Reviewerly/exHarmony huggingface dataset:
Determination of Hue Saturation Value (HSV) color feature in kidney histology image

Abstract The kidney is organ that plays an important role in the body’s metabolism, especially the process of filtration and reabsorption of food waste. Currently the determination of kidney parts through kidney histology is still done manually by experts based on experience and knowledge. Therefore, to make it easier to determine the parts of the kidney, a histological image segmentation of the kidney was carried out. In the segmentation process, it is necessary to extract the color features of the parts of the kidney, namely the glomerulus and proximal tubule. The color features used are Hue, saturation, value (HSV) color space. The hue means the representation of color type. The saturation defines the amount of white color is mixed with hue. The value in HSV color space denotes the intensity or lightness or brightness of the color. The method consists of three steps such as pre-processing step, extraction feature HSV and statistical analysis. The result of statistical analysis showed that the hue and value features, glomerulus and proximal tubule had different ranges of values. However, the features of saturation, glomerulus and proximal tubule is overlap.
AED-Net: A Single Image Dehazing

In the past decade, significant research effort has been directed toward developing single-image dehazing algorithms.Despite this effort, dehazing continues to present a challenge, particularly in complex real-world cases.Indeed, it is an ill-posed problem because scene transmission depends on unknown and nonhomogeneous depth information.This paper proposes a novel end-to-end adaptive enhancement dehazing network (AED-Net) to recover clean scenes from hazy images.We evaluate it quantitatively and qualitatively against several state-of-the-art methods on three commonly used dehazing benchmark datasets as well as hazy real-world images.Moreover, we evaluated it against the top-scoring methods of the Codalab NTIRE 2021 competition based on the dehazing challenge dataset.Extensive computer simulations demonstrated that AED-Net outperforms state-of-the-art single-image haze removal algorithms in terms of PSNR, SSIM, and other key metrics.Furthermore, it improves image texture, detail edges, boosts image contrast and color fidelity.Finally, AED-Net is more effective under complex real-world conditions.
Neural blockchain technology for a new anticorruption token: towards a novel governance model

Blockchain technology currently represents a great opportunity for e-government in general and for public procurement in particular, given their financial implications and potential political and social risks. Blockchain technology facilitates the procedures and processes of administrative records via smart contracts because of properties such as timeproof sealing and data record immutability. In the present paper, we present a truthfulness governance approach which uses a permissioned model based on neural blockchain technology and smart contracts to create blocks within which all information is held in an on-chain consensus system to avoid corruption in the field of public procurement. Our proposal represents a scalable, efficient, innovative solution that is aligned with Sustainable Development Goal requirements and constitutes a ‘Decentralized Autonomous Organization’ in itself. Our model highlights the benefits of blockchain technology in terms of transparency, immutability, security, inclusiveness and disintermediation in order to create new anticorruption policies and technical solutions.
Dynamic Reconfiguration of Automotive Architectures Using a Novel Plug-and-Play Approach

Innovation cycles in the automotive industry are shortening due to influences from the IT world and trends such as automated driving. In contrast, the life cycles of the vehicles remain substantially longer. This gives rise to problems such as the lack of availability of essential components. In addition, new business cases are emerging, such as retrofitting functionality at the customer’s site. However, the electrical/electronical and software architectures of today’s vehicles do not offer the required degree of flexibility during runtime.This paper therefore presents a plug-and-play approach to dynamically reconfigure vehicle architectures. This includes the registration of new components such as ECUs or intelligent sensors/actuators in the vehicle, including the transfer of required software components to the vehicle’s software repository. The focus here is on the exchange of components based on socalled capabilities: As a consequence, it is not only possible to dynamically switch to another capability in the event of a failure, but also when a capability with higher-rated properties becomes available after adding new components. This approach is demonstrated using two cameras that are switched between at runtime. Our findings indicate that feasible switching times can be achieved and that the approach is therefore suitable for productive use.
Concurrent incorrectness separation logic

Incorrectness separation logic (ISL) was recently introduced as a theory of under-approximate reasoning, with the goal of proving that compositional bug catchers find actual bugs. However, ISL only considers sequential programs. Here, we develop concurrent incorrectness separation logic (CISL), which extends ISL to account for bug catching in concurrent programs. Inspired by the work on Views, we design CISL as a parametric framework, which can be instantiated for a number of bug catching scenarios, including race detection, deadlock detection, and memory safety error detection. For each instance, the CISL meta-theory ensures the soundness of incorrectness reasoning for free, thereby guaranteeing that the bugs detected are true positives.
EgoGlass: Egocentric-View Human Pose Estimation From an Eyeglass Frame

We present a new approach, EgoGlass, towards egocentric motion-capture and human pose estimation. EgoGlass is a lightweight eyeglass frame with two cameras mounted on it. Our first contribution is a new egocentric motion-capture device that adds next to no extra burden on the user and a dataset of real people doing a diverse set of actions captured by EgoGlass. Second, we propose to utilize body part information for human pose detection - to help tackle the problems of limited body coverage and self-occlusions caused by the egocentric viewpoint and cameras’ proximity to the human body. We also propose a concept of pseudo-limb mask as an alternative for segmentation mask when ground truth segmentation mask is absent for egocentric images with real subject. We demonstrate that our method achieves better results than the counterpart method without body part information on our dataset. We also test our method on two existing egocentric datasets: xR-EgoPose and EgoCap. Our method achieves state-of-the-art results on xR-EgoPose and is on par with existing method for EgoCap without requiring temporal information or personalization for each individual user.
Performance Evaluation Metrics and Approaches for Target Tracking: A Survey

Performance evaluation (PE) plays a key role in the design and validation of any target-tracking algorithms. In fact, it is often closely related to the definition and derivation of the optimality/suboptimality of an algorithm such as that all minimum mean-squared error estimators are based on the minimization of the mean-squared error of the estimation. In this paper, we review both classic and emerging novel PE metrics and approaches in the context of estimation and target tracking. First, we briefly review the evaluation metrics commonly used for target tracking, which are classified into three groups corresponding to the most important three factors of the tracking algorithm, namely correctness, timeliness, and accuracy. Then, comprehensive evaluation (CE) approaches such as cloud barycenter evaluation, fuzzy CE, and grey clustering are reviewed. Finally, we demonstrate the use of these PE metrics and CE approaches in representative target tracking scenarios.
A Perceptron Based Neural Network Data Analytics Architecture for the Detection of Fraud in Credit Card Transactions in Financial Legacy Systems

Credit card fraud, a significant and growing problem in commerce that costs the global economy billions of dollars each year, has kept up with technological advancements as criminals devise new and innovative methods to defraud account holders, merchants, and financial institutions. While traditional fraudulent methods involved card cloning, skimming, and counterfeiting during transactional processes, the rapid adoption and evolution of Internet technologies aimed at facilitating trade has given rise to new digitally initiated illegitimate transactions, with online credit card fraud beginning to outpace physical world transactions. According to the literature, the financial industry has used statistical methods and Artificial Intelligence (AI) to keep up with fraudulent card patterns, but there appears to be little effort to provide neural network architectures with proven results that can be adapted to financial legacy systems. The paper examines the feasibility and practicality of implementing a proof-of-concept Perceptron-based Artificial Neural Network (ANN) architecture that can be directly plugged into a legacy paradigm financial system platform that has been trained on specific fraudulent patterns. When using a credit checking subscription service, such a system could act as a backup.
Visualizing Classification Results: Confusion Star and Confusion Gear

Recent developments in machine learning applications are deeply concerned with the poor interpretability of most of these techniques. To gain some insights in the process of designing data-based models it is common to graphically represent the algorithm’s results, either in their final or intermediate stage. Specially challenging is the task of plotting multiclass classification results as they involve categorical variables (classes) rather than numeric results. Using the well-known MNIST dataset and a simple neural network as an example, this paper reviews the existing techniques to visualize classification results, from those centered on a particular instance or set of instances, to those representing an overall performance metric. As classification results are commonly summarized in the form of a confusion matrix, special attention is paid to its graphical representation. From this analysis, a new visualization tool is derived, which is presented in two forms: confusion star and confusion gear. The confusion star is centered on the classification errors, while the confusion gear focuses on the classification hits. The proposed visualization tools are also evaluated when facing: (i) balanced and imbalanced classifiers issues; (ii) the problem of representing errors with different orders of magnitude. By using shapes instead of colors to represent the value of each matrix cell, the new tools significantly improve the readability of the confusion matrices. Furthermore, we show how the area enclosed by the confusion stars and gears are directly related to standard classification metrics. The new graphic tools can be also usefully employed to visualize the performances of a sequence of classifiers.
Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison

Recommender systems can assist with decision-making by delivering a list of item recommendations tailored to user preferences. Context-aware recommender systems additionally consider context information and adapt the recommendations to different situations. A process of context matching, therefore, enables the system to utilize rating profiles in the matched contexts to produce context-aware recommendations. However, it suffers from the sparsity problem since users may not rate items in various context situations. One of the major solutions to alleviate the sparsity issue is measuring the similarity of contexts and utilizing rating profiles with similar contexts to build the recommendation model. In this paper, we summarize the context-aware collaborative filtering methods using context similarity, and deliver an empirical comparison based on multiple context-aware data sets.
Spatial-Temporal Attentive Motion Planning Network for Autonomous Vehicles

Different deep learning approaches have been devised for Autonomous Vehicle (AV) motion planning. However, most of such learning approaches rely on generic visual features. To efficiently generate feasible trajectories, we introduce Spatial-Temporal Attentive Motion Planning Network (STAMPNet). STAMPNet consists of AttentiveFC and Attentive 3D-CNN modules. The former encodes the trajectory histories, and the latter models the Spatial-temporal representations from videos. Then we fuse both attentive intermediate features and feed them into an LSTM that outputs planned trajectories. Experimental results demonstrate that SAMPNet effectively generates feasible trajectories for autonomous vehicles.
LandChain: A Blockchain Based Secured Land Registration System

The Fourth Industrial Revolution will be solely driven by a set of cutting-edge technologies. Countries who are well prepared and trained to adopt these technologies, surely have a better chance of success in near future. Blockchain is treated as one of the major fundamental infrastructure of this revolution. For its high potentiality, several countries have started exploring the effective use of blockchain technology. However, land registration management is one of the most appealing sector where blockchain works very efficiently. In Bangladesh, the current land registration system has some major drawbacks and blockchain has the ability to overcome those by offering its strong features. If we connect these two dots, that will intersect at a point where we can use blockchain for a secured and transparent land registration management in Bangladesh. In this paper, we propose a system based on blockchain that have the capability to dramatically reduce the time taken to sell or buy land property, prevent frauding, and provide a high level of security in ownership. Introducing this system in land management will assist the government in tax collection, service delivery, and other areas of governance.
Reservoir Computing Meets Extreme Learning Machine in Real-Time MIMO-OFDM Receive Processing

In this paper, we consider a real-time deep learning-based symbol detection approach for MIMO-OFDM systems. To exploit the temporal correlation of the wireless channel and the time-frequency structure of OFDM signals, a recurrent neural network (RNN) with deep feedforward output layers is introduced, where the recurrent layers and feedforward output layers are designed to process time-domain and frequency-domain information respectively. Reservoir computing (RC), a special type of RNN, and extreme learning machine (ELM), a special type of feedforward neural network, are chosen as the corresponding building blocks to facilitate over-the-air training. An online training loss objective is introduced to recursively update the neural weights in real-time. We believe this is the first work in the literature to realize real-time machine learning for MIMO-OFDM symbol detection, i.e., conducting NN-based symbol detection on an OFDM symbol basis. We demonstrate that (1) the <i>IEEE</i> standardized WiFi training sequence can be directly applied as the real-time training sequence (2) the symbol detection performance can be further improved by using our theoretically derived pilot pattern. Evaluation results show that our RC-ELM-based symbol detection method outperforms traditional model-based techniques as well as state-of-the-art learning-based approaches in highly dynamic channel environments for real-time symbol detection.
NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset

Real-time load information in public transport is of high importance for both passengers and service providers. Neural algorithms have shown a high performance on various object counting tasks and play a continually growing methodological role in developing automated passenger counting systems. However, the publication of public-space video footage is often contradicted by legal and ethical considerations to protect the passengers&#x2019; privacy. This work proposes an end-to-end Long Short-Term Memory network with a problem-adapted cost function that learned to count boarding and alighting passengers on a publicly available, comprehensive dataset of approx.13,000 manually annotated low-resolution 3D LiDAR video recordings (depth information only) from the doorways of a regional train. These depth recordings do not allow the identification of single individuals. For each door opening phase, the trained models predict the correct passenger count (ranging from 0 to 67) in approx.96&#x0025; of boarding and alighting, respectively. Repeated training with different training and validation sets confirms the independence of this result from a specific test set.
Relevance attack on detectors

This paper focuses on high-transferable adversarial attacks on detectors, which are hard to attack in a black-box manner, because of their multiple-output characteristics and the diversity across architectures. To pursue a high attack transferability, one plausible way is to find a common property across detectors, which facilitates the discovery of common weaknesses. We are the first to suggest that the relevance map from interpreters for detectors is such a property. Based on it, we design a Relevance Attack on Detectors (RAD), which achieves a state-of-the-art transferability, exceeding existing results by above 20%. On MS COCO, the detection mAPs for all 8 black-box architectures are more than halved and the segmentation mAPs are also significantly influenced. Given the great transferability of RAD, we generate the first adversarial dataset for object detection and instance segmentation, i.e., Adversarial Objects in COntext (AOCO), which helps to quickly evaluate and improve the robustness of detectors.
Big Data Testing Framework for Recommendation Systems in e-Science and e-Commerce Domains

Software testing is an important process to evaluate whether the developed software applications meet the required specifications. There is an emerging need for testing frameworks for big data software projects to ensure the quality of the big data applications and satisfy the user requirements. In this study, we propose a software testing framework that can be utilized in big data projects both in e-science and e-commerce. In particular, we design the proposed framework to test big data-based recommendation applications. To show the usability of the proposed framework, we provide a reference prototype implementation and use the prototype to test a big data recommendation application. We apply the prototype implementation to test both functional and non-functional methods of the recommendation application. The results indicate that the proposed testing framework is usable and efficient for testing the recommendation systems that use big data processing techniques.
A Multi-dimensional Aesthetic Quality Assessment Model for Mobile Game Images

With the development of the game industry and the popularization of mobile devices, mobile games have played an important role in people's entertainment life. The aesthetic quality of mobile game images determines the users' Quality of Experience (QoE) to a certain extent. In this paper, we propose a multi-task deep learning based method to evaluate the aesthetic quality of mobile game images in multiple dimensions (i.e. the fineness, color harmony, colorfulness, and overall quality). Specifically, we first extract the quality-aware feature representation through integrating the features from all intermediate layers of the convolution neural network (CNN) and then map these quality-aware features into the quality score space in each dimension via the quality regressor module, which consists of three fully connected (FC) layers. The proposed model is trained through a multi-task learning manner, where the quality-aware features are shared by different quality dimension prediction tasks, and the multi-dimensional quality scores of each image are regressed by multiple quality regression modules respectively. We further introduce an uncertainty principle to balance the loss of each task in the training stage. The experimental results show that our proposed model achieves the best performance on the Multi-dimensional Aesthetic assessment for Mobile Game image database (MAMG) among state-of-the-art image quality assessment (IQA) algorithms and aesthetic quality assessment (AQA) algorithms.
“Unified Mobile, Financial, and Information Literacy Toolkit”: A Social Innovation for Public Libraries to Alleviate Poverty in Developing Countries

Social innovations implemented by public libraries rarely alleviate poverty. Mobile payments (i.e., financial transactions over mobile phones) represent the most widely used solution to alleviate poverty in developing countries, provided that people living in poverty have mobile, financial, and information literacy. This article reports a 3-year-plus study of proposing, testing, customizing, and disseminating a practice-based, outcome-driven, and community-oriented social innovation in the form of a “unified mobile, financial, and information literacy toolkit” to public librarians in India, who can assess mobile, financial, and information literacy of the poor at once and enhance their mobile payment readiness. Public libraries can be a strategic partner of the United Nations and governments in developing countries for addressing the grand challenge of poverty in society.
Cybersickness Measurement and Evaluation During Flying a Helicopter in Different Weather Conditions in Virtual Reality

The conflicts between the perceived sensation of the different sensory systems can cause adverse effects which is known as motion sickness (MS) and the side effects of MS include nausea, dizziness, stomach awareness etc. Virtual reality sickness (also called Cybersickness or visually induced motion sickness (VIMS)) happens during exposure to a virtual environment when senses transfer conflicting sensation signals to the brain. The symptoms of Cybersickness are similar to motion sickness symptoms. The adverse effects of this common phenomenon can negatively affect the training outcome and benefits using VR, undermine users’ health and usefulness of simulators as it involves health risk and contributes to the increase of dropout rates. Therefore, to mitigate these issues, MS should be detected and measured. The primary objective of this study is to subjectively and objectively detect and quantify cybersickness level using a helicopter simulator. This study has also investigated the change in cybersickness self-reported scores in different weather conditions such as clear and stormy. Simulator sickness questionnaire (SSQ) has been employed for subjective scoring. This research also aimed to correlate SSQ scores with physiological data such as Galvanic Skin Response (GSR). The findings demonstrated that the SSQ total score (TS) has increased significantly from clear weather to stormy for the participants. There is also a positive correlation found between the change in TS and the amount of GSR but not significant.