Reviewerly/exHarmony huggingface dataset:
Phisher Fighter: Website Phishing Detection System Based on URL and Term Frequency-Inverse Document Frequency Values

Fundamentally, phishing is a common cybercrime that is indulged by the intruders or hackers on naive and credible individuals and make them to reveal their unique and sensitive information through fictitious websites. The primary intension of this kind of cybercrime is to gain access to the ad hominem or classified information from the recipients. The obtained data comprises of information that can very well utilized to recognize an individual. The purloined personal or sensitive information is commonly marketed in the online dark market and subsequently these information will be bought by the personal identity brigands. Depending upon the sensitivity and the importance of the stolen information, the price of a single piece of purloined information would vary from few dollars to thousands of dollars. Machine learning (ML) as well as Deep Learning (DL) are powerful methods to analyse and endeavour against these phishing attacks. A machine learning based phishing detection system is proposed to protect the website and users from such attacks. In order to optimize the results in a better way, the TF-IDF (Term Frequency-Inverse Document Frequency) value of webpages is employed within the system. ML methods such as LR (Logistic Regression), RF (Random Forest), SVM (Support Vector Machine), NB (Naive Bayes) and SGD (Stochastic Gradient Descent) are applied for training and testing the obtained dataset. Henceforth, a robust phishing website detection system is developed with 90.68% accuracy.
Expert musical improvisations contain sequencing biases seen in language production.

Language production involves action sequencing to produce fluent speech in real time, placing a computational burden on working memory that leads to sequencing biases in production. Here we examine whether these biases extend beyond language to constrain one of the most complex human behaviors: music improvisation. Using a large corpus of improvised solos from eminent jazz musicians, we test for a production bias observed in language termed easy first-a tendency for more accessible sequences to occur at the beginning of a phrase, allowing incremental planning later in the same phrase. Our analysis shows consistent evidence of easy first in improvised music, with the beginning of musical phrases containing both more frequent and less complex sequences. The findings indicate that expert jazz musicians, known for spontaneous creative performance, reliably retrieve easily accessed melodic sequences before creating more complex sequences, suggesting that a domain-general sequencing system may support multiple forms of complex human behavior, from language production to music improvisation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Mobile learning in clinical settings: unveiling the paradox

The use of mobile devices among medical students and residents to access online material in real-time has become more prevalent. Most literature focused on the technical/functional aspects of mobile use. This study, on the other hands, explored students, doctors and patients' preferences and reasons towards the use of mobile devices in clinical settings underpinned by the Technology Acceptance Model 2 (TAM 2).This research employs an exploratory research design using survey and semi-structured interviews. An online survey was administered to clinical year medical students, followed by semi-structured interviews with the doctors and patients. Questions for the online survey and semi-structured interviews were derived from previous literature and was then reviewed by authors and an expert panel. A convenience sampling was used to invite voluntary participants.Survey findings showed that most medical students used their devices to find drug information and practice guidelines. The majority of the students accessed UpToDate followed by Google to access medical resources. Key barriers that students often encountered during the use of mobile devices were internet connectivity in the clinical settings, reliability of the information, and technical issues. Thematic analysis of the interviews revealed four themes: general usage by students, receptivity of the use of mobile devices by students, features in selecting resources for mobile learning, and limitation in the current use of mobile devices for learning.The findings from this study assist in recommending suitable material using mobile devices to enhance learning in the clinical environment and expand the TAM 2.
Design and Development of a VR Serious Game for Chemical Laboratory Safety

Virtual reality (VR) technologies are becoming more and more popular, not only as a gaming console, but also as a viable training tool. Especially for health and safety training programmes, VR can be very useful to train people in dangerous environments and situations without imposing real danger on them or others. Chemical laboratories are environments where risks of severe injury or even fatality are always present. For this, we developed a VR serious game, called VR LaboSafe Game, as a tool for laboratory safety training. However, designing a VR serious game is a challenging task. There are many factors to consider for an optimal game design. In this study, we discuss important design considerations and we present the game design of VR LaboSafe Game. Preliminary tests of an early version of the game show usability issues and minor discomforting symptoms for some participants. Nevertheless, participants do agree that VR LaboSafe Game is useful for learning laboratory safety; is more active and responsive in their learning process and makes safety training more engaging.
Quantum circuits for the realization of equivalent forms of one-dimensional discrete-time quantum walks on near-term quantum hardware

Quantum walks are a promising framework for developing quantum algorithms and quantum simulations. They represent an important test case for the application of quantum computers. Here we present different forms of discrete-time quantum walks (DTQWs) and show their equivalence for physical realizations. Using an appropriate digital mapping of the position space on which a walker evolves to the multiqubit states of a quantum processor, we present different configurations of quantum circuits for the implementation of DTQWs in one-dimensional position space. We provide example circuits for a five-qubit processor and address scalability to higher dimensions as well as larger quantum processors.
Machine Learning in Financial Market Surveillance: A Survey

The use of machine learning for anomaly detection is a well-studied topic within various application domains.However, the detection problem for market surveillance remains challenging due to the lack of labelled data and the nature of anomalous behaviours, which are often contextual and spread over a sequence of anomalous instances.This paper provides a comprehensive review of state-of-the-art machine learning methods used, particularly in financial market surveillance.We discuss the research challenges and progress in this field, mainly applied in other related application domains.In particular, we present a case of machine learning-based surveillance system design for a physical power trading market and discuss how the nature of input data affects the effectiveness of the methods on detecting anomalous market behaviours.Overall, our findings indicate that the regression tree-based ensemble algorithms robustly and effectively predict day-ahead future prices, showing their capability to detect abnormal price changes.
Identification of Usability Issues of Interactive Technologies in Cultural Heritage through Heuristic Evaluations and Usability Surveys

Usability is a principal aspect of the system development process to improve and augment system facilities and meet users’ needs and necessities in all domains. It is no exception for cultural heritage. Usability problems of the interactive technology practice in cultural heritage museums should be recognized thoroughly from the viewpoints of experts and users. This paper reports on a two-phase empirical study to identify the usability problems in audio guides and websites of cultural heritage museums in Vietnam, as a developing country, and Australia, as a developed country. In phase one, five-user experience experts identified usability problems using the set of usability heuristics, and proposed suggestions to mitigate these issues. Ten usability heuristics identified a total of 176 problems for audio guides and websites. In phase two, we conducted field usability surveys to collect the real users’ opinions to detect the usability issues and examine the negative-ranked usability. The outstanding issues for audio guides and websites were pointed out. Identification of relevant usability issues and users’ and experts’ suggestions for these technologies should be given immediate attention to helping organizations and interactive service providers improve technologies’ adoptions. The paper’s findings are reliable inputs for our future study about the preeminent UX framework for interactive technology in the CH domain.
Cross-Lingual Neural Network Speech Synthesis Based on Multiple Embeddings

The paper presents a novel architecture and method for speech synthesis in multiple languages, in voices of multiple speakers and in multiple speaking styles, even in cases when speech from a particular speaker in the target language was not present in the training data.The method is based on the application of neural network embedding to combinations of speaker and style IDs, but also to phones in particular phonetic contexts, without any prior linguistic knowledge on their phonetic properties.This enables the network not only to efficiently capture similarities and differences between speakers and speaking styles, but to establish appropriate relationships between phones belonging to different languages, and ultimately to produce synthetic speech in the voice of a certain speaker in a language that he/she has never spoken.The validity of the proposed approach has been confirmed through experiments with models trained on speech corpora of American English and Mexican Spanish.It has also been shown that the proposed approach supports the use of neural vocoders, i.e. that they are able to produce synthesized speech of good quality even in languages that they were not trained on.
Privacy-Preserving Scheme in the Blockchain Based on Group Signature with Multiple Managers

Group signature can provide the privacy-preserving authentication mechanism for the blockchain. In the traditional blockchain privacy-preserving scheme based on the group signature, there is only one group manager to revoke the anonymity. Thus, the traditional scheme will have single point of failure and key escrow problems. To solve these problems, we propose a privacy-preserving scheme in the blockchain based on the group signature with multiple managers. Our scheme is constructed based on bilinear pairing and the technique of distributed key generation. Finally, we analyze the application of the proposed scheme in the field of blockchain-based provable data possession (PDP), as well as the correctness and security of the scheme.
Language Learners’ Willingness to Communicate and Speaking Anxiety in Online versus Face-to-Face Learning Contexts

Foreign language instruction has moved from face-to-face to online learning in response to the COVID-19 pandemic. This study aimed to explore the extent to which Foreign Language Classroom Enjoyment (FLCE), Foreign Language Speaking Anxiety (FLSA) and demographic variables (i.e., age, gender, and course grades) are linked to English as a Foreign Language (EFL) learners’ Willingness to Communicate (WTC) in English under two conditions: online learning and conventional (face-to-face) learning. The study sample consisted of 106 Saudi undergraduate EFL learners. The research instrument was an online questionnaire. The data were analysed quantitatively through t-tests, Pearson correlational tests, and a hierarchical regression analysis. The research findings indicated that: (1) higher levels of WTC were found in the online learning context than in the face-to-face context; (2) FLCE was a positive predictor, while FLSA was a negative predictor, of WTC in the online and face-to-face learning contexts; (3) a high course grade seemed to be a positive predictor of EFL learners’ WTC only in the online learning context. These findings imply that utilizing online-based learning, particularly in the context of developing speaking skills, may foster EFL learners’ communication in the foreign language. Therefore, it is recommended that language teachers should support their students in practicing L2 communication during online classes in a more supportive atmosphere.
Ensemble of Deep Masked Language Models for Effective Named Entity Recognition in Health and Life Science Corpora

The health and life science domains are well known for their wealth of named entities found in large free text corpora, such as scientific literature and electronic health records. To unlock the value of such corpora, named entity recognition (NER) methods are proposed. Inspired by the success of transformer-based pretrained models for NER, we assess how individual and ensemble of deep masked language models perform across corpora of different health and life science domains—biology, chemistry, and medicine—available in different languages—English and French. Individual deep masked language models, pretrained on external corpora, are fined-tuned on task-specific domain and language corpora and ensembled using classical majority voting strategies. Experiments show statistically significant improvement of the ensemble models over an individual BERT-based baseline model, with an overall best performance of 77% macro F1-score. We further perform a detailed analysis of the ensemble results and show how their effectiveness changes according to entity properties, such as length, corpus frequency, and annotation consistency. The results suggest that the ensembles of deep masked language models are an effective strategy for tackling NER across corpora from the health and life science domains.
Exploring Users’ Mental Models for Anthropomorphized Voice Assistants through Psychological Approaches

Various perspectives are being studied to increase the usability and persistence of voice assistants (VA) as the use is rapidly expanding to various domains. Particularly, many studies note that users personify VA. Systems designed to suit users differing mental models while using specific systems can provide a positive user experience, increasing usability and persistence. Therefore, we tried to structure the mental model of users using personified VA and proposed these results as an important factor to consider in personifying VA. To determine important factors to consider in personifying VA, this research structures the mental model of users using personified VA. Furthermore, we used two types of psychological approaches that were not applied in previous studies to analyze users’ mental models. Using two types of psychological approaches that were not applied in previous studies, this research analyzed users’ mental models. In Study 1, each user’s thinking process is derived through ZMET (Zaltman metaphor elicitation techniques) as a consensus map. Afterward, in Study 2, correlations between the key components analyzed in Study 1 are validated through RG (repertory grid technique). As a result, the research found that there are three different psychological structures. The first structure is of users who feel human-like empathy and warmth in the use of VA. Meanwhile, the second structure is of users who seek help with problem-solving. The last is the psychological structure of users who regarded anthropomorphic VA as just a machine. Users with this mental model expect the potential for development as a machine rather than the personification of VA. Ultimately, this research is meaningful in that it analyzes each user’s psychological mechanism for personified VA through a psychological approach and derives three new mental models in detail.
Res2-Unet+, a Practical Oil Tank Detection Network for Large-Scale High Spatial Resolution Images

Oil tank inventory is significant for the economy and the military, as it can be used to estimate oil reserves. Traditional oil tank detection methods mainly focus on the geometrical characteristics and spectral features of remotely sensed images based on feature engineering. The methods have a limited application capability when the distribution pattern of ground objects in the image changes and the imaging condition varies largely. Therefore, we propose an end-to-end deep convolution network Res2-Unet+, to detect oil tanks in a large-scale area. The Res2-Unet+ method replaces the typical convolution block in the encoder of the original Unet method using hierarchical residual learning branches. A hierarchical branch is used to decompose the feature map into a few sub-channel features. To evaluate the generalization and transferability of the proposed model, we use high spatial resolution images from three different sensors in different areas to train the oil tank detection model. Images from yet another sensor in another area are used to evaluate the trained model. Three more widely used methods, Unet, Segnet, and PSPNet, are trained and evaluated for the same dataset. The experiments prove the effectiveness, strong generalization, and transferability of the proposed Res2-Unet+ method.
Exploiting robust unsupervised video person re‐identification

Unsupervised video person re-identification (reID) methods usually depend on global-level features. Many supervised reID methods employed local-level features and achieved significant performance improvements. However, applying local-level features to unsupervised methods may introduce an unstable performance. To improve the performance stability for unsupervised video reID, this paper introduces a general scheme fusing part models and unsupervised learning. In this scheme, the global-level feature is divided into equal local-level feature. A local-aware module is employed to explore the potentials of local-level feature for unsupervised learning. A global-aware module is proposed to overcome the disadvantages of local-level features. Features from these two modules are fused to form a robust feature representation for each input image. This feature representation has the advantages of local-level feature without suffering from its disadvantages. Comprehensive experiments are conducted on three benchmarks, including PRID2011, iLIDS-VID, and DukeMTMC-VideoReID, and the results demonstrate that the proposed approach achieves state-of-the-art performance. Extensive ablation studies demonstrate the effectiveness and robustness of proposed scheme, local-aware module and global-aware module. The code and generated features are available at https://github.com/deropty/uPMnet.
Are Proactive Interventions for Reddit Communities Feasible?

Reddit has found its communities playing a prominent role in originating and propagating problematic sociopolitical discourse. Reddit administrators have generally struggled to prevent or contain such discourse for several reasons including: (1) the inability for a handful of human administrators to track and react to millions of posts and comments per day and (2) fear of backlash as a consequence of administrative decisions to ban or quarantine hateful communities. Consequently, administrative actions (community bans and quarantines) are often taken only when problematic discourse within a community spills over into the real world with serious consequences. In this paper, we investigate the feasibility of deploying tools to proactively identify problematic communities on Reddit. Proactive identification strategies show promise for three reasons: (1) they have potential to reduce the manual efforts required to track communities for problematic content, (2) they give administrators a scientific rationale to back their decisions and interventions, and (3) they facilitate early and more nuanced interventions (than banning or quarantining) to mitigate problematic discourse.
Onset of non-Gaussian quantum physics in pulsed squeezing with mesoscopic fields

We study the emergence of non-Gaussian quantum features in pulsed squeezed light generation with a mesoscopic number (i.e., dozens to hundreds) of pump photons. Due to the strong optical nonlinearities necessarily involved in this regime, squeezing occurs alongside significant pump depletion, compromising the predictions made by conventional semiclassical models for squeezing. Furthermore, nonlinear interactions among multiple frequency modes render the system dynamics exponentially intractable in naïve quantum models, requiring a more sophisticated modeling framework. To this end, we construct a nonlinear Gaussian approximation to the squeezing dynamics, defining a “Gaussian interaction frame” in which non-Gaussian quantum dynamics can be isolated and concisely described using a few dominant (i.e., principal) supermodes. Numerical simulations of our model reveal non-Gaussian distortions of squeezing in the mesoscopic regime, largely associated with signal-pump entanglement. We argue that state of the art in nonlinear nanophotonics is quickly approaching this regime, providing an all-optical platform for experimental studies of the semiclassical-to-quantum transition in a rich paradigm of coherent, multimode nonlinear dynamics. Mesoscopic pulsed squeezing, thus, provides an intriguing case study of the rapid rise in dynamic complexity associated with semiclassical-to-quantum crossover, which we view as a correlate of the emergence of new information processing capacities in the quantum regime.
Bypassing Isolated Execution on RISC-V using Side-Channel-Assisted Fault-Injection and Its Countermeasure

RISC-V is equipped with physical memory protection (PMP) to prevent malicious software from accessing protected memory regions. PMP provides a trusted execution environment (TEE) that isolates secure and insecure applications. In this study, we propose a side-channel-assisted fault-injection attack to bypass isolation based on PMP. The proposed attack scheme involves extracting successful glitch parameters for fault injection from side-channel information under crossdevice conditions. A proof-of-concept TEE compatible with PMP in RISC-V was implemented, and the feasibility and effectiveness of the proposed attack scheme was validated through experiments in TEEs. The results indicate that an attacker can bypass the isolation of the TEE and read data from the protected memory region In addition, we experimentally demonstrate that the proposed attack applies to a real-world TEE, Keystone. Furthermore, we propose a software-based countermeasure that prevents the proposed attack.