Reviewerly/exHarmony huggingface dataset:
A Fractional Order General Type-2 Fuzzy PID Controller Design Algorithm

Fractional order PID controller was received attentions in control problems for it had 2 freedom adjustable parameters. Practices had proved that better results could be obtained by introduction of fractional order PID in control problems. And in recently years, fractional order PID combined with fuzzy logic system was gradually got attention, the typical of which was fractional order type-1 fuzzy PID controller. For fractional order PID and fractional order type-1 fuzzy PID controller couldn't deal with the uncertainty of system, so fractional order interval type-2 fuzzy PID controller was applied to solve this problem. But interval type-2 fuzzy sets were a simplification of type-2 fuzzy sets, and there may be have the defect of information loss. A fractional order general type-2 fuzzy PID controller was proposed in this article. The proposed controller based on general type-2 fuzzy logic system, which can make full use of the advantages of general type-2 fuzzy logic system in describing the uncertainty of the system. The fractional order general type-2 fuzzy PID controller utilized a simplified type reduction called NT type reduction algorithm. The NT type reduction algorithm can get the defuzzification result directly and avoided iterative process as KM type reduction commonly used in interval type-2 fuzzy controller. The simulations of 3 processes and a practical inverted pendulum system show that fractional order general type-2 fuzzy PID controller can reduce overshoot, improve system response speed and accelerate system stability time in comparing with other controllers. Especially, when the system has disturbance, parameters uncertainty or structure uncertainty, the fractional order general type-2 fuzzy PID controller has better control effects than other compared controllers.
Multiple fault localization of software programs: A systematic literature review

Multiple fault localization (MFL) is the act of identifying the locations of multiple faults (more than one fault) in a faulty software program. This is known to be more complicated, tedious, and costly in comparison to the traditional practice of presuming that a software contains a single fault. Due to the increasing interest in MFL by the research community, a broad spectrum of MFL debugging approaches and solutions have been proposed and developed. The aim of this study is to systematically review existing research on MFL in the software fault localization (SFL) domain. This study also aims to identify, categorize, and synthesize relevant studies in the research domain. Consequently, using an evidence-based systematic methodology, we identified 55 studies relevant to four research questions. The methodology provides a systematic selection and evaluation process with rigorous and repeatable evidence-based studies selection process. The result of the systematic review shows that research on MFL is gaining momentum with stable growth in the last 5 years. Three prominent MFL debugging approaches were identified, i.e. One-bug-at-a-time debugging approach (OBA), parallel debugging approach, and multiple-bug-at-a-time debugging approach (MBA), with OBA debugging approach being utilized the most. The study concludes with some identified research challenges and suggestions for future research. Although MFL is becoming of grave concern, existing solutions in the field are less mature. Studies utilizing real faults in their experiments are scarce. Concrete solutions to reduce MFL debugging time and cost by adopting an approach such as MBA debugging approach are also less, which require more attention from the research community.
Blockchain technology in international business: changing the agenda for global governance

Purpose Blockchain technology has extended beyond the border of cryptocurrency and taken hold in various areas of international business. This study aims to analyze the impacts of blockchain on international business and the resulting challenges and implications for global governance. Design/methodology/approach The analysis of multiple blockchain applications in international finance, banking and insurance, supply chain management and logistics and marketing and advertising shows that the use of blockchain in international business has different impacts on global governance. Findings Although the protection of property rights can be improved and transaction costs can be reduced, the effects on other functions of global governance are more ambivalent. Research limitations/implications As a recommendation for future studies, the need for more multidisciplinary and empirical research is proposed. Practical implications As the technology disrupts business activities, it also affects the governance of these activities on a global scale. Suggestions for the future regulation of blockchain applications in international business are developed. Originality/value Blockchain technology has extended beyond the border of cryptocurrency and taken hold in various areas of international business. This study aims to analyze the impacts of blockchain on international business and the resulting challenges and implications for global governance. The application of blockchain technology in international business across multiple industries is explored in order to draw conclusions about its impacts on global governance. It is determined that blockchain brings about both challenges and benefits for global governance.
Emotional Speech Synthesis with Rich and Granularized Control

This paper proposes an effective emotion control method for an end-to-end text-to-speech (TTS) system. To flexibly control the distinct characteristic of a target emotion category, it is essential to determine embedding vectors representing the TTS input. We introduce an inter-to-intra emotional distance ratio algorithm to the embedding vectors that can minimize the distance to the target emotion category while maximizing its distance to the other emotion categories. To further enhance the expressiveness of a target speech, we also introduce an effective interpolation technique that enables the intensity of a target emotion to be gradually changed to that of neutral speech. Subjective evaluation results in terms of emotional expressiveness and controllability show the superiority of the proposed algorithm to the conventional methods.
Visualizing 3D Molecular Structures Using an Augmented Reality App

We present a simple procedure to make an augmented reality app to visualize any chemical 3D model. The molecular structure may be based on crystallographic data or from computational modeling. This guide is made in such a way that no programming skills are needed, and the procedure uses free software and provides a way to visualize 3D structures that are normally difficult to comprehend in the 2D space of paper. The process can be applied to make a 3D representation of any 2D object, and we envisage the app to be useful when visualizing simple stereochemical problems, when presenting a complex 3D structure on a poster presentation or even in audio-visual presentations. The method works for all molecules including small molecules, supramolecular structures, MOFs, and biomacromolecules.
Using Mineral Chemistry to Aid Exploration: A Case Study from the Resolution Porphyry Cu-Mo Deposit, Arizona

Abstract The giant, high-grade Resolution porphyry Cu-Mo deposit in the Superior district of Arizona is hosted in Proterozoic and Paleozoic basement and in an overlying Cretaceous volcaniclastic breccia and sandstone package. Resolution has a central domain of potassic alteration that extends more than 1 km outboard of the ore zone, overlapping with a propylitic halo characterized by epidote, chlorite, and pyrite that is particularly well developed in the Laramide volcaniclastic rocks and Proterozoic dolerite sills. The potassic and propylitic assemblages were overprinted in the upper parts of the deposit by intense phyllic and advanced argillic alteration. The district was disrupted by Tertiary Basin and Range extension, and the fault block containing Resolution and its Cretaceous host succession was buried under thick mid-Miocene dacitic volcanic cover, obscuring the geologic, geophysical, and geochemical footprint of the deposit. To test the potential of propylitic mineral chemistry analyses to aid in the detection of concealed porphyry deposits, a blind test was conducted using a suite of epidote-chlorite ± pyrite-altered Laramide volcaniclastic rocks and Proterozoic dolerites collected from the propylitic halo, with samples taken from two domains located to the north and south and above the Resolution ore zone. Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) data of epidote provided indications of deposit fertility and proximity. Competition for chalcophile elements (As, Sb, Pb) between coexisting pyrite and epidote grains led to a subdued As-Sb fertility response in epidote, consistent with epidote collected between 0.7 and 1.5 km from the center of a large porphyry deposit. Temperature-sensitive trace elements in chlorite provided coherent spatial zonation patterns, implying a heat source centered at depth between the two sample clusters, and application of chlorite proximitor calculations based on LA-ICP-MS analyses provided a precisely defined drill target in this location in three dimensions. Drilling of this target would have resulted in the discovery of Resolution, confirming that epidote and chlorite mineral chemistry can potentially add value to porphyry exploration under cover.
Secure and Private Implementation of Dynamic Controllers Using Semihomomorphic Encryption

This article presents a secure and private implementation of linear time-invariant dynamic controllers using Paillier's encryption, a semihomomorphic encryption method. To avoid overflow or underflow within the encryption domain, the state of the controller is reset periodically. A control design approach is presented to ensure stability and optimize performance of the closed-loop system with encrypted controller.
PhishHaven—An Efficient Real-Time AI Phishing URLs Detection System

Different machine learning and deep learning-based approaches have been proposed for designing defensive mechanisms against various phishing attacks. Recently, researchers showed that phishing attacks can be performed by employing a deep neural network-based phishing URL generating system called DeepPhish. To prevent this kind of attack, we design an ensemble machine learning-based detection system called PhishHaven to identify AI-generated as well as human-crafted phishing URLs. To the best of our knowledge, this is the first study to consider detecting phishing attacks by both AI and human attackers. PhishHaven employs lexical analysis for feature extraction. To further enhance lexical analysis, we introduce URL HTML Encoding to classify URL on-the-fly and proactively compare with some of the existing methods. We also introduce a URL Hit approach to deal with tiny URLs, which is an open problem yet to be solved. Moreover, the final classification of URLs is made on an unbiased voting mechanism in PhishHaven, which aims to avoid misclassification when the number of votes is equal. To speed up the ensemble-based machine learning models, PhishHaven employs a multi-threading approach to execute the classification in parallel, leading to real-time detection. Theoretical analysis of our solution shows that (1) it can always detect tiny URLs, and (2) it can detect future AI-generated Phishing URLs based on our selected lexical features with 100% accuracy. Through experiments, we analyze our solution with a benchmark dataset of 100,000 phishing and normal URLs. The results show that PhishHaven can achieve 98.00% accuracy, outperforming the existing lexical-based human-crafted phishing URLs detection systems.
Blockchain for 5G: A Prelude to Future Telecommunication

5G and Blockchain are potentially revolutionizing future technologies. 5G promises high rates and QoS to the users and blockchain guarantees a high level of trust and security among the peers. Applications that would be using 5G have varying needs in terms of speed, bandwidth, latency and various other factors. Augmented reality, self-driving vehicles and other ioT applications tend to use 5G for reliable and fast communication. To work seamlessly and securely in such scenarios a more specialized and efficient approach would be required. in this article, we have identified the specific areas where blockchain could be utilized to enhance the security and privacy of the 5G services offered to the users. The current challenges faced in deployment and upliftment of 5G and their related solutions based on blockchain are discussed. A model for Multi-Operator Network Slicing in 5G using blockchain is also presented along with 5G blockchain implementation.
Certificateless authenticated key agreement for blockchain-based WBANs

Authenticated key agreement (AKA) enables communicating parties to mutually establish a session key, in a way that each entity is assured of the authentication of its peer. Certificateless AKA (CLAKA) protocols are widely designed since they avoid the key escrow problem found in identity-based systems and overcome certificateless management overheads in Public Key Infrastructure (PKI). Furthermore, the existing CLAKA were designed to operate in centralized network. Such architecture is a single point of management and single point of failure. We need lightweight protocols that fit with body sensors capabilities. In order to mitigate the aforementioned vulnerabilities, we propose a pairing-based certificateless against the key escrow problem. Our protocol operates in a decentralized system against the single point of failure and management. The protocol establishes a session key in the first phase for communications. The ring signature is used in the second phase for node authentication. The ring signature has advantage of reducing computation costs where a node signs a signature on behalf of others. The signing node remains anonymous and other nodes can verify the signature. Another advantage of ring signature is that there is no specification about the size of the group.
Learning a distance function with a Siamese network to localize anomalies in videos

This work introduces a new approach to localize anomalies in surveillance video. The main novelty is the idea of using a Siamese convolutional neural network (CNN) to learn a distance function between a pair of video patches (spatio-temporal regions of video). The learned distance function, which is not specific to the target video, is used to measure the distance between each video patch in the testing video and the video patches found in normal training video. If a testing video patch is not similar to any normal video patch then it must be anomalous. We compare our approach to previously published algorithms using 4 evaluation measures and 3 challenging target benchmark datasets. Experiments show that our approach either surpasses or performs comparably to current state-of-the-art methods.
Cross-resolution learning for Face Recognition

Convolutional Neural Networks have reached extremely high performances on the Face Recognition task. Largely used datasets, such as VGGFace2, focus on gender, pose and age variations trying to balance them to achieve better results. However, the fact that images have different resolutions is not usually discussed and resize to 256 pixels before cropping is used. While specific datasets for very low resolution faces have been proposed, less attention has been payed on the task of cross-resolution matching. Such scenarios are of particular interest for forensic and surveillance systems in which it usually happens that a low-resolution probe has to be matched with higher-resolution galleries. While it is always possible to either increase the resolution of the probe image or to reduce the size of the gallery images, to the best of our knowledge an extensive experimentation of cross-resolution matching was missing in the recent deep learning based literature. In the context of low- and cross-resolution Face Recognition, the contributions of our work are: i) we proposed a training method to fine-tune a state-of-the-art model in order to make it able to extract resolution-robust deep features; ii) we tested our models on the benchmark datasets IJB-B/C considering images at both full and low resolutions in order to show the effectiveness of the proposed training algorithm. To the best of our knowledge, this is the first work testing extensively the performance of a FR model in a cross-resolution scenario; iii) we tested our models on the low resolution and low quality datasets QMUL-SurvFace and TinyFace and showed their superior performances, even though we did not train our model on low-resolution faces only and our main focus was cross-resolution; iv) we showed that our approach can be more effective with respect to preprocessing faces with super resolution techniques.
A novel image encryption scheme of dynamic S-boxes and random blocks based on spatiotemporal chaotic system

In this paper, a novel image encryption scheme based on spatiotemporal chaotic system is proposed. In the scheme, we adopt the dynamic S-boxes and random blocks algorithms. Firstly, the image is brought into SHA-512 to generate system key. And the key is introduced into Logistic-Dynamic Coupled Logistic Map Lattices (LDCML) spatiotemporal chaos system to initialize the values of dynamic S-boxes and set the parameter values in image encryption. Then, the image is grouped horizontally and vertically in order. Each group consists of non-adjacent rows or non-adjacent columns and is encrypted by the different S-boxes. Lastly, every scrambled pixel is processed by random blocks algorithm. With this encryption scheme, the image is scrambled and diffused simultaneously, which can be scrambled sufficiency and diffused uniformity. Simulation results show that the scheme provides a satisfying level of security and has the advantages of large key space, anti-differential attack, high efficiency and good statistical performance in comparison with other image encryption schemes.
Neuromarketing, Subliminal Advertising, and Hotel Selection: An EEG Study

This study aims to understand how hotel videos embedded with a smiling face emoji as a subliminal message affect consumers’ selection of hotels, with their brain activities measured and collected while they watched the videos. Data was collected from sixteen participants who completed two rounds of experiments. A chi-square test of homogeneity, paired sample t-test, and Bayes factor were performed to address the two proposed research questions. The results of this study reveal that participants’ selection of hotels would be significantly affected by the subliminal stimuli of a smiling face emoji. Meanwhile, neuroscientific data identifies significant differences between participants’ two (theta and beta) out of five bands of brainwaves while they were viewing hotel videos with and without the subliminal message. Suggestions for future studies and practical operations are also discussed.
No Explainability without Accountability

Automatically generated explanations of how machine learning (ML) models reason can help users understand and accept them. However, explanations can have unintended consequences: promoting over-reliance or undermining trust. This paper investigates how explanations shape users' perceptions of ML models with or without the ability to provide feedback to them: (1) does revealing model flaws increase users' desire to "fix" them; (2) does providing explanations cause users to believe - wrongly - that models are introspective, and will thus improve over time. Through two controlled experiments - varying model quality - we show how the combination of explanations and user feedback impacted perceptions, such as frustration and expectations of model improvement. Explanations without opportunity for feedback were frustrating with a lower quality model, while interactions between explanation and feedback for the higher quality model suggest that detailed feedback should not be requested without explanation. Users expected model correction, regardless of whether they provided feedback or received explanations.
Nothing About Us Without Us

Accessibility concerns play an increasing role in Human-Computer Interaction (HCI) research. This workshop takes a look at the role Critical Disability Studies currently plays in the development of assistive technologies and the accessibility of technologies more generally. Accordingly, it has been ten years since Mankoff's seminal paper on "Disability Studies as a Source of Critical Inquiry for the Field of Assistive Technology'' drew out the requirement of actively involving disabled people in research about them. We find it a fitting time for reflecting on and revitalising the topic. We will examine untapped research opportunities and identify systemic obstacles that keep disabled scholars in the margins of associated research. The gathering additionally serves to establish a community of researchers interested in pursuing the perspective of Critical Disability Studies within HCI.
Intelligent phishing detection scheme using deep learning algorithms

Purpose Phishing attacks have evolved in recent years due to high-tech-enabled economic growth worldwide. The rise in all types of fraud loss in 2019 has been attributed to the increase in deception scams and impersonation, as well as to sophisticated online attacks such as phishing. The global impact of phishing attacks will continue to intensify, and thus, a more efficient phishing detection method is required to protect online user activities. To address this need, this study focussed on the design and development of a deep learning-based phishing detection solution that leveraged the universal resource locator and website content such as images, text and frames. Design/methodology/approach Deep learning techniques are efficient for natural language and image classification. In this study, the convolutional neural network (CNN) and the long short-term memory (LSTM) algorithm were used to build a hybrid classification model named the intelligent phishing detection system (IPDS). To build the proposed model, the CNN and LSTM classifier were trained by using 1m universal resource locators and over 10,000 images. Then, the sensitivity of the proposed model was determined by considering various factors such as the type of feature, number of misclassifications and split issues. Findings An extensive experimental analysis was conducted to evaluate and compare the effectiveness of the IPDS in detecting phishing web pages and phishing attacks when applied to large data sets. The results showed that the model achieved an accuracy rate of 93.28% and an average detection time of 25 s. Originality/value The hybrid approach using deep learning algorithm of both the CNN and LSTM methods was used in this research work. On the one hand, the combination of both CNN and LSTM was used to resolve the problem of a large data set and higher classifier prediction performance. Hence, combining the two methods leads to a better result with less training time for LSTM and CNN architecture, while using the image, frame and text features as a hybrid for our model detection. The hybrid features and IPDS classifier for phishing detection were the novelty of this study to the best of the authors' knowledge.