRI modalities and achieve multimodal MRI feature fusion based on their importance. The C-MPL module uses strong prior knowledge of tumor distribution as an important reference and assesses contextual information between adjacent images, thus achieving a more accurate prediction. The performance of MAC-Net is superior, with an area under the receiver operating characteristic curve of 0.878, surpassing that of several known MICNN approaches. Therefore, it can be used to assist clinical differentiation between BEOTs and MEOTs.
Rewrite rule inference using equality saturation

Many compilers, synthesizers, and theorem provers rely on rewrite rules to simplify expressions or prove equivalences. Developing rewrite rules can be difficult: rules may be subtly incorrect, profitable rules are easy to miss, and rulesets must be rechecked or extended whenever semantics are tweaked. Large rulesets can also be challenging to apply: redundant rules slow down rule-based search and frustrate debugging. This paper explores how equality saturation, a promising technique that uses e-graphs to apply rewrite rules, can also be used to infer rewrite rules. E-graphs can compactly represent the exponentially large sets of enumerated terms and potential rewrite rules. We show that equality saturation efficiently shrinks both sets, leading to faster synthesis of smaller, more general rulesets. We prototyped these strategies in a tool dubbed Ruler. Compared to a similar tool built on CVC4, Ruler synthesizes 5.8× smaller rulesets 25× faster without compromising on proving power. In an end-to-end case study, we show Ruler-synthesized rules which perform as well as those crafted by domain experts, and addressed a longstanding issue in a popular open source tool.
Techniques to Improve Ecological Interpretability of Black-Box Machine Learning Models

Abstract Statistical modeling of ecological data is often faced with a large number of variables as well as possible nonlinear relationships and higher-order interaction effects. Gradient boosted trees (GBT) have been successful in addressing these issues and have shown a good predictive performance in modeling nonlinear relationships, in particular in classification settings with a categorical response variable. They also tend to be robust against outliers. However, their black-box nature makes it difficult to interpret these models. We introduce several recently developed statistical tools to the environmental research community in order to advance interpretation of these black-box models. To analyze the properties of the tools, we applied gradient boosted trees to investigate biological health of streams within the contiguous USA, as measured by a benthic macroinvertebrate biotic index. Based on these data and a simulation study, we demonstrate the advantages and limitations of partial dependence plots (PDP), individual conditional expectation (ICE) curves and accumulated local effects (ALE) in their ability to identify covariate–response relationships. Additionally, interaction effects were quantified according to interaction strength (IAS) and Friedman’s $$H^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>H</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math> statistic. Interpretable machine learning techniques are useful tools to open the black-box of gradient boosted trees in the environmental sciences. This finding is supported by our case study on the effect of impervious surface on the benthic condition, which agrees with previous results in the literature. Overall, the most important variables were ecoregion, bed stability, watershed area, riparian vegetation and catchment slope. These variables were also present in most identified interaction effects. In conclusion, graphical tools (PDP, ICE, ALE) enable visualization and easier interpretation of GBT but should be supported by analytical statistical measures. Future methodological research is needed to investigate the properties of interaction tests. Supplementary materials accompanying this paper appear on-line.
Towards an integral pedagogy in the age of ‘digital <i>Gestell</i>’: Moving between embodied co-presence and telepresence in learning and teaching practices

Digitalisation offers a wide array of opportunities, but also challenges, for universities and business schools alike, regarding the provision and delivery of their teaching and learning activities. The COVID-19 pandemic has highlighted some of these challenges, as it forced educational institutions to move their pedagogic activities online in line with new governmental regulations. In this article, we identify and discuss critically the following three interconnected challenges: (1) shifting from direct embodied co-presence to technologically mediated telepresence, (2) re-embodying teaching and learning activities and (3) rethinking the purpose and relevance of teachings in business schools. We explore these challenges through a phenomenological lens, informed by the Heideggerian concepts of enframing ( Gestell) and releasement ( Gelassenheit), with a focus on (re-)embodiment. Finally, we discuss the need, for teachers and learners, to be able to reflectively move between embodied and digital(ised) forms of learning and teaching and outline some implications and perspectives regarding the development of an integral pedagogy.
Blockchain-Based Energy Applications: The DSO Perspective

This paper discusses blockchain-based energy applications from the distribution system operator (DSO) perspective. Blockchain has a potential impact on newly emergent actors, such as electric vehicles (EVs) and charging facility units (CFUs) of the electricity grid. Although Blockchain offers magnificent decentralized solutions, owing to the reality of the existing grid structure, the central management of DSOs still plays a significant, non-negligible role. Numerous studies of proposed blockchain-based EV systems have investigated the energy costs of EVs, fast and efficient charging, privacy and security, P2P energy trading, sharing economy, selection of appropriate CFUs location, and scheduling. However, cooperation with DSO organizations has not been adequately addressed. Blockchain-based solutions mainly suggest an entirely distributed and decentralized approach for energy trading; however, converting the entire power system infrastructure is considerably expensive. Building a thoroughly decentralized electricity network is nearly impossible in a short time, particularly at the national grid level. In this regard, the applicability of the solutions is as significant as their appropriateness, especially from the DSO perspective, and must be examined closely. We searched and analyzed the blockchain literature related to EVs, CFUs, DERs, microgrids, marketing, and DSOs to define the DSO-based requirements for potential blockchain applications in the energy sector, specifically EV evolution.
Digital Learning Enhancement Through Social Network Site (SNS)

In the last decade, the wide range of advanced technology development could be viewed into the number of recent electronic devices, and also services. Those include education development, business sector, and also public service. Moreover, the number could be seen into social media platform in facilitating global cross interaction. In this paper, the aims to examine the further development of social network site (SNS) are deployed in looking at the contribution for learning enhancement. Reviewing several critical literatures was deployed in the attempts to expand the initial value of SNS to play a significant role in developing the recent trends of technology development and transmitted into the social media basis. The finding points out that SNS with its valuable insight of developing adaptive learning technology is potential to play a significant role in facilitating the learning enhancement. This study is expected to contribute to give insights into enhancing the insightful contribution for learning technology together with the adaptive technology enhancement.
Earthquake Magnitude Estimation from High-Rate GNSS Data: A Case Study of the 2021 Mw 7.3 Maduo Earthquake

Peak ground displacement (PGD) and peak ground velocity (PGV) are critical parameters during earthquake early warning, as they can provide rapid magnitude estimation before rupture end. In this study, we used the high-rate Global Navigation Satellite System (GNSS) data from 55 continuous stations to estimate the magnitude of the 2021 Maduo earthquake in western China. We used the relative positioning method and variometric approach to acquire real-time GNSS displacement and velocity waveforms, respectively. The results showed the amplitude of displacement and velocity waveforms gradually decreased with increasing hypocentral distance. Our results showed that the fluctuation of PGD magnitudes over time is smaller than that of PGV magnitudes. Nonetheless, the earthquake magnitudes estimated from both methods were consistent with their counterparts (Mw 7.3) reported by the United States Geological Survey (USGS). The final magnitude estimated from the PGD and PGV methods were Mw 7.25 and Mw 7.31, respectively. In addition, our results highlighted how the number of high-rate GNSS stations could influence the stability and convergence time of magnitude estimation.
Resource-Efficient DAG Blockchain with Sharding for 6G Networks

The sixth-generation (6G) system is widely envisioned as a global network consisting of pervasive devices that interact with each other. Besides exchanging information, these peer entities also share heterogeneous and distributed edge resources. Blockchain is a promising technology to secure resource sharing in a peer-to-peer way. However, constrained by limited transaction throughput, existing blockchain systems (e.g., Bitcoin) cannot process numerous resource transactions concurrently produced in large-scale 6G networks. To cope with this issue, we propose a blockchain scaling scheme that combines Directed Acyclic Graph (DAG) with sharding. Our sharding method divides blockchain transactions into multiple mutually disjoint subsets maintained by different committees, which enables high-volume resource transactions to be processed and recorded concurrently. Additionally, all committees maintain a global DAG of blocks to resolve the security degradation raised by the sharding method. Moreover, we put forth a computation efficient consensus algorithm by leveraging miners' computing power to improve resource utilization efficiency. Numerical results show that the proposed scheme achieves good performance in resource utilization efficiency.
Explore Better Network Framework for High-Resolution Optical and SAR Image Matching

To fully explore the complementary information from optical and synthetic aperture radar (SAR) imageries, they need first to be coregistered with high accuracy. Due to the vast radiometric and geometric disparity, the problem to match high-resolution optical and SAR images is quite challenging. The present deep learning-based methods have shown advantages over the traditional approaches, but the performance increment is not significant. In this article, we explore a better network framework for high-resolution optical and SAR image matching from three aspects. First, we propose an effective multilevel feature fusion method, which helps to take advantage of both the low-level fine-grained features for precious feature location and the high-level semantic features for better discriminative ability. Second, a feature channel excitation procedure is conducted using a novel multifrequency channel attention module, which is able to make image features of different types and multiple levels effectively collaborate with each other and produce image matching features with high diversity. Third, the self-adaptive weighting loss is introduced, with which, each sample is assigned with an adaptive weighting factor, and therefore, information buried in all nearby samples can be better exploited. Under a pseudo-Siamese architecture, the proposed optical and SAR image matching network (OSMNet) is trained and tested on a large and diverse high-resolution optical and SAR dataset. Extensive experiments demonstrate that each component of the proposed deep framework helps to improve the matching accuracy. Also, the OSMNet shows overwhelming superior to the state-of-the-art handcrafted approaches on imageries of different land-cover types.
Reconsidering Tweets: Intervening during Tweet Creation Decreases Offensive Content

The proliferation of harmful and offensive content is a problem that many online platforms face today. One of the most common approaches for moderating offensive content online is via the identification and removal after it has been posted, increasingly assisted by machine learning algorithms. More recently, platforms have begun employing moderation approaches which seek to intervene prior to offensive content being posted. In this paper, we conduct an online randomized controlled experiment on Twitter to evaluate a new intervention that aims to encourage participants to reconsider their offensive content and, ultimately, seeks to reduce the amount of offensive content on the platform. The intervention prompts users who are about to post harmful content with an opportunity to pause and reconsider their Tweet. We find that users in our treatment prompted with this intervention posted 6% fewer offensive Tweets than non-prompted users in our control. This decrease in the creation of offensive content can be attributed not just to the deletion and revision of prompted Tweets - we also observed a decrease in both the number of offensive Tweets that prompted users create in the future and the number of offensive replies to prompted Tweets. We conclude that interventions allowing users to reconsider their comments can be an effective mechanism for reducing offensive content online.