Reviewerly/exHarmony huggingface dataset:
Adaptive provisioning for mobile cloud gaming at edges

Mobile cloud gaming (MCG), which is proposed to deliver high-quality gaming experience to users anywhere and anytime, suffers from tremendous wide-area traffic and long network delays. In order to shorten the delays and provide the gaming services in close proximity to end users, the mobile edge computing (MEC) is envisioned as a promising approach to enable related computations at the edge of the network. Unfortunately, the performance of MEC-enabled MCG highly depends on the provisioning of both gaming services and bandwidth, since the quality of gaming experience (QoE) is easily impaired by both delays and video frame rates. Furthermore, due to the erratic mobility of users, migrating the services accordingly actually decreases the QoE impairment, but incurs extra system cost, leading to the performance-cost tradeoff. In order to address these challenges, we jointly investigate adaptive service placement and bandwidth allocation for MEC-enabled MCG. Considering the system dynamics, we propose to minimize the QoE impairment in a long-term scope, under the constrained cost for migrations. We design an online two-layer iterative algorithm (OnTrial) to solve the problem. Theoretical analysis demonstrates that OnTrial achieves a near-optimal performance and the potential violation of the migration constraint is bounded. Extensive experiments with real-world traces confirm that OnTrial outperforms other algorithms regarding the long-term QoE impairment for games.
Perfectly convergent particle swarm optimisation in multidimensional space

In this paper a novel evolutionary algorithm, perfectly convergent particle swarm optimisation (PCPSO) has been proposed. This is an intelligent algorithm which does not get trapped in local minima by using personal best value along with new parameters and new velocity update equation for better exploration in the search space. Velocity clamping is used to avoid the particles from escaping the search space by preventing the initial explosion of particle velocity. The velocity clamping effectively help to control the maximum velocity of the particles by limiting the particle step size and hence the particle will have to perform further steps in the search space to travel the same distance which determines its search precision for exploration or exploitation and finally align them towards the true global minimum. Experimental results show that by using perfect convergent particle swarm optimisation (PCPSO) approach computational efficiency is increased as compared to other variants of PSO and finds fast true global minimum.
Model architecture and tile size selection for convolutional neural network training for non-small cell lung cancer detection on whole slide images

Recent advancements in Artificial-Intelligence-based computer vision systems demonstrated impressive image and pattern recognition capabilities. A special class of neural networks known as Convolutional Neural Networks are used in a wide variety of computer vision tasks such as image classification, object detection and autonomous driving. There is a huge potential to adopt such technology in the domain of pathology. Image data in pathology are considerably larger in size than in typical image recognition problems. Dissecting the image into smaller bits, known as tiling, are often carried out. This paper aims to compare and contrast common model architectures and input tile sizes systematically and find the optimal configuration in the context of lung cancer classification problems. A dataset composed of 87 annotated whole slide images of lung cancer specimens were collected and annotated by two pathologists. Annotated areas were grouped into four classes (Tumor area, Non-tumor area, Necrosis area, and Immune cells). Annotations were converted into labelled tiles at different tile sizes, from 296 to 10000 pixels, (74–2500 μm). The problem was framed as a supervised 4-class classification problem using deep learning. For each tile size, three models, VGG19, InceptionResnetv2 and EfficientNet b3, were trained. Model performances were measured on holdout dataset using standard quantitative metrics including F1-score and AUC-ROC. Our best model instance with tile size at 500 × 500 pixels (125 × 125 μm) achieved an F1-score at 0.9685 and AUC-ROC score at 0.9627. Our results showed that tile size had a significant impact on model performance. The optimal tile size was between 500 and 1000 pixel (125–250 μm) after both quantitative and qualitative assessments. VGG19 marginally outperformed other model architectures.
TAG: Gradient Attack on Transformer-based Language Models

Although distributed learning has increasingly gained attention in terms of effectively utilizing local devices for data privacy enhancement, recent studies show that publicly shared gradients in the training process can reveal the private training data (gradient leakage) to a third party.However, so far there hasn't been any systematic study of the gradient leakage mechanism of the Transformer based language models.In this paper, as the first attempt, we formulate the gradient attack problem on the Transformer-based language models and propose a gradient attack algorithm, TAG, to recover the local training data.Experimental results on Transformer, TinyBERT 4 , TinyBERT 6 , BERT BASE , and BERT LARGE using GLUE benchmark show that compared with DLG (Zhu et al., 2019), TAG works well on more weight distributions in recovering private training data and achieves 1.5× Recover Rate and 2.5× ROUGE-2 over prior methods without the need of ground truth label.TAG can obtain up to 88.9% tokens and up to 0.93 cosine similarity in token embeddings from private training data by attacking gradients on CoLA dataset.In addition, TAG is stronger than previous approaches on larger models, smaller dictionary size, and smaller input length.
Picture-wise just noticeable difference prediction model for JPEG image quality assessment

Introduction/purpose: The paper presents interesting research related to the performance analysis of the picture-wise just noticeable difference (JND) prediction model and its application in the quality assessment of images with JPEG compression. Methods: The performance analysis of the JND model was conducted in an indirect way by using the publicly available results of subject-rated image datasets with the separation of images into two classes (above and below the threshold of visible differences). In the performance analysis of the JND prediction model and image quality assessment, five image datasets were used, four of which come from the visible wavelength range, and one dataset is intended for remote sensing and surveillance with images from the infrared part of the electromagnetic spectrum. Results: The pap 86 er shows that using a picture-wise JND model, subjective image quality assessment scores can be estimated with better accuracy, leading to significant performance improvements of the traditional peak signal-to-noise ratio (PSNR). The gain achieved by introducing the picture-wise JND model in the objective assessment depends on the chosen dataset and the results of the initial simple to compute PSNR measure, and it was obtained on all five datasets. The mean linear correlation coefficient (for five datasets) between subjective and PSNR objective quality estimates increased from 74% (traditional PSNR) to 90% (picture-wise JND PSNR). Conclusion: Further improvement of the JND-based objective measure can be obtained by improving the picture-wise model of JND prediction.
CORY-Net: Contrastive Res-YOLOv5 Network for Intelligent Safety Monitoring on Power Grid Construction Sites

In power grid construction projects, ensuring the safety of construction workers by eliminating potential risks has always been important yet difficult.Thanks to the breakthrough in deep learning, it becomes possible to adopt deep learning based object detection technologies to enable intelligent safety monitoring on power grid construction sites.However, due to the complex terrain of the power grid construction site, there is a lack of a dataset of power grid construction scenes.In this paper, a high-quality power grid construction dataset is constructed.In order to reduce the expensive annotation work, we propose to adopt contrastive self-supervised learning to pretrain a feature extraction encoder and integrate it with YOLOv5 by modifying its backbone network for safety-related object detection.The proposed network is referred to as Contrastive Res-YOLOv5 Network (CORY-Net).Experimental results show that contrastive self-supervised pretraining can efficiently improve the object detection performance on the power grid construction dataset and speed up the convergence of the model.With only 1400 pretraining epochs, the proposed CORY-Net outperforms the original YOLOv5 by contributing 1.32% to the mAP@.5 and 3.54% to the mAP@.5:.95.Compared to the supervised object detection benchmarks, the proposed CORY-Net achieves the highest mAP@.5 in 9 out of 22 categories of targets and the highest overall mAP@.5.
Effective Integration of Distance Courses Through Project-Based Learning

The paper aims to single out critical success factors contributing to efficient implementation of distance learning practices and to explain the role of project-based learning in ensuring effective teaching of profession-oriented foreign language in a distance format. The authors argue in favor of distance learning as a beneficial educational tool providing for the training of large groups of people regardless of the place of residence and placing a priority on students’ individual capacities. Study design included a preliminary survey of 150 respondents specializing in the natural sciences and the humanities identifying the key analysis criteria, a complex of experimental online lessons incorporating project-based teaching principles, and a summarizing questionnaire verifying respondents’ post-experiment outlooks. The methodology of empirical research was based on a systematic approach and deployed the methods of pedagogical observation, project-based teaching, sociological research, and statistical analysis using Neural Designer software for calculating questionnaire results. Analyzing the obtained results, the authors conclude that: 1) Students specializing in the humanities have a higher motivation to study related minor subjects; 2) interdisciplinary integration using the English language allows to achieve a cumulative effect for major and related minor subjects; 3) interdisciplinary integration based on the project method is an effective means of improving academic performance in major subjects; 4) interdisciplinary integration based on the project method makes it possible to acquaint students with the best practices in their chosen specialty; 5) project method allows to achieve related didactic goals—self-expression, independent continuous learning and the formation of professional competencies.
Real-time object detector for low-end devices

YOLOv4-tiny is a lightweight network designed for low-end devices. It proposes a global model scaling technology, which uses the same scaling method with few convolution filters in each stage of the network, resulting in a small receptive field and low accuracy. To solve this problem, we use different scaling techniques in shallow and deep network stages. For the shallow network stage, the Simple-StemBlock scaling module is proposed to simplify network based on factors such as FLOPs and network fragmentation. The module can effectively reduce computation and improve the diversity of features. For the deep network stage, we consider depth of network and hardware resource constraints, the Depth-CSPBlock scaling module is designed to expand receptive field while keeping the computation as low as possible and layered residual connection is built in the module to enrich semantic information. Besides, mish activation function is adopted in the backbone for higher accuracy. The experimental results show that the accuracy of the proposed method achieves 23.2% AP on MSCOCO test-dev and 66.73 % AP50 on VOC datasets, compared with YOLOv4-tiny, the AP and AP50 increased by 3.3% and 3.7%, respectively. The speed of the proposed method on Jetson TX2 can reach 45.6 frames per second, which is 20% faster than YOLOv4-tiny.
A Scalable Computing Resources System for Remote Sensing Big Data Processing Using GeoPySpark Based on Spark on K8s

As a result of Earth observation (EO) entering the era of big data, a significant challenge relating to by the storage, analysis, and visualization of a massive amount of remote sensing (RS) data must be addressed. In this paper, we proposed a novel scalable computing resources system to achieve high-speed processing of RS big data in a parallel distributed architecture. To reduce data movement among computing nodes, the Hadoop Distributed File System (HDFS) is established on nodes of K8s, which are also used for computing. In the process of RS data analysis, we innovatively use the tile-oriented programming model instead of the traditional strip-oriented or pixel-oriented approach to better implement parallel computing in a Spark on Kubernetes (K8s) cluster. A large RS raster layer can be abstracted as a user-defined tile format of any size, so that a whole computing task can be divided into multiple distributed parallel tasks. The computing resources applied by users would be immediately assigned in the Spark on K8s cluster by simply configuring and initializing SparkContext through a web-based Jupyter notebook console. Users can easily query, write, or visualize data in any box size from the catalog module in GeoPySpark. In summary, the system proposed in this study can provide a distributed scalable resources system for assembling big data storage, parallel computing, and real-time visualization.
Gain-Some-Lose-Some: Reliable Quantification Under General Dataset Shift

When applying supervised learning to estimate class distributions of unlabelled samples (so-called quantification), dataset shift is an expected yet challenging problem. Existing quantification methods make strong assumptions on the nature of dataset shift that often will not hold in practice. We propose a novel Gain-Some-Lose-Some (GSLS) model that accounts for more general conditions of dataset shift. We present a method for fitting the GSLS model without any labelled instances from the target sample, and experimentally demonstrate that GSLS can produce reliable quantification prediction intervals under broader conditions of shift than existing quantification methods.
Interaction Design Patterns for Augmented Reality Fitting Rooms

In this work, we explore the role of augmented reality as a meta-user interface, with particular reference to its applications for interactive fitting room systems and the impact on the related shopping experience. Starting from literature and existing systems, we synthesized a set of nine interaction design patterns to develop AR fitting rooms and to support the shopping experience. The patterns were evaluated through a focus group with possible stakeholders with the aim of evaluating and envisioning the effects on the shopping experience. The focus group analysis shows as a result that the shopping experience related to an AR fitting room based on the proposed patterns is influenced by three main factors, namely: the perception of the utility, the ability to generate interest and curiosity, and the perceived comfort of the interaction and environment in which the system is installed. As a further result, the study shows that the patterns can successfully support these factors, but some elements that emerged from the focus group should be more investigated and taken into consideration by the designers.
Automatic Fake News Detection based on Deep Learning, FastText and News Title

As a range of daily phenomena, Fake News is quickly becoming a longstanding issue affecting individuals, public and private sectors.This major challenge of the connected and modern world can cause many severe and real damages such as manipulating public opinion, damaging reputations, contributing to the loss in stock market value and representing many risks to the global health.With the fast spreading of online misinformation, checking manually Fake News becomes ineffective solution (not obvious, difficult and takes a long time).The improvement of Deep Learning Networks (DLN) can support with high degree of accuracy and efficiency the classical processes of Fake News spotting.One of the keys improvement strategies are optimizing the Word Embedding Layer (WEL) and finding relevant Fake News predicting features.In this context, and based on six DLN architectures, FastText process as WEL and Inverted Pyramid as News Articles Pattern (IPP), the present paper focuses on the assessment of the first news article feature that is hypothesized as affecting the performances of fake news predicting: News Title.By assessing the impact that the Embedding Vector Size (EVS), Window Size (WS) and Minimum Frequency of Words (MFW) in News Titles corpus can have on DLN, the experiments carried out in this paper showed that the News Title feature and FastText process can have a significant improvement on DLN fake news detection with accuracy rates exceeding 98%.
Deep learning for prediction of population health costs

Accurate prediction of healthcare costs is important for optimally managing health costs. However, methods leveraging the medical richness from data such as health insurance claims or electronic health records are missing.Here, we developed a deep neural network to predict future cost from health insurance claims records. We applied the deep network and a ridge regression model to a sample of 1.4 million German insurants to predict total one-year health care costs. Both methods were compared to existing models with various performance measures and were also used to predict patients with a change in costs and to identify relevant codes for this prediction.We showed that the neural network outperformed the ridge regression as well as all considered models for cost prediction. Further, the neural network was superior to ridge regression in predicting patients with cost change and identified more specific codes.In summary, we showed that our deep neural network can leverage the full complexity of the patient records and outperforms standard approaches. We suggest that the better performance is due to the ability to incorporate complex interactions in the model and that the model might also be used for predicting other health phenotypes.
Convolutional Neural Network-Based Technique for Gaze Estimation on Mobile Devices

Eye tracking is becoming a very popular, useful, and important technology. Many eye tracking technologies are currently expensive and only available to large corporations. Some of them necessitate explicit personal calibration, which makes them unsuitable for use in real-world or uncontrolled environments. Explicit personal calibration can also be cumbersome and degrades the user experience. To address these issues, this study proposes a Convolutional Neural Network (CNN) based calibration-free technique for improved gaze estimation in unconstrained environments. The proposed technique consists of two components, namely a face component and a 39-point facial landmark component. The face component is used to extract the gaze estimation features from the eyes, while the 39-point facial landmark component is used to encode the shape and location of the eyes (within the face) into the network. Adding this information can make the network learn free-head and eye movements. Another CNN model was designed in this study primarily for the sake of comparison. The CNN model accepts only the face images as input. Different experiments were performed, and the experimental result reveals that the proposed technique outperforms the second model. Fine-tuning was also performed using the VGG16 pre-trained model. Experimental results show that the fine-tuned results of the proposed technique perform better than the fine-tuned results of the second model. Overall, the results show that 39-point facial landmarks can be used to improve the performance of CNN-based gaze estimation models.
An Efficient Outlier Detection and Classification Clustering-Based Approach for WSN

Wireless Sensor Network (WSN) is one of the main components of the Internet of things (IoT) for gathering information and monitoring the environment in a variety of applications (medical, agricultural, manufacturing, militarily, etc.). However, data collected and transferred from sensors to the base station are susceptible to have outliers. These outliers can occur due to sensor nodes itself or to the harsh environment where they are deployed. Thus, it is necessary for the WSN to be able to detect the outliers and take actions in order to ensure network quality of service (in terms of reliability, latency, etc.) and to avoid further degradation of the application efficiency. In this paper, we propose a distributed outlier detection and classification algorithm for WSN. Our approach is capable of distinguish between an error due to a faulty sensor and an error due to an interesting event. We take into consideration the spatial-temporal correlation between sensors&#x0027; data values and between neighbouring sensor nodes. Simulations with both synthetic and real datasets showed that our proposed approach outperforms other techniques by obtaining high Detection Rate (DR) and low False Alarm Rate (FAR).
A Decentralized Electronic Reporting Scheme with Privacy Protection Based on Proxy Signature and Blockchain

The electronic reporting system can alleviate the problems in terms of efficiency, content confidentiality, and reporter privacy imposed in the traditional reporting system. Relying on anonymity, the privacy of reporters can be protected, but the authentication of reporters with fake names should also be maintained. If authenticated anonymity is guaranteed, the reporters may still conduct misbehaviors such as submitting fake reports after the authentication. To address the above dilemma, we propose to apply a proxy signature to achieve authenticated anonymity and employ blockchain to maintain anonymity yet guarantee traceability for reporters’ misbehaviors. We also propose a new proxy signature scheme in this paper by module lattice for postquantum security. The extensive analysis justified our proposed scheme is secure and manageable.
Pseudo-Label-Free Weakly Supervised Semantic Segmentation Using Image Masking

Weakly-supervised semantic segmentation (WSSS) aims to train a semantic segmentation network using weak labels.Recent approaches generate the pseudo-label from the image-level label and then exploit it as a pixel-level supervision in the segmentation network training.A potential drawback of the conventional WSSS approaches is that the pseudo-label cannot accurately express the object regions and their classes, causing a degradation of the segmentation performance.In this paper, we propose a new WSSS technique that trains the segmentation network without relying on the pseudo-label.Key idea of the proposed approach is to train the segmentation network such that the object erased by the segmentation map is not detected by the classification network.From extensive experiments on the PASCAL VOC 2012 benchmark dataset, we demonstrate that our approach is effective in WSSS.