Reviewerly/exHarmony huggingface dataset:
Label noise and self-learning label correction in cardiac abnormalities classification

Objective. Learning to classify cardiac abnormalities requires large and high-quality labeled datasets, which is a challenge in medical applications. Small datasets from various sources are often aggregated to meet this requirement, resulting in a final dataset prone to label noise due to inter- and intra-observer variability and different expertise. It is well known that label noise can affect the performance and generalizability of the trained models. In this work, we explore the impact of label noise and self-learning label correction on the classification of cardiac abnormalities on large heterogeneous datasets of electrocardiogram (ECG) signals.Approach.A state-of-the-art self-learning multi-class label correction method for image classification is adapted to learn a multi-label classifier for electrocardiogram signals. We evaluated our performance using 5-fold cross-validation on the publicly available PhysioNet/Computing in Cardiology (CinC) 2021 Challenge data, with full and reduced sets of leads. Due to the unknown label noise in the testing set, we tested our approach on the MNIST dataset. We investigated the performance under different levels of structured label noise for both datasets.Main results.Under high levels of noise, the cross-validation results of self-learning label correction show an improvement of approximately 3% in the challenge score for the PhysioNet/CinC 2021 Challenge dataset and an improvement in accuracy of 5% and reduction of the expected calibration error of 0.03 for the MNIST dataset. We demonstrate that self-learning label correction can be used to effectively deal with the presence of unknown label noise, also when using a reduced number of ECG leads.
Lightweight Generative Network for Image Inpainting Using Feature Contrast Enhancement

With the help of the convolutional neural network (CNN) and generative adversarial network (GAN), image inpainting has achieved remarkable advances in performance. However, there are still some bottleneck problems to be solved, such as unnatural colors and heavy-weight network. In this paper, we propose a lightweight generative network for image inpainting using feature contrast enhancement. We achieve feature contrast enhancement based on dilated convolution and channel attention. Dilated convolution enlarges the receptive field of the convolution kernel with the same number of parameters, while channel attention successfully enhances feature contrast using adaptive weights. For loss function, we utilize color enhancement and symmetric mean absolute percentage error (SMAPE) losses to achieve image completion with high quality and natural color instead of the reconstruction losses. The proposed network needs only 4.5M parameters for image inpainting. Experimental results demonstrate that the proposed method outperforms state-of-the-art ones in terms of visual quality and quantitative measurements.
Query Integrity Meets Blockchain: A Privacy-Preserving Verification Framework for Outsourced Encrypted Data

Cloud outsourcing provides flexible storage and computation services for data users in a low cost, but it brings many security threats as the cloud server may not be fully trusted. Previous secure outsourcing solutions mostly assume that the server is honest-but-curious while the adversary model of a malicious server that may return incorrect results is rarely explored. Moreover, with the increasing popularity of verifiable computations, existing verification schemes are yet not efficient and cannot cater to different scenarios in practice. In this paper, we propose a blockchain-based verifiable search framework in the adversarial cloud outsourcing context. When outsourcing the encrypted data to the cloud or Interplanetary File System (IPFS), we also store the encrypted data index in a decentralized blockchain (i.e., Ethereum in this paper) which is public and cannot be modified. Once a user is authorized, he/she can flexibly obtain the query results and efficiently check the query integrity via the pre-deployed smart contract, without the need of the data owner being online. Moreover, for user&#x0027;s privacy protection, we construct a stealth authorization scheme to deliver the access authorization without any identity disclosure. Finally, theoretical analysis and performance evaluation validate the security and efficiency of our proposed framework.
Radar Target Tracking Based on Interactive Multi-model Cubature Kalman Filter

Aiming at the difficulty of model construction in the process of nonlinear maneuvering target tracking, the interactive multi-model algorithm based on CV and CT models is studied. Combined, an interactive multi-model cubature Kalman filter algorithm, which can be applied to the radar&#x2019;s need for air maneuvering target tracking, is designed. The simulation results show that compared with the single model CKF and IMM-UKF, the IMM-CKF has higher tracking accuracy and stronger model adaptability, and has important engineering application value.
Multi-Topic Categorization in a Low-Resource Ewe Language: A Modern Transformer Approach

The evolution of natural language processing (NLP) recently, paved the way for text categorization. With this mechanism, allocating a large volume of textual data to a category is much easier. This task is more challenging in dealing with multi-topic categorizations in a low-resource language. Transformer-based mechanisms have shown much strength in NLP tasks. However, low-resourced, low-data settings and a lack of benchmark datasets make it difficult to perform any NLP-related task in these extremely low-resource languages with data-points and dataset constraints. In this work, the authors focus on creating a new benchmark dataset for a low-resourced language and performed a multi-topic categorization using this dataset. We further propose an EweBERT model, which is built on the pre-trained transformer model known as Bidirectional Encoder Representations from Transformers (BERT) for multi topic categorization. The EweBERT is used to tokenize and represent the input articles as the initial stage in this system. The output of the EweBERT is then sent into a densely connected neural network, which classifies the articles according to six (6) diverse predefined topics. Experimental results prove that our proposed EweBERT-model records 86.2% accuracy, 85.6% F1-score micro, 85.4% F1-score macro, and F1-score mass of 85.7% compared with 3 benchmarked models.
Challenges and issues in energy efficient load balancing in the cloud computing environment

Cloud computing has played an important role in Information technology. Computational and storage needs are fulfilled by cloud providers around the globe by setting up data centers that involve thousands of servers (Xu, Li, Niu, &#x0026; Zhao, 2012). Different users operate many virtual machines, leading to more energy utilization and more power consumption. This issue can be resolved by the concept of load balancing; load balancing is an effective method for the system that works in a distributed method. This Load balancing concept helps in making cloud computing technology more efficient and fulfills the user needs more prominently. This paper will consider different load balancing algorithms and autoscaling to resolve load imbalance. We will compare different load balancing services of various cloud providers in the market.
Optimizing a Convolutional Neural Network using Particle Swarm Optimization

This study presents the application of the Particle Swarm Optimization (PSO) algorithm, a swarm algorithm which is based on the particle movement, to optimize the parameters of a Deep Neural Network (DNN), namely an architecture based on Convolutional Neural Networks (CNN). The model is optimized with respect to the image classification task on the MNIST dataset, consisting of images of handwritten digits. The study presents the results of training the model using different PSO hyperparameters and also compares the obtained performances with those obtained when training the model using gradient based optimizers such as Stochastic Gradient Descend (SGD) and Adam.
An Automated Learning Framework With Limited and Cross-Domain Data for Traffic Equipment Detection From Surveillance Videos

Traffic equipment detection from surveillance videos is of practical significance for temporary traffic element update in high-precision maps. However, there is little relative research developed due to limited labeled data. Based on a detector dubbed Faster R-CNN, we propose an automated learning framework that utilizes easy-to-obtain Internet images containing traffic equipment to acquire the capability of detecting traffic equipment from surveillance videos. In this framework, an appearance weighting module using a comprehensive feature aggregation method is designed to allow Faster R-CNN to converge and generalize quickly by taking limited data (i.e., less than 30 images per class) as input. To further address the cross-domain issue brought by the domain gap between the Internet images and the surveillance video frames, a domain adaptation learning scheme is developed, which aims to align the two domains and guide the framework to learn more robust domain-invariant features. Experimental results show that both the appearance weighting module and the domain adaptation learning scheme could bring a great performance improvement. Moreover, the combination of the two results in a state-of-the-art performance (mAP of 44.6%) even if only 30 training images per class are provided. To sum up, the proposed framework is suitable for traffic equipment detection from surveillance videos and provides an inspiration for other detection tasks with limited and cross-domain data, allowing humans to reduce their efforts and time required for arduous data collection and annotation.
A High-Throughput Full-Dataflow MobileNetv2 Accelerator on Edge FPGA

FPGA accelerators for lightweight neural networks, such as MobileNetv2, are of great need in edge computing applications with high throughput requirements. Dataflow architecture has been considered a promising approach to optimize throughput since the intermediate feature map transfers can be significantly saved. However, previous MobileNetv2 accelerators only achieved a partial-dataflow architecture, and just one-third of the feature map transfers can be saved. To solve this issue, we propose a scheme to achieve a full-dataflow MobileNetv2 accelerator on FPGA. The scheme contains four techniques. First, we improve the full-integer quantization for easier deployment on hardware. Second, we propose tunable activation weight imbalance transfer for less quantization accuracy loss. Third, we present several highly optimized accelerator components whose parallelism can be flexibly adjusted and implement residual connection with deeper FIFO so that the requirements of the full-dataflow architecture can be fully met. Finally, we present a computing resource allocation strategy to balance the latency of each layer, and a memory resource allocation strategy to effectively use the on-chip memory. Compared to the state-of-the-art, experimental results show that the accelerator achieves 1910 FPS with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.8\times $ </tex-math></inline-formula> speedup when implemented on the Xilinx ZCU102 FPGA. In addition, it reaches 72.98% Top-1 accuracy with 8-bit integer quantization that outperforms all the other MobileNetv2 accelerators.
Aspect Sentiment Triplet Extraction: A Seq2Seq Approach With Span Copy Enhanced Dual Decoder

Aspect Sentiment Triplet Extraction (ASTE) is a relatively new and very challenging task that attempts to provide an integral solution for aspect-based sentiment analysis. Aspect sentiment triplets in a sentence usually have overlaps when, e.g., one aspect is associated with multiple opinions and vice versa. Recently, end-to-end ASTE methods are becoming more and more popular for they can avoid the error propagation problem of pipeline-based methods. However, existing tagging-based end-to-end methods face difficulty to obtain a satisfactory recall, and generative methods fail to take a full account of the underlying interactions between aspects, opinions and their corresponding sentiments. In this paper, we formalize the ASTE task as a Seq2Seq learning problem with span copy mechanism for extracting multiple and possibly overlapped triplets. A novel dual decoder is devised purposefully for the ASTE task, where a multi-head attention based span copy mechanism is proposed to copy multi-token aspects and opinions. The dual decoder benefits from the rich output of encoder that can fuse multi-type information including word semantic, POS tag and BIO tag. Experiments on various benchmark datasets demonstrate that our approach achieves new state-of-the-art results. We also conduct analytical experiments to verify the effectiveness of various model components particularly for overlapped triplets extraction. We find that our model can be further improved through data augmentation and post-training.
Alice in Wonderland: The effects of body size and movement on children’s size perception and body representation in virtual reality

Previous work shows that in adults, illusory embodiment of a virtual avatar can be induced using congruent visuomotor cues. Furthermore, embodying different-sized avatars influences adults' perception of their environment's size. This study (N = 92) investigated whether children are also susceptible to such embodiment and size illusions. Adults and 5-year-old children viewed a first-person perspective of different-sized avatars moving either congruently or incongruently with their own body. Participants rated their feelings of embodiment over the avatar and also estimated the sizes of their body and objects in the environment. Unlike adults, children embodied the avatar regardless of visuomotor congruency. Both adults and children freely embodied different-sized avatars, and this affected their size perception in the surrounding virtual environment; they felt that objects were larger in a small body and vice versa in a large body. In addition, children felt that their body had grown in the large body condition. These findings have important implications for both our theoretical understanding of own-body representation, and our knowledge of perception in virtual environments.
Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

The combination of machine learning (ML) approaches in healthcare is a massive advantage designed at curing illness of millions of persons.Several efforts are used by researchers for detecting and providing primary phase insights as to cancer analysis.Lung cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the world.Lung disease is the unrestrained progress of irregular cells which begin off in one or both Lungs.The previous detection of cancer is not simpler procedure however if it can be detected, it can be curable, also finding the survival rate is a major challenging task.This study develops an Ant lion Optimization (ALO) with Deep Belief Network (DBN) for Lung Cancer Detection and Classification with survival rate prediction.The proposed model aims to identify and classify the presence of lung cancer.Initially, the proposed model undergoes min-max data normalization approach to preprocess the input data.Besides, the ALO algorithm gets executed to choose an optimal subset of features.In addition, the DBN model receives the chosen features and performs lung cancer classification.Finally, the optimizer is utilized for hyperparameter optimization of the DBN model.In order to report the enhanced performance of the proposed model, a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.
A Hybrid Clustering Method Based on the Several Diverse Basic Clustering and Meta-Clustering Aggregation Technique

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Evaluating Perceptual Hashing Algorithms in Detecting Image Manipulation Over Social Media Platforms

Perceptual hash is a fingerprint of features of multimedia content. Compared with crypto hash, perceptual hash shows many advantages when defending against image-based fake news attacks in terms of detecting deliberate image manipulation while still tolerating normal format or resolution changes conducted on user-uploaded images by content-hosting providers such as social media platforms. Previous research into perceptual hash has studied general image manipulation without considering legitimate image transformation by social media platforms. This paper evaluates and analyzes six state-of-the-art perceptual hash algorithms for detecting image manipulation over two major social media platforms: Facebook and Twitter. Our real-world image evaluation shows differences in the two platforms’ image processing and how the six algorithms perform in detecting image manipulation over these platforms. We also present a new approach to finding the optimal detection threshold for each perceptual hash algorithm in distinguishing the platform’s standard image processing from deliberate image manipulation.
CoReTM: An Approach Enabling Cross-Functional Collaborative Threat Modeling

Threat Modeling is a structured process to identify critical assets in an organization and the threats posed by adversarial agents. The goal of applying such a process is to achieve a shared understanding of the inherent risks and potential counter-measures that can be put in place. In practice, threat modeling is a collaborative process combining stakeholders' perceptions in a holistic view of the threat landscape. However, this paper points out that related work mainly focuses on adapting models to technical aspects of architectural decisions. Thus, non-technical stakeholders are not included in the process.This paper proposes CoReTM, a novel overarching approach to applying well-established threat modeling methodologies in a collaborative setting. The resulting approach allows organizations to extend threat modeling to non-technical stakeholders in an automated way while supporting on-site, remote, or hybrid operations in a synchronous or asynchronous fashion.
TriPlan: an interactive visual analytics approach for better tourism route planning

Continuous research and development of novel tourism routes is necessary for tourism service providers to improve the tourist experience and industrial competitiveness. However, the route planning is cumbersome due to the time-consuming, extensive, and costly field study. Most of the existing route planning studies focus on recommending tourism routes for users based on attraction characteristics or tourist behavior features, which are generally unexplainable due to the black-box approaches they use. Other solutions allow users to customize itineraries through an interactive interface but often lack guidance from the aspect of route evaluation or destination image perception. In this paper, we thoroughly discuss the requirements and design tasks with domain experts and propose TriPlan, an interactive visual analytics system that provides intuitive planning guidance for tourism product developers. We design and improve multiple coordinated visualizations to facilitate analysis from the perspectives of overall route pattern and individual destination image. We also develop a hierarchical planning view to display the structural information of a plan. In addition, we introduce an automatic route optimization algorithm and multiple interactions to assist users in optimizing and adjusting the itineraries. Finally, we evaluate the usability and effectiveness of our system through three case studies and quantitative and qualitative interviews with the domain experts on real-world datasets.
Sonarlizer xplorer

The advancement of artificial intelligence and the implementation of machine learning capabilities in programming languages such as Python, along with cloud services, allow researchers to apply methods to cluster and predict behaviors and patterns in software engineering data. On the other hand, these methods need a large amount of data in order to work with high accuracy in different contexts. This paper introduces Sonarlizer Xplorer: a tool that captures a large number of technical debt items and code metrics from public GitHub projects. Sonarlizer Xplorer is composed of two sub-tools. The first is Github Xplorer, responsible for mining public Github repositories from an initial project. The second is Sonarlizer, responsible for taking projects and analyzing them using SonarQube. We used the tool over four months, collecting technical debt items and code metrics on almost 46,000 public Java projects. In addition, we mined over 57 million repositories and 4 million users.
Merging smell detectors

Technical Debt estimation relies heavily on the use of static analysis tools looking for violations of pre-defined rules. Largely, Technical Debt principal is attributed to the presence of low-level code smells, unavoidably tying the effort for fixing the problems with mere coding inefficiencies. At the same time, despite their simple definition, the detection of most code smells is non-trivial and subjective, rendering the assessment of Technical Debt principal dubious. To this end, we have revisited the literature on code smell detection approaches backed by tools and developed an Eclipse plugin that incorporates six code smell detection approaches. The combined application of various smell detectors can increase the certainty of identifying actual code smells that matter to the development team. We also conduct a case study to investigate the agreement among the employed code smell detectors. To our surprise the level of agreement is quite low even for relatively simple code smells, threating the validity of existing TD analysis tools and calling for increased attention to the precise specification of code and design level issues.
User-Centered Evaluation and Design Suggestions for NFT Marketplaces

Non-Fungible Token (NFT) is a program similar to cryptocurrency that runs on blockchain. Due to its recent popularity, the user count increases every day. NFT, a relatively new technology compared to cryptocurrency brings many new terms and complicated user flows. The study was conducted to explore the motives of new NFT users, to identify the usability issues in those platforms that tend to be a barrier and to suggest relevant design approaches that help new users freely use the platform. A survey was conducted to identify the understanding of NFTs among various user groups. Further, usability testing and heuristic evaluation were carried out for the top 2 popular NFT marketplaces. The results show that the NFT platforms need more usability improvements, especially for first-time users. Based on the findings, relevant user-centered design approaches are suggested and are expected to improve the usability of the NFT platforms.