Reviewerly/exHarmony huggingface dataset:
Smooth unsupervised domain adaptation considering uncertainties

Collecting sufficient labeled data is time-consuming and even infeasible in streaming data applications. Unsupervised domain adaptation is a reasonable solution that has recently gained attention. However, most efforts have focused on stationary environments and have ignored the uncertainties that arise from mismatched distributions. Therefore, the proposed method addresses the classification of samples in streaming data in the presence of concept drift, employing a heterogeneous unsupervised domain adaptation method. Accordingly, a fuzzy rough set-based sample weighting approach is introduced to modulate the impact of uncertainties on feature alignment in non-stationary environments. The domain adaptation is carried out through a fuzzy approach and is optimized by a heuristic optimization technique that reduces the sensitivity to tunable parameters. Moreover, an incremental classifier is designed to enable rapid adaptation to changes. The advantages of the proposed method encompass an effective avoidance of excessive alignment, training cost optimization, and the gradual reduction in dependency on the source domain for domain adaptation. Regarding different types of concept drift, experiments on several tasks taken from four benchmark datasets demonstrate the superiority of the proposed method in terms of accuracy and computational time.
Depth Completion in Autonomous Driving: Adaptive Spatial Feature Fusion and Semi-Quantitative Visualization

The safety of autonomous driving is closely linked to accurate depth perception. With the continuous development of autonomous driving, depth completion has become one of the crucial methods in this field. However, current depth completion methods have major shortcomings in small objects. To solve this problem, this paper proposes an end-to-end architecture with adaptive spatial feature fusion by encoder–decoder (ASFF-ED) module for depth completion. The architecture is built on the basis of the network architecture proposed in this paper, and is able to extract depth information adaptively with different weights on the specified feature map, which effectively solves the problem of insufficient depth accuracy of small objects. At the same time, this paper also proposes a depth map visualization method with a semi-quantitative visualization, which makes the depth information more intuitive to display. Compared with the currently available depth map visualization methods, this method has stronger quantitative analysis and horizontal comparison ability. Through experiments of ablation study and comparison, the results show that the method proposed in this paper exhibits a lower root-mean-squared error (RMSE) and better small object detection performance on the KITTI dataset.
Interframe Saliency Transformer and Lightweight Multidimensional Attention Network for Real-Time Unmanned Aerial Vehicle Tracking

UAV visual-object-tracking technology based on Siamese neural networks has great scientific research and practical application value, and it is widely used in geological surveying, reconnaissance monitoring, and environmental monitoring. Due to the limited onboard computational resources and complex real-world environments of drones, most of the existing tracking systems based on Siamese neural networks struggle to combine excellent performance with high efficiency. Therefore, the key issue is to study how to improve the accuracy of target tracking under the challenges of real-time performance and the above factors. In response to this problem, this paper proposes a real-time UAV tracking system based on interframe saliency transformer and lightweight multidimensional attention network (SiamITL). Specifically, interframe saliency transformer is used to continuously perceive spatial and temporal information, making the network more closely related to the essence of the tracking task. Additionally, a lightweight multidimensional attention network is used to better capture changes in both target appearance and background information, improving the ability of the tracker to distinguish between the target and background. SiamITL is effective and efficient: extensive comparative experiments and ablation experiments have been conducted on multiple aerial tracking benchmarks, demonstrating that our algorithm can achieve more robust feature representation and more accurate target state estimation. Among them, SiamITL achieved success and accuracy rates of 0.625 and 0.818 in the UAV123 benchmark, respectively, demonstrating a certain level of leadership in this field. Furthermore, SiamITL demonstrates the potential for real-time operation on the embedded platform Xavier, highlighting its potential for practical application in real-world scenarios.
A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks

The efficiency and cognitive limitations of manual sample labeling result in a large number of unlabeled training samples in practical applications. Making full use of both labeled and unlabeled samples is the key to solving the semi-supervised problem. However, as a supervised algorithm, the stacked autoencoder (SAE) only considers labeled samples and is difficult to apply to semi-supervised problems. Thus, by introducing the pseudo-labeling method into the SAE, a novel pseudo label-based semi-supervised stacked autoencoder (PL-SSAE) is proposed to address the semi-supervised classification tasks. The PL-SSAE first utilizes the unsupervised pre-training on all samples by the autoencoder (AE) to initialize the network parameters. Then, by the iterative fine-tuning of the network parameters based on the labeled samples, the unlabeled samples are identified, and their pseudo labels are generated. Finally, the pseudo-labeled samples are used to construct the regularization term and fine-tune the network parameters to complete the training of the PL-SSAE. Different from the traditional SAE, the PL-SSAE requires all samples in pre-training and the unlabeled samples with pseudo labels in fine-tuning to fully exploit the feature and category information of the unlabeled samples. Empirical evaluations on various benchmark datasets show that the semi-supervised performance of the PL-SSAE is more competitive than that of the SAE, sparse stacked autoencoder (SSAE), semi-supervised stacked autoencoder (Semi-SAE) and semi-supervised stacked autoencoder (Semi-SSAE).
An Exploratory Study of SMT Versus NMT for the Resource Constraint English to Manipuri Translation

Recent advancements in machine translation have made neural machine translation (NMT) the preferred method over the previously popular statistical machine translation (SMT) method. However, SMT claims to be superior to NMT for language pairs with limited resources. For the linguistically distinct and under-resourced language pair English-Manipuri, we experiment and compare the SMT and NMT results for the English-to-Manipuri translations with the same data set sizes. We use BLEU (Bilingual Evaluation Understudy), Meteor, TER (Translation Error Rate) and F-mean scores as automatic evaluation metrics to evaluate the system’s results. Additionally, we sought expert evaluation of the results and examine the consistency between BLEU and expert judgement. We analyse which method is most appropriate for machine translation of the language pair under low resource settings through automatic and expert evaluation.
Formal Template-Based Generation of Attack–Defence Trees for Automated Security Analysis

Systems that integrate cyber and physical aspects to create cyber-physical systems (CPS) are becoming increasingly complex, but demonstrating the security of CPS is hard and security is frequently compromised. These compromises can lead to safety failures, putting lives at risk. Attack Defense Trees with sequential conjunction (ADS) are an approach to identifying attacks on a system and identifying the interaction between attacks and the defenses that are present within the CPS. We present a semantic model for ADS and propose a methodology for generating ADS automatically. The methodology takes as input a CPS system model and a library of templates of attacks and defenses. We demonstrate and validate the effectiveness of the ADS generation methodology using an example from the automotive domain.
In-Cognitive: A web-based Python application for fuzzy cognitive map design, simulation, and uncertainty analysis based on the Monte Carlo method

Fuzzy Cognitive Mapping is a semi-quantitative modelling method, widely used for decision support in various domains. However, existing software applications have been criticised over inadequate handling of uncertain information, lack of accessibility, and inability to converge to solutions for all modelled systems. Here we present In-Cognitive, an open-source, web-based application for the creation, visualisation, and simulation of Fuzzy Cognitive Maps, ensuring solution convergence and allowing for Monte Carlo uncertainty analysis. The application is built in Python and Bokeh and provides an accessible and user-friendly interface to model various systems quickly and reliably and evaluate the robustness of the modelling solutions.
Unbiased Estimates for Products of Moments and Cumulants for Finite Populations

Let FN be the distribution function of a finite real population of size N. Let Fn be the empirical distribution function of a sample of size n drawn from the population without replacement. Let TFN be any product of the moments or cumulants of FN, let TFn denote the sample version, and let Tn,NFN denote the expected value of TFn with respect to FN. We prove the following remarkable inversion principle that the expected value of TN,nFn is equal to TFN. We also obtain an explicit expression for Tn,NFN for all TFN of orders up to six.
Improving Abstractive Dialogue Summarization Using Keyword Extraction

Abstractive dialogue summarization aims to generate a short passage that contains important content for a particular dialogue spoken by multiple speakers. In abstractive dialogue summarization systems, capturing the subject in the dialogue is challenging owing to the properties of colloquial texts. Moreover, the system often generates uninformative summaries. In this paper, we propose a novel keyword-aware dialogue summarization system (KADS) that easily captures the subject in the dialogue to alleviate the problem mentioned above through the efficient usage of keywords. Specifically, we first extract the keywords from the input dialogue using a pre-trained keyword extractor. Subsequently, KADS efficiently leverages the keywords information of the dialogue to the transformer-based dialogue system by using the pre-trained keyword extractor. Extensive experiments performed on three benchmark datasets show that the proposed method outperforms the baseline system. Additionally, we demonstrate that the proposed keyword-aware dialogue summarization system exhibits a high-performance gain in low-resource conditions where the number of training examples is highly limited.
STRUCTURAL COMPONENTS OF PROFESSIONAL MOTIVATION OF FUTURE MANAGERS IN THE CONTEXT OF THE FORMATION OF A MODERN MANAGER-LEADER

Institutions of higher education that prepare future specialists in the field of management must take into account new requirements for managers when building their educational programs and provide for the education of new competencies of future managers. Motivation is one of the most important and difficult to analyze factors that should be taken into account when building a training system for future managers. 3 groups of differences in the modern interpretation of the category “motivation” were analyzed. The first difference in the perception of the category “motivation” is caused by the fact that we study motivation on the one hand as a psychological function, on the other - as a managerial function. We consider it appropriate to call the first approach psychological (cognitive), and the second managerial (instrumental). The second difference is that different authors in psychological research group definitions of motivation in different ways. The third difference lies in the different hierarchy of the concepts “motivation” and “motive” in different interpretations. The first category of authors proposes to understand “motivation” as a secondary phenomenon in relation to the motive. In this case, the motive appears as an exogenous factor that is given to a person in a ready-made form, and motivation as an endogenous factor that manifests itself as a process of activity regulation with the help of a motive. The second category emphasizes that motives are secondary. The system of motivating the professional formation of future managers due to the detailing of the components (needs, value orientations, cognitive control, professional intentions, motives, goals) at the stages of opting and professional training is considered. An empirical method was chosen for the research: Holland’s professional personality type test. Respondents were asked to choose from 42 pairs of professions. The results of the study were analyzed from the point of view of the dominant types of activities of the applicants studying at the educational program “Management” of the Alfred Nobel University during 2022. 62 students of the “Management” specialty of I-IV years of bachelor’s studies took part in the empirical study. Based on data analysis, it was found that the predominant types of activities for students who chose management as their future professional direction are Artistic, Social and Entrepreneurial, which have the highest average indicators for the entire group of subjects.
A Huffman code LSB based image steganography technique using multi-level encryption and achromatic component of an image

Abstract In the recent couple of years, due to the accelerated popularity of the internet, various organizations such as government offices, military, private companies, etc. use different transferring methods for exchanging their information. The Internet has various benefits and some demerits, but the primary bad mark is security of information transmission over an unreliable network, and widely uses of images. So, Steganography is the state of the art of implanting a message in the cover objects, that nobody can suspect or identify it. Therefore, in the field of cover steganography, it is very critical to track down a mechanism for concealing data by utilizing different blends of compression strategies. Amplifying the payload limit, and robustness, and working on the visual quality are the vital factors of this research to make a reliable mechanism. Different cover steganography research strategies have been recommended, and each adores its benefits and impediments but there is a need to foster some better cover steganography implements to accomplish dependability between the essential model of cover steganography. To handle these issues, in this paper we proposed a method in view of Huffman code, Least Significant Bits (LSB) based cover steganography utilizing Multi-Level Encryption (MLE) and colorless part (HC-LSBIS-MLE-AC) of the picture. It also used different substitution and flicking concepts, MLE, Magic matrix, and achromatic concepts for proving the proficiency, and significance of the method. The algorithm was also statistically investigated based on some Statistical Assessment Metrics (SAM) such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Normalized Cross Correlation (NCC), Structural Similarity Index Metric (SSIM), etc. and different perspectives. The observational outcomes show the likelihood of the proposed algorithm and the capacity to give unwavering quality between security, payload, perception, computation, and temper protection.
P‐89: Digital Holography Microscope with liquid crystal phase modulator

We proposed a DHM system with improved resolution property by using liquid crystal (LC) phase modulator. The LC phase modulator can stably control the phase modulation characteristics of the interference pattern and implements the four‐step phase shifting scheme. The proposed DHM system showed a four‐fold improvement in resolution compared to the conventional DHM with polarized camera. Finally, it was confirmed that the modulation transfer function (MTF) value was improved 2 times at the resolution target of 105 lines/mm.
Toward topic diversity in recommender systems: integrating topic modeling with a hashing algorithm

Purpose Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method. Design/methodology/approach This paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data. Findings (1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels. Originality/value The hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.
Updating mortality risk estimation in intensive care units from high-dimensional electronic health records with incomplete data

Abstract Background The risk of mortality in intensive care units (ICUs) is currently addressed by the implementation of scores using admission data. Their performances are satisfactory when complications occur early after admission; however, they may become irrelevant in the case of long hospital stays. In this study, we developed predictive models of short-term mortality in the ICU from longitudinal data. Methods Using data collected throughout patients’ stays of at least 48 h from the MIMIC-III database, several statistical learning approaches were compared, including deep neural networks and penalized regression. Missing data were handled using complete-case analysis or multiple imputation. Results Complete-case analyses from 19 predictors showed good discrimination (AUC &gt; 0.77 for several approaches) to predict death between 12 and 24 h onward, yet excluded 75% of patients from the initial target cohort, as data was missing for some of the predictors. Multiple imputation allowed us to include 70 predictors and keep 95% of patients, with similar performances. Conclusion This proof-of-concept study supports that automated analysis of electronic health records can be of great interest throughout patients’ stays as a surveillance tool. Although this framework relies on a large set of predictors, it is robust to data imputation and may be effective early after admission, when data are still scarce.
An empirically based model of software prototyping: a mapping study and a multi-case study

Abstract Context Prototyping is an established practice within product and user interface design that is also used as a requirements engineering (RE) practice within agile development. Even so, there is a lack of theory on prototyping. Aims Our main research objective is to support practitioners in improving on their prototyping practices. Method We have designed a model that describes key aspects of the practice of prototyping. The model is based on a systematic mapping study consisting of thirty-three primary studies and on empirical data from twelve case companies. We validate and demonstrate the applicability of our model through a focus group at one company and through semi-structured interviews at eleven (other) startup companies. Results Our prototyping aspects model (PAM) consists of five aspects of prototyping, namely purpose, prototype scope, prototype media, prototype use, and exploration strategy. This model has enabled practitioners to discuss their prototyping practices in terms of the concepts provided by our model. Conclusions The model can be used to categorise prototyping instances and can thereby support practitioners in reflecting and improving on their prototyping practices.
A study on copyright issues of different controlled digital lending (CDL) modes

In the recent years, CDL has been heatedly talked about, CDL should be treated objectively and rationally. Getting knowledge of CDL modes and their copyright issues is critical for sustainable development of CDL. Rather than CDL becomes a transient phenomenon as a result of many copyright hurdles. The paper will explore CDL modes by combing CDL practices and programs from research papers and official website documents of different library organizations. Then, based on legal frameworks of CDL in the US, Canada and the UK which are summarized, copyright issues of CDL modes are analyzed from perspectives of implementing institution, service resources, and usage mode. Finally, some copyright recommendations for sustainable development of CDL are proposed. We believe that library institutions can use CDL to advance their crucial mission for the public’s interest through making sense of different CDL modes and their copyright issues and implementing some proposals about copyright processing.
A Basis for AGM Revision in Bayesian Probability Revision

Abstract In standard Bayesian probability revision, the adoption of full beliefs (propositions with probability 1) is irreversible. Once an agent has full belief in a proposition, no subsequent revision can remove that belief. This is an unrealistic feature, and it also makes probability revision incompatible with belief change theory, which focuses on how the set of full beliefs is modified through both additions and retractions. This problem in probability theory can be solved in a model that (i) lets the codomain of the probability function be a hyperreal-valued rather than the real-valued closed interval [0, 1], and (ii) identifies the full beliefs as the propositions whose probability is either 1 or infinitesimally smaller than 1. In this model, changes in the probability function will result in changes in the set of full beliefs (belief set), which constitutes a submodel that can be conceived as the “tip of the iceberg” within the larger model that also contains beliefs on lower levels of probability. The patterns of change in the set of full beliefs in this modified Bayesian model coincides with the corresponding pattern in a slightly modified version of AGM revision, which is commonly conceived as the gold standard of (dichotomous) belief change. The modification only concerns the marginal case of revision by an inconsistent input sentence. These results show that probability revision and dichotomous belief change can be unified in one and the same framework, or – if we so wish – that belief change theory can be subsumed under a modified version of probability revision that allows for iterated change and for the removal of full beliefs.