Reviewerly/exHarmony huggingface dataset:
Development, experimental validation, and comparison of interval type-2 Mamdani fuzzy PID controllers with different footprints of uncertainty

Interval Type-2 Fuzzy PID (IT2FPID) controllers use Interval Type-2 Fuzzy Sets (IT2FSs). The difference between Type-1 Fuzzy PID (T1FPID) controllers and IT2FPID controllers is the Footprint of Uncertainty (FOU) which occurs due to the use of IT2FSs. FOU gives an extra degree of freedom to the controller structure, which is capable of handling uncertainty in the plant. However, the effect of FOU on the analytical structures of the IT2FPID controllers is not yet explored when uniformly/non-uniformly distributed multiple IT2FSs are used on the input side and uniformly/non-uniformly distributed multiple singleton fuzzy sets are considered on the output side of the controller. In this paper, controllers with three different types of FOUs and two different defuzzification methods are considered, and their analytical structures are derived. The suitability of the controllers is examined, and the properties of the suitable controllers are explored. Effects of FOU on the nonlinearity, gain, and computational aspects of the suitable controllers are studied. The applicability of proposed IT2FPID controllers is shown through simulation and real-time studies. The effectiveness of the proposed controllers is depicted by comparing their performances with that of the recently developed Integer Order Fuzzy PID (IOFPID) and Fractional Order Fuzzy PID (FOFPID) controllers.
Controlling Soft Robotic Arms Using Continual Learning

Learning-based modeling and control of soft robots is advantageous due to neural network’s ability to capture complex dynamical effects with low computational cost. Continual Learning techniques add further value to these methods by allowing networks to learn from continuously available data without incurring into catastrophic forgetting. In the context of soft robotic control, such capability can be exploited to design controllers able to continuously adapt to changes in robot dynamics, frequently due to material degradation or external interactions. This should be done without forgetting the control under normal working conditions which can be recovered as soon as the external interactions return to normal. In this letter elastic weight consolidation is used to continuously re-tune a neural network-based controller while changing the external loading of a soft robot. We demonstrate experimentally on a soft robot arm that this method outperforms plain stochastic gradient descent in tracking tasks, in the context of a continuously changing loading condition. We also show that the proposed control architecture can improve its performances when exposed to loading conditions already experienced. This letter represents a first step towards the introduction of continual learning methods in the soft robot control field.
Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy

Pathological examination is the gold standard for breast cancer diagnosis. The recognition of histopathological images of breast cancer has attracted a lot of attention in the field of medical image processing. In this paper, on the base of the Bioimaging 2015 dataset, a two-stage nuclei segmentation strategy, that is, a method of watershed segmentation based on histopathological images after stain separation, is proposed to make the dataset recognized to be the carcinoma and non-carcinoma recognition. Firstly, stain separation is performed on breast cancer histopathological images. Then the marker-based watershed segmentation method is used for images obtained from stain separation to achieve the nuclei segmentation target. Next, the completed local binary pattern is used to extract texture features from the nuclei regions (images after nuclei segmentation), and color features were extracted by using the color auto-correlation method on the stain-separated images. Finally, the two kinds of features were fused and the support vector machine was used for carcinoma and non-carcinoma recognition. The experimental results show that the two-stage nuclei segmentation strategy proposed in this paper has significant advantages in the recognition of carcinoma and non-carcinoma on breast cancer histopathological images, and the recognition accuracy arrives at 91.67%. The proposed method is also applied to the ICIAR 2018 dataset to realize the automatic recognition of carcinoma and non-carcinoma, and the recognition accuracy arrives at 92.50%.
Cryptoanalysis on the image encryption scheme based on interference and an amplitude mask

In this paper, the security of the optical image cryptosystem based on interference and an amplitude mask has been evaluated. The silhouette problem existing in the conventional interference-based cryptosystem is removed by eradicating the dependence of two phase-only masks (POM), which fixed the security leak. Moreover, the key space is enlarged by generating the amplitude mask in the encryption path as an additional private key. Since the number of unknown keys increases, the improved cryptosystem could be immune to the phase-retrieval technique-based iterative attack which the classical interference-based scheme is vulnerable to. However, the random phase mask (RPM) used as phase lock is predefined in the encryption path and irrelative to the plaintext. According to this finding, hybrid algorithms including a known-plaintext attack (KPA) and phase-retrieval technique-based iterative processes with different constraints have been proposed to crack the security-enhanced cryptosystem based on interference and an amplitude mask. Numerical simulations have been carried out to demonstrate that the silhouette problem existing in this enhanced scheme could be released by our proposed attacks.
Immersive machine learning for social attitude detection in virtual reality narrative games

Abstract People can understand how human interaction unfolds and can pinpoint social attitudes such as showing interest or social engagement with a conversational partner. However, summarising this with a set of rules is difficult, as our judgement is sometimes subtle and subconscious. Hence, it is challenging to program Non-Player Characters (NPCs) to react towards social signals appropriately, which is important for immersive narrative games in Virtual Reality (VR). We collaborated with two game studios to develop an immersive machine learning (ML) pipeline for detecting social engagement. We collected data from participants-NPC interaction in VR, which was then annotated in the same immersive environment. Game design is a creative process and it is vital to respect designer’s creative vision and judgement. We therefore view annotation as a key part of the creative process. We trained a reinforcement learning algorithm (PPO) with imitation learning rewards using raw data (e.g. head position) and socially meaningful derived data (e.g. proxemics); we compared different ML configurations including pre-training and a temporal memory (LSTM). The pre-training and LSTM configuration using derived data performed the best (84% F1-score, 83% accuracy). The models using raw data did not generalise. Overall, this work introduces an immersive ML pipeline for detecting social engagement and demonstrates how creatives could use ML and VR to expand their ability to design more engaging experiences. Given the pipeline’s results for social engagement detection, we generalise it for detecting human-defined social attitudes.
Enhancing Security with In-Depth Analysis of Brute-Force Attack on Secure Hashing Algorithms

Brute-force attack is considered one of the oldest attacks in the entire history of cybersecurity. Amid the diverse cyberattacks, the brute-force technique or exhaustive search has always gained its importance when it comes to access data in an unauthorized manner. Slowly and gradually, a lot more variants of brute-force have been released by the cybercriminals as they have also evolved. Alternatively, hashing has also evolved timely as a countermeasure of various cyberattacks. All variants of the SHA are illustrated here in this paper such as SHA-0, SHA-1, SHA-512, and SHA-256. SHA is also very popular as a network peer-to-peer technology blockchain also uses this for its security. The objective of the paper is to attain a measure of enhanced security. This paper discusses the in-depth analysis of brute-force attack on the secure hashing algorithms. Moreover, a study of diverse brute-force attacks is also highlighted in this paper.
Photonic Tensor Core with Photonic Compute-in-Memory

Here we demonstrate a photonic tensor core based on a silicon photonics dot-product engine. Utilizing compact electronic phase-change-material based photonic memory and WDM we show the highest throughput density to date of 3.8 MAC/s/mm 2 .
A scalable blockchain-based scheme for traffic-related data sharing in VANETs

Recent advances in wireless technology and embedded systems enable vehicles to share relevant traffic-related data to improve the transportation Quality-of-Service (QoS). However, due to the ubiquitousness of cyber-attacks, it is challenging to ensure the integrity of the data collected from cars. This paper proposes a novel architecture for road traffic events management in Vehicular Ad hoc NETworks (VANETs) relying on a permissioned blockchain. It also introduces the concept of micro-transactions to minimize communication and storage overhead. Through simulations, a rigorous performance evaluation of the proposed approach was conducted, and the micro-transactions effectiveness was assessed. In addition, a comparison with close works in the literature was performed. The proposed scheme ensures road traffic records integrity and traceability, and simulation results on the considered scenarios showed good performance.
An Medical Image File Formats and Digital Image Conversion

Medical image file formats make the confusing aspect to young researchers who start work with medical images.Medical image format conversion is still a tedious task due to the different structure of files. Digital image conversion from the medical image is an important pre-processing step to the process and visualizes the data. This article presents an overview of the major medical image file formats such as Analyze, neuro-imaging informatics technology initiative (NIFTI), MINC, and digital imaging and communications in medicine (DICOM). Then the characteristics and strengths of the various formats are discussed. Also the article describes the easiest way of digital image conversion from medical image format.
Entanglement dynamics for circularly accelerated two-level atoms coupled with electromagnetic vacuum fluctuations

We investigate, in the framework of open quantum systems, the entanglement dynamics of two circularly accelerated two-level atoms with the same centripetal acceleration interacting with a bath of fluctuating electromagnetic fields in the Minkowski vacuum. We assume that the two atoms rotate synchronically with their separation perpendicular to the rotating plane and study the entanglement degradation, creation, revival, and enhancement by solving the Markovian master equation. In contrast to the scalar-field case, the entanglement dynamics is crucially dependent on the atomic polarizations in the sense that the polarization directions may affect the entanglement decay rate and may determine the occurrences of entanglement creation, revival, and enhancement. Compared with the uniformly accelerated case and the thermal case, the decay rate of entanglement for circularly accelerated atoms is larger, while the revival and enhancement rates are smaller.
Reducing Cloud Data Breaches and Improving Data Security using Honey Encryption Algorithm

Data security is the most predominant measure that should be followed in any organization to prevent uncertified access to user’s data. A data breach is a security event in which sensitive data is viewed, transmitted, stolen or used by an unauthorized individual. A number of breaches include compromised password files which reveal the passwords of millions of users in an organization. The paper is intended to identify such password breaches through honey encryption technique. Using the concept of honey words, each user has a list of sweet words corresponding to their account, out of which only one is original password and remaining are fake passwords. During Honey Encryption, Distribution-transforming encoder (DTE) is applied on the password to acquire the seed space which is then encrypted by using secret key. The proposed model can be efficiently implemented in cloud applications to highly reduce cloud data breaches.
Security Risks in the Software Development Lifecycle

Security is a significant concern in software development. Risks and errors should be reduced and as much as possible eliminated. Especially with how the computer and internet provide numerous technological benefits and are being optimally utilized in the present times, users have high expectations when it comes to secure software products. The development cycle in software design is comprised of a systematic process wherein security risk assessment should be integrated into each phase. In order to ensure quality software with a high level of security, the best practices in risk management should be implemented from the beginning and should be performed throughout the process. While developing highly secure software is a complex task, therefore, it must involve more dedicated security activities that are usually ignored in traditional SDLC. In this paper, the nature and phases of secure software development will be discussed as well as the security risk assessment models and practices involved in it. This will provide software developers, programmers, or engineers the awareness on secure software development cycle which will allow them to plan efficient strategies and enhance their performance, thus, resulting in quality and reliable output. In addition, we present a qualitative study looking at real-life practice employed towards software security risk. We reflect on how well current risk practices follow best practice, identify pitfalls, and explore why these occur &amp; mitigate.
The construct validity of session RPE during an intensive camp in young male Karate athletes

Background: the aim of this study was to assess the validity of the session rating of perceived exertion (RPE) method and two objective HR-based methods for quantifying karate's training load (TL) in young Karatekas.Methods: eleven athletes (age 12.50±1.84years) participated in this study.The training period/camp was performed on 5 consecutive days with two training session (s) per-day (d).Construct validity of RPE method in young Karate athletes, was studied by correlation analysis between RPE session's training load and both Edwards and Banister's training impulse score' method.Results: significant relationship was found between inter-day (n-11 × d-5 × s-2 = 110) sessions RPE and Edwards (r values from 0.84 to 0.92 p < 0.001) and Banister's (r values from 0.84 to 0.97 p < 0.001), respectively.Conclusion: this study showed that session-RPE can be considered a valid method for quantifying karate's training load in young karate athletes.
Dynamic ranking of cloud services for web-based cloud communities: efficient algorithm for rating-based discovery and multi-level ranking of cloud services

Trust assessment in cloud depends on QoS attributes. While evaluating the trustworthiness of the CSPs, traditional methods of trust assessment do not take into account QoS attributes that keep changing dynamically. To address the issue of determining the trustworthiness of CSPs in web, rating based dynamic discovery (RBDD) of QoS attributes that keeps changing periodically and multi-layer ranking (MLR) algorithm that ranks the discovered CSPs in an efficient manner have been proposed. This approach allows us to evaluate CSPs trustworthiness from cloud auditors perception. evaluation results indicates that the RBDD is capable of sensing behavioural changes in CSPs and discovers the dynamic trustworthy service providers and MLR algorithm ranks them based on CC requirements with high accuracy and minimal time complexity compared to other approaches. The proposed system has been validated with synthetic dataset owing to absence of standardisation.
UAE’s Strategy Towards Most Cyber Resilient Nation

With the fast-paced industrial and Techni revolution, smart nations are emerging with the idea of integrating the developing technologies, like IoT and CPS, to make communications and networks easier and closely interconnected. With increasing technology usage globally, the UAE envisions transitioning into a global smart nation by innovating and deploying world class disruptive technologies for the better connectivity digitally. However, the frequency of cyber threats has also increased drastically with this ever-evolving technology. For a nation like UAE that is transforming into a digital nation at a rapid speed, while still having stakeholders with poor cybersecurity practices, UAE stands as a strong target for attacks by malicious hackers. With cyber-attacks becoming the top most concern globally, especially in the Middle East, UAE has built certain strategies to combat the evolving issue of cybersecurity. In this paper, the authors suggest that just as the collaboration of the key stakeholders – government, academics, industry and society – depicted under the Quadruple Model is needed to develop an innovative and creative nation, so is their coordination required to protect that nation from the potential cyber threats. In light of this theory, the four key stakeholders of the UAE are analyzed for their cybersecurity practices, initiatives and coordination to prove UAE’s commitment towards becoming the most cyber resilient nation in the world.
UPPER EOCENE CALCAREOUS NANNOFOSSIL BIOSTRATIGRAPHY: A NEW PRELIMINARY PRIABONIAN RECORD FROM NORTHERN SAUDI ARABIA

The current research work represents the first calcareous nannofossil biostratigraphic record of the upper Eocene Rashrashiyah Formation in northern Saudi Arabia.The Rashrashiyah Formation is an Eocene carbonate sedimentary unit, exposed adjacent to the Jordanian border, and yielded rich and diverse nannofossil assemblages.The Rashrashiyah Formation overlies the Paleogene rocks of the Umm Wu'al Formation and is unconformably topped by the Neogene (Miocene) Sirhan Formation.Our nannofossil biostratigraphic data recognized 44 species belonging to 21 genera.These taxa indicate a Priabonian age based on the presence of Chiasmolithus oamaruensis, and the recognition of bioevents in concordance with the global Priabonian zonations.Three comparable major zones and zonal boundaries (CP14b/CP15, NP18, and CNE17) were recognized in the study section indicative of a late Eocene age.The Base common (Bc) of Reticulofenestra erbae was detected at the same level as the top of Chiasmolithus grandis, supporting the bioevent reliability in the Tethys realm.A single and more isolated occurrence of Isthmolithus recurvus was observed within Zones CP15 and NP18 at the maximum increase abundance of R. erbae.Our calcareous nannofossil biostratigraphy from the Rashrashiyah Formation has coincided with the assignment for units 2 and 3 of the Wadi Esh-Shallalah Formation in Qa' Faydat ad Dahikiya in the eastern desert Jordan.
The 3$$^{\mathrm {rd}}$$ International Workshop on Narrative Extraction from Texts: Text2Story 2020

The Third International Workshop on Narrative Extraction from Texts ( Text2Story'20) [text2story20.inesctec.pt] held in conjunction with the 42$$^{\mathrm {nd}}$$ European Conference on Information Retrieval ( ECIR 2020 ) gives researchers of IR, NLP and other fields, the opportunity to share their recent advances in extraction and formal representation of narratives. This workshop also presents a forum to consolidate the multi-disciplinary efforts and foster discussions around the narrative extraction task, a hot topic in recent years.
Inauguration in Development for Data Deduplication Under Neural Network Circumstances

The Neural network system is an educational paradigm that unites several neural networks to solve a problem.This paper explores the relationship between the ensemble and its networks of neural components, both from the viewpoint of regression and classification, which reveals that certain networks are stronger than other neural networks.This result is surprising because the rest of the neural networks enter the ensemble at present.To prove that a GASEN algorithm efficiently selects the appropriate neural networks to construct an ensemble from different neural networks available.At first several neuronal networks were taught by GASEN.Then the network allocates random weights and uses genetic algorithms to establish these weights to classify the fitness of the neural system in one ensemble to a certain degree.Ultimately, it used the weights designed for the ensemble for certain neural networks.A comprehensive analytical analysis reveals that, in comparison to typical assemblies, such as luggage, GASEN can generate network assemblies with much smaller sizes but with a higher generalization efficiency.This study, in addition, gives the mistake a gradual regression, demonstrating that the performance of GASEN could be that it can greatly reduce its bias and uncertainty such that GASEN is well aware of its operating mechanism.
Extracting social networks from e-government by sentiment analysis of users' comments

Nowadays, the improvement of governance, ensurance the security and the timely detection of propaganda against the government are major problems of e-government. Extraction of hidden social networks is one of the most actual problems in the term of government security. The extraction of hidden social networks operating against the state in e-government is one of the key factors to ensure the security in e-government. In the paper, a method has been proposed for extracting hidden social networks to improve management in e-government, prevent promotion against the government and ensure the security. In this approach, hidden social networks are extracted through the analysis of user's comments via opinion and text mining technologies.
Teleoperation Control of an Underactuated Bionic Hand: Comparison between Wearable and Vision-Tracking-Based Methods

Bionic hands have been employed in a wide range of applications, including prosthetics, robotic grasping, and human–robot interaction. However, considering the underactuated and nonlinear characteristics, as well as the mechanical structure’s backlash, achieving natural and intuitive teleoperation control of an underactuated bionic hand remains a critical issue. In this paper, the teleoperation control of an underactuated bionic hand using wearable and vision-tracking system-based methods is investigated. Firstly, the nonlinear behaviour of the bionic hand is observed and the kinematics model is formulated. Then, the wearable-glove-based and the vision-tracking-based teleoperation control frameworks are implemented, respectively. Furthermore, experiments are conducted to demonstrate the feasibility and performance of these two methods in terms of accuracy in both static and dynamic scenarios. Finally, a user study and demonstration experiments are conducted to verify the performance of these two approaches in grasp tasks. Both developed systems proved to be exploitable in both powered and precise grasp tasks using the underactuated bionic hand, with a success rate of 98.6% and 96.5%, respectively. The glove-based method turned out to be more accurate and better performing than the vision-based one, but also less comfortable, requiring greater effort by the user. By further incorporating a robot manipulator, the system can be utilised to perform grasp, delivery, or handover tasks in daily, risky, and infectious scenarios.