Reviewerly/exHarmony huggingface dataset:
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning

In this work, we develop methods for few-shot image classification from a new perspective of optimal matching between image regions. We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance. The EMD generates the optimal matching flows between structural elements that have the minimum matching cost, which is used to calculate the image distance for classification. To generate the important weights of elements in the EMD formulation, we design a cross-reference mechanism, which can effectively alleviate the adverse impact caused by the cluttered background and large intra-class appearance variations. To implement <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -shot classification, we propose to learn a structured fully connected layer that can directly classify dense image representations with the EMD. Based on the implicit function theorem, the EMD can be inserted as a layer into the network for end-to-end training. Our extensive experiments validate the effectiveness of our algorithm which outperforms state-of-the-art methods by a significant margin on five widely used few-shot classification benchmarks, namely, miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100), Caltech-UCSD Birds-200-2011 (CUB), and CIFAR-FewShot (CIFAR-FS). We also demonstrate the effectiveness of our method on the image retrieval task in our experiments.
Knowledge graph embedding by relational rotation and complex convolution for link prediction

Knowledge graphs are organized as triplets to represent facts from the real world and play an important role in various intelligent information systems. Because knowledge graphs are frequently constructed using manual or semi-automatic methods, they often miss connections between entities. Link prediction was created to solve this problem. Many recent state-of-the-art studies, such as those introducing the RotatE and RotatHS models, have advocated for rotation transformations with entity and relation embeddings in complex vector spaces. However, using only rotation planes means that these models do not have the expressive power of models based on neural networks, such as the ConvE and the ConvR models. As a result, link prediction performance suffers. To address these shortcomings, this paper proposes the ConvRot model, which integrates a 2D convolution. Specifically, we perform convolution on embeddings of entities and relations to obtain support vector embeddings. These vectors are then integrated into an element-wise rotation from the head entity to the tail entity using the Hadamard product, enabling the model to capture local interactions among entities and relations through the neural network while still ensuring intuitiveness through a roto-transformation in the link prediction. In addition, we present two strategies for designing the complex convolution module and show their effects on model performance. The proposed method is evaluated on standard benchmark datasets and achieves significantly improved results on MRR and [email protected]K (K = 1, 3, 10). Overall, our model’s link prediction performance is superior by approximately 5–7 %. Moreover, the ConvRot model is also considered separately on many relation types, such as one-to-one, one-to-many, many-to-one, and many-to-many. Finally, we prove that type constraints can help increase the model’s overall performance, especially on complex and large datasets.
Metaverse: Threat or Opportunity for Our Social World? In understanding Metaverse on sociological context

Metaverse technology is a new technology that will focus on working, studying and entertainment in the future. In sociology, the existence of this technology will change the culture of society. The connection to social reality will be lost in the metaverse. This article aims to discuss in detail how the reality of technology metaverse in social life. This article will be discussed using the sociological descriptive analysis method in depth using various theories in the field of sociology. In this paper, will focus more on the discussion of how metaverse technology can affect human social life, what impact this technology has on humans, what challenges this technology faces, and the analysis of this technology from the sociological context. Although still in the design stage, the impact of this new technology seems to be obvious. In sociology, the existence of this metaverse technology will try to change the culture of society such as the culture of interacting, working to seeking entertainment. Although still in the design phase, the impact of this new technology seems to be real.
A relationship between service quality and customer satisfaction in e-banking services- a study with reference to commercial banks in Chennai City

Purpose: This paper's main aim is to validate the service quality dimensions offered by commercial banks in the study area and the relationship between service quality dimensions and customer satisfaction provided by commercial banks. Theoretical Framework: In India, three categories of commercial banks are the public sector, private sector, and foreign banks. These banks have heavy competition among themselves in maximising their customer base. Design/Methodology/Approach: The researcher collected 600 responses through convenience sampling. Applied exploratory factor analysis, confirmatory factor analysis, regression analysis and structural equation model to conclude that service quality of electronic banking service is not a unique phenomenon. Findings: E-Banking depends upon the five essential factors: reliability, safety and security, assurance, technological augmentation, and quickness since the electronic banking services offered by commercial banks are entirely based on the Internet process. Research, Practical &amp; Social Implications: The study identifies the customer requirements and how commercial banks could satisfy such requirements with their banking strategies. Also, the study identifies whether any demographic difference exists among the customers of commercial banks in perceiving the quality of e-banking services.
Arabic aspect sentiment polarity classification using BERT

Aspect-based sentiment analysis(ABSA) is a textual analysis methodology that defines the polarity of opinions on certain aspects related to specific targets. The majority of research on ABSA is in English, with a small amount of work available in Arabic. Most previous Arabic research has relied on deep learning models that depend primarily on context-independent word embeddings (e.g.word2vec), where each word has a fixed representation independent of its context. This article explores the modeling capabilities of contextual embeddings from pre-trained language models, such as BERT, and making use of sentence pair input on Arabic aspect sentiment polarity classification task. In particular, we develop a simple but effective BERT-based neural baseline to handle this task. Our BERT architecture with a simple linear classification layer surpassed the state-of-the-art works, according to the experimental results on three different Arabic datasets. Achieving an accuracy of 89.51% on the Arabic hotel reviews dataset, 73% on the Human annotated book reviews dataset, and 85.73% on the Arabic news dataset.
A review of deep learning-based deformable medical image registration

The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into five categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the point of view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publicly available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review.
Error-Mitigated Quantum Metrology via Virtual Purification

Quantum metrology with entangled resources aims to achieve sensitivity beyond the standard quantum limit by harnessing quantum effects even in the presence of environmental noise. So far, sensitivity has been mainly discussed from the viewpoint of reducing statistical errors under the assumption of perfect knowledge of a noise model. However, we cannot always obtain complete information about a noise model due to coherence time fluctuations, which are frequently observed in experiments. Such unknown fluctuating noise leads to systematic errors and nullifies the quantum advantages. Here, we propose an error-mitigated quantum metrology that can filter out unknown fluctuating noise with the aid of purification-based quantum error mitigation. We demonstrate that our protocol mitigates systematic errors and recovers superclassical scaling in a practical situation with time-inhomogeneous bias-inducing noise. Our result is the first demonstration to reveal the usefulness of purification-based error mitigation for unknown fluctuating noise, thus paving the way not only for practical quantum metrology but also for quantum computation affected by such noise.
Hotel robots: an exploratory study of Generation Z customers in China

Purpose Given little light has been shed on the preference of Generation Z tourists or tourists from different cultural backgrounds toward service robot preference in hospitality contexts, this study aims to explore robot service preferences in the accommodation sector in the Chinese market, with a specific focus of Generation Z customers. Design/methodology/approach This study followed an exploratory sequential research design including two qualitative methods (i.e. projective techniques and semistructured interviews) to reach its objectives. Findings This study suggests that service robots are more preferred for routine tasks and gender differences exist in the preference of service robot’s anthropomorphism level. The preferences are driven by four factors based on different levels of hedonic and utilitarian values: experienced-hedonic value, utilitarian-hedonic value, task-relevant value and utilitarian value. Research limitations/implications First, because of the exploratory nature of this study, the data interpretation is unavoidably subjective; therefore, the results can be confirmed by using a more rigorous research method. Second, this study only focuses on the preference of Generation Z customers in China in the hotel sector; as the cultural differences exist in different countries, this study’s findings cannot be readily generalized across populations and service contexts. Finally, this study fails to dig into the effect of gender differences on varying levels of anthropomorphic attributes. Practical implications As hotels adopt service robots more widely, managers need to identify customers’ service preferences and prioritize tasks between robots and human resources for more efficient service. Particularly important is understanding the benefits and challenges of specific robot implementations rather than looking for a one-size-fits-all mode of operation. Originality/value To the best of the authors’ knowledge, this is the first study to understand robot service preference with regard to specific tourist groups in illustrating their preference for service delivery. Drawing on self-determination theory, this study potentially provides a theoretical basis for hotel service process optimization.
A Bibliometric Analysis of Management Bioenergy Research Using Vosviewer Application

This study aims at analyzing and demonstrating bibliometric data analysis step by step using VOSViewer systematically. Step-by-step analysis was provided to make first-time users access and utilize VOSViewers more easily. This study provides data analysis regarding management bioenergy and its development throughout five years (2017–2021) by utilizing mapping tools in the VOSViewer. The method used was qualitative descriptive to describe the bibliometric analysis performance by producing network visualization of the chosen topic. From the search results, 1000 relevant published journals were found, ranging from 2017–2021, which were then grouped into five categories according to their published year. After grouping the journal data, we discovered that the total number of articles published on the topic of bioenergy management from 2017 to 2021 was 180. As a result, it conclude that management bioenergy is a field that can be combined with another field to be studied. This way, there is a lot of opportunities to find novelties in this topic. Furthermore, this study is expected to serve as a resource for researchers conducting research and determining the research theme.
Evaluating the Intention for the Adoption of Artificial Intelligence-Based Robots in the University to Educate the Students

Technology adoption is accepting, integrating, and using the latest innovative technologies in society. Artificial intelligence (AI) and robotics are changing the face of the industrial and service sectors. There is a need to change the traditional way of teaching by introducing the latest innovative methods. This study aims to measure the intention of adopting AI-based robots in the educational system of Indian universities. This study uses three theories technology acceptance model, the theory of planned behaviour, and the technology readiness index. Thirteen hypotheses are proposed for this study. The teachers and students survey Indian universities. This study also measures the users’ attitudes and the impact on their intention. Nine hypotheses got accepted, and four hypotheses got rejected. This study will benefit the university administration as they will understand the importance of AI-based robots and their applications. These will also be helpful in a way that the students and teachers both are in favours of the adoption process.
Infrared Invisible Clothing: Hiding from Infrared Detectors at Multiple Angles in Real World

Thermal infrared imaging is widely used in body temperature measurement, security monitoring, and so on, but its safety research attracted attention only in recent years. We proposed the infrared adversarial clothing, which could fool infrared pedestrian detectors at different angles. We simulated the process from cloth to clothing in the digital world and then designed the adversarial “QR code” pattern. The core of our method is to design a basic pattern that can be expanded periodically, and make the pattern after random cropping and deformation still have an adversarial effect, then we can process the flat cloth with an adversarial pattern into any 3D clothes. The results showed that the optimized “QR code” pattern lowered the Average Precision (AP) of YOLOv3 by 87.7%, while the random “QR code” pattern and blank pattern lowered the AP of YOLOv3 by 57.9% and 30.1%, respectively, in the digital world. We then manufactured an adversarial shirt with a new material: aerogel. Physical-world experiments showed that the adversarial “QR code” pattern clothing lowered the AP of YOLOv3 by 64.6%, while the random “QR code” pattern clothing and fully heat-insulated clothing lowered the AP of YOLOv3 by 28.3% and 22.8%, respectively. We used the model ensemble technique to improve the attack transferability to unseen models.
A Confusion Method for the Protection of User Topic Privacy in Chinese Keyword-based Book Retrieval

In this article, aiming at a Chinese keyword-based book search service, from a technological perspective, we propose to modify a user query sequence carefully to confuse the user query topics and thus protect the user topic privacy on the untrusted server, without compromising the accuracy of each book search service. First, we propose a client-based framework for the privacy protection of book search, and then a privacy model to formulate the constraints in terms of accuracy, efficiency, and security, which the cover queries generated based on a user query sequence should meet. Second, we present a modification algorithm for a user query sequence, based on some heuristic strategies, which can quickly generate a cover query sequence meeting the privacy model by replacing, deleting, and adding keywords for each user query. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ topic privacy on the untrusted server without compromising the efficiency, accuracy, and usability of an existing Chinese keyword book search service, so it has a positive impact for the construction of a privacy-preserving text retrieval platform under an untrusted network environment.
A Comparative Study of Time Series Anomaly Detection Models for Industrial Control Systems

Anomaly detection has been known as an effective technique to detect faults or cyber-attacks in industrial control systems (ICS). Therefore, many anomaly detection models have been proposed for ICS. However, most models have been implemented and evaluated under specific circumstances, which leads to confusion about choosing the best model in a real-world situation. In other words, there still needs to be a comprehensive comparison of state-of-the-art anomaly detection models with common experimental configurations. To address this problem, we conduct a comparative study of five representative time series anomaly detection models: InterFusion, RANSynCoder, GDN, LSTM-ED, and USAD. We specifically compare the performance analysis of the models in detection accuracy, training, and testing times with two publicly available datasets: SWaT and HAI. The experimental results show that the best model results are inconsistent with the datasets. For SWaT, InterFusion achieves the highest F1-score of 90.7% while RANSynCoder achieves the highest F1-score of 82.9% for HAI. We also investigate the effects of the training set size on the performance of anomaly detection models. We found that about 40% of the entire training set would be sufficient to build a model producing a similar performance compared to using the entire training set.
How the “lessons learned” from emergency remote teaching can enrich European higher education in the post-COVID-19 era

More than 2.5 years after the outbreak of the COVID-19 pandemic and the lessons learned from the implementation of emergency remote teaching in (European) higher education, this essay reflects on how universities, governments, and policy makers can re-imagine higher education in the post-COVID-19 era. It envisions universities as inclusive, student-centered, and accessible organizations capable of meeting diverse learning needs through technology-enhanced high-quality academic programs. This can be achieved through wide-scale uptake of blended learning in higher education; capacity building for stakeholders on online/blended learning; consideration of the unique needs of its stakeholders; and a holistic quality assurance framework. The author’s proposal is evolving, and its elements can be adjusted to the strategic priorities and characteristics of each institution.
Deterministic improvements of quantum measurements with grouping of compatible operators, non-local transformations, and covariance estimates

Abstract Obtaining the expectation value of an observable on a quantum computer is a crucial step in the variational quantum algorithms. For complicated observables such as molecular electronic Hamiltonians, one of the strategies is to present the observable as a linear combination of measurable fragments. The main problem of this approach is a large number of measurements required for accurate estimation of the observable’s expectation value. We consider three previously studied directions that minimize the number of measurements: (1) grouping commuting operators using the greedy approach, (2) involving non-local unitary transformations for measuring, and (3) taking advantage of compatibility of some Pauli products with several measurable groups. The last direction gives rise to a general framework that not only provides improvements over previous methods but also connects measurement grouping approaches with recent advances in techniques of shadow tomography. Following this direction, we develop two measurement schemes that achieve a severalfold reduction in the number of measurements for a set of model molecules compared to previous state-of-the-art methods.
Adaptive, problem-tailored variational quantum eigensolver mitigates rough parameter landscapes and barren plateaus

Variational quantum eigensolvers (VQEs) represent a powerful class of hybrid quantum-classical algorithms for computing molecular energies. Various numerical issues exist for these methods, however, including barren plateaus and large numbers of local minima. In this work, we consider Adaptive, Problem-Tailored (ADAPT)-VQE ans\"atze, and examine how they are impacted by these local minima. We find that while ADAPT-VQE does not remove local minima, the gradient-informed, one-operator-at-a-time circuit construction seems to accomplish two things: First, it provides an initialization strategy that is dramatically better than random initialization, and which is applicable in situations where chemical intuition cannot help with initialization, i.e., when Hartree-Fock is a poor approximation to the ground state. Second, even if an ADAPT-VQE iteration converges to a local trap at one step, it can still "burrow" toward the exact solution by adding more operators, which preferentially deepens the occupied trap. This same mechanism helps highlight a surprising feature of ADAPT-VQE: It should not suffer optimization problems due to "barren plateaus". Even if barren plateaus appear in the parameter landscape, our analysis and simulations reveal that ADAPT-VQE avoids such regions by design.
Voice Assistant Integrated with Chat GPT

AI has become deeply ingrained in the everyday life. The matter in question does not only touch upon the mobile phones that almost everyone carries within easy reach. Today, voice assistants and smart speakers are mainly used to turn on music, turn off the lights or forecast the weather. AI chatbots are getting smarter. The use of new technologies and the general development of neural networks makes it possible to chat or answer questions, write a script, a scientific work, or program code. One of the key differences from previous GPTs is that the new version is trained to continue the text and answer questions. The answers that the bot gives surprise users around the world. Yes, there are still questions about these answers and their validity, and everyone is sure that technology needs to be improved. For a technology to become revolutionary, it must find a better, new, breakthrough application. Although no, such an application has already been invented. Farcana decided to combine the functionality of the GPT chatbot and a voice assistant. By offering players a new approach to familiarizing themselves with game mechanics and general account management, Farcana promotes its advantages over others and rapidly contributes to AI's overall development in the digital society.