Reviewerly/exHarmony huggingface dataset:
Multi-output regression using polygon generation and conditional generative adversarial networks

• The paper proposes accurate and representative multi-output regression method. • The method is based on polygon generation and deep generative modeling. • It learns the true distribution of complex industrial data. • The method expresses all interrelationships between data inputs and outputs (KPIs). • It is tested for KPIs’ prediction using data collected from an industrial plant. This paper proposes an innovative multi-output regression method that processes and converts the numeric data variables into representative images (polygons) to build accurate predictive models in industrial applications with several dependent variables (responses). In this method, polygon images are generated from both the inputs and outputs of numeric data. The images representing the data inputs are then translated into those representing the outputs by training a conditional generative adversarial network (cGAN). The output images of the trained cGAN are then mapped into the outputs to get back the predicted numeric values. The advantage of the proposed method is that it makes use of the breakthrough of deep generative modeling to learn the true distribution of complex data, which is difficult to determine in many industrial applications. This is attributed to the fact that the generated polygons express all interrelationships between the data variables in the form of trustworthy representational images used to train the cGAN model. The performance of the proposed method was validated successfully using a complex industrial dataset acquired from a black liquor recovery boiler (BLRB) in a Kraft pulp & paper mill located in Canada. Three key performance indicators (KPIs), one economic and two environmental, are used as regression outputs in the BLRB dataset. The results of the proposed method demonstrate better performance than other comparable machine learning regression methods.
Generating Optimal Class Integration Test Orders Using Genetic Algorithms

In recent years, many intelligent optimization algorithms have been applied to the class integration and test order (CITO) problem. These algorithms also have been proved to be able to efficiently solve the problem. Here, the design of fitness function is a key task to generate the optimal solution. To better solve the class integration and test order problem, we propose a new fitness function to generate the optimal solution that achieves a balanced compromise between the different measures (objectives) such as the total number of stubs and the total stubbing complexity in this paper. We used some programs to compare and evaluate the different approaches. The experimental results show that our proposed approach is encouraging to some extent in solving the class integration and test order problem.
A Blockchain-Based Verifiable User Data Access Control Policy for Secured Cloud Data Storage

Adding the adequate level of security of information systems dealing with sensitive data, privacy, or defense systems involves some form of access control. The audits performed are dealing with the determination of the allowed activities of the legal users, when attempting to access resources of the system. Usually, full access is provided after the user has been successfully authenticated through an authentication mechanism (e.g., password), while the corresponding authorization control is based on the confidentiality level of the respective resources and the authorization level assigned to each user. A very important diversification occurring in modern digital technologies is related to the identification based on blockchain technology, which is presented as a public, distributed data series, unable to modify its history and grouped in time-numbered blocks. In this work, a blockchain-based verifiable user data access control policy for secured cloud data storage is suggested for a version associated with big data in health care. It is an innovative system of applying classified access policies to secure resources in the cloud, which operates based on blockchain technology. System evaluation is carried out by studying a case in its resilience to Eclipse attack under different malicious user capabilities for routing table poisoning.
A Dual Channel Multiscale Convolution U-Net Methodfor Liver Tumor Segmentation from Abdomen CT Images

Nowadays, the automatic segmentation of liver tumor is needed for various medical applications like pathological detection of liver diseases, surgical planning, and postoperative evaluation. The liver is a massive, meaty organ on the right part of the abdomen that plays an important part in our digestive system. Cancerous tumors in the liver can cause a serious threat to human life. Currently, automatic liver tumor segmentation has a lot of problems, such as complex tissues, fusion of features of the same scale, insufficient learning of features, and irregular shapes of tumors. Typically, more networks are used to learn the important features, and fuse the features of same scale between encoders and decoders. But still, segmentation of tumor is one of the main challenges in the liver. Besides, soft organ removal, noise removal, and edge detection are important stages in the segmentation of liver tumor. Aiming at these problems, this paper introduced a Dual Channel Multiscale Convolution Unet (DCMC-Unet) to segment the liver tumor. An Intensity based threshold method (IBTM) is used for removing the soft organs, and Z-score normalization is utilized to eliminate the noise. The Enhanced sobel method (ESM) is used to enhance the local features of the liver. Finally, the enhanced features are fed to the DCMC-Unetmodel to segment the tumor from the liver. Experimental analysis illustrates that the DCMC-Unet approach attains higher segmentation results than existing models in terms of several qualitative metrics.
A Fusion Algorithm of Object Detection and Tracking for Unmanned Surface Vehicles

To provide reliable input for obstacle avoidance and decision-making, unmanned surface vehicles (USV) need to have the function of sensing the position of other USV targets in the process of cooperation and confrontation. Due to the small size of the target and the interference of the water and sky background, the current algorithms are prone to missed detection and drift problems when detecting and tracking USV. Therefore, in this paper, we propose a fusion algorithm of detection and tracking for USV targets. To solve the problem of vague features in the single-frame image, high-resolution and deep semantic information are obtained through a cross-stage partial network, and the anchor and convolution structure in the network has been improved given the characteristics of USV; besides, to meet the real-time requirements, the detected target is quickly tracked through correlation filtering, and the correlation characteristics of multi-frame images are obtained; then, the correlation characteristics are used to significantly reduce missed detection, and the tracking drift problems are corrected, combined with high-resolution semantic features of a single frame. Finally, the fusion algorithm is designed. In this paper, we constructed a picture dataset and a video dataset to test the effect of detection, tracking, and fusion algorithm separately, which proves the superiority of the fusion algorithm in this paper. The results show that, compared with a single detection algorithm and tracking algorithm, the fusion one can increase the success rate by more than 10%.
A Motion-Based Tracking System Using the Lucas-Kanade Optical Flow Method

In video monitoring system, many challenges will affect its performance in tracking the movement of people. It was not tested in Lucas-Kanade Optical if it is affected by illumination changes. The basic purpose of this research article is to build and evaluate a proposed system for tracking a person’s movement and motion under various lighting conditions. The system used Lucas-Kanade Optical Flow and Canny Edge Detector. One objective is to use Independent T-Test as its statistical method to compare the processing time of the proposed system concerning motion detection in three different lightings. In the result of data gathering of processing time, the t-statistical value in Sunny and Dim lighting was 12.80, 3.23 t-statistical value in Sunny and Dark lighting, and 11.31 t-statistical value in Dim and Dark lighting. From the gathered data, the researchers fulfilled the research objectives that the proposed system can track a person’s motion even if there is a minimal difference in the environment’s lighting.
Designing for Speech Practice Systems: How Do User-Controlled Voice Manipulation and Model Speakers Impact Self-Perceptions of Voice?

Can you speak the way you desire without feeling the pressure to conform to standards of speaking? In this study, we investigated the impact of user-controlled voice manipulation and listening to recordings of model speakers on self-perceptions of voice and speech. Quantitative analysis showed that there was a significant improvement in the perceived confidence of tone by listening to model speakers, but there were no significant improvements due to voice manipulation. Qualitative analysis of interviews revealed that participants responded positively to the visual and auditory feedback provided by the voice manipulation software. The participants also evaluated the quality of model speakers to decide whether or not they wanted to refer to them for speech practice. Based on the results of these analyses, we summarized the design implications for a speech practice system that would allow further investigation of the impact of the system on self-perceptions of speech performance.
Long-Range 3D Self-Attention for MRI Prostate Segmentation

The problem of prostate segmentation from Magnetic Resonance Imaging (MRI) is an intense research area, due to the increased use of MRI in the diagnosis and treatment planning of prostate cancer. The lack of clear boundaries and huge variation of texture and shapes between patients makes the task very challenging, and the 3D nature of the data makes 2D segmentation algorithms suboptimal for the task. With this paper, we propose a novel architecture to fill the gap be-tween the most recent advances in 2D computer vision and 3D semantic segmentation. In particular, the designed model retrieves multi-scale 3D features with dilated convolutions and makes use of a self-attention transformer to gain a global field of view. The proposed Long-Range 3D Self-Attention block allows the convolutional neural network to build significant features by merging together contextual information collected at various scales. Experimental results show that the proposed method improves the state-of-the-art segmentation accuracy on MRI prostate segmentation.
Adaptation of a Multi-Site Network to a New Clinical Site Via Batch-Normalization Similarity

This paper tackles the challenging problem of medical site adaptation; i.e., learning a model from multi-site source data such that it can be modified and adapted to a new site using only unlabeled data from the new site. The method is based on Domain Specific Batch Normalization architecture and uses the Batch Normalization statistics of the new site to find the most similar internal site. The similarity measure is computed in an embedded space of the BN parameters. We evaluated our method on the task of MRI prostate segmentation. Public datasets from six different institutions were used, containing distribution shifts. The experimental results show that the proposed approach outperforms other generalization and adaptation methods.
Recognizing Handwritten Offline Tamil Character by using cGAN &amp; CNN

The handwritten characters were being recognized by various people from different geographical locations for long period. In this, machine transcripts of human writings are important for transferring the statistics like political history, social life, financial life, religion, philosophy, and much more. Though this popularity of handwritten character recognition is achieved for a lot of languages such as English, Chinese, and Arabic, it is not achieved for Indian languages. The demanding situations explored through researchers in spotting a lot of curves, strokes, and holes in characters, massive character set, complicated letter structure, and less dataset. Generative Adversarial Network (GANs) is an interesting innovation in Deep Learning. Due to the least availability of the dataset for handwritten Tamil characters, GAN assists to boost the dataset. The enriched method referred to as GAN, is far feasible to get the specified quantity of dataset. The images can be conditionally generated by using a model generator of Conditional Generative Adversarial Network (cGAN). It is based upon a class label that permits the generation of a specific kind of image. A Convolutional Neural Network (CNN) is a class of artificial neural networks, used to recognize Tamil handwritten characters. The implementation of such methods has an accuracy of over 99 %. The proposed work would improve the accuracy by enhancing the dataset order of the Tamil Handwritten Character Recognition using cGAN and CNN.
Detection and classification of cervical cancer images using CEENET deep learning approach

Earlier detection of cervical cancer in women can save their lives before a chronic development. The accurate detection in cancer tissues of cervix in the human body is very important. In this article, cervical images were classified into either affected or healthy images using deep learning architecture. The proposed approach was designed with the modules of Edge detector, complex wavelet transform, feature derivation and Convolutional Neural Networks (CNN) architecture with segmentation. The edge pixels in the source cervical image were detected using Kirsch’s edge detector, the Complex Wavelet Transform (CWT) was there used to decompose the edge detected cervical images into number of sub bands. Local Derivative Pattern (LDP) and statistical features were computed from the decomposed sub bands and feature map was constructed using the computed features. The featured map along with the source cervical image was fed into the Cervical Ensemble Network (CEENET) model for classifying of cervical images into the classes healthy or cancer (affected).
Ghost-based Convolutional Neural Network for Effective Facial Expression Recognition

Facial expression recognition(FER) has been improved with convolutional neural network(CNN) in the last few years. In particular, the annotation of facial expressions is more complicated. Annotating each facial expression is prohibitively time-consuming and difficult task for psychologists. Therefore, each FER database only contains limited labeled samples, thus it is prone to overfitting when using CNN. In this paper, an effective neural network for FER is proposed, named Ghost-based Convolutional Neural Network (GCNN), which is to overcome overfitting. Ghost-module architecture utilizes a series of low-cost linear transformations to reduce parameters and generate more feature maps. With the Ghost-module architecture, GCNN can extract and classify the facial expression features effectively. Experiments show that this method beats all advanced methods on three FER databases, including RAF-DB, FER2013 and FERPlus databases.
Integrating Gaze and Speech for Enabling Implicit Interactions

Gaze and speech are rich contextual sources of information that, when combined, can result in effective and rich multimodal interactions. This paper proposes a machine learning-based pipeline that leverages and combines users' natural gaze activity, the semantic knowledge from their vocal utterances and the synchronicity between gaze and speech data to facilitate users' interaction. We evaluated our proposed approach on an existing dataset, which involved 32 participants recording voice notes while reading an academic paper. Using a Logistic Regression classifier, we demonstrate that our proposed multimodal approach maps voice notes with accurate text passages with an average F1-Score of 0.90. Our proposed pipeline motivates the design of multimodal interfaces that combines natural gaze and speech patterns to enable robust interactions.
Session-Based Recommendation with GNN and Time-Aware Memory Network

The goal of the session-based recommendation system (SBRS) is to predict the user’s next behavior based on anonymous sessions. Since long-term historical information of users is not available, deep learning technology has become the mainstream technology in session-based recommendation systems instead of traditional content-based recommendation methods. However, most SBRS methods only consider the session itself, ignoring the collaborative information from other sessions. Even if some SBRS models consider collaborations between sessions, they mostly use the click order to calculate the similarity only and ignore the time the user spends on different items, which might imply the user’s varying interest on these items. In this paper, we propose a session-based recommendation model with GNN and time-aware memory networks (SR-GTM), which learns the user’s interest representation by combining the information from the session itself and the collaborative information from relevant neighbor sessions. Specifically, SR-GTM mainly includes inner feature extraction module (IFEM) and outer feature extraction module (OFEM). IFEM uses GNN to learn the session features based on its item sequence, and OFEM uses a memory network with dwell time information encoded to extract collaborative information. Finally, SR-GTM aggregates IFEM and OFEM by the gating mechanism and then decodes the output by a softmax layer to obtain the recommendation score for each candidate item. Experiments on three public datasets Yoochoose1/64, Yoochoose1/4, and RetailRocket show that SR-GTM achieves optimal performance compared with other state-of-the-art methods. More specifically, SR-GTM has improvements of 0.77%, 0.38%, and 3.63% over the best baseline method in P@20 and has improvements of 2.91%, 2.52%, and 2.49% in MRR@20, respectively.
Lateralized Learning to Solve Complex Boolean Problems

Modern classifier systems can effectively classify targets that consist of simple patterns. However, they can fail to detect hierarchical patterns of features that exist in many real-world problems, such as understanding speech or recognizing object ontologies. Biological nervous systems have the ability to abstract knowledge from simple and small-scale problems in order to then apply it to resolve more complex problems in similar and related domains. It is thought that lateral asymmetry of biological brains allows modular learning to occur at different levels of abstraction, which can then be transferred between tasks. This work develops a novel evolutionary machine-learning (EML) system that incorporates lateralization and modular learning at different levels of abstraction. The results of analyzable Boolean tasks show that the lateralized system has the ability to encapsulate underlying knowledge patterns in the form of building blocks of knowledge (BBK). Lateralized abstraction transforms complex problems into simple ones by reusing general patterns (e.g., any parity problem becomes a sequence of the 2-bit parity problem). By enabling abstraction in evolutionary computation, the lateralized system is able to identify complex patterns (e.g., in hierarchical multiplexer (HMux) problems) better than existing systems.
Identity Preserving Loss for Learned Image Compression

Deep learning model inference on embedded devices is challenging due to the limited availability of computation resources. A popular alternative is to perform model inference on the cloud, which requires transmitting images from the embedded device to the cloud. Image compression techniques are commonly employed in such cloud-based architectures to reduce transmission latency over low bandwidth networks. This work proposes an end-to-end image compression framework that learns domain-specific features to achieve higher compression ratios than standard HEVC/JPEG compression techniques while maintaining accuracy on downstream tasks (e.g., recognition). Our framework does not require fine-tuning of the downstream task, which allows us to drop-in any off-the-shelf downstream task model without retraining. We choose faces as an application domain due to the ready availability of datasets and off-the-shelf recognition models as representative downstream tasks. We present a novel Identity Preserving Reconstruction (IPR) loss function which achieves Bits-Per-Pixel (BPP) values that are ~ 38% and ~ 42% of CRF-23 HEVC compression for LFW (low-resolution) and CelebA-HQ (high-resolution) datasets, respectively, while maintaining parity in recognition accuracy. The superior compression ratio is achieved as the model learns to retain the domain-specific features (e.g., facial features) while sacrificing details in the background. Furthermore, images reconstructed by our proposed compression model are robust to changes in downstream model architectures. We show at-par recognition performance on the LFW dataset with an unseen recognition model while retaining a lower BPP value of ~ 38% of CRF-23 HEVC compression.
Put a Label On It! Approaches for Constructing and Contextualizing Bar Chart Physicalizations

Physicalizations represent data through their tangible and material properties. In contrast to screen-based visualizations, there is currently very limited understanding of how to label or annotate physicalizations to support people in interpreting the data encoded by the physicalization. Because of its spatiality, contextualization through labeling or annotation is crucial to communicate data across different orientations. In this paper, we study labeling approaches as part of the overall construction process of bar chart physicalizations. We designed a toolkit of physical tokens and paper data labels and asked 16 participants to construct and contextualize their own data physicalizations. We found that (i) the construction and contextualization of physicalizations is a highly intertwined process, (ii) data labels are integrated with physical constructs in the final design, and (iii) these are both influenced by orientation changes. We contribute with an understanding of the role of data labeling in the creation and contextualization of physicalizations.
News Informatics: Engaging Individuals with Data-Rich News Content through Interactivity in Source, Medium, and Message

This paper introduces the concept of "news informatics" to refer to journalistic presentation of big data in online sites. For users to be engaged with such data-driven public information, it is important to incorporate interactive tools so that each person can extract personally relevant information. Drawing upon a communication model of interactivity, we designed a data-rich site with three different types of interactive features—namely, modality interactivity, message interactivity, and source interactivity—and empirically tested their relative and combined effects on user engagement and user experience with a 2 (modality) × 3 (source) × 2 (message) field experiment (N =166). Findings shed light on how interface designers, online news editors and journalists can maximize user engagement with data-rich news content. Certain interactivity combinations are found to be better than others, with a structural equation model (SEM) revealing the underlying theoretical mechanisms and providing implications for the design of news informatics.