Reviewerly/exHarmony huggingface dataset:
Hybrid level anharmonicity and interference-induced photon blockade in a two-qubit cavity QED system with dipole–dipole interaction

We theoretically study a quantum destructive interference (QDI)-induced photon blockade in a two-qubit driven cavity quantum electrodynamics system with dipole–dipole interaction (DDI). In the absence of dipole–dipole interaction, we show that a QDI-induced photon blockade can be achieved only when the qubit resonance frequency is different from the cavity mode frequency. When DDI is introduced the condition for this photon blockade is strongly dependent upon the pump field frequency, and yet is insensitive to the qubit–cavity coupling strength. Using this tunability feature we show that the conventional energy-level-anharmonicity-induced photon blockade and this DDI-based QDI-induced photon blockade can be combined together, resulting in a hybrid system with substantially improved mean photon number and second-order correlation function. Our proposal provides a nonconventional and experimentally feasible platform for generating single photons.
ATTENTION-BASED LSTM NETWORK FOR ACTION RECOGNITION IN SPORTS

Understanding human action from the visual data is an important computer vision application for video surveillance, sports player performance analysis, and many IoT applications. The traditional approaches for action recognition used hand-crafted visual and temporal features for classifying specific actions. In this paper, we followed the standard deep learning framework for action recognition but introduced channel and spatial attention module sequentially in the network. In a nutshell, our network consists of four main components. First, the input frames are given to a pre-trained CNN for extracting the visual features and the visual features are passed through the attention module. The transformed features maps are given to the bi-directional LSTM network that exploits the temporal dependency among the frames for the underlying action in the scene. The output of bi-direction LSTM is given to a fully connected layer with a softmax classifier that assigns the probabilities to the actions of the subject in the scene. In addition to cross-entropy loss, the marginal loss function is used that penalizes the network for the inter action classes and complimenting the network for the intra action variations. The network is trained and validated on a tennis dataset and in total six tennis players' actions are focused. The network is evaluated on standard performance metrics (precision, recall) promising results are achieved.
Perspective: Does Realism Improve Presence in VR? Suggesting a Model and Metric for VR Experience Evaluation

The concepts of “immersion” and “presence” have been considered as staple metrics for evaluating the quality of virtual reality experiences for more than five decades, even as the concepts themselves have evolved in terms of both technical and psychological aspects. To enhance the user’s experience, studies have investigated the impact of different visual, auditory, and haptic stimuli in various contexts to mainly explore the concepts of “plausibility illusion” and “place illusion”. Previous research has sometimes shown a positive correlation between increased realism and an increase in presence, but not always, and thus, very little of the work around the topic of presence reports an unequivocal correlation. Indeed, one might classify the overall findings within the field around presence as “messy”. Better (or more) visual, auditory, or haptic cues, or increased agency, may lead to increased realism, but not necessarily increased presence, and may well depend on the application context. Rich visual and audio cues in concert contribute significantly to both realism and presence, but the addition of tactile cues, gesture input support, or a combination of these might improve realism, but not necessarily presence. In this paper, we review previous research and suggest a possible theory to better define the relationship between increases in sensory-based realism and presence, and thus help VR researchers create more effective experiences.
The quest of parsimonious XAI: A human-agent architecture for explanation formulation

With the widespread use of Artificial Intelligence (AI), understanding the behavior of intelligent agents and robots is crucial to guarantee successful human-agent collaboration since it is not straightforward for humans to understand an agent's state of mind. Recent empirical studies have confirmed that explaining a system's behavior to human users fosters the latter's acceptance of the system. However, providing overwhelming or unnecessary information may also confuse the users and cause failure. For these reasons, parsimony has been outlined as one of the key features allowing successful human-agent interaction with parsimonious explanation defined as the simplest explanation (i.e. least complex) that describes the situation adequately (i.e. descriptive adequacy). While parsimony is receiving growing attention in the literature, most of the works are carried out on the conceptual front. This paper proposes a mechanism for parsimonious eXplainable AI (XAI). In particular, it introduces the process of explanation formulation and proposes HAExA, a human-agent explainability architecture allowing to make it operational for remote robots. To provide parsimonious explanations, HAExA relies on both contrastive explanations and explanation filtering. To evaluate the proposed architecture, several research hypotheses are investigated in an empirical user study that relies on well-established XAI metrics to estimate how trustworthy and satisfactory the explanations provided by HAExA are. The results are analyzed using parametric and non-parametric statistical testing.
CBIR Using Features Derived by Deep Learning

In a Content-based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image and retrieve images that have a similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally, the choice of these features play a very important role in the success of this system, and high-level features are required to reduce the “semantic gap.” In this article, we propose to use features derived from pre-trained network models from a deep-learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method and also propose a pre-clustering of the database based on the above-mentioned features, which yields comparable results in a much shorter time in most of the cases.
Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis

When approaching thyroid gland tumor classification, the differentiation between samples with and without “papillary thyroid carcinoma-like” nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing interest in the use of machine learning approaches to provide pathologists real-time decision support. In this paper, we optimize and quantitatively compare two automated machine learning methods for thyroid gland tumor classification on two datasets to assist pathologists in decision-making regarding these methods and their parameters. The first method is a feature-based classification originating from common image processing and consists of cell nucleus segmentation, feature extraction, and subsequent thyroid gland tumor classification utilizing different classifiers. The second method is a deep learning-based classification which directly classifies the input images with a convolutional neural network without the need for cell nucleus segmentation. On the Tharun and Thompson dataset, the feature-based classification achieves an accuracy of 89.7% (Cohen’s Kappa 0.79), compared to the deep learning-based classification of 89.1% (Cohen’s Kappa 0.78). On the Nikiforov dataset, the feature-based classification achieves an accuracy of 83.5% (Cohen’s Kappa 0.46) compared to the deep learning-based classification 77.4% (Cohen’s Kappa 0.35). Thus, both automated thyroid tumor classification methods can reach the classification level of an expert pathologist. To our knowledge, this is the first study comparing feature-based and deep learning-based classification regarding their ability to classify samples with and without papillary thyroid carcinoma-like nuclei on two large-scale datasets.
Dense Dual-Attention Network for Light Field Image Super-Resolution

Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available. It is challenging to incorporate distinctive information from different views for LF image SR. Moreover, the long-term information from the previous layers can be weakened as the depth of network increases. In this paper, we propose a dense dual-attention network for LF image SR. Specifically, we design a view attention module to adaptively capture discriminative features across different views and a channel attention module to selectively focus on informative information across all channels. These two modules are fed to two branches and stacked separately in a chain structure for adaptive fusion of hierarchical features and distillation of valid information. Meanwhile, a dense connection is used to fully exploit multi-level information. Extensive experiments demonstrate that our dense dual-attention mechanism can capture informative information across views and channels to improve SR performance. Comparative results show the advantage of our method over state-of-the-art methods on public datasets.
Profiles Visiting Procrustes

Aggregate-level profile-based lectometry allows us to compare language varieties while aggregating over a wide range of linguistic features, thus allowing us to gauge robust, global differences between varieties. Moreover, refinements of the workflow were implemented that allow the researcher to have the best of both worlds, in the sense that next to global patterns, also information on how individual features relate or contribute to these patterns is visible. Even with these refinements, however, a remaining limitation of aggregate-level lectometry methods is that they assume, without testing, that the set of linguistic features fed to the method constitutes a “sensible set of features”. Sometimes, however, it is an empirical question whether aggregating over all features (as opposed to e.g. building separate solutions that aggregate over subsets of features) is the best choice. In this paper we propose a workflow, relying on Procrustes distances, that can help address this empirical question.
Colour and Texture Descriptors for Visual Recognition: A Historical Overview

Colour and texture are two perceptual stimuli that determine, to a great extent, the appearance of objects, materials and scenes. The ability to process texture and colour is a fundamental skill in humans as well as in animals; therefore, reproducing such capacity in artificial ('intelligent') systems has attracted considerable research attention since the early 70s. Whereas the main approach to the problem was essentially theory-driven ('hand-crafted') up to not long ago, in recent years the focus has moved towards data-driven solutions (deep learning). In this overview we retrace the key ideas and methods that have accompanied the evolution of colour and texture analysis over the last five decades, from the 'early years' to convolutional networks. Specifically, we review geometric, differential, statistical and rank-based approaches. Advantages and disadvantages of traditional methods vs. deep learning are also critically discussed, including a perspective on which traditional methods have already been subsumed by deep learning or would be feasible to integrate in a data-driven approach.
Recombinable Game Mechanics for Automated Design Support

Systems that provide automated game-design support — whether fully automated game generators, or tools to assist human designers — must be able to maintain a representation of a game design and add or remove game mechanics to support incrementally modifying the game. The system should then be able to reason about the design to support the designer. For example, it might point out that the set of mechanics makes the game unwinnable; or that there's only one complex possible way to beat the game; or that some room is impossible to get to. In addition, the same representation should be actually playable as a game. Existing game representations encode a fairly narrow range of games, most commonly symmetric board games; these representations are also difficult to extend or revise. We propose an architecture based on the event calculus, a logical representation designed for reasoning about time in an elaboration-tolerant way, meaning that designs can be changed by adding or removing sets of axioms rather than modifying brittle hard-coded representations. The resulting game design is a declarative specification in formal logic, so can be critiqued by making queries that are answered through logical inference. Since it specifies the game's simulation rules, it may be executed by logical inference as well; if symbols specifying input and graphical representation are appropriately mapped to input devices and a graphical display, the same declarative representation can be executed as a fully interactive, graphical game. We describe how to organize a library of game-design mechanics using this event-calculus framework, and describe a simple tile-based game, showing how it can be easily modified, critiqued and debugged, and played.
An analysis of investors’ behavior in Bitcoin market

As an emerging digital asset, Bitcoin has been traded for more than a decade, reaching an impressively high market capitalization and continuing to expand its volume of trading at a rapid pace. Many countries have legalized or are considering legalizing a trading platform for this asset, and a set of companies worldwide accept it as a medium of exchange. As a result of this expansion, many studies in finance literature have focused on studying the efficiency of this cryptocurrency. In line with this literature, this paper investigates, using the abnormal returns and abnormal trading volumes methodologies, the dynamics of investors’ reaction to the arrival of unexpected favorable and unfavorable information regarding the Bitcoin market in the context of the three famous hypotheses: the overreaction, the uncertain information, and the efficient market hypotheses. Overall, we find evidence confirming that the Bitcoin market tends to mature over time. More precisely, over the entire analyzed period, investors behave in accordance with the predictions of the uncertain information hypothesis when positive and negative events occur. However, splitting the timespan into sub-periods provides interesting insights. Remarkably in this respect is the fact that starting with the second sub-period, the response of investors in the Bitcoin market supports, in a moderate manner, the postulate of the efficient market hypothesis when favorable events are addressed. Moreover, our findings reveal that during the pandemic period, the efficiency of Bitcoin has increased, thus turning this stressful period into an advantage for this cryptocurrency. This improved market efficiency is also supported by the abnormal trading volume analysis.
SERVICE QUALITY ANALYSIS OF PRIVATE UNIVERSITIES LIBRARIES IN MALAYSIA IN THE ERA OF TRANSFORMATIVE MARKETING

This study aims to investigate and measure the satisfaction of students with the libraries' services of Malaysian Universities.To achieve this, a self-administered questionnaire was distributed among the students of private Universities.The survey instrument was adapted from Parasuraman's traditional SERVQUAL model.In this research total of 287 responses were collected from the users of libraries.After analysis of data with SPSS and Smart PLS 3, it has been concluded that the Service Quality dimension "Tangibles" is the most important factor in the satisfaction of the users.SERVQUAL components' reliability also has a significant positive impact on student's satisfaction.Compared to previous studies which say empathy has a positive relationship with the students' satisfaction we have found that the responsiveness plays a mediating role between empathy and customer satisfaction.If employees are empathic, their responsiveness increases and ultimately students become satisfied with the library services.This study is beneficial for the librarians and decision makers of the Universities.Library building, seating arrangement, printing and scanning facilities, and e-library content should be top priorities of all the management to increase library usage and keep the students satisfied.Library management should also focus on responding to student queries on a priority basis to improve the responsiveness which has a direct relationship with the students' satisfaction.
Distilling Knowledge From Object Classification to Aesthetics Assessment

In this work, we point out that the major dilemma of image aesthetics assessment (IAA) comes from the abstract nature of aesthetic labels. That is, a vast variety of distinct contents can correspond to the same aesthetic label. On the one hand, during inference, the IAA model is required to relate various distinct contents to the same aesthetic label. On the other hand, when training, it would be hard for the IAA model to learn to distinguish different contents merely with the supervision from aesthetic labels, since aesthetic labels are not directly related to any specific content. To deal with this dilemma, we propose to distill knowledge on semantic patterns for a vast variety of image contents from multiple pre-trained object classification (POC) models to an IAA model. Expecting the combination of multiple POC models can provide sufficient knowledge on various image contents, the IAA model can easier learn to relate various distinct contents to a limited number of aesthetic labels. By supervising an end-to-end single-backbone IAA model with the distilled knowledge, the performance of the IAA model is significantly improved by 4.8% in SRCC compared to the version trained only with ground-truth aesthetic labels. On specific categories of images, the SRCC improvement brought by the proposed method can achieve up to 7.2%. Peer comparison also shows that our method outperforms 10 previous IAA methods.
Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears

Background With the World Health Organization’s (WHO) publication of the 2021–2030 neglected tropical diseases (NTDs) roadmap, the current gap in global diagnostics became painfully apparent. Improving existing diagnostic standards with state-of-the-art technology and artificial intelligence has the potential to close this gap. Methodology/Principal findings We prototyped an artificial intelligence-based digital pathology (AI-DP) device to explore automated scanning and detection of helminth eggs in stool prepared with the Kato-Katz (KK) technique, the current diagnostic standard for diagnosing soil-transmitted helminths (STHs; Ascaris lumbricoides , Trichuris trichiura and hookworms) and Schistosoma mansoni (SCH) infections. First, we embedded a prototype whole slide imaging scanner into field studies in Cambodia, Ethiopia, Kenya and Tanzania. With the scanner, over 300 KK stool thick smears were scanned, resulting in total of 7,780 field-of-view (FOV) images containing 16,990 annotated helminth eggs ( Ascaris : 8,600; Trichuris : 4,083; hookworms: 3,623; SCH: 684). Around 90% of the annotated eggs were used to train a deep learning-based object detection model. From an unseen test set of 752 FOV images containing 1,671 manually verified STH and SCH eggs (the remaining 10% of annotated eggs), our trained object detection model extracted and classified helminth eggs from co-infected FOV images in KK stool thick smears, achieving a weighted average precision (± standard deviation) of 94.9% ± 0.8% and a weighted average recall of 96.1% ± 2.1% across all four helminth egg species. Conclusions/Significance We present a proof-of-concept for an AI-DP device for automated scanning and detection of helminth eggs in KK stool thick smears. We identified obstacles that need to be addressed before the diagnostic performance can be evaluated against the target product profiles for both STH and SCH. Given that these obstacles are primarily associated with the required hardware and scanning methodology, opposed to the feasibility of AI-based results, we are hopeful that this research can support the 2030 NTDs road map and eventually other poverty-related diseases for which microscopy is the diagnostic standard.
The COVID‐19 scamdemic: A survey of phishing attacks and their countermeasures during COVID‐19

The COVID-19 pandemic coincided with an equally-threatening scamdemic: a global epidemic of scams and frauds. The unprecedented cybersecurity concerns emerged during the pandemic sparked a torrent of research to investigate cyber-attacks and to propose solutions and countermeasures. Within the scamdemic, phishing was by far the most frequent type of attack. This survey paper reviews, summarises, compares and critically discusses 54 scientific studies and many reports by governmental bodies, security firms and the grey literature that investigated phishing attacks during COVID-19, or that proposed countermeasures against them. Our analysis identifies the main characteristics of the attacks and the main scientific trends for defending against them, thus highlighting current scientific challenges and promising avenues for future research and experimentation.