Reviewerly/exHarmony huggingface dataset:
LiveAE: Attention-based and Edge-assisted Viewport Prediction for Live 360° Video Streaming

Viewport prediction plays a crucial role in live 360° video streaming as it determines which tiles should be prefetched in high quality, thereby significantly impacting the user experience. However, the current approach to viewport prediction, which integrates content-level visual features with the viewer's head movement trajectory, faces the challenge of striking a balance between prediction accuracy and computational complexity. In this paper, we propose LiveAE, a novel attention-based and edge-assisted viewport prediction framework for live 360° video streaming. Specifically, we employ a pre-trained video encoder called Vision Transformer (ViT) for general visual feature extraction and a cross-attention mechanism for user-specific interest tracking. To address the computational complexity issue, we offload the aforementioned content-level operations to an edge server while retaining trajectory-related functions on the client side. Extensive experiments show that our proposed method not only outperforms state-of-the-art algorithms but also ensures the real-time requirements of live 360° video streaming.
Optimizing Real-Time Video Experience with Data Scalable Codec

Real-time video communication is becoming more and more important. However, packet loss is prevalent and resending packets, especially in long-latency networks, causes visual stalls. Previous solutions all perform suboptimally as they either add redundancy before sending the data, which reduces bitrate when no packet is lost, or fail to prevent video freeze when redundancy is not enough. User studies confirm that both bitrate decrease and video freeze significantly damage users' Quality of Experience (QoE). Through a user study comparing different artifacts during a quality drop period, we find that moderate quality drop is preferred over video freeze during packet loss. Inspired by this, we propose a new solution that trains a neural network Autoencoder to optimize frame quality under different packet loss rates. Our insight is that such training produces a Data Scalable codec, whose quality increases with each new packet arrival and reaches highest quality when no packet is lost. Specifically, with the arrival of any x encoded bytes of a frame, the decoded quality is closer to the quality than if the whole frame were encoded with x bytes in the first place. Thus, unless all packets are lost, our approach causes a moderate quality drop instead of video freeze during packet loss. In the end, we identify the technical challenges remaining in this approach and point out future opportunities.
Data Recognition for Multi-Source Heterogeneous Experimental Detection in Cloud Edge Collaboratives

This article proposes a multisource heterogeneous experimental data recognition method based on CRNN and DBNet in a cloud-edge collaborative environment in an attempt to address the issues of low efficiency and a high error rate that come with traditional manual data detection and recognition. Firstly, a recognition architecture for experimental detection data of intelligent measurement systems is designed based on a cloud-edge collaborative environment to improve the efficiency of data processing. Then, the improved DBNet network is used in the edge computing center to detect the text, and the correction module is used to correct the deviation of the detected text to ensure the standardization of the text. Finally, in the central cloud, the end-to-end indefinite length character recognition (CRNN) algorithm is used to analyze and identify the text order, rules, and other information of the image after the correction is completed, extract the test detection data, and convert the detection data into row data according to the two-dimensional table structure, and conduct structured storage and management through the relational database. An experimental analysis of the proposed method is conducted based on the deep learning framework, and results show that its accuracy rate and recall rate are close to 96% and 94%, respectively, with an average accuracy of 95.09%. This fully demonstrates the proposed method is effective and, therefore, applicable to power equipment experimental detection data recognition.
Deep transfer learning-based anomaly detection for cycling safety

Despite the general improvements in road safety, with the growing number of bicycle users, cycling safety is still a challenge as demonstrated by the fact that it is the only road transport mode with an increase in the number of fatalities in EU cities.Moreover, to analyze the problem to improve the road transport system, the traditional network screening based on crash statistics is a reactive approach and less effective due to the lack of suitable bicycle data availability, as well. In such a framework, new opportunities for data collection in smart cities and communities are emerging as proactive approaches to identify critical locations where safety treatments can be effectively applied to prevent bicycle crashes.This research applied a deep transfer learning model to detect anomalies in cycling behavior that can be associated with traffic conflicts or near-miss crashes.The paper presents how to build a users' tailored riding model named DTL AD to detect and localize riding anomalies by using a set of data in the National Marine Electronics Association (NMEA) string of Global Navigation Satellite System (GNSS) recorded with instrumented bicycles by different cyclists.More specifically, DTL AD exploits a convolutional autoencoder (CAE) with transfer learning to reduce data labelling and training effort.A case study demonstrates the identification of anomalies in cycling behavior visually represented on Geographic Information Systems (GIS) maps, showing how data clustering is well located in high-risk areas.
New Age Information Fusion Technology-Based Unique Multimedia Teaching Methods for English

English listening multimedia teaching courseware has the characteristics of both illustration and text, sound, and shape and provides a variety of possibilities for modern English listening teaching methods. Considering students' unique psychological characteristics such as strong self-awareness, physiological characteristics such as strong abstract thinking ability, and relevant theories such as schema theory and constructivism, this paper proposes that high school English listening multimedia teaching courseware can be innovative in many aspects. Multimedia is more and more widely used in the society, and the combination of multimedia and teaching is getting closer and closer. Through multimedia teaching, more teachers' resources are integrated, and students' extracurricular knowledge is broadened. This combination of multimedia and teachers achieves a different teaching effect than before. Due to its graphic, audio, and visual characteristics, it provides a variety of possibilities for modern English listening teaching methods.
Introducing the BlendedICU dataset, the first harmonized, international intensive care dataset

This study introduces the BlendedICU dataset, a massive dataset of international intensive care data. This dataset aims to facilitate generalizability studies of machine learning models, as well as statistical studies of clinical practices in the intensive care units.Four publicly available and patient-level intensive care databases were used as source databases. A unique and customizable preprocessing pipeline extracted clinically relevant patient-related variables from each source database. The variables were then harmonized and standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Format. Finally, a brief comparison was carried out to explore differences in the source databases.The BlendedICU dataset features 41 timeseries variables as well as the exposure times to 113 active ingredients extracted from the AmsterdamUMCdb, eICU, HiRID, and MIMIC-IV databases. This resulted in a database of more than 309000 intensive care admissions, spanning over 13 years and three countries. We found that data collection, drug exposure, and patient outcomes varied strongly between source databases.The variability in data collection, drug exposure, and patient outcomes between the source databases indicated some dissimilarity in patient phenotypes and clinical practices between different intensive care units. This demonstrated the need for generalizability studies of machine learning models. This study provides the clinical data research community with essential data to build efficient and generalizable machine learning models, as well as to explore clinical practices in intensive care units around the world.
Instructional Design in Modern Environments

This study highlights the most pertinent m-learning content quality features that should be considered by designers from the point of view of experts to determine students' perceptions about suggested quality features and to measure any statistically significant differences in students' perceptions due to their gender and technological skills. An exploratory sequential mixed research method was employed. A group of 20 experts in m-learning answered an open-ended question for the qualitative part, and a questionnaire with 31 items was answered by a purposeful sample of 200 students out of the entire population of 312 from seven Jordanian universities. The results revealed a list of content quality features. The overall perception of students was high with a mean of 4.15 and no statistically significant differences in students' perceptions due to their gender and technological skills. The study recommended studying the effectiveness of m-learning content features in Jordanian higher education institutions.
GAN-Based Handwritten Digit Recognition System

Machine learning and computer vision play a vital role in real-time applications such as in industries, marketing, education, finance, automative, security, manufacturing and health. Machine learning process includes understanding the data without being explicitly programmed, and computer vision process includes understanding image features from various sensing devices for further image processing with its extensive algorithms or deep learning techniques Computer applications gone intense transformation from simple data processing to complex problem solving aspects, and this may be possible due to the huge availability of data from different data sources. In this connection, machine learning adds the additional advantages to the computer applications with its diversity of models. In the current scenario, the research has been more focused on the latest computing technologies like computer vision, deep learning, artificial intelligence and data science. The main objective of this study is to elegant the various aspects of machine learning and computer vision with its applications to transform the changes in the field of computer applications. As an application concern, this paper also discussed the importance of Generative Adversarial Network (GAN) and GAN Lab implementation with handwritten digit images.
E-Learning on Semantic Web

E-Learning is the current trend that is gaining more significance post-pandemic, E-Learning has several distinctive features which has attracted many users across the globe, and there is a huge demand for E-Learning content. E-Learning is the delivery of learning material by any electronic gadget, and the communication channel used for the delivery of learning material is the World Wide Web. Currently, the content displayed on the World Wide Web is only understood by humans but not by machines, and in order to make machines understand the content and make intelligent decisions the World Wide Web can be replaced by the Semantic Web. In the current paper, SemELearn a Semantic Web-based E-Learning system is proposed that utilizes the underlying characteristic feature of the Semantic Web.
Cluster Query Optimization Technique Using Blockchain

Blockchain is an emerging technology, and it applies to time-stamped data that can be achieved by way of a cluster of processors now not possessed with the aid of any solitary entity. Here, cluster data records are to be considered as a set of blocks. Those blocks are protected and processed respectively for the usage of cryptographic standards, namely chain. The query processing is continued until achieved exact and optimal execution is on the blocks. The query is executed after connecting queries to databases, collecting statistics from exceptional places, and showing outcomes to the machine learning application. We propose a query optimization, which is the portion of the query procedure in the database system that compares distinctive query processes and selects the only one with the least expected value in every block. A query optimizer makes one or more query tactics for every query, each of which can be a device used to run a query. The most efficient query plan is chosen and used to run the query. The experimental result has calculated the latency for each query block node.
Karhunen–Loève data imputation in high-contrast imaging

The detection and characterization of extended structures is a crucial goal in high-contrast imaging. However, these structures face challenges in data reduction, leading to over-subtraction from speckles and self-subtraction with most existing methods. Iterative post-processing methods offer promising results, but their integration into existing pipelines is hindered by selective algorithms, the high computational cost, and algorithmic regularization. To address this for reference differential imaging (RDI), here we propose a data imputation concept for the Karhunen–Loève transform (DIKL) by modifying two steps in the standard Karhunen–Loève image projection (KLIP) method. Specifically, we partition an image to two matrices: an anchor matrix that focuses only on the speckles to obtain the DIKL coefficients, and a boat matrix that focuses on the regions of astrophysical interest for speckle removal using DIKL components. As an analytical approach, DIKL achieves high-quality results with significantly reduced computational cost (~3 orders of magnitude less than iterative methods). Being a derivative method of KLIP, DIKL is seamlessly integrable into high-contrast imaging pipelines for RDI observations.
Lossky N.O. and his Metacritique of Pure Reason

The publication of the Critique of Pure Reason by Immanuel Kant marked the beginning of an intellectual revolution not only in Philosophy, but also in other spheres of intellectual activity. Every year interest to this work is only growing up, especially in the context of the development of cognitive sciences and technologies related to the development and implementation of artificial intelligence systems. However, both Kant’s contemporaries and subsequent generations of researchers had questions about the basic concepts, outlined in the first Critique. Nikolay Lossky became one of such outstanding experts in Kant’s philosophy, who carried out a metacritique of transcendental idealism. His analysis is interesting because it is characterized by consistency, depth and inclusiveness, despite the fact that he was not a supporter of Kant’s philosophy. The proposed article explicates the main points of the metacritique of pure reason carried out by Lossky. The aim is to systematize the advantages and disadvantages of transcendental idealism highlighted by him in order to assess the validity of objections and identify points that paradoxically escaped Lossky’s attention. In particular, it is shown that the highest evaluation of Lossky is awarded to Kant’s efforts to create an epistemology that does not rely as presuppositions on any other branches of human knowledge; the resolution of contradictions between Empiricism and Rationalism; the creation of the doctrine of synthetic rationality and transcendental logic; the idea of the need for the immanence of knowledge to consciousness. Lossky identified as the key shortcomings of transcendental idealism a range of contradictions grouped around such concepts as the things in themselves, affection, experience, inner sense, transcendental schema, and unity of apperception. At the same time, it is shown that such a key concept for Kant’s theoretical philosophy as transcendental reflection has disappeared from the Lossky’s field of consideration. The study is preceded by a brief description of Lossky’s characteristics, including personal ones, as a meta-critic of pure reason.
Learning Scheduling Policies for Co-Located Workloads in Cloud Datacenters

Co-location, which deploys long running applications and batch-processing applications in the same computing cluster, has become a promising way to improve resource utility for large cloud datacenters. However, co-location brings huge challenges to task scheduling because different types of workloads may affect each other. Existing works on task scheduling rarely focus on the scenario of co-location. This article presents Co-ScheRRL, a scheduling algorithm delicately designed for co-located workloads. Co-ScheRRL consists of two major mechanisms: i) a self-attention encoding mechanism which encodes and represents states of the computing cluster as a set of embedding feature vectors; ii) a deep reinforcement learning (DRL) relational reasoning mechanism which calculates and compares different scheduling actions under different co-located workloads pattern via DRL feedback reward signals based on these feature vectors. Our two mechanisms can tackle complicatedly and dynamically varying behaviors of co-located workloads. With the help of these two mechanisms, Co-ScheRRL is able to construct high-quality scheduling policies. Trace-driven simulation demonstrates that Co-ScheRRL outperforms existing scheduling algorithms in terms of makespan by more than 38.4% and throughput by more than 166.7%.
Anchor Pseudo-Supervise Large-Scale Incomplete Multi-View Clustering

In real life, only partial information of samples is available everywhere, this makes Incomplete multi-view clustering (IMVC) becomes a significant research topic to handle data loss situations. Recently, several methods leverage the anchor strategy by selecting the fixed anchors to handle the challenging large-scale IMVC. However, all of them ignore the guidance of prior information hidden in the bipartite graph. Therefore, we propose a novel Anchor Pseudo-supervise Large-scale Incomplete Multi-view Clustering (AP-LIMC) method by introducing a prior indicator matrix as a pseudo-supervise anchor learning paradigm. Specifically, the prior indicator matrix is first introduced to control the distribution of anchors in each cluster. And then, an anchor pseudo-supervise learning framework is designed to generate high-quality anchors and a unified bipartite graph with prior indicator supervision. In addition, we design an optimize process with linear computational and extensive experiments on multiple public datasets with recent advances validates the effectiveness, superiority, and efficiency. For example, on the Stl10 dataset, the performance of the proposed AP-LIMC improved by 23.95%,15.71%,27.39%, and 18.24% in terms of four evaluation metrics, respectively.
Organ‐aware CBCT enhancement via dual path learning for prostate cancer treatment

Cone-beam computed tomography (CBCT) plays a crucial role in the intensity modulated radiotherapy (IMRT) of prostate cancer. However, poor image contrast and fuzzy organ boundaries pose challenges to precise targeting for dose delivery and plan reoptimization for adaptive therapy.In this work, we aim to enhance pelvic CBCT images by translating them to high-quality CT images with a particular focus on the anatomical structures important for radiotherapy.We develop a novel dual-path learning framework, covering both global and local information, for organ-aware enhancement of the prostate, bladder and rectum. The global path learns coarse inter-modality translation at the image level. The local path learns organ-aware translation at the regional level. This dual-path learning architecture can serve as a plug-and-play module adaptable to other medical image-to-image translation frameworks.We evaluated the performance of the proposed method both quantitatively and qualitatively. The training dataset consists of unpaired 40 CBCT and 40 CT scans, the validation dataset consists of 5 paired CBCT-CT scans, and the testing dataset consists of 10 paired CBCT-CT scans. The peak signal-to-noise ratio (PSNR) between enhanced CBCT and reference CT images is 27.22 ± 1.79, and the structural similarity (SSIM) between enhanced CBCT and the reference CT images is 0.71 ± 0.03. We also compared our method with state-of-the-art image-to-image translation methods, where our method achieves the best performance. Moreover, the statistical analysis confirms that the improvements achieved by our method are statistically significant.The proposed method demonstrates its superiority in enhancing pelvic CBCT images, especially at the organ level, compared to relevant methods.
TEmoX: Classification of Textual Emotion Using Ensemble of Transformers

Textual emotion classification (TxtEC) refers to the classification of emotion expressed by individuals in textual form. The widespread use of the Internet and numerous Web 2.0 applications has emerged in an expeditious growth of textual interactions. However, determining emotion from texts is challenging due to their unorganized, unstructured, and disordered forms. While research in textual emotion classification has made considerable breakthroughs for high-resource languages, it is yet challenging for low-resource languages like Bengali. This work presents a transformer-based ensemble approach (called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TEmoX</i> ) to categorize Bengali textual data into six integral emotions: joy, anger, disgust, fear, sadness, and surprise. This research investigates 38 classifier models developed using four machine learning LR, RF, MNB, SVM, three deep-learning CNN, BiLSTM, CNN+BiLSTM, five transformer-based m-BERT, XLM-R, Bangla-BERT-1, Bangla-BERT-2, and Indic-DistilBERT techniques with two ensemble strategies and three embedding techniques. The developed models are trained, tuned, and tested on the three versions of the Bengali emotion text corpus BEmoC-v1, BEmoC-v2, BEmoC-v3. The experimental outcomes reveal that the weighted ensemble of four transformer models En-22: Bangla-BERT-2, XLM-R, Indic-DistilBERT, Bangla-BERT-1 outperforms the baseline models and existing methods by providing the maximum weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> 1-score (80.24%) on BEmoC-v3. The dataset, models, and fractions of codes are available at https://github.com/avishek-018/TEmoX.