Reviewerly/exHarmony huggingface dataset:
Learning to Co-Embed Queries and Documents

Learning to Rank (L2R) methods that utilize machine learning techniques to solve the ranking problems have been widely studied in the field of information retrieval. Existing methods usually concatenate query and document features as training input, without explicit understanding of relevance between queries and documents, especially in pairwise based ranking approach. Thus, it is an interesting question whether we can devise an algorithm that effectively describes the relation between queries and documents to learn a better ranking model without incurring huge parameter costs. In this paper, we present a Gaussian Embedding model for Ranking (GERank), an architecture for co-embedding queries and documents, such that each query or document is represented by a Gaussian distribution with mean and variance. Our GERank optimizes an energy-based loss based on the pairwise ranking framework. Additionally, the KL-divergence is utilized to measure the relevance between queries and documents. Experimental results on two LETOR datasets and one TREC dataset demonstrate that our model obtains a remarkable improvement in the ranking performance compared with the state-of-the-art retrieval models.
XorShiftAnd Random Number Generator For Image Encryption

With the rapid development of computer science, fast and secure transmission of data has gained great importance. Undoubtedly, one of the most common data types is digital images. The attractiveness of digital images is result of wide range usage from social media to defense industry. In order to transmit through correct and secure channels, images must be encrypted before they are sent. The relevant process is realized by means of encryption algorithms. Symmetric steams encryption algorithms which have been proposed so far have weaknesses in terms of speed and processing power. Therefore, encryption quality drops dramatically in certain scenarios. A novel symmetric stream encryption algorithm called XorShiftAnd has been proposed in this study.
A Distributional Approach for Soft Clustering Comparison and Evaluation

The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison measures to SC, and are unable to account for the uncertainty represented in the results of SC algorithms. In this article, we propose a general method to address these limitations, grounding on a novel interpretation of SC as distributions over hard clusterings, which we call distributional measures. We provide an in-depth study of complexity- and metric-theoretic properties of the proposed approach, and we describe approximation techniques that can make the calculations tractable. Finally, we illustrate our approach through a simple but illustrative experiment.
TopC-CAMF: A Top Context-Based Matrix Factorization Recommender System

Online activities have been more and more vital as the digital business has expanded. Users can conduct most activities online such as online shops, hotel bookings, or online educations and courses. A large number of social users are drawn to the abundance of goods available on the Internet. The huge amount of information makes it impossible for social users to navigate it properly and efficiently. Many companies have offered a personalization to tackle this issue. It is proven that the personalized recommendation systems are able to suggest items to users based on their interests and needs that best suit them, which can be captured from user’s contextual information. However, most of the studies capture this contextual information from the predefined contexts such as location and time. In this study, the personalized user context from the user’s text review that they posted as they gave rating to an item was obtained. To this end, a new approach based on the matrix factorization recommendation model, TopC-CAMF, was proposed. TopC-CAMF investigates and finds the most important contexts or needs for each user by leveraging the deep learning model. First, all important contexts from user’s text reviews were extracted. The next step was representing user preferences with the variations of most important contexts, namely top 5, top 10, top 15, top 20, and top 25 contexts. Then, the best top context variation was evaluated and the optimal one was used as the input for the matrix factorization method in providing better recommendations. Extensive experiments using three real datasets were conducted to prove the effectiveness of the TopC-CAMF in terms of root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), normalized discounted cumulative gain (NDCG), and Recall.
The Moderating Effect of Academic Self-efficacy on the Relationship between Self-directed Learning Ability and Learning Performance in Online College English Class

Objectives This study aimed to examine the structural relationships among self-directed learning, self-directed English learning ability, learning satisfaction and academic achievement, and to analyze the differences in learning performance according to academic self-efficacy in online college English classes. Methods A web-based survey was conducted on 172 students who took English classes in the first and second semesters of 2021 at N University. The data were analyzed with SPSS 23.0 and AMOS 21.0. For the moderating effect of students' academic self-efficacy, a multi-group analysis of structural equations was used. Results The results were as follows: First, self-directed learning had a positive effect on the self-directed English learning ability. Second, the two factors, self-directed learning ability and self-directed English learning ability, had a positive effect on learning satisfaction, but only self-directed English learning ability had a significant effect on academic achievement. Third, a multi-group analysis for the moderating effect verified that students with high academic efficacy showed a significant effect on academic achievement. Conclusions It is meaningful that the effect of autonomous learning of college students on self-directed learning ability of English subjects was statistically verified. Based on the results of the study, it is suggested that the method to improve the self-directedness and academic self-efficacy of learners should be sought properly.
Epidemic Question Answering: question generation and entailment for Answer Nugget discovery

Abstract Objective The rapidly growing body of communications during the COVID-19 pandemic posed a challenge to information seekers, who struggled to find answers to their specific and changing information needs. We designed a Question Answering (QA) system capable of answering ad-hoc questions about the COVID-19 disease, its causal virus SARS-CoV-2, and the recommended response to the pandemic. Materials and Methods The QA system incorporates, in addition to relevance models, automatic generation of questions from relevant sentences. We relied on entailment between questions for (1) pinpointing answers and (2) selecting novel answers early in the list of its results. Results The QA system produced state-of-the-art results when processing questions asked by experts (eg, researchers, scientists, or clinicians) and competitive results when processing questions asked by consumers of health information. Although state-of-the-art models for question generation and question entailment were used, more than half of the answers were missed, due to the limitations of the relevance models employed. Discussion Although question entailment enabled by automatic question generation is the cornerstone of our QA system’s architecture, question entailment did not prove to always be reliable or sufficient in ranking the answers. Question entailment should be enhanced with additional inferential capabilities. Conclusion The QA system presented in this article produced state-of-the-art results processing expert questions and competitive results processing consumer questions. Improvements should be considered by using better relevance models and enhanced inference methods. Moreover, experts and consumers have different answer expectations, which should be accounted for in future QA development.
Global bifurcations in a dynamical model of recurrent neural networks

The dynamical behaviour of a continuous time recurrent neural network model with a special weight matrix is studied.The network contains several identical excitatory neurons and a single inhibitory one.This special construction enables us to reduce the dimension of the system and then fully characterize the local and global codimensionone bifurcations.It is shown that besides saddle-node and Andronov-Hopf bifurcations, homoclinic and cycle fold bifurcations may occur.These bifurcation curves divide the plane of weight parameters into nine domains.The phase portraits belonging to these domains are also characterized.
Fiber communication receiver models based on the multi-head attention mechanism

In this paper, an artificial-intelligence-based fiber communication receiver model is put forward. With the multi-head attention mechanism it contains, this model can extract crucial patterns and map the transmitted signals into the bit stream. Once appropriately trained, it can obtain the ability to restore the information from the signals whose transmission distances range from 0 to 100 km, signal-to-noise ratios range from 0 to 20 dB, modulation formats range from OOK to PAM4, and symbol rates range from 10 to 40 GBaud. The validity of the model is numerically demonstrated via MATLAB and Pytorch scenarios and compared with traditional communication receivers.
Fully homomorphic encryption scheme and Fermat’s little theorem

In this paper, we take an algebraic approach to the generalized DGHV scheme without using bootstrapping technique. We investigate the homomorphic evaluation algorithm in detail and provide several sufficient conditions for correct homomorphic evaluation of arbitrary polynomial circuits. Compared with the bootstrapping procedure, we show that our approach is much simpler and more efficient. Moreover, we prove that both of the key sizes are actually bounded above and that these upper-bounds depend only on the plaintext space and the security parameter. Hence we need only one pair of secret key and public key for correct homomorphic evaluation of all polynomial circuits, which implies the generalized DGHV scheme is a fully homomorphic encryption in itself. Thus our approach shows that the generalized DGHV scheme provides extremely simple and efficient algorithms.
Transadapting fable into a parable for Indonesian Muslim children: Strategies and impacts

The demand for domestication and localization of children’s literature compels translators to not only translate the texts but also transadapt them. Significant problems arise when the texts have to fit the cultures and religions of the target users. This qualitative study attempts to address this issue. Gathering teachers of Taman Pendidikan Al Qur’an (TPQ) or Qur’an study club for Muslim children in the Greater Boyolali area of Indonesia, children’s literature translators, and TPQ students in a Focus Group Discussion, we investigated the strategies of transadapting fables in English into Bahasa Indonesia with Islamic values as the core teaching along with the impacts ensued. Through the FGD constructed based on the purification strategy by Klingberg (1986), translation as adaptation and selection by Gengshen (2003), children picturebook translation by Oittinen (2000), narrative connectedness by Christman (2004), proairetic decoding by Nikolajeva (2010), and skopos by Reiss and Vermeer (2014), paratextualization, insertion, and bleaching strategies are constructed. Paratextualization adds clickable religious comments on the digital versions of the fables. Insertion adds religious lessons within the text. Bleaching refines any expressions considered unfit for the target religious values. These strategies trigger an impact called drifting. To reveal the extent of faithfulness, we constructed a drifting-level assessment. This assessment enables translators to reveal whether a transadapted children’s literature is still on track, slipped, or out of track. The study finding is expected to fill up the theoretical absence of transadaptation strategies and drifting level assessment. Its practical nature also brings benefits for children’s literature translators and TPQ teachers.
Better Heisenberg Limits, Coherence Bounds, and Energy-Time Tradeoffs via Quantum Rényi Information

An uncertainty relation for the Rényi entropies of conjugate quantum observables is used to obtain a strong Heisenberg limit of the form RMSE≥f(α)/(⟨N⟩+12), bounding the root mean square error of any estimate of a random optical phase shift in terms of average photon number, where f(α) is maximised for non-Shannon entropies. Related simple yet strong uncertainty relations linking phase uncertainty to the photon number distribution, such as ΔΦ≥maxnpn, are also obtained. These results are significantly strengthened via upper and lower bounds on the Rényi mutual information of quantum communication channels, related to asymmetry and convolution, and applied to the estimation (with prior information) of unitary shift parameters such as rotation angle and time, and to obtain strong bounds on measures of coherence. Sharper Rényi entropic uncertainty relations are also obtained, including time-energy uncertainty relations for Hamiltonians with discrete spectra. In the latter case almost-periodic Rényi entropies are introduced for nonperiodic systems.
Efficient Vision-Based Face Image Manipulation Identification Framework Based on Deep Learning

Image manipulation of the human face is a trending topic of image forgery, which is done by transforming or altering face regions using a set of techniques to accomplish desired outputs. Manipulated face images are spreading on the internet due to the rise of social media, causing various societal threats. It is challenging to detect the manipulated face images effectively because (i) there has been a limited number of manipulated face datasets because most datasets contained images generated by GAN models; (ii) previous studies have mainly extracted handcrafted features and fed them into machine learning algorithms to perform manipulated face detection, which was complicated, error-prone, and laborious; and (iii) previous models failed to prove why their model achieved good performances. In order to address these issues, this study introduces a large face manipulation dataset containing vast variations of manipulated images created and manually validated using various manipulation techniques. The dataset is then used to train a fine-tuned RegNet model to detect manipulated face images robustly and efficiently. Finally, a manipulated region analysis technique is implemented to provide some in-depth insights into the manipulated regions. The experimental results revealed that the RegNet model showed the highest classification accuracy of 89% on the proposed dataset compared to standard deep learning models.
An Experimental Analysis of Drift Detection Methods on Multi-Class Imbalanced Data Streams

The performance of machine learning models diminishes while predicting the Remaining Useful Life (RUL) of the equipment or fault prediction due to the issue of concept drift. This issue is aggravated when the problem setting comprises multi-class imbalanced data. The existing drift detection methods are designed to detect certain drifts in specific scenarios. For example, the drift detector designed for binary class data may not produce satisfactory results for applications that generate multi-class data. Similarly, the drift detection method designed for the detection of sudden drift may struggle with detecting incremental drift. Therefore, in this experimental investigation, we seek to investigate the performance of the existing drift detection methods on multi-class imbalanced data streams with different drift types. For this reason, this study simulated the streams with various forms of concept drift and the multi-class imbalance problem to test the existing drift detection methods. The findings of current study will aid in the selection of drift detection methods for use in developing solutions for real-time industrial applications that encounter similar issues. The results revealed that among the compared methods, DDM produced the best average F1 score. The results also indicate that the multi-class imbalance causes the false alarm rate to increase for most of the drift detection methods.
A crypto-steganography healthcare management: towards a secure communication channel for data COVID-19 updating

&lt;span lang="EN-US"&gt;Nowadays, secure transmission massive volumes of medical data (such as COVID-19 data) are crucial but yet difficult in communication between hospitals. The confidentiality and integrity are two concerning challenges must be addressing to healthcare data. Also, the data availability challenge that related to network fail which may reason concerns to the arrival the COVID-19 data. The second challenge solved with the different tools such as virtual privet network (VPN) or blockchain technology. Towards overcoming the aforementioned for first challenges, a new scheme based on crypto steganography is proposed to secure updating (COVID-19) data. Three main contributions have been consisted within this study. The first contribution is responsible to encrypt the COVID-19 data prior to the embedding process, called hybrid cryptography (HC). The second contribution is related with the security in random blocks and pixels selection in hosting image. Three iterations of the Hénon Map function used with this contribution. The last contribution called inversing method which used with embedding process. &lt;span&gt;Three important measurements were used the peak signal-to-noise ratio (PSNR), the Histogram analysis and structural similarity index measure (SSIM). Based on the findings, the present scheme gives evidence to increase capacity, imperceptibility, and security to ovoid the existing methods problem.&lt;/span&gt;&lt;/span&gt;
An interpretable RL framework for pre-deployment modeling in ICU hypotension management

Abstract Computational methods from reinforcement learning have shown promise in inferring treatment strategies for hypotension management and other clinical decision-making challenges. Unfortunately, the resulting models are often difficult for clinicians to interpret, making clinical inspection and validation of these computationally derived strategies challenging in advance of deployment. In this work, we develop a general framework for identifying succinct sets of clinical contexts in which clinicians make very different treatment choices, tracing the effects of those choices, and inferring a set of recommendations for those specific contexts. By focusing on these few key decision points, our framework produces succinct, interpretable treatment strategies that can each be easily visualized and verified by clinical experts. This interrogation process allows clinicians to leverage the model’s use of historical data in tandem with their own expertise to determine which recommendations are worth investigating further e.g. at the bedside. We demonstrate the value of this approach via application to hypotension management in the ICU, an area with critical implications for patient outcomes that lacks data-driven individualized treatment strategies; that said, our framework has broad implications on how to use computational methods to assist with decision-making challenges on a wide range of clinical domains.
Exploring the possibility of blockchain and smart contract-based digital certificate

The main objective of this research is to explore the possibility of using blockchain and smart contracts to provide a digital certificate for secure communication on the internet, which is currently provided by the certificate authority (CA) using Transport Layer Security (TLS) protocol based on the X.509 Public Key Infrastructure (PKI) standard. A digital certificate is a trusted certificate that is used to establish a secure connection between a client and a server through the internet. The digital certificates issued by CAs are having security problems, vulnerabilities, double spending, and so on. Blockchains are still in their infancy as far as the digital certificate in the web environment is concerned. This research study analyses the current PKI framework, examines the TLS protocol, checks the functioning of CAs, and explores the possibilities of using blockchain. This research also probes the implementation of smart contract to issue a digital certificate, verify the identity, validate the certificate, and manage the certificate without any third party's approval or intervention.
Buckling of a double sector thin elastic plate: Analytical solution

Analytical solutions for elastic buckling and post-buckling geometries are scarce. The present work presents such a solution for a sample geometry, initially planar, which buckles into a complex 3D shape under tensile load. The assumptions are that the sample thickness is small, so that flexural stiffness is low compared to in-plane strain stiffness, and hence the sample can be considered inextensible. The initial geometry is composed of two disk angular sectors sharing a common edge. Under tensile loading, the two radial edges of each sector tend to align so that each half turns into two regular cone sectors. As the tensile load increases, the cone angle progressively decreases, but the same generic form holds the sample extension all along the tensile direction. Such a solution may be useful for validating numerical simulation tools, or stereo-vision shape measurement procedures.
Lower bound estimation of recommendation error through user uncertainty modeling

In machine learning, the Bayesian error is the lower bound of the prediction error induced by data distribution. In recommender systems, this is also known as the magic barrier (MGBR). MGBR estimation is an important issue because the recommended data frequently contain considerable uncertainties that are difficult to quantify. It is possible to determine the extent to which the recommendation algorithm can be optimized by obtaining the MGBR for a given dataset. MGBR estimation generally requires real user ratings that are not affected by external factors such as human emotions and living environment, which can be extremely difficult or even impossible to gather. Existing theoretical approaches based on simple models, such as Gaussian distributions, have limited estimation capabilities. In this paper, we propose a more sophisticated mixture of exponential power (MoEP) model, which enables adaptive parameter selection for intricate uncertainty. To fit the distribution of the real data, we constructed a flexible learning model that automatically adjusts super- or sub-Gaussian uncertainties using the MoEP components. To select parameters adaptively, we employed an expectation-maximization algorithm to infer the parameters of the components. To estimate the MGBR, we explored an approach for calculating the lower bound of the prediction error under the guidance of a probability model. Experiments on the four datasets validated the rationality of the proposed method. The results show that the MGBR estimated using the new model is marginally lower than the prediction error of state-of-the-art algorithms.