Reviewerly/exHarmony huggingface dataset:
StoolNet for Color Classification of Stool Medical Images

The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians’ heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Thanks to its shallow structure and accurate segmentation, StoolNet can converge quickly. The sufficient experiments confirm the good performance of StoolNet and the impact of the different training sample numbers on StoolNet. The proposed method has several advantages, such as low cost, accurate automatic segmentation, and color classification. Therefore, it can be widely used in artificial intelligence (AI) healthcare.
An efficient blockchain‐based approach for cooperative decision making in swarm robotics

Swarm robotics is an emerging robotic domain that aims to accomplish complex tasks with the cooperative effort of a group of simple robots. These robots communicate and share information to coordinate their actions for achieving a common goal. However, existing mechanisms of control and communications are not very secure, efficient, and also rely on centralized controlling authority. It is not flexible, and also has low transparency and tracking capabilities. Blockchain, a decentralized technology based on peer to peer communication along with smart contracts, can be a potential contender for addressing these challenges. However, blockchains are resource‐intensive and not found suitable for resource‐constrained devices. We propose a blockchain‐based framework, which is based on Proof‐of‐Authority (PoA) consensus mechanism to ensure efficiency. We experiment with test‐bed implementation using RaspberryPi. The results obtained demonstrate that the PoA mechanism is not a resource‐hungry one (unlike other state‐of‐the‐art consensus mechanisms). The blockchain with PoA in swarm robotics can provide the necessary security, transparency, decentralization, reliability, autonomy, etc.
A Comprehensive Review on Face Recognition Methods and Factors Affecting Facial Recognition Accuracy

As of late, the need for biometric security framework is elevated for giving safety and security against frauds, theft, and so on. Face recognition has gained a significant position among all biometric-based systems. It can be used for authentication and surveillance to prove the identity of a person and detect individuals, respectively. In this paper, a point-by-point outline of some imperative existing strategies which are accustomed to managing the issues of face recognition has been introduced along with their face recognition accuracy and the factors responsible to degrade the performance of the study. In the first section of this paper, different factors that degrade the facial recognition accuracy have been investigated like aging, pose variation, partial occlusion, illumination, facial expressions, and so on. While in the second section, different techniques have been discussed that worked to mitigate the effect of discussed factors.
One-Shot PIR: Refinement and Lifting

We study a class of private information retrieval (PIR) methods that we call one-shot schemes. The intuition behind one-shot schemes is the following. The user's query is regarded as a dot product of a query vector and the message vector (database) stored at multiple servers. Privacy, in an information theoretic sense, is then achieved by encrypting the query vector using a secure linear code, such as secret sharing. Several PIR schemes in the literature, in addition to novel ones constructed here, fall into this class. One-shot schemes provide an insightful link between PIR and data security against eavesdropping. However, their download rate is not optimal, i.e., they do not achieve the PIR capacity. Our main contribution is two transformations of one-shot schemes, which we call refining and lifting. We show that refining and lifting one-shot schemes gives capacity-achieving schemes for the cases when the PIR capacity is known. In the other cases, when the PIR capacity is still unknown, refining and lifting one-shot schemes gives, for most parameters, the best download rate so far.
Veracity handling and instance reduction in big data using interval type-2 fuzzy sets

Within the aspect of big data, veracity refers to the existing uncertainty in the dataset. The continuous flow of unstructured data with unwanted noise may bring abnormality in the dataset making them unusable. In this paper, we propose a novel method to handle the veracity characteristic of the big data using the concept of footprint of uncertainty (FOU) in interval type-2 fuzzy sets (IT2 FSs). The proposed method helps in handling the veracity issue in big data and reduces the instances to a manageable extent. We have compared the results with the existing clustering based methods and examined the relationship between the clusters and the FOUs by comparing their centroids and defuzzified values. To scrutinize the validity of our results, we have also performed a number of additional experiments by appending extra instances to the datasets. To check its consistency and efficacy, the proposed methodology is assessed from three different aspects. Experimental result validates that the proposed method can suitably handle the veracity issue in big datasets and is efficient in reducing the instances.
Methods for measuring large shear strains in in-plane torsion tests

The in-plane torsion test achieves true strains far beyond 1.0 for sheet metals, especially using specimens with circular grooves. The accurate measurement of these high strains is a challenge for the conventional digital image correlation (DIC). Thus, the determination of flow curves is limited and fracture strains for very ductile materials cannot be measured. A new grooved specimen is introduced to avoid strain localization. Shear stress and shear strain along a defined area in the groove are constant so that strains can be measured independently of the DIC system setting without error due to strain localization. Furthermore, three methods for the measurement of very high shear strains in the in-plane torsion test are presented: Firstly, the limit of the optical strain measurement is extended by multiple renewal of the digital image correlation (DIC) pattern on the samples. Secondly, the shear strain for the planar specimen is calculated exactly from the rotation angle curve. Lastly, a new incremental method is presented. This method enables to determine shear strains for plane and grooved specimen exactly by only measuring the torque and the angle of rotation. All methods were applied for three steel sheet materials namely DP1000, CP1000 and DC04. The equivalent strain in a grooved in-plane torsion test of sheet steel DC04 was determined as 3.3 with the new incremental method. Such high strains far exceed conventional methods for determining the flow curve.
Human Activity Recognition Based on Evolution of Features Selection and Random Forest

Human Activity Recognition is a promising area having potential to benefit the human society by developing assistive technologies in order to aid elderly, chronically ill and for people with special needs. Accurate activity recognition is challenging because human activity is complex and highly diverse. A comparative study on Human Activity Recognition (HAR) dataset based on four methods, Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) with different features for selecting the best classifier among the models to test the dataset, has been carry out in this paper. The best classifier is choosing by accuracy of the model. We compare the result of dataset with and without important features selection by RF methods varImp(), Boruta, and Recursive Feature Elimination (RFE) to get the best accuracy. From the four methods, we found that the method RF have high accuracy from every group (98.16%, 98.09%, 93.6%), which is considered as a best classifier.
A novel method for tissue segmentation in high-resolution H&amp;E-stained histopathological whole-slide images

Tissue segmentation in whole-slide images is an important task in digital pathology, required for efficient and accurate computer-aided diagnostics. Precise tissue segmentation is particularly significant for a correct diagnosis in cases, when tissue structure of a specimen is very porous, such as skin specimens. In this paper, we addressed the problem of fore- and background segmentation in histopatological images of skin specimens stained with hematoxylin and eosin (H&E), which has not been solved yet, by a novel method based on a combination of statistical analysis, color thresholding, and binary morphology. We validated our algorithm on large extracts from 60 high-resolution whole slide images, with differing staining quality and captured under varying imaging conditions, from three laboratories. The size of extracts varies from 2000×1500 to 20000×30000 pixels and the number of images used in our study matches the number of H&E images used by other research teams. We compared our method to the published ones (GrabCut and FESI) and showed that our approach outperforms its counterparts (Jaccard index of 0.929 vs. 0.776 and 0.695).
An RGB Image Cipher Using Chaotic Systems, 15-Puzzle Problem and DNA Computing

The application of confusion and diffusion processes on the three individual components of an RGB image is not secure and efficient, so this problem needs to be addressed. In this paper, a novel RGB image cipher is proposed using chaotic systems, 15-puzzle artificial intelligence problem and DNA computing. First of all the given color image is decomposed into its red, green and blue gray scale images. Then these gray scale images are concatenated to make a single gray scale image. This single gray scale image is further divided into different blocks. A block level permutation (BLP) is proposed on this gray scale image by using the 15-puzzle problem. A pixel level permutation is applied to further randomize the image pixels. This confused image is then DNA encoded. Afterwards, a diffusion process is applied on this DNA encoded image. Lastly this DNA diffused image is converted back into the decimal. Further, this single gray scale image is broken into three gray scale images. These three images are combined to get the final color cipher image. To create the plaintext sensitivity, SHA 256 hash function has been used. Both the simulation and a comprehensive security analyses suggest the robustness and the impregnability of the proposed scheme which in turn signals towards the real world applicability of the scheme.
Distributed edge partitioning for trillion-edge graphs

We propose Distributed Neighbor Expansion (Distributed NE), a parallel and distributed graph partitioning method that can scale to trillion-edge graphs while providing high partitioning quality. Distributed NE is based on a new heuristic, called parallel expansion, where each partition is constructed in parallel by greedily expanding its edge set from a single vertex in such a way that the increase of the vertex cuts becomes local minimal. We theoretically prove that the proposed method has the upper bound in the partitioning quality. The empirical evaluation with various graphs shows that the proposed method produces higher-quality partitions than the state-of-the-art distributed graph partitioning algorithms. The performance evaluation shows that the space efficiency of the proposed method is an order-of-magnitude better than the existing algorithms, keeping its time efficiency comparable. As a result, Distributed NE can partition a trillion-edge graph using only 256 machines within 70 minutes.
An Evaluation of FaaS Platforms as a Foundation for Serverless Big Data Processing

Function-as-a-Service (FaaS), offers a new alternative to operate cloud-based applications. FaaS platforms enable developers to define their application only through a set of service functions, relieving them of infrastructure management tasks, which are executed automatically by the platform. Since its introduction, FaaS has grown to support workloads beyond the lightweight use-cases it was originally intended for, and now serves as a viable paradigm for big data processing. However, several questions regarding FaaS platform quality are still unanswered. Specifically, the impact of automatic infrastructure management on serverless big data applications remains unexplored.
Emerging Smart Home Technologies to Facilitate Engaging With Aging

Engaging with aging (EWA), the ongoing process of an individual to identify resources and implement adaptive strategies to maximize quality of life, may be enhanced by advances in information technology and adaptive upgrades to the built environment. Smart home technologies (SmHT) introduce passive monitoring features into the residential infrastructure to promote older adults' ability to manage day-to-day living and age in place. This article provides an overview of current and emerging SmHT and discusses opportunities to leverage this technology for enhancing the capacity of older adults to engage with their own aging. There are opportunities to create smart homes that enhance physical and cognitive capacity for older adults, but there are also ethical and practical challenges that will inform the design of future smart home systems. [Journal of Gerontological Nursing, 45(12), 41-48.].
Multi-stakeholder analysis to improve agricultural water management policy and practice in Malta

Malta faces a raft of water challenges which are negatively impacting on the sustainability of irrigated agriculture, and creating serious tensions with other sectors competing for water, including urban development, tourism and the environment. In this paper we argue for a transparent process centred on participatory stakeholder engagement to agree on the most challenging water-related risks and to identify solutions that both support the water governance framework and improve on-farm water management practices. Given Malta’s dependence on freshwater, this study focused on outdoor field-scale irrigated production. A three staged stakeholder-driven approach was developed. The first stage included Delphi analyses to identify the key constraints on water management and fuzzy cognitive mapping to enable stakeholders to analyse their mental models and formalise conceptual and causal relationships between different components impacting on Maltese agriculture. Secondly, questionnaires were used to inform understanding of national policy gaps in water management and thirdly, a “backcasting” stakeholder workshop was used to identify policy actions to achieve a more sustainable future for agriculture on the island. The study confirmed that Malta’s core challenge is tied to poor water governance and the need to define policies that are socially and environmentally acceptable and geared to tackling the complex water challenges the agricultural sector faces. Developing support for farmer training, knowledge translation, greater public awareness of the importance and value of water for high-value crop production and multi-sector collaboration to promote shared opportunities for water infrastructure investment were highlighted as potential solutions. The findings have direct relevance to other island communities where water scarcity poses serious agronomic risks to production and where agriculture underpins rural livelihoods and the economy.
MV-GCN: Multi-View Graph Convolutional Networks for Link Prediction

Link prediction is a demanding task in real-world scenarios, such as recommender systems, which targets to predict the unobservable links between different objects by learning network-structured data.In this paper, we propose a novel multi-view graph convolutional neural network (MV-GCN) model to solve this problem based on Matrix Completion method by simultaneously exploiting the interactive relationship and the content information of different objects.Unlike existing approaches directly concatenate the interactive and content information as a single view, the proposed MV-GCN improves the accuracy of the predictions by restricting the consistencies on the graph embedding from multiple views.Experimental results on six primary benchmark datasets, including two homogeneous datasets and four heterogeneous datasets, both show that MV-GCN outperforms the recent state-of-the-art methods.
Secure and Traceable Image Transmission Scheme Based on Semitensor Product Compressed Sensing in Telemedicine System

With the rapid development of the Internet of Things technology and the gradual upgrade of communication methods, a new type of telemedicine system encounters a golden opportunity for development. However, lots of portable sensors for medical health have generated a large amount of sensitive data while bringing convenience to people, which makes the original transmission scheme difficult to meet current transmission requirements. Aiming at realizing high efficiency of image transmission and high security of sensitive image data in the telemedicine system, this article proposes a secure and traceable image transmission scheme. The sensitive image is first compressed and encrypted by the optimized semitensor product compressed sensing algorithm. By the perceptual hash algorithm, the image hash value, also called image fingerprint, is generated from the image content to verify the authenticity of the image. All the image fingerprints are merged to form an image fingerprint chain, which provides the traceability of the image authentication. The digital watermarking technology is used to hide the encrypted sensitive image into a meaningful carrier image, which increases the transmission content without enlarging the transmission load. The experimental results show that the proposed image transmission scheme can effectively improve the transmission efficiency, the reconstruction effect, the security, and the authenticity of the image transmission in the telemedicine systems.
Big Data Analytics on Cyber Attack Graphs for Prioritizing Agile Security Requirements

In enterprise environments, the amount of managed assets and vulnerabilities that can be exploited is staggering. Hackers' lateral movements between such assets generate a complex big data graph, that contains potential hacking paths. In this vision paper, we enumerate risk-reduction security requirements in large scale environments, then present the Agile Security methodology and technologies for detection, modeling, and constant prioritization of security requirements, agile style. Agile Security models different types of security requirements into the context of an attack graph, containing business process targets and critical assets identification, configuration items, and possible impacts of cyber-attacks. By simulating and analyzing virtual adversary attack paths toward cardinal assets, Agile Security examines the business impact on business processes and prioritizes surgical requirements. Thus, handling these requirements backlog that are constantly evaluated as an outcome of employing Agile Security, gradually increases system hardening, reduces business risks and informs the IT service desk or Security Operation Center what remediation action to perform next. Once remediated, Agile Security constantly recomputes residual risk, assessing risk increase by threat intelligence or infrastructure changes versus defender's remediation actions in order to drive overall attack surface reduction.
Multimodal machine translation through visuals and speech

Abstract Multimodal machine translation involves drawing information from more than one modality, based on the assumption that the additional modalities will contain useful alternative views of the input data. The most prominent tasks in this area are spoken language translation, image-guided translation, and video-guided translation, which exploit audio and visual modalities, respectively. These tasks are distinguished from their monolingual counterparts of speech recognition, image captioning, and video captioning by the requirement of models to generate outputs in a different language. This survey reviews the major data resources for these tasks, the evaluation campaigns concentrated around them, the state of the art in end-to-end and pipeline approaches, and also the challenges in performance evaluation. The paper concludes with a discussion of directions for future research in these areas: the need for more expansive and challenging datasets, for targeted evaluations of model performance, and for multimodality in both the input and output space.
Obstacle Avoidance Drone by Deep Reinforcement Learning and Its Racing with Human Pilot

Drones with obstacle avoidance capabilities have attracted much attention from researchers recently. They typically adopt either supervised learning or reinforcement learning (RL) for training their networks. The drawback of supervised learning is that labeling of the massive dataset is laborious and time-consuming, whereas RL aims to overcome such a problem by letting an agent learn with the data from its environment. The present study aims to utilize diverse RL within two categories: (1) discrete action space and (2) continuous action space. The former has the advantage in optimization for vision datasets, but such actions can lead to unnatural behavior. For the latter, we propose a U-net based segmentation model with an actor-critic network. Performance is compared between these RL algorithms with three different environments such as the woodland, block world, and the arena world, as well as racing with human pilots. Results suggest that our best continuous algorithm easily outperformed the discrete ones and yet was similar to an expert pilot.
Learning Temporal Action Proposals With Fewer Labels

Temporal action proposals are a common module in action detection pipelines today. Most current methods for training action proposal modules rely on fully supervised approaches that require large amounts of annotated temporal action intervals in long video sequences. The large cost and effort in annotation that this entails motivate us to study the problem of training proposal modules with less supervision. In this work, we propose a semi-supervised learning algorithm specifically designed for training temporal action proposal networks. When only a small number of labels are available, our semi-supervised method generates significantly better proposals than the fully-supervised counterpart and other strong semi-supervised baselines. We validate our method on two challenging action detection video datasets, ActivityNet v1.3 and THUMOS14. We show that our semi-supervised approach consistently matches or outperforms the fully supervised state-of-the-art approaches.