Reviewerly/exHarmony huggingface dataset:
Slim-FCP: Lightweight-Feature-Based Cooperative Perception for Connected Automated Vehicles

Cooperative perception provides a novel way to conquer the sensing limitation on a single automated vehicle and potentially improves driving safety. To reduce the transmission data volume, existing solutions use the intermediate data generated by convolutional neural network (CNN) models, namely, feature maps, to achieve cooperative perception. The feature maps are however too large to be transmitted by the current V2X technology. We propose a novel approach, called Slim-FCP, to significantly reduce the transmission data size. It enables a channelwise feature encoder to remove irrelevant features for a better compression ratio. In addition, it adopts an intelligent channel selection strategy through which only representative channels of feature maps are selected for transmission. To evaluate the effectiveness of Slim-FCP, we further define a recall-to-bandwidth (RB) ratio metric to quantitatively measure how the recall of object detection changes with respect to the available network bandwidth. Experiment results show that Slim-FCP reduces the transmission data size by 75%, compared with the best state-of-the-art solution, with a subtle loss on object detection’s recall.
Modifications for the Differential Evolution Algorithm

Differential Evolution (DE) is a method of optimization used in symmetrical optimization problems and also in problems that are not even continuous, and are noisy and change over time. DE optimizes a problem with a population of candidate solutions and creates new candidate solutions per generation in combination with existing rules according to discriminatory rules. The present work proposes two variations for this method. The first significantly improves the termination of the method by proposing an asymptotic termination rule, which is based on the differentiation of the average of the function values in the population of DE. The second modification proposes a new scheme for a critical parameter of the method, which improves the method’s ability to better explore the search space of the objective function. The proposed variations have been tested on a number of problems from the current literature, and from the experimental results, it appears that the proposed modifications render the method quite robust and faster even in large-scale problems.
Human Pose Estimation and Object Interaction for Sports Behaviour

In the new era of technology, daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds. To understand the scenes and activities from human life logs, human-object interaction (HOI) is important in terms of visual relationship detection and human pose estimation. Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained. Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures, occluded regions, and unsatisfactory detection of objects, especially small-sized objects. The existing HOI detection techniques are instance-centric (object-based) where interaction is predicted between all the pairs. Such estimation depends on appearance features and spatial information. Therefore, we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the HOI. Furthermore, we detect the human body parts by using the Gaussian Matric Model (GMM) followed by object detection using YOLO. We predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction algorithm. The interactions are linked with the human and object to predict the actions. The experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.
Recent Applications of Artificial Intelligence in Early Cancer Detection

Cancer is a deadly disease often caused by the accumulation of various genetic mutations and pathological alterations. The death rate can only be reduced when it is detected in the early stages because treatment of cancer when the tumor has not metastasized in many regions of the body is more effective. However, early cancer detection is fraught with difficulties. Advances in artificial intelligence (AI) have developed a new scope for efficient and early detection of such a fatal disease. AI algorithms have a remarkable ability to perform well on a variety of tasks that are presented or fed to the system. Numerous studies have produced machine learning and deep learning-assisted cancer prediction models to detect cancer from previously accessible data with better accuracy, sensitivity, and specificity. It has been observed that the accuracy of prediction models in classifying fed data as benign, malignant, or normal is improved by implementing efficient image processing techniques and data segmentation augmentation methodologies, along with advanced algorithms. In this review, recent AI-based models for the diagnosis of the most prevalent cancers in the breast, lung, brain, and skin have been analysed. Available AI techniques, data preparation, modeling processes, and performance assessments have been included in the review.
Fusing Attention Network Based on Dilated Convolution for Superresolution

Deep neural networks with different filters or multiple branches have achieved good performance for single superresolution (SR) in recent years. However, they ignore the high-frequency components of the multiscale context information of the low-resolution image. To solve this problem, we propose a fusing attention network based on dilated convolution (DFAN) for SR. Specifically, we first propose a dilated convolutional attention module (DCAM), which captures multiscale contextual information from different regions of LR images by locking multiple regions with different sizes of receptive fields. Then, we propose a multifeature attention block (MFAB), further focus on high-frequency components of multiscale contextual information, and extract more high-frequency features. Experimental results demonstrate that the proposed DFAN achieves performance improvements in terms of visual quality evaluation and quantitative evaluation.
The material politics of mobile virtual reality: Oculus, data, and the technics of sensemaking

This paper contributes to an increasing occupation in media studies with Mobile Virtual Reality (MVR) – a form of ‘wireless’ VR, where all necessary sensing componentry is built into the system’s headset and controllers. Our analysis focuses on the Quest series of devices, offered by Facebook-owned VR company Oculus. Through the philosophy of Gilbert Simondon, we argue that the Quest represents a ‘concretisation’ of VR – of VR becoming internally coherent and synergistic, enabling its mobility and use in varied contexts. We suggest that this process of concretisation is what enables it to generate vast amounts of data with the potential for use by Facebook’s advertising arm and in future product development.
Learning by Imagination: A Joint Framework for Text-Based Image Manipulation and Change Captioning

Image and text are dual modalities of our semantic interpretation. Changing images based on text descriptions allows us to imagine and visualize the world (a.k.a. text-based image manipulation (TIM)). In this paper, we introduce a framework that combines TIM with change captioning (CC) and utilizes the benefits of co-training. CC aims to describe what has changed in a scene and can be regarded as the inverse version of TIM where both tasks rely on generative networks. These generative networks can be regarded as data producers of each other and unlike previous methods, we discover that integrating their learning procedures can benefit both. Since the CC module describes differences between two images as text, the CC module can be used as evaluation criteria and provide feedback. Furthermore, we utilize a shared attention mechanism in TIM and CC modules to localize towards prominent regions as well as enabling a change-aware discriminator. In the opposite direction, the output image synthesized by the TIM module can be assessed with the CC module, by checking whether the ground truth text description can be redescribed. Following this insight, not only do we boost the training of the TIM module, but we also utilize the TIM module as additional supervision for the CC training. Experimental results show that our framework outperforms existing TIM methods on several datasets substantially and we achieve marginal improvements in the CC module. To our best knowledge, this is the first study dedicated to the joint training of TIM and CC tasks.
Study of Natural Scene Categories in Measurement of Perceived Image Quality

One challenge facing image quality assessment (IQA) is that current models designed or trained on the basis of exiting databases are intrinsically suboptimal and cannot deal with the real-world complexity and diversity of natural scenes. IQA models and databases are heavily skewed toward the visibility of distortions. It is critical to understand the wider determinants of perceived quality and use the new understanding to improve the predictive power of IQA models. Human behavioral categorization performance is powerful and essential for visual tasks. However, little is known about the impact of natural scene categories (SCs) on perceived image quality. We hypothesize that different classes of natural scenes influence image quality perception—how image quality is perceived is not only affected by the lower level image statistics and image structures shared between different categories but also by the semantic distinctions between these categories. In this article, we first design and conduct a fully controlled psychovisual experiment to verify our hypothesis. Then, we propose a computational framework that integrates the natural SC-specific component into image quality prediction. Research demonstrates the importance and plausibility of considering natural SCs in future IQA databases and models.
Cognitive Loading and Knowledge Hiding in Art Design Education: Cognitive Engagement as Mediator and Supervisor Support as Moderator

The aim of the study is to document a new predictor of knowledge hiding from the perspective of Art design trainers and Art design trainees in virtual training's and this study tends to add new theoretical insights into the body of literature. For this purpose, this study approached a sample of 500 respondents under a cross-sectional research design and respondents who have participated in virtual trainings or their trainings were at the final stage were recruited through the snowball sampling technique. The useable responses remained at 406 and these have been analyzed through SPSS for demographic analysis and Smart-PLS has been used to test the structural model, while a process macro has been used to test the moderation. Results indicate that cognitive loading has the potency to reduce the knowledge hiding behavior of the trainees. Similarly, it has been observed that cognitive loading increases the cognitive engagement of the trainees, and it moreover reduces the knowledge hiding tendency of trainees. In case of mediation, a partial mediation has been documented through the variance accounted for (VAF) approach while testing moderation. The role of supervisor support has not been found to be statistically significant.
Generative models with kernel distance in data space

Generative models dealing with modeling a joint data distribution are generally autoencoder, or GAN based. Both have pros and cons, generating blurry images or being unstable in training. We propose a new generative model resembling a classical GAN transforming Gaussian noise into data space without adversarial optimization. Training of the proposed model is a two-step procedure. First, we train an autoencoder-based architecture to model a data manifold. Second, we use the Latent Trick to map Gaussian noise into the autoencoder's latent space. The resulting Latent Cramer-Wold (LCW) generator achieves competitive generative scores. Elimination of adversarial training and replacing the discriminator with kernel methods results in a stable training procedure that is not prone to mode collapse. We also show that the introduced Latent Trick can improve the generative capabilities of other latent-based models. We validate the model on standard benchmarks and compare it to different approaches.
MATLAB tool for probability density assessment and nonparametric estimation

<h2>Abstract</h2> A MATLAB function is presented for nonparametric probability density estimation, based on an iterative method that employs the principle of maximum entropy and characteristic properties of single order statistics. Featuring a robust and adaptive design, the method is well-suited for high throughput applications. The implementation comprises a MATLAB interface and underlying C++ code with extensible components that can be easily integrated into third party software. The functionality includes plotting capabilities and model independent diagnostics featuring the scaled quantile residual that is invariant to sample size, distribution, and estimation method.
Automatic labeling of river restoration project documents based on project objectives and restoration methods

River restoration projects are widely implemented around the world. Such projects are usually complicated endeavors. Engineering, environmental, and social impacts must be considered under various constraints. Gaining lessons from existing projects would be valuable in new project planning. However, because such projects vary greatly in objectives and used methods, it is difficult to pinpoint relevant projects for reference, even if a large number of cases are collected in a decision-support system. In this study, machine-learning-based approaches were used to classify the description documents for river restoration projects. Based on the characteristics of such projects, a deep neural network (DNN) was developed to label project objectives, and a dictionary-based multilabel classification (MLC) method was developed to label project methods. The resulting labels were validated using 1400 project description documents. In labeling project objectives, the method resulted in a weighted average f1-score of 0.82 and 0.55 for the training and testing dataset, respectively. In labeling project methods, the f1-score was found to be 0.70. Both results indicate that the developed automatic labeling methods perform satisfactorily. The labels attached to the project documents enable project planners to conveniently find the relevant documents for reference and understand the relationships among the objectives and methods.
Particle Filter Design for Robust Nonlinear Control System of Uncertain Heat Exchange Process with Sensor Noise and Communication Time Delay

In this paper, a particle filter design scheme for a robust nonlinear control system of uncertain heat exchange process against noise and communication time delay is presented. The particle filter employs a cluster of particles and associated weights to approximate the posterior distribution of states and is capable of handling nonlinear and non-Gaussian issues. However, when the realistic given noise is much larger than that of the one modeled by the particle filter, the estimated posterior distribution is no longer reliable. Considering that, the exponential weights take the place of the original absolute particle weights in this paper, which act as an adjustment to the particle filter weights for a better state estimation. This adjustment for the weight of the particle filter takes into account the practical significance and can ensure the stability, tracking performance, and continuous operation of the control process incorporated with the particle filter. The simulation verifies the feasibility and usefulness of the method.
SE<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1338" altimg="si1.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math>M: A model for software effort estimation using pre-trained embedding models

Software effort estimation from requirements texts, presents many challenges, mainly in getting viable features to infer effort. The most recent Natural Language Processing (NLP) initiatives for this purpose apply context-less embedding models, which are often not sufficient to adequately discriminate each analyzed sentence. Contextualized pre-trained embedding models have emerged quite recently and have been shown to be far more effective than context-less models in representing textual features. This paper proposes evaluating the effectiveness of pre-trained embedding models, to explore a more effective technique for representing textual requirements, which are used to infer effort estimates by analogy. Generic pre-trained models went through a fine-tuning process for both approaches — context-less and contextualized. The generated models were used as input in the applied deep learning architecture, with linear output. The results were very promising, realizing that contextualized pre-trained embedding models can be used to estimate software effort based only on requirements texts. We highlight the results obtained to apply the contextualized pre-trained model BERT with fine-tuning, applied in a single repository containing different projects, whose Mean Absolute Error (MAE) value is 4.25 and the standard deviation is only 0.17. This represents a result very positive when compared to similar works. The main advantages of the proposed estimation method are reliability, the possibility of generalization, speed, and low computational cost. Such advantages are provided by the fine-tuning process, enabling to infer effort estimation for new or existing requirements.
Identifying Counterfeit Products using Blockchain Technology in Supply Chain System

With the advent of globalization and the evergrowing rate of technology, the volume of production as well as ease of procuring counterfeit goods has become unprecedented. Be it food, drug or luxury items, all kinds of industrial manufacturers and distributors are now seeking greater transparency in supply chain operations with a view to deter counterfeiting. This paper introduces a decentralized Blockchain based application system (DApp) with a view to identifying counterfeit products in the supply chain system. With the rapid rise of Blockchain technology, it has become known that data recorded within Blockchain is immutable and secure. Hence, the proposed project here uses this concept to handle the transfer of ownership of products. A consumer can verify the product distribution and ownership information scanning a Quick Response (QR) code generated by the DApp for each product linked to the Blockchain.
Utilizing Blockchain for IoT Privacy through Enhanced ECIES with Secure Hash Function

Blockchain technology has been widely advocated for security and privacy in IoT systems. However, a major impediment to its successful implementation is the lack of privacy protection regarding user access policy while accessing personal data in the IoT system. This work aims to preserve the privacy of user access policy by protecting the confidentiality and authenticity of the transmitted message while obtaining the necessary consents for data access. We consider a Modified Elliptic Curve Integrated Encryption Scheme (ECIES) to improve the security strength of the transmitted message. A secure hash function is used in conjunction with a key derivation function to modify the encryption procedure, which enhances the efficiency of the encryption and decryption by generating multiple secure keys through one master key. The proposed solution eliminates user-dependent variables by including transaction generation and verification in the calculation of computation time, resulting in increased system reliability. In comparison to previously established work, the security of the transmitted message is improved through a reduction of more than 12% in the correlation coefficient between the constructed request transaction and encrypted transaction, coupled with a decrease of up to 7% in computation time.
Memory-augmented Dynamic Neural Relational Inference

Dynamic interacting systems are prevalent in vision tasks. These interactions are usually difficult to observe and measure directly, and yet understanding latent interactions is essential for performing inference tasks on dynamic systems like forecasting. Neural relational inference (NRI) techniques are thus introduced to explicitly estimate interpretable relations between the entities in the system for trajectory prediction. However, NRI assumes static relations; thus, dynamic neural relational inference (DNRI) was proposed to handle dynamic relations using LSTM. Unfortunately, the older information will be washed away when the LSTM updates the latent variable as a whole, which is why DNRI struggles with modeling long-term dependences and forecasting long sequences. This motivates us to propose a memory-augmented dynamic neural relational inference method, which maintains two associative memory pools: one for the interactive relations and the other for the individual entities. The two memory pools help retain useful relation features and node features for the estimation in the future steps. Our model dynamically estimates the relations by learning better embeddings and utilizing the long-range information stored in the memory. With the novel memory modules and customized structures, our memory-augmented DNRI can update and access the memory adaptively as required. The memory pools also serve as global latent variables across time to maintain detailed long-term temporal relations readily available for other components to use. Experiments on synthetic and real-world datasets show the effectiveness of the proposed method on modeling dynamic relations and forecasting complex trajectories.
COGNITIVE BASED ELECTRIC POWER MANAGEMENT SYSTEM

An electric power network can be evolved into smart grids, which are measured by providing energy efficiency and improving the available resources. With the development of software and hardware elements, the decision-making mechanism of existing smart grids is transformed into more robust uninterrupted and economical energy management systems. In this study, a cognitive-based algorithm using dynamic energy management flexibility, storage and energy management algorithm and cloud computing architecture is proposed. Using this approach, an uninterrupted and economical energy management system can be planned. In addition, the proposed approach provides the optimization of supply and demand sides.
Panoptic Narrative Grounding

This paper proposes Panoptic Narrative Grounding, a spatially fine and general formulation of the natural language visual grounding problem. We establish an experimental framework for the study of this new task, including new ground truth and metrics, and we propose a strong baseline method to serve as stepping stone for future work. We exploit the intrinsic semantic richness in an image by including panoptic categories, and we approach visual grounding at a fine-grained level by using segmentations. In terms of ground truth, we propose an algorithm to automatically transfer Localized Narratives annotations to specific regions in the panoptic segmentations of the MS COCO dataset. To guarantee the quality of our annotations, we take advantage of the semantic structure contained in WordNet to exclusively incorporate noun phrases that are grounded to a meaningfully related panoptic segmentation region. The proposed baseline achieves a performance of 55.4 absolute Average Recall points. This result is a suitable foundation to push the envelope further in the development of methods for Panoptic Narrative Grounding.