Reviewerly/exHarmony huggingface dataset:
Semantic Incompleteness of Hilbert System for a Combination of Classical and Intuitionistic Propositional Logic

The updated version of this paper has already been published in The Australasian Journal of Logic. You can access to the paper from the following link: https://ojs.victoria.ac.nz/ajl/article/view/7696. This paper shows Hilbert system $(\mathbf{C+J})^{-}$, given by del Cerro and Herzig (1996) is semantically incomplete. This system is proposed as a proof theory for Kripke semantics for a combination of intuitionistic and classical propositional logic, which is obtained by adding the natural semantic clause of classical implication into intuitionistic Kripke semantics. Although Hilbert system $(\mathbf{C+J})^{-}$ contains intuitionistic modus ponens as a rule, it does not contain classical modus ponens. This paper gives an argument ensuring that the system $(\mathbf{C+J})^{-}$ is semantically incomplete because of the absence of classical modus ponens. Our method is based on the logic of paradox, which is a paraconsistent logic proposed by Priest (1979).
Do Quantum Circuit Born Machines Generalize?

In recent proposals of quantum circuit models for generative tasks, the discussion about their performance has been limited to their ability to reproduce a known target distribution. For example, expressive model families such as Quantum Circuit Born Machines (QCBMs) have been almost entirely evaluated on their capability to learn a given target distribution with high accuracy. While this aspect may be ideal for some tasks, it limits the scope of a generative model's assessment to its ability to memorize data rather than generalize. As a result, there has been little understanding of a model's generalization performance and the relation between such capability and the resource requirements, e.g., the circuit depth and the amount of training data. In this work, we leverage upon a recently proposed generalization evaluation framework to begin addressing this knowledge gap. We first investigate the QCBM's learning process of a cardinality-constrained distribution and see an increase in generalization performance while increasing the circuit depth. In the 12-qubit example presented here, we observe that with as few as 30% of the valid data in the training set, the QCBM exhibits the best generalization performance toward generating unseen and valid data. Lastly, we assess the QCBM's ability to generalize not only to valid samples, but to high-quality bitstrings distributed according to an adequately re-weighted distribution. We see that the QCBM is able to effectively learn the reweighted dataset and generate unseen samples with higher quality than those in the training set. To the best of our knowledge, this is the first work in the literature that presents the QCBM's generalization performance as an integral evaluation metric for quantum generative models, and demonstrates the QCBM's ability to generalize to high-quality, desired novel samples.
DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation

Recently, one critical issue looms large in the field of recommender systems -- there are no effective benchmarks for rigorous evaluation -- which consequently leads to unreproducible evaluation and unfair comparison. We, therefore, conduct studies from the perspectives of practical theory and experiments, aiming at benchmarking recommendation for rigorous evaluation. Regarding the theoretical study, a series of hyper-factors affecting recommendation performance throughout the whole evaluation chain are systematically summarized and analyzed via an exhaustive review on 141 papers published at eight top-tier conferences within 2017-2020. We then classify them into model-independent and model-dependent hyper-factors, and different modes of rigorous evaluation are defined and discussed in-depth accordingly. For the experimental study, we release DaisyRec 2.0 library by integrating these hyper-factors to perform rigorous evaluation, whereby a holistic empirical study is conducted to unveil the impacts of different hyper-factors on recommendation performance. Supported by the theoretical and experimental studies, we finally create benchmarks for rigorous evaluation by proposing standardized procedures and providing performance of ten state-of-the-arts across six evaluation metrics on six datasets as a reference for later study. Overall, our work sheds light on the issues in recommendation evaluation, provides potential solutions for rigorous evaluation, and lays foundation for further investigation.
Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders

Deep Learning (DL) methods have dramatically increased in popularity in recent years. While its initial success was demonstrated in the classification and manipulation of image data, there has been significant growth in the application of DL methods to problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of Variational Autoencoders (VAEs), a popular unsupervised DL architecture commonly utilized for dimension reduction, imputation, and learning latent representations of complex data. We propose a new VAE architecture, NIMIWAE, that is one of the first to flexibly account for both ignorable and non-ignorable patterns of missingness in input features at training time. Following training, samples can be drawn from the approximate posterior distribution of the missing data can be used for multiple imputation, facilitating downstream analyses on high dimensional incomplete datasets. We demonstrate through statistical simulation that our method outperforms existing approaches for unsupervised learning tasks and imputation accuracy. We conclude with a case study of an EHR dataset pertaining to 12,000 ICU patients containing a large number of diagnostic measurements and clinical outcomes, where many features are only partially observed.
Near Lossless Time Series Data Compression Methods using Statistics and Deviation

The last two decades have seen tremendous growth in data collections because of the realization of recent technologies, including the internet of things (IoT), E-Health, industrial IoT 4.0, autonomous vehicles, etc. The challenge of data transmission and storage can be handled by utilizing state-of-the-art data compression methods. Recent data compression methods are proposed using deep learning methods, which perform better than conventional methods. However, these methods require a lot of data and resources for training. Furthermore, it is difficult to materialize these deep learning-based solutions on IoT devices due to the resource-constrained nature of IoT devices. In this paper, we propose lightweight data compression methods based on data statistics and deviation. The proposed method performs better than the deep learning method in terms of compression ratio (CR). We simulate and compare the proposed data compression methods for various time series signals, e.g., accelerometer, gas sensor, gyroscope, electrical power consumption, etc. In particular, it is observed that the proposed method achieves 250.8\%, 94.3\%, and 205\% higher CR than the deep learning method for the GYS, Gactive, and ACM datasets, respectively. The code and data are available at https://github.com/vidhi0206/data-compression .
Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shaping

In this paper, we study the application of DRL algorithms in the context of local navigation problems, in which a robot moves towards a goal location in unknown and cluttered workspaces equipped only with limited-range exteroceptive sensors, such as LiDAR. Collision avoidance policies based on DRL present some advantages, but they are quite susceptible to local minima, once their capacity to learn suitable actions is limited to the sensor range. Since most robots perform tasks in unstructured environments, it is of great interest to seek generalized local navigation policies capable of avoiding local minima, especially in untrained scenarios. To do so, we propose a novel reward function that incorporates map information gained in the training stage, increasing the agent's capacity to deliberate about the best course of action. Also, we use the SAC algorithm for training our ANN, which shows to be more effective than others in the state-of-the-art literature. A set of sim-to-sim and sim-to-real experiments illustrate that our proposed reward combined with the SAC outperforms the compared methods in terms of local minima and collision avoidance.
A privacy-preserving framework for blockchain-based multi-level marketing

The multi-level marketing (MLM) model allows customers to have numerous sub-groups and be rewarded based on subgroups’ activities. The ever-increasing large-scale transactions tempt individuals, hackers, and company owners, to commit fraud. However, no MLM-based market has been introduced that can provide privacy protection and conditional privacy. Transparency and immutability can help prevent fraud in these markets. Blockchain technology is a distributed database that supports the two mentioned features. This study presents a blockchain-based framework for MLM-based markets called “blockchain-based multi-level marketing” (BB-MLM). This framework is designed to allow registered users (i) to have conditional privacy, (ii) purchase anonymously, (iii) have untraceable accounts, and (iv) update their private information locally. Despite the privacy mentioned earlier, policies, and regulatory policies, including Know Your Customer (KYC) and Anti-Money Laundering (AML), are supported in the BB-MLM framework.
Waveguide Channel Splitting Induced by Artificial Gauge Fields

Initially considered not to interact with photons, a sort of neutral particle, gauge fields have been created artificially in engineered structures to control light, enabling fascinating phenomena such as topologically robust propagation, waveguiding, and negative refraction. Here we theoretically propose and experimentally demonstrate a mechanism to use uniform curl-free artificial gauge fields to split two counterpropagating transmission channels of a waveguide. By assigning oppositely oriented artificial gauge fields to a waveguide, two initially overlapped counterpropagating transmission channels can be separated spatially, resembling a photonic highway that was previously achieved in the form of chiral edge states in photonic Chern insulators. Interestingly, these two channels are naturally separated and do not need a cladding or wave-forbidden layer in between to avoid crosstalk, another unusual property enabled by gauge fields. This mechanism has been demonstrated experimentally in bilayer moiré metagratings with the synthetic gauge fields being continuously tuned. Our work provides an alternative mechanism to design photonic highways without requiring complicated band structure engineering and large-area bulks to separate two channels, which are favorable for future ultracompact and high-density photonic integrated circuits.
Virtual Reality Based Decision Support Model for Production Process of Museum Exhibition Projects

Quality and time are uncompromisable performance indicators of the physical exhibition production process. Design changes during the installation phase of the process, which arise from poor design representations, cause expensive rework and therefore cost increase. Since non-AEC (architecture/engineering) construction) decision-makers can only partially imagine the outcome through traditional 2D and 3D design representations; we proposed a model incorporating virtual reality technologies to solve design miscommunications and improve stakeholder participation. We adopted a qualitative method and tested the model through case studies with five expert museum professionals. We confirm that VR enhances the design communication among the contributing professionals and, unlike other traditional methods, makes it possible to present multimedia, lighting, sound effects, and other digital elements throughout the exhibition production process. Findings indicate that VR has the potential for significant cost reductions and quality improvement in the construction phase of the exhibitions.
Floquet engineering of non-equilibrium superradiance

We demonstrate the emergence of a non-equilibrium superradiant phase in the dissipative Rabi-Dicke model. This phase is characterized by a photonic steady state that oscillates with a frequency close to the cavity frequency, in contrast to the constant photonic steady state of the equilibrium superradiant phase in the Dicke model. We relate this superradiant phase to the population inversion of Floquet states by introducing a Schwinger representation of the driven two-level systems in the cavity. This inversion is depleted near Floquet energies that are resonant with the cavity frequency to sustain a coherent light-field. In particular, our model applies to solids within a two-band approximation, in which the electrons act as Schwinger fermions. We propose to use this Floquet-assisted superradiant phase to obtain controllable optical gain for a laser-like operation.
The Imagineering Learning Model with Inquiry-Based Learning via Augmented Reality to Enhance Creative Products and Digital Empathy

The objectives of this research are (1) to study and synthesise the conceptual framework of the imagineering learning model with inquiry-based learning via augmented reality to enhance creative products and digital empathy, (2) to develop the imagineering learning model with inquiry-based learning via augmented reality to enhance creative products and digital empathy, and (3) to study the results after using the imagineering learning model with inquiry-based learning via augmented reality to enhance creative products and digital empathy. The participants in this research include seven experts from various institutions, all of whom are specialised in the design and development of instruction models and instruction systems. The research tools consist of (1) the imagineering learning model with inquiry-based learning via augmented reality, and (2) the evaluation form on the suitability of the imagineering learning model with inquiry-based learning via augmented reality. According to the results of this research, it is found that (1) the conceptual framework of this research includes instruction system, imagineering learning, inquiry-based learning, augmented reality technology, creative products, and digital empathy, (2) the imagineering learning model with inquiry-based learning via augmented reality consist of four factors, i.e., input factor, learning process, output, and feedback, and (3) the study of the results after using the imagineering learning model by seven participants shows that 3.1) the overall suitability of the development of the imagineering learning model with inquiry-based learning via augmented reality (overall elements) is at the highest level (Mean = 4.69, SD. = 0.47), and 3.2) the overall suitability of the development of the imagineering learning model with inquiry-based learning via augmented reality is at the highest level (Mean = 4.70, SD. = 0.46).
Literasi Mitigasi Risiko dan Asuransi Perjalanan Wisata

This Community Service Takes the Title of Risk Mitigation Literacy and Travel Insurance. This Community Service aims to produce PKM Outcome Materials that will be published in Premium journals or journals of community service and entrepreneurship, Risk Mitigation Literacy Materials and travel insurance made in, zoom recordings, and disseminate via YouTube, and Increase literacy to the public through Management Sharing Sessions, posters and expansion of the idea of ​​Risk Mitigation, Travel Insurance, and the importance of understanding the underwriting process in insurance coverage. The method of this research is Implementation Time. The implementation time of the activity is April - August 2022. In the form of designing Literacy Materials for Risk Mitigation and Travel Insurance, publishing the idea of ​​Risk Mitigation and Travel Insurance, and conducting literacy by holding Zoom meetings in Management Sharing Sessions activities underwriting process insurance coverage. The result of this Community Service is the availability of literacy materials, posters, literacy activities in the 7.08th Management Session.
Measurement Indicators of Student Engagement and Investigation on Student Engagement in Blended Learning

With the influx of technology, Blended Learning (BL) has become a new and important teaching method in colleges and universities.Digital technology in BL environment inherently affecting all aspects of the student experience.Superstar platform can increase students' behavioural, affective(emotional) and cognitive (intellectual) engagement in various ways.The identification of influencing factors and measurement indicators of student engagement in blended learning environment are indistinct in current literature.This paper identifies influencing factors and measurement indicators of student engagement in blended learning through analysis, then clarify indicators regarding what should be focused on to improve student engagement and indicate the measurement indicators with Superstar platform.In addition, this paper uses quantitative research to provide analyses and countermeasures of the student engagement in blended learning.
Deepfake Technology and Current Legal Status of It

Deepfake refers to the artificial intelligence technology that splices individual sounds, facial expressions, and body movements into false content with the help of neural network technology.It makes it possible to tamper with or generate highly realistic audio and video contents and make it difficult to identify, which observers fail to distinguish with the naked eye.Therefore, the abuse of deepfake technology will accelerate human beings into the "post-truth era", which will cause a series of social risks, endangering personal legitimate rights and interests, social and public security, and even national security.This paper provides an overview of the main algorithm model-Autoencoder and Generative Adversarial Network-of deepfake, and then points out the existing risks and legal regulation of deepfake technology.
The Effects of e-Health Literacy on Preventive Health Behaviors among Nursing Students: the Mediating Effect of Awareness of COVID-19 Infection Control

This study was conducted to explore the levels of e-health literacy, awareness of COVID-19 infection control and preventive health behaviors, and to identify the mediating effect of awareness of COVID-19 infection control in the relationship between e-Health literacy and preventive health behaviors of nursing students. The survey data of 127 nursing students were analyzed. Data were collected from 127 nursing students based in D city from October 7 to October 18, 2021 with a structured questionnaire and analyzed by SPSS Statistics 25.0. Preventive health behaviors showed a positive correlation with e-health literacy and awareness of COVID-19 infection control. With regard to the relationship between e-health literacy and preventive health behaviors, awareness of COVID-19 infection control showed a partial mediating effect. To improve preventive health behaviors, it is necessary to improve e-health literacy to access accurate information about COVID-19. These results can also be used for COVID-19 infection prevention programs and should be developed to promote preventive health behaviors considering e-Health literacy and awareness of COVID-19 infection control.
Exploring the Relationship between Reading Maturity, Reading Strategy Use, and Gender with Reading Comprehension of EFL Undergraduates in Iran

This research investigated the relationship between reading maturity, reading strategy use, and gender with Iranian EFL undergraduates’ reading comprehension. The results revealed that the most preferred strategies were cognitive in nature. Moreover, the Iranian EFL undergraduates were intellectually enriched by most of what they read and enjoy reading materials that teach them the things that they did not know before. Also, it was found that the most observable effect of gender was on interpreting the text while reading, followed by reading the text again when some parts are difficult to understand. In both of these cognitive strategies, females outperformed the male students in using these two strategies. Furthermore, no correlation was observed between the most applicable reading strategy (b6: I read the text again when some parts are difficult to understand) and reading comprehension. What’s more, there was no correlation between reading maturity and reading comprehension. However, it was proved that there was a significant correlation between gender and reading comprehension. Reading maturity and the most applicable reading comprehension strategy (b6) had no significant correlation with reading comprehension as shown by the multiple regression results while gender revealed such a correlation. There was no significant correlation between the most applicable reading strategy (b6) and reading maturity. Furthermore, there was no relationship between gender and the mentioned reading maturity subgroups (c, d, e, and g) but gender correlated between subgroups f and h. At last, it was found that there was no relationship between gender and the most applicable reading strategy.
Inventory Grouping to Support IT Business Management with the K-Means Algorithm

Inventory control is an important factor in a company that functions to maintain smooth production. Inventory control is the most important activity in the survival of the company. Fulfillment of stocks of goods or products and recording transactions manually. The fluctuating number of requests from consumers resulted in the stock that Gaptech computer had to prepare to become unstable. In addition, the various and many types of products made stock management inaccurate. by extracting data using one of the data mining methods, namely data grouping. In this study grouping sales reports on Gaptech computer, the data is grouped into 2 groups using one of the data grouping algorithms, namely the K-Means Algorithm. The K-Means algorithm is a non-hierarchical data clustering method that divides data into one or more clusters, so that data with the same characteristics are grouped in the same cluster. The grouping results are used to find out which product groups are in great demand and which are less desirable. The results of this study will produce a software that is created using a method of managing incoming transaction data so that it will make it easier for Gaptech computer store owners to manage stock items.
The Impact of Technology Expenditure on the Commercial Banks’ Profitability in Canada

The growing cyber threat has created an uncontrollable financial mess for the global banking industry.This paper presents a case study that reviews Canada's largest financial data breach and investigates the spillover effects of cyber attack.To test the cyber investment efficiency of banks in Canada, we collect technology-related expenditures and profitability ratios from six Canadian central banks' annual reports.Moreover, we use a multiple linear regression model to examine the impact of technology spending on the financial performance of Canadian banks.The results show that technology expenditure has a significant negative impact on the financial performance of banks.However, we may find different results in future studies that can include more banks in the sample and obtain more comprehensive data sources.