Reviewerly/exHarmony huggingface dataset:
One-Step Multi-view Clustering Based on Low-Rank Tensor Proximity Learning

Multi-view clustering has achieved impressive performances by employing relationships and complex structures hidden in multi-view data. However, most of methods still suffer from the following two problems. (1) They aim to study a common representation or pairwise correlations between views, ignoring the comprehensiveness and deeper higher-order correlations among multiple views. (2) They can-not obtain optimal clustering performance since the unified graph construction is separated from the subsequent clustering step. Both factors may cause insufficient utilization of multi-view information, leading to unsatisfying clustering performance. In this paper, we propose a novel one-step multi-view clustering method based on low-rank tensor proximity learning to address these issues. Low-rank tensor proximity learning, consensus fusion learning and labels learning are performed in a unified framework. Through the three steps of mutual negotiation, the final clustering label is directly obtained. Experimental results on four benchmark datasets demonstrate that our method outperforms other state-of-the-art methods.
Expert Annotation Tools for Labeling Student Capstone Project based on ACM CCS Ontology

Students enrolled in an undergraduate computer science program must complete a capstone project as their final project. The selection of a capstone topic that is not appropriate can cause delays in the completion of this activity. Consequently, a thorough comprehension of the disciplines represented in each capstone title is required as a reference point for students when selecting topics that are appropriate for their academic skills. The popular ontology produced by the Association of Computing Machinery (ACM) can be used to learn about different computer science disciplines. Capstone title labeling on this ontology is applicable in topic recommendation systems that use clustering techniques to group titles into groups of similar topics. This technique requires a manual labeling process carried out by an expert, which has proven to be superior in other studies. However, this process is cumbersome and prone to errors. As a result, we present annotation tools that can assist experts in labeling based on the rules of the ACM Computing Classification System (CCS) ontology. Such tools have never been developed before in the education domain, despite the fact that educational datasets are widely available. In order to evaluate the efficacy, reliability, and accuracy of our system, we employ expert services to make manual annotations with and without the use of tools. The results of the comparison show that the manual labeling process with our tools are able to speed up the annotation process and increase the accuracy of the results achieved, as well as assist the resolution process of labeling disagreements among experts.
An Efficient Load Balancing Using Genetic Algorithm in Cloud Computing

Distributing jobs to servers is an essential factor affecting the performance of a cluster server. Load balancing is a method to distribute jobs to a group of computers to optimize resource utilization, throughput, or response time. A large number of requests increase the load on the cloud. The load has to be balanced to distribute the dynamic workload throughout a couple of nodes to make sure that no single resource is both crushed or underutilized. Machine learning is used to automate the decision of the load balancing algorithm, and machine learning is considered to provide advantages in load balancing. Various strategies can be used inside the Genetic Algorithm (GA) approach selection procedure on the problem. Selection techniques used depend on the problem to be solved. This paper proposes GA with Tournament Selection and Roulette Wheel Selection. These two selection techniques have been proposed as load balancing techniques for cloud computing to find global optimal processors for tasks in the cloud. The result of this research is that the GA using Tournament Selection is better than the GA using the Roulette Wheel Selection.
A Real-Time Object Tracking Method

Artificial Intelligence is one of the most vital prospects in our current life; its applications vary from robotics, transport, houses, to industrial facilities and planes. While neural networks offer the ability to recognize and track new objects, it takes time during learning process especially deep neural networks. There are specific computer vision systems, which require high accuracy and high speed conditions, regardless the knowledge of tracked object. Such as a tracking system for a measurement instrument which scans a certain area and follows the location of object(s). In this paper, we introduce a new tracking method for one object in real time system, based on a theory, building a new powerful algorithm to track the center of object in sub-pixel accuracy.
Intelligent Warning System for the Elderly Living Alone Based on OpenPose

Nowadays, the aging of the global population phenomenon has emerged. However, the development of intelligent anti-fall detection systems for the elderly is not mature. Based on this phenomenon, our team developed an intelligent anti-fall warning system for the elderly living alone. We record the posture behavior of human body under the current monitoring range through electronic monitoring equipment. Based on OpenPose algorithm, it collects the data of human key points and draws the motion curve through the movement track of key points. The above generated data can be used to determine whether someone falls, and the corresponding level of early warning can be given through risk level assessment. Ensure the safety of the elderly living alone.
Performance of ITU-T P.1204.4 on Video Encoded with AV1, AVS2, VVC

The recent standard P.1204.4 contains a reduced-full-reference video quality assessment model for the prediction of mean opinion scores for videos encoded with codecs H.264, HEVC, VP9. In this paper the performance of P.1204.4 on videos encoded with new codecs AV1, AVS2, VVC outside of the scope of the standard P.1204.4 is analyzed using two open datasets, the Waterloo IVC-Lab dataset and the Bristol VI-Lab dataset. The pixel-based model should be codec agnostic. This is analyzed by comparing subset performance on data encoded with new codecs versus within-scope codecs. The equally good performance on the subset of samples encoded with new codecs relative to the performance of the model for data using the codecs within scope of the standard indicate the possibility to use the model for data encoded with these new codecs.
BiLSTM-based Quality of Experience Prediction using Physiological Signals

This paper presents an evaluation of a deep learning (DL) model to predict the user quality of experience (QoE) from physiological signals using a publicly available multimodal dataset, SoPMD. The subjective scores related to QoE factors, namely, perceptual video quality, immersion level, surrounding awareness, interest in video content and audio content are evaluated. A DL model, Bidirectional Long-short-term memory (BiLSTM), is trained on the fusion of electrocardiogram (ECG) and respiration features to predict subjective scores for the five QoE factors. This study achieved classification accuracies and Fl-scores ranging between 58% and 67% for different QoE factors. The results of the BiLSTM model were compared with machine learning techniques. The experimental results demonstrated that the proposed BiLSTM network has the potential to predict QoE from physiological signals.
An Interactive Annotation Tool for Perceptual Video Compression

Human perception is at the core of lossy video compression and yet, it is challenging to collect data that is sufficiently dense to drive compression. In perceptual quality assessment, human feedback is typically collected as a single scalar quality score indicating preference of one distorted video over another. In reality, some videos may be better in some parts but not in others. We propose an approach for collecting finer-grained user feedback through an interactive tool that allows direct optimization of perceptual quality given a fixed bitrate. To this end, we built a novel web-tool which allows users to paint spatio-temporal importance maps over videos. The tool allows for interactive successive refinement: we iteratively re-encode the original video according to the painted importance maps, while maintaining the same bitrate, thus allowing the user to visually see the trade-off of assigning higher importance to one spatio-temporal part of the video at the cost of others. We use this tool to collect data in-the-wild (10 videos, 17 users) and utilize the obtained importance maps in the context of x264 coding to demonstrate that the tool can indeed be used to generate videos which, at the same bitrate, look perceptually better through a subjective study (n = 26) - and are 1.9 times more likely to be preferred by viewers. We plan on collecting a large-scale dataset using the tool for automated perceptual compression in the future. The code for the tool and dataset can be found at https://github.com/jenyap/video-annotation-tool.git.
QoE Assessment for Multi-Video Object Based Media

Recent multimedia experiences using techniques such as DASH allow the streaming delivery to be adapted to suit network context. Object Based Media (OBM) provides even more flexibility as distinct media objects are streamed and combined based on user preferences, allowing the experience to be personalised for the user. As adaptation can lead to degradation, modelling and measuring Quality of Experience (QoE) are crucial to ensure a perceptibly-optimal user experience. QoE models proposed for DASH include quality-related factors from single video-object streams and hence, are unsuitable for multi-video OBM experiences. In this paper, we propose an objective method to quantify QoE for video-based OBM experiences. Our model provides different strategies to aggregate individual object QoE contributions for different OBM experience genres. We apply our model to a case study and contrast it with the QoE levels obtained using a standard QoE model for DASH.
Enrichment of Product Presentation Video: Methods and Impacts on User Experience

Product presentation video (PPV) is a genre of short-form video in online retailing that presents product features and facilitates online shopping. To improve PPVs, quality and provide a better experience for viewers, PPV producers, most of whom are online retailers and non-professionals in video production, have made efforts to enrich these short-form videos. Despite the great demand for PPV enrichment, these methods have not been systematically explored, and their impacts on user experience remain unclear, impeding the improvement of PPVs. This study combined qualitative and quantitative methods to explore the impacts of PPV enrichment methods on user experience. As an exploratory study, we focused on PPVs of female fashion products on Chinese e-commerce platforms. We collected 240 PPVs and summarized ten enrichment methods from them accordingly. A questionnaire-based experiment, including 48 participants, was then conducted to explore the impacts of these methods on the experience of PPVs. Results indicated that eight out of ten methods effectively improved PPV experience from multiple dimensions. This study brings insights for exploring PPV enrichment from the perspective of user experience and provides support for PPV production process.
Improved Adjacency Matrix-Based Graph Convolutional Network Aspect-Level Sentiment Analysis

Existing aspect-based sentiment classification methods use graph-based models to integrate the syntactic structure of sentences. Although these methods are practical, they give less consideration to the aspect-context relative position distance relationship when creating the adjacency matrix, and ignore the point that contexts with different distances from aspects have different effects on the feature extraction of aspects, and simply give the same weight to contexts only. To address the above problems, this paper proposes to fuse the context and aspect relative position distance weight relationship in the syntactic relationship adjacency matrix of sentences. Firstly, the context information of the sentence is obtained using a two-way long and short-term memory network. Then the adjacency matrix is created based on the aspect-context relative distance and syntactic relationship. Finally, a graph convolutional network is used to extract the sentiment features of aspects by combining sentence context and syntactic relations. The experimental results on five public datasets demonstrate that the adjacency matrix incorporating the distance relationship is effective.
Better Experience, Better Performance? Results of a Study on VR Training Effectiveness in Healthcare

Training in virtual reality (VR) is widely considered as promising approach to address the rapidly rising demand for adequately trained healthcare professionals. In this paper, we present an experimental study assessing the experience and learning effects of a VR-based training for healthcare professionals (scenario: preparation of an endotracheal intubation). 41 participants from the field of healthcare and nursing were randomly assigned to an intervention group training in VR and a control group training with traditional printed materials. Subsequently, both groups had to apply their newly acquired skills in a real-world practice environment to objectively assess training effectiveness at high validity. Our results demonstrate the added value of the VR training in terms of trainees' significantly reduced time to solve the task in the real-world environment. Moreover, statistical analysis of experience measures (hedonic and pragmatic user experience, sense of presence, VR-induced symptoms, quality of experience, overall satisfaction) confirms participants' appreciation of the quality of the VR-based training. Surprisingly, however, our results do not indicate a significant link between VR training experience and real-world task performance. We discuss implications of these findings and outline a research agenda to better understand the key factors that influence the effectiveness of VR-based training.
Between Two and Six? Towards Correct Estimation of JND Step Sizes for VMAF-based Bitrate Laddering

We currently witness the rapidly growing importance of intelligent video streaming quality optimization and reduction of video delivery costs. Per-Title encoding, in contrast to a fixed bitrate ladder, shows significant promise to deliver higher quality video streams by addressing the trade-off between compression efficiency and video characteristics such as resolution and frame rate. Selecting encodings with noticeable quality differences in between prevents the construction of an inefficient bitrate ladder that suffers from too similar quality representations. In this respect, the VMAF metric represents a promising foundation for bitrate laddering, as it currently yields the highest video quality prediction performance. However, the minimum noticeable quality difference, referred as to just-noticeable-difference (JND), has not been properly validated for VMAF yet, with existing sources proposing highly diverse ΔVMAF step sizes ranging from two [1] to six [2]. This paper investigates JND for VMAF to facilitate the efficient construction of content-specific bitrate ladders. Using a publicly available JND multimedia dataset, we invalidate existing step size rules of thumb by revealing the large errors they introduce when applied to real-world video content. We investigate different factors that influence the actual ΔVMAF step sizes required to obtain correct JNDs and develop a model using content features that more accurately determines the optimal quality step sizes required for properly laddering individual clips.
Global Information Progressive Aggregation Network for Lightweight Salient Object Detection

Most of the research on salient object detection pursues performance but ignores efficiency, resulting in poor practicability. In this paper, we propose a lightweight SOD solution, named GIPANet, which better alleviates the contradiction between model size and performance. Firstly, to solve the problems of shallow depth and insufficient information extraction in lightweight feature extraction networks, a Global Pooling Aggregation Module (GPAM) is constructed. Secondly, a Feature Aggregation Enhancement Module (FAEM) is constructed to fuse features more efficiently in each layer. Then, we use a hybrid loss function that fuses Binary Cross Entropy (BCE), Intersection-over-Union (IoU) and Progressive Self-Guided (PSG) losses to efficiently locate and segment salient objects. Finally, the experimental results on five public benchmark datasets show that the proposed network reaches a running speed of 125fps on a single GTX 1080Ti GPU for 352×352 images and uses only 3.68MB parameters to achieve an equivalent or even better performance than current state-of-the-art methods.
GAN-VAE: Elevate Generative Ineffective Image Through Variational Autoencoder

We present a general learning framework which combine a deep convolutional GANs network with a variational autoencoder network. To begin with, we found a solution which could solve the problem that the images create from generator of GANs are usually unsharp and breezing. In this case, GAN merge with VAE could be a better choice which will avoid to waste the resource of GPU or TPU. We put what we have done on https://github.com/charlesshowup/GAN-AE.
A general cardinalized probability hypothesis density filter

Abstract Based on random finite set, the probability hypothesis density (PHD) filter and the cardinalized PHD (CPHD) filter have been proposed for multitarget tracking as they are computational tractable. But the classical PHD and CPHD filter is not applicable when a target generates multiple detections. The general PHD filter was proposed to apply the PHD filter to arbitrary clutter and target measurement process. However, as a filter with better performance than the PHD filter, the CPHD filter for multiple-detection tracking is not proposed except the extended target CPHD filter and the multisensor CPHD filter. In our work, the general CPHD filter, which allows the clutter process and target measurement process to be arbitrary and performs better than the general PHD filter, is proposed. The proposed general CPHD filter is the general form of all kinds of CPHD filters, as well as all kinds of PHD filters and Bernoulli filters. In the simulation, the multiple-detection model of the over-the-horizon radar is used to demonstrate the tracking performance. The simulation results show that the proposed filter improves the accuracy of the state estimates and reduces the variance of the estimated number of targets.
Mathematical insights into the original Retinex algorithm for image enhancement

The Retinex theory, originally developed by Land and McCann as a computation model of the human color sensation, has become, with time, a pillar of digital image enhancement. In this area, the Retinex algorithm is widely used to improve the quality of any input image by increasing the visibility of its content and details, enhancing its colorfulness, and weakening, or even removing, some undesired effects of the illumination. The algorithm was originally described by its creators in terms of a sequence of image processing operations and was not fully formalized mathematically. Later, works focusing on aspects of the original formulation and adopting some of its principles tried to frame the algorithm within a mathematical formalism: this yielded every time a partial rendering of the model and resulted in several interesting model variants. The purpose of the present work is to fill a gap in the Retinex-related literature by providing a complete mathematical formalization of the original Retinex algorithm. The overarching goals of this work are to provide mathematical insights into the Retinex theory, promote awareness of the use of the model within image enhancement, and enable better appreciation of differences and similarities with later models based on Retinex principles. For this purpose, we compare our model with others proposed in the literature, paying particular attention to the work published in 2005 by Provenzi and others.
A Framework for Considering Uncertainty in Software Systems

There are many aspects involved in the development and operation of a software system, including system artefacts, activities, and infrastructure. Most of these aspects are vulnerable to uncertainty, which can result in risks to system quality and performance. Thus it is important to identify, represent and manage uncertainty in software systems. We hypothesise that using an underlying conceptual framework for characterising uncertainty can facilitate these activities. This paper demonstrates the use of an extensible framework, which defines a foundation for the systematic and explicit consideration of uncertainty in software systems. A software architecture case study is used to illustrate and evaluate the framework. A discussion of potential uses for the framework and future research is also provided.
Efficient Multi-Layer Stochastic Gradient Descent Algorithm for Federated Learning in E-health

E-health systems consist of intelligent devices, medical institutions, edge nodes, and cloud servers to improve healthcare service quality and efficiency. In e-health systems, patients’ data are cooperatively collected by their wearable devices and the hospital they have visited, i.e., vertically distributed data. The data on wearable devices share the same feature set but are different in sample spaces, i.e., horizontally partitioned data. Meanwhile, hospitals target various user groups resulting in high data diversity, i.e., non-identically distributed data. These three characteristics cause that existing federated learning frameworks cannot efficiently train models on medical data. Furthermore, model training in e-health is time-sensitive because some diseases mutate very quickly and spread easily, which requires fast convergence of machine learning algorithms. In this paper, we address the problem of how to efficiently and rapidly train global models on e-health data. Specifically, we propose a multilayer federated learning framework to cope with data that are vertically, horizontally, and non-identically distributed. Moreover, we develop a Multi-Layer Stochastic Gradient Descent (MLSGD) algorithm towards the proposed framework to learn the optimal global model. To improve training efficiency, partial models learned by devices are aggregated on edge nodes before exchanging intermediate results with hospitals. The weight of local models is proportional to local data size when performing global aggregation to balance the impact of local models on the global model. We also prove the convergence of the MLSGD algorithm from a theoretical perspective. The experimental results from the real-world dataset MIMIC-III validate that the proposed algorithm converges fast and achieves desired accuracy.