Reviewerly/exHarmony huggingface dataset:
A HEVC Video Steganalysis Against DCT/DST-Based Steganography

The development of video steganography has put forward a higher demand for video steganalysis. This paper presents a novel steganalysis against discrete cosine/sine transform (DCT/DST)-based steganography for high efficiency video coding (HEVC) videos. The new steganalysis employs special frames extraction (SFE) and accordion unfolding (AU) transformation to target the latest DCT/DST domain HEVC video steganography algorithms by merging temporal and spatial correlation. In this article, the distortion process of DCT/DST-based HEVC steganography is firstly analyzed. Then, based on the analysis, two kinds of distortion, the intra-frame distortion and the inter-frame distortion, are mainly caused by DCT/DST-based steganography. Finally, to effectively detect these distortions, an innovative method of HEVC steganalysis is proposed, which gives a combination feature of SFE and a temporal to spatial transformation, AU. The experiment results show that the proposed steganalysis performs better than other methods.
Evaluation and Analysis of Mental Health Level of College Students With Financial Difficulties Under the Background of COVID-19

Against the backdrop of COVID-19, the mental health of college students with financial difficulties deserves scientific attention. This paper on the relationship between mental resilience and the mental health of students with financial difficulties summarizes the research on the psychological resilience of students with financial difficulties during the COVID-19 pandemic. It also suggests ways in which to improve the mental health levels of students with financial difficulties by improving their mental resilience.
Hierarchical Learning for Generation with Long Source Sequences

One of the challenges for current sequence to sequence (seq2seq) models is processing long sequences, such as those in summarization and document level machine translation tasks. These tasks require the model to reason at the token level as well as the sentence and paragraph level. We design and study a new Hierarchical Attention Transformer-based architecture (HAT) that outperforms standard Transformers on several sequence to sequence tasks. Furthermore, our model achieves state-of-the-art ROUGE scores on four summarization tasks, including PubMed, arXiv, CNN/DM, SAMSum, and AMI. Our model outperforms document-level machine translation baseline on the WMT20 English to German translation task. We investigate what the hierarchical layers learn by visualizing the hierarchical encoder-decoder attention. Finally, we study hierarchical learning on encoder-only pre-training and analyze its performance on classification tasks.
Attainable and usable coherence in X states over Markovian and non-Markovian channels

The relations between the resource theoretic measures of quantum coherence are rigorously investigated for various Markovian and non-Markovian channels for the two-qubit X states with specific attention to the maximum and minimum attainable coherence and usefulness of these states in performing quantum teleportation in noisy environment. The investigation has revealed that under both dephasing and dissipative type noises the maximally entangled mixed states and Werner states lose their form and usefulness. However, maximally non-local mixed states (MNMSs) lose their identity in dissipative noise only. Thus, MNMSs are established to be useful in teleporting a qubit with fidelity greater than the classical limit in the presence of dephasing noise. MNMSs also remain useful for device independent quantum key distribution in this case as they still violate Bell's inequality. In the presence of noise, coherence measured by relative entropy of coherence is found to fall faster than the same measured using $$l_1$$ norm of coherence. Further, information back-flow from the environment to the system is observed over non-Markovian channels which leads to revival in coherence. Additionally, sequential interaction of two qubits with the same environment is found to result in correlated noise on both qubits, and coherence is observed to be frozen in this case under dephasing channel. Under the effect of Markovian and non-Markovian dephasing channels studied here, we observed that MNMSs have maximum relative coherence, i.e. they have the maximum amount of $$l_1$$ norm of coherence among the states with the same amount of relative entropy of coherence. However, this feature is not visible in any X state evolving over dissipative channels.
Multi-valued neural networks I: a multi-valued associative memory

A new concept of a multi-valued associative memory is introduced, generalizing a similar one in fuzzy neural networks. We expand the results on fuzzy associative memory with thresholds, to the case of a multi-valued one: we introduce the novel concept of such a network without numbers, investigate its properties, and give a learning algorithm in the multi-valued case. We discovered conditions under which it is possible to store given pairs of network variable patterns in such a multi-valued associative memory. In the multi-valued neural network, all variables are not numbers, but elements or subsets of a lattice, i.e., they are all only partially-ordered. Lattice operations are used to build the network output by inputs. In this paper, the lattice is assumed to be Brouwer and determines the implication used, together with other lattice operations, to determine the neural network output. We gave the example of the network use to classify aircraft/spacecraft trajectories.
Multi level Recommendation System of College Online Learning Resources Based on Multi Intelligence Algorithm

Abstract In order to accurately grasp the actual search habits of online learning objects and establish a more reasonable multi-level recommendation scheme, a multi-level recommendation system of online learning resources in Colleges and Universities Based on multi intelligent algorithm is designed. With the help of the given topology, the multi-level recommendation behavior in the system is analyzed. Then combined with various types of Resource Recommendation application units, the hardware execution environment of the system is built. On this basis, a multiple intelligent neural network model is established. Combined with multi intelligent recommendation technology, the actual Resource Recommendation behavior related to learning objects is determined, and the software execution environment of the system is built. Combined with the structure of relevant hardware equipment, the design of multi-level recommendation system for online learning resources in colleges and universities based on multiple intelligent algorithms is completed. The experimental results show that the system designed in this paper records a larger number of learning objects, but the recommended waiting time is relatively shorter. The system can accurately grasp the actual search habits of online learning objects and establish a more reasonable multi-level recommendation implementation scheme.
Determination of a Good Indicator for Estimated Prime Factor and Its Modification in Fermat’s Factoring Algorithm

Fermat’s Factoring Algorithm (FFA) is an integer factorisation methods factoring the modulus N using exhaustive search. The appearance of the Estimated Prime Factor (EPF) method reduces the cost of FFA’s loop count. However, the EPF does not work for balanced primes. This paper proposed the modified Fermat’s Factoring Algorithm 1-Estimated Prime Factor (mFFA1-EPF) that improves the EPF method. The algorithm works for factoring a modulus with unbalanced and balanced primes, respectively. The main results of mFFA1-EPF focused on three criteria: (i) the approach to select good candidates from a list of convergent continued fraction, (ii) the establishment of new potential initial values based on EPF, and (iii) the application of the above modification upon FFA. The resulting study shows the significant improvement that reduces the loop count of FFA1 via (improved) EPF compared to existing methods. The proposed algorithm can be executed without failure and caters for both the modulus N with unbalanced and balanced primes factor. The algorithm works for factoring a modulus with unbalanced and balanced primes.
ANALYSIS OF WEB-BASED COLLEGE ENGLISH TEACHING FROM 2000 TO 2017 IN CHINA

Based on 812 papers on web-based college English teaching published in Chinese journals from 2000 to 2017, this study adopted modern statistical methods to explore the spatio-temporal development of the research. The results revealed the research on web-based college English teaching started in 2000 and had been increasing from 2006 to 2011; however, scholars’ attention to it was on the decline after 2012. During the period of 2000 -2017, the average annual number of papers published in Chinese journals was up to 45 while that in core journal was only 4. Meanwhile, there were significantly spatial differences. The research on web-based college English teaching in China has mainly focused on eastern region, particularly, Beijing and Guangdong as two main research regions, its average annual number of papers issued in core journals accounted for 9.5% of the total amount of the country respectively. We can see many well-known experts in "double first-class" universities pay little attention to the research on web-based college English teaching and there are weak foundation and low faculty. In recent years, college English teaching has been closely related to internet, also, colleges and universities increasingly put more emphasis on the significant influence of network on college English teaching, and however, the research level of web-based college English teaching needs to be improved further from the perspective of the quality and quantity of the published papers.
Prediction research of cervical cancer clinical events based on recurrent neural network

Through the research on the existing time series prediction technology, most researchers mainly make predictions based on clinical events, without thinking about whether the previous clinical process is standard or not. This paper proposes a two-stage prediction model, RNN-2-DT, based on word vector representation and integrated into the standardized judgment of the diagnosis and treatment process. The model mines standardized clinical mode which is a standard of clinical process, using the method of binary K-means. Meantime, the clinical events prediction model using gated recurrent units (GRU) based on recurrent neural network (RNN) is constructed. The experimental results indicate that, compared with the clinical processes no considering standardized judgments, our model’s recall rate and mean average precision are increased by 7.2% and 4.3%, respectively.
Revealing Persona Biases in Dialogue Systems

Dialogue systems in the form of chatbots and personal assistants are being increasingly integrated into people's lives. Modern dialogue systems may consider adopting anthropomorphic personas, mimicking societal demographic groups to appear more approachable and trustworthy to users. However, the adoption of a persona can result in the adoption of biases. In this paper, we present the first large-scale study on persona biases in dialogue systems and conduct analyses on personas of different social classes, sexual orientations, races, and genders. We define persona biases as harmful differences in responses (e.g., varying levels of offensiveness, agreement with harmful statements) generated from adopting different demographic personas. Furthermore, we introduce an open-source framework, UnitPersonaBias, to explore and aggregate persona biases in dialogue systems. By analyzing the Blender and DialoGPT dialogue systems, we observe that adopting personas can actually decrease harmful responses, compared to not using any personas. Additionally, we find that persona choices can affect the degree of harms in generated responses and thus should be systematically evaluated before deployment. We also analyze how personas can result in different amounts of harm towards specific demographics.
Applying Digital Arts Experience to Strengthen the Organizational Culture in Higher Education During the Pandemic

During the COVID-19, schools were faced with changes in organizational culture under the digital education model. It is a new challenge facing higher education universities to promote cultural competitiveness, enhancing the confidence of educators in organizational culture, and communicating the cultural atmosphere to educators through digital technology. This concept paper mainly emphasizes the introduction of Digital Arts experience into organizational culture, thereby enhancing the appeal of the university organizational culture. This requires the university to combine both ideology and technology, highly advocating the organization's core concept through the global digital trend during the pandemic. This concept paper provides a new perspective of change for constructing organizational culture in universities facing education and digital challenges during the pandemic. The Digital Arts experience will be an essential part of the global university organization culture after COVID-19.
Context- and sequence-aware convolutional recurrent encoder for neural machine translation

Neural Machine Translation1 model is a sequence-to-sequence converter based on neural networks. Existing models use recurrent neural networks to construct both the encoder and decoder modules. In alternative research, the recurrent networks were substituted by convolutional neural networks for capturing the syntactic structure in the input sentence and decreasing the processing time. We incorporate the goodness of both approaches by proposing a convolutional-recurrent encoder for capturing the context information as well as the sequential information from the source sentence. Word embedding and position embedding of the source sentence is performed prior to the convolutional encoding layer which is basically a n-gram feature extractor capturing phrase-level context information. The rectified output of the convolutional encoding layer is added to the original embedding vector, and the sum is normalized by layer normalization. The normalized output is given as a sequential input to the recurrent encoding layer that captures the temporal information in the sequence. For the decoder, we use the attention-based recurrent neural network. Translation task on the German-English dataset verifies the efficacy of the proposed approach from the higher BLEU scores achieved as compared to the state of the art.
A novel bivariate autoregressive model for predicting and forecasting irregularly observed time series

In several disciplines it is common to find time series measured at irregular observational times. In particular, in astronomy there are a large number of surveys that gather information over irregular time gaps and in more than one passband. Some examples are Pan-STARRS, ZTF and also the LSST. However, current commonly used time series models that estimate the time dependency in astronomical light curves consider the information of each band separately (e.g, CIAR, IAR and CARMA models) disregarding the dependency that might exist between different passbands. In this paper we propose a novel bivariate model for irregularly sampled time series, called the bivariate irregular autoregressive (BIAR) model. The BIAR model assumes an autoregressive structure on each time series, it is stationary, and it allows to estimate the autocorrelation, the cross-correlation and the contemporary correlation between two unequally spaced time series. We implemented the BIAR model on light curves, in the g and r bands, obtained from the ZTF alerts processed by the ALeRCE broker. We show that if the light curves of the two bands are highly correlated, the model has more accurate forecast and prediction using the bivariate model than a similar method that uses only univariate information. Further, the estimated parameters of the BIAR are useful to characterize LongPeriod Variable Stars and to distinguish between classes of stochastic objects, providing promising features that can be used for classification purposes
A study of sensor signal danger detector system using Arduino programming method

A distance sensor system was used as wireless danger signal detector system controlled by Arduino programming that was detected any potential danger coming in target range. The system operated by sending signals based on the sensor reading. The purpose of the safety system was to increase the safety procedure and precaution for maintenance workers that currently doing their job at the emergency lane of the highway. In certain range of radar sensor detection, when the potential danger or accident was detected to approach, the sensor send the signal to the system and carried out the duty as medium of sending alarm caution in three ways, which were through sound, vibration and visual.
Material model identification from set of experiments and validation by DIC

A methodology for calibration and validation of material models using more simultaneous experiments is presented. The procedure contains the selection of suitable parameters for identification using a sensitivity analysis, parameters identification, analysis of usability of the material model, and validation of the material model. Three elastoplastic models are evaluated in this study: the bilinear isotropic hardening model with Von Mises yield condition, the bilinear isotropic hardening model with the Hill anisotropic yield condition, and the nonlinear isotropic hardening model with Hill anisotropic yield condition. Five proportional as well as nonproportional monotonic loading experiments conducted on aluminium alloy 2124-T851 are used for the presentation of the inverse approach application, whereas four of them are the source of identification. The validation of calibrated material models is done based on the strain field measurement realised by the Digital Image Correlation method. The nonlinear isotropic hardening rule together with Hill yield condition brings the best description of stress–strain behaviour of the material under investigation.
Lattice-Based Proof of Shuffle and Applications to Electronic Voting

A verifiable shuffle of known values is a method for proving that a collection of commitments opens to a given collection of known messages, without revealing a correspondence between commitments and messages. We propose the first practical verifiable shuffle of known values for lattice-based commitments.
An extensive study on smell-aware bug localization

Bug localization is an important aspect of software maintenance because it can locate modules that should be changed to fix a specific bug. Our previous study showed that the accuracy of the information retrieval (IR)-based bug localization technique improved when used in combination with code smell information. Although this technique showed promise, the study showed limited usefulness because of the small number of: (1) projects in the dataset, (2) types of smell information, and (3) baseline bug localization techniques used for assessment. This paper presents an extension of our previous experiments on Bench4BL, the largest bug localization benchmark dataset available for bug localization. In addition, we generalized the smell-aware bug localization technique to allow different configurations of smell information, which were combined with various bug localization techniques. Our results confirmed that our technique can improve the performance of IR-based bug localization techniques for the class level even when large datasets are processed. Furthermore, because of the optimized configuration of the smell information, our technique can enhance the performance of most state-of-the-art bug localization techniques.
Breiman's Two Cultures: You Don't Have to Choose Sides

Breiman's classic paper casts data analysis as a choice between two cultures: data modelers and algorithmic modelers.Stated broadly, data modelers use simple, interpretable models with well-understood theoretical properties to analyze data.Algorithmic modelers prioritize predictive accuracy and use more flexible function approximations to analyze data.This dichotomy overlooks a third set of models -mechanistic models derived from scientific theories (e.g., ODE/SDE simulators).Mechanistic models encode application-specific scientific knowledge about the data.And while these categories represent extreme points in model space, modern computational and algorithmic tools enable us to interpolate between these points, producing flexible, interpretable, and scientifically-informed hybrids that can enjoy accurate and robust predictions, and resolve issues with data analysis that Breiman describes, such as the Rashomon effect and Occam's dilemma.Challenges still remain in finding an appropriate point in model space, with many choices on how to compose model components and the degree to which each component informs inferences.
Deliberative acting, planning and learning with hierarchical operational models

In AI research, synthesizing a plan of action has typically used descriptive models of the actions that abstractly specify what might happen as a result of an action, and are tailored for efficiently computing state transitions. However, executing the planned actions has needed operational models, in which rich computational control structures and closed-loop online decision-making are used to specify how to perform an action in a nondeterministic execution context, react to events and adapt to an unfolding situation. Deliberative actors, which integrate acting and planning, have typically needed to use both of these models together—which causes problems when attempting to develop the different models, verify their consistency, and smoothly interleave acting and planning. As an alternative, we define and implement an integrated acting and planning system in which both planning and acting use the same operational models. These rely on hierarchical task-oriented refinement methods offering rich control structures. The acting component, called Reactive Acting Engine (RAE), is inspired by the well-known PRS system. At each decision step, RAE can get advice from a planner for a near-optimal choice with respect to an utility function. The anytime planner uses a UCT-like Monte Carlo Tree Search procedure, called UPOM, whose rollouts are simulations of the actor's operational models. We also present learning strategies for use with RAE and UPOM that acquire, from online acting experiences and/or simulated planning results, a mapping from decision contexts to method instances as well as a heuristic function to guide UPOM. We demonstrate the asymptotic convergence of UPOM towards optimal methods in static domains, and show experimentally that UPOM and the learning strategies significantly improve the acting efficiency and robustness.
A Comparative Study of Feature Types for Age-Based Text Classification

The ability to automatically determine the age audience of a novel provides many opportunities for the development of information retrieval tools. Firstly, developers of book recommendation systems and electronic libraries may be interested in filtering texts by the age of the most likely readers. Further, parents may want to select literature for children. Finally, it will be useful for writers and publishers to determine which features influence whether the texts are suitable for children. In this article, we compare the empirical effectiveness of various types of linguistic features for the task of age-based classification of fiction texts. For this purpose, we collected a text corpus of book previews labeled with one of two categories – children’s or adult. We evaluated the following types of features: readability indices, sentiment, lexical, grammatical and general features, and publishing attributes. The results obtained show that the features describing the text at the document level can significantly increase the quality of machine learning models.